This study aims to examine how marketing mix and trust theories influence users’ intentions to adopt herbal platform services in Thailand and examine the impact of these intentions on actual service usage, placing a special focus on the integration of technologies in the context. The significant potential for growth in Thailand’s herbal business and the currently underutilized online platforms, it is crucial for stakeholders to understand the determinants of investment intentions. Merging marketing mix and trust theories, this research offers a comprehensive analysis of factors influencing the use of herbal platform, highlighting the relevance of herbal in enhancing service adoption. This study utilized a quantitative approach, gathering data through online surveys from 416 users of online herbal platforms in Thailand using SEM to examine the impact of gender on consumers’ decisions to use these platforms. This study provides insights into effective business strategies for herbal companies and contributes novel perspectives to the literature on herbal services. It specifically examines cognitive and emotional trust impacts and explores gender dynamics within the context of Health development. The study clarifies the roles of these factors and assesses the impact of gender on platform adoption, highlighting the importance of m-Health services in facilitating this process. Enhancing user engagement with herbal platform services requires prioritizing influential determinants, streamlining the investment experience, and underscoring the sector’s contribution to economic revitalization. Authorities should prioritize simplifying the investment landscape and initiating advocacy campaigns, while platform developers are advised to improve the user experience, bolster educational efforts, and heighten awareness of the investment advantages within the herbal industry. This research provides stakeholders with insights into the factors that enhance Thais’ engagement with herbal market platforms, especially via online channels. Identifying these key drivers is anticipated to boost participation in the herbal market, thereby contributing positively to Thailand’s economy.

This scientific study aims to thoroughly assess the current status and evaluate key indicators influencing healthcare and the workforce in selected European Union (EU) member states. Building upon this ambitious research agenda, we focused on a comprehensive descriptive analysis of selected indicators within the healthcare sector, including healthcare financing schemes, overall employment in healthcare and social care, the number of graduates in healthcare (including physicians and general practitioners), as well as migration patterns within the healthcare sector. The data forming the basis of this analysis were systematically gathered from Organization for Economic Co-operation and Development (OECD) and Eurostat databases. Subsequently, we conducted a robust correlation analysis to explore the intricate relationships among these indicators. Our research endeavour aimed to identify and quantify the impact of these indicators on each other, with a focus on their implications for overall healthcare and the workforce in the respective countries. Based on the findings obtained, we derived several significant conclusions and recommendations. For instance, we identified that increasing employment in the healthcare sector may be associated with the overall quality of healthcare provision in a given country. These findings have important implications for policymaking and decision-making at the EU level. Therefore, we recommend that policymakers in these countries consider implementing measures to further develop the healthcare sector while also helping to retain and attract qualified professionals in the healthcare industry. Such recommendations could include improving healthcare infrastructure, incentivizing professional education and further training in the healthcare sector, and implementing policies to support healthcare provision more broadly.

Ecuador acknowledges the need to improve infrastructure and resources for educational inclusion, but it faces challenges in effective implementation compared to developed countries that have made advancements in this area. The objective of this research was to map the regulations and practices related to the implementation of inclusive infrastructure and educational resources at the international level, identifying knowledge gaps and opportunities for adaptation in Ecuador. An exploratory theoretical review was conducted following PRISMA-ScR guidelines, using searches in academic databases and official documents. Qualitative and regulatory studies from the United States, Finland, Canada, and Japan were selected, analyzing 16 scientific articles and 11 official documents. The results reveal that Ecuador faces challenges in the implementation of inclusive regulations, particularly in infrastructure and resources, highlighting the need to establish national accessibility standards, invest in assistive technologies, and offer continuous teacher training to enhance educational inclusion. The research uncovered a negative cycle where the lack of effective implementation of inclusive regulations perpetuates inequality and reinforces institutional inertia. For successful reform, the regulatory structure, resource management, and educational culture in Ecuador must be addressed simultaneously.

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.

Olive production is threatened by a fungal pathogen, Armillaria mellea (Vahl. Fr.) P. Kumm.,causing decline in trees worldwide. Effectiveness of once and twice applications of fungicides hexaconazole, propicoconazole and thiophanate-methyl and application of biological agent (Trichoderma harzianum) to control A. mellea was studied at orchard scale during four years. T. harzianum inhibited the pathogen growth on agar media. This antagonistic fungus provided a 25% control efficiency of A. mellea on olive trees younger than 15 years which was the same as control efficiency of once application of hexaconazole. Control efficiencies as perfect as 100% were determined on younger (<15 years old) diseased olive trees treated with once applications of thiophanate-methyl and hexaconazole, and twice applications of thiophanate-methyl. Moreover, olive tree age was significantly effective on fungicidal control efficiency. Hence, this four-year research advanced our understanding of sustainable olive production in study region and other geographical areas with similar agro-ecological characteristics.