Analyzing the Impact of Financial Resilience on the Lives of Individuals and Household Post Covid-19 in Indian Economy

Purpose: Economic crisis is a global phenomenon. When the crisis hits, many people are affected. This paper is an attempt to analyze the impact of financial resilience on the lives of individuals and households. In this study we analyze what are common financial shocks that individuals can face and how they tend to cope with this and our recommendations.&#x0D; Methodology: Literature review and comparative study of consumption and consumer credit across diverse market.&#x0D; Main Findings: The intensity of the economic crisis may vary from one person to another, depending on the individual financial resilience. Some households are less resilient to financial shocks than others. This may be because they have low levels of savings, have limited access to affordable credit, already hold high levels of debt or lack the skills required to manage household budgets.&#x0D; Implications: Financial resilience is difficult to estimate because it is a dynamic concept – the ability to recover quickly from an income or expenditure shock. Savings are in anticipation to the challenges face which might hinder the achievement of financial goals, hence there is a scope for new saving/investment products which are more comprehensive in nature.&#x0D; Novelty: We over a period have witnessed that there is a social support to the society during economic crisis but what is required is organization resilience rather than developing personal capabilities. The resilience should go beyond financial vulnerabilities and encompass mental stability, emotional wellbeing, education attainment etc.</jats:p

Aspects of programming for implementation of convolutional neural networks on multisystem HPC architectures

Abstract The training of deep learning convolutional neural networks is extremely compute intensive and takes long times for completion, on all except small datasets. This is a major limitation inhibiting the widespread adoption of convolutional neural networks in real world applications despite their better image classification performance in comparison with other techniques. Multidirectional research and development efforts are therefore being pursued with the objective of boosting the computational performance of convolutional neural networks. Development of parallel and scalable deep learning convolutional neural network implementations for multisystem high performance computing architectures is important in this background. Prior analysis based on computational experiments indicates that a combination of pipeline and task parallelism results in significant convolutional neural network performance gains of up to 18 times. This paper discusses the aspects which are important from the perspective of implementation of parallel and scalable convolutional neural networks on central processing unit based multisystem high performance computing architectures including computational pipelines, convolutional neural networks, convolutional neural network pipelines, multisystem high performance computing architectures and parallel programming models.</jats:p

Comparative evaluation of performance and scalability of convolutional neural network implementations on a multisystem HPC architecture

Abstract The convolutional neural network training algorithm has been implemented for a central processing unit based high performance multisystem architecture machine. The multisystem or the multicomputer is a parallel machine model which is essentially an abstraction of distributed memory parallel machines. In actual practice, this model corresponds to high performance computing clusters. The proposed implementation of the convolutional neural network training algorithm is based on modeling the convolutional neural network as a computational pipeline. The various functions or tasks of the convolutional neural network pipeline have been mapped onto the multiple nodes of a central processing unit based high performance computing cluster for task parallelism. The pipeline implementation provides a first level performance gain through pipeline parallelism. Further performance gains are obtained by distributing the convolutional neural network training onto the different nodes of the compute cluster. The two gains are multiplicative. In this work, the authors have carried out a comparative evaluation of the computational performance and scalability of this pipeline implementation of the convolutional neural network training with a distributed neural network software program which is based on conventional multi-model training and makes use of a centralized server. The dataset considered for this work is the North Eastern University’s hot rolled steel strip surface defects imaging dataset. In both the cases, the convolutional neural networks have been trained to classify the different defects on hot rolled steel strips on the basis of the input image. One hundred images corresponding to each class of defects have been used for the training in order to keep the training times manageable. The hyperparameters of both the convolutional neural networks were kept identical and the programs were run on the same computational cluster to enable fair comparison. Both the convolutional neural network implementations have been observed to train to nearly 80% training accuracy in 200 epochs. In effect, therefore, the comparison is on the time taken to complete the training epochs.</jats:p

Glutor, a Glucose Transporter Inhibitor, Exerts Antineoplastic Action on Tumor Cells of Thymic Origin: Implication of Modulated Metabolism, Survival, Oxidative Stress, Mitochondrial Membrane Potential, pH Homeostasis, and Chemosensitivity

Neoplastic cells overexpress glucose transporters (GLUT), particularly GLUT1 and GLUT3, to support altered metabolism. Hence, novel strategies are being explored to effectively inhibit GLUTs for a daunting interference of glucose uptake. Glutor, a piperazine-2-one derivative, is a newly reported pan-GLUT inhibitor with a promising antineoplastic potential. However, several aspects of the underlying mechanisms remain obscure. To understand this better, tumor cells of thymic origin designated as Dalton’s lymphoma (DL) were treated with glutor and analyzed for survival and metabolism regulatory molecular events. Treatment of tumor cells with glutor caused a decrease in cell survival with augmented induction of apoptosis. It also caused a decrease in glucose uptake associated with altered expression of GLUT1 and GLUT3. HIF-1α, HK-2, LDH-A, and MCT1 also decreased with diminished lactate production and deregulated pH homeostasis. Moreover, glutor treatment modulated the expression of cell survival regulatory molecules p53, Hsp70, IL-2 receptor CD25, and C-myc along with mitochondrial membrane depolarization, increased intracellular ROS expression, and altered Bcl-2/BAX ratio. Glutor also enhanced the chemosensitivity of tumor cells to cisplatin, accompanied by decreased MDR1 expression. Adding fructose to the culture medium containing glutor reversed the latter’s inhibitory action on tumor cell survival. These results demonstrate that in addition to inhibited glucose uptake, modulated tumor growth regulatory molecular pathways are also implicated in the manifestation of the antineoplastic action of glutor. Thus, the novel findings of this study will have a long-lasting clinical significance in evaluating and optimizing the use of glutor in anticancer therapeutic strategies.</jats:p

Molecular docking studies of 3-bromopyruvate and its derivatives to metabolic regulatory enzymes: Implication in designing of novel anticancer therapeutic strategies.

Altered metabolism is an emerging hallmark of cancer, as malignant cells display a mammoth up-regulation of enzymes responsible for steering their bioenergetic and biosynthetic machinery. Thus, the recent anticancer therapeutic strategies focus on the targeting of metabolic enzymes, which has led to the identification of specific metabolic inhibitors. One of such inhibitors is 3-bromopyruvate (3-BP), with broad spectrum of anticancer activity due to its ability to inhibit multiple metabolic enzymes. However, the molecular characterization of its binding to the wide spectrum of target enzymes remains largely elusive. Therefore, in the present study we undertook in silico investigations to decipher the molecular nature of the docking of 3-BP with key target enzymes of glycolysis and TCA cycle by PatchDock and YASARA docking tools. Additionally, derivatives of 3-BP, dibromopyruvate (DBPA) and propionic acid (PA), with reported biological activity, were also investigated for docking to important target metabolic enzymes of 3-BP, in order to predict their therapeutic efficacy versus that of 3-BP. A comparison of the docking scores with respect to 3-BP indicated that both of these derivatives display a better binding strength to metabolic enzymes. Further, analysis of the drug likeness of 3-BP, DBPA and PA by Lipinski filter, admetSAR and FAF Drug3 indicated that all of these agents showed desirable drug-like criteria. The outcome of this investigation sheds light on the molecular characteristics of the binding of 3-BP and its derivatives with metabolic enzymes and thus may significantly contribute in designing and optimizing therapeutic strategies against cancer by using these agents