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Abstract: Drug discovery and development process is very lengthy, highly expensive and extremely complex in nature. Traditional methods involve expensive techniques and take many years to bring a new drug to the market. With the advent of new tools and technologies in this field, the major challenge is to reduce the time and cost required for the development of a new drug. These complex problems involve extremely high computations and can be addressed with the help of Artificial Intelligence based techniques. In this paper, we have broadly discussed different emerging applications of artificial intelligence in the field of drug discovery and development including identification of gene targets for diseases, repurposing of existing drugs through pathway networks, improvements in structure modelling, virtual screenings and hit identification, ADMET prediction, lead identification, clinical trials etc. using various artificial intelligence methods and their inter comparisons. This review presents the literature survey of different research articles published in reputed journals of international publishers such as Springer, Science Direct, IEEE Xplore, Elsevier etc. This is a systematic review of 143 publications to provide an organized summary. In addition to the in-depth analysis the foreseen challenges and existing limitations associated with drug discovery and development process are also pointed out in bold and humble suggestions have been made for necessary improvements. Readers, who are new to the field, will find it useful for enhancing their view about the field. PubDate: 2021-10-12
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Abstract: Bacteria are important in a variety of practical domains, including industry, agriculture, medicine etc. A very few species of bacteria are favourable to humans. Whereas, majority of them are extremely dangerous and causes variety of life threatening illness to different living organisms. Traditionally, this class of microbes is detected and classified using different approaches like gram staining, biochemical testing, motility testing etc. However with the availability of large amount of data and technical advances in the field of medical and computer science, the machine learning methods have been widely used and have shown tremendous performance in automatic detection of bacteria. The inclusion of latest technology employing different Artificial Intelligence techniques are greatly assisting microbiologist in solving extremely complex problems in this domain. This paper presents a review of the literature on various machine learning approaches that have been used to classify bacteria, for the period 1998–2020. The resources include research papers and book chapters from different publishers of national and international repute such as Elsevier, Springer, IEEE, PLOS, etc. The study carried out a detailed and critical analysis of penetrating different Machine learning methodologies in the field of bacterial classification along with their limitations and future scope. In addition, different opportunities and challenges in implementing these techniques in the concerned field are also presented to provide a deep insight to the researchers working in this field. PubDate: 2021-10-12
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Abstract: Along with horizontal drilling techniques, multi-stage hydraulic fracturing has improved shale gas production significantly in past decades. In order to understand the mechanism of hydraulic fracturing and improve treatment designs, it is critical to conduct modelling to predict stimulated fractures. In this paper, related physical processes in hydraulic fracturing are firstly discussed and their effects on hydraulic fracturing processes are analysed. Then historical and state of the art numerical models for hydraulic fracturing are reviewed, to highlight the pros and cons of different numerical methods. Next, commercially available software for hydraulic fracturing design are discussed and key features are summarised. Finally, we draw conclusions from the previous discussions in relation to physics, method and applications and provide recommendations for further research. PubDate: 2021-10-11
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Please help us test our new pre-print finding feature by giving the pre-print link a rating. A 5 star rating indicates the linked pre-print has the exact same content as the published article.
Abstract: The article focuses on the gentle introduction of Artificial Intelligence and the concepts of machine learning (ML) and deep learning (DL). The rapid developments made in DL techniques has motivated us to delve into this study. The concept of DL flourishing from basics theoretical concepts to applications. Deep neural networks are now state-of-the-art ML models commonly used in academia and industry in several fields, from image recognition to natural language processing. These advances have an immense potential for medical imaging technology, medical data processing, medical diagnostics and general healthcare. Our aim is two-fold: (1) the survey on DL approaches to medical images (2) the DL-based object detection approaches. The article delivers an effective description of the recent advances, advanced learning technologies and the platforms used for DL approaches. Object detection is the most explored and challenging concept in the field of computer vision systems. This field is receiving greater attention among the researchers since it covers real-time applications such as the face, pedestrian, text etc. The role of object detection is to detect the target objects presented in the image (or) video frames by appropriately classifying into their relevant classes. The review study of object detection begins with the recent works, the datasets used, and the real-time applications are explored from the learning strategies. Finally, the article investigates the challenges of the DL models and discusses promising future directions in both the research areas. PubDate: 2021-10-04
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Abstract: Nanocomposites are making way for potential application in various fields of engineering. The present article reviews the work carried out on vibration, static and buckling analysis of nano sandwich, nano functionally graded (FG), and carbon nanotube reinforced nanocomposite plates, beams, and shells. A review of available theories and methodologies adopted by various researchers is highlighted and presented in tabulated form. Size-dependent and size-independent theories are also discussed in detail. Present work also incorporates hygrothermal-based analysis of nanostructures. The review work is first categorized based on the material used (FG, CNTRC, etc.), and then in subsequent categorizations, the theory-based grouping of research is presented. Critical discussion is also carried out for each type of material, along with the scope of future study. PubDate: 2021-10-04
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Abstract: We present an approach to studying and predicting the spatio-temporal progression of infectious diseases. We treat the problem by adopting a partial differential equation (PDE) version of the Susceptible, Infected, Recovered, Deceased (SIRD) compartmental model of epidemiology, which is achieved by replacing compartmental populations by their densities. Building on our recent work (Computat Mech 66:1177, 2020), we replace our earlier use of global polynomial basis functions with those having local support, as epitomized in the finite element method, for the spatial representation of the SIRD parameters. The time dependence is treated by inferring constant parameters over time intervals that coincide with the time step in semi-discrete numerical implementations. In combination, this amounts to a scheme of field inversion of the SIRD parameters over each time step. Applied to data over ten months of 2020 for the pandemic in the US state of Michigan and to all of Mexico, our system inference via field inversion infers spatio-temporally varying PDE SIRD parameters that replicate the progression of the pandemic with high accuracy. It also produces accurate predictions, when compared against data, for a three week period into 2021. Of note is the insight that is suggested on the spatio-temporal variation of infection, recovery and death rates, as well as patterns of the population’s mobility revealed by diffusivities of the compartments. PubDate: 2021-10-01
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Abstract: In this work, a critical comparison between three different numerical approaches for the computational modelling of quasi-brittle structural failure is presented. Among the many finite element approaches devised to solve the problem, both using continuous and discontinuous methods, the present study examines the relative performance of the XFEM, the mixed strain/displacement FE and phase-field models. These numerical techniques are selected as the current representatives of embedded, smeared and regularized models for analyzing the phenomenon of fracture with different mathematical descriptions for the cracking induced discontinuities in the displacement and strain fields. The present investigation focusses on the main differences of the formulation of these models both at the continuum and discrete level and discusses the main assets and burdens that ensue in their practical application. The relative advantages and difficulties related to their use in the computation of localized structural failure in engineering practice are evaluated against a 10-point checklist that cover the main challenges met by these models. The paper includes an extensive comparison of selected numerical benchmark problems analyzed with the three examined methods. Relative performance is assessed in terms of load capacity, force–displacement curves, crack paths, collapse mechanisms, cost-efficiency and other key issues. PubDate: 2021-09-29
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Abstract: CNT/Graphene reinforced composites due to their exceptional mechanical, electrical, thermal, and optical characteristics that have attracted the scientific community working in the area of material development. In protraction, this review work on fracture analysis of CNT/Graphene reinforced composites is summarized from an exponentially growing literature inclusive of fabrication methods and present and potential applications. Owing to these composites' enormous structural applications, the number of literature works on the fracture of the structures using various models is summarized here. The literature contains several techniques to model the CNT/graphene composites that include: Halpin Tsai model, modified Halpin Tsai model, Mori–Tanaka model, homogenization approach, etc. which are discussed in detail. Various combined analysis for fracture toughness and crack growth analysis based on fracture mechanics are cited here using different computational formulations like finite element method (FEM), element free Galerkin method, reproducing kernel particle method, meshless local Petrov–Galerkin method, extended finite element method (XFEM), isogeometric analysis (IGA), etc. In particular, the authors have reported the work done in the area of fracture analysis of these novel composites since its inception that is inclusive of its structure, fabrication, properties, mathematical modeling, applications, and analysis. PubDate: 2021-09-28
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Abstract: Artificial intelligence has aided in the advancement of healthcare research. The availability of open-source healthcare statistics has prompted researchers to create applications that aid cancer detection and prognosis. Deep learning and machine learning models provide a reliable, rapid, and effective solution to deal with such challenging diseases in these circumstances. PRISMA guidelines had been used to select the articles published on the web of science, EBSCO, and EMBASE between 2009 and 2021. In this study, we performed an efficient search and included the research articles that employed AI-based learning approaches for cancer prediction. A total of 185 papers are considered impactful for cancer prediction using conventional machine and deep learning-based classifications. In addition, the survey also deliberated the work done by the different researchers and highlighted the limitations of the existing literature, and performed the comparison using various parameters such as prediction rate, accuracy, sensitivity, specificity, dice score, detection rate, area undercover, precision, recall, and F1-score. Five investigations have been designed, and solutions to those were explored. Although multiple techniques recommended in the literature have achieved great prediction results, still cancer mortality has not been reduced. Thus, more extensive research to deal with the challenges in the area of cancer prediction is required. PubDate: 2021-09-27
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Abstract: Concrete is the most widely used man-made material in the construction of structures, pavements, bridges, dams, and infrastructures. Depending on the type of components and mixture proportions, different behavior can be expected from different types of concretes, which necessitates the study of concrete behavior in designing procedures. The properties of the concrete mixtures and elements can be estimated through expensive and time-taking laboratory-based experiments. Alternatively, these properties can be estimated through predictive models developed using statistical or artificial intelligence (AI) techniques. AI techniques, because of their capabilities in knowledge processing and pattern recognition, are among the leading methods to find solutions for engineering problems. In this paper, the available studies on the applications of AI techniques to model the behavior of concrete elements and estimate the properties of concrete mixtures are reviewed. In addition, the capabilities of various AI techniques in handling different types of data are discussed. This paper also provides recommendations on the selection of the appropriate input variables in developing the predictive models. It is hoped that this paper will provide the interested practicing engineers with the information needed to fully exploit the resources available on the use of AI techniques in the concrete industry. Moreover, this paper will be helpful to the researchers to explore future avenues of research on the applications of AI techniques in the field of concrete mixtures and elements. PubDate: 2021-09-27
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Abstract: Gas hydrates is one the most complex flow assurance problems encountered in pipelines. The gas hydrate formation in pipelines eventually leads to the blockage of pipelines interrupting pipeline operations and affects transmission safety. Over the years, many mitigation techniques are employed to resolve gas hydrate issues in pipelines. But it is equally important to predict the formation and dissociation conditions of the hydrate formation as the experimental investigation is not always viable. So, an accurate prediction modelling approach is required for it. This paper provides a detailed overview of various gas hydrate modelling techniques. Initially, the details about the thermodynamic and kinetic properties of the gas hydrates are discussed. Furthermore, the discussion on the major aspects of the thermodynamic models, Kinetic models, Statistical Models, Models developed using Computational Fluid Dynamics, and Artificial Neural Networks techniques are highlighted. Finally, shortcomings and potential prospects of gas hydrate modelling procedures are addressed. PubDate: 2021-09-26
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Abstract: This is a survey paper that aims to give reviews about that finest architectures of machine learning, the use of algorithms and the applications of the system and speech and vision processes. The current technology poses vast areas of research in the field of architectures and algorithms which are used in machine learning, they can further be used for the planning new ideas and recreation of speech system and vision systems intelligently. The level of personal computing and its commercialization is at an all-time high. The machine learning can used for learning and training using large sets of sensor data and computing through clouds, also not to forget the mobile and embedded technology which is even more sophisticated. The survey is presented by giving a detailed background and the evolving nature of the most used models of deep learning which are used effectively in the vision and speech systems. The survey aims to give a perspective into the large scale research at the industrial level, it also highlights the efforts taken for future focus and upcoming demands of the intelligent use of speech and vision processes. The demand in a strong, robust and high intelligent system is that of lower latency and fidelity to be high. This is mostly seen in hardware devices with limited resources like automobiles, robots and mobile phones. These points are kept in mind and a detail of the major challenges and success rates especially in machine learning with platforms that have limited resources. The restrictions are in memory, life of the battery and the capacity of processing. The conclusion is based on the applications which are fast emerging based on the usage of speech and vision systems. Examples include effective evaluation, smart and quick system transportation and correct medicine prescriptions. This paper promise to deliver a comprehensive survey emphasizing on the demands of speech and vision systems with the view of both hardware and software systems. The technologies which are discussed in machine learning are fast gaining access and aim to revolutionize the areas of research and development in speech and vision systems. PubDate: 2021-09-22
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Abstract: This paper presents a comprehensive state-of-the-art review of the research carried out on the energy-based structural seismic design methods. Since earthquake exerts energy to the structure, it is realistic to use the energy as the main design criteria of the structure. The energy-based seismic design method is based on the concept of energy balance in the structures, which states that the earthquake input energy to the structure must be less than its capacity to dissipate the energy; otherwise, local or global damage will occur. Although the energy-based design method has received increasing attention in recent years, it has not yet become an applicable engineering design procedure. This could be due to the lack of specified and reliable criteria for this method. This paper is intended to provide the reader with a thorough review of energy-based seismic design methods, highlighting the unresolved issues and identifying the gaps that will require attention in future research. PubDate: 2021-09-20
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Abstract: Coronavirus disease is communicable and inhibits the infected person’s immune system. It belongs to the Coronaviridae family and has affected 213 nations and territories so far. Many kinds of studies are being carried out to filter advice and provide oversight to monitor this outbreak. A comparative and brief review was carried out in this paper on research concerning the early identification of symptoms, estimation of the end of the pandemic, and examination of user-generated conversations. Chest X-ray images, abdominal computed tomography scan, tweets shared on social media are several of the datasets used by researchers. Using machine learning and deep learning methods such as K-means clustering, Random Forest, Convolutional Neural Network, Long Short-Term Memory, Auto-Encoder, and Regression approaches, the above-mentioned datasets are processed. The studies on COVID-19 with machine learning and deep learning models with their results and limitations are outlined in this article. The challenges with open future research directions are discussed at the end. PubDate: 2021-09-18
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Abstract: In modern days, image segmentation is one of the most important processing step in the field of computer vision and image processing. It helps to identify object, reconstruct shape, classify and estimate volume of an object. In the last few decades, many algorithms have been developed to eradicate the various segmentation problems such as weak edge detection, inhomogeneous image segmentation, accurate object shape identification and classification. Among them, one of the popular active contour models namely level set model is extensively used to eliminate the problem of topological changes during curve evolution. Earlier, the active contour models were unable to deal with sudden topological changes which led to poor segmentation results. Thus, the paper investigates several level set models in various applications of modern imaging. Therefore, it is necessary to understand the formulation of various level set models with their characteristics before applying them to solve the segmentation problem. In this paper, authors have extensively studied the formulation of various level set models and their application in different types of images. Further, the authors have discussed their contributions to level set framework and open research challenges for researchers. PubDate: 2021-09-17
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Abstract: Based on the orientation of the rotor, horizontal axis wind turbines (HAWTs) and vertical axis wind turbines (VAWTs) are two broad families of wind turbines in the world. The HAWT is today’s mainstream wind turbine type that has been widely used. Nevertheless, there continues to be widespread interest in VAWTs in recent years because of their unparalleled advantages over HAWTs such as simple and compact design, constant operation regardless of the wind direction, lower rotation speed, lower noise, etc. However, due to their complex unsteady aerodynamics, VAWTs are generally less efficient than HAWTs, resulting in a lower efficiency of energy conversion. To improve the airflow over the blade surface and thus enhance the aerodynamic performance of VAWTs, a wide variety of different types of flow control strategies (active or passive) have been developed and implemented over the years. The purpose of this paper is to provide a survey of recent numerical and experimental studies regarding the effectiveness of different passive flow control methods for manipulating the flow field around the SB-VAWT blade in order to enhance its aerodynamic performance. Special attention has been devoted to comparing and assessing these passive flow control techniques with respect to their underlying mechanism, effectiveness, and operational complexity. Then, identified research gaps and suggestions for future research are also presented. PubDate: 2021-09-13
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Abstract: Microorganisms or microbes comprise majority of the diversity on earth and are extremely important to human life. They are also integral to processes in the ecosystem. The process of their recognition is highly tedious, but very much essential in microbiology to carry out different experimentation. To overcome certain challenges, machine learning techniques assist microbiologists in automating the entire process. This paper presents a systematic review of research done using machine learning (ML) and deep leaning techniques in image recognition of different microorganisms. This review investigates certain research questions to analyze the studies concerning image pre-processing, feature extraction, classification techniques, evaluation measures, methodological limitations and technical development over a period of time. In addition to this, this paper also addresses the certain challenges faced by researchers in this field. Total of 100 research publications in the chronological order of their appearance have been considered for the time period 1995–2021. This review will be extremely beneficial to the researchers due to the detailed analysis of different methodologies and comprehensive overview of effectiveness of different ML techniques being applied in microorganism image recognition field. PubDate: 2021-08-31
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Abstract: The timing and sequence of safe campus reopening has remained the most controversial topic in higher education since the outbreak of the COVID-19 pandemic. By the end of March 2020, almost all colleges and universities in the United States had transitioned to an all online education and many institutions have not yet fully reopened to date. For a residential campus like Stanford University, the major challenge of reopening is to estimate the number of incoming infectious students at the first day of class. Here we learn the number of incoming infectious students using Bayesian inference and perform a series of retrospective and projective simulations to quantify the risk of campus reopening. We create a physics-based probabilistic model to infer the local reproduction dynamics for each state and adopt a network SEIR model to simulate the return of all undergraduates, broken down by their year of enrollment and state of origin. From these returning student populations, we predict the outbreak dynamics throughout the spring, summer, fall, and winter quarters using the inferred reproduction dynamics of Santa Clara County. We compare three different scenarios: the true outbreak dynamics under the wild-type SARS-CoV-2, and the hypothetical outbreak dynamics under the new COVID-19 variants B.1.1.7 and B.1.351 with 56% and 50% increased transmissibility. Our study reveals that even small changes in transmissibility can have an enormous impact on the overall case numbers. With no additional countermeasures, during the most affected quarter, the fall of 2020, there would have been 203 cases under baseline reproduction, compared to 4727 and 4256 cases for the B.1.1.7 and B.1.351 variants. Our results suggest that population mixing presents an increased risk for local outbreaks, especially with new and more infectious variants emerging across the globe. Tight outbreak control through mandatory quarantine and test-trace-isolate strategies will be critical in successfully managing these local outbreak dynamics. PubDate: 2021-08-23
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Abstract: The work provides an exhaustive comparison of some representative families of topology optimization methods for 3D structural optimization, such as the Solid Isotropic Material with Penalization (SIMP), the Level-set, the Bidirectional Evolutionary Structural Optimization (BESO), and the Variational Topology Optimization (VARTOP) methods. The main differences and similarities of these approaches are then highlighted from an algorithmic standpoint. The comparison is carried out via the study of a set of numerical benchmark cases using industrial-like fine-discretization meshes (around 1 million finite elements), and Matlab as the common computational platform, to ensure fair comparisons. Then, the results obtained for every benchmark case with the different methods are compared in terms of computational cost, topology quality, achieved minimum value of the objective function, and robustness of the computations (convergence in objective function and topology). Finally, some quantitative and qualitative results are presented, from which, an attempt of qualification of the methods, in terms of their relative performance, is done. PubDate: 2021-08-20
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