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.

Ukrainian Human Resource (HR) practices have multiple difficulties from economic changes combined with digital transformation and workforce instability brought on by the war in 2022. The study examines Ukrainian HR practices between 2015 and 2024, focusing on the digitalization of HR systems, talent development, staff engagement, and hiring strategies. It considers the effects of organizational size and industry type. The study combined interviews with 30 HR professionals and surveyed 150 organizations from different industry groups and sizes. Our data required both quantitative statistical tests and manual content breakdown with codes. Research has shown significant differences between Information Technology (IT) and farming firms, as 89% of IT businesses have integrated artificial intelligence (AI)-powered HR tools. In comparison, only 15% of agricultural companies have adopted them. Small and medium-sized enterprises (SMEs) showed less commitment to digital transformation and European Union (EU) requirements than large enterprises, which adopted these systems at rates of 75% and 88%, respectively. Western Ukraine first established mental health initiatives during the crisis, and Eastern Ukraine moved toward decentralized administration. Digitalization assistance for small businesses, along with EU and local human resources frameworks, should form the basis of our suggestions. This research calls for flexible people management methods to boost the Ukrainian workspace’s ability to recover from shocks.

This paper uses existing studies to explore how Artificial Intelligence (AI) advancements enhance recruitment, retention, and the effective management of a diverse workforce in South Africa. The extensive literature review revealed key themes used to contextualize the study. This study uses a meta-narrative approach to literature to review, critique and express what the literature says about the role of AI in talent recruitment, retention and diversity mapping within South Africa. An unobtrusive research technique, documentary analysis, is used to analyze literature. The findings reveal that South Africa’s Human Resource Management (HRM) landscape, marked by a combination of approaches, provides an opportunity to cultivate alternative methods attuned to contextual conditions in the global South. Consequently, adopting AI in recruiting, retaining, and managing a diverse workforce demands a critical examination of the colonial/apartheid past, integrating contemporary realities to explore the potential infusion of contextually relevant AI innovations in managing South Africa’s workforce.

Using company size as a moderator, this article examines the MENA region’s gender balance on boards and how it influences capital structure. The study uses the Generalized Method of Moments (GMM) estimate technique to analyze data from a sample of 556 non-financial organizations across 10 MENA countries from 2010 to 2023. The results show that a lower debt ratio is connected with a higher percentage of female board members. Further steps towards debt reduction include increasing the number of independent female board members and decreasing the board’s overall size. The opposite is true for larger enterprises, more profitability, more expansion opportunities, and macroeconomic variables like inflation and GDP growth, which tend to raise the debt ratio. Capital structure decisions in the MENA area are influenced by gender diversity on boards and business characteristics. Therefore, Companies in the MENA area would do well to support initiatives that increase the representation of women on corporate boards. One way to achieve this goal is to establish gender diversity targets or launch programs to increase the number of women serving on boards of directors, particularly in positions of power.

Since the proposal of the low-carbon economy plan, all countries have deeply realized that the economic model of high energy and high emission poses a threat to human life. Therefore, in order to enable the economy to have a longer-term development and comply with international low-carbon policies, enterprises need to speed up the transformation from a high-carbon to a low-carbon economy. Unfortunately, due to the massive volume of data, developing a low-carbon economic enterprise management model might be challenging, and there is no way to get more precise forecast data. This study tackles the challenge of developing a low-carbon enterprise management mode based on the grey digital paradigm, with the aim of finding solutions to these issues. This paper adopts the method of grey digital model, analyzes the strategy of the enterprise to build the model, and makes a comparative experiment on the accuracy and performance of the model in this paper. The results show that the values of MAPE, MSE and MAE of the model in this paper are the lowest. And the r^2 of the model in this paper is also the highest. The MAPE value of the model in this paper is 0.275, the MSE is 0.001, and the MAE is 0.003. These three indicators are much lower than other models, indicating that the model has high prediction accuracy. r² is 0.9997, which is much higher than other models, indicating that the performance of this model is superior. With the support of this model, the efficiency of building an enterprise model has been effectively improved. As a result, developing an enterprise management model for the low-carbon economy based on the gray numerical model can offer businesses new perspectives into how to quicken the shift to the low-carbon economy.

This study aims to determine the extent to which talent identification is implemented in talent management. A Systematic Literature Review (SLR) was conducted to summarize the application of talent identification in the last six years. Researchers use Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) to process scientific articles. The literature reveals that while topics related to talent management garner significant attention, research on talent identification within talent management remains relatively scarce despite a gradual increase each year. We compared documents indexed by Scopus Q1 and Q2. The results show that the United States accounted for a significant portion of research on talent identification, representing 16% of the total existing research. Researchers have conducted extensive studies on the medical and pharmaceutical sectors, public services, tourism, and hospitality. The number of citations varied greatly from 1 to 93, with a median value of 20. These studies have also used various research methods with different theoretical bases and produced different analyses. This finding enriches the perspective of talent identification.