The main objective of the study is to discuss the application of a participatory approach that involves the community of a small rural area in Italy to develop and maintain a sustainable local food system based on a very ancient and high-quality typical local bean. The efficacy of the approach in terms of the active involvement of local actors (farming communities, local administration, social associations, and civil society) and knowledge transfer for preserving the local food culture has been demonstrated. Possible improvements to the approach through digital technologies for stimulating the effective engagement of teenagers have also been discussed.

Since its inception in 2013, “The Belt and Road Initiative” has become an important engine driving global economic growth. The initiative has not only promoted infrastructure construction in countries along the Belt and Road but also strengthened financial integration, unimpeded trade, people-to-people exchanges, and policy communication. In this context, higher education, as an important avenue for talent training and scientific and technological innovation, is of great significance to promoting the economic and social development of countries along the Belt and Road. By strengthening academic cooperation with Chinese universities, Kyrgyzstan can enhance its curriculum, adopt advanced teaching methods, and integrate cutting-edge research to foster more skilled labor. In addition, innovation and technology transfer through higher education partnerships can drive sustainable economic growth and diversification. This paper explores the strategic path of integrating higher education into the Belt and Road. Initiative, focusing on academic collaboration, enhancing R&D capabilities, and fostering an entrepreneurial ecosystem.

The study evaluates to what extent logistics performance and its components impact Vietnam’s bilateral export value. The augmented Gravity model is applied on panel data in the period from 2010 to 2018. Logistics efficiency is measured by Logistic performance index (LPI) and its sub-indices developed by the World Bank. A variety of diagnostic tests and estimation methods are employed to ensure the stability of the results. The main findings confirm that all explanatory variables demonstrate the expected signs, and aggregate logistics performance and its sub-indices have positive impacts on Vietnam’s export flows, with the magnitude of logistics impacts is greater than other factors in the research model. Among LPI components of Vietnam, Ease of arranging shipments index is the most influential factor on exports, followed by Infrastructure, Timeliness, and Quality of logistics services. These export’s effects are also identified by partners’ LPI indicators namely Quality of logistics services, Customs, Infrastructure, and Tracking and tracing.

This study aims to structure guidelines for an intervention model from the perspective of Integral Project Management to improve the competitiveness level of cacao associations in south region of Colombia. The research followed a mixed-method approach with a non-experimental cross-sectional design and a descriptive scope. The study employed a stage-based analytical framework which included: identifying the factors influencing the competitiveness of the cacao sector; grouping these factors under the six primary determinants of competitiveness with reference to Porter’s Diamond Model; and proposing guidelines for an intervention model to enhance the competitiveness of the studied associations through project management. The first stage was conducted via literature review. The second stage involved primary data collected through surveys and interviews with the associations, members, and cacao sector experts in Huila. The third stage entailed grouping the factors within the main determinants that promote and limit the competitiveness of the cacao sector in the context of Porter’s Diamond Model. Based on the analysis of the corresponding restrictive and promoting factors, strategic recommendations were formulated for the various sector stakeholders on the measures that can be adopted to address restrictive factors and maintain promoting factors to enhance and sustain the sector's competitiveness.

This study conducts a comparative analysis of various machine learning and deep learning models for predicting order quantities in supply chain tiers. The models employed include XGBoost, Random Forest, CNN-BiLSTM, Linear Regression, Support Vector Regression (SVR), K-Nearest Neighbors (KNN), Multi-Layer Perceptron (MLP), Recurrent Neural Network (RNN), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Conv1D-BiLSTM, Attention-LSTM, Transformer, and LSTM-CNN hybrid models. Experimental results show that the XGBoost, Random Forest, CNN-BiLSTM, and MLP models exhibit superior predictive performance. In particular, the XGBoost model demonstrates the best results across all performance metrics, attributed to its effective learning of complex data patterns and variable interactions. Although the KNN model also shows perfect predictions with zero error values, this indicates a need for further review of data processing procedures or model validation methods. Conversely, the BiLSTM, BiGRU, and Transformer models exhibit relatively lower performance. Models with moderate performance include Linear Regression, RNN, Conv1D-BiLSTM, Attention-LSTM, and the LSTM-CNN hybrid model, all displaying relatively higher errors and lower coefficients of determination (R²). As a result, tree-based models (XGBoost, Random Forest) and certain deep learning models like CNN-BiLSTM are found to be effective for predicting order quantities in supply chain tiers. In contrast, RNN-based models (BiLSTM, BiGRU) and the Transformer show relatively lower predictive power. Based on these results, we suggest that tree-based models and CNN-based deep learning models should be prioritized when selecting predictive models in practical applications.

This study deals with the impact of Vietnam bank size, loans, credit risk, and liquidity on Vietnam banks’ net interest margin, which are crucial for economic development. High profit margins result in a lower bad debt ratio due to timely loan collection and good liquidity. This study applies a panel data model to evaluate the relationship among bank size, loans, credit risk, liquidity, and marginal profitability, which are increasingly important in commercial bank growth. Data were collected from 2010 to 2022, and test methods were applied to select a good-fit model. Realizing that the factors that have a close correlation and affect the profit margin are 33.6% and 16.07%, 75.2%, 37.51%, 64.30%, and 41.11%, and R2 is 59.04%, respectively, this suggests that financial managers need to develop appropriate strategies and policies to adjust the factors that adversely affect commercial bank profitability.