The livelihood of ethnic minority households in Vietnam is mainly in the fields of agriculture and forestry. The percentage of ethnic minorities who have jobs in industry, construction, and services is still limited. Moreover, due to harsh climate conditions, limited resources, poor market access, low education level, lack of investment capital for production, and inadequate policies, job opportunities in the off-farm and non-farm activities are very limited among ethnic minority areas. This paper assessed the contribution of livelihood diversification activities to poverty reduction of ethnic minority households in Son La Province of Vietnam. The analysis was based on the data using three stages sampling procedure of 240 ethnic minority households in Son La Province. The finding showed that the livelihood diversification activities had positively significant contribution to poverty reduction of ethnic minority households in Son La Province. In addition, the factors positively affecting the livelihood choices of ethnic minority households in Son La Province of Vietnam are education level, labor size, access to credit, membership of associations, support policies, vocational training, and district. Thus, improving ethnic minority householder’s knowledge through formal educational and training, expanding availability of accessible infrastructure, and enhancing participation of social/political associations were recommended as possible policy interventions to diversify livelihood activities so as to mitigate the level of poverty in the study area.

The study examines the factors shaping inflation in 2022–2023 and explores why inflation in the Hungarian economy has increased more sharply than in neighboring countries with similar structures. The research hypothesis suggests that the inflationary surge, which is notable both globally and within the European Union, is not solely due to market economy mechanisms, but also to specific circumstances in Hungary, including the state’s radical interventions aimed at curbing inflation. The study seeks to highlight these effects and provide recommendations for economic policymakers to develop a more resilient inflation policy. Additionally, it focuses on analyzing inflation in the agricultural sector. The results indicate that, alongside global inflationary pressures, several country-specific factors have driven up the inflation rate in Hungary. Energy prices have risen sharply, and some supply chains from the East have been disrupted. The country under study is less productive, and the impact of the energy price shock on the energy-intensive food industry is higher than in surrounding countries. Consequently, the exchange rate volatility in 2022–2023, combined with short- and medium-term factors, has had a significant impact on food inflation, causing substantial deviations from long-term equilibrium. The research concludes that, in addition to increasing food self-sufficiency, special attention should be given to the domestic development of the agricultural supply chain.

The emergence of the COVID-19 pandemic led to the need to move educational processes to virtual environments and increase the use of digital tools for different teaching uses. This led to a change in the habits of using information and communication technologies (ICT), especially in higher education. This work analyzes the impact of the COVID-19 pandemic on the frequency of use of different ICT tools in a sample of 950 Latin American university professors while focusing on the area of knowledge of the participating professors. To this end, a validated questionnaire has been used, the responses of which have been statistically analyzed. As a result, it has been proven that participants give high ratings to ICT but show insufficient digital competences for its use. The use of ICT tools has increased in all areas after the pandemic but in a diverse way. Differences have been identified in the areas of knowledge regarding the use of ICT for different uses before the pandemic. In this sense, the results suggest that Humanities professors are the ones who least use ICT for didactic purposes. On the other hand, after the pandemic, the use of ICT for communication purposes has been homogenized among the different knowledge areas.

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.

The endogenous, human, and social factors influencing the economic development of the municipalities of San Juan Cotzocón and San Pedro y San Pablo Ayutla in the Istmo de Tehuantepec region of the state of Oaxaca are analyzed. The hypothesis posits that the dimensions of endogenous development, social capital, and human capital directly impact the economic development of the respective municipalities. The study involved administering 262 questionnaires to the residents of these municipalities during the month of May 2023. The collected data were examined using exploratory factor analysis to determine the underlying structure and structural equation modeling to estimate the effects and relationships between variables. Results indicate that endogenous development, social capital, and human capital are factors in the economic development of the studied communities, with endogenous development being the most influential factor due to its statistical significance. Notably, the existence of tourist and cultural attractions in the municipalities emerges as a catalyst for local economic development in response to the establishment and operation of the Isthmus of Tehuantepec Interoceanic Corridor.

In rural areas, land use activities around primary arterial roads influence the road section’s traffic characteristics. Regulations dictate the design of primary arterial roads to accommodate high speeds. Hence, there is a mix of traffic between high-speed vehicles and vulnerable road users (pedestrians, bicycles, and motorcycles) around the land. As a result, researchers have identified several arterial roads in Indonesia as accident-prone areas. Therefore, to improve the road user’s safety on primary arterial roads, it is necessary to develop models of the influence of various factors on road traffic accidents. This research uses binary logistic regression analysis. The independent variables are carelessness, disorderliness, high speed, horizontal alignment, road width, clear zone, road shoulder width, signs, markings, and land use. Meanwhile, the dependent variable is the frequency of accidents, where the frequency of accidents consists of multi-accident vehicles (MAV) and single-accident vehicles (SAV). This study collects data for a traffic accident prediction model based on collision frequency in accident-prone areas. The results, road shoulder width, and road sign factor all have an impact on the frequency of traffic accidents. According to a realistic risk analysis, MAV and SAV have no risk difference. After validation, this model shows a confidence level of 92%. This demonstrates that the model generates estimations that accurately reflect reality and are applicable to a wider population. This research has the potential to assist engineers in improving road safety on primary arterial roads. In addition, the model can help the government measure the impact of implemented policies and engage the public in traffic accident prevention efforts.