Global economic competition is leading companies to improve their competitiveness by increasing production and eliminating the main obstacles to the process of making products available. This approach concerns both SMEs and SMIs as well as multinationals. Thus, the Compagnie Minière de l’Ogooué (COMILOG), a subsidiary of the French group ERAMET, which until recently had a monopoly on manganese mining in Gabon, must now face competition from Asian operators. To export its ore, COMILOG must first transport it by rail for nearly 650 km, from the Moanda site (south-east of the country) to the port of Owendo. However, port operations, which until then took place exclusively during the day, limited the company’s export capacities and the profits made, while increasing the stopover time of ships and their operating costs. To remedy this, the French company introduced nighttime docking and departures. This work addresses the challenges of the performance of port operations at the Owendo ore terminal and the security and natural risks of night manoeuvres. The general objective of the study is to assess the impact of these night services on ship traffic, on the one hand, and to identify the related socio-economic and security issues, on the other hand. Data collection was carried out using documentary research in libraries and research centres, consultation of websites, semi-directed interviews, questionnaire surveys and participatory observation. The sample of 50 people surveyed took into account management staff, supervisors and line managers, integrating the diversity of actors involved in the processing of ships calling at the port of Owendo. Finally, the surveys attest to a clear reduction in the time spent by ships at the Owendo Ore Port and an increase in their number calling. They also confirm the improvement in tonnages embarked and the improvement in turnover achieved by COMILOG. This study led to the conclusion that the introduction of night manoeuvres at the port of Owendo allowed COMILOG to increase its exports and the number of ore carriers received in stopover and then improve its turnover.

In order to optimize the environmental factors for cucumber growth, a fertilizer and water control system was designed based on the Internet of Things (IoT) system. The IoT system monitors environmental factors such as temperature, light and soil Ec value, and uses image processing to obtain four growth indicators such as cucumber stem height, stem diameter size, number of leaves and number of fruit set to establish a single growth indicator model for temperature, light, soil Ec value and growth stage, and the four growth indicators were fused to obtain the comprehensive growth indicator I_c for cucumber, and calculates its deviation to determine the cucumber growth status. Based on the integrated growth index I_c of cucumber, a soil Ec control model was established to provide the optimal environment and fertilizer ration for cucumber at different growth stages to achieve stable and high yield of cucumber.

This article explores a method for evaluating the achievement of learning effectiveness based on virtual reality technology. The research analyzed the design and construction of a virtual learning environment, data collection of learner behavior, data analysis and evaluation methods, evaluation indicators and personalized feedback, as well as a case study of a virtual learning evaluation system. By using virtual reality technology to create an immersive learning environment, learners can gain an immersive learning experience, and evaluators can accurately record learners' behavior and performance. The learning effectiveness evaluation method based on virtual reality technology can improve learning effectiveness and teaching quality, promote educational innovation and development. These research results are of great significance for the evaluation of virtual learning effectiveness and personalized teaching in the field of education.

The Science and Technology Innovation Center holds a pivotal position in the national science and technology innovation system, and a scientific evaluation of the “Sci-tech Innovation Center” will guide its construction direction. This study found the advantages and disadvantages of the four cities through comparison; Hence improvement suggestions were proposed for the weaknesses of the four cities. There are two main paths for the government to drive technology innovation: STI (Science and Technology Innovation) mode and DUI (Doing, Using, Interacting) mode. With the aid of the evaluation index system of the Sci-tech Innovation Center, this article uses fuzzy sets, rough sets and fuzzy dynamic clustering methods to comprehensively evaluate the effects of driving technology innovation in the four cities of Beijing, Shanghai, Shenzhen and Guangzhou. The results found that Shenzhen has a significant effect in DUI, and Beijing has a significant effect in STI. The choice of path is related to the abundance of innovation resources.

Vehicle detection stands out as a rapidly developing technology today and is further strengthened by deep learning algorithms. This technology is critical in traffic management, automated driving systems, security, urban planning, environmental impacts, transportation, and emergency response applications. Vehicle detection, which is used in many application areas such as monitoring traffic flow, assessing density, increasing security, and vehicle detection in automatic driving systems, makes an effective contribution to a wide range of areas, from urban planning to security measures. Moreover, the integration of this technology represents an important step for the development of smart cities and sustainable urban life. Deep learning models, especially algorithms such as You Only Look Once version 5 (YOLOv5) and You Only Look Once version 8 (YOLOv8), show effective vehicle detection results with satellite image data. According to the comparisons, the precision and recall values of the YOLOv5 model are 1.63% and 2.49% higher, respectively, than the YOLOv8 model. The reason for this difference is that the YOLOv8 model makes more sensitive vehicle detection than the YOLOv5. In the comparison based on the F1 score, the F1 score of YOLOv5 was measured as 0.958, while the F1 score of YOLOv8 was measured as 0.938. Ignoring sensitivity amounts, the increase in F1 score of YOLOv8 compared to YOLOv5 was found to be 0.06%.

Strategically managing production systems is crucial for creating value and enhancing the competitive capabilities of companies. However, research on organizational culture within these systems is scarce, particularly in the Colombian context. This research aims to evaluate cultural profiles and their impact on the performance of production systems in Colombian firms. The regional focus is vital as cultural and contextual factors can vary significantly between regions, influencing organizational behavior and performance outcomes. To achieve this, we make a study in a sample of Colombian companies, with participation from working students of the Universidad Nacional Abierta y a Distancia (UNAD). We used a data analytics approach to collected data. The results will be relevant to both the scientific community and business practitioners. This research seeks to determine whether the perception of the work environment within a company influences the perceived performance of the company. The findings will provide a deeper understanding of the relationship between organizational culture and production system performance, offering a foundation for business decision-making and enhancing competitiveness in Latin American context.