This study aimed to determine the socio-economic poverty status of those living in rural areas using data surveys obtained from household expenditure and income. Machine learning-based classification and clustering models were proven to provide an overview of efforts to determine similarities in poverty characteristics. Efforts to address poverty classification and clustering typically involve comprehensive strategies that aim to improve socio-economic conditions in the affected areas. This research focuses on the combined application of machine learning classification and clustering techniques to analyze poverty. It aims to investigate whether the integration of classification and clustering algorithms can enhance the accuracy of poverty analysis by identifying distinct poverty classes or clusters based on multidimensional indicators. The results showed the superiority of machine learning in mapping poverty in rural areas; therefore, it can be adopted in the private sector and government domains. It is important to have access to relevant and reliable data to apply these machine learning techniques effectively. Data sources may include household surveys, census data, administrative records, satellite imagery, and other socioeconomic indicators. Machine learning classification and clustering analyses are used as a decision support tool to gain an understanding of poverty data from each village. These strategies are also used to describe the profile of poverty clusters in the community in terms of significant socio-economic indicators present in the data. Village clusters based on an analysis of existing poverty indicators are grouped into high, moderate, and low poverty levels. Machine learning can be a valuable tool for analyzing and understanding poverty by classifying individuals or households into different poverty categories and identifying patterns and clusters of poverty. These insights can inform targeted interventions, policy decisions, and resource allocation for poverty reduction programs.

The paper examines the motivations, financing, expansion and challenges of the Belt and Road Initiative (BRI). The BRI was initially designed to address China’s overcapacity and promote economic growth in both China and in countries along the “Belt” and “Road” through infrastructure investment and industrial capacity cooperation. It took into account China’s strategic transition in its opening-up policy and foreign policy to pay more attention to the neighboring countries in Southeast Asia and Central and West Asia when facing greater strategic pressure from the United States in East Asia and the Pacific region. More themes have been added to the initiative’s original framework since its inception in 2013, including the vision of the BRI as China’s major solution to improve international economic cooperation and practice to build a “community of shared future for mankind”, and the idea of the Green Silk Road and the Digital Silk Road. Chinese state-owned enterprises and policy and commercial banks have dominated investment and financing for BRI projects, which explains the root of the problems and risks facing the initiative, such as unsustainable debt, non-transparency, corruption and low economic efficiency. Measures taken by China to tackle these problems, for example, mitigating the debt distress and improving debt sustainability, are unlikely to make a big difference anytime soon due to the tenacity of China’s long-held state-driven investment model.

Paraffin wax is the most common phase change material (PCM) that has been broadly studied, leading to a reliable optimal for thermal energy storage in solar energy applications. The main advantages of paraffin are its high latent heat of fusion and low melting point that appropriate solar thermal energy application. In addition to its accessibility, ease of use, and ability to be stored at room temperature for extended periods of time, Nevertheless, improving its low thermal conductivity is still a big, noticeable challenge in recently published work. In this work, the effect of adding nano-Cu₂O, nano-Al₂O₃ and hybrid nano-Cu₂O-Al₂O₃ (1:1) at different mass concentrations (1, 3, and 5 wt%) on the thermal characteristics of paraffin wax is investigated. The measured results showed that the peak values of thermal conductivity and diffusivity are achieved at a wight concentration of 3% when nano-Cu₂O and nano-Al₂O₃ are added to paraffin wax with significant superiority for nano-Cu₂O. While both of those thermal properties are negatively affected by increasing the concentration beyond this value. The results also showed the excellence of the proposed hybrid nanoparticles compared to nano-Cu₂O and nano-Al₂O₃ as they achieve the highest values of thermal conductivity and diffusivity at a weight concentration of 5.0 wt%.

Solar energy is a reliable and abundant resource for both heating and power generation. The current research examines how the novel class of nano-embedded Bees wax phase change materials (NEBPCMs) improves heat storage qualities. The synthetic NEBPCMs were subjected to experimental testing using, XRD, Bees wax and Al2O3 FESEM. A typical solar water heating system features a flat plate collector unit incorporating Bees Wax phase change material (NEBPCM) combined with varying concentrations of Al2O3 (0.01%, 0.015%, and 0.02%). The absorber plate surface is coated with a Nano-hybrid coating consisting of Black Paint, Al2O3, and additional Fe3O4 at a 2% concentration. Pure water is frequently used in these solar water heaters (SWH), with performance evaluations conducted using different Bees Wax and Al2O3 concentrations of NEBPCM (Bees Wax + Al2O3). The system’s efficiency is assessed across different flow rates (60, 90, and 120 kg/hr) and tilt angles (15, 30, and 45 degrees). This study aims to examine the feasibility of using PCMs to store solar energy for night time water heating, ensuring a continuous supply of hot water maximum efficiency achieved by using NEBPCM in solar water heater 52.26% at a flow rate of 120 Kg/hr, at angle of 45 degrees and Concentration 0.015%.