Disclosures relating to Covid-19 in the Malaysian banking industry: Theory and Practice

Purpose: Covid-19 has impacted all the spheres of human lives and so on the stakeholders’ demands. This paper is aimed to discuss various strategies the banking industry may be asked to perform for coping against the Covid-19. This paper also analyzed the volume of real in-time disclosures created by the banking industry. These disclosures were also differentiated between public and private banks and between lowly and highly disclosing banks as well. Design/methodology/approach: Different strategies were used theoretically under the triple bottom line of sustainability. For empirical analysis, the data was taken from Malaysian listed and non-listed banks. Group differences and correlation analyses were performed. Findings: Banks with bigger size, more profitability, and previous engagements in CSR were more active in disclosing their strategies for Covid-19. Banks were doing least for their disclosures on environmental strategies on Covid-19. Overall, the disclosures about Covid-19 can be taken as a nexus of CSR disclosures of the banks since they have similar correlating variables and have significant correlations. Moreover, the findings were robust against alternative measures of CSR. Originality: This research is the first in time to discuss disclosures about Covid-19 generated by the banking industry. Research limitations/implications: The study was limited from the banking industry of Malaysia and had not been able to run regression analysis due to a limited number of observations. Practical implications: Various aspects of strategies under economic, social and environmental concerns had been discussed. Pertinent examples from different countries around the globe had also been given. These strategies can help practitioners in formulating their Covid-19 strategies to satisfy the stakeholders’ demands

VR-ZYCAP: A versatile resourse-level ICAP controller for ZYNQ SOC

This article belongs to the Special Issue Architecture and CAD for Field-Programmable Gate Arrays (FPGAs)Hybrid architectures integrating a processor with an SRAM-based FPGA fabric—for example, Xilinx ZynQ SoC—are increasingly being used as a single-chip solution in several market segments to replace multi-chip designs. These devices not only provide advantages in terms of logic density, cost and integration, but also provide run-time in-field reconfiguration capabilities. However, the current reconfiguration capabilities provided by vendor tools are limited to the module level. Therefore, incremental run-time configuration memory changes require a lengthy compilation time for off-line bitstream generation along with storage and reconfiguration time overheads with traditional vendor methodologies. In this paper, an internal configuration access port (ICAP) controller that provides a versatile fine-grain resource-level incremental reconfiguration of the programmable logic (PL) resources in ZynQ SoC is presented. The proposed controller implemented in PL, called VR-ZyCAP, can reconfigure look-up tables (LUTs) and Flip-Flops (FF). The run-time reconfiguration of FF is achieved through a reset after reconfiguration (RAR)-featured partial bitstream to avoid the unintended state corruption of other memory elements. Along with versatility, our proposed controller improves the reconfiguration time by 30 times for FFs compared to state-of-the-art works while achieving a nearly 400-fold increase in speed for LUTs when compared to vendor-supported software approaches. In addition, it achieves competitive resource utilization when compared to existing approaches.This research was funded by Spanish Ministry of Science and Innovation under the ACHILLES project, grant number PID2019-104207RB-I00 and by Taif University Researchers Supporting fund, grant number (TURSP-2020/144), Taif University, Taif, Saudi Arabia

Isolation Design Flow Effectiveness Evaluation Methodology for Zynq SoCs

Static Random-Access Memory (SRAM)-based Field Programmable Gate Arrays (FPGAs) are increasingly being used in many application domains due to their higher logic density and reconfiguration capabilities. However, with state-of-the-art FPGAs being manufactured in the latest technology nodes, reliability is becoming an important issue, particularly for safety-critical avionics, automotive, aerospace, industrial robotics, medical, and financial systems. Therefore, fault tolerant system design methodologies have become essential in the aforementioned application domains. The Isolation Design Flow (IDF) is one such design methodology that has promising prospects due to its ability to isolate logic design modules at the physical level for fault containment purposes. This paper proposes a methodology to evaluate the effectiveness of the IDF. To do so, reverse engineering is used to enable fault injection on the IDF designs with minimal changes in the bit-stream. This reduces the time needed to inject a fault significantly thus accelerating the evaluation process. Then this methodology is applied to a case study of a single-chip cryptography application on a ZynQ SoC. Specifically, an Advanced Encryption Standard (AES) Duplication With Comparison (DWC) design is physically isolated with IDF and subsequently subjected to frame-level Fault Injection (FI) in the configuration memory

A predictive mimicker for mechanical properties of eco-efficient and sustainable bricks incorporating waste glass using machine learning

Urbanization, industrialization, and economic growth all contribute to the rising demand for bricks. The world is heading towards sustainable, eco-friendly, recyclable materials to enhance the circular economy and mitigate the issues of carbon footprint, overburdened landfills, and waste of natural resources. Therefore, the mix design of bricks has evolved with time by incorporating recycled waste materials, i.e., glass, etc. This paper presents a unique approach to developing machine learning models to predict the mechanical properties of eco-efficient bricks incorporating waste glass, as it requires extensive experimentation to comprehend the properties. This research assesses four essential input parameters affecting mechanical properties. To predict the outcomes, four machine learning models were generated, and their results were compared. These four models include artificial neural networks (ANN), the Gaussian process of regression (GPR), the classification and regression tree (CART), and the support vector machine (SVM). A unique and advanced approach known as the generative adversarial network (GAN) has been employed for augmenting data and enhancing accuracy as the data available in published literature were limited. As a result, artificial neural network (ANN) has the highest accuracy among all models. Therefore, it is the most efficient model with RMSE and R2 of 3.86 MPa and 0.81 for predicting the compressive strength, and RMSE and R2 of 0.82 % and 0.995 for predicting the Shrinkage of bricks incorporating glass. This study proposes a distinctive tool using machine learning for the sustainable brick production sector via predicting the mechanical properties of brick incorporating waste glass

Isolation design flow effectiveness evaluation methodology for Zynq SoCs

Static Random-Access Memory (SRAM)-based Field Programmable Gate Arrays (FPGAs) are increasingly being used in many application domains due to their higher logic density and reconfiguration capabilities. However, with state-of-the-art FPGAs being manufactured in the latest technology nodes, reliability is becoming an important issue, particularly for safety-critical avionics, automotive, aerospace, industrial robotics, medical, and financial systems. Therefore, fault tolerant system design methodologies have become essential in the aforementioned application domains. The Isolation Design Flow (IDF) is one such design methodology that has promising prospects due to its ability to isolate logic design modules at the physical level for fault containment purposes. This paper proposes a methodology to evaluate the effectiveness of the IDF. To do so, reverse engineering is used to enable fault injection on the IDF designs with minimal changes in the bit-stream. This reduces the time needed to inject a fault significantly thus accelerating the evaluation process. Then this methodology is applied to a case study of a single-chip cryptography application on a ZynQ SoC. Specifically, an Advanced Encryption Standard (AES) Duplication With Comparison (DWC) design is physically isolated with IDF and subsequently subjected to frame-level Fault Injection (FI) in the configuration memory

Self-reported health and smoking status, and body mass index: a case-control comparison based on GEN SCRIP (GENetics of SChizophRenia In Pakistan) data

Introduction Individuals with schizophrenia are at a high risk of physical health comorbidities and premature mortality. Cardiovascular and metabolic causes are an important contributor. There are gaps in monitoring, documenting and managing these physical health comorbidities. Because of their condition, patients themselves may not be aware of these comorbidities and may not be able to follow a lifestyle that prevents and manages the complications. In many low-income and middle-income countries including Pakistan, the bulk of the burden of care for those struggling with schizophrenia falls on the families.Objectives To determine the rate of self-reported physical health disorders and risk factors, like body mass index (BMI) and smoking, associated with cardiovascular and metabolic disorders in cases of schizophrenia compared with a group of mentally healthy controls.Design A case-controlled, cross-sectional multicentre study of patients with schizophrenia in Pakistan.Settings Multiple data collection sites across the country for patients, that is, public and private psychiatric OPDs (out patient departments), specialised psychiatric care facilities, and psychiatric wards of teaching and district level hospitals. Healthy controls were enrolled from the community.Participants We report a total of 6838 participants’ data with (N 3411 (49.9%)) cases of schizophrenia compared with a group of healthy controls (N 3427 (50.1%)).Results BMI (OR 0.98 (CI 0.97 to 0.99), p=0.0025), and the rate of smoking is higher in patients with schizophrenia than in controls. Problems with vision (OR 0.13 (0.08 to 0.2), joint pain (OR 0.18 (0.07 to 0.44)) and high cholesterol (OR 0.13 (0.05 to 0.35)) have higher reported prevalence in controls. The cases describe more physical health disorders in the category ‘other’ (OR 4.65 (3.01 to 7.18)). This captures residual disorders not listed in the questionnaire.Conclusions Participants with schizophrenia in comparison with controls report more disorders. The access in the ‘other’ category may be a reflection of undiagnosed disorders