The study explores improving opportunities of forecasting accuracy from the traditional method through advanced forecasting techniques. This enables companies to optimize inventory management, production planning, and reducing the travelling time thorough vehicle route optimization. The article introduced a holistic framework by deploying advanced demand forecasting techniques i.e., AutoRegressive Integrated Moving Average (ARIMA) and Recurrent Neural Network-Long Short-Term Memory (RNN-LSTM) models, and the Vehicle Routing Problem with Time Windows (VRPTW) approach. The actual milk demand data came from the company and two forecasting models, ARIMA and RNN-LSTM, have been deployed using Python Jupyter notebook and compared them in terms of various precision measures. VRPTW established not only the optimal routes for a fleet of six vehicles but also tactical scheduling which contributes to a streamlined and agile raw milk collection process, ensuring a harmonious and resource-efficient operation. The proposed approach succeeded on dropping about 16% of total travel time and capable of making predictions with approximately 2% increased accuracy than before.

A salinity gradient solar pond (SGSP) is a large and deep artificial basin of layered brine, that collects and stores simultaneous solar energy for use in various applications. Experimental and theoretical studies have been launched to understand the thermal behavior of SGSPs, under different operating conditions. This article then traces the history of SGSPs, from their natural discovery to their current artificial applications and the progress of studies and research, according to their chronological sequence, in terms of determining their physical and dynamic aspects, their operation, management, and maintenance. It has extensively covered the theoretical and experimental studies, as well as the direct and laboratory applications of this technology, especially the most famous and influential in this field, classified according to the aspect covered by the study, with a comparison between the different results obtained. In addition, it highlighted the latest methods to improve the performance of an SGSP and facilitate its operation, such as the use of a magnetic field and the adoption of remote data acquisition, with the aim of expanding research and enhancing the benefit of this technology.

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%.

This study examines the spatial distribution of consumption competitiveness and carrying capacity across regions, exploring their interrelationship and implications for sustainable regional development. An evaluation index system is constructed for both consumption competitiveness and carrying capacity using a range of economic, social, and environmental indicators. We apply this framework to regional data in China and analyze the resultant spatial patterns. The findings reveal significant regional disparities: areas with strong consumption competitiveness are often concentrated in economically developed regions, while high carrying capacity is notable in less populated or resource-rich areas. Notably, a mismatch emerges in some regions—high consumer demand is not always supported by adequate carrying capacity, and vice versa. These disparities highlight potential sustainability challenges and opportunities. In the discussion, we address reasons behind the spatial mismatch and propose policy implications to better align consumer market growth with regional resource and environmental capacity. The paper concludes that integrating consumption-driven growth strategies with carrying capacity considerations is essential for balanced and sustainable regional development.

Objective: This study aimed to examine the psychometric properties of the 21-item Depression, Anxiety, and Stress Scale (DASS-21) in a sample of Moroccan students. Method: A total of 208 Moroccan students participated in this study. The dimensionality of the DASS-21 scale was assessed using exploratory factor analysis. Construct validity was assessed using the Stress Perception (PSS-10), State Anxiety (SAI), and Depression (CESD-10) scales. Results: Correlation analyses between Depression, Anxiety, and Stress subscales showed significant results. The exploratory factor analysis results confirmed the DASS’s three-dimensional structure. Furthermore, correlation analyses revealed positive correlations between the DASS-18 sub-dimensions and the three scales for Stress (PSS-10), Anxiety (SAI), and Depression (CESD-10). Conclusion: In line with previous work, the results of this study suggest that the DASS-18 reflect adequate psychometric properties, making it an appropriate tool for use in the university context.