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This study addresses the growing cyber risks of banks by proposing an innovative, end-to-end dual-layer blockchain-based cyber fraud (CF) response system that integrates Safeguard (SG) and Block guard (BG) mechanisms. The comprehensive solution offers an actionable framework for bank managers to mitigate CFs by prioritizing fraud detection, leveraging early warning signals (EWS), and implementing tailored, need-based control measures before, during, and after a fraud event.

The study uses a multi-method approach, beginning with an extensive literature review on fraud identification, assessment, and prevention strategies. A theoretical framework is constructed to support the proposed SG and BG measures. Machine learning-based data analysis, using Artificial Neural Networks, is employed to dynamically assess the severity of CFs in real time. A managerial action plan for each phase of the fraud lifecycle is presented.

The research underscores the necessity for an adaptable, dual-layered response system that transitions from reactive to proactive and predictive mitigation strategies. The study introduces a novel approach incorporating SG and BG mitigation measures, enabling managers to detect early warning signals and implement robust post-fraud interventions.

The dual-layer approach enhances the sector's resilience to CFs by providing a robust, adaptive framework for fraud prevention and mitigation. This approach helps maintain stability, SG the bank's reputation, and improve overall risk management practices.

This study is unique in its development of an integrated SG and BG response system, combining machine learning, blockchain technology, early warning signals, and a structured before-during-after fraud control model. The research also highlights the critical role of bank managers in implementing and overseeing this innovative response system.

The purpose of the paper is to conduct a numerical and experimental investigation into the properties of nanofluids containing spherical nanoparticles of random sizes flowing through a porous medium. The study aims to understand how the thermophysical properties of the nanofluid are affected by factors such as nanoparticle volume fraction, permeability of the porous medium, and pore size. The paper provides insights into the behavior of nanofluids in complex environments and explores the impact of varying conditions on key properties such as thermal conductivity, density, viscosity, and specific heat. Ultimately, the research contributes to the broader understanding of nanofluid dynamics and has potential implications for engineering and industrial applications in porous media.

This paper investigates nanofluids with spherical nanoparticles in a porous medium, exploring thermal conductivity, density, specific heat, and dynamic viscosity. Studying three compositions, the analysis employs the classical Maxwell model and Koo and Kleinstreuer’s approach for thermal conductivity, considering particle shape and temperature effects. Density and specific heat are defined based on mass and volume ratios. Dynamic viscosity models, including Brinkman’s and Gherasim et al.'s, are discussed. Numerical simulations, implemented in Python using the Langevin model, yield results processed in Origin Pro. This research enhances understanding of nanofluid behavior, contributing valuable insights to porous media applications.

This study involves a numerical examination of nanofluid properties, featuring spherical nanoparticles of varying sizes suspended in a base fluid with known density, flowing through a porous medium. Experimental findings reveal a notable increase in thermal conductivity, density, and viscosity as the volume fraction of particles rises. Conversely, specific heat experiences a decrease with higher particle volume concentration.xD; xA; The influence of permeability and pore size on particle volume fraction variation is a key focus. Interestingly, while the permeability of the medium has a significant effect, it is observed that it increases with permeability. This underscores the role of the medium’s nature in altering the thermophysical properties of nanofluids.

This paper presents a novel numerical study on nanofluids with randomly sized spherical nanoparticles flowing in a porous medium. It explores the impact of porous medium properties on nanofluid thermophysical characteristics, emphasizing the significance of permeability and pore size. The inclusion of random nanoparticle sizes adds practical relevance. Contrasting trends are observed, where thermal conductivity, density, and viscosity increase with particle volume fraction, while specific heat decreases. These findings offer valuable insights for engineering applications, providing a deeper understanding of nanofluid behavior in porous environments and guiding the design of efficient systems in various industrial contexts.

The dual epidemic of non-communicable diseases (NCDs) and human immuno-deficiency virus (HIV) in Sub-Saharan Africa has increased substantially in recent years, with cardiovascular disease representing a significant contributor to the regional burden of disease. Very little is known about the cardiovascular health of people deprived of their liberty in the region. The purpose of this study was to collate extant literature on the topic.

A scoping review mapped and described what is known about cardiovascular disease in prison populations in Sub-Saharan Africa. A systematic search of empirical literature with no date limitation was conducted in English. Sixteen studies representing six Sub-Saharan African countries (Cameroon, Nigeria, Guinea, Burkina Faso, Ghana and Ethiopia) were charted, categorised and thematically analysed.

Seven key themes were identified: custodial deaths and autopsy; cardiorespiratory fitness and exercise; cardiovascular disease and elderly people in prison; cardiovascular disease and women in prison; dietary deficiencies; influence of sleep patterns on cardiovascular disease; and other associated risk factors. Most natural deaths at autopsy of custodial deaths were due to cardiovascular disease. Cardiorespiratory fitness was low in prisons, and poor sleep patterns and dietary deficiencies are likely contributors to the burden of cardiovascular disease in prisons. The needs of elderly and female prison populations are ill-considered.

To the best of the authors’ knowledge, this is the first known attempt to scope extant literature on cardiovascular disease in Sub-Saharan African prisons. A strategic focus on the cardiovascular health of people in prison is warranted. Routine monitoring and expansion of existing prison health-care services and integration of NCD services with infectious disease (HIV and tuberculosis) programmes in prisons are required.

This study examines the impact of climate legislation on green agricultural production and tests the heterogeneous impact of different types of climate legislation on agricultural green production.

In this study, the super-slacks-based measure (super-SBM) model is used to calculate agricultural green total factor productivity (AGTFP). The impact of climate legislation (including legislative acts and executive orders) on AGTFP is examined through regression analysis. The transmission mechanism of climate legislation affecting agricultural green production is further investigated.

This study shows that climate legislation has a positive long-term effect on AGTFP. It stimulates innovation in agricultural green technology but has a negative impact on resource allocation efficiency. Executive orders have a more significant effect on AGTFP than climate legislative acts. The effectiveness of climate legislation is more significant in countries with stronger legislation. Moreover, climate legislation reduces AGTFP in low-income countries while enhancing AGTFP in high-income countries. This effect is most prominent in upper-middle-income countries.

This study examines the different effects of various types of climate legislation, considering the level of economic development and the strength of the legal system on AGTFP. The findings can offer a global perspective and insights for China’s policymaking.

For over 25 years, the United States Green Building Council (USGBC) has significantly influenced the US sustainable construction through its leadership in energy and environmental design (LEED) certification program. This study aims to delve into how Baton Rouge, Louisiana, fares in green building adoption relative to other US capital cities and regions.

The study leverages statistical and geospatial analyses of data sourced from the USGBC, among other databases. It scrutinizes Baton Rouge’s LEED criteria performance using the mean percent weighted criteria to pinpoint the LEED criteria most readily achieved. Moreover, unique metrics, such as the certified green building per capita (CGBC), were formulated to facilitate a comparative analysis of green building adoption across various regions.

Baton Rouge’s CGBC stands at 0.31% (C+), markedly trailing behind the frontrunner, Santa Fe, New Mexico, leading at 3.89% (A+) and in LEED building per capita too. Despite the notable concentration of certified green buildings (CGBs) within Baton Rouge, the city’s green building development appears to be in its infancy. Innovation and design was identified as the most attainable LEED benchmark in Baton Rouge. Additionally, socioeconomic factors, including education and income per capita, were associated with a mild to moderate positive correlation (0.25 = r = 0.36) with the adoption of green building practices across the capitals, while sociocultural infrastructure exhibited a strong positive correlation (r = 0.99).

This study is beneficial to policymakers, urban planners and developers for sustainable urban development and a reference point for subsequent postoccupancy evaluations of CGBs in Baton Rouge and beyond.

This study pioneers the comprehensive analysis of green building adoption rates and probable influencing factors in capital cities in the contiguous US using distinct metrics.

This paper aims to present a comprehensive overview of open-access publications by Indian non-profit organizations over the past two decades. The study explores the growth, licensing patterns, citations, authorship patterns and other parameters to understand the scholarly output.

The study involves data collection from OpenAlex scholarly catalog. Data analysis uses OpenRefine, a data carpentry tool, to examine and extract various aspects of scholarly output. A total of 89,149 scholarly outputs from 2004 to 2023 were analyzed using statistical and bibliometric methods.

The findings revealed a positive publication growth trend, with 57.74% open access. Gold OA dominates, with 69.61% of papers in 2023. Licensing patterns reveal that 63.75% of OA papers have licenses. Most papers have multiple authors, with 24.83% of over ten authors receiving 60.12% of citations. “Medknow” is the leading publisher, and “The Indian Journal of Ophthalmology” tops journals. Contributions from repositories like SSRN and PubMed are significant. The study also examines citation patterns across different OA types and identifies the top 30 research areas, emphasizing “Medicine” as the most prevalent.

The identified trends and patterns offer valuable insights for policymakers, researchers and organizations to enhance accessibility and impact. This study stresses sustained efforts for transparency and democratization of knowledge in the non-profit sector.

This study filled a gap in existing research by focusing on Indian non-profits, highlighting their roles and impacts often overlooked in scholarly literature. This study provides insights into the growth of open-access publications and their implications in the non-profit sector.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

This study aims to assess the effect of water variation on bioethanol production from cassava peels (CP) using Saccharomyces cerevisiae yeast as the ethanologenic agent.

The milled CP was divided into three treatment groups in a small-scale flask experiment where each 20 g CP was subjected to two-stage hydrolysis. Different amount of water was added to the fermentation process of CP. The fermented samples were collected every 24 h for various analyses.

The results of the fermentation revealed that the highest ethanol productivity and fermentation efficiency was obtained at 17.38 ± 0.30% and 0.139 ± 0.003 gL⁻¹ h⁻¹. The study affirmed that ethanol production was increased for the addition of water up to 35% for the CP hydrolysate process.

The finding of this study demonstrates that S. cerevisiae is the key player in industrial ethanol production among a variety of yeasts that produce ethanol through sugar fermentation. In order to design truly sustainable processes, it should be expanded to include a thorough analysis and the gradual scaling-up of this process to an industrial level.

This paper is an original research work dealing with bioethanol production from CP using S. cerevisiae microbe.

1. Hydrolysis of cassava peels using 13.1 M H₂SO₄ at 100 ^oC for 110 min gave high Glucose productivity

2. Highest ethanol production was obtained at 72 h of fermentation using Saccharomyces cerevisiae

3. Optimal bioethanol concentration and yield were obtained at a hydration level of 35% agitation

4. Highest ethanol productivity and fermentation efficiency were 17.3%, 0.139 g.L⁻¹.h⁻¹

Hydrolysis of cassava peels using 13.1 M H₂SO₄ at 100 ^oC for 110 min gave high Glucose productivity

Highest ethanol production was obtained at 72 h of fermentation using Saccharomyces cerevisiae

Optimal bioethanol concentration and yield were obtained at a hydration level of 35% agitation

Highest ethanol productivity and fermentation efficiency were 17.3%, 0.139 g.L⁻¹.h⁻¹