The characteristics of the anatomical Duke model.
\r\n\tHydroxyapatite (HA) is an important member of the calcium phosphate chemical family. It has been used in several medical applications for the past decades, due to its chemical similarity to the mineral phase of bone and high biocompatibility. Several studies demonstrated that bone mineral presents several ion substitutions, so in order to prepare a synthetic material with an even closer composition to bone mineral, HA has been prepared with the incorporation of several ions like, silicon or fluoride. These ions induced not only structural changes on HA lattice, but also on its biocompatibility.
\r\n\tSignificant advances in nanotechnologies resulted in the preparation of HA in different forms, with a wider range of applications, from support to drug and gene delivery.
\r\n\tThis book aims to collect the most relevant information regarding HA properties, modifications and its application in the biomedical field.
Many of the MR-related injuries and the few fatalities that have occurred were the apparent result of failure to follow safety guidelines or of the use of inappropriate information related to the safety aspects of biomedical implants and devices [1, 2, 3, 4, 5, 6, 7]. The preservation of a safe MR environment requires constant attention to the care of patients and individuals with metallic implants and devices, because the variety and complexity of these objects constantly changes [5, 6, 7]. Therefore, to guard against accidents in the MR environment, it is important to understand the risk associated with implantable medical devices which may cause potential problems.
\nThe radiofrequency coils could send energy, in the form of electromagnetic radiation, into the human body. Since the energy is in the radio frequency range, the radiation is not ionizing. But it still can influence biological tissue. During MR procedures, the majority of the RF power transmitted for imaging or spectroscopy (especially for carbon decoupling) is transformed into heat within the patient’s tissue as a result of resistive losses, through convection, conduction, radiation or evaporation [8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]. Thus, a potential concern in MRI is the heating of the body during image acquisition.
\nTo evaluate the RF-induced heating, the specific absorption rate (SAR) is applied to determine how much electromagnetic energy is absorbed by the body. SAR is typically expressed in unites of watts per kilogram, or W/kg. So the SAR could be defined as:
\nwhere
The first study of human thermal responses to RF radiation-induced heating during an MR procedure was conducted by Schaefer et al. [19]. Temperature changes and other physiological parameters were assessed in volunteer subjects exposed to relatively high, whole-body averaged SARs (approximately 4.0 W/kg). The data indicated that there were no excessive temperature elevations or other deleterious physiological consequences related to the exposure to RF radiation [19].
\nHowever, for patients with medical implants, MRI-related RF induced heating is potentially problematic. The evaluation of heating for an implant or device is particularly challenging because of the many factors that affect temperature increase in these items. Variables that impact heating include the following: the specific type of implant or device; the electrical characteristic of the implant or device; the RF wavelength of the MR system; the type of transmit RF coil that is used (i.e., transmit head versus transmit body RF coil); the amount of RF energy delivered (i.e., the specific absorption rate, SAR); the landmark position or body part undergoing MRI relative to the transmit RF coil; and the orientation or configuration of the implant or device relative to the source of transmit RF coil.
\nIn this chapter, it shows the importance of evaluation the MRI-related RF induced heating issues for patient with implantable medical devices. Generally, the estimation and measurement is based on
MRI may be contraindicated for a given patient primarily due to its potential risks associated with a metallic implant or device. Although many investigations have been performed using laboratory animals to determine thermoregulatory reactions to tissue heating associated with exposure to RF radiation, these experiments do not directly apply to the conditions that occur during MR procedures, nor can they be extrapolated to provide useful information for various reasons [20, 21]. For example, the pattern of RF absorption or the coupling of radiation to biological tissues is primarily dependent on the organism’s size, anatomical features, duration of exposure, sensitivity of the involved tissues (e.g., some tissues are more “thermal sensitive” than others), and a myriad of other variables [14, 21, 22]. Furthermore, there is no laboratory animal that sufficiently mimics or simulates the thermoregulatory responses of an organism with the dimensions and specific responses to that of a human subject. Therefore, experimental results obtained in laboratory animals cannot be simply “scaled” or extrapolated to predict thermoregulatory or other physiological changes in human subjects exposed to RF radiation-induced heating during MR procedures [14, 15, 22], and. In consideration of the above,
One of
Although the RF-induced heating evaluating method using the phantom filled with gelled-saline is widely used, it is obvious that the RF environment of a human body and a phantom filled with gelled-saline are quite different. The power deposition due to an implant for a given incident RF field is a function of the physical properties of the implant and electrical properties of the surrounding medium. Compared with homogeneous gelled-saline in phantom, human body is an inhomogeneous circumstance which includes different tissues with various permittivity and conductivity in a wide range. Hence, it is necessary to study a feasible guide with
With the development of computational electromagnetics, anatomical computer models of the human body have been used for nearly four decades for dosimetric applications in electromagnetics (EM) [33] and in medical radiation physics [34]. The most prominent numerical methods used in computational dosimetry of electromagnetic fields are based on finite-difference formulations of the underlying differential equations. For the simulation of both RF fields and induced tissue heating, the finite-difference time-domain (FDTD) method in its formulations by Yee [35] and Patankar [36] is applied to rectilinear grids to optimally handle large voxel models. The reconstructed human model used in this Chapter is from the Virtual Family [37]. It is based on high resolution magnetic resonance images of healthy volunteers. Seventy seven different tissue types were distinguished during the segmentation. Currently, the models are being widely applied in several studies on electromagnetic exposure, device optimization and medical applications. \nTable 1\n shows the characteristics of the anatomical model. Duke model is an anatomical model of adult male which is shown in \nFigures 1\n and \n2\n. And \nTable 2\n shows the segmented tissues and organs of the model, as well as the electromagnetic properties.
\nName | \nAge (years) | \nGender | \nHeight (m) | \nMass (kg) | \nBMI (kg/m2) | \n
---|---|---|---|---|---|
Duke | \n34 | \nMale | \n1.74 | \n70 | \n23.1 | \n
The characteristics of the anatomical Duke model.
The segmented tissues and organs of anatomic body.
The segmented tissues and organs of anatomic brain.
Tissue or organ | \nElectric conductivity (S/m) | \nRelative permittivity | \nDensity (kg/m3) | \n||
---|---|---|---|---|---|
1.5 T/64 MHz | \n3 T/128 MHz | \n1.5 T/64 MHz | \n3 T/128 MHz | \n||
Adrenal gland | \n0.778305 | \n0.804166 | \n73.9472 | \n66.7839 | \n1027.5 | \n
Air internal | \n0 | \n0 | \n1 | \n1 | \n1.2 | \n
Artery | \n1.20667 | \n1.24863 | \n86.4441 | \n73.159 | \n1049.75 | \n
Bladder | \n0.287352 | \n0.298014 | \n24.5943 | \n21.8607 | \n1035 | \n
Blood vessel | \n1.20667 | \n1.24863 | \n86.4441 | \n73.159 | \n1049.75 | \n
Bone | \n0.0595255 | \n0.0673524 | \n16.6812 | \n14.7171 | \n1908 | \n
Brain gray matter | \n0.510868 | \n0.58673 | \n97.4294 | \n73.5204 | \n1044.5 | \n
Brain white matter | \n0.291504 | \n0.342151 | \n67.8358 | \n52.5338 | \n1041 | \n
Bronchi | \n0.528415 | \n0.559346 | \n58.8896 | \n50.5714 | \n1101.5 | \n
Bronchi lumen | \n0 | \n0 | \n1 | \n1 | \n1.2 | \n
Cartilage | \n0.452103 | \n0.488375 | \n62.9145 | \n52.9242 | \n1099.5 | \n
Cerebellum | \n0.719003 | \n0.829397 | \n116.35 | \n79.7377 | \n1045 | \n
Cerebrospinal fluid | \n2.06597 | \n2.14301 | \n97.3124 | \n84.0406 | \n1007 | \n
Commissure anterior | \n0.291504 | \n0.342151 | \n67.8358 | \n52.5338 | \n1041 | \n
Commissure posterior | \n0.291504 | \n0.342151 | \n67.8358 | \n52.5338 | \n1041 | \n
Connective tissue | \n0.474331 | \n0.498727 | \n59.4892 | \n51.8568 | \n1525 | \n
Cornea | \n1.00058 | \n1.05874 | \n87.3779 | \n71.4566 | \n1050.5 | \n
Diaphragm | \n0.688213 | \n0.719235 | \n72.2347 | \n63.4948 | \n1090.4 | \n
Ear cartilage | \n0.452103 | \n0.488375 | \n62.9145 | \n52.9242 | \n1099.5 | \n
Ear skin | \n0.43575 | \n0.522704 | \n92.1679 | \n65.437 | \n1109 | \n
Epididymis | \n0.884871 | \n0.926404 | \n84.5272 | \n72.1279 | \n1082 | \n
Esophagus | \n0.877842 | \n0.912807 | \n85.8204 | \n74.895 | \n1040 | \n
Esophagus lumen | \n0 | \n0 | \n1 | \n1 | \n1.2 | \n
Eye lens | \n0.28588 | \n0.312684 | \n50.3392 | \n42.7911 | \n1075.5 | \n
Eye sclera | \n0.882673 | \n0.917665 | \n75.2998 | \n64.9991 | \n1032 | \n
Eye vitreous humor | \n1.50315 | \n1.50536 | \n69.1264 | \n69.0619 | \n1004.5 | \n
Fat | \n0.0661558 | \n0.0697299 | \n13.6436 | \n12.3711 | \n911 | \n
Gall bladder | \n1.48179 | \n1.5764 | \n105.443 | \n88.8995 | \n928 | \n
Heart lumen | \n1.20667 | \n1.24863 | \n86.4441 | \n73.159 | \n1049.75 | \n
Heart muscle | \n0.678423 | \n0.766108 | \n106.514 | \n84.2573 | \n1080.8 | \n
Hippocampus | \n0.510868 | \n0.58673 | \n97.4294 | \n73.5204 | \n1044.5 | \n
Hypophysis | \n0.778305 | \n0.804166 | \n73.9472 | \n66.7839 | \n1053 | \n
Hypothalamus | \n0.778305 | \n0.804166 | \n73.9472 | \n66.7839 | \n1053 | \n
Intervertebral disc | \n0.452103 | \n0.488375 | \n62.9145 | \n52.9242 | \n1099.5 | \n
Kidney cortex | \n0.741316 | \n0.852313 | \n118.556 | \n89.6168 | \n1049 | \n
Kidney medulla | \n0.741316 | \n0.852313 | \n118.556 | \n89.6168 | \n1044 | \n
Large intestine | \n0.638152 | \n0.705214 | \n94.6639 | \n76.5722 | \n1088 | \n
Large intestine lumen | \n0.688213 | \n0.719235 | \n72.2347 | \n63.4948 | \n1045.2 | \n
Larynx | \n0.452103 | \n0.488375 | \n62.9145 | \n52.9242 | \n1099.5 | \n
Liver | \n0.447984 | \n0.510897 | \n80.5595 | \n64.2507 | \n1078.75 | \n
Lung | \n0.288977 | \n0.315616 | \n37.1022 | \n29.4677 | \n394 | \n
Mandible | \n0.0595255 | \n0.0673524 | \n16.6812 | \n14.7171 | \n1908 | \n
Marrow | \n0.154335 | \n0.162021 | \n16.4355 | \n13.5377 | \n1028.5 | \n
Medulla oblongata | \n0.719003 | \n0.829397 | \n116.35 | \n79.7377 | \n1045.5 | \n
Meniscus | \n0.452103 | \n0.488375 | \n62.9145 | \n52.9242 | \n1099.5 | \n
Midbrain | \n0.719003 | \n0.829397 | \n116.35 | \n79.7377 | \n1045.5 | \n
Mucosa | \n0.488039 | \n0.544202 | \n76.7233 | \n61.5852 | \n1102 | \n
Muscle | \n0.688213 | \n0.719235 | \n72.2347 | \n63.4948 | \n1090.4 | \n
Nerve | \n0.312174 | \n0.353802 | \n55.0621 | \n44.0653 | \n1075 | \n
Pancreas | \n0.778305 | \n0.804166 | \n73.9472 | \n66.7839 | \n1086.5 | \n
Patella | \n0.0595255 | \n0.0673524 | \n16.6812 | \n14.7171 | \n1908 | \n
Penis | \n0.429311 | \n0.478934 | \n68.6368 | \n55.9888 | \n1101.5 | \n
Pharynx | \n0 | \n0 | \n1 | \n1 | \n1.2 | \n
Pineal body | \n0.778305 | \n0.804166 | \n73.9472 | \n66.7839 | \n1053 | \n
Pons | \n0.719003 | \n0.829397 | \n116.35 | \n79.7377 | \n1045.5 | \n
Prostate | \n0.884871 | \n0.926404 | \n84.5272 | \n72.1279 | \n1045 | \n
SAT | \n0.0661558 | \n0.0697299 | \n13.6436 | \n12.3711 | \n911 | \n
Skin | \n0.43575 | \n0.522704 | \n92.1679 | \n65.437 | \n1109 | \n
Skull | \n0.0595255 | \n0.0673524 | \n16.6812 | \n14.7171 | \n1908 | \n
Small intestine | \n1.59145 | \n1.69285 | \n118.363 | \n87.9725 | \n1030 | \n
Small intestine lumen | \n0.688213 | \n0.719235 | \n72.2347 | \n63.4948 | \n1045.2 | \n
Spinal cord | \n0.312174 | \n0.353802 | \n55.0621 | \n44.0653 | \n1075 | \n
Spleen | \n0.743914 | \n0.835186 | \n110.559 | \n82.8917 | \n1089 | \n
Stomach | \n0.877842 | \n0.912807 | \n85.8204 | \n74.895 | \n1088 | \n
Stomach lumen | \n0.688213 | \n0.719235 | \n72.2347 | \n63.4948 | \n1045.2 | \n
Teeth | \n0.0595255 | \n0.0673524 | \n16.6812 | \n14.7171 | \n2180 | \n
Tendon ligament | \n0.474331 | \n0.498727 | \n59.4892 | \n51.8568 | \n1142 | \n
Testis | \n0.884871 | \n0.926404 | \n84.5272 | \n72.1279 | \n1082 | \n
Thalamus | \n0.510868 | \n0.58673 | \n97.4294 | \n73.5204 | \n1044.5 | \n
Thymus | \n0.778305 | \n0.804166 | \n73.9472 | \n66.7839 | \n1023 | \n
Thyroid gland | \n0.778305 | \n0.804166 | \n73.9472 | \n66.7839 | \n1050 | \n
Tongue | \n0.652145 | \n0.687137 | \n75.2998 | \n64.9991 | \n1090.4 | \n
Trachea | \n0.528415 | \n0.559346 | \n58.8896 | \n50.5714 | \n1080 | \n
Trachea lumen | \n0 | \n0 | \n1 | \n1 | \n1.2 | \n
Ureter Urethra | \n0.429311 | \n0.478934 | \n68.6368 | \n55.9888 | \n1101.5 | \n
Vein | \n1.20667 | \n1.24863 | \n86.4441 | \n73.159 | \n1049.75 | \n
Vertebrae | \n0.0595255 | \n0.0673524 | \n16.6812 | \n14.7171 | \n1908 | \n
The electromagnetic properties of the segmented tissues and organs.
The standard F2182 describe a test method for measurement of RF induced heating on or near passive implants and its surrounding during MRI procedure. A design of phantom container is introduced in the standard with its dimension shown in \nFigure 3\n. The material of phantom container are electrical insulators and non-magnetic and non-metallic. The phantom container is filled with a gelled-saline which has a relative permittivity εr = 80.4 and conductivity of σ = 0.47 S/m. In order to have a great conductivity and viscosity, a suitable gelled saline should be made with 1.32 g/L NaCl and 10 g/L polyacrylic acid (PAA) in water. Numerical simulations indicate that the maximum electric field inside the ASTM phantom is at mid-axial plane about 2 cm away from the vertical phantom side wall. To maximize the heating, and thereby maximizing the signal-to-noise ratio, we placed the implants at this location.
\nThe structure and dimension of standard ASTM phantom.
A generic RF transmit body coil is developed and shown in \nFigure 4\n. The upper plots represent a 1.5 T RF coil and the lower two plots represent a 3 T RF coil. A physical coil is usually difficult to model and it takes much longer simulation time to reach the steady state of the simulation. It has been shown that using a non-physical coil could significantly reduce the simulation time while providing the same result as that from a physical coil. Thus, rather than modeling the exact physical coil, the non-physical coils were modeled in this study. The two coils have the same dimensions, and both have 8 rungs. The diameter of the RF coil is 63 cm, and the height of the RF coil is 65 cm. The eight parallel lines or the rungs are one dimensional line current excitation. The end rings on top and bottom of the RF coils are tuning capacitors which are also modelled as one dimensional line segments.
\nThe generic coil model of 1.5-T RF coil (top) and 3-T RF coil (bottom) in SEMCAD X.
The capacitance value is determined from several broadband simulations so that the second highest resonant frequency was adjusted to 64 MHz for 1.5-T and 128 MHz for 3-T systems. The detailed steps are: set an initial capacitance value for all capacitors on end rings and add a broadband pulse signal on one single rung. The other seven rungs are modeled as zero ohm resistors. After the simulation is finished, the power spectrum is extracted. If the second highest resonant frequency is not at appropriate resonant frequencies, the capacitance needs to be adjusted. From this study, the capacitance for the end ring tuning capacitor values is 7.2 pF for 1.5-T RF coil and 1.3 pF for 3-T RF coil.
\nAny device intended to be totally or partially introduced into the human body through surgical intervention and intended to remain in place after the procedure for at a long-term duration is considered as an implantable device. Passive devices in terms of their form of operation can be classified as device used for transportation and storage of pharmaceutical liquid, device for alteration of blood, body fluids, medical dressing, surgical instruments; reusable surgical instruments, disposable aseptic device, implantable device, device for contraception and birth control, device for sterilization and cleaning, patient care device, in vitro diagnostic reagent, as well as other passive contacting device or passive supplementary device.
\nIn this chapter, three typical categories of orthopedic implantable devices, bone plate system, hip prostheses and tibia intramedullary nails, are selected for MRI related RF induced heating study which are shown in \nFigures 5\n–\n7\n. The configuration of each implantable device is shown in figure. For bone fragment compression plate, it is designed to offer multiple compression and reconstruction plating options for the treatment of bone fractures. The application of hip prostheses is related to hip revision and arthroplasty. As for intramedullary nails, they are characterized by the anatomic shape, which is intended to replicate the natural anatomic shape of the bones. They have been designed to help restore the shape of the bone and treat the fractured bones.
\nThe bone plant system of AxSOS system from Stryker®.
The hip prostheses of Excia® T from Aesculap®.
The tibia intramedullary nails of PROTect™ from Depuy Synthes.
In this numerical investigation, we use the finite difference time domain (FDTD) based SEMCAD X 14.8 (SPEAG) simulation platform. Graphics processing unit (GPU) hardware acceleration was achieved using the SPEAG CUDA library with Tesla C2075 graphic card which is can handle up to 90 million cells. To assure convergence of the numerical simulations, the simulation time was set to 20 periods for each simulation. Additionally, the convergence was checked after the simulations were finished. The material of orthopedic devices is set to perfect electric conductor (PEC), and all the numerical results are normalized to a whole body average SAR of 2 W/kg. The SAR distribution is studied for each case.
\nTo ensure a comprehensive comparison, the 1g local average peak SAR value at device is extracted for each configuration of femur and humerus system. \nTables 3\n and \n4\n show the value for femur and humerus system. For each numerical result, whole-body average SAR is normalized to 2 W/kg. Since the interaction between RF induced field and implant is dependent on the physical structure of device, the heating effect variations related to the length of plate and screw are studied separately. For femur system, the plate length varies from 100 to 300 mm, and the screw length changes from 10 to 32 mm. For humerus system, the screw dimension is the same as femur system. But the plate length varies only from 100 to 250 mm due to the limit of bone structure. The plate length is studied at first for minimum and maximum screw length. Then the screw length is investigated under the worst case of plate length study which has the highest 1g average peak SAR value for
Plate length (mm) | \nScrew length (mm) | \n1.5 T/64 MHz | \n3 T/128 MHz | \n|||
---|---|---|---|---|---|---|
\n | \n\n | \n\n | \n\n | \n|||
100 | \n10 | \n64.20 | \n125.97 | \n79.75 | \n80.90 | \n|
150 | \n10 | \n94.82 | \n178.62 | \n74.52 | \n64.01 | \n|
175 | \n10 | \n107.00 | \n190.44 | \n68.50 | \n50.20 | \n|
200 | \n10 | \n116.65 | \n188.87 | \n63.37 | \n44.69 | \n|
225 | \n10 | \n117.00 | \n185.04 | \n63.10 | \n37.72 | \n|
250 | \n10 | \n123.00 | \n169.75 | \n61.23 | \n37.27 | \n|
275 | \n10 | \n117.00 | \n149.81 | \n53.80 | \n35.33 | \n|
300 | \n10 | \n105.00 | \n134.91 | \n42.37 | \n38.53 | \n|
100 | \n32 | \n85.02 | \n100.22 | \n88.26 | \n71.90 | \n|
150 | \n32 | \n108.17 | \n135.90 | \n55.48 | \n47.74 | \n|
200 | \n32 | \n104.91 | \n140.27 | \n51.56 | \n41.59 | \n|
250 | \n32 | \n111.73 | \n123.17 | \n48.94 | \n37.09 | \n|
300 | \n32 | \n79.94 | \n128.93 | \n27.77 | \n40.70 | \n|
250(1.5 T) | \n100(3 T) | \n15 | \n120.00 | \n150.91 | \n79.30 | \n72.19 | \n
250(1.5 T) | \n100(3 T) | \n20 | \n121.00 | \n135.71 | \n76.20 | \n68.00 | \n
250(1.5 T) | \n100(3 T) | \n25 | \n123.00 | \n132.15 | \n64.2 | \n66.52 | \n
Peak 1g averaged SAR of femur system for
Plate length (mm) | \nScrew length (mm) | \n1.5 T/64 MHz | \n3 T/128 MHz | \n||
---|---|---|---|---|---|
\n | \n\n | \n\n | \n\n | \n||
100 | \n10 | \n38.02 | \n135.61 | \n65.47 | \n90.51 | \n
150 | \n10 | \n63.74 | \n192.14 | \n94.68 | \n59.90 | \n
200 | \n10 | \n69.47 | \n204.57 | \n81.84 | \n45.54 | \n
250 | \n10 | \n104.57 | \n193.61 | \n86.73 | \n41.50 | \n
100 | \n32 | \n30.70 | \n92.17 | \n63.46 | \n64.6 | \n
150 | \n32 | \n54.10 | \n124.76 | \n85.39 | \n53.70 | \n
200 | \n32 | \n55.90 | \n161.97 | \n75.19 | \n43.39 | \n
250 | \n32 | \n109.00 | \n156.38 | \n108.92 | \n42.51 | \n
250 | \n15 | \n95.53 | \n161.64 | \n77.82 | \n35.33 | \n
250 | \n20 | \n91.02 | \n163.55 | \n72.91 | \n38.94 | \n
250 | \n25 | \n88.27 | \n164.54 | \n68.40 | \n41.32 | \n
The peak 1g average SAR value of humerus system for
The femur bone plate length study of 10 mm screw for 1.5 T (left) and 3 T (right).
The femur bone plate length study of 32 mm screw for 1.5 T (left) and 3 T (right).
The femur screw length study for 1.5 T (left) and 3 T (right).
The humerus bone plate length study of 10 mm screw for 1.5 T (left) and 3 T (right).
The humerus bone plate length study of 32 mm screw for 1.5 T (left) and 3 T (right).
The humerus screw length study for 1.5 T (left) and 3 T (right).
For hip prostheses, the 1g average local peak SAR value at device is also extracted for each configuration. \nTable 5\n shows the value for hip system of various dimensions. The height of hip prostheses stem ranges from 100 to 170 mm. For
Stem height (mm) | \n1.5 T/64 MHz | \n3 T/128 MHz | \n||
---|---|---|---|---|
\n | \n\n | \n\n | \n\n | \n|
100 | \n83.2764 | \n270.843 | \n43.4746 | \n55.4143 | \n
110 | \n81.5782 | \n260.502 | \n35.8247 | \n56.3111 | \n
120 | \n77.8568 | \n248.116 | \n24.4627 | \n57.0498 | \n
130 | \n74.9259 | \n247.635 | \n21.9093 | \n60.352 | \n
140 | \n64.5142 | \n237.51 | \n16.3059 | \n61.7026 | \n
150 | \n62.8772 | \n205.328 | \n13.1482 | \n63.2055 | \n
160 | \n59.6273 | \n221.781 | \n11.5599 | \n63.9114 | \n
170 | \n55.0469 | \n213.602 | \n11.1877 | \n63.8915 | \n
Peak 1g average SAR of hip prostheses system for
The hip prostheses stem length study for 1.5 T (left) and 3 T (right).
The 1g average local peak SAR value at device is also extracted for each configuration of tibia intramedullary nails. The length of stem ranges from 255 to 360 mm. The entire nail is penetrated into the bone marrow. The four screws are inserted perpendicularly through the nail and bone. \nTable 6\n shows the value for nail system of various dimensions. \nFigure 15\n represents the results of tibia intramedullary nails. The solid and dash curve and indicate the
Nail length (mm) | \n1.5 T/64 MHz | \n3 T/128 MHz | \n||
---|---|---|---|---|
\n | \n\n | \n\n | \n\n | \n|
255 | \n77.6968 | \n136.331 | \n93.5532 | \n55.4068 | \n
270 | \n77.6346 | \n129.249 | \n93.2341 | \n53.6038 | \n
285 | \n78.9892 | \n122.28 | \n89.1767 | \n49.8946 | \n
300 | \n82.6751 | \n115.745 | \n91.5648 | \n46.8385 | \n
315 | \n81.7577 | \n109.28 | \n92.7346 | \n44.0529 | \n
330 | \n74.7444 | \n103.319 | \n88.9989 | \n41.6544 | \n
345 | \n66.6095 | \n97.3232 | \n90.7795 | \n39.3275 | \n
360 | \n67.3469 | \n91.8168 | \n90.1809 | \n37.1875 | \n
Peak 1g average SAR of tibia nails system for
The nail length study for 1.5 T (left) and 3 T (right).
From the comparison between
Based on the comparison result, conservatively, the
The mention of commercial products, their sources, or their use in connection with material reported herein is not to be construed as either an actual or implied endorsement of such products by the Department of Health and Human Services.
\nIn recent decades, the application of geographic information systems (GIS) in solid waste management (SWM) has been widely adopted in many cities around the world, from developed to developing nations. Moreover, the utilization of GIS is not limited to the management of solid waste. It has been widely applied in agriculture, natural resource management, planning and economic development, disaster management and mitigation, public health, and related areas. In solid waste management, the primary objective for the adoption of GIS is to reduce cost and time (feasibility) and also to help planners make better decisions in designing solid waste management.
Essentially, planning a sustainable waste management approach is complex, tedious, and time-consuming, and decision-makers are frequently subjected to conflicting factors in SWM planning. Sustainable waste management, according to the United Nations, is aimed at the integration of SWM at the national and local level, utilizing a life-cycle approach for resource efficiency and environmentally safe management of solid waste [1]. Waste reduction, resource reuse, recycling, and recovery are all part of sustainable waste management, which helps to reduce pollution while also extending the life of resources to be wasted. As a result, sustainable waste management should be economically feasible, socially acceptable, and environmentally effective upon implementation.
The geographic information system can be used to simplify the ease of implementation of sustainable SWM. Most often, due to the complex nature of SWM, there is poor performance in its technical functions and management functions (policy and legislation, financial, and stakeholders). Owing to this fact, there are different optimization techniques developed and implemented in different areas. Among the techniques used for spatial and non-spatial data information, GIS is common. In most cases, GIS is used with the integration of remote sensing and/or multicriteria decision-making analysis (MCDA). In spatial multicriteria analysis, the combination of GIS and MCDA capabilities is crucial. GIS allows for the acquisition, storage, retrieval, manipulation, and analysis of data to obtain information for decision-making. Whereas MCDA techniques provide tools for aggregating geographic data and decision-maker preferences into a single-dimensional value or utility of alternative decisions [2].
A brief description of how GIS is used in solid waste management was presented in this chapter. Our main goal was to show the fundamentals of GIS in SWM to individuals who have little or no experience with SWM planning, especially in developing countries. Furthermore, experts who specialized in GIS or SWM and have more experience with one than the other should understand the fundamentals of integrating the two to maximize efficiency. In addition, this chapter covers the concepts of sustainable waste management, GIS-based SWM, and MCDA. The application of GIS in solid waste collection optimization and waste disposal planning is discussed also.
Solid waste is defined as any waste discarded by households, commercial, mining, and agricultural operations, as well as the residue sludge from wastewater treatment plants, water supply treatment plants, and air pollution control centers, and it is other than liquids and gases [3]. Solid wastes can be categorized based on sources, reusable potential, degree of biodegradability, and potential impact on the environment, as shown in Table 1. Solid waste management (SWM) is a discipline related to the proper management of solid waste generation, storage, collection, transfer, transport, processing, and disposal of solid waste [5].
Classification | Type | Description | Composition |
---|---|---|---|
Source (based on [4]) | Residential | From Single and multifamily dwellings | Food wastes, paper, cardboard, plastics, textiles, leather, yard wastes, wood, glass, metals, ashes, special wastes (e.g., bulky items, consumer electronics, white goods, batteries, oil, tires), and household hazardous wastes.). |
Commercial | From stores, hotels, restaurants, markets, office buildings, etc. | Paper, cardboard, plastics, wood, food wastes, glass, metals, special wastes, hazardous wastes. | |
Industrial | From light and heavy manufacturing, fabrication, construction sites, power, and chemical plants. | Housekeeping wastes, packaging, food wastes, construction and demolition materials, hazardous wastes, ashes, special wastes. | |
Institutional | From schools, hospitals, prisons, government centers. | Same as the commercial wastes | |
Construction and demolition | New construction sites, road repair, renovation sites, demolition of buildings | Wood, steel, concrete, dirt, etc. | |
Agriculture | Crops, orchards, vineyards, dairies, feedlots, farms. | Spoiled food wastes, agricultural wastes, hazardous wastes (e.g., pesticides). | |
Degree of biodegradability | Biodegradable | Can be decomposed easily by bacteria or any other natural organisms | Food wastes, garden wastes, paper, cardboard |
Nonbiodegradable | Cannot be decomposed or degraded by the biological process | Plastics | |
Potential impact | Hazardous | Wastes whose uses or disposal pose a threat to human health or the environment | Toxic, corrosive explosive, and/ or inflammable Pesticides, herbicides, paints, industrial solvents, fluorescent light bulbs, and mercury-containing batteries |
Nonhazardous | Wastes that are considered less harmful to the environment or human health | Paper, plastics, glass, metals |
The categories and classification of solid wastes.
Solid waste management (SWM) consists of six functional elements, which include solid waste generation, collection, transfer, storage, processing, and disposal. It is a discipline related to the proper management of each of these functional elements. Waste collection is the process of gathering solid waste and recyclable materials and transporting them to a destination where the collection vehicle is emptied, such as a materials-processing facility, a transfer station, or a landfill, once they have been collected [6]. Following the collection of these wastes, the wastes are sorted, processed, and reused to recover, recycle, or reuse them. Additionally, the waste could be treated to recover energy. After the recovery of products, the majority of the waste is compacted to reduce volume, weight, and size. Finally, the remaining waste is either disposed of through regulated methods such as landfilling or thrown in the open.
Integrated solid waste management (ISWM) is a strategic approach to sustainable solid waste management in which solid waste generation, segregation, transportation, sorting, treatment, recovery, and disposal are all combined with the goal of resource efficiency. The US Environmental Protection Agency defines ISWM as the prevention, recycling, composting, and disposal of solid waste to protect public health and the environment, with a focus on reducing recycling, and managing waste [7]. Solid waste management that is integrated can provide both environmental and economic sustainability. No single waste management technology can deal with all waste products in an environmentally sustainable manner. A wide range of management options is ideal. As a result, any waste management system is made up of several interconnected processes. This method examines the entire waste management system and offers methods for estimating overall environmental and economic costs [8].
To bring about a sustainable world for present and future generations, besides the ISWM, there are many approaches to SWM today, including the “zero waste” approach and the life cycle inventory approach. The “Zero waste” approach has been defined in various ways by different individuals. Literally, zero waste is defined as the complete elimination and absence of waste. However, the generation of waste is inevitable as it is a result of extraction and manufacture, distribution, consumption, and other daily activities of human beings. As per the US Environmental Protection Agency, “zero waste” is defined as “the conservation of all resources through responsible production, consumption, reuse, and recovery of products, packaging, and materials without burning or discharges to land, water, or air that endanger the environment or human health”. It is an approach that aims to optimize waste recycling and reduction; products are designed in a manner that can be reused, mended, or recycled back into nature [9].
The collection, transportation, treatment, and disposal of diverse types of waste in a manner that does not damage the environment, human health, or future generations is referred to as sustainable waste management. It encompasses all aspects of waste management organization, from production to final disposal [10]. To minimize irreversible negative impacts on human health and the environment, the sustainability concept suggests that industries and their operations be encouraged to become more efficient in terms of resource utilization, production of less pollution and process waste, and use of nonrenewable resources [11]. The waste management hierarchy, on the other hand, frequently necessitates more effort, invention, and creativity, as well as good regulations, stakeholders, and financial support, to achieve the “zero waste” goal.
Solid waste management (SWM) has become a tough task for many cities around the world due to a significant increase in waste generation as a result of demo-technic growth, population growth combined with technological advancement [12, 13]. Waste collection, treatment, and disposal, among other issues, are crucial due to their costs [13]. Poor SW management has serious environmental implications and puts public health at risk. Moreover, the problem is worse in developing countries that lack basic infrastructure and are known for their less organized waste management practices coupled with a high waste generation rate [14]. According to Tan [12], in these countries, the collection rate is as low as 30–50%, and even the collected waste ends up in unmanaged landfills. Another major challenge is inadequate policy and legislation, lack of public commitment and awareness, lack of technical capacity, and poor financing in these countries [15].
Furthermore, gas and leachate generation are unavoidable outcomes of solid waste disposal in landfills, owing to microbial decomposition, climatic circumstances, refuse properties, and landfilling processes. The migration of gas and leachate away from landfill limits and their release into the surrounding ecosystem pose major environmental concerns at both existing and new sites. These concerns include but are not limited to, fires and explosions, vegetation damage, foul odors, landfill settlement, groundwater pollution, air pollution, and global warming, among others [16].
Recently, the implementation of integrated solid waste management (ISWM) has become popular for better management of growing MSW. It ideally proposes the waste reduction at source, before even generation of wastes utilizing different techniques, innovations, and optimal management practices [17]. In ISWM, there is effective management of waste at all levels, from generation to disposal. Despite its popularity, little is known about its technical operation, and it is also subjective from a management perspective.
Integrated solid waste management compromises the complex multi-objective criteria in each management element. This resulted in making the ISWM more subjective for decision-makers as uncertain solid waste generation, conflicting factors, and economic constraints are dealt with. To overcome these problems, there are some innovations in ISWM, including mathematical modeling, computer-based modeling, geographic information systems, and remote sensing, to name a few. Besides, Gaeta et al. [18] divided the innovations in ISWM grouping into four main typologies of innovations in the solid waste market system as follows: a traditional landfill-oriented system; a modern waste-to-energy incinerator-oriented system; a light recycling system; and a hard recycling system. In this chapter, the focus is on GIS, also detailed discussions on other approaches are presented by [14, 18, 19, 20, 21].
To optimize solid waste management, there were some early attempts. For example, Anderson & Nigam [22] were the first to propose mathematical modeling in SWM. Followed by the development of models for technology selection, siting, and sizing of waste processing facilities in SWM [23, 24, 25]. On the other hand, geographic information systems combined with remote sensing are becoming widely common in the area of SWM. GIS is preferred because of its simplicity, easy access, and low cost [26]. The use of GIS in solid waste management supports capturing, handling, and transmitting the required information promptly and properly, and it is a well-known, innovative technology that has contributed a lot to SWM in a short period.
Historically, early society relied heavily on spatial data to represent geographical locations. In the American War of Independence, at the Battle of Yorktown, the French cartographer prepared the map overlays of troop movements [27]. Also, in 1854, Dr. John Snow, in providing evidence for a water-borne cholera outbreak, mapped the incidence of cases with water supply sources. Even though there was a huge reliance on spatial data in early societies, the breakthrough was witnessed with the introduction of GIS after a century. The precise articulation of GIS’s brief history is muddled at best. This is because most organizations involved in the use of GIS at the time of its inception refused to give up their data, and the early writers of GIS history were not practical users of the technology [28]. Even though the book did not give any description of the overlay processing with defined methodology, the publication of a book named “Design with Nature” by McHarg [29] is known to have been a crucial factor in the evolution of GIS. Many research articles, reviews, and books concur that the evolution of GIS over time is due to improvements in geography, environmental awareness, technology advancements, and the enhanced practical and technical skills of GIS users.
Geographic information arose as integrating and powerful technology in the context of these breakthroughs because it allowed researchers and geographers to include their methods and information in a variety of ways that supported traditional kinds of geographic analysis. For example, map overlay analysis and other forms of analysis and modeling that were previously impossible to achieve using manual approaches [30]. Researchers can now map, model, query, and analyze large volumes of data in a single database thanks to GIS [31]. GIS can play a part in SWM because it is complicated and the components are interconnected. As a result, the planning and monitoring operations are based on spatial data. Customer service, analyzing optimal transfer station locations, planning routes for vehicles transporting waste from residential, commercial, and industrial customers to transfer stations and from transfer stations to landfills, locating new landfills, and monitoring the landfill are all important aspects of SWM that GIS can help with.
In the management of solid waste, a geographic information system is an excellent instrument as it is used in the planning of technical elements. Many researchers have used GIS principles in SWM to optimize the practice of SWM. For example, Chang et al. [32] used GIS in conjunction with a mathematical programming model to develop a multi-objective, mixed-integer programming model for collection vehicle routing and scheduling for solid waste management systems synthesized within a GIS environment and concluded the effectiveness of the model, as well as recommendations for application to other environmental planning and management problems. In recent decades, efforts have been undertaken to shorten the distance between waste collection stations and landfills, hence reducing the number of trucks involved in waste collection and disposal [33, 34, 35, 36].
GIS can be used to save costs and improve waste collection and transportation efficiency. Many elements influence route optimization, including the location of waste bins, collection details, vehicle kinds, trip impedances, and the road network’s integrity [37]. Chang and Lin [38] used a GIS and a mixed-integer programming model to locate proper waste bin locations and waste transfer stations, which resulted in lower direct costs and more manageable operational programs. Even though the process varies, the technique has recently become popular. El-Hallaq & Mosabeh [39] used the GIS integrated location-allocation methodology to rebuild the existing waste bin sites and were successful in finding misplaced bins and recommending an equitable waste bin distribution.
Another key application of GIS in SWM is the selection of disposal sites. Muttiah et al. [40] used a GIS and a Markov-chain-based simulated annealing algorithm to find prospective waste disposal sites, and the simulated annealing method saved order of magnitude of time over an exhaustive search strategy. Multi-criteria coupled with GIS have recently become popular in SWM for assessing conflicting criteria. Asefa et al. [41] used GIS in combination with the multicriteria decision-making method and the analytical hierarchy process to find the best landfill location by balancing competing environmental and socioeconomic concerns. In addition, Rahimi et al. [42] used GIS techniques and fuzzy Multi-Criteria Decision-Making methods in landfill site selection problems, where the criteria weights are determined using the group fuzzy Best-Worst Method, suitability maps are generated using GIS analysis, and the sites are analyzed and ranked using the group fuzzy MULTIMOORA method.
A geographic information system (GIS) has a broad area of application and is a computer-based system that helps in the manipulation of data about specific geographic areas. GIS, as presented by Rolf and Deby [43], is a georeferenced data entry, analysis, and presentation tool based on the computer interface. Data preparation and entry is the initial phase in GIS processing, which entails acquiring, preparing, and entering the data needed for information production into the GIS database system. The second stage is data analysis, which entails going over and analyzing the information that has been gathered and uploaded into the GIS system. Finally, during the data presentation phase, the analytical results are displayed and/or saved appropriately. Data input, data management, analysis, and final output are usually common processes in most GIS technique development for various purposes and applications, depending on the basic concept of the above-mentioned stages. Table 2 summarizes and presents these stages, as well as their fundamental principles and instances.
Stages | Description | Principles/basics | Examples |
---|---|---|---|
Input | Identifying and gathering data related to SWM. Acquisition, reformatting, georeferencing, compiling and documenting these data. | Digitizing, scanning, remote sensing, GPS, internet | Daily waste generation rate, waste collection routes, landfill site location, type of waste, land use map, the elevation of the area. |
Data storage and management | Includes those functions needed to store and retrieve data from the database can be thought of as a representation or model of real-world geographical systems | Geographical entity (towns, road network, and town boundary represented by point, line, and polygon). Object (spatial and nonspatial data) | Data model location of the existing landfill site (spatial) and types of waste dumped with the rate of dumping (nonspatial data). |
Data manipulation and storage | To obtain information useful for waste collection optimization or waste disposal planning. | Fundamental analysis (measurement, classification, overlay operations, and neighborhood and connectivity operations) and advanced analysis (statistical modeling and mathematical modeling) | Vehicle route optimization, suitability analysis of landfill, optimal waste bin location |
Data output | A way to see the analyzed SWM-related data or information in the form of maps, tables, diagrams. | Display monitors, pen plotters, electrostatic plotters, laser printers, line printers, and dot matrix printers and plotters | Optimal route for waste collection (map), schedule for waste collection (table), thematic map of a suitable landfill site. |
Basic principles of GIS.
SWM: solid waste management; GPS: global positioning service.
Geospatial analysis is the process of calculating data that has been entered or saved to generate new information that can be used to improve SWM decisions. The decision on which geographical analysis to use is based on the decision maker’s needs and objectives. For example, when deciding where to build a new landfill, various conflicting environmental, social, and political variables must be considered. When these criteria are entered into the GIS interface, other geographical analyses, such as reclassification, overlaying, buffering, and so on, can be performed. As a result, GIS can assist in the computation of such cases using up-to-date criteria that are examined cost-effectively. The following are some of the most often used spatial analysis methodologies as discussed in [2, 43, 44].
It is usually used at the beginning of any analysis as it allows data exploration without making a significant change. In the measurement function, distances between features or along their perimeters, the counting frequency of features, and the computation of area size features are computed. The retrieval functions allow the selection of specific features based on logical functions and user preferences. Classification is the re-arrangement of specific features into a common data value layer. Usually, measurement, retrieval, and classification are performed using a single vector or raster data layer combined with non-spatial data, sometimes.
The overlay capability, which allows disparate data layers to be joined to create new data, is considered the most important feature of GIS interfaces. It can be used for both vector and raster data types, however, it is most commonly employed for raster overlay computations. It includes operations of intersection, union, difference, and complement using sets of positions. Many GISs allow overlays using an algebraic language, which expresses an overlay function as a formula with data layers as parameters. Arithmetic, relational, and conditional operators, as well as a variety of functions, can be used to combine different layers [2].
Neighborhood functions allow the evaluation of the surrounding areas of the location of the features and operate on the neighboring features of a given feature or set of features. It includes search functions to allow the retrieval of features, line-in-polygon and point-in-polygon functions to compute a given linear or point feature is located within a given polygon, and buffering functions which allow determining a fixed-width environment surrounding a feature. Network analysis is concerned with network computations in the GIS interface. A network is a series of interconnected lines that depict a geographic phenomenon, most commonly transportation. People, cars, and other vehicles can be transported along with a road network; commercial goods can be transported along with a logistic network; phone calls can be transported along with a telephone network, and water pollution can be transported along with a stream or river network. There are two types of network analysis functions: optimal pathfinding and network partitioning. Optimal path finding generates the least cost path on a network between two predefined locations using both geometric and attributes data. Network partitioning assigns network elements (nodes or line segments) to different locations based on predefined criteria.
We have seen that the capability of GIS is reliant on spatial analysis functions such as overlay, connectivity, and proximity. These functions, however, always do not provide the best decision alternatives when there are complex and conflicting sets of criteria presents. For instance, in siting a new waste disposal site, the overlay function can be used to combine different factors such as proximity to a road, surface water, and groundwater, or site slope, elevation, and soil type. However, this function does not provide enough analytical support because of the limited capabilities for incorporating decision makers’ preferences into the GIS-based decision-making process. Thus, the combination of geographic data and the decision maker’s preferences into analysis for better output is required.
As a result, GIS can assist in minimizing value conflicts among conflicting interest parties by giving more and better information, whereas multicriteria decision-making analysis (MCDA) methodologies can aid in lowering factual disagreements [2]. MCDA is a strategy for assisting decision-makers through the essential process of establishing evaluation criteria and determining relevant values in a choice circumstance. Based on literature MCDA has six components as a primary goal, the decision-maker(s), set of criteria, decision alternatives, decision environment, and outcome. Furthermore, the components of MCDA can be achieved through the steps shown in Figure 1. As it is shown in the figure, any spatial decision problem can be structured into three major phases according to [45]; intelligence which examines the existence of a problem or the opportunity for change, design which determines the alternatives, and choice which decides the best alternative. The components and steps of MCDA were discussed in detail by [2, 46, 47].
Framework for spatial multicriteria decision analysis based on [
According to a recent study aimed to present a literature review of MCDA applications used in SWM, the top five and most commonly used MCDA methods are Analytic Hierarchy Process (AHP), Simple Additive Weighting (SAW), Elimination and choice expressing the reality (ELECTRE), and Preference Ranking Organization Method for Enrichment (PROMETHEE) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Table 3 shows the commonly used MCDA methodologies in SWM, their description, advantages, and disadvantages of these methodologies.
MCDA method | Description | Advantage | Disadvantage |
---|---|---|---|
SAW | Value based method Use of measurement of the utility of an alternative | Easy to use and well understandable. Applicable when exact and total information is collected. Well-proven technique. Good performance when compared with more sophisticated methods. | Normalization is required to solve multidimensional problems |
AHP | Use of value-based, compensatory, and pairwise comparison approaches. Use of Hierarchical structure to present complex decision problem | Applicable when exact and total information is collected. A decision problem can be fragmented into its smallest elements, making evidence of each criterion applied. Applicable for either single or multiple problems, since it incorporates qualitative and quantitative criteria. Generation of inconsistency index to assure decision-makers | Due to aggregation, compensation between good scores on some criteria and bad scores on other criteria can occur Implementation is quite inconvenient due to the complexity Complex computation is required Time-consuming |
TOPSIS | Use of the value-based compensatory method Measures the distances of the alternatives from the ideal solution Selection of the one closest to the ideal solution | Easy to implement the understandable principle Applicable when exact and total information is collected Consideration of both the positive and negative ideal solutions Provision of a well-structured analytical framework for alternatives ranking Use of fuzzy numbers to deal with uncertainty problems | • Normalization is required to solve multidimensional problems |
ELECTRE | Use of outranking method Use of pairwise comparison, compensatory Use of indirect method that ranks alternatives utilizing pairwise comparison | Applicable even when there is missing information Applicable even when there are incomparable alternatives Applicable even when the incorporation of uncertainties is required | Time-consuming without using specific software due to complex computational procedure May or may not reach the preferred alternative |
PROMETHEE | Use of outranking method, pairwise comparison, and compensatory method Use of positive and negative preference flows for each alternative in the valued outranking Applicable even when simple and efficient information is needed Generation of ranking with decision weights | Applicable even when there is missing information | Time-consuming without using specific software When using many criteria, it becomes difficult for decision-makers to obtain a clear view of the problem |
Comparison of MCDA methodologies applied to SWM based on [48].
As mentioned in Table 3, we have to understand that each MCDA available has its strength and drawback, and also there is no general rule in adopting one. Therefore, in SWM the decision-maker decides to depend on the criteria and alternatives available. This also depends on the decision-maker’s previous experience and the availability of adequate software [47].
As discussed in previous sections, GIS-based MCDA is used to overcome complex and conflicting criteria in SWM. In fact, in MCDA methodology development there are goals and alternatives where decision outputs are made based on the decision-maker preferences. In addition, in sustainable waste management or ISWM, the aim is to reduce waste generation, improve reuse, recycle, and recovery, and otherwise properly manage these wastes. So, without any compromises, there are always environmental, economic, political, and social considerations in ISWM. Due to these facts, ISWM planning has been a challenge for the decision-makers as different criteria are taken into consideration under the specific goal of sustainable solid waste management. For instance, [49] used sixteen criteria for landfill site selection, including topography, urban and rural settlements, highways and village roads, railways, airports, wetlands, pipelines, and power line infrastructure, slope, geology, land use, floodplains, aquifers, and surface water. In general, the criteria used in ISWM planning can be categorized as environmental criteria, political criteria, financial and economic criteria, hydrologic and hydrogeologic criteria, topographical criteria, geological criteria, availability of construction materials, and other criteria as shown in Figure 2.
Criteria used for decision making in SWM.
Routing in solid waste collection entails planning and specifying routes for trucks to follow during the collection process [50]. When it comes to SWM optimization, routing is crucial. The rate of solid waste collection is frequently determined by the efficiency of transportation in the SWM component. Due to their complicated structures, transportation of solid waste to the final disposal or treatment facility is a major problem in many cities. As a result, more emphasis should be placed on optimizing waste collection routes, as failure to do so would result in exorbitant costs. As a result, defining the routes reduces waste collection costs while delivering the best service to the community. Routing is essentially the process of choosing a path for traffic within a network, as well as between or across various networks. The five basic steps of route planning are as follows, and they are accomplished in GIS utilizing the previously mentioned principles: (a) Identifying the potential location, (b) Identifying the storage capacity and volume to be collected, (c) Grouping the potential locations for a single truck cover, (d) Planning the shortest route between different groups, (e) Choosing the optimum route that is a shorter distance, less traffic volume, and less expensive.
Over time, routing has been applied in varying ways in solid waste management, giving rise to many models and strategies. According to EPA categorization, these models can be divided into macro-routing, districting and route balancing, and micro-routing [51]. The major difference is that macro-routing aims to optimize the use of the waste collection in daily and long-term capacity by minimizing round trip and haul time. While districting and route balancing divide the workload between the workers, micro-routing considers the details of each daily waste collection so that unnecessary truck movements will be reduced [51]. Among the many identified strategies for route optimization, heuristic routing, right turns, onboard computers, and round trips were presented in Table 4.
Strategies | Description |
---|---|
Heuristic Routing | Heuristic routing is a system used to describe how deliveries are made when problems in a network topology arise. In heuristic routing, routes should not be fragmented or overlapping |
Right Turns | Using only right turn during the collection of solid waste. This helped not only reduce cost by reducing fuel consumption but promoted the safety of drivers. |
Computer-based | This allows the collectors to track routes in realtime. It is also GPS-based and every detail of the route is presented. |
Dump trips | Scheduling the collection trips for the full containers twice per week. This also involves when the containers are full, they should be taken by the nearby truck. |
Strategies to optimize the routing in the solid waste management.
Residential collections are often routed using arc routing or side-of-street routing in high-density routing. This enables the software to arrange automatic collection with two passes on a street segment, as well as a semi-automated collection to serve both sides of a street segment at the same time. Point-to-point routing can be done on a variety of software and web-based platforms. When using point-to-point, the collected side of the roadway is usually ignored. Point-to-point routing can be used to develop commercial routes for solid waste collection as well as for routing calls for services like cart or bin delivery.
For waste to be collected efficiently, proper waste bin allocation and distribution are critical. It simplifies waste sorting, recycling, and transportation at the source. As a result, the second functional element of solid waste management is waste storage at the source. The waste is usually placed in bins on both sides of streets, close to buildings and other sources of waste generation. As a result, due to health concerns, attention should be exercised when storing hazardous material near residential areas. Wastes should also be stripped away regularly. GIS can be used to appropriately place waste storage containers or waste bins so that they are no longer a menace, are evenly distributed among the households, and the cost is decreased.
Several criteria were used to determine where the waste bins should be placed, including proximity to a road and a waste-producing source, land usage, sensitive areas, and so on. Using GIS, the criteria are integrated with the preferences of the decision-makers to obtain optimized waste bin sites using spatial analysis such as buffering. For example, a decision-maker could choose a buffer zone that is 20 meters away from a waste-producing source, 10 meters away from roadways, but 100 meters away from sensitive places such as hospitals, historical sites, and schools. The number of waste bins allocated can also be decided based on the waste generation rate and dispersed evenly. Furthermore, one of the most common strategies for arranging waste bins is using Location-Allocation models to determine the best position. The p-median model is used in several investigations. Because it averages the locations of multiple points, this model decreases the distance and expense of the facility from the source.
In waste disposal planning, a geographic information system plays an important role. GIS is used in waste disposal for a variety of reasons, including decision support for locating suitable landfills and temporal monitoring of disposal locations, including landfills. Due to the competing criteria illustrated in Figure 2, solid waste landfill siting is a complex and time-consuming operation in the traditional method of SWM. Furthermore, the primary purpose of the landfill site selection procedure is to ensure that the disposal facility is located in the best possible area, with the least amount of detrimental influence on the environment and population. Furthermore, a thorough review process is required to determine the best possible disposal place that complies with government standards while also minimizing costs.
Typically, landfilling is the least preferred method of ISWM according to the waste management hierarchy. But proper management of residue from reuse, recycling, and recovery, as well as the ashes from incineration, is a must. Therefore, the GIS-based MCDA approach is common in landfill site selection analysis. The basics and principles of GIS-based MCDA have been discussed in previous sections, so in this section, an overview of how GIS can be applied to landfill suitability analysis is presented. Figure 3 depicts the methodology for adopting GIS, MCDA, and waste disposal site analysis. Also, as presented in Figure 2, the process always starts with setting a goal (suitable landfill site). Then, after defining a set of criteria, GIS functions are combined with decision makers’ preferences to produce the final, most suitable landfill site.
Framework of the study to select a suitable sanitary landfill site, adopted from [
This chapter deals with the application of geographic information systems to solid waste management. GIS is a powerful tool that can assist in minimizing value conflicts among conflicting interest parties by giving more and better information. Essentially, planning a sustainable waste management approach is complex, tedious, and time-consuming, and decision-makers are frequently subjected to conflicting factors in SWM planning. There is an increasing trend of waste generation worldwide, and the situation is worse in developing countries owing to poor infrastructure, finance, and political reasons. To cope with the high waste generation and different problems in SWM, integrated solid waste management is widely used as a sustainable waste management practice. ISWM is a complex and tedious process to implement. Following the challenges in ISWM, many researchers came up with many innovations, like mathematical modeling and computer-based modeling, to mention a few. GIS is a computer-based spatial analysis method applied to SWM, enabling decision-makers to make better judgments by combining the alternatives and their preferences. In this chapter, the basics, and principles of how GIS works, what multicriteria decision making is, how to apply it to GIS, and the utilization of the GIS-based MCDA method in SWM was discussed. Also, information on how to apply GIS to waste collection optimization (routing and waste bin allocation) and waste disposal planning (landfill) was presented. Furthermore, the key principles of GIS-based MCDA method development were supported with references for further reading. Finally, we hope the readers will get some insights and, with some digging, be able to solve problems with SWM in their area easily.
The authors declare no conflict of interest.
Geographic information system
Solid waste management
Integrated waste management
Multicriteria decision making analysis
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",metaTitle:"Open Access Publishing Fees",metaDescription:"Open Access Publishing Fees",metaKeywords:null,canonicalURL:"/page/OA-publishing-fees",contentRaw:'[{"type":"htmlEditorComponent","content":"As a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 140,000 international scientists and researchers.
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\\n\\nAs a gold Open Access publisher, an Open Access Publishing Fee is payable on acceptance following peer review of the manuscript. In return, we provide high quality publishing services and exclusive benefits for all contributors. IntechOpen is the trusted publishing partner of over 140,000 international scientists and researchers.
\n\nThe Open Access Publishing Fee (OAPF) is payable only after your book chapter, monograph or journal article is accepted for publication.
\n\nOAPF Publishing Options
\n\nDuring the launching phase journals do not charge an APC, rather they will be funded by IntechOpen.
\n\n*These prices do not include Value-Added Tax (VAT). Residents of European Union countries need to add VAT based on the specific rate in their country of residence. Institutions and companies registered as VAT taxable entities in their own EU member state will not pay VAT as long as provision of the VAT registration number is made during the application process. This is made possible by the EU reverse charge method.
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\n\nWhat isn't covered by the Open Access Publishing Fee?
\n\nIf your manuscript:
\n\nYour Author Service Manager will inform you of any items not covered by the OAPF and provide exact information regarding those additional costs before proceeding.
\n\nOpen Access Funding
\n\nTo explore funding opportunities and learn more about how you can finance your IntechOpen publication, go to our Open Access Funding page. IntechOpen offers expert assistance to all of its Authors. We can support you in approaching funding bodies and institutions in relation to publishing fees by providing information about compliance with the Open Access policies of your funder or institution. We can also assist with communicating the benefits of Open Access in order to support and strengthen your funding request and provide personal guidance through your application process. You can contact us at funders@intechopen.com for further details or assistance.
\n\nFor Authors who are still unable to obtain funding from their institutions or research funding bodies for individual projects, IntechOpen does offer the possibility of applying for a Waiver to offset some or all processing feed. Details regarding our Waiver Policy can be found here.
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\n\nThis chapter considers the use of different capacitated clustering problems and models that fits better in real-life applications such as household waste collection, IT teams layout in software factories, wholesales distribution, and staff’s home collection or delivery to/from workplace. Each application is explored in its regular form as it is being developed by contractors and/or users. We consider for each application the aspects of solving the problem by the appropriate mathematical programming model and decision support methodology (using aggregated Geographical Information System and mobile technology) to hold correctly and most precisely the problems and difficulties related to instances in evaluation. The experience on these fields is here revealed in detailed form as the results obtained by using the techniques here explained.
',book:{id:"11082",title:"Operations Management",coverURL:"https://cdn.intechopen.com/books/images_new/11082.jpg"},signatures:"Marcos J. Negreiros, Nelson Maculan, Augusto W.C. Palhano, Albert E.F. Muritiba and Pablo L.F. Batista"},{id:"81676",title:"Multiscale Modeling Framework for Defect Generation in Metal Powder Bed Fusion Process to Correlate Process Parameters and Structural Properties",slug:"multiscale-modeling-framework-for-defect-generation-in-metal-powder-bed-fusion-process-to-correlate-",totalDownloads:10,totalDimensionsCites:0,doi:"10.5772/intechopen.104493",abstract:"Powder Bed Fusion (PBF) is one of the most popular additive manufacturing methods employed extensively to fabricate complex parts especially in industries with stringent standard criteria, including aerospace, medical, and defense. DMLS/PBF fabrication of parts that is free of defects represents major challenges. A comprehensive study of thermal defects, contributing parameters, and their correlation is necessary to better understand how process specifications initiate these defects. Monitoring & controlling temperature and its distribution throughout a layer under fabrication is an effective and efficient proxy to controlling process thermal evolution, which is a completely experimental technique. This being highly costly specifically for metal printing, computer-based numerical simulation can significantly help the identification of temperature distribution during the printing process. In this paper, a multiscale modeling technique is demonstrated with commercially available software tools to correlate the defect generation in metal PBF process and significant process parameters. This technique can help efficiently design the process setting in addition to or even absence of experimental monitoring data. This research work is a part of a larger project of closed-loop control strategy development using physics-based modeling and graph-based artificial neural network implementation for reducing thermally induced part defects in metal 3D printed process.",book:{id:"11171",title:"Trends and Opportunities of Rapid Prototyping Technologies",coverURL:"https://cdn.intechopen.com/books/images_new/11171.jpg"},signatures:"Suchana Akter Jahan and Hazim El-Mounayri"},{id:"1089787",title:"Differences between Universal-Deterministic and Probabilistic Hypotheses in Operations Management Research",slug:null,totalDownloads:4,totalDimensionsCites:0,doi:"10.5992/intechopen.1000218",abstract:'Very few papers in the operations management (OM) field have taken the themes of universal-deterministic (UD) and probabilistic hypotheses as their main topics of investigation and discussion. Our investigation continues a recent line of research that focuses on a better understanding of these critical issues. Specifically, we attempt to respond to some pointed criticisms that experts in the field have made when the topic UD and probabilistic hypotheses have emerged in academic settings/discussions. A detailed analysis of those criticisms shows that they lack merit, thereby reinforcing our argument that it is most important to distinguish between the two types of scientific hypotheses in order to advance in the rigor of OM theoretical and empirical research. Ideas for future research are outlined.
',book:{id:"11082",title:"Operations Management",coverURL:"https://cdn.intechopen.com/books/images_new/11082.jpg"},signatures:"Roberto Sarmiento"},{id:"1083885",title:"Design and Planning Robust and Competitive Supply Chains",slug:null,totalDownloads:5,totalDimensionsCites:0,doi:"10.5992/intechopen.1000208",abstract:'In recent years, supply chains in the manufacturing industry have become more and more complicated, and many cases of supply chain disruptions due to natural disasters have been confirmed. It is necessary for manufacturers to build a system that can help them alleviate losses and shorten recovery periods due to supply chain disruptions. Supplier diversification, as well as supplier evaluation and selection, are discussed as risk aversion measures in many papers. However, even if the procurement source has been evaluated enough, there are problems, such as opportunity loss during recovery periods and soaring procurement costs during normal periods. In this chapter, to help Japanese manufacturers to alleviate opportunity loss under component procurement disruption situations and keep cost competitiveness in normal periods, decision-making models of supply chain structure assessment, supplier selection, procurement allocation, and trading contracts are designed and verified.
',book:{id:"11082",title:"Operations Management",coverURL:"https://cdn.intechopen.com/books/images_new/11082.jpg"},signatures:"Kotomichi Matsuno, Jiahua Weng, Noriyuki Hosokawa and Takahiro Ohno"},{id:"1085055",title:"Performance Measurement Using Deterministic and Stochastic Multiplicative Directional Distance Functions",slug:null,totalDownloads:5,totalDimensionsCites:0,doi:"10.5992/intechopen.1000179",abstract:'Performance measurement is essential for fostering continuous improvement of the production and operation management in a firm or organization. We consider a deterministic scenario based on a flexible structure of production technology and establish a multiplicative relationship between the generalized multiplicative directional distance function (GMDDF) and geometric distance function (GDF). We also introduce a stochastic multiplicative directional distance function (SMDDF). Based on a stochastic scenario, the SMDDF can be estimated by the method of convex nonparametric least squares. As an illustrative application, we investigate the productive performance of Japanese life insurance companies using a panel dataset spanning 2016 to 2020.
',book:{id:"11082",title:"Operations Management",coverURL:"https://cdn.intechopen.com/books/images_new/11082.jpg"},signatures:"Yu Zhao"},{id:"1085559",title:"Assessment of Medical Equipment Maintenance Management",slug:null,totalDownloads:20,totalDimensionsCites:0,doi:"10.5992/intechopen.1000210",abstract:'Today's modern hospital is highly dependent on different types of medical equipment to help diagnose, monitor, and treat patients. Medical equipment maintenance is important to reduce costs, reduce patient dissatisfaction, treat the patient in a timely manner, and reduce mortality and risks during patient care. Good maintenance management is important to have well-planned and implemented programs through which hospitals can minimize medical device failures or other problems with the operation of medical equipment. Medical equipment plays an important role in the hospital system; therefore, the acquisition, maintenance, and replacement of medical equipment are key factors in hospitals for the implementation of the health service. Thus, in order to ensure the quality of medical devices for the provision of medical care, it is imperative to evaluate the safety of using hospital maintenance management. In order to achieve these goals, hospitals must develop checklists that identify the state of performance of medical equipment maintenance. It is essential for clinical managers and engineers not only to increase the capacity of the hospital but also to predict the risks of sudden failure. Given the lack of unique and comprehensive maintenance management checklists, the current goal is to design and develop medical equipment maintenance management checklists.
',book:{id:"11082",title:"Operations Management",coverURL:"https://cdn.intechopen.com/books/images_new/11082.jpg"},signatures:"Călin Corciovă, Robert Fuior, Doru Andriţoi and Cătălina Luca"}],onlineFirstChaptersTotal:10},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:107,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:139,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:21,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:"13",text:"The collaboration with and support of the technical staff of IntechOpen is fantastic. The whole process of submitting an article and editing of the submitted article goes extremely smooth and fast, the number of reads and downloads of chapters is high, and the contributions are also frequently cited.",author:{id:"55578",name:"Antonio",surname:"Jurado-Navas",institutionString:null,profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRisIQAS/Profile_Picture_1626166543950",slug:"antonio-jurado-navas",institution:{id:"720",name:"University of Malaga",country:{id:null,name:"Spain"}}}},{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"}}}}]},series:{item:{id:"11",title:"Biochemistry",doi:"10.5772/intechopen.72877",issn:"2632-0983",scope:"Biochemistry, the study of chemical transformations occurring within living organisms, impacts all areas of life sciences, from molecular crystallography and genetics to ecology, medicine, and population biology. Biochemistry examines macromolecules - proteins, nucleic acids, carbohydrates, and lipids – and their building blocks, structures, functions, and interactions. Much of biochemistry is devoted to enzymes, proteins that catalyze chemical reactions, enzyme structures, mechanisms of action and their roles within cells. Biochemistry also studies small signaling molecules, coenzymes, inhibitors, vitamins, and hormones, which play roles in life processes. Biochemical experimentation, besides coopting classical chemistry methods, e.g., chromatography, adopted new techniques, e.g., X-ray diffraction, electron microscopy, NMR, radioisotopes, and developed sophisticated microbial genetic tools, e.g., auxotroph mutants and their revertants, fermentation, etc. More recently, biochemistry embraced the ‘big data’ omics systems. Initial biochemical studies have been exclusively analytic: dissecting, purifying, and examining individual components of a biological system; in the apt words of Efraim Racker (1913 –1991), “Don’t waste clean thinking on dirty enzymes.” Today, however, biochemistry is becoming more agglomerative and comprehensive, setting out to integrate and describe entirely particular biological systems. The ‘big data’ metabolomics can define the complement of small molecules, e.g., in a soil or biofilm sample; proteomics can distinguish all the comprising proteins, e.g., serum; metagenomics can identify all the genes in a complex environment, e.g., the bovine rumen. This Biochemistry Series will address the current research on biomolecules and the emerging trends with great promise.",coverUrl:"https://cdn.intechopen.com/series/covers/11.jpg",latestPublicationDate:"August 2nd, 2022",hasOnlineFirst:!0,numberOfPublishedBooks:33,editor:{id:"31610",title:"Dr.",name:"Miroslav",middleName:null,surname:"Blumenberg",slug:"miroslav-blumenberg",fullName:"Miroslav Blumenberg",profilePictureURL:"https://mts.intechopen.com/storage/users/31610/images/system/31610.jpg",biography:"Miroslav Blumenberg, Ph.D., was born in Subotica and received his BSc in Belgrade, Yugoslavia. He completed his Ph.D. at MIT in Organic Chemistry; he followed up his Ph.D. with two postdoctoral study periods at Stanford University. Since 1983, he has been a faculty member of the RO Perelman Department of Dermatology, NYU School of Medicine, where he is codirector of a training grant in cutaneous biology. Dr. Blumenberg’s research is focused on the epidermis, expression of keratin genes, transcription profiling, keratinocyte differentiation, inflammatory diseases and cancers, and most recently the effects of the microbiome on the skin. He has published more than 100 peer-reviewed research articles and graduated numerous Ph.D. and postdoctoral students.",institutionString:null,institution:{name:"New York University Langone Medical Center",institutionURL:null,country:{name:"United States of America"}}},editorTwo:null,editorThree:null},subseries:{paginationCount:4,paginationItems:[{id:"14",title:"Cell and Molecular Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/14.jpg",isOpenForSubmission:!0,editor:{id:"165627",title:"Dr.",name:"Rosa María",middleName:null,surname:"Martínez-Espinosa",slug:"rosa-maria-martinez-espinosa",fullName:"Rosa María Martínez-Espinosa",profilePictureURL:"https://mts.intechopen.com/storage/users/165627/images/system/165627.jpeg",biography:"Dr. Rosa María Martínez-Espinosa has been a Spanish Full Professor since 2020 (Biochemistry and Molecular Biology) and is currently Vice-President of International Relations and Cooperation development and leader of the research group 'Applied Biochemistry” (University of Alicante, Spain). Other positions she has held at the university include Vice-Dean of Master Programs, Vice-Dean of the Degree in Biology and Vice-Dean for Mobility and Enterprise and Engagement at the Faculty of Science (University of Alicante). She received her Bachelor in Biology in 1998 (University of Alicante) and her PhD in 2003 (Biochemistry, University of Alicante). She undertook post-doctoral research at the University of East Anglia (Norwich, U.K. 2004-2005; 2007-2008).\nHer multidisciplinary research focuses on investigating archaea and their potential applications in biotechnology. She has an H-index of 21. She has authored one patent and has published more than 70 indexed papers and around 60 book chapters.\nShe has contributed to more than 150 national and international meetings during the last 15 years. Her research interests include archaea metabolism, enzymes purification and characterization, gene regulation, carotenoids and bioplastics production, antioxidant\ncompounds, waste water treatments, and brines bioremediation.\nRosa María’s other roles include editorial board member for several journals related\nto biochemistry, reviewer for more than 60 journals (biochemistry, molecular biology, biotechnology, chemistry and microbiology) and president of several organizing committees in international meetings related to the N-cycle or respiratory processes.",institutionString:null,institution:{name:"University of Alicante",institutionURL:null,country:{name:"Spain"}}},editorTwo:null,editorThree:null},{id:"15",title:"Chemical Biology",coverUrl:"https://cdn.intechopen.com/series_topics/covers/15.jpg",isOpenForSubmission:!0,editor:{id:"441442",title:"Dr.",name:"Şükrü",middleName:null,surname:"Beydemir",slug:"sukru-beydemir",fullName:"Şükrü Beydemir",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0033Y00003GsUoIQAV/Profile_Picture_1634557147521",biography:"Dr. Şükrü Beydemir obtained a BSc in Chemistry in 1995 from Yüzüncü Yıl University, MSc in Biochemistry in 1998, and PhD in Biochemistry in 2002 from Atatürk University, Turkey. He performed post-doctoral studies at Max-Planck Institute, Germany, and University of Florence, Italy in addition to making several scientific visits abroad. He currently works as a Full Professor of Biochemistry in the Faculty of Pharmacy, Anadolu University, Turkey. Dr. Beydemir has published over a hundred scientific papers spanning protein biochemistry, enzymology and medicinal chemistry, reviews, book chapters and presented several conferences to scientists worldwide. He has received numerous publication awards from various international scientific councils. He serves in the Editorial Board of several international journals. Dr. Beydemir is also Rector of Bilecik Şeyh Edebali University, Turkey.",institutionString:null,institution:{name:"Anadolu University",institutionURL:null,country:{name:"Turkey"}}},editorTwo:{id:"13652",title:"Prof.",name:"Deniz",middleName:null,surname:"Ekinci",slug:"deniz-ekinci",fullName:"Deniz Ekinci",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002aYLT1QAO/Profile_Picture_1634557223079",biography:"Dr. Deniz Ekinci obtained a BSc in Chemistry in 2004, MSc in Biochemistry in 2006, and PhD in Biochemistry in 2009 from Atatürk University, Turkey. He studied at Stetson University, USA, in 2007-2008 and at the Max Planck Institute of Molecular Cell Biology and Genetics, Germany, in 2009-2010. Dr. Ekinci currently works as a Full Professor of Biochemistry in the Faculty of Agriculture and is the Head of the Enzyme and Microbial Biotechnology Division, Ondokuz Mayıs University, Turkey. He is a member of the Turkish Biochemical Society, American Chemical Society, and German Genetics society. Dr. Ekinci published around ninety scientific papers, reviews and book chapters, and presented several conferences to scientists. He has received numerous publication awards from several scientific councils. Dr. Ekinci serves as the Editor in Chief of four international books and is involved in the Editorial Board of several international journals.",institutionString:null,institution:{name:"Ondokuz Mayıs University",institutionURL:null,country:{name:"Turkey"}}},editorThree:null},{id:"17",title:"Metabolism",coverUrl:"https://cdn.intechopen.com/series_topics/covers/17.jpg",isOpenForSubmission:!0,editor:{id:"138626",title:"Dr.",name:"Yannis",middleName:null,surname:"Karamanos",slug:"yannis-karamanos",fullName:"Yannis Karamanos",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002g6Jv2QAE/Profile_Picture_1629356660984",biography:"Yannis Karamanos, born in Greece in 1953, completed his pre-graduate studies at the Université Pierre et Marie Curie, Paris, then his Masters and Doctoral degree at the Université de Lille (1983). He was associate professor at the University of Limoges (1987) before becoming full professor of biochemistry at the Université d’Artois (1996). He worked on the structure-function relationships of glycoconjugates and his main project was the investigations on the biological roles of the de-N-glycosylation enzymes (Endo-N-acetyl-β-D-glucosaminidase and peptide-N4-(N-acetyl-β-glucosaminyl) asparagine amidase). From 2002 he contributes to the understanding of the Blood-brain barrier functioning using proteomics approaches. He has published more than 70 papers. His teaching areas are energy metabolism and regulation, integration and organ specialization and metabolic adaptation.",institutionString:null,institution:{name:"Artois University",institutionURL:null,country:{name:"France"}}},editorTwo:null,editorThree:null},{id:"18",title:"Proteomics",coverUrl:"https://cdn.intechopen.com/series_topics/covers/18.jpg",isOpenForSubmission:!0,editor:{id:"200689",title:"Prof.",name:"Paolo",middleName:null,surname:"Iadarola",slug:"paolo-iadarola",fullName:"Paolo Iadarola",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bSCl8QAG/Profile_Picture_1623568118342",biography:"Paolo Iadarola graduated with a degree in Chemistry from the University of Pavia (Italy) in July 1972. He then worked as an Assistant Professor at the Faculty of Science of the same University until 1984. In 1985, Prof. Iadarola became Associate Professor at the Department of Biology and Biotechnologies of the University of Pavia and retired in October 2017. Since then, he has been working as an Adjunct Professor in the same Department at the University of Pavia. His research activity during the first years was primarily focused on the purification and structural characterization of enzymes from animal and plant sources. During this period, Prof. Iadarola familiarized himself with the conventional techniques used in column chromatography, spectrophotometry, manual Edman degradation, and electrophoresis). Since 1995, he has been working on: i) the determination in biological fluids (serum, urine, bronchoalveolar lavage, sputum) of proteolytic activities involved in the degradation processes of connective tissue matrix, and ii) on the identification of biological markers of lung diseases. In this context, he has developed and validated new methodologies (e.g., Capillary Electrophoresis coupled to Laser-Induced Fluorescence, CE-LIF) whose application enabled him to determine both the amounts of biochemical markers (Desmosines) in urine/serum of patients affected by Chronic Obstructive Pulmonary Disease (COPD) and the activity of proteolytic enzymes (Human Neutrophil Elastase, Cathepsin G, Pseudomonas aeruginosa elastase) in sputa of these patients. More recently, Prof. Iadarola was involved in developing techniques such as two-dimensional electrophoresis coupled to liquid chromatography/mass spectrometry (2DE-LC/MS) for the proteomic analysis of biological fluids aimed at the identification of potential biomarkers of different lung diseases. He is the author of about 150 publications (According to Scopus: H-Index: 23; Total citations: 1568- According to WOS: H-Index: 20; Total Citations: 1296) of peer-reviewed international journals. He is a Consultant Reviewer for several journals, including the Journal of Chromatography A, Journal of Chromatography B, Plos ONE, Proteomes, International Journal of Molecular Science, Biotech, Electrophoresis, and others. He is also Associate Editor of Biotech.",institutionString:null,institution:{name:"University of Pavia",institutionURL:null,country:{name:"Italy"}}},editorTwo:{id:"201414",title:"Dr.",name:"Simona",middleName:null,surname:"Viglio",slug:"simona-viglio",fullName:"Simona Viglio",profilePictureURL:"https://s3.us-east-1.amazonaws.com/intech-files/0030O00002bRKDHQA4/Profile_Picture_1630402531487",biography:"Simona Viglio is an Associate Professor of Biochemistry at the Department of Molecular Medicine at the University of Pavia. She has been working since 1995 on the determination of proteolytic enzymes involved in the degradation process of connective tissue matrix and on the identification of biological markers of lung diseases. She gained considerable experience in developing and validating new methodologies whose applications allowed her to determine both the amount of biomarkers (Desmosine and Isodesmosine) in the urine of patients affected by COPD, and the activity of proteolytic enzymes (HNE, Cathepsin G, Pseudomonas aeruginosa elastase) in the sputa of these patients. Simona Viglio was also involved in research dealing with the supplementation of amino acids in patients with brain injury and chronic heart failure. She is presently engaged in the development of 2-DE and LC-MS techniques for the study of proteomics in biological fluids. The aim of this research is the identification of potential biomarkers of lung diseases. 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Dr. Koprowski has authored more than a hundred research papers with dozens in impact factor (IF) journals and has authored or co-authored six books. Additionally, he is the author of several national and international patents in the field of biomedical devices and imaging. Since 2011, he has been a reviewer of grants and projects (including EU projects) in biomedical engineering.",institutionString:null,institution:{name:"University of Silesia",institutionURL:null,country:{name:"Poland"}}},subseries:[{id:"7",title:"Bioinformatics and Medical Informatics",keywords:"Biomedical Data, Drug Discovery, Clinical Diagnostics, Decoding Human Genome, AI in Personalized Medicine, Disease-prevention Strategies, Big Data Analysis in Medicine",scope:"Bioinformatics aims to help understand the functioning of the mechanisms of living organisms through the construction and use of quantitative tools. The applications of this research cover many related fields, such as biotechnology and medicine, where, for example, Bioinformatics contributes to faster drug design, DNA analysis in forensics, and DNA sequence analysis in the field of personalized medicine. Personalized medicine is a type of medical care in which treatment is customized individually for each patient. Personalized medicine enables more effective therapy, reduces the costs of therapy and clinical trials, and also minimizes the risk of side effects. Nevertheless, advances in personalized medicine would not have been possible without bioinformatics, which can analyze the human genome and other vast amounts of biomedical data, especially in genetics. The rapid growth of information technology enabled the development of new tools to decode human genomes, large-scale studies of genetic variations and medical informatics. 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We encourage the submission of manuscripts that provide novel and mechanistic insights that report significant advances in the fields. Topics can include but are not limited to: Biotechnology such as biotechnological products and process engineering; Biotechnologically relevant enzymes and proteins; Bioenergy and biofuels; Applied genetics and molecular biotechnology; Genomics, transcriptomics, proteomics; Applied microbial and cell physiology; Environmental biotechnology; Methods and protocols. Moreover, topics in biosensor technology, like sensors that incorporate enzymes, antibodies, nucleic acids, whole cells, tissues and organelles, and other biological or biologically inspired components will be considered, and topics exploring transducers, including those based on electrochemical and optical piezoelectric, thermal, magnetic, and micromechanical elements. 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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