This study investigates the core competencies essential for product designers to excel in cross-cultural global markets, with particular emphasis on implications for human resource development and organizational leadership. As design practices increasingly transcend cultural and geographical boundaries, designers are required to integrate advanced technical proficiency, creative problem-solving, technological adaptability, and cultural intelligence to create inclusive, socially responsible, and market-relevant products. Employing a mixed-methods approach—including focus groups and surveys with design professionals, industry executives, and academic leaders—the research identifies key competencies such as flexibility, intercultural communication, ethical integrity, and systems thinking. The findings underscore the necessity of balancing technical expertise with emotional intelligence and transformational leadership capabilities to effectively lead diverse, cross-functional teams. These competencies contribute significantly to fostering innovation, enhancing employee well-being and job satisfaction, and strengthening organizational resilience, thereby supporting sustainable human resource strategies. Furthermore, the study highlights the importance of continuous professional development and lifelong learning in cultivating culturally competent and ethically driven design talent. The insights offer strategic guidance for human resource professionals, organizational leaders, and educational institutions aiming to develop adaptive, inclusive, and future-ready design capabilities aligned with evolving global demands.

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.

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.

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.

A comprehensive proteomic analysis was carried out to evaluate leaf proteome changes of Brassica napus cultivars as an important oilseed crop inoculated with the bacterium Pseudomonas fluorescens FY32 under salt stress. Based on the physiochemical characteristics of canola, Hyola308 was a tolerant and Sarigol was a salt sensitive cultivar. Gel-based proteomics indicated that proteins related to energy/metabolism, cell/membrane maintenance, signalins, stress, and development respond to salt stress and bacterial inoculation in both cultivars. Under salt stress, Hyola308 launches mechanisms similar to Sarigol, but the tolerance was related to consuming less energy consumption than Sarigol for launching the proper pathway/mechanism. Inoculation with plant growth promoting bacteria promotes relative growth rate and net assimilation rate; causes increase in soluble sugar content (12–32% varing to cultivars and salt treatments), as an osmo-protectant, in leaves of Sarigol and Hyola308 in control and salt stress conditions. The groups of proteins that are affected due to inoculation (18 and14 functional groups in Hyola308 and Sarigol, respectively) are varying to stress-influenced groups (10 and 6 functional groups in Hyola308 and Sarigol, respectively) that might be because of regulating tolerance mechanism of plant and/or plant-growth promoting bacteria inoculation. Furthermore, it is recognized that P. fluorescens FY32 has a dual effect on the cultivars including a pathogenic effect and a growth promoting effect on both cultivars under salt stress.