Generational differences shape technological preferences and fundamentally influence workplace motivation and interactions. Our research aims to examine in detail how different generations assess the importance of workplace communication and leadership styles and how these diverse preferences impact workplace motivation and commitment. In our analysis, we studied the behavioral patterns of four generations—Baby Boomers, Generations X, Y, and Z—through anonymous online questionnaires supplemented by in-depth interviews conducted with a leader and a Generation Z employee. To verify our hypotheses, we employed statistical methods, including the Chi-Square test, Spearman’s rank correlation, and cross-tabulation analysis. Our results clearly demonstrated that different generations evaluate the importance of applied leadership and communication styles differently. While Generations Y and Z highly value flexible, supportive leadership styles, older generations, such as the Baby Boomers prefer more traditional, structured approaches. The study confirmed that aligning leadership and communication styles is crucial, as it significantly impacts the workplace atmosphere and employee performance. Our research findings hold both theoretical and practical significance. This research highlights how understanding generational preferences in leadership and communication styles can enhance workplace cohesion and efficiency. The results provide specific guidance for leaders and HR professionals to create a supportive and adaptable environment that effectively meets the needs of diverse generations.

Among contemporary computational techniques, Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) are favoured because of their capacity to tackle non-linear modelling and complex stochastic datasets. Nondeterministic models involve some computational intricacies when deciphering real-life problems but always yield better outcomes. For the first time, this study utilized the ANN and ANFIS models for modelling power generation/electric power output (EPO) from databases generated in a combined cycle power plant (CCPP). The study presents a comparative study between ANNs and ANFIS to estimate the power output generation of a combined cycle power plant in Turkey. The inputs of the ANN and ANFIS models are ambient temperature (AT), ambient pressure (AP), relative humidity (RH), and exhaust vacuum (V), correlated with electric power output. Several models were developed to achieve the best architecture as the number of hidden neurons varied for the ANNs, while the training process was conducted for the ANFIS model. A comparison of the developed hybrid models was completed using statistical criteria such as the coefficient of determination (R²), mean average error (MAE), and average absolute deviation (AAD). The R² of 0.945, MAE of 3.001%, and AAD of 3.722% for the ANN model were compared to those of R² of 0.9499, MAE of 2.843% and AAD of 2.842% for the ANFIS model. Even though both ANN and ANFIS are relevant in estimating and predicting power production, the ANFIS model exhibits higher superiority compared to the ANN model in accurately estimating the EPO of the CCPP located in Turkey and its environment.

This paper explores the interconnected dynamics between governance, public debt, and domestic investment (also known as gross fixed capital formation (GFCF) in South Africa). It also highlights domestic investment as a key driver of economic growth, noting a consistent decline in investment since the country’s democratic transition in 1994. Moreover, this downward trend is exacerbated by excessive public debt, poor governance, and increased economic risks, discouraging domestic and foreign investments. The analysis incorporates two theoretical perspectives: endogenous growth theory, which stresses the significance of local capital investment and innovation, and institutional governance theory, which focuses on the role of governance in promoting economic development. The study reveals that poor governance, rising debt, and high economic risks have impeded GFCF and economic stability. By utilizing quantitative data from 1995 to 2023, the research concludes that reducing public debt, improving governance, and minimizing economic risk are critical to revitalizing domestic investment in South Africa. These findings suggest that policy reforms centered on good governance, effective debt management, and economic stabilization can stimulate investment, promote growth, and address the country’s economic challenges. This study offers insights into how governance and fiscal policies shape investment and capital formation in a developing nation, providing valuable guidance for policymakers and stakeholders working towards sustainable economic growth in South Africa.

In the current context of multicultural collision, online information is impacting traditional gender values. To analyze the changes in gender role attitudes and gender awareness among Chinese Generation Z college students under the influence of various social factors, the study focuses on Generation Z college students and explores the impact of cultural, media, educational, and family factors on gender role attitudes and gender awareness among Chinese Generation Z college students through questionnaire surveys and quantitative analysis methods. The research results show that Generation Z college students exhibit extremely favorable gender perspectives, with the proportion of bisexual gender roles approaching 38%, surpassing the number of students with traditional understanding of single sex gender roles. At the same time, in school gender awareness education, research has found that the proportion of bisexual gender roles is the highest among students who accept open mindedness, at 46.6%. In family gender awareness education, students who receive parental gender awareness sharing education have the highest proportion of bisexual gender roles, accounting for 48.5%. Therefore, the current gender education for the new Generation of students in China needs to abandon traditional avoidance-based teaching methods and adopt an open and supportive attitude to guide students’ gender values.

This document outlines the advancements in AI- accelerated frame generation utilizing Neural Processing Units (NPU) in mobile devices. The integration of NPU technology enhances the processing efficiency of mobile graphics, enabling real-time frame generation that significantly improves video and image quality. By leveraging specialized hardware designed for AI computations, the system reduces latency and optimizes power consumption, making it ideal for demanding applications such as gaming and augmented reality. This paper discusses the underlying architecture of NPUs, their role in accelerating frame generation, and the potential impacts on user experience in mobile environments. The findings illustrate how NPU-driven solutions can transform mobile graphics, offering a more immersive and responsive experience while efficiently managing resources.