This work investigates epoxy composites reinforced by randomly oriented, short glass fibres and silica microparticles. A full-factorial experiment evaluates the effects of glass fibre mass fraction (15 wt% and 20 wt%) and length (5 mm and 10 mm), and the mass fraction of silica microparticles (5 wt% and 10 wt%) on the apparent density and porosity, as well as the compressive and tensile strength and modulus of the hybrid composites. Hybrid epoxy composites present significantly higher tensile strength (9%) and modulus (57%), as well as compressive strength (up to 15%) relative to pure epoxy.

A logistics service company in Batam faces challenges related to warehouse load fulfillment and sorting inaccuracies. This study aims to identify proposed efficiency improvements to the goods distribution system using the cross-docking method. The research method chosen is cross-docking, a technique that eliminates the storage process in the warehouse, thus saving time and cost. The research findings show significant benefits, especially in achieving zero inventory efficiency. Data processing and discussion revealed that efficiencies were apparent by increasing the sorting tables from 1 to 6, with an output of 90,000 kg during aircraft loading and unloading (compared to approximately 77,000 kilograms). This efficiency arises from the larger output of the sorting tables compared to the input, eliminating the need for warehousing and adding ten trucks. As a result, the shipment can be completed in one trip, with no goods stored in the warehouse. The analysis shows that implementing cross-docking in the company increases efficiency in distributing goods to forwarding partners.

This study examines the relationship between Russian FDI carried out by large MNCs and investment development path (IDP). Although statistical analysis does not establish a significant relationship between outward FDI and GDP, the behavior of Russian outward FDI contradicts traditional models. Two primary factors contribute to this paradox. First, the complex business environment in Russia, characterized by a combination of both improvements and contradictions, has a significant impact on outward FDI behavior. Secondly, the duality of the Russian economy and society plays a decisive role. This segment resembles a high-income country with ample resources, while most face lower income levels, raising concerns about wealth distribution. Historical factors, including Russia’s transition from a state-controlled to a market-oriented economy, contribute to the internationalization of Russian MNCs. Both state-owned enterprises and privatized firms are influenced by the state, although to varying degrees. Government involvement in international business strategies increases the knowledge and experience of Russian MNCs, but also raises concerns about political influence.

This contribution questions young people’s access to digital networks at the scale of intermediate cities in Saint-Louis. Thus, it analyzes the prescriptions of digital actors responsible for the development of digital economy in relation with the orientations of the Senegal Digital 2025 strategy. This is a pretex to highlight the gaps between official political discourses and the level of deployment of digital infrastructures. The study highlights the need to repoliticize the needs of populations for broadband and very high-speed connections to promote local initiatives for youth participation in Saint-Louis. Indeed, datas relating to access and use of the Internet by young people reveal inequalities linked to household income, the disparity of infrastructure and digital equipment, and the discontinuity in neighborhood development, but also to the adaptability of the internet service marketed. Through urban and explanatory sociology mobilized through the approach of young people’s real access to the Internet, our analyzes have shown at the scale of urban neighborhoods the impact of the actions recommended by those involved in the development of populations’ access to Internet. The result is that the majority of young people are forced to access the Internet through medium-speed mobile networks.

Infrastructure investment has long been held as an accelerator or a driver of the economy. Internationally, the UK ranks poorly with the performance of infrastructure and ranks in the lower percentile for both infrastructure investment and GDP growth rate amongst comparative nations. Faced with the uncertainty of Brexit and the likely negative economic impact this will bring, infrastructure investment may be used to strengthen the UK economy. This study aims to examine how infrastructure funding impacts economic growth and how best the UK can maximize this potential by building on existing work.
The research method is based on interviews carried out with respondents involved in infrastructure operating across various sectors. The findings show that investment in infrastructure is vital in the UK as it stimulates economic growth through employment creation due to factor productivity. However, it is critical for investment to be directed to regional opportunity areas with the potential to unlock economic growth and maximize returns whilst stimulating further growth to benefit other regions. There is also a need for policy consistency and to review UK infrastructure policy to streamline the process and to reduce cost and time overrun, with Brexit likely to impact negatively on infrastructure investment.

In this study, we utilized a convolutional neural network (CNN) trained on microscopic images encompassing the SARS-CoV-2 virus, the protozoan parasite “plasmodium falciparum” (causing of malaria in humans), the bacterium “vibrio cholerae” (which produces the cholera disease) and non-infected samples (healthy persons) to effectively classify and predict epidemics. The findings showed promising results in both classification and prediction tasks. We quantitatively compared the obtained results by using CNN with those attained employing the support vector machine. Notably, the accuracy in prediction reached 97.5% when using convolutional neural network algorithms.