The multifaceted nature of the skills required by new-age professions, reflecting the dynamic evolution of the global workforce, is the focal point of this study. The objective was to synthesize the existing academic literature on these skills, employing a scientometric approach . This involved a comprehensive analysis of 367 articles from the merged Scopus and Web of Science databases. Science. We observed a significant increase in annual scientific output, with an increase of 87.01% over the last six years. The United States emerged as the most prolific contributor, responsible for 21.61% of total publications and receiving 34.31% of all citations. Using the Tree algorithm of Science (ToS), we identified fundamental contributions within this domain. The ToS outlined three main research streams: the convergence of gender, technology, and automation; defining elements of future work; and the dualistic impact of AI on work, seen as both a threat and an opportunity. Furthermore, our study explored the effects of automation on quality of life, the evolving meaning of work, and the emergence of new skills. A critical analysis was also conducted on how to balance technology with humanism, addressing challenges and strategies in workforce automation. This study offers a comprehensive scientometric view of new-age professions, highlighting the most important trends, challenges, and opportunities in this rapidly evolving field.

Fog computing (FC) has been presented as a modern distributed technology that will overcome the different issues that Cloud computing faces and provide many services. It brings computation and data storage closer to data resources such as sensors, cameras, and mobile devices. The fog computing paradigm is instrumental in scenarios where low latency, real-time processing, and high bandwidth are critical, such as in smart cities, industrial IoT, and autonomous vehicles. However, the distributed nature of fog computing introduces complexities in managing and predicting the execution time of tasks across heterogeneous devices with varying computational capabilities. Neural network models have demonstrated exceptional capability in prediction tasks because of their capacity to extract insightful patterns from data. Neural networks can capture non-linear interactions and provide precise predictions in various fields by using numerous layers of linked nodes. In addition, choosing the right inputs is essential to forecasting the correct value since neural network models rely on the data fed into the network to make predictions. The scheduler may choose the appropriate resource and schedule for practical resource usage and decreased make-span based on the expected value. In this paper, we suggest a model Neural Network model for fog computing task time execution prediction and an input assessment of the Interpretive Structural Modeling (ISM) technique. The proposed model showed a 23.9% reduction in MRE compared to other methods in the state-of-arts.

This paper investigates the potential of a concept for the commercial utilization of surplus intermittent wind-generated electricity for municipal district heating based on the development of an electric-driven heat storage. The article is divided into three sections: (1) A review of energy storage systems; (2) Results and calculations after a market analysis based on electricity consumption statistics covering the years 2005–2013; and (3) Technology research and the development of an innovative thermal energy storage (TES) system. The review of energy storage systems introduces the basic principles and state-of-the-art technologies of TES. The market analysis describes the occurrence of excess wind power in Germany, particularly the emergence of failed work and negative electricity rates due to surplus wind power generation. Based on the review, an innovative concept for a prototype of a large-scale underwater sensible heat storage system, which is combined with a latent heat storage system, was developed. The trapezoidal prism-shaped storage system developed possesses a high efficiency factor of 0.98 due to its insulation, large volume, and high rate of energy conversion. Approximate calculations showed that the system would be capable of supplying about 40,000 people with hot water and energy for space heating, which is equivalent to the population of a medium-sized city. Alternatively, around 210,000 inhabitants could be supplied with hot water only. While the consumer´s costs for hot water generation and space heating would be lowered by approximately 20.0–73.4%, the thermal energy storage would generate an estimated annual profit of 3.9 million euros or more (excluding initial costs and maintenance costs).

Polyurethane is a multipurpose polymer with valuable mechanical, thermal, and chemical stability, and countless other physical features. Polyurethanes can be processed as foam, elastomer, or fibers. This innovative overview is designed to uncover the present state and opportunities in the field of polyurethanes and their nanocomposite sponges. Special emphasis has been given to fundamentals of polyurethanes and foam materials, related nanocomposite categories, and associated properties and applications. According to literature so far, adding carbon nanoparticles such as graphene and carbon nanotube influenced cell structure, overall microstructure, electrical/thermal conductivity, mechanical/heat stability, of the resulting polyurethane nanocomposite foams. Such progressions enabled high tech applications in the fields such as electromagnetic interference shielding, shape memory, and biomedical materials, underscoring the need of integrating these macromolecular sponges on industrial level environmentally friendly designs. Future research must be intended to resolve key challenges related to manufacturing and applicability of polyurethane nanocomposite foams. In particular, material design optimization, invention of low price processing methods, appropriate choice of nanofiller type/contents, understanding and control of interfacial and structure-property interplay must be determined.

The fast-growing field of nanotheranostics is revolutionizing cancer treatment by allowing for precise diagnosis and targeted therapy at the cellular and molecular levels. These nanoscale platforms provide considerable benefits in oncology, including improved disease and therapy specificity, lower systemic toxicity, and real-time monitoring of therapeutic outcomes. However, nanoparticles' complicated interactions with biological systems, notably the immune system, present significant obstacles for clinical translation. While certain nanoparticles can elicit favorable anti-tumor immune responses, others cause immunotoxicity, including complement activation-related pseudoallergy (CARPA), cytokine storms, chronic inflammation, and organ damage. Traditional toxicity evaluation approaches are frequently time-consuming, expensive, and insufficient to capture these intricate nanoparticle-biological interactions. Artificial intelligence (AI) and machine learning (ML) have emerged as transformational solutions to these problems. This paper summarizes current achievements in nanotheranostics for cancer, delves into the causes of nanoparticle-induced immunotoxicity, and demonstrates how AI/ML may help anticipate and create safer nanoparticles. Integrating AI/ML with modern computational approaches allows for the detection of potentially dangerous nanoparticle qualities, guides the optimization of physicochemical features, and speeds up the development of immune-compatible nanotheranostics suited to individual patients. The combination of nanotechnology with AI/ML has the potential to completely realize the therapeutic promise of nanotheranostics while assuring patient safety in the age of precision medicine.