The effects of pH and Phosphorus concentrations on the chlorophyll content of
\r\n\tDiagnosis (clinical, radiological, cytogenetic, and molecular criteria), pathogenesis (risk factors, pre-myeloma conditions, and bone marrow microenvironment), cytogenetic abnormalities and molecular profiles disease staging and risk stratification, novel therapies such as proteasome inhibitors, immunomodulatory agents as well as monoclonal antibodies, drug resistance (primary and secondary resistance as well as evolution of new genetic mutations that may be disease or therapy-related), hematopoietic stem cell transplantation (HSCT) (autologous HSCT, allogeneic HSCT, and tandem transplantation), relapsed and refractory multiple myeloma, minimal residual disease (evaluation by flow cytometry or various sequencing techniques, importance of MRD in prognosis and prediction of disease relapse), chimeric antigen receptor (CAR) T-cell therapy, infectious complications in multiple myeloma (viral infections, bacterial infections, fungal infections, disease-related infections and therapy-related infections).
\r\n\r\n\tThe book chapters will intend to be written by scientists and experts in the field from various institutions around the world.
",isbn:"978-1-80356-093-9",printIsbn:"978-1-80356-092-2",pdfIsbn:"978-1-80356-094-6",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,isSalesforceBook:!1,isNomenclature:!1,hash:"c8e2b12df4fc2d313aced448fe08a63e",bookSignature:"Dr. Khalid Ahmed Al-Anazi",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/11600.jpg",keywords:"Risk Factors, Angiogenesis, Signaling Pathways, Therapeutic Targets, Drug Resistance, Genetic Mutations, Disease-Related Infections, Therapy-Related Infections, Complete Remission, Overall Survival, Immunomodulatory Agents, Bone Marrow Microenvironment",numberOfDownloads:13,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"January 26th 2022",dateEndSecondStepPublish:"March 29th 2022",dateEndThirdStepPublish:"May 28th 2022",dateEndFourthStepPublish:"August 16th 2022",dateEndFifthStepPublish:"October 15th 2022",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"3 months",secondStepPassed:!0,areRegistrationsClosed:!0,currentStepOfPublishingProcess:4,editedByType:null,kuFlag:!1,biosketch:"Dr. Khalid Al-Anazi established the Hematopoietic Stem Cell Transplantation Services in Saudi Arabia. He is a distinguished researcher in the fields of stem cell therapies & infections in immunocompromised individuals.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"37255",title:"Dr.",name:"Khalid",middleName:"Ahmed",surname:"Al-Anazi",slug:"khalid-al-anazi",fullName:"Khalid Al-Anazi",profilePictureURL:"https://mts.intechopen.com/storage/users/37255/images/system/37255.jpg",biography:"Dr. Khalid Ahmed Al-Anazi is a consultant Hemato-Oncologist and the Chairman of the Department of Adult Hematology and Hematopoietic Stem Cell Transplantation (HSCT) at King Fahad Specialist Hospital (KFSH) in Dammam, Saudi Arabia. \r\nHe graduated from the college of medicine, King Saud University (KSU) in Riyadh in 1986. After having his Boards in Internal Medicine, he trained in clinical hematology and HSCT at King’s College Hospital, University of London, U.K. He has 4 year experience in internal medicine and 28 year experience in adult clinical hematology and HSCT at: Riyadh Armed Forces Hospital; King Faisal Specialist Hospital and Research Centre (KFSH&RC) in Riyadh; King Khalid University Hospital (KKUH) and the College of Medicine, KSU in Riyadh; and KFSH in Dammam, Saudi Arabia. \r\nHe established the adult HSCT program at KFSH in Dammam in the year 2010. 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In its natural habitat,
Unfortunately, very little information has been documented on the cultivation of this species. Cultivation of medicinal plants is gaining traction worldwide; it is seen as a tool for biodiversity conservation, poverty alleviation and cultural preservation [12]. However, good knowledge of plant physiology must be attained in order to develop enhanced cultivation protocols that could result in high yield and high‐quality medicinal materials. Effects of nutrients and nutrient ratios on many food and medicinal crop plants, such as soya bean, thyme, wheat cultivars, barley, spinach and pelargoniums, have been studied. In most cases, a positive result in growth is noticed with the addition of some macro‐nutrients such as N, P, K, Mg or Ca [13–21]. It is therefore crucial that adequate plant nutrition and soil pH levels are met for any given plant so that the cell\'s functioning is not impeded. Chlorophyll is a fundamental part of the light‐dependent reactions of the photosynthesis process, capturing light rays from the sun and producing energy‐storing ATP molecules that are essential for the functioning of a healthy plant [22, 23]. The effects of poor nutrition, be it through infertile soils or incorrect soil pH level, directly affect the production of chlorophyll molecules resulting in chlorosis of leaves and a reduced photosynthetic rate, thus inhibiting some biological processes and decreasing the general health of the plants [23–25]. There are plausible mechanisms through which the production of chlorophyll could be affected, for example, the pH level of a growing medium affects the uptake of P [26] and the P level influences the nutrient uptake by plants [27]. The relationship between the nutrient P and chlorophyll is not fully understood. According to Nicholls and Dillon [28], there are substantial variations of the published phosphorus‐chlorophyll relationship, which they ascribed to variations in sampling and analytical techniques.
\nThis chapter aims to investigate the effects of P and pH on the chlorophyll production, leaf colour and the nutrient uptake of medicinal
The experiment took place in the research glasshouse at the Cape Peninsula University of Technology (CPUT), Cape Town campus, South Africa, latitude and longitude S33°55′ 58 E18°25′ 57, from June 2012 to August 2012. Inside the glasshouse was a 40%‐Aluminet shade cloth, raised 2 m above the floor, resulting in light intensities ranging from 10 to 13 Klx, determined by using a Toptronic T630 light meter. The climate was controlled between 16 and 28°C during the day while 10–20°C during the night, with an average relative humidity of 42%.
\nThe experiment was laid out in a randomized block design with plants being spaced 30 cm apart and consisted of 12 treatments of four differing nutrient solutions offering a low concentration of P, a balanced concentration of supplementary P, a moderate concentration of supplementary P and a high concentration of supplementary P at three differing pH levels. The control treatment of 31 ppm was chosen due to the nature of fynbos soils being low in available P [29–31].
\nHoagland solution, a well‐known hydroponic nutrient solution modified by Hershey [32, 33], offering all the necessary macro‐ and micro‐nutrients for healthy plant growth, was used as a base nutrient and supplemented with P.
\nThe plants for the experiment were rooted tip cuttings sourced from healthy mother stock plants at the CPUT Glass House Nursery. The rooted cuttings were gently rinsed in deionized water to remove any rooting media from the root\'s zone. They were then weighed and planted into 25‐cm plastic pots filled with leca clay and placed into a recirculating closed hydroponics system at a spacing of 30 cm, where their heights were recorded (Figure 1).
The plants were irrigated with the treatments 15 times per day at equal timed intervals for the duration of the experiment. For each treatment, there were 10 plants. The treatments were as follows:\n
Hoagland hydroponic nutrient solution with 31 ppm of P at a pH of 4.
Hoagland hydroponic nutrient solution with 31 ppm of P at a pH of 6.
Hoagland hydroponic nutrient solution with 31 ppm of P at a pH of 8.
Hoagland hydroponic nutrient solution supplemented with 90 ppm of P at a pH of 4.
Hoagland hydroponic nutrient solution supplemented with 90 ppm of P at a pH of 6.
Hoagland hydroponic nutrient solution supplemented with 90 ppm of P at a pH of 8.
Hoagland hydroponic nutrient solution supplemented with 150 ppm of P at a pH of 4.
Hoagland hydroponic nutrient solution supplemented with 150 ppm of P at a pH of 6.
Hoagland hydroponic nutrient solution supplemented with 150 ppm of P at a pH of 8.
Hoagland hydroponic nutrient solution supplemented with 210 ppm of P at a pH of 4.
Hoagland hydroponic nutrient solution supplemented with 210 ppm of P at a pH of 6.
Hoagland hydroponic nutrient solution supplemented with 210 ppm of P at a pH of 8.
The pH levels of the nutrient solutions were monitored using a Martini Instrument PH55 pH probe and were adjusted accordingly using either hydrochloric acid (HCl) to lower the pH or sodium hydroxide (NaOH) to raise the pH.
The treatments were set to irrigate 15 times daily for a duration of 15 min using a 1350 L/h Boyu submersible pump and a Tedelex analogue timer to regulate irrigation frequencies.
Green leaf colour intensity was measured using a hand‐held, dual‐wavelength SPAD meter (SPAD 502, chlorophyll meter, Minolta Camera Co., Ltd., Japan). Readings were taken from the top three fully developed leaves of each plant. For each treatment, 30 fully developed leaves were used weekly. The SPAD meter stored and automatically averaged the recordings to generate one reading per plant.
The extraction of leaf chlorophyll using dimethylsulphoxide (DMSO) was carried out as described in Hiscox and Israelsta [34]. A third of plant leaves from the tip were collected from each plant. About 100 mg of the middle portion of the fresh leaf slices was placed in a 15‐mL vial containing 7 mL DMSO and incubated at 4°C for 72 h. After the incubation, the extract was diluted to 10 mL with DMSO. A 3‐mL sample of chlorophyll extract was then transferred into curvets for absorbance determination. A spectrophotometer (UV/Visible Spectrophotometer, Pharmacia LKB. Ultrospec II E) was used to determine absorbance values at 645 and 663 nm, which were then used in the equation proposed by Arnon [35] to determine the total leaf chlorophyll content against DMSO blank, expressed as mg L-1 as follows: Chl
The measurements of macronutrients (N, P, K, Ca and Mg) and micronutrients (Cu, Zn, Mn, Fe and B) were determined by ashing a 1 g ground sample in a porcelain crucible at 500°C overnight. This was followed by dissolving the ash in 5 mL of 6 M HCl and putting it in an oven at 50°C for 30 min; 35 mL of deionized water was added, and the extract was filtered through Whatman no. 1 filter paper. Nutrient concentrations in plant extracts were determined using an inductively coupled plasma (ICP) emission spectrophotometer (IRIS/AP HR DUO Thermo Electron Corporation, Franklin, Massachusetts, USA) [36].
Data collected was analysed for statistical significance using the two‐way analysis of variance (ANOVA), with the computations being done using the software program STATISTICA. Fisher\'s least significance difference (LSD) was used to compare treatment means at
Treatment significantly (
Treatments | Chlorophyll A | Chlorophyll B | Total chlorophyll |
---|---|---|---|
pH 4, P 31 ppm | 12.242 ± 1.7a | 3.446 ± 0.5a | 15.684 ± 2.2a |
pH 6, P 31 ppm (Control) | 10.384 ± 1.0cd | 2.848 ± 0.3cde | 13.229 ± 1.3cd |
pH 8, P 31 ppm | 10.173 ± 1.1cde | 2.784 ± 0.3ef | 12.954 ± 1.5cde |
pH 4, P 90 ppm | 11.419 ± 0.5ab | 3.233 ± 0.2ab | 14.649 ± 0.6ab |
pH 6, P 90 ppm | 8.348 ± 1.1g | 2.227 ± 0.3hi | 10.574 ± 1.4g |
pH 8, P 90 ppm | 9.327 ± 1.3ef | 2.600 ± 0.4g | 11.924 ± 1.7ef |
pH 4, P 150 ppm | 10.929 ± 0.7bc | 3.014 ± 0.3bcd | 13.941 ± 0.9bc |
pH 6, P 150 ppm | 8.463 ± 1.4fg | 2.282 ± 0.4h | 10.744 ± 1.8fg |
pH 8, P 150 ppm | 7.063 ± 0.6h | 1.988 ± 0.2i | 9.049 ± 0.7h |
pH 4, P 210 ppm | 10.900 ± 0.7bc | 3.108 ± 0.3bc | 14.005 ± 0.9bc |
pH 6, P 210 ppm | 9.817 ± 1.0de | 2.650 ± 0.3g | 12.465 ± 1.3de |
pH 8, P 210 ppm | 3.547 ± 0.5i | 0.910 ± 0.2j | 4.456 ± 0.7i |
One‐way ANOVA ( | 46.757*** | 43.425*** | 46.388*** |
The effects of pH and Phosphorus concentrations on the chlorophyll content of
a-jMeans followed by same lowercase letters in the same column are not significantly different (
***represents a statistical significance of (
Effects of various P treatments at differed pH levels induced varied colour intensities, ranging from 16 to 31.7 from week 1 to week 8 on the leaf colour of
Observable variations in the leaf\'s green colour among plants (
Treatments | Wk1 | Wk2 | Wk3 | Wk4 | Wk5 | Wk6 | Wk7 | Wk8 |
---|---|---|---|---|---|---|---|---|
pH 4, P 31 ppm | 30.156 ± 3.6ab | 31.667 ± 4.1a | 31.433 ± 3.0a | 30.878 ± 1.3a | 30.189 ± 3.1ab | 30.011 ± 2.2a | 31.078 ± 1.8a | 28.467 ± 1.9ab |
pH 6, P 31 ppm (Control) | 32.800 ± 2.5a | 31.644 ± 4.0a | 30.533 ± 1.3ab | 29.933 ± 2.0a | 30.122 ± 2.4ab | 28.822 ± 2.6ab | 28.644 ± 1.6bcd | 28.922 ± 1.4a |
pH 8, P 31 ppm | 30.033 ± 4.5ab | 30.567 ± 3.8ab | 28.933 ± 1.4b | 29.944 ± 2.1a | 29.189 ± 1.6ab | 30.111 ± 2.4a | 29.589 ± 1.7abc | 28.278 ± 2.3ab |
pH4, P 90 ppm | 29.689 ± 3.8ab | 29.911 ± 5.3ab | 30.867 ± 2.2ab | 31.111 ± 1.5a | 30.711 ± 2.1ab | 29.656 ± 2.4a | 30.567 ± 2.1a | 28.122 ± 2.0ab |
pH 6, P 90 ppm | 31.789 ± 3.6ab | 31.156 ± 4.6ab | 31.078 ± 2.8ab | 30.578 ± 2.0a | 29.444 ± 1.6ab | 27.000 ± 1.4b | 28.067 ± 2.2cde | 27.056 ± 1.0bc |
pH 8, P 90 ppm | 29.411 ± 3.3b | 27.356 ± 3.0bc | 22.756 ± 3.0c | 20.067 ± 2.3b | 20.278 ± 1.2c | 24.489 ± 1.6c | 27.000 ± 1.9e | 27.800 ± 1.7abc |
pH 4, P 150 ppm | 30.289 ± 3.6ab | 30.044 ± 3.7ab | 31.233 ± 2.7ab | 30.622 ± 1.5a | 29.189 ± 2.6ab | 29.989 ± 4.0a | 30.178 ± 1.8ab | 27.956 ± 1.1ab |
pH 6, P 150 ppm | 29.333 ± 3.5b | 30.944 ± 3.6ab | 29.933 ± 2.1ab | 29.356 ± 2.7a | 28.633 ± 2.3b | 28.233 ± 2.2ab | 26.456 ± 1.5e | 25.100 ± 2.1de |
pH 8, P 150 ppm | 29.267 ± 3.0b | 22.422 ± 4.9d | 15.978 ± 2.8d | 15.756 ± 3.5c | 15.156 ± 2.7d | 15.267 ± 3.1e | 21.756 ± 1.6f | 24.278 ± 2.3e |
pH 4, P 210 ppm | 30.944 ± 2.8ab | 30.456 ± 3.1ab | 28.956 ± 2.4b | 31.033 ± 2.0a | 30.911 ± 3.2a | 28.767 ± 2.0ab | 29.533 ± 0.7abc | 29.278 ± 1.4a |
pH 6, P 210 ppm | 31.756 ± 3.9ab | 28.489 ± 3.2abc | 29.478 ± 2.4ab | 29.444 ± 1.2a | 29.722 ± 1.0ab | 28.622 ± 1.7ab | 27.756 ± 2.0de | 26.278 ± 1.7cd |
pH 8, P 210 ppm | 31.411 ± 2.6ab | 24.922 ± 5.7cd | 16.167 ± 3.6d | 13.900 ± 2.7c | 14.511 ± 1.8d | 18.044 ± 1.7d | 18.567 ± 1.6g | 16.067 ± 1.8f |
Two‐way ANOVA ( | 1.013NS | 4.333*** | 45.53*** | 78.77*** | 66.25*** | 38.79*** | 41.52*** | 37.33*** |
The effects of pH and phosphorus concentrations on the leaf colour of
NS represents no statistical significance,
*represents a statistical significance of (
**represents a statistical significance of (
***represents a statistical significance of (
Macro‐nutrient uptake of P, K and Mg was significantly (
Treatments | N (%) | P (%) | K (%) | Ca (%) | Mg (%) |
---|---|---|---|---|---|
pH 4, P 31 ppm | 4.18 ± 0.29bcd | 0.64 ± 0.06g | 4.23 ± 0.29g | 1.13 ± 0.07a | 0.28 ± 0.01h |
pH 6, P 31 ppm | 4.24 ± 0.55abc | 0.73 ± 0.06f | 4.41 ± 0.23fg | 1.12 ± 0.10a | 0.36 ± 0.03e |
pH 8, P 31 ppm | 4.19 ± 0.18abcd | 0.62 ± 0.08g | 4.47 ± 0.26efg | 1.10 ± 0.11ab | 0.43 ± 0.04c |
pH 4, P 90 ppm | 4.27 ± 0.26abc | 0.77 ± 0.08ef | 4.64 ± 0.36cdef | 1.10 ± 0.10ab | 0.31 ± 0.02g |
pH 6, P 90 ppm | 4.41 ± 0.20a | 0.82 ± 0.08cde | 4.53 ± 0.13defg | 1.08 ± 0.07ab | 0.38 ± 0.03d |
pH 8, P 90 ppm | 4.20 ± 0.12abcd | 0.80 ± 0.07def | 4.45 ± 0.24efg | 1.14 ± 0.05a | 0.48 ± 0.03b |
pH 4, P 150 ppm | 4.37 ± 0.19ab | 0.82 ± 0.04cde | 4.79 ± 0.58cd | 1.07 ± 0.06abc | 0.32 ± 0.03fg |
pH 6, P 150 ppm | 4.09 ± 0.22cd | 0.88 ± 0.07bc | 4.87 ± 0.19c | 1.01 ± 0.07cd | 0.36 ± 0.02de |
pH 8, P 150 ppm | 4.00 ± 0.08de | 1.07 ± 0.08a | 6.29 ± 0.39a | 0.77 ± 0.03e | 0.55 ± 0.02a |
pH 4, P 210 ppm | 4.13 ± 0.18cd | 0.84 ± 0.06bcd | 4.73 ± 0.32cde | 1.05 ± 0.06bc | 0.31 ± 0.02g |
pH 6, P 210 ppm | 4.05 ± 0.18cd | 0.87 ± 0.08bcd | 4.23 ± 0.35g | 0.97 ± 0.06d | 0.35 ± 0.02ef |
pH 8, P 210 ppm | 3.77 ± 0.16e | 0.91 ± 0.13b | 5.71 ± 0.38b | 0.46 ± 0.03f | 0.48 ± 0.03b |
One‐way ANOVA ( | 4.35*** | 21.34*** | 31.67*** | 68.64*** | 89.74*** |
The effects of pH and Phosphorus concentrations on the uptake of macro‐nutrients in
NS represents no statistical significance,
*represents a statistical significance of (
**represents a statistical significance of (
***represents a statistical significance of (
The micro‐nutrient uptake of Na, Mn, Fe, Cu, Zn and B was significantly (
Treatments | Na (mg/kg) | Mn (mg/kg) | Fe (mg/kg) | Cu (mg/kg) | Zn (mg/kg) | B (mg/kg) |
---|---|---|---|---|---|---|
pH 4, P 31 ppm | 477.89 ± 36.27fg | 84.67 ± 7.48efg | 151.56 ± 7.32cde | 5.22 ± 1.99a | 39.56 ± 2.88bc | 37.78 ± 3.63ab |
pH 6, P 31 ppm (Control) | 479.78 ± 57.99fg | 105.89 ± 11.40c | 139.11 ± 10.17def | 2.89 ± 0.60d | 38.00 ± 3.20c | 38.56 ± 3.09a |
pH 8, P 31 ppm | 472.89 ± 58.58g | 156.78 ± 9.11a | 137.11 ± 8.25ef | 2.89 ± 0.33d | 37.89 ± 3.44c | 37.33 ± 2.29ab |
pH4, P 90 ppm | 548.44 ± 74.72ef | 84.00 ± 8.19fg | 144.11 ± 10.59def | 4.11 ± 0.60bc | 40.11 ± 4.31bc | 38.67 ± 3.67a |
pH 6, P 90 ppm | 505.78 ± 39.02fg | 101.00 ± 4.69cd | 153.33 ± 13.87bcd | 2.56 ± 0.53de | 41.33 ± 6.12bc | 37.44 ± 2.40ab |
pH 8, P 90 ppm | 532.56 ± 70.06efg | 150.33 ± 12.56a | 167.33 ± 13.27abc | 3.22 ± 0.44cd | 39.78 ± 6.28bc | 36.56 ± 1.24abc |
pH 4, P 150 ppm | 604.22 ± 102.07de | 82.67 ± 9.84g | 168.56 ± 23.51ab | 4.67 ± 1ab | 41.33 ± 6.24bc | 37.67 ± 2.29ab |
pH 6, P 150 ppm | 680.33 ± 55.08bc | 94.00 ± 19.68de | 151.00 ± 34.86de | 3.11 ± 1.90d | 38.00 ± 6.75c | 35.22 ± 3.03bcd |
pH 8, P 150 ppm | 716.00 ± 117.06b | 131.44 ± 7.32b | 152.78 ± 16.20bcde | 4.33 ± 0.5ab | 39.44 ± 4.48bc | 31.67 ± 3.35e |
pH 4, P 210 ppm | 696.78 ± 69.99bc | 82.11 ± 4.43g | 175.00 ± 14.42a, | 5.11 ± 0.60a | 44.00 ± 2.92ab | 34.56 ± 2.51cd |
pH 6, P 210 ppm | 640.89 ± 36.55cd | 90.78 ± 9.38efg | 145.11 ± 14.16def | 2.44 ± 0.53de | 43.89 ± 5.69ab | 35.33 ± 2.45bcd |
pH 8, P 210 ppm | 867.67 ± 131.72a | 93.44 ± 8.14def | 129.33 ± 21.17f | 1.89 ± 0.60e | 46.78 ± 7.31a | 33.33 ± 2.40de |
One‐way ANOVA ( | 22.746*** | 62.30*** | 5.590*** | 11.975*** | 2.573*** | 5.56*** |
The effects of pH and phosphorus concentrations on the uptake of micro‐nutrients in
NS represents no statistical significance,
*represents a statistical significance of (
**represents a statistical significance of (
***represents a statistical significance of (
In this chapter, the significantly (
Despite the relatively high nutrient uptake values in plants receiving a nutrient solution with a pH 8, chlorosis of their leaves was apparent during the growth period. This suggests that the uptake of some essential nutrients responsible for chlorophyll development was affected at this pH level, namely the mineral nutrients Cu, B, N and Fe which are directly involved in photosynthesis, respiration, cell division and protein formation [23, 40]. In soil‐less media, the affinity of soluble nutrients to negatively charged surfaces and the interactions between charged cations can have a profound effect on nutrient availability and subsequently, the uptake of nutrients by plants. For example, fertilization with phosphorous increases the soil\'s nitrogen absorption in young plants of
In conclusion, this chapter gives insight into the unknown cultivation requirements of the leaf\'s chlorophyll development of
This study was funded by Cape Peninsula University of Technology through CPUT Bursary and University Research Funds.
More than ever, the world has to work together to find renewable energy solutions to combat the Climate Crisis. Since 2015, all UN Member States have committed to ensuring that everyone has access to cheap, dependable, sustainable, and contemporary clean energy by 2030. Clean energy is derived from renewable natural resources such as the sun, wind, tides and waves, and geothermal [1].
Renewable energy sources, such as solar and wind energy, are more volatile than traditional energy sources since they are weather-dependent. As many countries throughout the world expand their renewable energy supply [2, 3], it is critical to ensure that these clean energy sources offer a consistent supply while replacing fossil fuel-based energy sources. The renewable energy applications range from large-scale and off-grid electricity generating (for rural and remote areas) [1] to heating/cooling systems and transportation.
Wind energy is one of the most widely used renewable energy sources, accounting for 4.8 percent of global electricity production in 2018 [4, 5] and 15 percent of Europe’s electricity consumption in 2019 [6]. The mechanical power of the wind is used to power turbines that generate electricity, which generates wind energy. Because wind has a fluctuating intensity over time and might stop blowing at any time, electricity generated by this source is frequently coupled with other power sources to improve reliability and stability.
Wind energy is one of the RES with the lowest electricity production costs and the largest available resource. As a result, a growing number of countries are realizing that wind power offers a great future power generation opportunity.
By dealing with the intermittence characteristic of wind, forecasting methods can improve wind position. Although wind energy cannot currently be dispatched, the financial impacts of wind can be greatly decreased if wind energy can be scheduled using precise wind predictions. As a result, improving wind power output and developing a wind speed forecasting tool has a huge economic and technical impact on the system, Figure 1 detailed classification of deterministic wind speed and power forecasting.
Detailed classification of deterministic wind speed and power forecasting.
A number of institutes and organizations with extensive experience in the subject have dedicated numerous studies to the advancement of wind forecasting techniques. Models like WPMS, WPPT, Prediktor, ARMINES, Previento, and others have been developed and deployed in wind farms all around the world. Physical, statistical, and hybrid methodologies were used to develop these models, Table 1 presents a list of wind power software prediction models developed internationally.
Model name | Developer(s) | Method | Some geographical locations of applications |
---|---|---|---|
Prediktor | L. Landberg at Risø, Denmark | Physical | Spain, Denmark, Republic of Ireland, Northern Ireland, France, Germany, USA, Scotland & Japan |
WPPT | Eltra/Elsam collaboration with Informatics and Mathematical Modeling at Denmarks Tekniske Universities (DTU), Denmark | Statistical | Denmark, Australia, Canada, Republic of Ireland, Holland, Sweden, Greece & Northern Ireland |
Zephyr | Risø & IMM ay DTU, Denmark | Hybrid | Denmark& Australia |
Previento | Oldenburg University | Hybrid | Germany, Northern Ireland |
e-WindTM | True Wind Inc., USA | Hybrid | USA |
Sipreólico | University Carlos III, Madrid, Spain | Statistical | Spain |
WPMS | Institute of solar energy technology (ISET), Germany | Statistical | Germany |
WEPROG | J. Jorgensen & C. Möhrlen at University College Cork | Hybrid | Ireland, Denmark and Germany |
GH Forecaster | Garrad Hassan | Statistical | Greece, Great Britain & USA |
AWPPS | École des Mines, Paris | Statistical | Crete, Madeira, Azores & Ireland |
LocalPred&RegioPred | M. Perez at center national energy renewable (CENER) | Hybrid | Spain and Ireland |
Alea Wind | Aleasoft at the Polytechnic University of Catalonia Spain (UPC) | Statistical | Spain SOWIE Eurowind GmbH, Germany Physical Germany, Austria & Switzerland |
EPREV | Institute of Systems and Computer Engineering of Porto (INESC), Institute of Mechanical Engineering and Industrial Management (INEGI) and Center for the Study of Wind Energy and Atmospheric Flows (CEsA) in Portugal | Statistical | Portugal |
Scirocco | Aeolis Forecasting Services, Netherlands | Hybrid | Netherlands, Germany & Spain |
Presents wind power software models prediction internationally.
In general, wind forecasting is mostly concerned with the immediate-short-term of minutes to hours to commonly up to 1 day and the long-term of up to 2 days. WPMS, as an example of immediate-short-term models, currently predicts wind generation for over 95 percent of Germany’s territory. Reference [5] discusses immediate short-term wind forecasting models. In addition, various models for short-term wind forecasting have been created, such as Predictor, Zephyr, AWPPS, and Ewind, which are all based on high precision numerical weather prediction (NWP) [6, 7]. Previento, which employs a hybrid technique, can anticipate wind for up to 48 hours. References [8, 9] include more studies on long-term forecasting models.
Variations in energy production (induced by variations in wind speed) will become more noticeable on the electrical system as the penetration of wind power generation grows (in terms of the overall energy mix). To avoid balancing concerns, Transmission System Operators (TSOs) operating to balance supply and demand on regional or national grid systems will need to foresee and manage this unpredictability. The moment at which this is necessary varies by system, although it has been noted that it becomes critical when wind energy penetration reaches roughly 5% of installed capacity.
As wind energy’s penetration into individual networks grows, it will be important to make wind farms look more like conventional plants, necessitating the ability to estimate how much energy will be produced over short to medium periods (1 hour to 7 days). Operators, managers, and TSOs commonly anticipate the output from their wind farms in European nations where there is already a substantial level of penetration, such as Spain, Germany, and Denmark. These estimates are used to plan the operations of other factories and for trading.
As the amount of installed capacity develops, forecasting wind energy generation will become more important. The wind industry must expect to do everything possible to enable TSOs to use wind energy to its full potential, which necessitates reliable aggregated output estimates from wind farms.
At the same time as improving the predictability of wind energy plant production through better forecasting tools, it is important to be aware of the true behavior of conventional plants. All of the different energy forms must be considered on an equal level in order to produce the best mix of plants and technologies. As a result, a comprehensive statistical analysis of renewable and conventional plants is critical. This task should be viewed as a critical component of a wind energy development plan, and it should be approached from a comprehensive power system standpoint.
Electricity producers, which include corporations that run wind farms, sell predetermined amounts of energy (measured in kWh) to regional or national energy companies (in the case of wind energy). Because the grid is intended to provide a constant supply of electricity, governments may punish energy producers with large fines if there are power outages.
Energy trade businesses play a critical role in assessing the risk of energy transaction shortfalls by assisting in the forecasting of expected energy production (especially in the case of wind, as a non-steady energy source). Energy dealers, on behalf of energy producers, forecast energy production (in our case, wind energy) using two scenarios:
If there is a shortfall below the forecast, electricity is purchased on the spot market to keep the system running (with prices above the average energy price).
Energy producers are not rewarded for surplus energy produced in excess of predicted output.
In this regard, accurate energy output forecasting is critical to the financial performance of wind farms (i.e. wind energy producers).
The following steps must be completed in order to anticipate the wind farm’s electricity production:
Predict the variation in long-term wind speed over the site at the machine hub height based on long-term wind speeds at the mast locations;
Predict the wake losses that occur when one turbine operates behind another.
In addition to the wind data, the inputs to this process are typically as follows:
Wind farm layout and hub height;
Wind turbine characteristics, such as the power curve (which depicts a turbine’s power production as a function of wind speed) and the thrust curve (which plots the force exerted by the wind at the top of the tower as a function of wind speed);
Air density and turbulence intensity at the place with time (the “gustiness” of the wind).
The topography of the place and its environs; and
Overlay of surface ground cover on the site and in the adjacent region.
Low variability and great predictability are required for a reliable energy source. While the modest variance is acceptable, poor predictability is not, and can result in significant revenue loss. Wind energy fluctuation is caused by a heavy reliance on weather, which varies during the day and annually. As a result, precise weather forecasting is required to produce a useful wind power forecast, Figure 2 shows Dual-step wind power prediction approach based on a hybrid wavelet transform (WT)-ant colony optimization algorithm (ACO)-feedforward artificial neural network (FFANN).
Dual-step wind power prediction approach based on hybrid wavelet transform (WT)-ant colony optimization algorithm (ACO)-feedforward artificial neural network (FFANN).
It is commonly known that the accuracy of weather forecasts improves as the forecast horizon shortens. Combining forecasts from multiple numerical techniques can also be advantageous. As a result, wind farms rely on a variety of weather forecasts given by different models at different times of day or week. Although the weather cannot be controlled, the wind sector may take advantage of advances in artificial intelligence to increase the predictability of the energy supply.
In the chapter, there are several ways for forecasting wind power are categorized as Physical models, statistical models, and hybrid.
Models for wind energy forecasting can be classified into two categories. The first is based on historical wind time series analysis, while the second is based on anticipated values from a numerical weather prediction (NWP) model. However, physical methods, classic statistical or ‘black box’ methods, and more recently, so-called learning approaches, artificial intelligence, or “gray box” methods are used to characterize wind power forecasts. All of these can be included into hybrid approaches.
The first category of models utilizes a statistical approach to anticipate mean hourly wind speed or directly forecast electric power production. To anticipate wind power N-steps ahead, the models in the second category use explanatory variables (often hourly mean wind speed and direction) generated from a meteorological model of wind dynamics. In the majority of cases, the models in the first group produce good results in the estimation of mean monthly or even higher temporal scale (quarterly, annual) wind speed.
However, the influence of atmospheric dynamics becomes more important in the short term (mean daily or hourly wind speed predictions), making the adoption of the second group’s models necessary [10].
In wind power forecasting, there are three steps: first, determining wind speed from a model; second, calculating the wind power output forecast or prediction; and finally, regional forecasting or upscaling or downscaling, which can be implemented over various time horizons. Statistical models are typically used in very short-term forecasting. Ensemble forecasting is utilized to overcome these statistical and learning method conditions [11].
Nielsen et al. [11] demonstrated that if several NWP forecasts are used the forecast error decreases. Louka et al. [12] showed that the Kalman filter can remove systematic forecast errors in NWP wind speed forecasts. Wind forecasting can be separated based on the prediction horizon into three categories:
For short-term forecasting, several tools have been created, including WPPT, Predictor, Zephyr, Ewind, WPFS Ver1.0, and AWPPS. A number of case studies in Spain, Germany, Denmark, Ireland, Greece, and France have used these models [14, 15].
The Wind Power Prediction Tool is a well-known model with a wide range of applications for this time frame (WPPT). It can be used to generate short-term (say, up to 120 hours, or 36 hours) wind power output projections. Because the system can provide prediction values as a total including not only a single wind farm, but also a region, it is extremely flexible. The system also gives accurate estimates of the tools’ uncertainty, which is critical for efficient scheduling or trading. WPPT uses advanced nonlinear techniques.
Because it may produce prediction values as a total spanning not just a particular wind farm, but also a region, the technique is extremely versatile. The system also provides accurate estimations of the tools’ uncertainty, which is critical for optimum trading or scheduling. Advanced nonlinear statistical models underpin WPPT. A semi-parametric power curve model for wind farms that take both wind speed and direction into account, as well as dynamical forecasting models that describe the dynamics of wind power and any diurnal variations, are among the models included in the package. Self-calibrating and self-adaptive models have been developed.
As a result, they update parameters automatically in response to changes in the number of turbines and their features, the environment, the NWP models, and non-explicit model attributes like roughness and filthy blades. WPPT can automatically calibrate to the observed circumstances using artificial intelligence [14]. The system requires online wind power measurements in its simplest configuration. However, the following data is taken into account depending on the configuration: Wind power measurements are now available online. Energy readings from all (or almost all) turbines in a region aggregated (for regional forecasting). Wind speed and direction forecasts by meteorologists for wind farms and regions.
Other measurements or predictions, such as local wind speed, stability, and the number of active turbines, are available. Prediktor, a tool developed by the meteorology research program, is another useful tool (MET). Unlike WPPT, however, Prediktor’s main goal is to represent as much as possible using physical models. Every 6 hours, the system provides the predicted production of wind farms for up to 48 hours. All it requires is online access to NWP model output.
The basic processes are as follows: a NWP model predicts overall weather patterns. Only the entire wind can be predicted by such a model, and only correct forecasts can be made at a given site. Then, if needed, these projections are tailored. The WAsP model tailors the wind turbines to each other by modeling local characteristics such as roughness, horography (ridges and hills), and obstructions, as well as the influence of the wind turbines on each other.
Since no model can simulate nature perfectly, two MOS (model output statistics) filters are used in Prediktor to correct shortcomings. The wind power observed is used to adjust the parameters of these filters. The final output of the model is the expected production of the wind farm every 3 hours over the next 48 hours. Furthermore, Prediktor forecasts or will forecast in the near future for up to 50 wind farms in Ireland, Denmark, Germany, France, and Spain in 2025 [14].
The AWPPS is the only instrument available that estimates confidence intervals for wind power predictions at a predetermined level of certainty (i.e. 85 percent, 90 percent, and 95 percent). The intervals are generated using an important international dedicated to the problem of wind prediction. The Prediction Risk Module allows to forecast uncertainty for the next 24 hours based on projected weather stability. Furthermore, the online use of this module allows for the development of appropriate techniques for optimizing the value of power forecasts [16, 17].
A general overview of wind forecasting models is presented in Table 2. This section is divided into three parts based on the time-scales, and for each of them and its applications.
Time-scale | Range | Applications |
---|---|---|
Immediate-short-term | 8 hours-ahead | • Real-time grid operations |
• Regulation actions | ||
Short-term | Day-ahead | • Economic load dispatch planning |
• Load reasonable decisions | ||
• Operational security in electricity market | ||
Long-term | Multiple-days-ahead | • Maintenance planning |
• Operation management | ||
• Optimal operating cost |
Time-scale classification for wind forecasting [18].
Immediate short-term forecasting Models
Medium-term forecasts (from 6 hours up to a day) are used to make decisions for switching the turbine on or off for safety or conditions on the market.
WPMS has been adapted for performance in the ICT settings of various grid operators and carriers of major wind parks, as one prominent example of immediate-short-term wind forecasting [19].
WPMS deployed artificial neural networks (ANN) in wind farms that were trained using a large amount of historical data. A preprocessor translated input data, output data measured in wind farms, and forecasted meteorological parameters into XML-format before being sent to the program core, which consists of prediction and transformation modules.
Long-term forecasting
Long-term wind forecasting methods have been studied in a few researches. And there aren’t many prediction tools available for this timeframe. Simple models can no longer match the criteria because to the extended ahead-forecasting time, hence NWP or hybrid NWP models are being investigated. Modern wind power forecasting methods, which are typically based on NWP, provide forecasts over a time range of up to several days. To put it another way, the NWP is the source of all information about the future of wind forecasting.
The national weather service or private weather data provider supplies a collection of NWP data that can be used to predict wind speed and power. In the future, it is becoming more common to use NWP for long-term forecasting [20]. Previento is comparable to Prediktor, but it utilizes more severe physical downscaling and specific upscaling techniques. It provides a reliable forecast of projected wind power for any locations and regions in Germany, Europe, and the rest of the world up to 10 days ahead of time, with a temporal resolution of up to 15 minutes. The wind power forecast is based on the best possible mix of meteorological models, as well as the local conditions of the wind farm’s surrounds and the NWP [21].
The Previento system involves a physical approach with data from a large-scale weather prediction model, such as the German Weather Service’s Lokalmodell. It simulates roughness, horography, and wake effects in the boundary layer. The daily variation of the thermal stratification of the atmosphere, which is employed to adjust the logarithmic profile, is critical for calculating wind speed at hub height. The expected power output for single sites is derived using the turbine’s particular power characteristic. The total amount of power generated by wind in a certain region is computed using data from chosen wind farms.
For long-term planning, long-term forecasts (from a day to a week or even a year) are utilized (to schedule the maintenance or unit commitment, optimize the cost of operation). Maintenance of offshore wind farms can be extremely costly, thus proper planning of maintenance activities is essential. Wind power predictions have a temporal resolution of 10 minutes to a few hours (depending on the forecast length). Wind power forecasting improvements are concentrating on using additional data as input to the models involved, as well as offering uncertainty estimates alongside the standard predictions.
Wind forecasting schemes as Figure 3 can also be classified based on their methodology into many categories:
The conceptual mind on wind energy prediction.
Approaching the situation physically (deterministic approach), the physical approach, also known as the deterministic technique, is based on weather forecast data such as temperature, pressure, surface roughness, and obstructions in the lower atmosphere, or numerical weather prediction (NWP).
Established several physical models based on weather data to predict wind speed and estimated wind power [22]. Physical models often rely on global databases of meteorological information or atmospheric mesoscale models, but to provide accurate results, they require massive computer systems [23].
To estimate wind power production, the physical method uses a thorough description of the lower atmosphere. Cellura et al. [22] provide an overview of some of the neural, geostatistical, and hybrid models that have been applied in space-temporal wind forecasting. Dynamic models (also known as prognostic) and kinematic models (also known as diagnostic) are the two main forms of numerical codes for wind field modeling across rugged terrain [24, 25]. The momentum and energy equations are not explicitly solved in these models; instead, parametric relations and/or wind data are used to examine them implicitly [26].
To account for the local circumstances of the physical topography, computational fluid dynamics (CFD) is utilized as an alternative to the power law [27]. Model output statistics (MOS) are frequently employed to reduce systematic forecasting mistakes and to compensate for unknowns in the expected power output [28].
Forecasts are provided at specified nodes on a grid that covers a certain area. Due to the fact that wind farms are not located on these nodes, these estimates must be extrapolated to the required location and turbine hub height. Physical-based forecasting methods are comprised of multiple sub-models that work together to translate wind forecasts at various grid points and model levels to power forecasts at the actual site.
Converting wind speed to power at the level of the wind farm and at hub height depending on the using theoretical power curves supplied by the wind turbine manufacturer. However, since multiple studies have demonstrated a preference for empirically obtained power curves over theoretical ones; theoretical power curves are becoming less and less important. When using a physical methodology, the function that calculates wind generation from NWPs at various locations around the wind farm is modeled once and for all. The calculated transfer function is then applied to the current weather predictions. Physical simulations frequently integrate Model Output Statistics (MOS) for post-processing power forecasts to account for systematic forecasting errors that may be due to the NWP model or modeling approach, Figure 4 shows steps forecasting wind farm with NPW.
Steps forecasting wind farm with NPW.
Statistical approach statistical method is based on the vast amount of historical data without considering meteorological conditions. It usually involved artificial intelligence (neural networks, neuron-fuzzy networks) and time series analysis approaches [29, 30]. Statistical models, the set of models includes a semi-parametric power curve model for wind farms taking into account both wind speed and direction, and dynamical forecasting models describing the dynamics of the wind power and any weather variation, etc.
Statistical forecasting approaches are based on one or more models that establish the relationship between historical power values, historical and future values of meteorological variables, and wind power measurements. The physical events are not deconstructed and accounted for, despite the fact that problem expertise is required for selecting the appropriate meteorological variables and developing appropriate models.
Model parameters are calculated using a collection of previously known data, and they are updated on a frequent basis during online operation to account for any new information that becomes available (i.e. meteorological forecasts and power measurements).
Linear and nonlinear statistical models, as well as structural and black-box models, are all examples of statistical models. Structural models rely on the analyst’s knowledge of the phenomenon of interest, whereas black-box models are built from data in a fairly mechanical manner and require little subject-matter knowledge.
Structural models for wind power forecasting would include diurnal wind speed changes modeling or an explicit function of meteorological variable predictions. Neural-Networks (NNs) and Support Vector Machines are examples of black-box models (SVMs). Some models, on the other hand, are ‘in-between’ the extremes of being entirely structural or completely black-box. Expert systems, for example, learn from experience (from a dataset) and can be programmed with prior information. The subject of gray-box modeling is then discussed.
Statistical models are often made up of two parts: an autoregressive portion for capturing the wind’s persistent behavior, and a “meteorological” part for nonlinear transformation of meteorological variable projections. The autoregressive component provides for considerable gains in forecast accuracy across horizons up to 6–10 hours ahead, when the use of meteorological forecast information alone may not be adequate to exceed persistence.
Statistical approaches to wind power prediction are currently focusing on the use of multiple meteorological forecasts as input and forecast combination, as well as the best use of spatially distributed measurement data for prediction error correction or issuing warnings on potentially large uncertainty.
Calculate a statistical relationship between the essential input data and the generation of wind energy. They entail utilizing a statistical model to directly turn the input factors into wind generation. With these models, a one-step direct calculation of wind power from input parameters is achievable. Most data mining-based models (e.g., ANN, SVM, fuzzy model, model trees), as well as time series analysis methods, can be used as output models (e.g. ARIMA, fractional ARIMA).
A massive quantity of data is processed in the statistical technique, and meteorological processes are not clearly represented. The relationship between historical power output and weather is established, and this information is then used to anticipate future power output. Statistical methods, unlike physical methods, simply require one step to convert input variables to power output. As a result, the procedures used are referred to as “black box.” In most cases, a statistical relationship is established between the weather forecast or projection and the wind farm’s prospective power output. Other statistical approaches employed include the Box-Jenkins methodology, the use of the Kalman filter, and the use of autoregressive (AR), moving average (MA), autoregressive moving average model (ARMA), and autoregressive integrated moving average model (ARIMA).
Torres et al. [29] discovered that compared to persistence, it was possible to get a 20% error reduction when forecasting average hourly wind speed for a 10 h forecast horizon at a number of locations using nine variables.
Classical time series analysis is not the only approach to model a statistical relationship between data points. Artificial neural networks (ANN) and fuzzy systems are the most common soft computing (or machine learning) techniques utilized, however other models such as gray predictors and support vector machines (SVM) have also been used. Artificial intelligence (AI) approaches are another term for learning approaches. They’re known as learning techniques since they take historical time series to learn about the relationship between projected wind and predicted power production. They’ve been dubbed “gray box” approaches in recent years.
The presentation of parametric statistical methods directly inspired from the physical equation. Parametric modeling according to the wind speed only, the investigated the simplest parametric models, namely linear regression and logistic regression, with the wind speed as the unique explanatory variable. If the predicted power at time t is denoted by Yˆt, these models are given by
where the parameters a0, a1, C are estimated using the associated methodology.
Logistic regression has also been considered to mimic more closely Eq. (1). More precisely, the model is then defined by:
where ai, i = 0, …, 3 and C are estimated parameters.
This model is using not only wind speed as a predictor, but also wind direction, (coded by its cosine and sine: Dcos and Dsin), temperature T, and the variances of the wind speed WS and direction, DS, Re and DS, Im.
The Lasso method, which simultaneously performs variable selection and regularization through the least squares criterion penalized by the ` 1 norm of the regression coefficients has been investigated as well (see for instance [20]). The model is defined by.
with a0, …, a7 minimizing.
Hybrid method, which combines physical methods and statistical methods particularly uses weather forecasts and time series analysis.
ANEMOS is a hybrid wind forecast tool that takes into account a variety of time horizons. The development of combining high-resolution meteorological predictions and appropriate prediction models for the offshore is emphasized [18, 30].
Hybrid models aim to combine the advantages of each model in order to produce the best predicting results possible. Because the information provided in individual forecasting techniques is restricted, a hybrid approach can take use of the available data, integrate individual model data, and maximize the benefits of many forecasting methods, improving prediction accuracy [31].
Many techniques, such as mixing physical and statistical procedures or short-term and medium-term models, are included in hybrid methods. A number of hybrid models were utilized to anticipate wind power. Here are some examples of potential combinations:
A combination of physical and artificial intelligence approaches.
Using a combination of artificial intelligence models Zhao et al. [32] looked into a hybrid wind forecasting system that included both NWP and ANN models. To anticipate meteorological characteristics, the NWP model combines the Global Forecasting System (GFS) with the Weather Research and Forecasting (WRF) system. Figure 5 Shows an example of an ANN structure with 4 inputs and 2 hidden layer.
ANN structure with 4 inputs and 2 hidden layer.
Shi et al. [33] proposed two hybrid models for wind speed and power forecasting: ARIMA-ANN and ARIMA-SVM. Based on two case studies on wind speed and wind power generation, this research analyses the application of the suggested hybrid models in a systematic and thorough manner. The findings imply that hybrid approaches are feasible alternatives for predicting both wind speed and wind power generation time series, but that they do not always provide better forecasting performance for all forecasting time horizons investigated.
Guo et al. [34] proposed a novel hybrid wind speed forecasting method based on a back propagation neural network and the notion of seasonal exponential adjustment to exclude seasonal effects from real wind speed datasets. A proposed technique outperformed the single back propagation neural network in the tests.
For short-term wind power forecasting in Portugal, Catalo et al. [18] presented a hybrid approach based on the combination of ANN and wavelet transform. To deconstruct the wind power series into a set of better-behaved constituent series, the wavelet transform is applied. The test findings show that the proposed hybrid technique for forecasting wind output has a lot of potential.
Finally, hybrid models (e.g. [18, 35]) are based on the combination of the physical and statistical models, the combination of models with several time horizons, and the combination of alternative statistical models
The spatial correlation models take into account the spatial link between wind speeds at different sites. The wind speed time-series of the projected point and its neighbors is used to predict the wind speed in spatial correlation models [35]. When predicting wind speed at one location based on observations taken at another, a spatial correlation model is used. Data obtained over a seven-year period [36] was used to test its behavior and provide adequate verification.
Based on cross-correlation at surrounding sites, Alexiadis et al. [37] demonstrated a technique for forecasting wind speed and power output up to several hours ahead. This research established an ANN technique based on spatial correlation models that outperform the persistence forecasting model in terms of forecasting accuracy [38].
Barbounis and Theocharis [39] proposed the use of local feedback dynamic fuzzy neural network (LF-DFNN) to forecast wind speed using spatial correlation. Remote meteorological stations are installed at two reference sites in accordance with the location of the base site so that the three sites are aligned along the prevailing wind direction. Using spatial information from remote meteorological stations, the LF-DFNN is used in this paper to predict multi-step forward wind speed in the base site. The LF-DFNN outperforms other network models tested in this application, according to simulation data.
Various novel AI algorithms for wind speed and power prediction have recently been developed as a result of the advancement of artificial intelligence (AI). Artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), fuzzy logic approaches, support vector machine (SVM), neuro-fuzzy network, and evolutionary optimization algorithms are among the newly developed methodologies.
Through the training process, ANN models can represent a complex nonlinear relationship and extract the dependency between variables [39]. Back propagation neural networks, recurrent neural networks, radial basis function (RBF) neural networks, ridgelet neural networks, and adaptive linear element neural networks are examples of ANN-based methods. The application of an ANN-based method to the problem of wind power forecasting is appropriate.
ANN might handle nonlinear and complex scenarios in terms of categorization or forecasting. ANN models can depict a complex nonlinear relationship and extract the link between variables through the training phase [39]. Examples of ANN-based techniques include back propagation neural networks, recurrent neural networks, radial basis function (RBF) neural networks, ridgelet neural networks, and adaptive linear element neural networks. It appears that applying an ANN-based technique to the problem of wind power forecasting is a good idea.
Using time series analysis, Sfetsos [40] proposed an ANN technique for forecasting mean hourly wind speed data. The proposed methodology also has a benefit for utilities that have a high level of wind penetration and utilize hourly intervals for power system operational procedures like economic dispatch and unit commitment.
Chang [41] discussed back propagation neural network-based wind power forecasting algorithms. The created model for short-term wind forecasting demonstrated excellent accuracy when utilized to supply energy to a 2400 kW (WECS) on the Taichung coast. Back propagation neural networks and recurrent neural networks were used in More and Deo’s [42] wind forecasting methodology. Traditional statistical time series analysis has been found to be less accurate than neural network forecasting [43].
Chang [44] described a method for forecasting wind power generation time series using an RBF neural network. The numerical results show that the suggested forecasting method is accurate and dependable, with good matches between realistic values and predicting values.
Guo et al. [45] studied a feed-forward neural network (FNN) wind forecasting approach based on modified empirical mode decomposition (EMD). Through multi-step forecasting of mean monthly and daily wind speeds in Zhangye, China, the proposed technique outperforms basic FNN and unmodified EMD-based FNN [46]. Li and Shi [47] used three types of conventional ANNs to anticipate wind speed: adaptive linear element, back propagation, and radial basis function.
The outcomes of comparing three types of ANN reveal that no single ANN model outperforms another universally in terms of all evaluation measures, even for the same wind dataset. Furthermore, the type of ANN to use for the best results is determined by the data sources.
Yang et al. [48] proposed an ANFIS approach for interpolating missing and incorrect wind data. Twelve measured wind data sets from a wind farm in North China are interpolated and examined for performance testing. The ANFIS method’s effectiveness was demonstrated by the test results. A SVM-based technique for wind power forecasting was described by Zeng and Qiao [31]. Real wind speed and wind power data obtained from the National Renewable Energy Laboratory are used in simulation research.
The suggested SVM method outperforms the persistence model and the RBF neural network-based model, according to the results. For one-step ahead, wind speed forecasting, Zhou et al. [49] described a systematic investigation on fine-tuning least-squares support vector machines (LSSVM) model parameters. Three SVM kernels are implemented: linear, Gaussian, and polynomial kernels. LSSVM approaches are proven to outperform the persistence model in the vast majority of scenarios. For short-term wind power forecasting, Xia et al. [50] introduced a neuro-fuzzy network technique.
For the wind power forecasting of a practical wind farm in China, the forecasting approach is used. The results of the tests revealed that the trained neuro-fuzzy networks are capable of predicting and forecasting wind power.
Jursa and Rohrig [51] proposed a new short-term prediction technique based on the automated specification of neural networks and the nearest
Wind Forecasting in the Future The forecast accuracy of wind power prediction systems is becoming increasingly significant due to the high penetration of wind power in the energy grid. Many academics have been working on wind power forecasting in recent years. Forecast accuracy has steadily increased and intensive research and development efforts are projected to be underway soon. In order to improve wind power projections even more, various literature [32, 52] suggest that future studies should focus on the following areas:
Research new artificial intelligence approaches and enhance training algorithms in order to increase forecast accuracy. Future studies will also focus on new strategies for dealing with complicated terrain.
More research into hybrid methods is needed to combine different approaches, such as combining physical and statistical approaches, to achieve good results in both long-term and short-term prediction.
In actual WECS, the existing forecast approach should be used. Continue your investigation into the practical application of the methodologies rather than just the theory.
Develop a more precise assessment methodology and a standard for measuring technique performance.
Improved input data for wind power forecasting will come from improved NWP models and more regular weather forecast updates.
Expand research into the use of online wind data, particularly for short-term wind forecasting.
More study on adaptive parameter estimation is needed. The models can respond to changes in the farms and their surroundings automatically.
Conduct additional research into the NWP models designed for use in an offshore environment. Improve meteorological data available to evaluate NWP results for offshore areas.
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\n\nThe first step in obtaining funds for your Open Access publication begins with your institution or library. IntechOpen’s publishing standards align with most institutional funding programs. Our advice is to petition your institution for help in financing your Open Access publication.
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\n\nPlease consult our Open Access Funding page to explore some of these funding opportunities and learn more about how you could finance your IntechOpen publication. Keep in mind that this list is not definitive, and while we are constantly updating and informing our Authors of new funding opportunities, we recommend that you always check with your institution first.
\n\nFor Authors who are unable to obtain funding from their institution or research funding bodies and still need help in covering publication costs, IntechOpen offers the possibility of applying for a Waiver.
\n\nOur mission is to support Authors in publishing their research and making an impact within the scientific community. Currently, 14% of Authors receive full waivers and 6% receive partial waivers.
\n\nWhile providing support and advice to all our international Authors, waiver priority will be given to those Authors who reside in countries that are classified by the World Bank as low-income economies. In this way, we can help ensure that the scientific work being carried out can make an impact within the worldwide scientific community, no matter where an Author might live.
\n\nThe application process is open after your submitted manuscript has been accepted for publication. To apply, please fill out a Waiver Request Form and send it to your Author Service Manager. If you have an official letter from your university or institution showing that funds for your OA publication are unavailable, please attach that as well. The Waiver Request will normally be addressed within one week from the application date. All chapters that receive waivers or partial waivers will be designated as such online.
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Wettability is strongly dependent on time from modification.",book:{id:"5319",slug:"wetting-and-wettability",title:"Wetting and Wettability",fullTitle:"Wetting and Wettability"},signatures:"Nikola Slepickova Kasalkova, Petr Slepicka, Zdenka Kolska and\nVaclav Svorcik",authors:[{id:"144929",title:"Prof.",name:"Vaclav",middleName:null,surname:"Svorcik",slug:"vaclav-svorcik",fullName:"Vaclav Svorcik"},{id:"146297",title:"Dr.",name:"Petr",middleName:null,surname:"Slepicka",slug:"petr-slepicka",fullName:"Petr Slepicka"},{id:"147600",title:"Ph.D.",name:"Nikola",middleName:null,surname:"Slepičková Kasálková",slug:"nikola-slepickova-kasalkova",fullName:"Nikola Slepičková Kasálková"},{id:"153983",title:"Dr.",name:"Zdeňka",middleName:null,surname:"Kolská",slug:"zdenka-kolska",fullName:"Zdeňka Kolská"}]},{id:"30975",doi:"10.5772/36619",title:"Solution Properties of κ-Carrageenan and Its Interaction with Other Polysaccharides in Aqueous Media",slug:"solution-properties-of-k-carrageenan-and-its-interaction-with-other-polysaccharides-in-aqueous-media",totalDownloads:7630,totalCrossrefCites:6,totalDimensionsCites:33,abstract:null,book:{id:"1601",slug:"rheology",title:"Rheology",fullTitle:"Rheology"},signatures:"Alberto Tecante and María del Carmen Núñez Santiago",authors:[{id:"109087",title:"Prof.",name:"Alberto",middleName:null,surname:"Tecante",slug:"alberto-tecante",fullName:"Alberto Tecante"},{id:"109098",title:"Dr.",name:"Maria Del Carmen",middleName:null,surname:"Nunez-Santiago",slug:"maria-del-carmen-nunez-santiago",fullName:"Maria Del Carmen Nunez-Santiago"}]},{id:"48822",doi:"10.5772/60808",title:"Wettability of Nanostructured Surfaces",slug:"wettability-of-nanostructured-surfaces",totalDownloads:3166,totalCrossrefCites:12,totalDimensionsCites:33,abstract:"There are many studies in literature concerning contact angle measurements on different materials/substrates. It is documented that textiles can be coated with multifunctional materials in form of thin films or nanoparticles to acquire characteristics that can improve the protection and comfort of the wearer. The capacity of oxide nanostructures to inhibit fungal development and neutralize bacteria is a direct consequence of their wetting behavior [1–6]. Moreover, the radical modification of wetting behavior of nanostructures from hydrophilic to hydrophobic when changing the pulsed laser deposition (PLD) ambient will be thoroughly discussed.",book:{id:"5319",slug:"wetting-and-wettability",title:"Wetting and Wettability",fullTitle:"Wetting and Wettability"},signatures:"L. Duta, A.C. Popescu, I. Zgura, N. Preda and I.N. Mihailescu",authors:[{id:"17636",title:"Prof.",name:"Ion N.",middleName:null,surname:"Mihailescu",slug:"ion-n.-mihailescu",fullName:"Ion N. Mihailescu"},{id:"23532",title:"Dr.",name:"Andrei",middleName:null,surname:"Popescu",slug:"andrei-popescu",fullName:"Andrei Popescu"},{id:"174343",title:"Dr.",name:"Liviu",middleName:null,surname:"Duta",slug:"liviu-duta",fullName:"Liviu Duta"},{id:"174344",title:"Dr.",name:"Irina",middleName:null,surname:"Zgura",slug:"irina-zgura",fullName:"Irina Zgura"},{id:"174345",title:"Dr.",name:"Ligia",middleName:null,surname:"Frunza",slug:"ligia-frunza",fullName:"Ligia Frunza"}]},{id:"64392",doi:"10.5772/intechopen.80542",title:"Corrosion Inhibitors",slug:"corrosion-inhibitors",totalDownloads:3373,totalCrossrefCites:21,totalDimensionsCites:32,abstract:"Corrosion is a natural process driven by energy consideration. Inhibition is a preventive measure against corrosive attack on metallic materials. Corrosion inhibitors have been frequently studied, since they offer simple solution for protection of metals against corrosion in aqueous environment. Mineral acids like hydrochloric and sulfuric acids are most widely used in pickling baths to remove the metal oxides formed on the surface. The multidisciplinary aspect of corrosion problems combined with the distributed responsibilities associated with such problems only increase the complexity of the subject. Inhibitors are used in industrial and commercial processes to minimize both the metal loss and acid consumption.",book:{id:"7550",slug:"corrosion-inhibitors",title:"Corrosion Inhibitors",fullTitle:"Corrosion Inhibitors"},signatures:"Geethamani Palanisamy",authors:[{id:"253697",title:"Dr.",name:"Geethamani",middleName:null,surname:"Palanisamy",slug:"geethamani-palanisamy",fullName:"Geethamani Palanisamy"}]}],mostDownloadedChaptersLast30Days:[{id:"48768",title:"TiO2 -Based Surfaces with Special Wettability – From Nature to Biomimetic Application",slug:"tio2-based-surfaces-with-special-wettability-from-nature-to-biomimetic-application",totalDownloads:5140,totalCrossrefCites:3,totalDimensionsCites:4,abstract:"Super-wetting/antiwetting surfaces with extremely high contrast of surface energy and liquid adhesion have attracted a lot of interest in both fundamental research and industry. Various types of special wetting surfaces can be constructed by adjusting the topographical structure and chemical composition. In this chapter, recent advance of the super-wetting/antiwetting surfaces with special solid/liquid adhesion has been reviewed, with a focus on the biomimetic fabrication and applications of TiO2-based surfaces. Special super-wettability examples include lotus-leaf-inspired surfaces with low adhesion, rose-petal-inspired surfaces with high adhesion, spider silk bio-inspired surfaces with directional adhesion, fish-scale-inspired underwater superoleophobic surface, and artificial surfaces with controllable or stimuli-responsive liquid adhesion. In addition, we will review some potential applications related to artificial antiwetting surface with controllable adhesion, e.g., self-cleaning, antifogging/anti-icing, micro-droplet manipulation, fog/water collection, water/oil separation, anti-bioadhesion, micro-template for patterning, and friction reduction. Finally, the difficulty and prospects of this renascent and rapidly developing field are also briefly proposed and discussed.",book:{id:"5319",slug:"wetting-and-wettability",title:"Wetting and Wettability",fullTitle:"Wetting and Wettability"},signatures:"Jian-Ying Huang and Yue-Kun Lai",authors:[{id:"175512",title:"Prof.",name:"Yuekun",middleName:null,surname:"Lai",slug:"yuekun-lai",fullName:"Yuekun Lai"}]},{id:"64392",title:"Corrosion Inhibitors",slug:"corrosion-inhibitors",totalDownloads:3365,totalCrossrefCites:21,totalDimensionsCites:31,abstract:"Corrosion is a natural process driven by energy consideration. Inhibition is a preventive measure against corrosive attack on metallic materials. Corrosion inhibitors have been frequently studied, since they offer simple solution for protection of metals against corrosion in aqueous environment. Mineral acids like hydrochloric and sulfuric acids are most widely used in pickling baths to remove the metal oxides formed on the surface. The multidisciplinary aspect of corrosion problems combined with the distributed responsibilities associated with such problems only increase the complexity of the subject. Inhibitors are used in industrial and commercial processes to minimize both the metal loss and acid consumption.",book:{id:"7550",slug:"corrosion-inhibitors",title:"Corrosion Inhibitors",fullTitle:"Corrosion Inhibitors"},signatures:"Geethamani Palanisamy",authors:[{id:"253697",title:"Dr.",name:"Geethamani",middleName:null,surname:"Palanisamy",slug:"geethamani-palanisamy",fullName:"Geethamani Palanisamy"}]},{id:"68236",title:"The Arrhenius Acid and Base Theory",slug:"the-arrhenius-acid-and-base-theory",totalDownloads:1444,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Swedish Svante Arrhenius, in 1884 proposed the concept of acid and base based on the theory of ionization. According to Arrhenius, the acids are the hydrogen-containing compounds which give H+ ions or protons on dissociation in water and bases are the hydroxide compounds which give OH− ions on dissociation in water. This concept is only applicable to those compounds which dissolved in aqueous solution (or you can say where water is the solvent). It covers many common acids, bases and their chemical reactions, but there are also other compounds that have the characteristics of acids and bases but they do not fit into Arrhenius concept.",book:{id:"10050",slug:"corrosion",title:"Corrosion",fullTitle:"Corrosion"},signatures:"Shikha Munjal and Aakash Singh",authors:[{id:"290524",title:"Ms.",name:"Shikha",middleName:null,surname:"Munjal",slug:"shikha-munjal",fullName:"Shikha Munjal"}]},{id:"71924",title:"Technological Background and Properties of Thin Film Semiconductors",slug:"technological-background-and-properties-of-thin-film-semiconductors",totalDownloads:919,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Especially with the development of nanotechnology and polymer science, interest in research and production of both efficient and lower-cost semiconductor thin film materials is increasing day by day. The use of nano-structured thin films for efficient use of solar cells in production of n-type semiconductor materials is one of the most important sources of energy and new-generation energy. Considering the indicated trends and energy requirements, it has been important to transfer this technology in detail regarding the surface technologies related to the semiconductor materials produced with thin film technologies instead of bulk materials. With this aim, this book chapter “Technological Background and Properties of Thin Film Semiconductors” includes a brief story about semiconductors, band gap theory, thin film applications, and besides traditional thin film processing methods finally a new technology called aerosol deposition technique which allows room temperature processing of several materials for semiconductor applications, respectively. It is thought that it will make important contributions to the relevant field and bring a new perspective and direct scientific research in “process-structure–property-performance” relation.",book:{id:"10061",slug:"21st-century-surface-science-a-handbook",title:"21st Century Surface Science",fullTitle:"21st Century Surface Science - a Handbook"},signatures:"Orkut Sancakoglu",authors:[{id:"177188",title:"Dr.",name:"Orkut",middleName:null,surname:"Sancakoğlu",slug:"orkut-sancakoglu",fullName:"Orkut Sancakoğlu"}]},{id:"60426",title:"Applications of Viscoelastic Fluids Involving Hydrodynamic Stability and Heat Transfer",slug:"applications-of-viscoelastic-fluids-involving-hydrodynamic-stability-and-heat-transfer",totalDownloads:1364,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Rayleigh and Marangoni convection and rheology are linked in the thermal convection of viscoelastic fluids to some recent technological applications. Such technology developments as the ones presented here undoubtedly shall be based on interdisciplinary projects involving not only rheology or fluid mechanics but several other disciplines. Three practical applications which use Rayleigh or Marangoni convection in their working principle are presented along with some technical details. This contribution focus mainly on the physical mechanism and the involved hydrodynamics of some lab and industrial applications. Finally, a short discussion on the role play by the convective mechanisms is given in order to provide integration of the exposed ideas.",book:{id:"6702",slug:"polymer-rheology",title:"Polymer Rheology",fullTitle:"Polymer Rheology"},signatures:"Ildebrando Pérez-Reyes, René Osvaldo Vargas-Aguilar, Samuel\nBernardo Pérez-Vega and Alejandro Sebastián Ortiz-Pérez",authors:[{id:"183938",title:"Dr.",name:"Samuel",middleName:null,surname:"Perez-Vega",slug:"samuel-perez-vega",fullName:"Samuel Perez-Vega"},{id:"186659",title:"Prof.",name:"Ildebrando",middleName:null,surname:"Pérez-Reyes",slug:"ildebrando-perez-reyes",fullName:"Ildebrando Pérez-Reyes"},{id:"242858",title:"Prof.",name:"Rene Osvaldo",middleName:null,surname:"Vargas-Aguilar",slug:"rene-osvaldo-vargas-aguilar",fullName:"Rene Osvaldo Vargas-Aguilar"},{id:"242859",title:"Prof.",name:"Alejandro Sebastian",middleName:null,surname:"Ortiz-Perez",slug:"alejandro-sebastian-ortiz-perez",fullName:"Alejandro Sebastian Ortiz-Perez"}]}],onlineFirstChaptersFilter:{topicId:"160",limit:6,offset:0},onlineFirstChaptersCollection:[],onlineFirstChaptersTotal:0},preDownload:{success:null,errors:{}},subscriptionForm:{success:null,errors:{}},aboutIntechopen:{},privacyPolicy:{},peerReviewing:{},howOpenAccessPublishingWithIntechopenWorks:{},sponsorshipBooks:{sponsorshipBooks:[],offset:8,limit:8,total:0},allSeries:{pteSeriesList:[{id:"14",title:"Artificial Intelligence",numberOfPublishedBooks:9,numberOfPublishedChapters:89,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:104,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:32,numberOfPublishedChapters:318,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:12,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:11,numberOfPublishedChapters:141,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:8,numberOfPublishedChapters:129,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:113,numberOfOpenTopics:3,numberOfUpcomingTopics:1,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:107,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2632-0517",doi:"10.5772/intechopen.73681",isOpenForSubmission:!0}],sshSeriesList:[{id:"22",title:"Business, Management and Economics",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:5,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:0,numberOfPublishedChapters:15,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:null,doi:"10.5772/intechopen.100361",isOpenForSubmission:!0}],testimonialsList:[{id:"6",text:"It is great to work with the IntechOpen to produce a worthwhile collection of research that also becomes a great educational resource and guide for future research endeavors.",author:{id:"259298",name:"Edward",surname:"Narayan",institutionString:null,profilePictureURL:"https://mts.intechopen.com/storage/users/259298/images/system/259298.jpeg",slug:"edward-narayan",institution:{id:"3",name:"University of Queensland",country:{id:null,name:"Australia"}}}},{id:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"June 29th, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:32,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. 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She is also the Global Harmonization Initiative (GHI)",institutionString:"Australian College of Business & Technology",institution:null}]},{type:"book",id:"6820",title:"Keratin",subtitle:null,coverURL:"https://cdn.intechopen.com/books/images_new/6820.jpg",slug:"keratin",publishedDate:"December 19th 2018",editedByType:"Edited by",bookSignature:"Miroslav Blumenberg",hash:"6def75cd4b6b5324a02b6dc0359896d0",volumeInSeries:2,fullTitle:"Keratin",editors:[{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. 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Her research interests include microalgal biotechnology with an emphasis on microalgae-based products.",institutionString:"Universidade Federal de Santa Maria",institution:{name:"Universidade Federal de Santa Maria",institutionURL:null,country:{name:"Brazil"}}}]},{type:"book",id:"7953",title:"Bioluminescence",subtitle:"Analytical Applications and Basic Biology",coverURL:"https://cdn.intechopen.com/books/images_new/7953.jpg",slug:"bioluminescence-analytical-applications-and-basic-biology",publishedDate:"September 25th 2019",editedByType:"Edited by",bookSignature:"Hirobumi Suzuki",hash:"3a8efa00b71abea11bf01973dc589979",volumeInSeries:4,fullTitle:"Bioluminescence - Analytical Applications and Basic Biology",editors:[{id:"185746",title:"Dr.",name:"Hirobumi",middleName:null,surname:"Suzuki",slug:"hirobumi-suzuki",fullName:"Hirobumi Suzuki",profilePictureURL:"https://mts.intechopen.com/storage/users/185746/images/system/185746.png",biography:"Dr. Hirobumi Suzuki received his Ph.D. in 1997 from Tokyo Metropolitan University, Japan, where he studied firefly phylogeny and the evolution of mating systems. 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Radiotherapy and Nuclear Medicine Technology has always been my aspiration and my life. As years passed I accumulated a tremendous amount of skills and knowledge in Radiotherapy and Nuclear Medicine, Conventional Radiology, Radiation Protection, Bioinformatics Technology, PACS, Image processing, clinically and lecturing that will enable me to provide a valuable service to the community as a Researcher and Consultant in this field. My method of translating this into day to day in clinical practice is non-exhaustible and my habit of exchanging knowledge and expertise with others in those fields is the code and secret of success.",institutionString:null,institution:{name:"Majmaah University",country:{name:"Saudi Arabia"}}},{id:"313277",title:"Dr.",name:"Bartłomiej",middleName:null,surname:"Płaczek",slug:"bartlomiej-placzek",fullName:"Bartłomiej Płaczek",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/313277/images/system/313277.jpg",biography:"Bartłomiej Płaczek, MSc (2002), Ph.D. (2005), Habilitation (2016), is a professor at the University of Silesia, Institute of Computer Science, Poland, and an expert from the National Centre for Research and Development. His research interests include sensor networks, smart sensors, intelligent systems, and image processing with applications in healthcare and medicine. He is the author or co-author of more than seventy papers in peer-reviewed journals and conferences as well as the co-author of several books. He serves as a reviewer for many scientific journals, international conferences, and research foundations. Since 2010, Dr. Placzek has been a reviewer of grants and projects (including EU projects) in the field of information technologies.",institutionString:"University of Silesia",institution:{name:"University of Silesia",country:{name:"Poland"}}},{id:"35000",title:"Prof.",name:"Ulrich H.P",middleName:"H.P.",surname:"Fischer",slug:"ulrich-h.p-fischer",fullName:"Ulrich H.P Fischer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/35000/images/3052_n.jpg",biography:"Academic and Professional Background\nUlrich H. P. has Diploma and PhD degrees in Physics from the Free University Berlin, Germany. He has been working on research positions in the Heinrich-Hertz-Institute in Germany. Several international research projects has been performed with European partners from France, Netherlands, Norway and the UK. He is currently Professor of Communications Systems at the Harz University of Applied Sciences, Germany.\n\nPublications and Publishing\nHe has edited one book, a special interest book about ‘Optoelectronic Packaging’ (VDE, Berlin, Germany), and has published over 100 papers and is owner of several international patents for WDM over POF key elements.\n\nKey Research and Consulting Interests\nUlrich’s research activity has always been related to Spectroscopy and Optical Communications Technology. Specific current interests include the validation of complex instruments, and the application of VR technology to the development and testing of measurement systems. He has been reviewer for several publications of the Optical Society of America\\'s including Photonics Technology Letters and Applied Optics.\n\nPersonal Interests\nThese include motor cycling in a very relaxed manner and performing martial arts.",institutionString:null,institution:{name:"Charité",country:{name:"Germany"}}},{id:"341622",title:"Ph.D.",name:"Eduardo",middleName:null,surname:"Rojas Alvarez",slug:"eduardo-rojas-alvarez",fullName:"Eduardo Rojas Alvarez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/341622/images/15892_n.jpg",biography:null,institutionString:null,institution:{name:"University of Cuenca",country:{name:"Ecuador"}}},{id:"215610",title:"Prof.",name:"Muhammad",middleName:null,surname:"Sarfraz",slug:"muhammad-sarfraz",fullName:"Muhammad Sarfraz",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/215610/images/system/215610.jpeg",biography:"Muhammad Sarfraz is a professor in the Department of Information Science, Kuwait University. His research interests include computer graphics, computer vision, image processing, machine learning, pattern recognition, soft computing, data science, intelligent systems, information technology, and information systems. Prof. Sarfraz has been a keynote/invited speaker on various platforms around the globe. He has advised various students for their MSc and Ph.D. theses. He has published more than 400 publications as books, journal articles, and conference papers. He is a member of various professional societies and a chair and member of the International Advisory Committees and Organizing Committees of various international conferences. Prof. Sarfraz is also an editor-in-chief and editor of various international journals.",institutionString:"Kuwait University",institution:{name:"Kuwait University",country:{name:"Kuwait"}}},{id:"32650",title:"Prof.",name:"Lukas",middleName:"Willem",surname:"Snyman",slug:"lukas-snyman",fullName:"Lukas Snyman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/32650/images/4136_n.jpg",biography:"Lukas Willem Snyman received his basic education at primary and high schools in South Africa, Eastern Cape. He enrolled at today's Nelson Metropolitan University and graduated from this university with a BSc in Physics and Mathematics, B.Sc Honors in Physics, MSc in Semiconductor Physics, and a Ph.D. in Semiconductor Physics in 1987. After his studies, he chose an academic career and devoted his energy to the teaching of physics to first, second, and third-year students. After positions as a lecturer at the University of Port Elizabeth, he accepted a position as Associate Professor at the University of Pretoria, South Africa.\r\n\r\nIn 1992, he motivates the concept of 'television and computer-based education” as means to reach large student numbers with only the best of teaching expertise and publishes an article on the concept in the SA Journal of Higher Education of 1993 (and later in 2003). The University of Pretoria subsequently approved a series of test projects on the concept with outreach to Mamelodi and Eerste Rust in 1993. In 1994, the University established a 'Unit for Telematic Education ' as a support section for multiple faculties at the University of Pretoria. In subsequent years, the concept of 'telematic education” subsequently becomes well established in academic circles in South Africa, grew in popularity, and is adopted by many universities and colleges throughout South Africa as a medium of enhancing education and training, as a method to reaching out to far out communities, and as a means to enhance study from the home environment.\r\n\r\nProfessor Snyman in subsequent years pursued research in semiconductor physics, semiconductor devices, microelectronics, and optoelectronics.\r\n\r\nIn 2000 he joined the TUT as a full professor. Here served for a period as head of the Department of Electronic Engineering. Here he makes contributions to solar energy development, microwave and optoelectronic device development, silicon photonics, as well as contributions to new mobile telecommunication systems and network planning in SA.\r\n\r\nCurrently, he teaches electronics and telecommunications at the TUT to audiences ranging from first-year students to Ph.D. level.\r\n\r\nFor his research in the field of 'Silicon Photonics” since 1990, he has published (as author and co-author) about thirty internationally reviewed articles in scientific journals, contributed to more than forty international conferences, about 25 South African provisional patents (as inventor and co-inventor), 8 PCT international patent applications until now. Of these, two USA patents applications, two European Patents, two Korean patents, and ten SA patents have been granted. A further 4 USA patents, 5 European patents, 3 Korean patents, 3 Chinese patents, and 3 Japanese patents are currently under consideration.\r\n\r\nRecently he has also published an extensive scholarly chapter in an internet open access book on 'Integrating Microphotonic Systems and MOEMS into standard Silicon CMOS Integrated circuitry”.\r\n\r\nFurthermore, Professor Snyman recently steered a new initiative at the TUT by introducing a 'Laboratory for Innovative Electronic Systems ' at the Department of Electrical Engineering. The model of this laboratory or center is to primarily combine outputs as achieved by high-level research with lower-level system development and entrepreneurship in a technical university environment. Students are allocated to projects at different levels with PhDs and Master students allocated to the generation of new knowledge and new technologies, while students at the diploma and Baccalaureus level are allocated to electronic systems development with a direct and a near application for application in industry or the commercial and public sectors in South Africa.\r\n\r\nProfessor Snyman received the WIRSAM Award of 1983 and the WIRSAM Award in 1985 in South Africa for best research papers by a young scientist at two international conferences on electron microscopy in South Africa. He subsequently received the SA Microelectronics Award for the best dissertation emanating from studies executed at a South African university in the field of Physics and Microelectronics in South Africa in 1987. In October of 2011, Professor Snyman received the prestigious Institutional Award for 'Innovator of the Year” for 2010 at the Tshwane University of Technology, South Africa. This award was based on the number of patents recognized and granted by local and international institutions as well as for his contributions concerning innovation at the TUT.",institutionString:null,institution:{name:"University of South Africa",country:{name:"South Africa"}}},{id:"317279",title:"Mr.",name:"Ali",middleName:"Usama",surname:"Syed",slug:"ali-syed",fullName:"Ali Syed",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/317279/images/16024_n.png",biography:"A creative, talented, and innovative young professional who is dedicated, well organized, and capable research fellow with two years of experience in graduate-level research, published in engineering journals and book, with related expertise in Bio-robotics, equally passionate about the aesthetics of the mechanical and electronic system, obtained expertise in the use of MS Office, MATLAB, SolidWorks, LabVIEW, Proteus, Fusion 360, having a grasp on python, C++ and assembly language, possess proven ability in acquiring research grants, previous appointments with social and educational societies with experience in administration, current affiliations with IEEE and Web of Science, a confident presenter at conferences and teacher in classrooms, able to explain complex information to audiences of all levels.",institutionString:null,institution:{name:"Air University",country:{name:"Pakistan"}}},{id:"75526",title:"Ph.D.",name:"Zihni Onur",middleName:null,surname:"Uygun",slug:"zihni-onur-uygun",fullName:"Zihni Onur Uygun",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/75526/images/12_n.jpg",biography:"My undergraduate education and my Master of Science educations at Ege University and at Çanakkale Onsekiz Mart University have given me a firm foundation in Biochemistry, Analytical Chemistry, Biosensors, Bioelectronics, Physical Chemistry and Medicine. After obtaining my degree as a MSc in analytical chemistry, I started working as a research assistant in Ege University Medical Faculty in 2014. In parallel, I enrolled to the MSc program at the Department of Medical Biochemistry at Ege University to gain deeper knowledge on medical and biochemical sciences as well as clinical chemistry in 2014. In my PhD I deeply researched on biosensors and bioelectronics and finished in 2020. Now I have eleven SCI-Expanded Index published papers, 6 international book chapters, referee assignments for different SCIE journals, one international patent pending, several international awards, projects and bursaries. In parallel to my research assistant position at Ege University Medical Faculty, Department of Medical Biochemistry, in April 2016, I also founded a Start-Up Company (Denosens Biotechnology LTD) by the support of The Scientific and Technological Research Council of Turkey. Currently, I am also working as a CEO in Denosens Biotechnology. The main purposes of the company, which carries out R&D as a research center, are to develop new generation biosensors and sensors for both point-of-care diagnostics; such as glucose, lactate, cholesterol and cancer biomarker detections. My specific experimental and instrumental skills are Biochemistry, Biosensor, Analytical Chemistry, Electrochemistry, Mobile phone based point-of-care diagnostic device, POCTs and Patient interface designs, HPLC, Tandem Mass Spectrometry, Spectrophotometry, ELISA.",institutionString:null,institution:{name:"Ege University",country:{name:"Turkey"}}},{id:"267434",title:"Dr.",name:"Rohit",middleName:null,surname:"Raja",slug:"rohit-raja",fullName:"Rohit Raja",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/267434/images/system/267434.jpg",biography:"Dr. Rohit Raja received Ph.D. in Computer Science and Engineering from Dr. CVRAMAN University in 2016. His main research interest includes Face recognition and Identification, Digital Image Processing, Signal Processing, and Networking. Presently he is working as Associate Professor in IT Department, Guru Ghasidas Vishwavidyalaya (A Central University), Bilaspur (CG), India. He has authored several Journal and Conference Papers. He has good Academics & Research experience in various areas of CSE and IT. He has filed and successfully published 27 Patents. He has received many time invitations to be a Guest at IEEE Conferences. He has published 100 research papers in various International/National Journals (including IEEE, Springer, etc.) and Proceedings of the reputed International/ National Conferences (including Springer and IEEE). He has been nominated to the board of editors/reviewers of many peer-reviewed and refereed Journals (including IEEE, Springer).",institutionString:"Guru Ghasidas Vishwavidyalaya",institution:{name:"Guru Ghasidas Vishwavidyalaya",country:{name:"India"}}},{id:"246502",title:"Dr.",name:"Jaya T.",middleName:"T",surname:"Varkey",slug:"jaya-t.-varkey",fullName:"Jaya T. Varkey",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/246502/images/11160_n.jpg",biography:"Jaya T. Varkey, PhD, graduated with a degree in Chemistry from Cochin University of Science and Technology, Kerala, India. She obtained a PhD in Chemistry from the School of Chemical Sciences, Mahatma Gandhi University, Kerala, India, and completed a post-doctoral fellowship at the University of Minnesota, USA. She is a research guide at Mahatma Gandhi University and Associate Professor in Chemistry, St. Teresa’s College, Kochi, Kerala, India.\nDr. Varkey received a National Young Scientist award from the Indian Science Congress (1995), a UGC Research award (2016–2018), an Indian National Science Academy (INSA) Visiting Scientist award (2018–2019), and a Best Innovative Faculty award from the All India Association for Christian Higher Education (AIACHE) (2019). She Hashas received the Sr. Mary Cecil prize for best research paper three times. She was also awarded a start-up to develop a tea bag water filter. \nDr. Varkey has published two international books and twenty-seven international journal publications. She is an editorial board member for five international journals.",institutionString:"St. Teresa’s College",institution:null},{id:"250668",title:"Dr.",name:"Ali",middleName:null,surname:"Nabipour Chakoli",slug:"ali-nabipour-chakoli",fullName:"Ali Nabipour Chakoli",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/250668/images/system/250668.jpg",biography:"Academic Qualification:\r\n•\tPhD in Materials Physics and Chemistry, From: Sep. 2006, to: Sep. 2010, School of Materials Science and Engineering, Harbin Institute of Technology, Thesis: Structure and Shape Memory Effect of Functionalized MWCNTs/poly (L-lactide-co-ε-caprolactone) Nanocomposites. Supervisor: Prof. Wei Cai,\r\n•\tM.Sc in Applied Physics, From: 1996, to: 1998, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Determination of Boron in Micro alloy Steels with solid state nuclear track detectors by neutron induced auto radiography, Supervisors: Dr. M. Hosseini Ashrafi and Dr. A. Hosseini.\r\n•\tB.Sc. in Applied Physics, From: 1991, to: 1996, Faculty of Physics & Nuclear Science, Amirkabir Uni. of Technology, Tehran, Iran, Thesis: Design of shielding for Am-Be neutron sources for In Vivo neutron activation analysis, Supervisor: Dr. M. Hosseini Ashrafi.\r\n\r\nResearch Experiences:\r\n1.\tNanomaterials, Carbon Nanotubes, Graphene: Synthesis, Functionalization and Characterization,\r\n2.\tMWCNTs/Polymer Composites: Fabrication and Characterization, \r\n3.\tShape Memory Polymers, Biodegradable Polymers, ORC, Collagen,\r\n4.\tMaterials Analysis and Characterizations: TEM, SEM, XPS, FT-IR, Raman, DSC, DMA, TGA, XRD, GPC, Fluoroscopy, \r\n5.\tInteraction of Radiation with Mater, Nuclear Safety and Security, NDT(RT),\r\n6.\tRadiation Detectors, Calibration (SSDL),\r\n7.\tCompleted IAEA e-learning Courses:\r\nNuclear Security (15 Modules),\r\nNuclear Safety:\r\nTSA 2: Regulatory Protection in Occupational Exposure,\r\nTips & Tricks: Radiation Protection in Radiography,\r\nSafety and Quality in Radiotherapy,\r\nCourse on Sealed Radioactive Sources,\r\nCourse on Fundamentals of Environmental Remediation,\r\nCourse on Planning for Environmental Remediation,\r\nKnowledge Management Orientation Course,\r\nFood Irradiation - Technology, Applications and Good Practices,\r\nEmployment:\r\nFrom 2010 to now: Academic staff, Nuclear Science and Technology Research Institute, Kargar Shomali, Tehran, Iran, P.O. Box: 14395-836.\r\nFrom 1997 to 2006: Expert of Materials Analysis and Characterization. Research Center of Agriculture and Medicine. Rajaeeshahr, Karaj, Iran, P. O. Box: 31585-498.",institutionString:"Atomic Energy Organization of Iran",institution:{name:"Atomic Energy Organization of Iran",country:{name:"Iran"}}},{id:"248279",title:"Dr.",name:"Monika",middleName:"Elzbieta",surname:"Machoy",slug:"monika-machoy",fullName:"Monika Machoy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/248279/images/system/248279.jpeg",biography:"Monika Elżbieta Machoy, MD, graduated with distinction from the Faculty of Medicine and Dentistry at the Pomeranian Medical University in 2009, defended her PhD thesis with summa cum laude in 2016 and is currently employed as a researcher at the Department of Orthodontics of the Pomeranian Medical University. She expanded her professional knowledge during a one-year scholarship program at the Ernst Moritz Arndt University in Greifswald, Germany and during a three-year internship at the Technical University in Dresden, Germany. She has been a speaker at numerous orthodontic conferences, among others, American Association of Orthodontics, European Orthodontic Symposium and numerous conferences of the Polish Orthodontic Society. She conducts research focusing on the effect of orthodontic treatment on dental and periodontal tissues and the causes of pain in orthodontic patients.",institutionString:"Pomeranian Medical University",institution:{name:"Pomeranian Medical University",country:{name:"Poland"}}},{id:"252743",title:"Prof.",name:"Aswini",middleName:"Kumar",surname:"Kar",slug:"aswini-kar",fullName:"Aswini Kar",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/252743/images/10381_n.jpg",biography:"uploaded in cv",institutionString:null,institution:{name:"KIIT University",country:{name:"India"}}},{id:"204256",title:"Dr.",name:"Anil",middleName:"Kumar",surname:"Kumar Sahu",slug:"anil-kumar-sahu",fullName:"Anil Kumar Sahu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/204256/images/14201_n.jpg",biography:"I have nearly 11 years of research and teaching experience. I have done my master degree from University Institute of Pharmacy, Pt. Ravi Shankar Shukla University, Raipur, Chhattisgarh India. I have published 16 review and research articles in international and national journals and published 4 chapters in IntechOpen, the world’s leading publisher of Open access books. I have presented many papers at national and international conferences. I have received research award from Indian Drug Manufacturers Association in year 2015. My research interest extends from novel lymphatic drug delivery systems, oral delivery system for herbal bioactive to formulation optimization.",institutionString:null,institution:{name:"Chhattisgarh Swami Vivekanand Technical University",country:{name:"India"}}},{id:"253468",title:"Dr.",name:"Mariusz",middleName:null,surname:"Marzec",slug:"mariusz-marzec",fullName:"Mariusz Marzec",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/253468/images/system/253468.png",biography:"An assistant professor at Department of Biomedical Computer Systems, at Institute of Computer Science, Silesian University in Katowice. Scientific interests: computer analysis and processing of images, biomedical images, databases and programming languages. He is an author and co-author of scientific publications covering analysis and processing of biomedical images and development of database systems.",institutionString:"University of Silesia",institution:null},{id:"212432",title:"Prof.",name:"Hadi",middleName:null,surname:"Mohammadi",slug:"hadi-mohammadi",fullName:"Hadi Mohammadi",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/212432/images/system/212432.jpeg",biography:"Dr. Hadi Mohammadi is a biomedical engineer with hands-on experience in the design and development of many engineering structures and medical devices through various projects that he has been involved in over the past twenty years. Dr. Mohammadi received his BSc. and MSc. degrees in Mechanical Engineering from Sharif University of Technology, Tehran, Iran, and his PhD. degree in Biomedical Engineering (biomaterials) from the University of Western Ontario. He was a postdoctoral trainee for almost four years at University of Calgary and Harvard Medical School. He is an industry innovator having created the technology to produce lifelike synthetic platforms that can be used for the simulation of almost all cardiovascular reconstructive surgeries. He’s been heavily involved in the design and development of cardiovascular devices and technology for the past 10 years. He is currently an Assistant Professor with the University of British Colombia, Canada.",institutionString:"University of British Columbia",institution:{name:"University of British Columbia",country:{name:"Canada"}}},{id:"254463",title:"Prof.",name:"Haisheng",middleName:null,surname:"Yang",slug:"haisheng-yang",fullName:"Haisheng Yang",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/254463/images/system/254463.jpeg",biography:"Haisheng Yang, Ph.D., Professor and Director of the Department of Biomedical Engineering, College of Life Science and Bioengineering, Beijing University of Technology. He received his Ph.D. degree in Mechanics/Biomechanics from Harbin Institute of Technology (jointly with University of California, Berkeley). Afterwards, he worked as a Postdoctoral Research Associate in the Purdue Musculoskeletal Biology and Mechanics Lab at the Department of Basic Medical Sciences, Purdue University, USA. He also conducted research in the Research Centre of Shriners Hospitals for Children-Canada at McGill University, Canada. Dr. Yang has over 10 years research experience in orthopaedic biomechanics and mechanobiology of bone adaptation and regeneration. He earned an award from Beijing Overseas Talents Aggregation program in 2017 and serves as Beijing Distinguished Professor.",institutionString:null,institution:{name:"Beijing University of Technology",country:{name:"China"}}},{id:"89721",title:"Dr.",name:"Mehmet",middleName:"Cuneyt",surname:"Ozmen",slug:"mehmet-ozmen",fullName:"Mehmet Ozmen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/89721/images/7289_n.jpg",biography:null,institutionString:null,institution:{name:"Gazi University",country:{name:"Turkey"}}},{id:"242893",title:"Ph.D. Student",name:"Joaquim",middleName:null,surname:"De Moura",slug:"joaquim-de-moura",fullName:"Joaquim De Moura",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/242893/images/7133_n.jpg",biography:"Joaquim de Moura received his degree in Computer Engineering in 2014 from the University of A Coruña (Spain). In 2016, he received his M.Sc degree in Computer Engineering from the same university. He is currently pursuing his Ph.D degree in Computer Science in a collaborative project between ophthalmology centers in Galicia and the University of A Coruña. His research interests include computer vision, machine learning algorithms and analysis and medical imaging processing of various kinds.",institutionString:null,institution:{name:"University of A Coruña",country:{name:"Spain"}}},{id:"294334",title:"B.Sc.",name:"Marc",middleName:null,surname:"Bruggeman",slug:"marc-bruggeman",fullName:"Marc Bruggeman",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/294334/images/8242_n.jpg",biography:"Chemical engineer graduate, with a passion for material science and specific interest in polymers - their near infinite applications intrigue me. \n\nI plan to continue my scientific career in the field of polymeric biomaterials as I am fascinated by intelligent, bioactive and biomimetic materials for use in both consumer and medical applications.",institutionString:null,institution:null},{id:"255757",title:"Dr.",name:"Igor",middleName:"Victorovich",surname:"Lakhno",slug:"igor-lakhno",fullName:"Igor Lakhno",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/255757/images/system/255757.jpg",biography:"Igor Victorovich Lakhno was born in 1971 in Kharkiv (Ukraine). \nMD – 1994, Kharkiv National Medical Univesity.\nOb&Gyn; – 1997, master courses in Kharkiv Medical Academy of Postgraduate Education.\nPh.D. – 1999, Kharkiv National Medical Univesity.\nDSC – 2019, PL Shupik National Academy of Postgraduate Education \nProfessor – 2021, Department of Obstetrics and Gynecology of VN Karazin Kharkiv National University\nHead of Department – 2021, Department of Perinatology, Obstetrics and gynecology of Kharkiv Medical Academy of Postgraduate Education\nIgor Lakhno has been graduated from international training courses on reproductive medicine and family planning held at Debrecen University (Hungary) in 1997. Since 1998 Lakhno Igor has worked as an associate professor in the department of obstetrics and gynecology of VN Karazin National University and an associate professor of the perinatology, obstetrics, and gynecology department of Kharkiv Medical Academy of Postgraduate Education. Since June 2019 he’s been a professor in the department of obstetrics and gynecology of VN Karazin National University and a professor of the perinatology, obstetrics, and gynecology department. He’s affiliated with Kharkiv Medical Academy of Postgraduate Education as a Head of Department from November 2021. Igor Lakhno has participated in several international projects on fetal non-invasive electrocardiography (with Dr. J. A. Behar (Technion), Prof. D. Hoyer (Jena University), and José Alejandro Díaz Méndez (National Institute of Astrophysics, Optics, and Electronics, Mexico). He’s an author of about 200 printed works and there are 31 of them in Scopus or Web of Science databases. Igor Lakhno is a member of the Editorial Board of Reproductive Health of Woman, Emergency Medicine, and Technology Transfer Innovative Solutions in Medicine (Estonia). He is a medical Editor of “Z turbotoyu pro zhinku”. Igor Lakhno is a reviewer of the Journal of Obstetrics and Gynaecology (Taylor and Francis), British Journal of Obstetrics and Gynecology (Wiley), Informatics in Medicine Unlocked (Elsevier), The Journal of Obstetrics and Gynecology Research (Wiley), Endocrine, Metabolic & Immune Disorders-Drug Targets (Bentham Open), The Open Biomedical Engineering Journal (Bentham Open), etc. He’s defended a dissertation for a DSc degree “Pre-eclampsia: prediction, prevention, and treatment”. Three years ago Igor Lakhno has participated in a training course on innovative technologies in medical education at Lublin Medical University (Poland). Lakhno Igor has participated as a speaker in several international conferences and congresses (International Conference on Biological Oscillations April 10th-14th 2016, Lancaster, UK, The 9th conference of the European Study Group on Cardiovascular Oscillations). His main scientific interests: are obstetrics, women’s health, fetal medicine, and cardiovascular medicine. \nIgor Lakhno is a consultant at Kharkiv municipal perinatal center. He’s graduated from training courses on endoscopy in gynecology. He has 28 years of practical experience in the field.",institutionString:null,institution:null},{id:"244950",title:"Dr.",name:"Salvatore",middleName:null,surname:"Di Lauro",slug:"salvatore-di-lauro",fullName:"Salvatore Di Lauro",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0030O00002bSF1HQAW/ProfilePicture%202021-12-20%2014%3A54%3A14.482",biography:"Name:\n\tSALVATORE DI LAURO\nAddress:\n\tHospital Clínico Universitario Valladolid\nAvda Ramón y Cajal 3\n47005, Valladolid\nSpain\nPhone number: \nFax\nE-mail:\n\t+34 983420000 ext 292\n+34 983420084\nsadilauro@live.it\nDate and place of Birth:\nID Number\nMedical Licence \nLanguages\t09-05-1985. Villaricca (Italy)\n\nY1281863H\n474707061\nItalian (native language)\nSpanish (read, written, spoken)\nEnglish (read, written, spoken)\nPortuguese (read, spoken)\nFrench (read)\n\t\t\nCurrent position (title and company)\tDate (Year)\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. Private practise.\t2017-today\n\n2019-today\n\t\n\t\nEducation (High school, university and postgraduate training > 3 months)\tDate (Year)\nDegree in Medicine and Surgery. University of Neaples 'Federico II”\nResident in Opthalmology. Hospital Clinico Universitario Valladolid\nMaster in Vitreo-Retina. IOBA. University of Valladolid\nFellow of the European Board of Ophthalmology. Paris\nMaster in Research in Ophthalmology. University of Valladolid\t2003-2009\n2012-2016\n2016-2017\n2016\n2012-2013\n\t\nEmployments (company and positions)\tDate (Year)\nResident in Ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl.\nFellow in Vitreo-Retina. IOBA. University of Valladolid\nVitreo-Retinal consultant in ophthalmology. Hospital Clinico Universitario Valladolid. Sacyl. National Health System.\nVitreo-Retinal consultant in ophthalmology. Instituto Oftalmologico Recoletas. Red Hospitalaria Recoletas. \n\t2012-2016\n2016-2017\n2017-today\n\n2019-Today\n\n\n\t\nClinical Research Experience (tasks and role)\tDate (Year)\nAssociated investigator\n\n' FIS PI20/00740: DESARROLLO DE UNA CALCULADORA DE RIESGO DE\nAPARICION DE RETINOPATIA DIABETICA BASADA EN TECNICAS DE IMAGEN MULTIMODAL EN PACIENTES DIABETICOS TIPO 1. Grant by: Ministerio de Ciencia e Innovacion \n\n' (BIO/VA23/14) Estudio clínico multicéntrico y prospectivo para validar dos\nbiomarcadores ubicados en los genes p53 y MDM2 en la predicción de los resultados funcionales de la cirugía del desprendimiento de retina regmatógeno. Grant by: Gerencia Regional de Salud de la Junta de Castilla y León.\n' Estudio multicéntrico, aleatorizado, con enmascaramiento doble, en 2 grupos\nparalelos y de 52 semanas de duración para comparar la eficacia, seguridad e inmunogenicidad de SOK583A1 respecto a Eylea® en pacientes con degeneración macular neovascular asociada a la edad' (CSOK583A12301; N.EUDRA: 2019-004838-41; FASE III). Grant by Hexal AG\n\n' Estudio de fase III, aleatorizado, doble ciego, con grupos paralelos, multicéntrico para comparar la eficacia y la seguridad de QL1205 frente a Lucentis® en pacientes con degeneración macular neovascular asociada a la edad. (EUDRACT: 2018-004486-13). Grant by Qilu Pharmaceutical Co\n\n' Estudio NEUTON: Ensayo clinico en fase IV para evaluar la eficacia de aflibercept en pacientes Naive con Edema MacUlar secundario a Oclusion de Vena CenTral de la Retina (OVCR) en regimen de tratamientO iNdividualizado Treat and Extend (TAE)”, (2014-000975-21). Grant by Fundacion Retinaplus\n\n' Evaluación de la seguridad y bioactividad de anillos de tensión capsular en conejo. Proyecto Procusens. Grant by AJL, S.A.\n\n'Estudio epidemiológico, prospectivo, multicéntrico y abierto\\npara valorar la frecuencia de la conjuntivitis adenovírica diagnosticada mediante el test AdenoPlus®\\nTest en pacientes enfermos de conjuntivitis aguda”\\n. National, multicenter study. Grant by: NICOX.\n\nEuropean multicentric trial: 'Evaluation of clinical outcomes following the use of Systane Hydration in patients with dry eye”. Study Phase 4. Grant by: Alcon Labs'\n\nVLPs Injection and Activation in a Rabbit Model of Uveal Melanoma. Grant by Aura Bioscience\n\nUpdating and characterization of a rabbit model of uveal melanoma. Grant by Aura Bioscience\n\nEnsayo clínico en fase IV para evaluar las variantes genéticas de la vía del VEGF como biomarcadores de eficacia del tratamiento con aflibercept en pacientes con degeneración macular asociada a la edad (DMAE) neovascular. Estudio BIOIMAGE. IMO-AFLI-2013-01\n\nEstudio In-Eye:Ensayo clínico en fase IV, abierto, aleatorizado, de 2 brazos,\nmulticçentrico y de 12 meses de duración, para evaluar la eficacia y seguridad de un régimen de PRN flexible individualizado de 'esperar y extender' versus un régimen PRN según criterios de estabilización mediante evaluaciones mensuales de inyecciones intravítreas de ranibizumab 0,5 mg en pacientes naive con neovascularización coriodea secunaria a la degeneración macular relacionada con la edad. CP: CRFB002AES03T\n\nTREND: Estudio Fase IIIb multicéntrico, randomizado, de 12 meses de\nseguimiento con evaluador de la agudeza visual enmascarado, para evaluar la eficacia y la seguridad de ranibizumab 0.5mg en un régimen de tratar y extender comparado con un régimen mensual, en pacientes con degeneración macular neovascular asociada a la edad. CP: CRFB002A2411 Código Eudra CT:\n2013-002626-23\n\n\n\nPublications\t\n\n2021\n\n\n\n\n2015\n\n\n\n\n2021\n\n\n\n\n\n2021\n\n\n\n\n2015\n\n\n\n\n2015\n\n\n2014\n\n\n\n\n2015-16\n\n\n\n2015\n\n\n2014\n\n\n2014\n\n\n\n\n2014\n\n\n\n\n\n\n\n2014\n\nJose Carlos Pastor; Jimena Rojas; Salvador Pastor-Idoate; Salvatore Di Lauro; Lucia Gonzalez-Buendia; Santiago Delgado-Tirado. Proliferative vitreoretinopathy: A new concept of disease pathogenesis and practical\nconsequences. Progress in Retinal and Eye Research. 51, pp. 125 - 155. 03/2016. DOI: 10.1016/j.preteyeres.2015.07.005\n\n\nLabrador-Velandia S; Alonso-Alonso ML; Di Lauro S; García-Gutierrez MT; Srivastava GK; Pastor JC; Fernandez-Bueno I. Mesenchymal stem cells provide paracrine neuroprotective resources that delay degeneration of co-cultured organotypic neuroretinal cultures.Experimental Eye Research. 185, 17/05/2019. DOI: 10.1016/j.exer.2019.05.011\n\nSalvatore Di Lauro; Maria Teresa Garcia Gutierrez; Ivan Fernandez Bueno. Quantification of pigment epithelium-derived factor (PEDF) in an ex vivo coculture of retinal pigment epithelium cells and neuroretina.\nJournal of Allbiosolution. 2019. ISSN 2605-3535\n\nSonia Labrador Velandia; Salvatore Di Lauro; Alonso-Alonso ML; Tabera Bartolomé S; Srivastava GK; Pastor JC; Fernandez-Bueno I. Biocompatibility of intravitreal injection of human mesenchymal stem cells in immunocompetent rabbits. Graefe's archive for clinical and experimental ophthalmology. 256 - 1, pp. 125 - 134. 01/2018. DOI: 10.1007/s00417-017-3842-3\n\n\nSalvatore Di Lauro, David Rodriguez-Crespo, Manuel J Gayoso, Maria T Garcia-Gutierrez, J Carlos Pastor, Girish K Srivastava, Ivan Fernandez-Bueno. A novel coculture model of porcine central neuroretina explants and retinal pigment epithelium cells. Molecular Vision. 2016 - 22, pp. 243 - 253. 01/2016.\n\nSalvatore Di Lauro. Classifications for Proliferative Vitreoretinopathy ({PVR}): An Analysis of Their Use in Publications over the Last 15 Years. Journal of Ophthalmology. 2016, pp. 1 - 6. 01/2016. DOI: 10.1155/2016/7807596\n\nSalvatore Di Lauro; Rosa Maria Coco; Rosa Maria Sanabria; Enrique Rodriguez de la Rua; Jose Carlos Pastor. Loss of Visual Acuity after Successful Surgery for Macula-On Rhegmatogenous Retinal Detachment in a Prospective Multicentre Study. Journal of Ophthalmology. 2015:821864, 2015. DOI: 10.1155/2015/821864\n\nIvan Fernandez-Bueno; Salvatore Di Lauro; Ivan Alvarez; Jose Carlos Lopez; Maria Teresa Garcia-Gutierrez; Itziar Fernandez; Eva Larra; Jose Carlos Pastor. Safety and Biocompatibility of a New High-Density Polyethylene-Based\nSpherical Integrated Porous Orbital Implant: An Experimental Study in Rabbits. Journal of Ophthalmology. 2015:904096, 2015. DOI: 10.1155/2015/904096\n\nPastor JC; Pastor-Idoate S; Rodríguez-Hernandez I; Rojas J; Fernandez I; Gonzalez-Buendia L; Di Lauro S; Gonzalez-Sarmiento R. Genetics of PVR and RD. Ophthalmologica. 232 - Suppl 1, pp. 28 - 29. 2014\n\nRodriguez-Crespo D; Di Lauro S; Singh AK; Garcia-Gutierrez MT; Garrosa M; Pastor JC; Fernandez-Bueno I; Srivastava GK. Triple-layered mixed co-culture model of RPE cells with neuroretina for evaluating the neuroprotective effects of adipose-MSCs. Cell Tissue Res. 358 - 3, pp. 705 - 716. 2014.\nDOI: 10.1007/s00441-014-1987-5\n\nCarlo De Werra; Salvatore Condurro; Salvatore Tramontano; Mario Perone; Ivana Donzelli; Salvatore Di Lauro; Massimo Di Giuseppe; Rosa Di Micco; Annalisa Pascariello; Antonio Pastore; Giorgio Diamantis; Giuseppe Galloro. Hydatid disease of the liver: thirty years of surgical experience.Chirurgia italiana. 59 - 5, pp. 611 - 636.\n(Italia): 2007. ISSN 0009-4773\n\nChapters in books\n\t\n' Salvador Pastor Idoate; Salvatore Di Lauro; Jose Carlos Pastor Jimeno. PVR: Pathogenesis, Histopathology and Classification. Proliferative Vitreoretinopathy with Small Gauge Vitrectomy. Springer, 2018. ISBN 978-3-319-78445-8\nDOI: 10.1007/978-3-319-78446-5_2. \n\n' Salvatore Di Lauro; Maria Isabel Lopez Galvez. Quistes vítreos en una mujer joven. Problemas diagnósticos en patología retinocoroidea. Sociedad Española de Retina-Vitreo. 2018.\n\n' Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor Jimeno. iOCT in PVR management. OCT Applications in Opthalmology. pp. 1 - 8. INTECH, 2018. DOI: 10.5772/intechopen.78774.\n\n' Rosa Coco Martin; Salvatore Di Lauro; Salvador Pastor Idoate; Jose Carlos Pastor. amponadores, manipuladores y tinciones en la cirugía del traumatismo ocular.Trauma Ocular. Ponencia de la SEO 2018..\n\n' LOPEZ GALVEZ; DI LAURO; CRESPO. OCT angiografia y complicaciones retinianas de la diabetes. PONENCIA SEO 2021, CAPITULO 20. (España): 2021.\n\n' Múltiples desprendimientos neurosensoriales bilaterales en paciente joven. Enfermedades Degenerativas De Retina Y Coroides. SERV 04/2016. \n' González-Buendía L; Di Lauro S; Pastor-Idoate S; Pastor Jimeno JC. Vitreorretinopatía proliferante (VRP) e inflamación: LA INFLAMACIÓN in «INMUNOMODULADORES Y ANTIINFLAMATORIOS: MÁS ALLÁ DE LOS CORTICOIDES. 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rapidly publish contributions on all aspects of cell and molecular biology, including aspects related to biochemical and genetic research (not only in humans but all living beings). We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics include, but are not limited to: Advanced techniques of cellular and molecular biology (Molecular methodologies, imaging techniques, and bioinformatics); Biological activities at the molecular level; Biological processes of cell functions, cell division, senescence, maintenance, and cell death; Biomolecules interactions; Cancer; Cell biology; Chemical biology; Computational biology; Cytochemistry; Developmental biology; Disease mechanisms and therapeutics; DNA, and RNA metabolism; Gene functions, genetics, and genomics; Genetics; Immunology; Medical microbiology; Molecular biology; Molecular genetics; Molecular processes of cell and organelle dynamics; Neuroscience; Protein biosynthesis, degradation, and functions; Regulation of molecular interactions in a cell; Signalling networks and system biology; Structural biology; Virology and microbiology.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",keywords:"Omics (Transcriptomics; Proteomics; Metabolomics), Molecular Biology, Cell Biology, Signal Transduction and Regulation, Cell Growth and Differentiation, Apoptosis, Necroptosis, Ferroptosis, Autophagy, Cell Cycle, Macromolecules and Complexes, Gene Expression"},{id:"15",title:"Chemical Biology",scope:"Chemical biology spans the fields of chemistry and biology involving the application of biological and chemical molecules and techniques. In recent years, the application of chemistry to biological molecules has gained significant interest in medicinal and pharmacological studies. This topic will be devoted to understanding the interplay between biomolecules and chemical compounds, their structure and function, and their potential applications in related fields. Being a part of the biochemistry discipline, the ideas and concepts that have emerged from Chemical Biology have affected other related areas. This topic will closely deal with all emerging trends in this discipline.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",keywords:"Phenolic Compounds, Essential Oils, Modification of Biomolecules, Glycobiology, Combinatorial Chemistry, Therapeutic peptides, Enzyme Inhibitors"},{id:"17",title:"Metabolism",scope:"Metabolism is frequently defined in biochemistry textbooks as the overall process that allows living systems to acquire and use the free energy they need for their vital functions or the chemical processes that occur within a living organism to maintain life. Behind these definitions are hidden all the aspects of normal and pathological functioning of all processes that the topic ‘Metabolism’ will cover within the Biochemistry Series. Thus all studies on metabolism will be considered for publication.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",keywords:"Biomolecules Metabolism, Energy Metabolism, Metabolic Pathways, Key Metabolic Enzymes, Metabolic Adaptation"},{id:"18",title:"Proteomics",scope:"With the recognition that the human genome cannot provide answers to the etiology of a disorder, changes in the proteins expressed by a genome became a focus in research. Thus proteomics, an area of research that detects all protein forms expressed in an organism, including splice isoforms and post-translational modifications, is more suitable than genomics for a comprehensive understanding of the biochemical processes that govern life. The most common proteomics applications are currently in the clinical field for the identification, in a variety of biological matrices, of biomarkers for diagnosis and therapeutic intervention of disorders. From the comparison of proteomic profiles of control and disease or different physiological states, which may emerge, changes in protein expression can provide new insights into the roles played by some proteins in human pathologies. Understanding how proteins function and interact with each other is another goal of proteomics that makes this approach even more intriguing. Specialized technology and expertise are required to assess the proteome of any biological sample. Currently, proteomics relies mainly on mass spectrometry (MS) combined with electrophoretic (1 or 2-DE-MS) and/or chromatographic techniques (LC-MS/MS). MS is an excellent tool that has gained popularity in proteomics because of its ability to gather a complex body of information such as cataloging protein expression, identifying protein modification sites, and defining protein interactions. The Proteomics topic aims to attract contributions on all aspects of MS-based proteomics that, by pushing the boundaries of MS capabilities, may address biological problems that have not been resolved yet.",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",keywords:"Mono- and Two-Dimensional Gel Electrophoresis (1-and 2-DE), Liquid Chromatography (LC), Mass Spectrometry/Tandem Mass Spectrometry (MS; MS/MS), Proteins"}],annualVolumeBook:{},thematicCollection:[],selectedSeries:null,selectedSubseries:null},seriesLanding:{item:null},libraryRecommendation:{success:null,errors:{},institutions:[]},route:{name:"profile.detail",path:"/profiles/117993",hash:"",query:{},params:{id:"117993"},fullPath:"/profiles/117993",meta:{},from:{name:null,path:"/",hash:"",query:{},params:{},fullPath:"/",meta:{}}}},function(){var e;(e=document.currentScript||document.scripts[document.scripts.length-1]).parentNode.removeChild(e)}()