Global pandemic has provoked industries with unprecedented challenges. Stratagems to squash the COVID-19 bow like communal lockdown, social isolation, work at home, containment zones and all restraints sited on travel with stay home orders issued by the authorities led to sharp failure in revenues of service and manufacturing industries. The COVID-19 pandemic has shattered the transportation links with supply chain amongst suppliers, production amenities and consumers. Mostly the business executives are penetrating about passable strategies and plans for restoring production lines to encounter customer mandates. In this paper the pandemic effect of Indian business is considered which are affected by critical barriers of supply chains in the Indian scenario. These critical barriers are identified on a priority basis using the MADM approach. Furthermore, the study will help the scholars to grow conceptual models for maintaining a better supply chain to overwhelm this world-wide problem.

This research aims to explore the significance of supply chain (SC) resilience by integrating SC-Resilience and SC-Ambidexterity concepts. SC-Ambidexterity refers to the simultaneous application of SC-Adaptability and SC-Alignment capabilities within the supply chain. In line with the dynamic capabilities view (DCV) of the firm, this research adopts a quantitative approach to investigate the relationship between variables in the context of manufacturing and production companies in Pakistan, specifically in Karachi. The results of this case study have revealed a strong positive impact of SC-Ambidexterity on SC-Resilience, confirming the significance of adopting concurrent and synchronized supply chain capabilities. Furthermore, the analysis indicated that SC-Agility plays a crucial role as a mediator in the relationship between SC-Ambidexterity and SC-Resilience. The findings suggest that organizations that proactively invest in developing both ambidextrous capabilities and agility are more likely to achieve a higher level of supply chain resilience, enabling them to effectively navigate turbulent business environments.

Access to safe drinking water is essential for human health. In Abbottabad, tap and bore water are commonly used, but there has been a recent increase in bottled water consumption. This study aimed to compare tap and bottled water quality in Jinnahabad, Abbottabad. Physicochemical and bacteriological analysis was conducted on water samples collected from various sources. Surveys and interviews were also conducted to assess consumer perceptions and costs. The study found that, on average, bottled water had better physicochemical quality, although both alternatives met WHO limits. Tap water had higher levels of E. coli due to a weak sanitation system. Interestingly, despite perceiving bottled water as safer, most respondents still consumed tap water daily. Shopkeepers reported higher bottled water purchases for travel but lower daily consumption. Tap water was the main source, according to the Cantonment Board Abbottabad, though resources were insufficient. Doctors confirmed tap water-related diseases. The study suggests further research into consumer behavior and recommends monitoring measures, staff evaluations, and penalties to reduce costs and waste.

This study delved into the repercussions of economic instability on household food security while aiming to construct a robust framework for enhancing the resilience of the food supply chain. It thoroughly analyzed diverse factors, including government policies, economic conditions, environmental variables, and social dynamics, to gauge their reciprocal impact on food security. The research employed a meticulously chosen probability-based sample to ensure the representativeness of findings within the population, specifically focusing on residents in the Tanjung Kupang region of Johor. The study holds paramount significance as it provides novel insights for researchers, academic practitioners, and policymakers, shedding light on the far-reaching consequences of economic uncertainty on household purchasing power and its pivotal role in upholding food security. Moreover, it aspires to devise a tailored and sustainable food supply chain framework for Malaysia's unique context. By employing a quantitative approach enriched with robust statistical analysis and insights gathered through a meticulously designed questionnaire, this study sought to illuminate the intricate dynamics at play. The findings underscore the profound impact of economic instability on diminishing the income of B40 households, thereby curtailing their purchasing power. These findings align with comprehensive literature reviews from authoritative sources, underscoring factors such as inflation, soaring prices of essential commodities, and stagnant incomes contributing to the decline in income among B40 households. The implications of this research extend to policymakers, offering invaluable insights and promoting public awareness of potential challenges, along with plausible solutions.

Nomophobia is a term describing a growing fear in today’s world, the fear of being without a mobile device or beyond mobile phone contact. It is the biggest non-drug addiction of the 21st century and is mainly affected by teen-aged students. Those experiencing nomophobia may feel a sense of panic, anxiety, or distress when they are separated from their mobile phones. This work uses different statistical tools to identify the risk factor of nomophobia and machine learning techniques to propose a fresh way to measure and understand nomophobia. To create a predictive model for nomophobia, we gathered information from a broad sample (n = 357) of smartphone users and used a variety of machine learning methods. Using a questionnaire on 17 different factors and performing a statistically significant test (p<0.05) and ordinal logistic regression analysis on respondents age, level of education, CGPA, self-evaluation, per-day mobile phone usage, and use of media, we can recognize the most important features causative of nomophobia. The context of maximum phone usage is an important feature that highly affects nomophobia. About 201 respondents are at a moderate level. To develop a predictive model, decision tree (DT), random forest (RF), Gaussian Naïve Bayes (NB), and support vector machine (SVM) are utilized in this study for recognition of nomophobia addiction. Proposing an ensemble method to refine the predictive performance. From the analysis, we have found that the SVM feature selector with ensemble algorithm has classified the extent of smartphone addiction with a 57% accuracy rate. Our findings show that nomophobia tendencies can be accurately captured and predicted by machine learning approaches. The model distinguished between students who had symptoms of nomophobia and those who did not with remarkable accuracy. This study of machine learning-based methods presents a viable tool for diagnosing and treating nomophobia in students, eventually assisting in the creation of focused interventions and preventive measures.

Recent organizational trends give more importance to the social and responsible behavior of organizations to their sustainability. Numerous factors are responsible for prioritizing social responsiveness and pro-environmental or employee-green behavior for organizational sustainability. Nevertheless, no studies have yet considered how voluntary green behavior (VGB) and socially responsible human resource management (SRHRM) influence this perception. Hence, this study determines the explanatory link of VGB along with the impacts of SRHRM on organizational sustainability (environmental [EnS], social [SS], and economic [ES] sustainability). Data was collected from a developing nation context. An explanatory research strategy was used for the present study. To acquire data from 100 Bangladeshi pharmaceutical manufacturing organizations, structured questionnaires were used. For testing the proposed hypotheses of the study, we used partial least squares structural equation modeling (PLS-SEM). The outcomes of the analysis reveal that SRHRM has a considerable impact, both positively and significantly, on organizational sustainability (EnS, SS, and ES). Once again, SRHRM has a substantial positive effect on VGB. Furthermore, VGB plays an influential role as a mediator in the relationship between SRHRM and organizational sustainability. The findings have significant implications for pharmaceutical manufacturing organization management in Bangladesh and other Southeast Asian contexts. Based on the findings, pharmaceutical company managers will have a stronger rationale to invest in SRHRM while simultaneously establishing strong ties with employees and CSR-oriented green behavior to accomplish their organizational sustainability objectives. This study contributes to the existing body of research on the sustainability of organizations and the triple bottom line, along with SRHRM and VGB, by providing evidence from a country that is rapidly industrializing and developing. In this work, SRHRM was measured as a whole, even though it has three dimensions. Since the sample consists of Bangladeshi pharmaceutical manufacturing organizations, it is also uncertain whether the results can be generalized.

A wide range of obstacles faced by people with disabilities, such as visual impairment, motor disability, and communication difficulties, have shown significant promise for being addressed by computer vision. The state-of-the-art in computer vision-based assistive technology is examined in this report along with major future research topics and obstacles. In particular, this study explores how computer vision can be used for object recognition, navigation, facial recognition, sign language interpretation, and gesture-based control interfaces. It also discusses the benefits and drawbacks of various methodologies and technologies and offers examples of how computer vision can be incorporated into current assistive technologies to boost their efficacy. The ethical and privacy issues surrounding the use of computer vision in assistive technologies are covered in this study effort. The study also highlights the need for protocol standardization, better user-centered design, and the assessment of real-world effectiveness as future research objectives for improving the use of computer vision in assistive technology. Overall, this paper sheds light on how computer vision might completely alter the world of assistive technologies for people with impairments.

Solar panel energy output is an essential parameter for the design and operation of renewable energy systems. Previously, little was known about the precise relationship between the energy outputs of solar panels with various meteorological, radiometric, and weather conditions in the southern California region. Without precise modeling or prediction systems, solar energy can potentially be wasted due to grid energy fluctuation. Thus, it is intended to use an artificial neural network (ANN) to develop solar panel energy output prediction model with a high degree of accuracy. A self-developed feedforward ANN model utilizing the Rectified linear unit (ReLu) activation function was used in the present study. Meteorological, weather, and sun irradiation data collected throughout the last year from a residential location have been used to train the models. The model’s performance was identified based on the minimum mean absolute error (MAE) and root mean square error (RMSE) and maximum linear correlation coefficient (R2). Further, the present self-developed ANN model was consistent with other solar energy experimental results and theoretical analysis. The developed ANN model using the Python programming language achieved a high R2 of more than 85% which ascertains the accuracy and suitability of the model to predict the daily solar energy output in local southern California area. This ANN modeling approach can be extended to many other applications such as SCORE, commercial, and residential building design.

The pharmaceutical industry is vital for global health, supplying necessary medicines, yet its conventional supply chain has notable environmental and social impacts. Amid a growing sustainability focus across sectors, the pharmaceutical industry must also adopt sustainable practices throughout its supply chain. This includes lessening its ecological impact, curbing waste, and endorsing social responsibility. Assessing a supplier's environmental performance, or "green performance," is of great interest. This involves gauging their eco-friendly actions like energy efficiency, waste management, and carbon footprint reduction. Metrics cover certifications, resource conservation, and responsible sourcing. In a study within a renowned Bangladeshi pharmaceutical firm, a key drugs manufacturer, seven criteria were used to evaluate suppliers' green performance. For this multi-criteria decision-making (MCDM) task, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was applied, considering a fuzzy environment. It ranked alternative suppliers via a widely used approach using linguistic terms expressed as Triangular Fuzzy Numbers (TFN). Important weights were determined via the Center of Area (COA) method. The study revealed supplier 4 as the top performer in green performance among five alternatives. This study introduces an innovative strategy for manufacturing decision-makers to choose the most suitable green supplier. It's anticipated to aid decision-makers in emerging economy pharmaceutical industries, facilitating the efficient evaluation of economically viable and environmentally sustainable suppliers for the long term.

During the Covid-19 pandemic, when strict restrictions were imposed to protect public health, e-commerce played a significant role in providing products on time. E-commerce technology and big data analytics enable companies to gain competitive advantages and respond to customers more efficiently. To make e-commerce more sustainable, the three dimensions of sustainability must be met, otherwise it can have negative consequences that lead to ecosystem destruction. Thus, e-commerce must learn how to effectively manage certain aspects of sustainability and adapt its operations to achieve balance. E-commerce's impact on sustainability can be measured in three pillars: economic, social and environmental and achieving a balance among these is the ultimate goal of sustainable development. Although the sustainability issue and big data analytics have gained increasing popularity in recent years, there is still a gap in evaluating applications of big data based on sustainable development indicators. In this study, we used a hybrid multi-criteria decision-making technique combining fuzzy TOPSIS and BWM to assess big data applications in e-commerce considering sustainable development indicators. The results showed environmental sustainability and energy consumption efficiency received the highest weight for the main pillars and sub criteria of sustainability indicators. Coordinating and monitoring supply chain processes, innovating product, process and business models, and creating new products and services are the top three applications of big data in e-commerce considering sustainable development indicators. E-commerce managers and experts can make better decisions about sustainable approaches by prioritizing big data applications based on sustainable development indicators. In addition, the proposed approach can also be used to evaluate big data analytics in other industries that consider sustainable development indicators.