Objectives: This study aims to examine the impact of Sun Tzu’s Art of War Five Virtues Leadership on innovation and the efficiency of the Chinese brand passenger vehicle industry, explore the role of innovation in enhancing industry efficiency, and propose strategies for leveraging the Five Virtues Leadership to improve operational performance and competitiveness in the sector. Methodology: A mixed research method using quantitative research (questionnaire survey) as the main method and qualitative research (in-depth interview) as the auxiliary method. Result: Quantitative and qualitative research results confirm the positive correlation between the Five Virtues Leadership, innovation, and the efficiency of Chinese brand passenger vehicle companies. And through effective data analysis, it explains the importance of the five virtues of leadership in traditional Chinese culture. Further understanding of the effectiveness and competitiveness of China’s passenger car brands, with leadership references. Conclusion: Five Virtues Leadership can foster a favorable environment for innovation, enhance time utilization, optimize resource allocation, and strengthen brand image. By developing and validating a measurement for Five Virtues Leadership, this study enhances the understanding of its role and significance in modern management, paving the way for future research.

In the human and economic development context, this study examines the relationship between human capital, life expectancy, labor force participation rate, and education level in Indonesia, Malaysia, and Thailand. The World Bank’s 2001–2021 data are examined using a panel vector autoregressive model. The findings demonstrate the substantial influence of health expenditure from the prior period on present health expenditure. Though not significantly different, life expectancy and education levels from earlier periods also impact present health spending. A slight positive correlation exists between prior labor force involvement and present healthcare costs. An increase in current health expenditure supports an increase in life expectancy. Health expenditure in the previous period had a significant positive effect on education, although insignificant. Life expectancy in the previous period harms current education but is also insignificant. Education in the previous period significantly positively affects current education, indicating a sustained impact of education investment. Labor force participation in the previous period also positively affected education, although not significantly. The prior period’s health spending, life expectancy, and educational attainment impact the current labor force participation rate. The length of life has a significant favorable impact on entering the labor sector. Currently being in the job field has a good correlation with prior education as well. These findings support that higher education levels lead to higher labor force participation rates. Life expectancy, health care costs, education level, and prior work experience all influence current life expectancy. While prior life expectancy significantly influences current life expectancy, health expenditures have a negligible negative impact. Prior education positively impacts life expectancy but negatively impacts prior labor force engagement. These results reject the hypothesis that increasing life expectancy causes current health expenditure to increase.

This research aims to build an appropriate leadership model for regional heads in mitigating disasters due to climate change that is occurring in Papua. Papua Island is one of the islands that is included in disaster-prone areas, namely earthquakes, flash floods, tidal floods and landslides. This disaster occurred due to Papua’s geological conditions in the form of activity on the Indo-Australian plate (southern part) and the Pacific plate (north-eastern part). Exploitation of nature carried out by companies and communities themselves in a particular area has an impact on the balance of the natural ecosystem. So far, disaster management has only focused on emergency response. Aid movements coordinated by ordinary people also focus more on raising aid for emergency situations. In fact, comprehensive disaster management includes before, during and after a disaster occurs. So a combination of leadership styles is needed that must be carried out at each phase of a disaster so that the right model can be produced. The results of this research found that the leadership model of regional heads in mitigating climate change in Papua is in accordance with the disaster management cycle with leadership styles, and traditional Papuan leadership styles. This combination is called a collaborative leadership model for disaster management in Papua. It is hoped that by implementing this model, climate change disaster mitigation can be effective.

Distributed Energy Resources (DERs), such as solar photovoltaic (PV) systems, wind turbines, and energy storage systems, offer many benefits, including increased energy efficiency, sustainability, and grid reliability. However, their integration into the smart grid also introduces new vulnerabilities to cyber threats. The smart grid is becoming more digitalized, with advanced technologies like Internet of Things (IoT) devices, communication networks, and automation systems that enable the integration of DER systems. While this enhances grid efficiency and control, it creates more entry points for attackers and thus expands the attack surface for potential cyber threats. Protecting DERs from cyberattacks is crucial to maintaining the overall reliability, security, and privacy of the smart grid. The adopted cybersecurity strategies should not only address current threats but also anticipate future dangers. This requires ongoing risk assessments, staying updated on emerging threats, and being prepared to adapt cybersecurity measures accordingly. This paper highlights some critical points regarding the importance of cybersecurity for Distributed Energy Resources (DERs) and the evolving landscape of the smart grid. This research study shows that there is need for a proactive and adaptable cybersecurity approach that encompasses prevention, detection, response, and recovery to safeguard these critical energy systems against cyber threats, both today and in the future. This work serves as a valuable tool in enhancing the cybersecurity posture of utilities and grid-connected DER owners and operators. It allows them to make informed decisions, protect critical infrastructure, and ensure the reliability and security of grid-connected DER systems in an evolving energy landscape.

Introduction: Many detrimental effects on employees’ health and wellbeing might result from inadequate illumination in the workplace. Headaches and trouble focusing can result from eye strain brought on by inadequate illumination. The purpose of this study was to simulate and optimize workplace illumination in the ceramic industry. Materials and methods: A common Luxmeter ST-1300 was used to measure the illumination in seven workplaces at a height of 100 cm above the floor. DIALux evo version 7.1 software was used to simulate the illumination of workplaces. To optimize the illumination conditions, a numerical experiment design consisting of 16 scenarios was used for each of the workplaces. Four factors were considered for each scenario: luminaire height, number of luminaires, luminous flux, and light loss factor. The Design-Expert program version 13.0.5.0 was applied for developing the scenarios. Finally, by developing quadratic models for each workplace, the optimization process was implemented. Results: Every workplace had illumination levels that were measured to be between 250 and 300 lux. Instead of using compact fluorescent luminaires, LED technology was recommended to maximize the illumination conditions for the workers. Following optimization, 376 lux of illumination were visible at each workstation in every workspace. For the majority of the workspaces, the simulated illumination was expected to have a desirability degree greater than 0.9. The uniformity and illumination of the workplace were significantly impacted by the two factors of luminaire height and luminaire count. Conclusion: The primary outcomes of this optimization were the environmental, political, and socioeconomic ones, including reduced consumption power, high light flux, and environmental compatibility. Nonetheless, the optimization technique applied in this work can be applied to the design of similar situations, such as residential infrastructure.

The telecommunications services market faces essential challenges in an increasingly flexible and customer-adaptable environment. Research has highlighted that the monopolization of the spectrum by one operator reduces competition and negatively impacts users and the general dynamics of the sector. This article aims to present a proposal to predict the number of users, the level of traffic, and the operators’ income in the telecommunications market using artificial intelligence. Deep Learning (DL) is implemented through a Long-Short Term Memory (LSTM) as a prediction technique. The database used corresponds to the users, revenues, and traffic of 15 network operators obtained from the Communications Regulation Commission of the Republic of Colombia. The ability of LSTMs to handle temporal sequences, long-term dependencies, adaptability to changes, and complex data management makes them an excellent strategy for predicting and forecasting the telecom market. Various works involve LSTM and telecommunications. However, many questions remain in prediction. Various strategies can be proposed, and continued research should focus on providing cognitive engines to address further challenges. MATLAB is used for the design and subsequent implementation. The low Root Mean Squared Error (RMSE) values and the acceptable levels of Mean Absolute Percentage Error (MAPE), especially in an environment characterized by high variability in the number of users, support the conclusion that the implemented model exhibits excellent performance in terms of precision in the prediction process in both open-loop and closed-loop.