The United States, Mexico, and Canada (USMCA) seek to promote fair wages and adequate working conditions, especially in Mexico, by strengthening labor rights and freedom of association. The objective of this research is to determine the factors that influence salary levels in the Mexican Automotive Industry (MAI), through a causality analysis in the Granger sense, to generate a panorama that allows a decision-making process in the Mexican salary policy. With data from the National Institute of Statistics and Geography, the Bank of Mexico and Statista, autoregressive vector models were estimated to determine causalities in the Granger sense. It was proven that minimum wage, employed personnel, production, total sales, and exports are some causes of remuneration in the sector, with the minimum wage being the most significant. The above suggests that the salary increase involves several actors, such as the government (minimum wage), the organization (production, sales and exports) and the market (employed personnel), therefore, the design of appropriate labor policies will contribute to the dignification of salaries inside the MAI.

The present research is on the propagation of Rayleigh waves in a homogenous thermoelastic solid half-space by considering the compact form of six different theories of thermoelasticity. The medium is subjected to an insulated boundary surface that is free from normal stress, tangential stress, and a temperature gradient normal to the surface. After developing a mathematical model, a dispersion equation is obtained with irrational terms. To apply the algebraic method, this equation must be converted into a rational polynomial equation. From this, only those roots are filtered out, which has satisfied both of the above equations for the propagation of waves decaying with depth. With the help of these roots, different characteristics are computed numerically, like phase velocity, attenuation coefficient, and path of particles. Various particular cases are compared graphically by using phase velocity and attenuation coefficient. The elliptic path of surface particles in Rayleigh wave propagation is also presented for the different theories using physical constants of copper material for different depths and thermal conductivity.

It is critical for urban and regional planners to examine spatial relationships and interactions between a port and its surrounding urban areas within a region’s spatial structure. This paper seeks to develop a targeted framework of causal relationships influencing the spatial structure changes in the Bushehr port-city. Hence, the study utilizes Fuzzy Cognitive Maps (FCMs), a computational technique adept at analyzing complex decision-making processes. FCMs are employed to identify concepts that act as drivers or barriers in the spatial structure changes of Bushehr port-city, thereby elucidating the causal relationships within this context. Additionally, the study evaluates these concepts’ relative significance and interrelationships. Data was collected through interviews with ten experts from diverse backgrounds, including specialists, academics, policymakers, and urban managers. The insights from these experts were analyzed using FCMapper and Pajek software to construct a collective FCM, which depicts the influential and affected concepts within the system. The resulting collective FCM consists of 16 concepts, representing the varied perspectives and expertise of the participants. Among these, the concepts of management and planning reform, economic growth of the city-port, and port development emerged as the three most central concepts. Moreover, the effects of all influential concepts on the spatial structure change in Bushehr port-city were evaluated through simulations conducted across four different scenarios. The analysis demonstrated that the system experiences the most significant impact under the fourth scenario, where the most substantial changes are observed in commercial and industrial growth and the planning of port-city separation policies.

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.

This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.