Assessing the impact of blockchain technology on the overall performance of sustainable supply chains: an analytical perspective.

Supply chain control and sustainability can be significantly improved using distributed ledger technologies such as blockchain. The blockchain has the potential to facilitate responsible sourcing appropriately, compliance with weather requirements, and sustainable delivery chains. The purpose of this study is to address the hassle of managing conservatism when approaching era adoption and to explore the performance enhancements in blockchain-generated implementations. To achieve this goal, we introduce a scientific approach aimed at studying the outcomes of various factors in the adoption process in the blockchain era and verifying their impact on the overall performance of the delivery chain. Furthermore, a team of multidisciplinary professionals will establish causal relationships among these elements through a consensus-based approach. Ultimately, fuzzy reasoning tools can be used to determine the relative weights between identified factors and delivery chain performance goals. We will assemble causal representations of diagnoses using a dense scientific map model and dynamically generate scenarios for each. The study demonstrates that the integration of blockchain power generation can significantly improve the effectiveness of mineral supply chains. It uses smart contracts to promote environmental sustainability, traceability, and transparency.

Circular value creation: business models and supply chain strategies.

This paper aims to advance research on the circular economy, sustainable innovation through adopting a circular business model (CBM), and circular supply chain management (CSCM). The circular economy is gradually acknowledged as promising to attain ecological growth by minimising resource input, waste, emissions and energy loss. This article investigates the environmental efficacy of circular value creation and its implications for business models and supply chain strategies. It intends to incorporate CBM and CSCM for sustainable innovation and ecological growth, relying on a review of the literature and four case analyses. The context identifies five distinct CBM propelling supply chain strategies and sustainable innovation, supply chain loops, which differ in intricacy and worth. The study demonstrates that circular business models (CBM) and circular CSCM models can facilitate organisations in accomplishing ecological objectives. The companies examined in the study have different characteristics, but all face comparable challenges in persuading consumers and suppliers to adopt circular business models and supply chain management. A significant challenge is that customers perceive products made from recycled or remanufactured materials as inferior to traditional products, leading to lower prices despite meeting comparable quality standards. Therefore, we review the current literature on the business model background to technological, organisational and social innovation. Since the existing literature does not provide a general conceptual definition of sustainable innovation and circular business mode for circular supply chain management, we present normative examples of requirements that circular business models should meet to support sustainable innovation. Finally, we outline the research agenda by asking some guiding questions.

Annual trading patterns and risk factors of avian influenza A/H5 and A/H9 virus circulation in turkey birds (Meleagris gallopavo) at live bird markets in Dhaka city, Bangladesh.

The impacts of the avian influenza virus (AIV) on farmed poultry and wild birds affect human health, livelihoods, food security, and international trade. The movement patterns of turkey birds from farms to live bird markets (LBMs) and infection of AIV are poorly understood in Bangladesh. Thus, we conducted weekly longitudinal surveillance in LBMs to understand the trading patterns, temporal trends, and risk factors of AIV circulation in turkey birds. We sampled a total of 423 turkeys from two LBMs in Dhaka between May 2018 and September 2019. We tested the swab samples for the AIV matrix gene (M-gene) followed by H5, H7, and H9 subtypes using real-time reverse transcriptase-polymerase chain reaction (rRT-PCR). We used exploratory analysis to investigate trading patterns, annual cyclic trends of AIV and its subtypes, and a generalized estimating equation (GEE) logistic model to determine the factors that influence the infection of H5 and H9 in turkeys. Furthermore, we conducted an observational study and informal interviews with traders and vendors to record turkey trading patterns, demand, and supply and turkey handling practices in LBM. We found that all trade routes of turkey birds to northern Dhaka are unidirectional and originate from the northwestern and southern regions of Bangladesh. The number of trades from the source district to Dhaka depends on the turkey density. The median distance that turkey was traded from its source district to Dhaka was 188 km (Q1 = 165, Q3 = 210, IQR = 45.5). We observed seasonal variation in the median and average distance of turkey. The qualitative findings revealed that turkey farming initially became reasonably profitable in 2018 and at the beginning of 2019. However, the fall in demand and production in the middle of 2019 may be related to unstable market pricing, high feed costs, a shortfall of adequate marketing facilities, poor consumer knowledge, and a lack of advertising. The overall prevalence of AIV, H5, and H9 subtypes in turkeys was 31% (95% CI: 26.6-35.4), 16.3% (95% CI: 12.8-19.8), and 10.2% (95% CI: 7.3-13.1) respectively. None of the samples were positive for H7. The circulation of AIV and H9 across the annual cycle showed no seasonality, whereas the circulation of H5 showed significant seasonality. The GEE revealed that detection of AIV increases in retail vendor business (OR: 1.71; 95% CI: 1.12-2.62) and the bird's health status is sick (OR: 10.77; 95% CI: 4.31-26.94) or dead (OR: 11.33; 95% CI: 4.30-29.89). We also observed that winter season (OR: 5.83; 95% CI: 2.80-12.14) than summer season, dead birds (OR: 61.71; 95% CI: 25.78-147.75) and sick birds (OR 8.33; 95% CI: 3.36-20.64) compared to healthy birds has a higher risk of H5 infection in turkeys. This study revealed that the turkeys movements vary by time and season from the farm to the LBM. This surveillance indicated year-round circulation of AIV with H5 and H9 subtypes in turkey birds in LBMs. The seasonality and health condition of birds influence H5 infection in birds. The trading pattern of turkey may play a role in the transmission of AIV viruses in the birds. The selling of sick turkeys infected with H5 and H9 highlights the possibility of virus transmission to other species of birds sold at LBMs and to people.

Comparative safety analysis of newly prepackaged backed food products and those approaching the expiry date in Bangladesh.

PURPOSE: The enrichment of microbial growth in prepackaged, frozen food goods from the day of manufacturing to the day of expiration has been the subject of recurrent concerns. These fortified foods are widely consumed by individuals of all ages in poor nations due to their ability to satisfy even the smallest of appetites. People often disregard the expiration dates printed on food packaging despite the fact that manufacturers are required by law to do so. This research looked into whether or not it was safe to consume packaged foods that were getting close to their expiration date. Finding out if people are exposed to hazardous microorganisms and how much bacteria is created daily on them. MATERIALS AND METHODS: We collected six prepackaged backed food products samples of three types separately, where three were collected around manufacturing days and three were nearly expired days from different companies. We have assayed and identified the foodborne microbial communities among the samples by morphological study and different types of biochemical tests. After that, we tested how well various popular antibiotics worked against those isolates. RESULTS: It showed that there are more bacterial communities that grow gradually day by day on prepackaged backed food products and nearly expired products that contain a large number of food-borne disease-causing bacteria that show mostly resistance against commonly used antibiotics. CONCLUSION: Although nowadays the demand for prepackaged backed food products is increasing as ready-to-eat processed foods, mostly in developing countries, there's a serious health risk if we take the products that were produced a long time ago.

An In-Silico Identification of Potential Flavonoids against Kidney Fibrosis Targeting TGFßR-1.

Fibrosis is a hallmark of progressive kidney diseases. The overexpression of profibrotic cytokine, namely transforming growth factor ß (TGF-ß) due to excessive inflammation and tissue damage, induces kidney fibrosis. The inhibition of TGF-ß signaling is markedly limited in experimental disease models. Targeting TGF-ß signaling, therefore, offers a prospective strategy for the management of kidney fibrosis. Presently, the marketed drugs have numerous side effects, but plant-derived compounds are relatively safer and more cost-effective. In this study, TGFßR-1 was targeted to identify the lead compounds among flavonoids using various computational approaches, such as ADME/T (absorption, distribution, metabolism, and excretion/toxicity) analysis, molecular docking, and molecular dynamics simulation. ADME/T screening identified a total of 31 flavonoids with drug-like properties of 31 compounds, a total of 5 compounds showed a higher binding affinity to TGFßR-1, with Epicatechin, Fisetin, and Luteolin ranking at the top three (-13.58, -13.17, and -10.50 kcal/mol, respectively), which are comparable to the control drug linagliptin (-9.074 kcal/mol). The compounds also exhibited outstanding protein-ligand interactions. The molecular dynamic simulations revealed a stable interaction of these compounds with the binding site of TGFßR-1. These findings indicate that flavonoids, particularly Epicatechin, Fisetin, and Luteolin, may compete with the ligand-binding site of TGFßR-1, suggesting that these compounds can be further evaluated for the development of potential therapeutics against kidney fibrosis. Further, in-vitro and in-vivo studies are recommended to support the current findings.

COVID-19 pandemic, lockdown, and consequences for a fossil fuel-dominated electricity system.

In South Asian countries, the spread of COVID-19 was not treated seriously until mid-March 2020. Measures similar to those considered in Europe and other developed countries, such as maintaining social distance and lockdowns, were imposed. Lockdowns imposed a significant impact on the power sector, and this has been well explored in the literature for developed countries. A country-specific assessment of the impact of COVID-19 on the energy sector is crucial for future crisis management and underpinning sustainable power sector development plans. The impact of COVID-19 on Bangladesh's fossil-fuel dominated electricity sector is explored in this study. The analyses were conducted for 2019 and for the pandemic lockdown period in 2020. Daily hourly demand variations for different electricity generation zones in the country were investigated. The impact of these demand variations on greenhouse gas (GHG) emissions was assessed through time-varying carbon intensity analysis. Nationwide, the analysis revealed that the maximum hourly demand reduced by about 14% between 5 and 6 pm whereas the minimum demand reduction (3%-4%) occurred between 7:30 and 8 pm. Peak time demand reduction was found to be minimal during lockdowns. The national absolute GHG emission reduced by about 1075 kt CO2 e, an ∼16% reduction compared with that in 2019. Time-varying carbon intensity patterns varied significantly between zones.

Hybrid LSTM Self-Attention Mechanism Model for Forecasting the Reform of Scientific Research in Morocco.

Education is the cultivation of people to promote and guarantee the development of society. Education reforms can play a vital role in the development of a country. However, it is crucial to continually monitor the educational model's performance by forecasting the outcome's progress. Machine learning-based models are currently a hot topic in improving the forecasting research area. Forecasting models can help to analyse the impact of future outcomes by showing yearly trends. For this study, we developed a hybrid, forecasting time-series model by long short-term memory (LSTM) network and self-attention mechanism (SAM) to monitor Morocco's educational reform. We analysed six universities' performance and provided a prediction model to evaluate the best-performing university's performance after implementing the latest reform, i.e., from 2015-2030. We forecasted the six universities' research outcomes and tested our proposed methodology's accuracy against other time-series models. Results show that our model performs better for predicting research outcomes. The percentage increase in university performance after nine years is discussed to help predict the best-performing university. Our proposed algorithm accuracy and performance are better than other algorithms like LSTM and RNN.