This study explores the determinants of political participation among Thai youth, focusing on the roles of political interest, knowledge, and efficacy. Employing stratified random sampling, data were collected from 191 university students in Bangkok. Structural Equation Modeling (SEM) via Smart PLS was utilized to test hypotheses regarding the direct and mediating effects of political interest and knowledge on participation, highlighting the mediating role of political efficacy. The findings indicate that political efficacy significantly enhances participation, while political interest boosts knowledge significantly but does not directly influence efficacy. Furthermore, political knowledge positively affects efficacy but not participation directly. Notably, the indirect effects of political interest on participation through efficacy alone are insignificant, but the pathways from interest to participation through both knowledge and efficacy, and from knowledge to participation through efficacy, are significant. These results elucidate the complex interactions between political interest, knowledge, and efficacy in shaping the political engagement of Thai youth.

The H3N2 influenza virus is spiking dramatically, which is a major concern worldwide and in India. The multifunctional hetero-trimer influenza virus RNA-dependent RNA polymerase (RdRP) is involved in the generation of viral mRNA and is crucial for viral infectivity, which is directly related to the virus’s ability to survive. The goal of the current work was to use molecular docking to determine how the RdRP protein might be affected by powerful bioactive chemicals found in Calotropis gigantia latex. By applying CB-dock 2 analysis and 2D interactions, an in-silico docking study was conducted using a GC-FID (gas chromatography with flame-ionization detection) based composition profile. Tocospiro A (15%), Amyrin (7%), and Gombasterol A were found by GC-FID to be the main phytocompounds in the latex of Calotropis gigantia. The docking result showed that ligands were effectively bound to RdRP. According to interaction studies, RdRP/ligand complexes create hydrogen bonds, van der Waals forces, pi-alkyl bonds, alkyl bonds, and pi-Sigma bonds. Therefore, it was suggested that Calotropis gigantia latex may represent a possible herbal remedy to attenuate H3N2 infections based on the above findings of the fragrance profile and docking.

A theoretical investigation of the effect of an inverse parabolic potential on third harmonic generation in cylindrical quantum wires is presented. The wave functions are obtained as solutions to Schrödinger equation solved within the effective mass approximation. It turns out that peaks of the third harmonic generation susceptibility (THGS) associated with nanowires of small radii occur at larger photon energies as compared to those associated with quantum wires of larger radii. The inverse parabolic potential red-shifts peaks of the THGS, and suppresses the amplitude of the THGS. THGS associated with higher radial quantum numbers is diminished in magnitude and blue-shifted, as a function of the photon energy. As a function of the inverse parabolic potential, the THGS still characterized by peaks, and the peaks shift to lower values of the potential as the photon energy increases.

Foodborne diseases are a global health problem. Every year, millions of people die worldwide from these diseases. It has been determined that the high prevalence of these diseases is related to unfavorable socioeconomic conditions of the population. In this study, the relationship between foodborne diseases and socioeconomic conditions of the population was determined using principal component analysis as a multivariate statistical analysis technique. In this study, the socioeconomic variables of each Ecuador province and the prevalence of foodborne diseases (hepatitis A, salmonella, shigellosis and typhoid fever) during the years 2018 and 2019 were considered. The results show the relationship between foodborne diseases and the socioeconomic conditions of the population, as well as identifying regions more vulnerable to present high levels of prevalence of foodborne diseases, thus facilitating the implementation of social investment programs to reduce the prevalence of these diseases.

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.