The usage of cybersecurity is growing steadily because it is beneficial to us. When people use cybersecurity, they can easily protect their valuable data. Today, everyone is connected through the internet. It’s much easier for a thief to connect important data through cyber-attacks. Everyone needs cybersecurity to protect their precious personal data and sustainable infrastructure development in data science. However, systems protecting our data using the existing cybersecurity systems is difficult. There are different types of cybersecurity threats. It can be phishing, malware, ransomware, and so on. To prevent these attacks, people need advanced cybersecurity systems. Many software helps to prevent cyber-attacks. However, these are not able to early detect suspicious internet threat exchanges. This research used machine learning models in cybersecurity to enhance threat detection. Reducing cyberattacks internet and enhancing data protection; this system makes it possible to browse anywhere through the internet securely. The Kaggle dataset was collected to build technology to detect untrustworthy online threat exchanges early. To obtain better results and accuracy, a few pre-processing approaches were applied. Feature engineering is applied to the dataset to improve the quality of data. Ultimately, the random forest, gradient boosting, XGBoost, and Light GBM were used to achieve our goal. Random forest obtained 96% accuracy, which is the best and helpful to get a good outcome for the social development in the cybersecurity system.

This study aims to examine the role of automotive industry development in the regional growth of Hungarian counties. Through word frequency analysis, the counties were grouped, and their unique characteristics were highlighted. Some counties already play a prominent role in the domestic automotive industry hosting established Original Equipment Manufacturers (OEMs), a significant number of automotive suppliers and high R&D and innovation potential. Another group includes counties that currently lack a significant automotive industry and did not identify it as a key focus area for future development. Additionally, an intermediate group has also emerged, including counties where the automotive industry is either in its early stages of investment, or such development is prioritized in regional planning documents. The study details the direction of automotive development in counties where the industry plays a significant role, focusing on labor market characteristics and human resource development. The findings have significant implications for the future of the automotive industry in these counties, underlining the urgent and immediate need for well-managed and well-established human resource development and ensuring effective partnership to realize its full potential in the automotive industry.

Air cargo transportation accounts for less than 1% of the global trade volume, yet it represents approximately 35% of the total value of goods transported, highlighting its strategic importance in trade and economic development. This study investigates the relationship between domestic air cargo transport in Brazil and key macroeconomic variables, focusing on how regional economic dynamism, logistical infrastructure, and population density impact the country’s development. Using a panel data regression model covering the period from 2000 to 2020, the study analyzes the evolution of air cargo transportation and its role in redistributing economic growth across Brazil’s regions. The findings emphasize the key factors influencing the air cargo sector and demonstrate how these factors can be leveraged to optimize public policies and business strategies. This research provides valuable insights into the relevance of air cargo transportation for regional and national development, particularly in emerging economies like Brazil, offering guidance for the formulation of strategies that promote balanced economic growth across regions.

This study investigates the impact of corporate carbon performance on financing costs, focusing on S&P 500 companies from 2015 to 2022. Utilizing a fixed-effects regression model, the research reveals a complex U-shaped nonlinear relationship between carbon intensity (CI) and cost of debt (COD). The sample comprises 2896 firm-year observations, with CI measured by the ratio of Scope 1 and 2 greenhouse gas (GHG) emissions to annual sales. The findings indicate that companies with higher CI initially face increased COD due to heightened regulatory and operational risks. However, as CI falls below a certain threshold, further reductions in emissions can paradoxically lead to increased COD, likely due to the substantial investments required for advanced technologies. Additionally, a positive relationship between CI and cost of equity (COE) is observed, suggesting that shareholders demand higher returns from companies with greater environmental risks. These results underscore the importance of balancing short-term and long-term environmental strategies. The study highlights the need for corporate managers to communicate the long-term benefits of environmental efforts effectively to creditors and investors. Policymakers should consider these dynamics when designing regulations that incentivize lower carbon emissions.

Research issue: The study is driven by contemporary global challenges regarding the stability and efficiency of production processes, the necessity to enhance competitiveness, and ensuring workplace safety, which demands a systematic approach to monitoring and supervising adherence to labour discipline. The research is theoretical in nature. The aim/objective of the study is to analyse the specifics of state policy on supervision and control over employees’ adherence to labour discipline, the peculiarities of its practical implementation, and perspectives for improvement. Method: The study employed a logical-semantic method, analytical and documentary methods of analysis, and the method of expert assessment of labour discipline of employees and employers based on their evaluation of certain aspects of labour discipline. The research methodology included a sample size of 30 respondents, and the research instrument was expert evaluation. Data collection was conducted through surveys, and the calculation method was quantitative. Results: The article examines the impact of the main incentives and methods on ensuring labour discipline, determining their essence and forms of manifestation. It also considers the extent of application of each method in enterprise practices. It was found that economic methods are widely used and aimed at increasing employee motivation and maintaining their labour discipline. The analysis revealed that the main manifestations of employee labour discipline and managerial duties are differences in the perception of labour discipline by both parties. It was found that employers underestimate the productivity and abilities of employees, indicating potential systemic deficiencies in human resource management. Conversely, employees note that managers ignore their needs and problems. The results of the expert evaluation showed that employees rated their discipline higher than employers did. These discrepancies in evaluations could affect internal relations within the team and require managerial attention to improve interaction and cooperation. Conclusion: Based on the assessment of labour discipline, systemic deficiencies in human resource management were identified, highlighting the need for appropriate monitoring and employee motivation mechanisms. The study proposes innovative personnel management methods to ensure labour discipline in enterprises, including HR branding, team building, mentoring, and grading. It is proven that these approaches allow for the creation of a fundamentally new management system to ensure compliance with labour discipline and the development of professionalism and employee motivation.

The COVID-19 epidemic has given rise to a new situation that requires the qualification and training of teachers to operate in educational crises. Amidst the pandemic, online training has emerged as the predominant approach for delivering teacher training. The COVID-19 pandemic has created potential opportunities and challenges for online training, which may have a long-lasting impact on online training procedures in the post-pandemic era. This study aims to determine the primary potential and constraints of online training as seen by instructors. The Technology Acceptance Model (TAM) identified online training opportunities and challenges by examining the to-be-applied behavioral intention variables that influence trainees. These variables include individual, system, social, and organizational factors. The study has applied the Phenomenological technique to address the research issues, using the Semi-structured interview tool to get a comprehensive knowledge of the online training phenomena amongst the pandemic. A total of seven participants were selected from a list of general education teachers at the Central Education Office of the Education Department in Bisha Governorate. These people were deliberately selected because of their high frequency of completing training sessions throughout the epidemic. A series of interviews was conducted with these participants. The findings indicated that the primary prospects included both equal opportunities and digital culture within the individual factors, enrollment in training programs and variation in training programs across organizational characteristics, the use of digital material and electronic archiving within the system variables, engaging in the exchange of personal experiences, providing constructive criticism, and fostering favorable communication within the realm of social factors. However, the primary obstacles included deficiencies in digital competencies, compatibility of trainees’ attributes, and dearth of desire as per individual factors, the temporal arrangement of training programs, as well as the lack of prior preparation and preparedness within the realm of organizational factors. Other challenges included the absence of trainer assessment, limited diversity of training exercises, and technological obstacles within the system factors, and ultimately the absence of engagement with the instructor, and lack of engagement with peers are within the social variable.