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.

The freight transport chain brings together several types of players, particularly upstream and downstream players, where it is connected to both nodal and linear logistics infrastructures. The territorial anchoring of the latter depends on a good level of collaboration between the various players. In addition to the flow of goods from various localities in the area, the Autonomous Port of Lomé generates major flows to and through the port city of Lomé, which raises questions about the sustainability of these various flows, which share the road with passenger transport flows. The aim of this study is to analyse the challenges associated with the sustainability of goods flows. The methodology is based on direct observations of incoming and outgoing flows in the Greater Lomé Autonomous District (DAGL) and semi-directive interviews with the main players in urban transport and logistics. The results show that the three main challenges to the sustainability of goods transport are congestion (28%), road deterioration (22%) and lack of parking space (18%).

This project analyzes the evolution of the manufacturing sector in Portugal from 2009 to 2021, focusing on the variations in the number of active companies across various subcategories, such as food, textiles, and metal product industries. The goal of this analysis is to understand the dynamics of growth and contraction within each sector, providing insights for companies to adjust their market and operational strategies. Key objectives include analyzing the overall evolution in the number of companies, identifying subcategories with notable changes, and providing a comprehensive analysis of observed trends and patterns. The study is based on data from PORDATA 2024, and the research employs temporal trend analysis, linear and quadratic regression, and the Pareto representation to identify patterns of growth and decline. By comparing annual data, the project uncovers periods of growth and decline, allowing for a deeper understanding of the sector’s dynamics. The findings also highlight variations in periods of economic crises and during the Covid-19 pandemic, and recommendations for action are presented to support businesses resilience and continuity. These results are valuable for companies within the manufacturing sectors analyzed and policy makers, guiding strategic decisions to navigate the complexities of the market dynamics and to ensuring long-term organizational sustainable success.

Entrepreneurial Orientation (EO) emphasizes the identification and exploitation of business opportunities, while entrepreneurial action learning (EAL) underscores the acquisition of knowledge through practical experience and continuous improvement. Breakthroughs in both aspects contribute to maintaining flexibility, adapting to changes, and enabling success in competitive markets. The key to the development of small and medium-sized enterprises (SMEs) lies in a clear Entrepreneurial Orientation, a focus on Entrepreneurial Action Learning, and the cultivation of innovation spirit through continuous practice and experience accumulation, thereby enhancing entrepreneurial performance (EP). This study aims to explore the impact of Entrepreneurial Orientation on the Entrepreneurial Performance of SMEs, clarify the mediating role of Entrepreneurial Action Learning between Entrepreneurial Orientation and Entrepreneurial Performance, and investigate the variability of Entrepreneurial Performance among different industries. By means of data collection from 598 SMEs, data analysis was conducted using Structural Equation Modeling (SEM) and Analysis of Variance (ANOVA). The analysis results indicate that entrepreneurial orientation has a positive impact on entrepreneurial action learning and entrepreneurial performance, and entrepreneurial action learning has a positive impact on entrepreneurial performance. The study also found that entrepreneurial action learning partially mediates the relationship between entrepreneurial orientation and entrepreneurial performance. There are certain differences in entrepreneurial performance among different industries. This study enriches the relevant literature in the field of entrepreneurship. Additionally, research on entrepreneurial orientation, entrepreneurial action learning, and entrepreneurial performance in specific regional contexts is very limited, making this study valuable for subsequent research in related areas.

Work can be demanding, imposing challenges that can be detrimental to the job performance of employees. Efforts are therefore underway to develop practices and initiatives that may improve job performance and well-being. These include interventions based on mindfulness, inclusive leadership and work engagement. In the present study, authors have presented an association of inclusive leadership and mindfulness towards job performance through employee work engagement among secondary teachers in the context of Hong Kong. The sample size of 263 teachers working from three secondary schools in Sha Tin, Hong Kong has been incorporated in this study. A structured questionnaire designed on a 5-point Likert scale has been used based on purposive sampling by analysis of IBM SPSS 27 and Smart PLS version 4.0.9 by applying a structural equation modelling approach (SEM). The results indicated a strong positive influence on employee work engagement and job performance. Moreover, the bootstrap investigation showed that mindfulness and inclusive leadership were significantly associated with employees’ work engagement in the presence of mediators’ work engagement. This study adds to the very scarce literature on inclusive leadership and mindfulness. In addition, this research is the first study to test the mindfulness skill, inclusive leadership and job performance relationship. Furthermore, this is the first study to explore the concept of mindfulness and inclusive leadership in the Hong Kong context. Moreover, the findings of this research can be beneficial for future theory development on mindfulness skill and inclusive leadership in cross-cultural contexts.