RDC/TMD criteria for axis I diagnoses [3].
\\n\\n
IntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\\n\\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\\n\\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\\n\\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\\n\\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\\n\\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\\n\\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\\n\\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\\n\\nFeel free to share this news on social media and help us mark this memorable moment!
\\n\\n\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/237"}},components:[{type:"htmlEditorComponent",content:'
After years of being acknowledged as the world's leading publisher of Open Access books, today, we are proud to announce we’ve successfully launched a portfolio of Open Science journals covering rapidly expanding areas of interdisciplinary research.
\n\n\n\nIntechOpen was founded by scientists, for scientists, in order to make book publishing accessible around the globe. Over the last two decades, this has driven Open Access (OA) book publishing whilst levelling the playing field for global academics. Through our innovative publishing model and the support of the research community, we have now published over 5,700 Open Access books and are visited online by over three million academics every month. These researchers are increasingly working in broad technology-based subjects, driving multidisciplinary academic endeavours into human health, environment, and technology.
\n\nBy listening to our community, and in order to serve these rapidly growing areas which lie at the core of IntechOpen's expertise, we are launching a portfolio of Open Science journals:
\n\nAll three journals will publish under an Open Access model and embrace Open Science policies to help support the changing needs of academics in these fast-moving research areas. There will be direct links to preprint servers and data repositories, allowing full reproducibility and rapid dissemination of published papers to help accelerate the pace of research. Each journal has renowned Editors in Chief who will work alongside a global Editorial Board, delivering robust single-blind peer review. Supported by our internal editorial teams, this will ensure our authors will receive a quick, user-friendly, and personalised publishing experience.
\n\n"By launching our journals portfolio we are introducing new, dedicated homes for interdisciplinary technology-focused researchers to publish their work, whilst embracing Open Science and creating a unique global home for academics to disseminate their work. We are taking a leap toward Open Science continuing and expanding our fundamental commitment to openly sharing scientific research across the world, making it available for the benefit of all." Dr. Sara Uhac, IntechOpen CEO
\n\n"Our aim is to promote and create better science for a better world by increasing access to information and the latest scientific developments to all scientists, innovators, entrepreneurs and students and give them the opportunity to learn, observe and contribute to knowledge creation. Open Science promotes a swifter path from research to innovation to produce new products and services." Alex Lazinica, IntechOpen founder
\n\nIn conclusion, Natalia Reinic Babic, Head of Journal Publishing and Open Science at IntechOpen adds:
\n\n“On behalf of the journal team I’d like to thank all our Editors in Chief, Editorial Boards, internal supporting teams, and our scientific community for their continuous support in making this portfolio a reality - we couldn’t have done it without you! With your support in place, we are confident these journals will become as impactful and successful as our book publishing program and bring us closer to a more open (science) future.”
\n\nWe invite you to visit the journals homepage and learn more about the journal’s Editorial Boards, scope and vision as all three journals are now open for submissions.
\n\nFeel free to share this news on social media and help us mark this memorable moment!
\n\n\n'}],latestNews:[{slug:"intechopen-supports-asapbio-s-new-initiative-publish-your-reviews-20220729",title:"IntechOpen Supports ASAPbio’s New Initiative Publish Your Reviews"},{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"}]},book:{item:{type:"book",id:"1305",leadTitle:null,fullTitle:"Advances in Endoscopic Surgery",title:"Advances in Endoscopic Surgery",subtitle:null,reviewType:"peer-reviewed",abstract:"Surgeons from various domains have become fascinated by endoscopy with its very low complications rates, high diagnostic yields and the possibility to perform a large variety of therapeutic procedures. Therefore during the last 30 years, the number and diversity of surgical endoscopic procedures has advanced with many new methods for both diagnoses and treatment, and these achievements are presented in this book. Contributing to the development of endoscopic surgery from all over the world, this is a modern, educational, and engrossing publication precisely presenting the most recent development in the field. New technologies are described in detail and all aspects of both standard and advanced endoscopic maneuvers applied in gastroenterology, urogynecology, otorhinolaryngology, pediatrics and neurology are presented. 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A highly respected surgeon, teacher and researcher, he is one of the leading authorities on advanced laparoscopic surgery in Romania. Beside laparoscopic cholecystectomy and anti-reflux surgery Dr. Iancu has extensive experience in performing advanced laparoscopic procedures for gastric tumors, colo-rectal malignancies, pancreatic and liver tumors and complicated hydatic disease. Among research in the field of digestive surgical oncology and endoscopic surgery, Dr. Iancu’s research was also focused in the area of nanotechnology, bio-nano-medical research and treatment by bioconjugated nanomaterials with priority on the selective laser ablation and optical damage of the pancreatic and liver cancer cells mediated by antibodies functionalized nanoparticles.",institutionString:null,position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"1",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"Institutul Regional de Gastroenterologie Prof. Dr. Octavian Fodor",institutionURL:null,country:{name:"Romania"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"1152",title:"Reconstructive Surgery",slug:"reconstructive-surgery"}],chapters:[{id:"24320",title:"Endoscopic Endonasal Skull Base Surgery: Current State of the Art and Future Trends",doi:"10.5772/24768",slug:"endoscopic-endonasal-skull-base-surgery-current-state-of-the-art-and-future-trends",totalDownloads:10792,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:null,signatures:"Jouanneau Emmanuel, Messerer Mahmoud and Berhouma Moncef",downloadPdfUrl:"/chapter/pdf-download/24320",previewPdfUrl:"/chapter/pdf-preview/24320",authors:[{id:"58283",title:"Prof.",name:"Moncef",surname:"Berhouma",slug:"moncef-berhouma",fullName:"Moncef Berhouma"},{id:"59436",title:"Prof.",name:"Emmanuel",surname:"Jouanneau",slug:"emmanuel-jouanneau",fullName:"Emmanuel Jouanneau"},{id:"59438",title:"Dr.",name:"Mahmoud",surname:"Messerer",slug:"mahmoud-messerer",fullName:"Mahmoud Messerer"}],corrections:null},{id:"24321",title:"Endoscopic Surgery of Maxillary Sinuses in Oral Surgery and Implantology",doi:"10.5772/22758",slug:"endoscopic-surgery-of-maxillary-sinuses-in-oral-surgery-and-implantology",totalDownloads:7113,totalCrossrefCites:1,totalDimensionsCites:1,hasAltmetrics:0,abstract:null,signatures:"Miroslav Andrić",downloadPdfUrl:"/chapter/pdf-download/24321",previewPdfUrl:"/chapter/pdf-preview/24321",authors:[{id:"49081",title:"Dr.",name:"Miroslav",surname:"Andric",slug:"miroslav-andric",fullName:"Miroslav Andric"}],corrections:null},{id:"24322",title:"Objective Outcomes in Endoscopic Sinus Surgery",doi:"10.5772/22191",slug:"objective-outcomes-in-endoscopic-sinus-surgery",totalDownloads:9189,totalCrossrefCites:0,totalDimensionsCites:2,hasAltmetrics:0,abstract:null,signatures:"David W.J. 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Temporomandibular joint dysfunction (TMD) is the most common cause of non-odontogenic pain in the oro-facial region, having a significant impact on quality of life [1].
TMD is a common term used to describe a range of disorders affecting the temporomandibular joint. TMD can affect the temporomandibular joint (TMJ), the jaw muscles, or both, TMD has also been associated with ear and neck pain. Patients demonstrate clinical signs such as pain from the TMJ, muscle pain, TMJ sounds including clicking and crepitus, restricted mouth opening and deviation on mouth opening or closing [2].
The Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) presented by Dworkin and LeResche in 1992 classified TMD patients according to their physical diagnosis (axis I) and pain-related disability and psychological status (axis II). Axis I is divided into three groups. Group I is muscle disorders, Group II disc displacements and Group III consists of joint disorders as seen in the box below [3] (Table 1, Figure 1).
Group I—Muscle disorders | Group II—Disc displacements | Group III—Joint disorders |
---|---|---|
|
|
|
|
|
|
|
|
RDC/TMD criteria for axis I diagnoses [3].
PRISMA flow diagram, illustrating method by which screening of articles was undertaken.
Classification systems have been conceived to further classify and delineate the specific groups discussed above. The Wilkes classification is used to classify TMJ Internal Derangement (Table 2).
Stage | Clinical | Imaging | Surgical |
---|---|---|---|
I Early | Painless clicking. No restricted motion | Slightly forward disc, reducing. Normal osseous contours | Normal disc form. Slight anterior displacement. Passive incoordination (clicking) |
II Early/Intermediate | Occasional painful clicking. Intermittent locking. Headaches | Slightly forward disc, reducing. Early disc deformity. Normal osseous contours | Anterior disc displacement. Thickened disc |
III Intermediate | Frequent pain. Joint tenderness, headaches. Locking. Restricted motion. Painful chewing | Anterior disc displacement. Reducing early progressing to non-reducing late. Moderate to marked disc thickening. Normal osseous contours | Disc deformed and displaced. Variable adhesions. No bone changes |
IV Intermediate/Late | Chronic pain, headache. Restricted motion | Anterior disc displacement non-reducing. Marked disc thickening. Abnormal bone contours | Degenerative remodelling of bony surfaces. Osteophtyes. Adhesions. Deformed disc without perforation |
V Late | Variable pain. Joint crepitus. Painful function | Anterior disc displacement non-reducing with perforation and gross disc deformity. Degenerative osseous changes | Gross degenerative changes of disc and hard tissue. Perforation and multiple adhesions |
The Wilkes classification [4].
The causes of TMD are wide and varied due to the homogeneity demonstrated in the classification. They include a wide range of direct injuries, such as fractures of the mandibular condyle, systemic diseases, including as immune mediated arthritis, growth disturbances and tumours. Non-functional movements of the mandible such as bruxing and tooth-clenching are clinically correlated with a variety of jaw muscle symptoms and are associated with internal joint disk derangements. It has been postulated however that these behaviours are not established causes of TMD but may only be propagating factors [5].
Malocclusion, previously thought to be causative is no longer widely established as an important factor in TMD. It has also been demonstrated that orthodontic treatment neither increases nor decreases the chances of developing TMD [5].
Different approaches have been described for the management of TMD depending on severity and aetiology. Initial management is non-surgical. This can range from physical therapy, occlusal appliance therapy, drug therapies, intra-articular injections, diet alteration and life style adaptation. Studies have demonstrated 70–80% of cases can be treated successfully with non-surgical interventions [4, 6].
Occlusal splint therapy has been reported with success. Various types of splints exist with distinctive indications and functions. The stabilisation splint is widely used; it is a hard acrylic splint and provides a temporary and removable ideal occlusion. Affording an occlusion reduces atypical muscle activity and produces neuromuscular balance to the TMJ [7].
Occlusal modification was proposed as a treatment however meta-analysis showed no evidence that it was beneficial in the management of TMD. Physiotherapy has been shown to be beneficial for a select group of patients [6].
Should conservative management strategies prove unsuccessful they may be followed by surgical intervention. These include menisectomy, disc repositioning, condylotomy and joint replacement. These procedures are aggressive and invasive with risks and complications of their own. Arthocentsis is considered less invasive and refers to lavage of the upper joint space, hydraulic pressure and manipulation to liberate adhesions and improve motion. Another less invasive procedure, TMJ arthroscopy is widely undertaken [8].
A review of arthroscopy and arthrocentesis found no statistically significant difference between these interventions in terms of pain, however a statistically significant difference in favour of arthroscopy was discovered in maximum incisal opening. The review concluded that there is insufficient reliable evidence to either encourage or disprove the use of arthrocentesis for treating patients with TMD and that further high quality studies are needed before firm conclusions can be stated [9].
Disc repositioning as a treatment for internal derangement of the temporomandibular disc was first reported to have been performed by Annandale in 1887 [10]. However, it was not until Wilkes first described the nature of the temporomandibular joint (TMJ) in TMD using arthrography in 1987 that surgical procedures such as disc repositioning were accepted as a means of management [11]. Notably, McCarty and Farrar were first to describe a rationale and technique for arthroplasty and disc repositioning for internal derangement of the TMJ in 1988 [12]. Since then, there have been multiple variations and modifications in technique that have been proposed by surgeons, each in an attempt to improve outcome [13]. Examples of such innovations are the introduction of orthopaedic suture anchors to with the hope of stabilising the disk more reliably and the advent of arthroscopy as an approach to repositioning the disc [14, 15].
The authors aim to look at current practice for meniscopexy, give an overview of the different TMJ articular disc repositioning techniques described as well as analysing clinical studies to establish their success by looking at measured functional outcomes. This enables us to scrutinise different approaches allowing us to conclude as to whether Meniscopexy remains to be a viable treatment modality in the management of TMD.
Evidence was searched, using the Ovid Medline, Embase, Scopus and Cochrane Library medical databases. A literature search was carried out, in accordance with Prisma guidelines, on the PubMed/MEDLINE database as well as SCOPUS, Embase, Cochrane library medical databases and Google Scholar using key medical subject headings (MeSH) relevant to TMJ meniscopexy restricted to the past 11 years from the time of writing of this paper (Table 3).
Type | No. of Articles (%) |
---|---|
Review | 3 (13) |
Technical Note | 6 (26) |
Retrospective | 12 (52) |
Prospective | 2 (9) |
Articles found within literature search classified by type (published between 28/10/2008 and 28/10/2019).
The Key Terms we used included:
Temporomandibular joint disorders
TMD
Temporomandibular joint
TMJ
Meniscopexy/Discopexy
Meniscoplasty
Disc Repositioning
We further scrutinised the list of papers using our inclusion criteria (below) to yield 23 relevant papers.
Inclusion criteria:
Articles must be directly relevant to the to be evaluated
Articles must be peer-reviewed
Articles must be written in English
Articles must be less than 11 years old (i.e., all articles published after 28/10/2008)
Quantitative and Qualitative studies can be included
Excluded papers were patents, letters, articles not pertaining to meniscopexy treatment, articles not in the English language and those outside the stated timeline.
Of the 23 papers, we found 3 review articles, 12 retrospective studies, 2 prospective studies and 6 technical notes.
Meniscopexy has been indicated in cases of anterior displacement of the TMJ articular disc without reduction, in an effort to slow progression to more advanced TMJ-related symptoms [16]. There are, however, a range of various surgical techniques that may be employed for TMD management, thus it is important to delineate where meniscopexy may be used in preference to these. Disc repositioning is favoured in patients who have not responded to initial non-surgical treatment [17] or diagnostic arthroscopy/arthrocentesis [18].
Studies have also attempted to identify causes of TMD that may have a more favourable surgical outcome with meniscopexy. One prospective cohort study looked at the stage of internal derangement of the articular disc according to the Wilkes classification as a factor in determining success using Meniscopexy in management. Patients had previously undergone both conservative and primary arthroscopic treatment with lysis and lavage of the superior joint space. Patients were classified into two groups, Wilkes Stages II and III and Wilkes Stages IV and V and underwent arthroscopic disc repositioning surgery to reduce and fix the articular disc. The primary successful outcome was determined by absence of pain at 12 months postoperatively. The study found an 86.7% success rate for the Wilkes II/III group in comparison to 25% for the Wilkes IV/V group [18].
This suggests meniscopexy as being an effective means of management for earlier stages of internal derangement, but less successful with increasing degenerative changes of the TMJ. A literature review of discectomy versus preservation of the articular disc evaluated current evidence to determine a rationale for meniscopexy versus removal/replacement of the disc. Adequate remaining anatomy and mobility of the disc, with minimal damage or perforation was key to the decision to preserve [19, 20].
Surgical management of disc displacement without reduction arising acutely following facial trauma without condylar fracture where no TMJ symptoms existed prior to the event was evaluated. Evidence suggests that there is a more rapid progression of disease following acute displacement. There is a much weaker response to conservative treatment and greater chance of developing osteoarthiritis or even ankylosis of the joint, with fast degeneration of the condyle within the first 3 months. Such adverse degenerative changes in some patients would justify surgical intervention at an earlier stage rather than prolonged conservative treatment as first line management where necessary. Eight8 patients were offered disc repositioning surgery and seven total joint replacement following rapid onset of end-stage disease. The study observed patients following trauma, after both conservative treatment and surgery. All patients demonstrated limited maximum interincisal opening (MIO) and deterioration of the condyle as seen on MRI following initial conservative treatment, with significant improvement in MIO post-operatively [21].
Studies like this are useful in demonstrating the place for meniscopexy within the surgical armamentarium when managing TMDs. They give an appreciation where the procedure is most effective and can be indicated as a first line of approach. From the literature search there are no studies available that investigate the efficacy of meniscopexy over other techniques at various stages of disease. More data is required to inform surgeons of circumstances where meniscopexy has the best outcome.
There are multiple surgical approaches described in the literature to TMJ articular disc repositioning, which can be broadly classified into three areas:
Meniscopexy through an open incision or arthrotomy (described in 4 articles within our search) [16, 19, 22, 23]
Meniscopexy with the use of suture anchors or mini-anchors to aid disc fixation (described in 10 articles within our search) [13, 15, 24, 25, 26, 27, 28, 29, 30, 31]
Arthroscopic meniscopexy (described in 4 articles within our search) [3, 14, 17, 32]
Disc repositioning techniques first developed, such as that initially described by McCarty and Farrar in 1988, rely on an endaural open incision to access the TMJ, which remains to be the approach of choice for many surgeons. Dissection is carried out to access the disc and is released anteriorly, repositioned and subsequently sutured, either to the capsule or auricular cartilage. This technique is favoured by many due to its relative ease, providing a better view of the disc and its attachments, making the anterior release and suturing of the disc more predictable, compared to arthroscopic surgery [16].
Modifications of this technique have been introduced, with the use of mini suture anchors screwed into the condyle, in the hope of achieving better long-term fixation and stability of the reduced disc. A variety of orthopaedic suture anchors have been used for this purpose, most commonly the Mitek [29] and Arthrex mini-anchors [15], however alternatives have also demonstrated success, such as one study that reports the use of orthodontic mini-screws [24]. Such techniques aim to offer a predictable long-term result, however, may not be most appropriate where it is difficult to insert a screw into the condyle, such as in the small, resorbed or osteoporotic condyle [16].
Arthroscopic repositioning of the disc and suturing provides an alternative to procedures involving an open incision. Arthroscopy is a more minimally invasive approach, permitting the selective operation on articular disc without causing damage to the other tissues of the TMJ, as well as reducing risk of iatrogenic facial nerve injury which can be associated with open surgery (arthrotomy). Surgically, this involves the creation of three portals, one for the arthroscope and two for instrumentation permitting the passing of a suture through the disc. This technique, despite its conservative nature, is mainly limited by its technical difficulty, and is best performed by an experienced surgeon to ensure a long-term stable outcome [16, 17, 32].
One article also reported the use of a modified meniscopexy technique, whereby a splint was used by patients for a period before surgery, opening the joint space. An anterior-repositioning splint was during the surgical procedure to place the mandible into an ideal relationship with the maxilla. The disc is finally repositioned and sutured anteriorly and laterally to the capsular ligament using an open preauricular approach. Patients then wear the splint postoperatively which is gradually made smaller and narrower, slowly reducing the joint space to its normal anatomical size with the aim of reducing displacement or relapse of the disc repositioning [22].
Several outcome variables were used as measures post-surgery. These included maximal interincisal opening (MIO), post-operative pain using the visual analogue scale (VAS) or questionnaire, changes in diet consistency, mandibular range of motion, joint clicking (including other noises), the use of medications, joint loading signs, MRI to evaluate disc position, disc rupture, and muscular pain.
MIO is the measurement of the distance between the incisal edges of maxillary central incisors to mandibular central incisors at maximum mouth opening. During anterior disc displacement, mandibular opening becomes limited, thus corrective meniscopexy should increase mouth opening in successful cases allowing MIO to be an indicator for success (Table 4).
Name of study | Technique | Number of patients | Preoperative MIO mean (mm) | Postoperative MIO mean (mm) | Follow-up period (time post-op) | |
---|---|---|---|---|---|---|
He et al. [21] | Disc repositioning/Total joint replacement | 8/7 | 19.8 | 33.9 | Not specified | |
McCain et al. [18] | Arthroscopic suture meniscopexy | 32 | 30.0 | 37.9 | 12 months | |
Ruiz Valero et al. [28] | Open Meniscopexy using Mitek anchors | 50 | 23.5 | 38.3 | Not specified | |
Goizueta Adame et al. [17] | Arthroscopic Meniscopexy using posterior double pass suture | 16 | 31.1 | 39.9 | 12 months | |
Göçmen et al. [26] | Open Meniscopexy using Mitek anchors | 7 | 22.8 | 31.5 | 12 months | |
Rajkumar et al. [24] | Open Meniscopexy with orthodontic anchors | 3 patients with DDwoR* | 25.6 | 35.0 39.6 | 1 month 6 months |
Pre-operative and post-operative mouth opening.
DDwoR, disc displacement without reduction.
When comparing the different techniques used in the studies, the greatest increase in mean MIO was seen for patients who had undergone arthrotomy using Mitek mini-anchors compared to arthroscopic techniques [17, 18, 21, 26, 28]. However, it should be noted that the severity of TMD within the patient cohorts is likely to differ, with higher Wilkes’ classifications having further disc degeneration and thus limited mouth opening. Further to this, cohort size has implications on reliability of the results, with smaller cohorts being less reliable to compare surgical techniques based on outcomes. Rajkumar et al. found that MIO increased most significantly, in patients with disc displacement without reduction (DDwoR), within the first month after surgery, with significantly smaller increases seen up to 6 months post-operatively. Although it is difficult to compare between these studies due to differing techniques used and other difference in key variables, similar trends can be seen from the values provided, highlighting the importance of correct disc position in mechanical action of the mandible.
Pain can be used as a subjective measure of surgical outcomes. Within the studies we looked at, there were three methods of measuring pain: using a questionnaire to give a pain score out of 10, using the Visual Analogue Scale (VAS) or by reporting the percentage of patients with pain pre and post operatively. These have been summarised in the table as average values within the patient cohorts (Table 5).
Study | Surgical technique | No. of patients in cohort | Pre-op pain | Post-op pain | Follow-up period (time post-op) |
---|---|---|---|---|---|
McCain et al. [18] | Arthroscopic suture Meniscopexy | 32 | VAS score for joint function* 41.9 | VAS score for joint function* 71.5 | Not specified |
Ruiz Valero et al. [28] | Open Meniscopexy with Mitek anchors | 50 | 100% of patients | 8% of patients | 12 months |
Sheikh et al. [22] | Splint assisted Meniscopexy | 30 | See separate table below | See Separate table below | 8.5 years |
Abramowicz et al. [23] | Not specified | 18 | 9/10 | 1.3/10 | 20 years |
Goizueta Adame et al. [17] | Arthroscopic Meniscopexy using posterior double pass suture | 16 | VAS score 7.6 | VAS score 0.9 | 12 months |
Sharma et al. [19] | Open Meniscopexy using Suture | 10 | Not specified | 100% “Pain relief” | 12 months |
Göçmen et al. [26] | Open Meniscopexy with Mitek anchors | 7 | VAS Score 7.9 | VAS score 0.6 | 12 months |
Rajkumar et al. [24] | Open Meniscopexy with orthodontic anchors | 10 | Not specified | 3.5 | 6 months |
Pre-operative and post-operative pain.
This paper only presented scores for successful surgeries. Visual analogue scale in this paper ranged from 0 to 100 rather than 0 to –10, 0 indicating maximum pain and 100 no pain.
Overall, all studies showed an improvement in pain scores on follow up. The VAS was the most commonly used measure of quantifying pain improvement post-operatively. This can be said to be due to its relative simplicity and quantitative nature. However, there has been debate with regards to its validity. Patients may report pain with a certain degree of bias and also may not be able to report pain relief reliably owing to difficulties in recalling previous pain experiences, hence quantitative comparison is challenging [33]. Some studies looked at the simple absence/presence of pain: Ruiz Valero et al. demonstrated a significantly smaller number of patients presenting with painful TMJ symptoms 12 months post-operatively (pre-100%, post-8%). McCain et al. also looked at pain medication use among patients at pre-operative assessment (15/32 patients) and at last visit (6/32) as a secondary outcome variable. The subjective nature of pain presents difficulty when data gathering techniques are employed. Furthermore, only two articles measuring pain as an outcome variable in our search followed-up patients for more than 12 months [22, 23]. More studies are needed to look into more long-term data as a measure of success, especially with concerns of patients going on to develop secondary joint diseases in the long-term following disc-repositioning procedures [34]. Also, more sensitive means of pain measurement can be considered to establish pain relief post-operatively, if this is to be used as a primary outcome variable, such as the McGill pain questionnaire [35].
Sheikh et al. investigated pain using a different method. They asked patients about the frequency of pain compared to the severity as a measure of outcomes and found improvements from constant pain to rare and no pain. The table illustrates the distribution of pain frequency pre and post operatively [22] (Table 6).
Frequency of pain | Pre-operatively (% of patients) | Post-operatively (% of patients) |
---|---|---|
Constant | 66.67 | 3.33 |
Moderate | 10 | 6.67 |
Occasionally | 6.67 | 33.33 |
Rare | 10 | 33.33 |
None | 6.67 | 23.33 |
Pre-operative and post-operative pain distribution.
This study looked at the pre and post-operative frequency of pain in 30 patients at a mean of 8.5 years post-op. Although the data illustrates a clear reduction in the frequency of pain within the cohort of patients, it would have been further enhanced by quantitative measures of frequency (e.g. pain occurring between 5 and 10 times per day, etc.). Currently, the interpretation of the terms used to describe pain by patients remains subjective. Further to this, if more data points were collected throughout the post-op period, the course of recovery could be monitored. Nevertheless, frequency of pain remains to be an interesting and useful alternative when measuring pain.
Arguably this is one of the most important outcomes as a patient’s quality of life is greatly determined by this (ability to speak and chew). Goizueta Adame et al. specifically used lateral movements of the mandible as an outcome variable. They found on average in a cohort of 16 patients, the average lateral movement increased from 3.9 mm pre-op to 10.3 mm post-op [17].
Moreover, within the cohort patients looked into by Rajkumar et al., three patients had disc displacement without reduction and their lateral mandibular movements were recorded pre-op and at 6 months post-op. The pre-op average was 1.67 mm increasing to 4.67 mm at 6 months. However, this small patient size cannot be used to determine trends within the data. Further to this, other mandibular movements such as protrusion and retrusion could have been recorded over the long term throughout the studies, in order to create a more complete picture of mandibular function [24].
Four studies used MRI evaluation post-operatively looking at disc positioning and condylar changes, as a means to qualify the effectiveness of TMJ meniscopexy procedures.
Zhang et al. conducted a study with 81 patients with internal derangement of the articular disc, ranging from Wilkes III to V that underwent meniscopexy using bone anchors for fixation. MRI was performed 1–7 days post-operatively to evaluate the position of the disc as poor (none or only reposition in one sagittal plane), good (reposition in two2 sagittal planes) or excellent (reposition in three sagittal planes). They termed a successful outcome as good or better and found that 77 patients were excellent, 1 patient had good outcome and 3 patients had poor outcomes, suggesting a 96.3% success rate using bone anchors in arthrotomy [27].
Comparable results are seen in a study of 764 joints treated with an arthroscopic disc repositioning technique, with 729/764 joints deemed as having an excellent outcome [32]. In this particular study however, the specific suture technique was not described, making comparison difficult.
Such data proves that various techniques can be effectively used to reposition the disc accurately into its anatomic position. However, the efficacy of the techniques described can be attributed to the skill of the operator, and such results may not be reproducible universally. Furthermore, while this data gives a good indication of how well the disc is reduced immediately post-operatively, more information regarding long-term relapse would be useful to determine success. The study also gives no mention of post-operative pain or function. Outcomes that are more important to patients than knowing their disc is in the correct position.
Rajkumar et al. conducted MRI assessment at 6 months for 10 patients managed with meniscopexy with orthodontic mini-screws, finding stable positioning of the disc and lack of progression of arthritic changes of the condyle on evaluation [24]. Zhou et al. when using MRI scans to follow up patients post-operatively found 4.7% of patients relapsed with anterior disc displacement. Of the 149 patients, 5 relapsed after 1 year and 2 after 2 years [25]. However, further studies investigating the long-term follow up of all cases is required to accurately ascertain incidence of relapse.
Other outcomes such as reduction in joint noises, increase in diet consistency, muscle pain and joint loading signs were also looked at in a few isolated studies as a means to assess the success of surgery. However, there were not enough studies available to effectively use these to determine outcomes.
From the results of our search it is reasonable to conclude that there is data to support the efficacy of meniscopexy for the management of temporomandibular joint dysfuction. Studies evaluating various techniques of articular disc repositioning demonstrated a successful outcome in the majority of cases, with little evidence to prove otherwise. However, there are certain limitations present in the literature available:
There is a lack of high-level evidence to evaluate outcomes.
There is limited evidence looking at long-term follow up of patients who have received meniscopexy.
No study currently compares the efficacy of meniscopexy directly relative to other surgical techniques available (
There is a lack of consistency in outcome variables evaluated and method in which these were determined, as well as surgical techniques used between papers. Some studies did not describe technique used. Therefore, it is difficult to compare and collate the results of various studies.
Many of the procedures described are sensitive techniques, and therefore results may not necessarily be reproducible across centres or among different operators.
According to the
Our literature search only yielded 2 articles following up patients for more than 12 months after having received disc repositioning surgery, neither of which was conducted prospectively [22, 23]. Concerns exist with regard to long-term outlook of this surgery, specifically incidence of relapse, reoccurrence of symptoms and secondary joint disease [34, 37]. Well-designed, prospective studies of patients receiving meniscopexy with prolonged follow-up are required to address these concerns.
No one surgical technique in the management of advanced TMD seems to predominate, with multiple options described in the literature. Where procedures such as arthrocentesis of the joint space are successful in the management of internal derangement, the role of disc position in the pathology of TMD should be questioned [37]. Ribeiro et al. [38] found articular disc displacement without symptoms to be a common occurrence in the general population (34% of subjects) when conducting an MRI study of 56 asymptomatic volunteers. Other authors [39, 40] have also questioned the role of disc position in TMJ pathology, arguing that pathological changes such as synovitis, osteoarthritis and adhesions to be the causative agents of symptoms, which should be treated separately.
However, in instances where there is no response to other treatment, there seems to be benefit in meniscopexy. This suggests there may still be a place for disc mobilisation, with many patients showing immediate improvement in mechanical function potentially leading to better regeneration of the tissues [17].
Other surgeries performed such as discectomy, joint replacement procedures and various other arthrotomies may also be beneficial in particular circumstances [20]. Since a single procedure has not yet been identified as being preferable in all instances of TMD, the role of the surgeon then becomes to identify the modality which will achieve the best outcome on a case-by-case basis.
To establish the place of meniscopexy relative to other techniques at the operator’s disposal research is required comparing surgical modalities. Despite positive findings no study exists offering a direct comparison between repositioning the disc and alternative procedures.
A number of different approaches are described to reposition the articular disc, some of which are more technically demanding. No study was found directly comparing the efficacy of different techniques. This is also hard to determine since the efficacy of certain techniques will largely depend on the skill of the operator [13]. For instance, excellent outcomes have been reported with arthroscopic disc repositioning and suturing techniques [32]. However, such results may not be reproducible due to this technically demanding technique, resulting in data that is not universally acceptable [16].
Despite the fact that an increasingly greater proportion of TMD is being managed conservatively and minimally invasively, there remains a place for surgical procedures in refractory cases. There is evidence in the literature to suggest meniscopexy is an effective procedure in the management of some instances of TMD, however high-level evidence is lacking. Outcome variables between papers varied, making comparison difficult. In addition, it is apparent that many other techniques are available at a surgeon’s disposal, many of which may be more effective than attempts to reposition the disc. Therefore, case selection is vital when deciding to use meniscopexy as a primary means of management, something which is currently largely based on clinical experience. More research into the pathological processes underlying TMD is required to allow surgeons to make more justified and informed decisions on appropriate means of management for each individual case.
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. Image taken from: ref. [
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, 8]. Previento, which employs a hybrid technique, can anticipate wind for up to 48 hours. References [9, 10] 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 [11].
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 [12].
Nielsen et al. [12] demonstrated that if several NWP forecasts are used the forecast error decreases. Louka et al. [13] 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 [15, 16].
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 [15]. 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 [15].
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 [17, 18].
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 [19].
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 [20].
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 [21]. 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 [22].
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 [23]. Physical models often rely on global databases of meteorological information or atmospheric mesoscale models, but to provide accurate results, they require massive computer systems [24].
To estimate wind power production, the physical method uses a thorough description of the lower atmosphere. Cellura et al. [23] 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 [25, 26]. 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 [27].
To account for the local circumstances of the physical topography, computational fluid dynamics (CFD) is utilized as an alternative to the power law [28]. Model output statistics (MOS) are frequently employed to reduce systematic forecasting mistakes and to compensate for unknowns in the expected power output [29].
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 [30, 31]. 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. [30] 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 [21]). 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 [19, 31].
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 [32].
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. [33] 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. [34] 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. [35] 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. [19] 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. [19, 36]) 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 [36]. 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 [37] was used to test its behavior and provide adequate verification.
Based on cross-correlation at surrounding sites, Alexiadis et al. [38] 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 [39].
Barbounis and Theocharis [40] 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 [40]. 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 [40]. 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 [41] 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 [42] 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 [43] wind forecasting methodology. Traditional statistical time series analysis has been found to be less accurate than neural network forecasting [44].
Chang [45] 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. [46] 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 [47]. Li and Shi [48] 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. [49] 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 [32]. 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. [50] 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. [51] 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 [52] 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 [33, 53] 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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Numerical methods are included in computational algorithms to solve large turbulent scales using large eddy simulation (LES) techniques to obtain acceptable results of turbulent flows. However, microphysics processes involving evaporation, condensation and precipitation water using LES techniques are parameterized. These atmospheric processes are simulated using the advanced regional prediction systems (ARPS) code. On the contrary, atmospheric transport of pollutants is simulated using ARPS code coupled with a Lagrangian stochastic one-particle method. The theoretical details of this coupling are presented. Later, we show some laboratory experiments of plume dispersion emitted from gaseous sources, and the results of the computational simulation tool are compared after obtaining good agreement of the gas concentrations on the stream-wise vertical plane and over the ground. Finally, we present a simulation of a pollution event of copper solid particles at San Miguel de Tucumán city, Argentina. The geographical distributions of copper particle concentrations are in good agreement with the measurements carried out experimentally.",book:{id:"5158",slug:"atmospheric-hazards-case-studies-in-modeling-communication-and-societal-impacts",title:"Atmospheric Hazards",fullTitle:"Atmospheric Hazards - Case Studies in Modeling, Communication, and Societal Impacts"},signatures:"César Augusto Aguirre and Armando Benito Brizuela",authors:[{id:"179132",title:"Dr.",name:"César",middleName:"Augusto",surname:"Aguirre",slug:"cesar-aguirre",fullName:"César Aguirre"},{id:"185180",title:"Prof.",name:"Armando Benito",middleName:null,surname:"Brizuela",slug:"armando-benito-brizuela",fullName:"Armando Benito Brizuela"}]},{id:"51345",doi:"10.5772/64019",title:"Analysis of the Twitter Response to Superstorm Sandy: Public Perceptions, Misconceptions, and Reconceptions of an Extreme Atmospheric Hazard",slug:"analysis-of-the-twitter-response-to-superstorm-sandy-public-perceptions-misconceptions-and-reconcept",totalDownloads:1346,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Superstorm Sandy was the second-costliest hurricane in U.S. history, causing catastrophic flooding and prolonged power outages in New Jersey and New York. The public’s response to this extreme event on the social network Twitter is examined using statistical analysis and manual inspection of 185,000 “tweets” relating to Sandy. Sentiment analysis of tweets from Manhattan Island reveals a statistically significant trend toward negative perceptions, especially on the southern half of the island, as Sandy made landfall. Inspection of all tweets uncovered scientific misconceptions regarding hurricanes, and a surprising and disquieting anthropomorphic reconception of Sandy. This reconception, divorced from factual information about the storm, dominated the “Twittersphere” compared to official scientific information. The implications of such reconceptions for social media communication during future extreme events, and the utility of the methodology employed for analysis of other events, are discussed.",book:{id:"5158",slug:"atmospheric-hazards-case-studies-in-modeling-communication-and-societal-impacts",title:"Atmospheric Hazards",fullTitle:"Atmospheric Hazards - Case Studies in Modeling, Communication, and Societal Impacts"},signatures:"John A. Knox, Brendan Mazanec, Emily Sullivan, Spencer Hall and\nJared A. Rackley",authors:[{id:"179985",title:"Dr.",name:"John",middleName:"A.",surname:"Knox",slug:"john-knox",fullName:"John Knox"},{id:"180238",title:"Mr.",name:"Brendan",middleName:null,surname:"Mazanec",slug:"brendan-mazanec",fullName:"Brendan Mazanec"},{id:"180239",title:"Ms.",name:"Emily",middleName:null,surname:"Sullivan",slug:"emily-sullivan",fullName:"Emily Sullivan"},{id:"180240",title:"Mr.",name:"Spencer",middleName:null,surname:"Hall",slug:"spencer-hall",fullName:"Spencer Hall"},{id:"180241",title:"M.Sc.",name:"Jared",middleName:null,surname:"Rackley",slug:"jared-rackley",fullName:"Jared Rackley"}]},{id:"50596",doi:"10.5772/63319",title:"A Case Study of Atmospheric Dynamics and Thermodynamics in Derechos and the Societal Impacts",slug:"a-case-study-of-atmospheric-dynamics-and-thermodynamics-in-derechos-and-the-societal-impacts",totalDownloads:937,totalCrossrefCites:0,totalDimensionsCites:1,abstract:"The word “derecho” is used to differentiate a storm having straight‐line winds as opposed to rotational, tornadic winds. Although the term “derecho” is relatively old, derechos were not readily recognized by the general public until recent outbreaks caused significant widespread damage and associated fatalities. Most notably, the 2012 Mid‐Atlantic Derecho in the USA brought these types of storms to the public's attention as a variety of societal impacts including infrastructural damage, power outages, and fatalities occurred over an extensive area from outside of Chicago to Washington, DC The associated damage can be more widespread than tornadoes, and the number of fatalities is comparable to those found in medium‐intensity tornadoes.",book:{id:"5158",slug:"atmospheric-hazards-case-studies-in-modeling-communication-and-societal-impacts",title:"Atmospheric Hazards",fullTitle:"Atmospheric Hazards - Case Studies in Modeling, Communication, and Societal Impacts"},signatures:"Kevin Law",authors:[{id:"15961",title:"Dr.",name:"Kevin",middleName:null,surname:"Law",slug:"kevin-law",fullName:"Kevin Law"}]}],mostDownloadedChaptersLast30Days:[{id:"50772",title:"Lightning Occurrence and Social Vulnerability",slug:"lightning-occurrence-and-social-vulnerability",totalDownloads:1580,totalCrossrefCites:3,totalDimensionsCites:10,abstract:"The occurrence of lightning in time and space around the world is well known. Lightning fatalities and injuries are well delineated in the United States; however, there is much less information about lightning impacts on people in the developing world. It is estimated that between 6000 and 24,000 people are killed globally per year, and 10 times as many are injured. The fatality rate per capita has become very low in the developed countries during the past century due to the availability of lightning-safe structures and vehicles, less labor-intensive agriculture, and other factors, but this reduction has not occurred where people continue to work and live in lightning-unsafe situations. Lightning safety advice often mistakenly expects that the direct strike is most common, but ground current, direct contact, side flash, and upward streamers are much more frequent mechanisms. In developed countries, the injury:death ratio is approximately 10:1, meaning that 90% survive but may have permanent disabling injuries. The proximate cause of death is cardiac arrest and anoxic brain injury at the time of the lightning strike, and, at this time, the damage from a lightning strike cannot be reversed or decreased in survivors. Lightning vulnerability in many developing countries continues to be a major issue due to widespread exposure during labor-intensive agriculture during the day when thunderstorms are the most frequent and while occupying lightning-unsafe dwellings at night.",book:{id:"5158",slug:"atmospheric-hazards-case-studies-in-modeling-communication-and-societal-impacts",title:"Atmospheric Hazards",fullTitle:"Atmospheric Hazards - Case Studies in Modeling, Communication, and Societal Impacts"},signatures:"Ronald L. Holle and Mary Ann Cooper",authors:[{id:"179023",title:"Mr.",name:"Ronald",middleName:null,surname:"Holle",slug:"ronald-holle",fullName:"Ronald Holle"},{id:"183336",title:"Dr.",name:"Mary Ann",middleName:null,surname:"Cooper",slug:"mary-ann-cooper",fullName:"Mary Ann Cooper"}]},{id:"50497",title:"Extreme Precipitation Events over East Asia: Evaluating the CMIP5 Model",slug:"extreme-precipitation-events-over-east-asia-evaluating-the-cmip5-model",totalDownloads:1499,totalCrossrefCites:3,totalDimensionsCites:4,abstract:"Extreme hydrological events are a direct threat to society and the environment, and their study within the framework of global climate change remains crucial. However, forecasts present numerous uncertainties.",book:{id:"5158",slug:"atmospheric-hazards-case-studies-in-modeling-communication-and-societal-impacts",title:"Atmospheric Hazards",fullTitle:"Atmospheric Hazards - Case Studies in Modeling, Communication, and Societal Impacts"},signatures:"Nicolas Freychet, Huang-Hsiung Hsu and Chi-Hua Wu",authors:[{id:"179232",title:"Dr.",name:"Nicolas",middleName:null,surname:"Freychet",slug:"nicolas-freychet",fullName:"Nicolas Freychet"},{id:"184846",title:"Prof.",name:"Huang-Hsiung",middleName:null,surname:"Hsu",slug:"huang-hsiung-hsu",fullName:"Huang-Hsiung Hsu"},{id:"184847",title:"Dr.",name:"Chi-Hua",middleName:null,surname:"Wu",slug:"chi-hua-wu",fullName:"Chi-Hua Wu"}]},{id:"51345",title:"Analysis of the Twitter Response to Superstorm Sandy: Public Perceptions, Misconceptions, and Reconceptions of an Extreme Atmospheric Hazard",slug:"analysis-of-the-twitter-response-to-superstorm-sandy-public-perceptions-misconceptions-and-reconcept",totalDownloads:1346,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"Superstorm Sandy was the second-costliest hurricane in U.S. history, causing catastrophic flooding and prolonged power outages in New Jersey and New York. The public’s response to this extreme event on the social network Twitter is examined using statistical analysis and manual inspection of 185,000 “tweets” relating to Sandy. Sentiment analysis of tweets from Manhattan Island reveals a statistically significant trend toward negative perceptions, especially on the southern half of the island, as Sandy made landfall. Inspection of all tweets uncovered scientific misconceptions regarding hurricanes, and a surprising and disquieting anthropomorphic reconception of Sandy. This reconception, divorced from factual information about the storm, dominated the “Twittersphere” compared to official scientific information. The implications of such reconceptions for social media communication during future extreme events, and the utility of the methodology employed for analysis of other events, are discussed.",book:{id:"5158",slug:"atmospheric-hazards-case-studies-in-modeling-communication-and-societal-impacts",title:"Atmospheric Hazards",fullTitle:"Atmospheric Hazards - Case Studies in Modeling, Communication, and Societal Impacts"},signatures:"John A. Knox, Brendan Mazanec, Emily Sullivan, Spencer Hall and\nJared A. Rackley",authors:[{id:"179985",title:"Dr.",name:"John",middleName:"A.",surname:"Knox",slug:"john-knox",fullName:"John Knox"},{id:"180238",title:"Mr.",name:"Brendan",middleName:null,surname:"Mazanec",slug:"brendan-mazanec",fullName:"Brendan Mazanec"},{id:"180239",title:"Ms.",name:"Emily",middleName:null,surname:"Sullivan",slug:"emily-sullivan",fullName:"Emily Sullivan"},{id:"180240",title:"Mr.",name:"Spencer",middleName:null,surname:"Hall",slug:"spencer-hall",fullName:"Spencer Hall"},{id:"180241",title:"M.Sc.",name:"Jared",middleName:null,surname:"Rackley",slug:"jared-rackley",fullName:"Jared Rackley"}]},{id:"51374",title:"Computational Tools for the Simulation of Atmospheric Pollution Transport During a Severe Wind Event in Argentina",slug:"computational-tools-for-the-simulation-of-atmospheric-pollution-transport-during-a-severe-wind-event",totalDownloads:1300,totalCrossrefCites:1,totalDimensionsCites:2,abstract:"This chapter details the theoretical aspects of numerical methods for the simulation of atmospheric phenomena, such as severe thunderstorms and turbulent transport of the dangerous gases and solid particles into the atmospheric boundary layer. Numerical methods are included in computational algorithms to solve large turbulent scales using large eddy simulation (LES) techniques to obtain acceptable results of turbulent flows. However, microphysics processes involving evaporation, condensation and precipitation water using LES techniques are parameterized. These atmospheric processes are simulated using the advanced regional prediction systems (ARPS) code. On the contrary, atmospheric transport of pollutants is simulated using ARPS code coupled with a Lagrangian stochastic one-particle method. The theoretical details of this coupling are presented. Later, we show some laboratory experiments of plume dispersion emitted from gaseous sources, and the results of the computational simulation tool are compared after obtaining good agreement of the gas concentrations on the stream-wise vertical plane and over the ground. Finally, we present a simulation of a pollution event of copper solid particles at San Miguel de Tucumán city, Argentina. The geographical distributions of copper particle concentrations are in good agreement with the measurements carried out experimentally.",book:{id:"5158",slug:"atmospheric-hazards-case-studies-in-modeling-communication-and-societal-impacts",title:"Atmospheric Hazards",fullTitle:"Atmospheric Hazards - Case Studies in Modeling, Communication, and Societal Impacts"},signatures:"César Augusto Aguirre and Armando Benito Brizuela",authors:[{id:"179132",title:"Dr.",name:"César",middleName:"Augusto",surname:"Aguirre",slug:"cesar-aguirre",fullName:"César Aguirre"},{id:"185180",title:"Prof.",name:"Armando Benito",middleName:null,surname:"Brizuela",slug:"armando-benito-brizuela",fullName:"Armando Benito Brizuela"}]},{id:"51134",title:"Communication and Preparedness Issues on Various Scales from Extreme Atmospheric Hazards",slug:"communication-and-preparedness-issues-on-various-scales-from-extreme-atmospheric-hazards",totalDownloads:1373,totalCrossrefCites:0,totalDimensionsCites:0,abstract:"Most federally declared disasters are from atmospheric hazards. These could be from floods, tropical cyclones, tornadoes, or winter storms. Some of these hazards are events with relatively short warning times such as tornadoes or have sufficient warnings from tropical cyclones. This research examined communication of weather information and personal preparedness following the Florida landfall from Tropical Storm Debby in 2012. Another case study examined emergency management issues such as preparedness and response after the 2011 tornado in Tuscaloosa, Alabama. The concentration was on emergency management agencies at the County, University, State, and Federal levels. A sample of elderly residents of Pinellas and Pasco Counties in Florida completed a self-administered survey to examine various means of receiving weather information along with hurricane preparedness actions. In-depth interviews were conducted with representatives of various agencies on different scales in regard to preparedness and response following the Tuscaloosa tornado. The elderly used television as the primary means of receiving weather information, thus stressing the importance of utilizing both traditional and newer forms of communications to reach all citizens. One of the major issues on all levels following the Tuscaloosa tornado is related to communications such as resource allocations and response actions.",book:{id:"5158",slug:"atmospheric-hazards-case-studies-in-modeling-communication-and-societal-impacts",title:"Atmospheric Hazards",fullTitle:"Atmospheric Hazards - Case Studies in Modeling, Communication, and Societal Impacts"},signatures:"Robert M. Schwartz",authors:[{id:"184116",title:"Dr.",name:"Robert",middleName:null,surname:"Schwartz",slug:"robert-schwartz",fullName:"Robert Schwartz"}]}],onlineFirstChaptersFilter:{topicId:"627",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:90,numberOfOpenTopics:6,numberOfUpcomingTopics:0,issn:"2633-1403",doi:"10.5772/intechopen.79920",isOpenForSubmission:!0},{id:"7",title:"Biomedical Engineering",numberOfPublishedBooks:12,numberOfPublishedChapters:108,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2631-5343",doi:"10.5772/intechopen.71985",isOpenForSubmission:!0}],lsSeriesList:[{id:"11",title:"Biochemistry",numberOfPublishedBooks:33,numberOfPublishedChapters:330,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2632-0983",doi:"10.5772/intechopen.72877",isOpenForSubmission:!0},{id:"25",title:"Environmental Sciences",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2754-6713",doi:"10.5772/intechopen.100362",isOpenForSubmission:!0},{id:"10",title:"Physiology",numberOfPublishedBooks:14,numberOfPublishedChapters:145,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-8261",doi:"10.5772/intechopen.72796",isOpenForSubmission:!0}],hsSeriesList:[{id:"3",title:"Dentistry",numberOfPublishedBooks:9,numberOfPublishedChapters:141,numberOfOpenTopics:2,numberOfUpcomingTopics:0,issn:"2631-6218",doi:"10.5772/intechopen.71199",isOpenForSubmission:!0},{id:"6",title:"Infectious Diseases",numberOfPublishedBooks:13,numberOfPublishedChapters:123,numberOfOpenTopics:4,numberOfUpcomingTopics:0,issn:"2631-6188",doi:"10.5772/intechopen.71852",isOpenForSubmission:!0},{id:"13",title:"Veterinary Medicine and Science",numberOfPublishedBooks:11,numberOfPublishedChapters:112,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:22,numberOfOpenTopics:3,numberOfUpcomingTopics:0,issn:"2753-894X",doi:"10.5772/intechopen.100359",isOpenForSubmission:!0},{id:"23",title:"Education and Human Development",numberOfPublishedBooks:0,numberOfPublishedChapters:11,numberOfOpenTopics:1,numberOfUpcomingTopics:1,issn:null,doi:"10.5772/intechopen.100360",isOpenForSubmission:!0},{id:"24",title:"Sustainable Development",numberOfPublishedBooks:1,numberOfPublishedChapters:19,numberOfOpenTopics:5,numberOfUpcomingTopics:0,issn:"2753-6580",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. 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Her focus is on quality, innovation, leadership, and personalised learning. She works primarily at the strategic and policy levels, both nationally and internationally, and with key international organisations. She is committed to promoting and improving OFDL in the context of SDG4 and the future of education. Ossiannilsson has more than 20 years of experience in her current field, but more than 40 years in the education sector. She works as a reviewer and expert for the European Commission and collaborates with the Joint Research Centre for Quality in Open Education. Ossiannilsson also collaborates with ITCILO and ICoBC (International Council on Badges and Credentials). She is a member of the ICDE Board of Directors and has previously served on the boards of EDEN and EUCEN. Ossiannilsson is a quality expert and reviewer for ICDE, EDEN and the EADTU. She chairs the ICDE OER Advocacy Committee and is a member of the ICDE Quality Network. 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Évora",institutionURL:null,country:{name:"Portugal"}}}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null}]},subseriesFiltersForPublishedBooks:[{group:"subseries",caption:"Animal Nutrition",value:20,count:2},{group:"subseries",caption:"Animal Reproductive Biology and Technology",value:28,count:4},{group:"subseries",caption:"Animal Science",value:19,count:5}],publicationYearFilters:[{group:"publicationYear",caption:"2022",value:2022,count:3},{group:"publicationYear",caption:"2021",value:2021,count:3},{group:"publicationYear",caption:"2020",value:2020,count:3},{group:"publicationYear",caption:"2019",value:2019,count:1},{group:"publicationYear",caption:"2018",value:2018,count:1}],authors:{paginationCount:148,paginationItems:[{id:"165328",title:"Dr.",name:"Vahid",middleName:null,surname:"Asadpour",slug:"vahid-asadpour",fullName:"Vahid Asadpour",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/165328/images/system/165328.jpg",biography:"Vahid Asadpour, MS, Ph.D., is currently with the Department of Research and Evaluation, Kaiser Permanente Southern California. He has both an MS and Ph.D. in Biomedical Engineering. He was previously a research scientist at the University of California Los Angeles (UCLA) and visiting professor and researcher at the University of North Dakota. He is currently working in artificial intelligence and its applications in medical signal processing. In addition, he is using digital signal processing in medical imaging and speech processing. Dr. Asadpour has developed brain-computer interfacing algorithms and has published books, book chapters, and several journal and conference papers in this field and other areas of intelligent signal processing. He has also designed medical devices, including a laser Doppler monitoring system.",institutionString:"Kaiser Permanente Southern California",institution:null},{id:"169608",title:"Prof.",name:"Marian",middleName:null,surname:"Găiceanu",slug:"marian-gaiceanu",fullName:"Marian Găiceanu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/169608/images/system/169608.png",biography:"Prof. Dr. Marian Gaiceanu graduated from the Naval and Electrical Engineering Faculty, Dunarea de Jos University of Galati, Romania, in 1997. He received a Ph.D. (Magna Cum Laude) in Electrical Engineering in 2002. Since 2017, Dr. Gaiceanu has been a Ph.D. supervisor for students in Electrical Engineering. He has been employed at Dunarea de Jos University of Galati since 1996, where he is currently a professor. Dr. Gaiceanu is a member of the National Council for Attesting Titles, Diplomas and Certificates, an expert of the Executive Agency for Higher Education, Research Funding, and a member of the Senate of the Dunarea de Jos University of Galati. He has been the head of the Integrated Energy Conversion Systems and Advanced Control of Complex Processes Research Center, Romania, since 2016. He has conducted several projects in power converter systems for electrical drives, power quality, PEM and SOFC fuel cell power converters for utilities, electric vehicles, and marine applications with the Department of Regulation and Control, SIEI S.pA. (2002–2004) and the Polytechnic University of Turin, Italy (2002–2004, 2006–2007). He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and cofounder-member of the IEEE Power Electronics Romanian Chapter. He is a guest editor at Energies and an academic book editor for IntechOpen. He is also a member of the editorial boards of the Journal of Electrical Engineering, Electronics, Control and Computer Science and Sustainability. Dr. Gaiceanu has been General Chairman of the IEEE International Symposium on Electrical and Electronics Engineering in the last six editions.",institutionString:'"Dunarea de Jos" University of Galati',institution:{name:'"Dunarea de Jos" University of Galati',country:{name:"Romania"}}},{id:"4519",title:"Prof.",name:"Jaydip",middleName:null,surname:"Sen",slug:"jaydip-sen",fullName:"Jaydip Sen",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/4519/images/system/4519.jpeg",biography:"Jaydip Sen is associated with Praxis Business School, Kolkata, India, as a professor in the Department of Data Science. His research areas include security and privacy issues in computing and communication, intrusion detection systems, machine learning, deep learning, and artificial intelligence in the financial domain. He has more than 200 publications in reputed international journals, refereed conference proceedings, and 20 book chapters in books published by internationally renowned publishing houses, such as Springer, CRC press, IGI Global, etc. Currently, he is serving on the editorial board of the prestigious journal Frontiers in Communications and Networks and in the technical program committees of a number of high-ranked international conferences organized by the IEEE, USA, and the ACM, USA. He has been listed among the top 2% of scientists in the world for the last three consecutive years, 2019 to 2021 as per studies conducted by the Stanford University, USA.",institutionString:"Praxis Business School",institution:null},{id:"320071",title:"Dr.",name:"Sidra",middleName:null,surname:"Mehtab",slug:"sidra-mehtab",fullName:"Sidra Mehtab",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00002v6KHoQAM/Profile_Picture_1584512086360",biography:"Sidra Mehtab has completed her BS with honors in Physics from Calcutta University, India in 2018. She has done MS in Data Science and Analytics from Maulana Abul Kalam Azad University of Technology (MAKAUT), Kolkata, India in 2020. Her research areas include Econometrics, Time Series Analysis, Machine Learning, Deep Learning, Artificial Intelligence, and Computer and Network Security with a particular focus on Cyber Security Analytics. Ms. Mehtab has published seven papers in international conferences and one of her papers has been accepted for publication in a reputable international journal. She has won the best paper awards in two prestigious international conferences – BAICONF 2019, and ICADCML 2021, organized in the Indian Institute of Management, Bangalore, India in December 2019, and SOA University, Bhubaneswar, India in January 2021. Besides, Ms. Mehtab has also published two book chapters in two books. Seven of her book chapters will be published in a volume shortly in 2021 by Cambridge Scholars’ Press, UK. Currently, she is working as the joint editor of two edited volumes on Time Series Analysis and Forecasting to be published in the first half of 2021 by an international house. Currently, she is working as a Data Scientist with an MNC in Delhi, India.",institutionString:"NSHM College of Management and Technology",institution:{name:"Association for Computing Machinery",country:{name:"United States of America"}}},{id:"226240",title:"Dr.",name:"Andri Irfan",middleName:null,surname:"Rifai",slug:"andri-irfan-rifai",fullName:"Andri Irfan Rifai",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/226240/images/7412_n.jpg",biography:"Andri IRFAN is a Senior Lecturer of Civil Engineering and Planning. He completed the PhD at the Universitas Indonesia & Universidade do Minho with Sandwich Program Scholarship from the Directorate General of Higher Education and LPDP scholarship. He has been teaching for more than 19 years and much active to applied his knowledge in the project construction in Indonesia. His research interest ranges from pavement management system to advanced data mining techniques for transportation engineering. He has published more than 50 papers in journals and 2 books.",institutionString:null,institution:{name:"Universitas Internasional Batam",country:{name:"Indonesia"}}},{id:"314576",title:"Dr.",name:"Ibai",middleName:null,surname:"Laña",slug:"ibai-lana",fullName:"Ibai Laña",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314576/images/system/314576.jpg",biography:"Dr. Ibai Laña works at TECNALIA as a data analyst. He received his Ph.D. in Artificial Intelligence from the University of the Basque Country (UPV/EHU), Spain, in 2018. He is currently a senior researcher at TECNALIA. His research interests fall within the intersection of intelligent transportation systems, machine learning, traffic data analysis, and data science. He has dealt with urban traffic forecasting problems, applying machine learning models and evolutionary algorithms. He has experience in origin-destination matrix estimation or point of interest and trajectory detection. Working with large volumes of data has given him a good command of big data processing tools and NoSQL databases. He has also been a visiting scholar at the Knowledge Engineering and Discovery Research Institute, Auckland University of Technology.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"314575",title:"Dr.",name:"Jesus",middleName:null,surname:"L. Lobo",slug:"jesus-l.-lobo",fullName:"Jesus L. Lobo",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/314575/images/system/314575.png",biography:"Dr. Jesús López is currently based in Bilbao (Spain) working at TECNALIA as Artificial Intelligence Research Scientist. In most cases, a project idea or a new research line needs to be investigated to see if it is good enough to take into production or to focus on it. That is exactly what he does, diving into Machine Learning algorithms and technologies to help TECNALIA to decide whether something is great in theory or will actually impact on the product or processes of its projects. So, he is expert at framing experiments, developing hypotheses, and proving whether they’re true or not, in order to investigate fundamental problems with a longer time horizon. He is also able to design and develop PoCs and system prototypes in simulation. He has participated in several national and internacional R&D projects.\n\nAs another relevant part of his everyday research work, he usually publishes his findings in reputed scientific refereed journals and international conferences, occasionally acting as reviewer and Programme Commitee member. Concretely, since 2018 he has published 9 JCR (8 Q1) journal papers, 9 conference papers (e.g. ECML PKDD 2021), and he has co-edited a book. He is also active in popular science writing data science stories for reputed blogs (KDNuggets, TowardsDataScience, Naukas). Besides, he has recently embarked on mentoring programmes as mentor, and has also worked as data science trainer.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"103779",title:"Prof.",name:"Yalcin",middleName:null,surname:"Isler",slug:"yalcin-isler",fullName:"Yalcin Isler",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRyQ8QAK/Profile_Picture_1628834958734",biography:"Yalcin Isler (1971 - Burdur / Turkey) received the B.Sc. degree in the Department of Electrical and Electronics Engineering from Anadolu University, Eskisehir, Turkey, in 1993, the M.Sc. degree from the Department of Electronics and Communication Engineering, Suleyman Demirel University, Isparta, Turkey, in 1996, the Ph.D. degree from the Department of Electrical and Electronics Engineering, Dokuz Eylul University, Izmir, Turkey, in 2009, and the Competence of Associate Professorship from the Turkish Interuniversity Council in 2019.\n\nHe was Lecturer at Burdur Vocational School in Suleyman Demirel University (1993-2000, Burdur / Turkey), Software Engineer (2000-2002, Izmir / Turkey), Research Assistant in Bulent Ecevit University (2002-2003, Zonguldak / Turkey), Research Assistant in Dokuz Eylul University (2003-2010, Izmir / Turkey), Assistant Professor at the Department of Electrical and Electronics Engineering in Bulent Ecevit University (2010-2012, Zonguldak / Turkey), Assistant Professor at the Department of Biomedical Engineering in Izmir Katip Celebi University (2012-2019, Izmir / Turkey). He is an Associate Professor at the Department of Biomedical Engineering at Izmir Katip Celebi University, Izmir / Turkey, since 2019. In addition to academics, he has also founded Islerya Medical and Information Technologies Company, Izmir / Turkey, since 2017.\n\nHis main research interests cover biomedical signal processing, pattern recognition, medical device design, programming, and embedded systems. He has many scientific papers and participated in several projects in these study fields. He was an IEEE Student Member (2009-2011) and IEEE Member (2011-2014) and has been IEEE Senior Member since 2014.",institutionString:null,institution:{name:"Izmir Kâtip Çelebi University",country:{name:"Turkey"}}},{id:"339677",title:"Dr.",name:"Mrinmoy",middleName:null,surname:"Roy",slug:"mrinmoy-roy",fullName:"Mrinmoy Roy",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/339677/images/16768_n.jpg",biography:"An accomplished Sales & Marketing professional with 12 years of cross-functional experience in well-known organisations such as CIPLA, LUPIN, GLENMARK, ASTRAZENECA across different segment of Sales & Marketing, International Business, Institutional Business, Product Management, Strategic Marketing of HIV, Oncology, Derma, Respiratory, Anti-Diabetic, Nutraceutical & Stomatological Product Portfolio and Generic as well as Chronic Critical Care Portfolio. A First Class MBA in International Business & Strategic Marketing, B.Pharm, D.Pharm, Google Certified Digital Marketing Professional. Qualified PhD Candidate in Operations and Management with special focus on Artificial Intelligence and Machine Learning adoption, analysis and use in Healthcare, Hospital & Pharma Domain. Seasoned with diverse therapy area of Pharmaceutical Sales & Marketing ranging from generating revenue through generating prescriptions, launching new products, and making them big brands with continuous strategy execution at the Physician and Patients level. Moved from Sales to Marketing and Business Development for 3.5 years in South East Asian Market operating from Manila, Philippines. Came back to India and handled and developed Brands such as Gluconorm, Lupisulin, Supracal, Absolut Woman, Hemozink, Fabiflu (For COVID 19), and many more. In my previous assignment I used to develop and execute strategies on Sales & Marketing, Commercialization & Business Development for Institution and Corporate Hospital Business portfolio of Oncology Therapy Area for AstraZeneca Pharma India Ltd. Being a Research Scholar and Student of ‘Operations Research & Management: Artificial Intelligence’ I published several pioneer research papers and book chapters on the same in Internationally reputed journals and Books indexed in Scopus, Springer and Ei Compendex, Google Scholar etc. Currently, I am launching PGDM Pharmaceutical Management Program in IIHMR Bangalore and spearheading the course curriculum and structure of the same. I am interested in Collaboration for Healthcare Innovation, Pharma AI Innovation, Future trend in Marketing and Management with incubation on Healthcare, Healthcare IT startups, AI-ML Modelling and Healthcare Algorithm based training module development. I am also an affiliated member of the Institute of Management Consultant of India, looking forward to Healthcare, Healthcare IT and Innovation, Pharma and Hospital Management Consulting works.",institutionString:null,institution:{name:"Lovely Professional University",country:{name:"India"}}},{id:"310576",title:"Prof.",name:"Erick Giovani",middleName:null,surname:"Sperandio Nascimento",slug:"erick-giovani-sperandio-nascimento",fullName:"Erick Giovani Sperandio Nascimento",position:null,profilePictureURL:"https://intech-files.s3.amazonaws.com/0033Y00002pDKxDQAW/ProfilePicture%202022-06-20%2019%3A57%3A24.788",biography:"Prof. Erick Sperandio is the Lead Researcher and professor of Artificial Intelligence (AI) at SENAI CIMATEC, Bahia, Brazil, also working with Computational Modeling (CM) and HPC. He holds a PhD in Environmental Engineering in the area of Atmospheric Computational Modeling, a Master in Informatics in the field of Computational Intelligence and Graduated in Computer Science from UFES. He currently coordinates, leads and participates in R&D projects in the areas of AI, computational modeling and supercomputing applied to different areas such as Oil and Gas, Health, Advanced Manufacturing, Renewable Energies and Atmospheric Sciences, advising undergraduate, master's and doctoral students. He is the Lead Researcher at SENAI CIMATEC's Reference Center on Artificial Intelligence. In addition, he is a Certified Instructor and University Ambassador of the NVIDIA Deep Learning Institute (DLI) in the areas of Deep Learning, Computer Vision, Natural Language Processing and Recommender Systems, and Principal Investigator of the NVIDIA/CIMATEC AI Joint Lab, the first in Latin America within the NVIDIA AI Technology Center (NVAITC) worldwide program. He also works as a researcher at the Supercomputing Center for Industrial Innovation (CS2i) and at the SENAI Institute of Innovation for Automation (ISI Automação), both from SENAI CIMATEC. He is a member and vice-coordinator of the Basic Board of Scientific-Technological Advice and Evaluation, in the area of Innovation, of the Foundation for Research Support of the State of Bahia (FAPESB). He serves as Technology Transfer Coordinator and one of the Principal Investigators at the National Applied Research Center in Artificial Intelligence (CPA-IA) of SENAI CIMATEC, focusing on Industry, being one of the six CPA-IA in Brazil approved by MCTI / FAPESP / CGI.br. He also participates as one of the representatives of Brazil in the BRICS Innovation Collaboration Working Group on HPC, ICT and AI. He is the coordinator of the Work Group of the Axis 5 - Workforce and Training - of the Brazilian Strategy for Artificial Intelligence (EBIA), and member of the MCTI/EMBRAPII AI Innovation Network Training Committee. He is the coordinator, by SENAI CIMATEC, of the Artificial Intelligence Reference Network of the State of Bahia (REDE BAH.IA). He leads the working group of experts representing Brazil in the Global Partnership on Artificial Intelligence (GPAI), on the theme \"AI and the Pandemic Response\".",institutionString:"Manufacturing and Technology Integrated Campus – SENAI CIMATEC",institution:null},{id:"1063",title:"Prof.",name:"Constantin",middleName:null,surname:"Volosencu",slug:"constantin-volosencu",fullName:"Constantin Volosencu",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/1063/images/system/1063.png",biography:"Prof. Dr. Constantin Voloşencu graduated as an engineer from\nPolitehnica University of Timișoara, Romania, where he also\nobtained a doctorate degree. He is currently a full professor in\nthe Department of Automation and Applied Informatics at the\nsame university. Dr. Voloşencu is the author of ten books, seven\nbook chapters, and more than 160 papers published in journals\nand conference proceedings. He has also edited twelve books and\nhas twenty-seven patents to his name. He is a manager of research grants, editor in\nchief and member of international journal editorial boards, a former plenary speaker, a member of scientific committees, and chair at international conferences. His\nresearch is in the fields of control systems, control of electric drives, fuzzy control\nsystems, neural network applications, fault detection and diagnosis, sensor network\napplications, monitoring of distributed parameter systems, and power ultrasound\napplications. He has developed automation equipment for machine tools, spooling\nmachines, high-power ultrasound processes, and more.",institutionString:'"Politechnica" University Timişoara',institution:null},{id:"221364",title:"Dr.",name:"Eneko",middleName:null,surname:"Osaba",slug:"eneko-osaba",fullName:"Eneko Osaba",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/221364/images/system/221364.jpg",biography:"Dr. Eneko Osaba works at TECNALIA as a senior researcher. He obtained his Ph.D. in Artificial Intelligence in 2015. He has participated in more than twenty-five local and European research projects, and in the publication of more than 130 papers. He has performed several stays at universities in the United Kingdom, Italy, and Malta. Dr. Osaba has served as a program committee member in more than forty international conferences and participated in organizing activities in more than ten international conferences. He is a member of the editorial board of the International Journal of Artificial Intelligence, Data in Brief, and Journal of Advanced Transportation. He is also a guest editor for the Journal of Computational Science, Neurocomputing, Swarm, and Evolutionary Computation and IEEE ITS Magazine.",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"275829",title:"Dr.",name:"Esther",middleName:null,surname:"Villar-Rodriguez",slug:"esther-villar-rodriguez",fullName:"Esther Villar-Rodriguez",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/275829/images/system/275829.jpg",biography:"Dr. Esther Villar obtained a Ph.D. in Information and Communication Technologies from the University of Alcalá, Spain, in 2015. She obtained a degree in Computer Science from the University of Deusto, Spain, in 2010, and an MSc in Computer Languages and Systems from the National University of Distance Education, Spain, in 2012. Her areas of interest and knowledge include natural language processing (NLP), detection of impersonation in social networks, semantic web, and machine learning. Dr. Esther Villar made several contributions at conferences and publishing in various journals in those fields. Currently, she is working within the OPTIMA (Optimization Modeling & Analytics) business of TECNALIA’s ICT Division as a data scientist in projects related to the prediction and optimization of management and industrial processes (resource planning, energy efficiency, etc).",institutionString:"TECNALIA Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"49813",title:"Dr.",name:"Javier",middleName:null,surname:"Del Ser",slug:"javier-del-ser",fullName:"Javier Del Ser",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/49813/images/system/49813.png",biography:"Prof. Dr. Javier Del Ser received his first PhD in Telecommunication Engineering (Cum Laude) from the University of Navarra, Spain, in 2006, and a second PhD in Computational Intelligence (Summa Cum Laude) from the University of Alcala, Spain, in 2013. He is currently a principal researcher in data analytics and optimisation at TECNALIA (Spain), a visiting fellow at the Basque Center for Applied Mathematics (BCAM) and a part-time lecturer at the University of the Basque Country (UPV/EHU). His research interests gravitate on the use of descriptive, prescriptive and predictive algorithms for data mining and optimization in a diverse range of application fields such as Energy, Transport, Telecommunications, Health and Industry, among others. In these fields he has published more than 240 articles, co-supervised 8 Ph.D. theses, edited 6 books, coauthored 7 patents and participated/led more than 40 research projects. He is a Senior Member of the IEEE, and a recipient of the Biscay Talent prize for his academic career.",institutionString:"Tecnalia Research & Innovation",institution:{name:"Tecnalia",country:{name:"Spain"}}},{id:"278948",title:"Dr.",name:"Carlos Pedro",middleName:null,surname:"Gonçalves",slug:"carlos-pedro-goncalves",fullName:"Carlos Pedro Gonçalves",position:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRcmyQAC/Profile_Picture_1564224512145",biography:'Carlos Pedro Gonçalves (PhD) is an Associate Professor at Lusophone University of Humanities and Technologies and a researcher on Complexity Sciences, Quantum Technologies, Artificial Intelligence, Strategic Studies, Studies in Intelligence and Security, FinTech and Financial Risk Modeling. He is also a progammer with programming experience in:\n\nA) Quantum Computing using Qiskit Python module and IBM Quantum Experience Platform, with software developed on the simulation of Quantum Artificial Neural Networks and Quantum Cybersecurity;\n\nB) Artificial Intelligence and Machine learning programming in Python;\n\nC) Artificial Intelligence, Multiagent Systems Modeling and System Dynamics Modeling in Netlogo, with models developed in the areas of Chaos Theory, Econophysics, Artificial Intelligence, Classical and Quantum Complex Systems Science, with the Econophysics models having been cited worldwide and incorporated in PhD programs by different Universities.\n\nReceived an Arctic Code Vault Contributor status by GitHub, due to having developed open source software preserved in the \\"Arctic Code Vault\\" for future generations (https://archiveprogram.github.com/arctic-vault/), with the Strategy Analyzer A.I. module for decision making support (based on his PhD thesis, used in his Classes on Decision Making and in Strategic Intelligence Consulting Activities) and QNeural Python Quantum Neural Network simulator also preserved in the \\"Arctic Code Vault\\", for access to these software modules see: https://github.com/cpgoncalves. He is also a peer reviewer with outsanding review status from Elsevier journals, including Physica A, Neurocomputing and Engineering Applications of Artificial Intelligence. 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Recently, bioinspired systems have been successfully employing biomechanics to develop and improve assistive technology and rehabilitation devices. The research topic "Bioinspired Technology and Biomechanics" welcomes studies reporting recent advances in bioinspired technologies that contribute to individuals\' health, inclusion, and rehabilitation. 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Chapters exploring biomaterial approaches such as polymer synthesis and characterization, drug and gene vector design, biocompatibility, immunology and toxicology, and self-assembly at the nanoscale, are welcome. 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