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.

This article aims to examine the influence of top management green commitment and green intellectual capital of Thailand’s thrift and credit cooperatives on sustainable business performance: the mediating role of collaboration. A questionnaire was used to collect data from 400 Thailand’s thrift and credit cooperatives. The measurement and structural models were analyzed with Structural Equation Modeling (SEM). The finding of the study demonstrated that top management green commitment and green intellectual capital have a positive influence on sustainable business performance and collaboration. Moreover, collaboration has a positive influence on sustainable business performance and mediates the relationship between top management green commitment on sustainable business performance, and also green intellectual capital on sustainable business performance. This empirical finding contributes to the body of knowledge in Resource Based View theory.

As the population grows and demand increases, cities have seen a rise in the number of chain stores. To remain competitive, these companies must reduce costs and attract more customers. A key factor in achieving this is the strategic placement of store branches, which reduces the distance between stores and customers, instilling trust and increasing their appeal while also cutting costs by reducing the need for employees to navigate longer distances. In this study, an integer linear programming model is presented with the goal of dividing a zone in Ahvaz city into several scenarios to determine the optimal number of stores while maintaining control over the distance between active stores. This research is the first to include this specific limitation in the mathematical model of the problem. The results of the study demonstrate a significant reduction in the distance between customers and stores.

This paper aims to improve the ABC analysis method used for inventory management by applying the Pythagorean Fuzzy TODIM approach. ABC analysis is one the well-known and widely used inventory classification techniques which divides inventory items into three categories according to their importance and value. However, the traditional ABC analysis does not consider the imprecision and vagueness of real-world inventory data, which can lead to inaccurate results and poor inventory management decisions. The proposed approach enhances the traditional ABC analysis by incorporating fuzzy numbers to be considered in real-world inventory data. The improved ABC analysis helps companies to optimize inventory levels, reduce costs, improve customer service, and increase overall operational efficiency. To check for the reliability and effectiveness of the developed model under different scenarios sensitivity analysis is conducted. Additionally, the comparative analysis among other existing models further demonstrates the model's accuracy. The model prepared shows that the Pythagorean Fuzzy TODIM approach is superior to the conventional ABC analysis in terms of reliability and dealing with the uncertain inventory data. Overall, this paper provides a novel and effective approach to inventory management and offers valuable insights for practitioners and researchers in the field.

In disease mapping, it is preferable to estimate the risk rather than the significance in general, but the variation in estimation precision across the geographical map of the study region must also be taken into consideration. In such a situation the conventional methods would not yield the best estimates. Heterogeneity is an important aspect to be considered as significant in Disease Mapping and relative risk estimation. The simple regression models often do not capture the extent of the variation exhibited in the spatial count data. This is the case when the spatial data is over-dispersed or there is spatial correlation due to unobserved confounders. In such situations, it is appropriate to include some additional terms, which may be in the form of the prior distribution. In this paper, a Poisson model with Gamma prior is used to model and map the dengue incidences in Tamil Nadu to explain the patterns of variations.

The global value chain (GVC) recognizes post production processes that involve socio economic engagements of stakeholders with electrical and electronic equipment (EEE or e-products) at their near or end of life. This paper attempts to measure some of the key social, economic, environmental, and sustainability indicators in line with GVC for lithium-ion batteries from e-products at their near or end of life. The assessment was based on the socioeconomic impacts on the battery sector for managing spent lithium-ion (Li-ion) batteries in Southeastern Nigeria. The study methodology highlighted strategic features in line with socioeconomic assessment by deploying questionnaire administration. Ten (10) local government areas (LGAs) were purposely selected from five mutually exclusive States, with 100 stakeholders investigated. Descriptive statistics were used to analyze data for innovation potentials for the battery recycling/refurbishing sector, and discussed accordingly under sustainability, social, economic and environmental impacts. The socioeconomic drivers (or outcomes) for the recycling and refurbishing of lithium ion batteries touched on self-sustainability, institutionalization, service demand, service utilization and prevalence.

The purpose of this study is to examine the relationships of master production scheduling (MPS), total quality management (TQM), blood production (BP) and blood transfusion sustainability in the blood bank sector of Uganda. This study considers MPS as an independent variable and examines its relationship with BTS through TQM and BP. Based on the dynamic capability theory, this study concurrently incorporates and considers TQM and BP as independent and serial mediators. Data was collected from 213 blood bank respondents using a self-administered questionnaire. Random simple sampling was used to draw the sample. SEM approach was used in the study to assess the model. The major findings are positive total effect relationship between MPS and BTS; BP was found to be an independent mediator, TQM was found not to be an independent mediator, and TQM and BP mediated the MPS and BTS relationship serially. The findings suggest that MPS is related to BTS through a serial mediation model with two mediators. Therefore, blood banks in Uganda need to pay attention to develop and maintain a better MPS program so that positive perceptions of blood transfusion can be attained. Future research should investigate the mediating effect of other TQM and BP dimensions in the link between MPS and BTS in other countries.