This research analyses digital nomads’ relationship with tourism, their motivations for travelling and their expectations of the destinations they visit. In addition, it aims to understand the lifestyle of this public and their preference for sustainable destinations, as well as the implications for policies and the organisation of tourism infrastructure, in line with their specific needs. A questionnaire was administered to users of open-access social networks or members of online digital nomad communities (n = 34), between December 2022 and March 2023. Descriptive statistics, construct validations, reliability and internal consistency of the measures were carried out and Pearson’s linear correlation coefficient (r) was applied between items of the same scale and different scales. The results indicate that quality of life, life-work balance, living with other cultures, being in contact with nature, escaping from large urban centres, indulging in tourism all year round and travelling for long stays, are the main motivations of this public. The importance of quality Wi-Fi, flexible tourist services and support services is emphasised as the main attributes to be considered in tourist destinations.

The freight transport chain brings together several types of players, particularly upstream and downstream players, where it is connected to both nodal and linear logistics infrastructures. The territorial anchoring of the latter depends on a good level of collaboration between the various players. In addition to the flow of goods from various localities in the area, the Autonomous Port of Lomé generates major flows to and through the port city of Lomé, which raises questions about the sustainability of these various flows, which share the road with passenger transport flows. The aim of this study is to analyse the challenges associated with the sustainability of goods flows. The methodology is based on direct observations of incoming and outgoing flows in the Greater Lomé Autonomous District (DAGL) and semi-directive interviews with the main players in urban transport and logistics. The results show that the three main challenges to the sustainability of goods transport are congestion (28%), road deterioration (22%) and lack of parking space (18%).

The Malaysian government has been actively strengthening the information and communication industry’s ecosystem through talent retention to realize Malaysia 5.0 and transform the country into a developed human-centered society that balances economic advancement with the resolution of talent problems. This is done to recognize the significance of emerging in building a vibrant and dynamic economy for the country. Few of these studies, however, had developed comprehensive policy recommendations for keeping information specialists in Malaysia’s information businesses. To address this gap, a comprehensive literature review was conducted to understand the factors driving information professionals to leave the sector. The findings aim to inform talent retention strategies that will strengthen the industry’s sustainability and attract skilled leaders, ensuring the information sector’s readiness for a successful digital transition.

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.

Introduction: In Central Europe, in Hungary, the state guarantees access to health care and basic health services partly through the Semmelweis Plan adopted in 2011. The primary objectives of the Semmelweis Plan include the optimisation and transformation of the health care system, starting with the integration of hospitals and the state control of previously municipally owned hospitals. The transformation of the health care system can have an impact on health services and thus on meeting the needs of the population. In addition to reducing health inequalities and costs, the relevant benefits include improving patients’ chances of recovery and increasing patient safety. The speciality under study is decubitus care. Our hypothesis is that integration will improve the chances of recovery for decubitus patients through access to smart dressings to promote patient safety. Objective: to investigate and demonstrate the effectiveness of integration in improving the chances of recovery for decubitus ulcer patients. Material and methods: The research compared two time periods in the municipality of Kalocsa, Bács-Kiskun County, Southern Hungary. We collected the number of decubitus patients arriving and leaving the hospital from the nursing records and compared the pre-integration period when decubitus patients were provided with conventional dressings (01.01.2006–2012.12.31) and the post-integration period, which entailed the introduction of smart dressings in decubitus care (01.01.2013–2012.12.31). The target population of the study was men and women aged 0–99 years who had developed some degree of decubitus. The sample size of the study was 4456. Independent samples t-test, Chow test and linear trend statistics were used to evaluate the results. Based on the empirical evidence, a SWOT analysis was conducted to further examine the effectiveness of integration. Results: The independent samples t-test model used was significant (for Phase I: t (166) = −16.872, p < 0.001; for Phase II: t (166) = −19.928, p < 0.001; for Phase III: t (166) = −19.928, p < 0.001; for Phase III: t (166) = −16.872, p < 0.001). For stage III: t (166) = −10.078, p < 0.001; for stage IV: t (166) = −10.078, p < 0.001; for stage III: t (166) = −10.078, p < 0.001). for stage III: t (166) = −14.066, p < 0.001). For the Chow test, the p-values were highly significant, indicating a structural break. Although the explanatory power of the regression models was variable (R-squared values ranged from 0.007 to 0.617), they generally supported the change in patient dynamics after integration. Both statistical analyses and SWOT analysis supported our hypothesis and showed that integration through access to smart dressings improves patients’ chances of recovery. Conclusions: Although only one segment of the evidence on the effectiveness of hospital integration was examined in this study, integration in the study area had a positive impact on the effective care of patients with decubitus ulcers, reduced inequalities in care and supported patient safety. In the context of the results obtained, these trends may reflect different systemic changes in patient management strategies in addition to efficient allocation of resources and quality of care.

This study investigates the impact of digital payment infrastructure accessibility on the social influence of microenterprises in Barranquilla, Colombia, while examining the mediating roles of financial inclusion, digital literacy, social support networks, and collaboration with social innovation initiatives. Employing a mixed-methods approach, the study analyzes data from a sample of 25 microenterprises operating in various sectors. The findings, based on statistical techniques such as multiple regression, path analysis, and structural equation modeling (SEM), provide strong evidence for the positive influence of digital payment infrastructure accessibility on the social relationship of microenterprises. The results also highlight the crucial roles played by financial inclusion and social support networks in mediating this relationship. The study contributes to the growing body of literature on the factors driving the social effect of microenterprises and offers valuable insights for policymakers and practitioners aiming to foster inclusive economic development in the region. The findings suggest that investing in the development and expansion of digital payment systems, alongside efforts to promote financial inclusion and strengthen social support networks, can have far-reaching benefits for microenterprises and their communities.