The welding occupation has a great deal of risk factors that may conspire to dire health complications and occupational pitfalls. Majority of the welders learn welding without any well-thought-out edifying system in health and safety. Health effects led by welding work are many; some of them to mention being respiratory and cardiac diseases, vision and hearing impairments, dermatological and musculoskeletal maladies. The occupational health hazards of welding largely involve lack of personal protective equipment availability and usage. Hitherto not much information is in hand about the adversities of welding fume exposure to neurological, dermatological and reproductive systems. Hence, it's imperative to know about the plausible hostile effects of welding on health and simultaneously to be acquainted about the advices by the occupational safety and health administrators to develop prevention tactics and thereby decrease the level of vulnerability of the welders to threats of welding.

In the dynamic world of business, where constant adaptation is the accepted norm, Indian entrepreneurs have shown remarkable tenacity & creativity in generating innovative solutions by uniting the practicality of social entrepreneurship & catalyzing effective social change simultaneously. This spirit of the social enterprises of the country has become well represented in it’s start-up economic landscape, affirming the commitment to innovation & progress. It has also emerged as a powerful tool for addressing the innumerable social challenges like economic inequality, poor infrastructure, and bureaucratic red tape that are faced by the diverse Indian populace. Striving to eradicate these pressing obstacles, Indian entrepreneurs have hitherto espoused values of inclusivity, sustainability, and social responsibility as central tenets in their innovative strategies. They have successfully leveraged local resources while orchestrating value-added activities that engage the impoverished in a meaningful economic endeavour, uplifting them from their poverty-stricken conditions. Entrepreneurs have managed to wield these pressures, as a catalyst for growth and development of India. While the social entrepreneurship landscape in India faces hurdles owing to certain system-level constraints, there exists an opportunity to rectify these barriers. Reinforcing the infrastructure around social entrepreneurship, further facilitating access to capital, broadening awareness of innovative strategies, and refining regulatory frameworks can significantly contribute to the growth and scalability of these enterprises. Such development will be pivotal in solving the socially entrenched problem of poverty, and working towards an equitable, accessible, and high-quality social system in India.

AbstractThis article illustrates how a social knowledge management (SKM) platform can be conceptualized, developed and implemented that can provide users with multi‐community connectivity, making it quicker and easier for members to connect, communicate, collaborate, and learn. By conducting case studies on rural craft producers in West Bengal, India, we have demonstrated how this platform fosters meaningful collaboration among rural artisans and various stakeholders within the craft production system, ultimately resulting in the formation of vibrant virtual communities. The platform promotes purposeful knowledge exchanges among variety of agents, which helps to reduce the knowledge divide and the market isolation that rural artisans experience. The platform enhances inter and intra‐group communication that supports rural artisans to establish their independent credibility in their trade without the assistance of intermediaries or middlemen.

In this current era, the identification of both known and novel cell types, the representation of cells, predicting cell fates, classifying various tumor types, and studying heterogeneity in various cells are the key areas of interest in the analysis of single-cell RNA sequencing (scRNA-seq) data. Due to the nature of the data, cluster identification in single-cell sequencing data with high dimensions presents several difficulties. In this paper, we introduce a new framework that combines various strategies such as imputed matrix, minimum redundancy maximum relevance (MRMR) feature selection, and shrinkage clustering to discover gene signatures from scRNA-seq data. Firstly, we conducted the pre-filtering of the “drop-out” value in the data focusing solely on imputing the identified “drop-out” values. Next, we applied the MRMR feature selection method to the imputed data and obtained the top 100 features based on the MRMR feature selection optimization scores for further downstream analysis. Thereafter, we employed shrinkage clustering on the selected feature matrix to identify the cell clusters using a global optimization approach. Finally, we applied the Limma-Voom R tool employing voom normalization and an empirical Bayes test to detect differentially expressed features with a false discovery rate (FDR) < 0.001. In addition, we performed the KEGG pathway and gene ontology enrichment analysis of the identified biomarkers using David 6.8 software. Furthermore, we conducted miRNA target detection for the top gene markers and performed miRNA target gene interaction network analysis using the Cytoscape online tool. Subsequently, we compared our detected 100 markers with our previously detected top 100 cluster-specified markers ranked by FDR of the latest published article and discovered three common markers; namely, Cyp2b10, Mt1, Alpi, along with 97 novel markers. In addition, the Gene Set Enrichment Analysis (GSEA) of both marker sets also yields similar outcomes. Apart from this, we performed another comparative study with another published method, demonstrating that our model detects more significant markers than that model. To assess the efficiency of our framework, we apply it to another dataset and identify 20 strongly significant up-regulated markers. Additionally, we perform a comparative study of different imputation methods and include an ablation study to prove that every key phase of our framework is essential and strongly recommended. In summary, our proposed integrated framework efficiently discovers differentially expressed stronger gene signatures as well as up-regulated markers in single-cell RNA sequencing data.

Abstract The rural handicrafts industry is a highly fragmented industry with many small-scale businesses. Despite its cultural and economic importance, this industry encounters challenges like intermediary exploitation, restricted market entry, and lack of a credible product authentication system, making it susceptible to competition from cheaper machine-made alternatives. It can lead to loss of sales and reputational damage endangering both the artisans' livelihoods and the craft's sustainability. To address these challenges, establishing end-to-end visibility within the rural handicrafts supply chain offers enhanced transparency, traceability, and authentication. This fosters trust among the various participants in the supply chain. However, ensuring the integrity of the supply chain data is essential, necessitating a mechanism to secure production process information, verify product and artisan authenticity, and enable transparent transactions. This paper proposes a blockchain-enabled crafts supply chain for rural artisans and presents an innovative implementation approach using the real data (artisan and product profiles) from rural kantha artisans in Birbhum district, West Bengal. By leveraging the decentralized, immutable, and transparent nature of blockchain, our approach provides a practical framework that could be adapted to other similar contexts.

The present days passes through Industry 4.0. It emphasizes on intelligent computing, smart computing, cloud computing, internet of things (IoT), artificial intelligence (AI), and machine learning (ML). In addition, the function of smart devices in these computing techniques make the work user friendly. Nowadays, health issues are a big challenge in society. In this industry revolution period, smart devices play significant role to provide health assistance in a finger touch. It is possible due to the increase in the number of smart devices and use of IoT, cloud, AI, and ML. Fog computing is the mechanism of providing quality services through optimization of the resources. This paper presents the hybridization of cloud computing with fog computing in the field of healthcare applications.

Abstract This study introduces a time dependent fuzzy multi-objective reliability redundancy allocation problem (TF-MORRAP) for the $n$-stage (level) series parallel system. System reliability maximization and system cost minimization according to time by optimizing the redundant components counting at every stage of the system is the main objective of this study. This optimization is done by satisfying the entropy constraints with limited redundant components at every stage and in the whole system. The reliability and cost of every component are represented as triangular fuzzy numbers (TFN) to handle the uncertainty of input information of the system. According to time the component reliability and cost decrease by some factor of their previous existing value. This factor follows the change in the length of radius of the inverse logarithmic spiral with respect to angle which is regarded as time here. The proposed problem is analysed by using an over-speed protection system of a gas turbine. We compare the membership values of optimal solutions obtained by using two well-known techniques namely Non-dominated sorting genetic algorithm-II (NSGA-II) and a multi-objective particle swarm optimization algorithm called NF-MOPSO. Various performances of the algorithms are compared to solve the aforementioned problem by using some performance metrics. NF-MOPSO shows the high satisfaction level of objective functions and better performance than NSGA-II.