The recent outbreak of coronavirus infectious disease 2019 (COVID-19) has gripped the world with apprehension and has evoked a scare of epic proportion regarding its potential to spread and infect ...humans worldwide. As we are in the midst of an ongoing pandemic of COVID-19, scientists are struggling to understand how it resembles and differs from the severe acute respiratory syndrome coronavirus (SARS-CoV) at the genomic and transcriptomic level. In a short time following the outbreak, it has been shown that, similar to SARS-CoV, COVID-19 virus exploits the angiotensin-converting enzyme 2 (ACE2) receptor to gain entry inside the cells. This finding raises the curiosity of investigating the expression of ACE2 in neurological tissue and determining the possible contribution of neurological tissue damage to the morbidity and mortality caused by COIVD-19. Here, we investigate the density of the expression levels of ACE2 in the CNS, the host–virus interaction and relate it to the pathogenesis and complications seen in the recent cases resulting from the COVID-19 outbreak. Also, we debate the need for a model for staging COVID-19 based on neurological tissue involvement.
We show that opportunistic insiders can be identified through the profitability of their trades prior to quarterly earnings announcements (QEAs) and that opportunistic trading is associated with ...various kinds of firm or managerial misconduct. A value-weighted trading strategy based on (not necessarily pre-QEA) trades of opportunistic insiders earns monthly four-factor alphas of over 1%, which is much higher than in past insider trading literature and substantial and significant even on the short side. Firms with opportunistic insiders have higher levels of earnings management, restatements, US Securities and Exchange Commission enforcement actions, shareholder litigation, and executive compensation. These findings suggest that opportunism is a domain-general trait.
Shape from focus (SFF) reconstructs 3D shape of the scene from a sequence of multi-focus images, and the quality of reconstructed shape mainly depends on the accuracy of image focus volume (FV). ...Traditional SFF techniques exhibit poor performance in preserving structural edges and fine details while removing noisy artifacts, and mostly they do not incorporate any additional shape prior. Therefore, in this paper, we propose to refine FV by formulating an energy minimization framework that employs a nonconvex regularizer and incorporates two types of shape priors. The proposed regularizer is robust against noisy focus values. The first proposed shape prior is input image sequence and it is a single and static shape prior. While, the second shape prior corresponds to a series of shape priors. These shape priors are FVs which are iteratively obtained on-the-fly. Both of these shape priors constrain the solution space for output FV. We optimize nonconvex energy function through majorize-minimization algorithm which iteratively guarantees a local minimum and converges quickly. Experiments have been conducted to evaluate accuracy and convergence properties of the proposed method. Experimental results of synthetic and real image sequences demonstrate that our method achieves superior results in terms of ability to reconstruct accurate 3D shapes as compared to existing approaches.
Artificial intelligence (AI) in healthcare plays a pivotal role in combating many fatal diseases, such as skin, breast, and lung cancer. AI is an advanced form of technology that uses ...mathematical-based algorithmic principles similar to those of the human mind for cognizing complex challenges of the healthcare unit. Cancer is a lethal disease with many etiologies, including numerous genetic and epigenetic mutations. Cancer being a multifactorial disease is difficult to be diagnosed at an early stage. Therefore, genetic variations and other leading factors could be identified in due time through AI and machine learning (ML). AI is the synergetic approach for mining the drug targets, their mechanism of action, and drug-organism interaction from massive raw data. This synergetic approach is also facing several challenges in data mining but computational algorithms from different scientific communities for multi-target drug discovery are highly helpful to overcome the bottlenecks in AI for drug-target discovery. AI and ML could be the epicenter in the medical world for the diagnosis, treatment, and evaluation of almost any disease in the near future. In this comprehensive review, we explore the immense potential of AI and ML when integrated with the biological sciences, specifically in the context of cancer research. Our goal is to illuminate the many ways in which AI and ML are being applied to the study of cancer, from diagnosis to individualized treatment. We highlight the prospective role of AI in supporting oncologists and other medical professionals in making informed decisions and improving patient outcomes by examining the intersection of AI and cancer control. Although AI-based medical therapies show great potential, many challenges must be overcome before they can be implemented in clinical practice. We critically assess the current hurdles and provide insights into the future directions of AI-driven approaches, aiming to pave the way for enhanced cancer interventions and improved patient care.
•The potential of Artificial Intelligence (AI) to revolutionize cancer research, diagnosis, and treatment through its analytical power and utilization of big cancerous research data.•Autonomic neuropathy disease diagnostic technologies, and the use of artificial intelligence and machine learning for early cancer detection.•The role of the tumor microenvironment (TME) in tumor development and metastasis, and the effect of artificial intelligence on the clinical diagnosis and treatment of cancer.•The utilization of AI algorithms such as Deep Learning, random forests, and neural networks for accurate cancer diagnosis, recurrence, and survival predictions.•AI in cancer medical imaging for skin, lung, prostate, breast, colorectal, and gastric cancer diagnosis and its potential to enhance drug discovery and cancer treatment.
Drought stress is a severe environmental constraint among the emerging problems. Plants are highly vulnerable to drought stress and a severe decrease in yield was recorded in the last few decades. ...So, it is highly desirable to understand the mechanism of drought tolerance in plants and consequently enhance the tolerance against drought stress. Phytohormones are known to play vital roles in regulating various phenomenons in plants to acclimatize to varying drought environment. Abscisic acid (ABA) is considered the main hormone which intensifies drought tolerance in plants through various morpho-physiological and molecular processes including stomata regulation, root development, and initiation of ABA-dependent pathway. In addition, jasmonic acid (JA), salicylic acid (SA) ethylene (ET), auxins (IAA), gibberellins (GAs), cytokinins (CKs), and brassinosteroids (BRs) are also very important phytohormones to congregate the challenges of drought stress. However, these hormones are usually cross talk with each other to increase the survival of plants in drought conditions. On the other hand, the transgenic approach is currently the most accepted technique to engineer the genes responsible for the synthesis of phytohormones in drought stress response. Our present review highlights the regulatory circuits of phytohormones in drought tolerance mechanism.
The present analysis focuses on the synthetic methods used for the application of supercapacitors with various mysterious architectures derived from zeolitic imidazolate frameworks (ZIFs). ZIFs ...represent an emerging and unique class of metal-organic frameworks with structures similar to conventional aluminosilicate zeolites, consisting of imidazolate linkers and metal ions. Their intrinsic porous properties, robust functionalities, and excellent thermal and chemical stabilities have resulted in a wide range of potential applications for various ZIF materials. In this rapidly expanding area, energetic research activities have emerged in the past few years, ranging from synthesis approaches to attractive applications of ZIFs. In this analysis, the development of high-performance supercapacitor electrodes and recent strategies to produce them, including the synthesis of various heterostructures and nanostructures, are analyzed and summarized. This analysis goes
via
the ingenuity of modern science when it comes to these nanoarchitecture electrodes. Despite these significant achievements, it is still difficult to accurately monitor the morphologies of materials derived from metal-organic frameworks (MOFs) because the induction force during structural transformations at elevated temperatures is in high demand. It is also desirable to achieve the direct synthesis of highly functionalized nanosized materials derived from zeolitic imidazolate frameworks (ZIFs) and the growth of nanoporous structures based on ZIFs encoded in specific substrates for the construction of active materials with a high surface area suitable for electrochemical applications. The latest improvements in this field of supercapacitors with materials formed from ZIFs as electrodes using ZIFs as templates or precursors are discussed in this review. Also, the possibility of usable materials derived from ZIFs for both existing and emerging energy storage technologies is discussed.
The present analysis focuses on the synthetic methods used for the application of supercapacitors with various mysterious architectures derived from zeolitic imidazolate frameworks (ZIFs).
Pakistan has enormous coal and biomass resources, however, it imports coal from other parts of the world. Whereas, utilization of indigenous coal and biomass may be beneficial for the economy and ...local energy security. Hence, this study focuses on the likely effects of using coal blends and biomass co-firing with indigenous and imported coal on the performance of a supercritical power plant. A coal fired power plant is modeled using Aspen plus. Three different case studies are considered, which are; constant heat input (CHI), constant fuel input (CFI), and constant Reynold number (CRN). Conclusively, coal blending has resulted in reduced emissions than the use of low rank coal. Further, biomass co-firing has shown reduced emissions. For the CHI case, efficiency remains the same for all fuels. The highest efficiency in the CFI case is for South African coal and the lowest is for biomass. In the CRN case, South African coal has maximum efficiency while Thar coal has the minimum efficiency. Moreover, economic analysis of this study proposes that native resources must be used, provided the technical issues have resolved. Therefore, this study resulted in the comparison and applicability of different coal blends and renewable biomass fuel in a coal fired power plant.
•Supercritical coal fired power plant model is developed using Aspen plus.•Simulation is done for three different case studies; CHI, CFI, and CRN.•Coal blending along with biomass co-firing is taken into account for all three case studies.•CRN case is a novel approach with a purpose to minimize design changes for different fuels.•Economic analysis proposes that native resources must be used, provided the technical issues are resolved.
Fault diagnosis in photovoltaic (PV) arrays is essential in enhancing power output as well as the useful life span of a PV system. Severe faults such as Partial Shading (PS) and high impedance ...faults, low location mismatch, and the presence of Maximum Power Point Tracking (MPPT) make fault detection challenging in harsh environmental conditions. In this regard, there have been several attempts made by various researchers to identify PV array faults. However, most of the previous work has focused on fault detection and classification in only a few faulty scenarios. This paper presents a novel approach that utilizes deep two-dimensional (2-D) Convolutional Neural Networks (CNN) to extract features from 2-D scalograms generated from PV system data in order to effectively detect and classify PV system faults. An in-depth quantitative evaluation of the proposed approach is presented and compared with previous classification methods for PV array faults - both classical machine learning based and deep learning based. Unlike contemporary work, five different faulty cases (including faults in PS - on which no work has been done before in the machine learning domain) have been considered in our study, along with the incorporation of MPPT. We generate a consistent dataset over which to compare ours and previous approaches, to make for the first (to the best of our knowledge) comprehensive and meaningful comparative evaluation of fault diagnosis. It is observed that the proposed method involving fine-tuned pre-trained CNN outperforms existing techniques, achieving a high fault detection accuracy of 73.53%. Our study also highlights the importance of representative and discriminative features to classify faults (as opposed to the use of raw data), especially in the noisy scenario, where our method achieves the best performance of 70.45%. We believe that our work will serve to guide future research in PV system fault diagnosis.
The wide bandgap Sb2S3 is considered to be one of the most promising absorber layers in single‐junction solar cells and a suitable top‐cell candidate for multi‐junction (tandem) solar cells. However, ...compared to mature thin‐film technologies, Sb2S3 based thin‐film solar cells are still lagging behind in the power conversion efficiency race, and the highest of just 7.5% has been achieved to date in a sensitized single‐junction structure. Furthermore, to break single junction solar cell based Shockley–Queisser (S–Q) limits, tandem devices with wide bandgap top‐cells and low bandgap bottom‐cells hold a high potential for efficient light conversion. Though matured and desirable bottom‐cell candidates like silicon (Si) are available, the corresponding mature wide bandgap top‐cell candidates are still lacking. Hence, a literature review based on Sb2S3 solar cells is urgently warranted. In this review, the progress and present status of Sb2S3 solar cells are summarized. An emphasis is placed mainly on the improvement of absorber quality and device performance. Moreover, the low‐performance causes and possible overcoming mechanisms are also explained. Last but not least, the potential and feasibility of Sb2S3 in tandem devices are vividly discussed. In the end, several strategies and perspectives for future research are outlined.
This review summarizes the progress that has been reported to date for low‐cost, non‐toxic, and wide‐bandgap Sb2S3 thin film solar cells. The significant breakthroughs achieved in improving the absorber quality and device performance have been comprehensively collected and analyzed. The critical challenges, possible strategies, high potential as a top‐cell candidate in tandem devices, and future perspectives are vividly discussed.
Machine learning techniques are widely used to understand and predict data trends and therefore can provide a huge computational advantage over conventional numerical techniques. In this work, an ...artificial neural network (ANN) model is coupled with a rate-dependant crystal plasticity finite element method (CPFEM) formulation to predict the stress-strain behavior and texture evolution in AA6063-T6 under uniaxial tension and simple shear. Firstly, stress-strain and texture evolution results from the crystal plasticity simulations were verified with experimental observations for AA6063-T6 under simple shear and tension. Next, results from crystal plasticity simulations were used to train, validate and test the ANN model. The proposed ANN framework, was successfully applied on single crystal simulation results to predict stress-strain and texture data. Then, the proposed ANN framework was applied to predict the stress-strain curves and texture evolution of AA6063-T6 during uniaxial tension and simple shear. The flexibility of the proposed ANN model was also tested, for simple shear, with a completely new data set and the predicted results showed excellent agreement with corresponding crystal plasticity simulations. Finally, the predictive capability of the proposed model was further demonstrated by successfully validating the ANN model for non-proportional loading paths such as uniaxial tension followed by simple shear and simple shear followed by tension. The results presented in this research clearly demonstrate that the proposed ANN model provided significant computational time improvements without any major sacrifice in accuracy.
•Artificial neural network crystal plasticity finite element framework is proposed.•Model successfully captures single crystal stress-strain and texture response.•Polycrystal AA6063-T6 stress-strain and texture evolution are also validated.•ANN model reproduces non-proportional loading (shear/tension) cases successfully.•Huge computational time savings are achieved with the proposed framework.
PDF
