The success of a city’s entrepreneurial ecosystem (EE) depends on a combination of interconnected factors that foster innovation, collaboration and growth. Urban planning, infrastructure management and an entrepreneurial culture are essential factors for the success of cities’ Entrepreneurial Ecosystems (EEs). Land use and infrastructure management create opportunities for growth and industry expansion. EEs are local, social, business, institutional and cultural stakeholders that encourage and enhance the formation and growth of new businesses, which are supported by enabling infrastructure. The objective of this study was to investigate how urban planning affects EEs in the metropolitan region, Nelson Mandela Bay (NMB), South Africa. NMB is known for poor land use management, which hinders the management of diverse spatial needs, as well as bureaucratic processes for land rezoning for commercial activity. In order to better understand the fundamental issues, a qualitative case study was conducted. The data were collected from fifteen economic development role players from NMB using semi-structured interviews combined with secondary data from the NMB Integrated Development Plan (IDP). The data analysis included thematic analysis using Atlas.ti and Claude 2.0. In order to validate the findings, qualitative data were cross-referenced with secondary sources from the NMB IDP. The key themes that emerged effect the NMB metropole’s management of infrastructure to support the EE. These include, Land use issues, Poor oversight by metropolitan leadership, Lack of infrastructure maintenance and pushing out potential investment and economic growth. The results highlight that the NMB metropole fails to prioritise land use and infrastructure challenges, impacting the NMB metropolitan area’s economic development and worsening inequality among different groups. The findings from this study add to the current research on cities’ EEs and The Right to the City Theory, which supports the UN Sustainable Development Goals 8, 9 and 11.

In recent years, how farmers leverage social capital to improve their well-being has become a crucial question in post-poverty alleviation China. This study assessed the impact of ‘linking social capital’ on farmers’ well-being, as mediated by self-efficacy. The study was conducted using data collected from 443 randomly selected farmers from two villages in Guizhou Province, China. The Partial Least Squares Structural Equation Model (PLS-SEM) was employed to analyze the proposed relationships in the study. The results indicate that linking social capital, when mediated by self-efficacy, positively impacted farmers’ well-being. This suggests that policymakers and implementers exercising hierarchical power in social improvement programs in disadvantaged provinces, such as Guizhou, should take full advantage of linking social capital to effectively improve farmers’ well-being. In doing so, the study concludes, they should consider the positive role farmers’ self-efficacy can play in the process.

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.

The business environment in the modern era is witnessing numerous Intellectual Changes, Technological developments, and increasingly Complex Situations, which has led to a need for effective Leadership in the Business Sectors. This leadership plays a role in transforming companies into giant corporations that serve as a true foundation for enhancing and improving Job Competencies (JC)., The study aimed to analyze the impact of the Soft Skills approach in Human Resources (analytical and critical thinking, decision-making and problem-solving, planning and organization, teamwork) on developing Job Competencies (productivity, technical, managerial) in Petroleum Sector Companies in Egypt. The researchers employed the descriptive-analytical method to study the phenomenon, conducting the study on stratified random samples consisting of 379 managers and a sample of 382 employees from Petroleum Sector Companies. The study utilized the SPSS and AMOS Software Programs. The study found statistically significant differences at the (0.01) level between the average scores of managers and employees regarding soft skills in human resources and job competencies, with managers scoring higher. Additionally, the study revealed a statistically significant direct causal effect at the (0.01) level of Human Resources Soft Skills on Job Competencies in Petroleum Sector Companies., Finally, a proposal was developed for enhancing Job Competencies in Petroleum Companies in Egypt based on the application of human resources Soft Skills, alongside future research directions and practical implications.

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.