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Membrane sensors for pollution problems(Elsevier, 2023-02-08) [Book Chapter]The in-field detection of pollutants and the assessment of the quality of industrial exhausts and wastewaters are essential activities in the environmental management. Thus, the modern society continuously demands the development of cost-effective, quick, real-time, and reliable sensors. Membrane technology consists of a solid background for the development of advanced sensors to detect pollutants. In fact, highly selective membranes responsible for the molecular recognition and/or selective capture of target analyte(s) are the core of highly sensitive sensors. In this chapter, we discuss the implementation of membranes in sensors aimed to detect gaseous pollutants and elucidate the mechanism of detection. Also, the key features of membranes for the detection of nano/microplastics and pathogens, two of the trending emerging questions, are highlighted.
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A Textural Perspective on the Magmatic System and Eruptive Behaviour of Merapi Volcano(Springer International Publishing, 2023-02-02) [Book Chapter]Quantitative textural analysis of crystals, including their number density, shapes, sizes, overall abundance and size distribution can be used to shed light on magmatic processes and the timescales over which they operate. At Merapi, textural analysis of phenocrysts in dome lavas, lava flows, tephra, and in plutonic cumulates has revealed that open system steady state conditions prevail throughout the crustal magma plumbing system over short time periods, with non-steady state conditions prevailing over the longer term. Phenocryst crystallisation likely takes place over tens to hundreds of years prior to eruption. Quantitative textural analysis of feldspar microlites, in conjunction with compositional data, elucidate magma ascent and degassing processes within the conduit during dome forming eruptions, and additionally reveal the driving forces behind transitions between effusive and explosive eruptive behaviour. For example, microlite textures from different stages of the 2010 eruption show that transitions between explosive and effusive activity in 2010 were driven primarily by the dynamics of magma ascent in the shallow conduit.
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MOF-Based Materials for CO2 Conversion(BENTHAM SCIENCE PUBLISHERS, 2023-01-29) [Book Chapter]Due to the rapid and continuous increase in CO2 concentrations in the atmosphere by the massive combustion of fossil fuels, the global ecosystem is being affected severely. Therefore, balancing the CO2 content in the atmosphere should be our main agenda nowadays. For minimization of CO2 concentration, carbon capture and its conversion to valuable chemicals are being perused worldwide. Metal-organic framework (MOF)-based materials having a porous structure and tuneable structural features, are best candidates for the purpose. Herein, we provide a detailed discussion on the design, synthesis and catalytic applications of MOF-based materials for various CO2 conversion reactions.
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Metabolic biomarkers in cancer(Elsevier, 2023-01-27) [Book Chapter]Over the course of years healthcare systems have utilized various “-omics” approaches to prognose, diagnose, and evaluate the treatment efficacy of cancer diseases. Metabolomics is one of the latest prominent additions to the -omics approaches, characterized by its versatile methodology. Owing to constant improvements in the field, metabolomics aims to provide a faster and a more accurate diagnosis, as well as personalized and optimal strategies of treatment. In recent years, a growing number of studies have utilized metabolomics approach to find new disease-related biomarkers of cancer diseases. Here we present the summary of recent advances in biomarker discovery for various types of cancers such as leukemia, ovarian, lung, breast, and liver cancers as well as cancer-related cachexia.
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Laser Absorption(American Institute of Aeronautics and Astronautics, Inc., 2023-01-18) [Book Chapter]
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Discontinuous Galerkin Time-Domain Method in Electromagnetics: From Nanostructure Simulations to Multiphysics Implementations(IEEE, 2022-11-18) [Book Chapter]In the past few decades, the discontinuous Galerkin time-domain (DGTD) method has become widely popular in various fields of engineering because of the fact that it benefits from computational advantages that come with finite volume and finite element formulations. Similarly, in the field of computational electromagnetics, the superiority of the DGTD method has been quickly recognized after the first few works on its formulation and implementation to solve Maxwell equations. With further developments in more recent years, the DGTD method has become one of the preeminent solutions to tackle a wide variety of challenging large-scale electromagnetic problems including those that require multi-physics modeling.This chapter starts with a brief introduction to the DGTD method. This introduction provides the fundamentals of numerical flux, discretization techniques that rely on vector and nodal basis functions, and incorporation of absorbing boundary conditions. This is followed by descriptions of a time-domain boundary integral (TDBI) scheme, which replaces absorbing boundary conditions within the DGTD method, and a multi-step time integration technique, which uses different time step sizes for the DGTD and TDBI parts. Numerical results show that both techniques significantly improve the efficiency, accuracy, and stability of the traditional DGTD method. Then, the chapter continues with the applications of the DGTD method to several real-life practical problems. More specifically, it describes various novel techniques developed to enable the application of the DGTD method to electromagnetic analysis of nanostructures and graphene-based devices and multi-physics simulation of optoelectronic antennas and source generators. For each application, several numerical examples are provided to demonstrate the accuracy, efficiency, and robustness of the developed techniques.
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Explicit Marching-on-in-time Solvers for Second-kind Time Domain Integral Equations(IEEE, 2022-11-18) [Book Chapter]Time domain methods are often preferred over their frequency domain counterparts for numerical electromagnetic analysis since they can produce broadband data from a single simulation, can account for nonlinearities directly, and often provide immediate physical interpretation of the problem's solution. Among the time domain methods, time domain integral equation (TDIE) solvers have recently found widespread use, and they have become an attractive alternative to differential equation solvers such as finite difference time domain schemes and time domain finite element method, especially for open region scattering problems. This is because TDIE solvers do not suffer from numerical phase dispersion, do not require approximate radiation boundary conditions (such as absorbing boundary conditions and perfectly matched layers), and their time step size is not restricted by a Courant–Friedrichs–Lewy-like condition. Almost all traditional TDIE solvers utilize an implicit time marching scheme, i.e. they call for solution of a (sparse) matrix at every iteration. This reduces the computational effciency under low-frequency excitations and makes the use of TDIE solvers in multiphysics frameworks a challenge. To address these challenges, recently, various explicit marching-on-in-time (MOT) schemes have been developed to solve the second kind TDIEs for perfect electrically conducting and dielectric scatterers. This chapter details the formulation and implementation of these TDIE solvers. These schemes cast the second kind TDIE in the form of an ordinary differential equation (ODE). A classical scheme such as those making use of Rao–Wilton–Glisson and Schubert–Wilton–Glisson basis functions and Nyström integration is used for spatial discretization. Then, a predictor-corrector scheme is used to integrate the spatially discretized ODE systems in time for the expansion coefficients of the unknowns. The explicit TDIE solvers described in this chapter use the same time step size as their implicit counterparts without sacrificing from accuracy and stability of the solution. In addition, they are significantly faster under low-frequency excitations. Numerical results are presented to demonstrate these computational benefits.
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The Chromatin Interaction System(Wiley, 2022-11-18) [Book Chapter]Chromatin consists of genomic DNA, histone, and nonhistone proteins, as well as RNAs and small molecules. A plethora of proteins stably or transiently associate with the different components of chromatin, often sequentially or simultaneously. Abundant posttranslational modifications (PTMs) in particular of the histone but also other chromatin proteins, as well as reversible modifications of DNA and RNA bases, regulate the chromatin interaction system by directing recruitment or repulsion of associated factors. Here, we summarize state-of-the-art methods for the unbiased or targeted identification and characterization of histone PTM readers, of proteins that are affected by DNA and RNA methylation, as well as for the study of chromatin remodelers. We describe technical aspects of in vitro and biochemical assays, cellular approaches, and computational studies in this intense research area. Using the DNA methylation maintenance protein UHRF1 as an example, we illustrate the variety of experimental approaches that are used to uncover the working mode of epigenetic regulators. Finally, we highlight challenges, open questions, and future directions in the stimulating and rich field of chromatin interactomics.
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Breeding intermediate wheatgrass for grain production(Wiley, 2022-11-18) [Book Chapter]Intermediate wheatgrass [IWG; Thinopyrum intermedium (Host) Barkworth & D. R. Dewey] is a perennial grass with the unique distinction of having been, for more than 30 years, the target of active breeding for use as a grain crop for human consumption. Improving the grain production characteristics of a perennial forage grass to economically viable levels is a long-term endeavor that was undertaken because of the potential for profound benefits to farmers, human society, and the environment. Even before research as a perennial grain, IWG has had a history of improvement as a forage species, and as one of wheat's closest perennial relatives it has also been used to transfer desirable traits into annual wheat. Since initial work in the 1980s, long-term breeding programs have been initiated in Kansas, Minnesota, and Utah (United States), Manitoba (Canada), and Uppsala (Sweden). Coupling advances in molecular technologies, many of these programs have harnessed the power of genomic selection and other cutting-edge tools to rapidly improve IWG. This has resulted in estimated gains of up to 8% per year for spike yield, and across eight breeding cycles grain yield has increased 9% per cycle, yet another 23 breeding cycles may be required before IWG yields are comparable to annual wheat. In addition to improving key domestication and agronomic traits, molecular research has provided a wealth of information about the genomic regions controlling trait expression through linkage mapping and genome-wide association studies. These results suggest that leveraging new molecular and breeding tools could potentially lead to de novo domestication of new crops in approximately 40 years or less.
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Spatio-temporal variation of biomass burning fires over Indian region using satellite data(Elsevier, 2022-11-11) [Book Chapter]The present study analyses the satellite derived active fire occurrences over Indian region during 2003–2018 based on moderate resolution imaging spectroradiometer (MODIS) and visible infrared imaging radiometer suite (VIIRS) sensors. The biomass fires show a large spatial and temporal variation with maxima observed in two phases: (1) premonsoon (combination forest fires and crop residue burning) and (2) postmonsoon period (crop residue burning only). On an annual average, the fraction of fire occurrences during premonsoon and postmonsoon is observed to be 57% and 24% from MODIS and 61% and 19% from VIIRS, respectively. Significant interannual variability of fire count was observed over Indian region. The annual average of active fire counts was found to be ∼75,786 for MODIS and ∼574,381 for VIIRS, respectively and also show the significant (95% confidence) increasing trend with a rate of 2.7% yr–1 and 3.4% yr–1 for MODIS and VIIRS, respectively. Regionally, most of the states (8 states) show the significantly increasing trends (10–30% yr–1) except north-east states (6 states) were found the decreasing trends (2–5% yr–1). Further, there were seven biomass burning (BB) hotspot regions are identified over India based on the 16 years of MODIS statistical fire density map. Annual fraction of fire types (either forest fire or crop residue burning) derived using MODIS land cover type product and were found range from 40% to 57% of annual total fires from forest and 39% to 55% from crop residue respectively on an all-India basis and has a strong regional/seasonal variation. These results on BB hotspots will be useful to address the regional fire mitigation strategies and emissions sources in India.
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Structure by single crystal X-ray diffraction(Elsevier, 2022-10-14) [Book Chapter]Structure determination by X-ray diffraction is key to understand its function. In this chapter we have summarized the major developments in atomic structures of NCs solved by single crystal X-ray diffraction. Different techniques to grow single crystals and challenges to get good quality crystals are mentioned. We discussed various structural building blocks, their growth to build the atomic structure and complexity of surfaces of NCs. Important crystal structures of gold and silver nanoclusters are talked about. Finally, cocrystallization of NCs are explained with examples.
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An Overview on Deep Learning Techniques in Solving Partial Differential Equations(Springer International Publishing, 2022-10-13) [Book Chapter]Despite great advances in solving partial differential equations (PDEs) using the numerical discretization, some high- dimensional problems with large number of parameters cannot be handled easily. Owing to the rapid growth of accessible data and computing expedients, recent developments in deep learning techniques for the solution of (PDEs) have yielded outstanding results on distinctive problems. In this chapter, we give an overview on diverse deep learning techniques namely; Physics-Informed Neural Networks (PINNs), Int-Deep, BiPDE-Net etc., which are all devised based on Deep Neural Networks (DNNs). We also discuss on several optimization methods to enrich the accuracy of the training and minimize training time.
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Physics-Constrained Deep Learning for Isothermal CSTR(Springer International Publishing, 2022-10-13) [Book Chapter]This research study investigates the approach of using physics-constrained deep learning in modelling isothermal continuous stirred-tank reactor (CSTR) to address the challenges in its current process control and optimisation. An inaccurate system identification affects prediction and consequently deteriorates the control performance. Physics-constrained deep learning is a promising machine learning framework that can better govern the system dynamics. Therefore, this research study attempts to investigate its application in predicting the behaviour of isothermal continuous stirred-tank reactor, particularly in modelling the concentration of reactant at the outlet of the reactor. The research methodology comprises data preparation, network architecture design, model training, model validation, and solution prediction. Different activation functions, optimizers, and epochs are used in the design. The prediction made by physics-constrained deep learning converged to that of the exact solution whereby the lowest error obtained at 4000 epochs is 2.1076e−5, when using Adam optimizer and tanh activator in the design. Increasing the number of epochs increases the prediction accuracy. The selection of the network architecture requires extensive numerical experimentation and is often depending on the problem.
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Heat Transfer Modelling with Physics-Informed Neural Network (PINN)(Springer International Publishing, 2022-10-13) [Book Chapter]The numerical simulations of partial differential equations aid us in studying the nanofluid flow in the porous media, the analysis of the dispersion of pollutants, and many other physical phenomena. However, to simulate such phenomena requires tremendous computational power, and it increases with the number of parameters. In this chapter, we will explore the application of the Physics-Informed Neural Network (PINN) in solving heat equation with distinct types of materials. To leverage the GPU performance and cloud computing, we perform the simulations on the Google Cloud Platform.
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Virtual element approximation of eigenvalue problems(Accepted as a chapter in "The Virtual Element Method and its Applications", P.F. Antonietti, L. Beirão da Veiga, and G. Manzini Eds., Springer-Verlag, Springer, 2022-10-09) [Book Chapter]We discuss the approximation of eigenvalue problems associated with elliptic partial differential equations using the virtual element method. After recalling the abstract theory, we present a model problem, describing in detail the features of the scheme, and highligting the effects of the stabilizing parameters. We conlcude the discussion with a survey of several application examples.
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Microgrids in mission-critical applications(Institution of Engineering and Technology, 2022-09-15) [Book Chapter]Similar to utility grid and other terrestrial microgrids, aircraft electric power systems and shipboard microgrids have their own power generation, distribution, utilization and generation storage. The rapid development of power electronics technology has allowed the converters to operate at DC voltage levels required for transmission, distribution and consumption. However, the coordination among power electronic converters can lead to malicious intrusions that aim to manipulate microgrids' operation and cause deviation from their mission-critical objectives. Such manipulations may lead to disturbances during motion, thus posing serious risks to passenger lives and cargo. The disturbances can also affect operations in critical sectors such as commercial transportation and defence, which may directly influence the global economy and national security. This chapter overviews the industrial security measures considered for microgrids in mission-critical applications, such as those found in electric aircrafts and shipboard power systems, with further insights on vulnerable areas, which can be exploited by stealthy attackers.
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Introduction to Coral Reef Conservation and Restoration in the Omics Age(Springer International Publishing, 2022-09-13) [Book Chapter]The rapid decline of coral reefs worldwide has led to an increased focus on conservation and restoration of these marvelous ecosystems. Conservation and restoration interventions benefit from scientific data including those that describe species distribution, ocean currents, the abiotic and biotic reef environment, and the degree of disturbance on coral reefs. Various types of omics data are becoming increasingly available for coral reef organisms, and these can also advance coral reef conservation and restoration and can sometimes provide insights that cannot be obtained from other data types. This book brings together genomic, transcriptomic, proteomic, and metabolomic data for reef-building corals that bear relevance to their protection, describes how these data can be obtained, what their value in coral reef conservation and restoration is and where the field is heading in the near future. This chapter provides an introduction to the book.
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Synthesis: Coral Reef Conservation and Restoration in the Omics Age(Springer International Publishing, 2022-09-13) [Book Chapter]The ongoing decline of coral reef ecosystems has stimulated new research to improve current coral reef conservation and restoration methods. In this book, we have focused on the potential of omics technologies to inform and improve coral reef conservation and restoration actions and to monitor the success of such initiatives. This closing chapter highlights the importance of integration among the various omics approaches and data types discussed in this book to advance human activities aimed at conserving and improving the resilience of reef-building corals.
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Epigenetics and Acquired Tolerance to Environmental Stress(Springer International Publishing, 2022-09-13) [Book Chapter]The unprecedented rate of increase in global temperatures is threatening to outpace the evolutionary adaptability of corals. With climate change endangering the very existence of coral reefs, the ability of corals to mitigate detrimental changes in their environment within their lifetime is becoming more critical than ever. A range of experiments and observations suggest that corals might be able to retain environmental memory from previous stress that provides increased resilience to recurrent events, and examples from other organisms suggest that such responses could potentially be transferred across generations. However, the underlying molecular mechanisms and the extent to which they can improve resilience and survivability in light of climate change have yet to be elucidated. This chapter provides an overview of the current knowledge on acquired tolerance to environmental stress in corals and the potential role of epigenetic mechanisms in this process. Based on the current evidence from corals and other organisms, I provide a theoretical model by which epigenetic mechanisms could confer transcriptional memory and, thus, promote acquired tolerance in these organisms.
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Land evaporation in “State of the Climate in 2021"(Bulletin of the American Meteorological Society, American Meteorological Society, 2022-09-07) [Book Chapter]
