This is an overview of the papers published in the special issue.

Entrepreneurial intentions among Gen Z university students in Bangladesh remain underexplored, particularly regarding the influence of education, access to finance and socioeconomic status. This study aims to investigate how these factors shape entrepreneurial intentions within a developing economy context. A quantitative research design was employed, using stratified random sampling of university students in Dhaka, and data were collected through structured questionnaires. Analysis was conducted using covariance-based structural equation modeling (CB-SEM) with AMOS, which showed that all structural paths were statistically significant at p < 0.05. The study found a significant positive association between education and entrepreneurial intentions among Gen Z students in Bangladesh. Access to resources, particularly digital tools and crowdfunding platforms, was also significantly and positively associated with entrepreneurial intentions. Socioeconomic status demonstrated a further significant positive relationship with entrepreneurial intentions. Access to digital resources emerged as a strong direct predictor of entrepreneurial intention. These findings extend the Theory of Planned Behavior (TPB) within a developing economy context. In conclusion, education, resource access and socioeconomic status are key positive determinants of entrepreneurial intentions. Strengthening digital infrastructure and entrepreneurship education may enhance youth entrepreneurial outcomes. Broader studies beyond Dhaka are recommended to improve generalizability.

Purpose: This study evaluated tibiofemoral loading and medial meniscal stress distribution in individuals with flexible flatfoot (FFF) during walking under different foot progression angle (FPA) conditions. Methods: This study analyzed the gait of 28 FFF patients (16 males, 12 females) under three FPA conditions (neutral, toe-in, toe-out). Kinematic (Vicon) and kinetic (Kistler) data were used to estimate tibiofemoral forces in OpenSim. Subsequently, joint angles and muscle forces at peak tibiofemoral forces were used to drive a finite element (FE) model of the knee, enabling the comparison of meniscal von Mises stress, maximum shear stress, and contact pressure across FPA conditions. Results: Tibiofemoral force increased during early stance (9–11%) in the toe-in condition with this increase reaching statistical significance in males (p = 0.008, mean partial η2=0.70 within the SPM-identified cluster). FE analysis showed that peak stresses and contact pressure were primarily localized in the anterior region of the medial meniscus. A consistent directional response to FPA was observed with the lowest peak values occurring in the toe-in condition and the highest values in the toe-out condition. Conclusion: Adjusting FPA modulates intra-articular knee loading via the kinetic chain. For FFF patients, neutral FPA provides stable loading. The toe-in condition presents a complex mechanism: despite increasing tibiofemoral force (notably in males), it reduces peak stress by altering contact mechanics and stress distribution. Therefore, FFF gait interventions must be individualized based on factors like foot morphology, sex, and functional goals.

Background Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. Methods GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. Findings The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6–47·0) in 1990 to 63·4 years (63·1–63·7) in 2023. For males, mean age increased from 45·4 years (45·1–45·7) to 61·2 years (60·7–61·6), and for females it increased from 48·5 years (48·1–48·8) to 65·9 years (65·5–66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9–81·0) and for males 74·8 years (74·8–74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5–38·4) for females and 35·6 years (35·2–35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. Interpretation We examined global mortality patterns over the past three decades, highlighting—with enhanced estimation methods—the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. Funding Gates Foundation.

COVID-19, officially SARS-CoV-2, which originated in China in December 2019, has fundamentally disrupted globalization and economic growth. The strain on supply chains is difficult to manage, and it is expected that the problems can only be resolved once the pandemic is over, which could lead to a further increase in economic and globalization growth. Consumer goods reach the final consumer through supply chains and supply networks, and these supply chains are increasingly playing a role in fostering collaborative relationships across international companies. As a result, companies are becoming stronger, more developed, and growing. Logistics is an important part of a company's operations, managing the flow of materials. The development and positioning of logistics within a company have a major impact on the company's performance, its role in the supply chain, and its competitiveness. Advanced, large companies now consider the senior manager responsible for material flow processes to be the head of the supply chain within their company, as the process from raw material to consumer must be managed and controlled as a whole. Performance must also be assessed in context, recognizing the differences between companies that give them a competitive advantage or disadvantage. In addition, the aim is to develop sustainable logistics at the company level, which will be achieved in companies that pay particular attention to the strategic role of logistics within the company. The use of statistical methods to analyze these relationships is not common in business practice, but it can provide important information and can also be a major aid to future decision-making.

Parkinson disease (PD) is a progressive neurodegenerative disease whose diagnosis is not an easy task because of subjective clinical examination and late onset of motor symptoms. Immediate and correct diagnosis is imperative in the prompt intervention and better patient outcomes. This paper introduces a machine learning-based system of earlystage Parkinsonian disorder detection with neuro-motor voice features. There was a publicly available biomedical voice dataset (22 acoustic features, 195 samples) in which each sample was classified as either Parkinson disease or healthy control. Some of the supervised machine learning classifier such as Support Vector Machine (SVM), k-Nearest Neighbors (KNN), Random Forest, Extra Trees and XGBoost were tested. In order to further improve classification performance and minimize falsenegative predictions, a hybrid stacking ensemble model was set forward that uses XGBoost and Extra Trees as base learners and Logistic Regression as a meta-learner. Adequate preprocessing methods like stratified data splitting and feature standardization were used. The results of the experiment show that the proposed hybrid stacking model is better than single classifiers; with an accuracy of 98.31 percent and is able to remove false negative cases of the Parkinson disease. The results suggest the usefulness of ensemble learning in addressing the issue of class imbalance and enhancing diagnostic accuracy, which means voice-based machine learning models might be proposed as a useful decision-supporting instrument to detect Parkinson disease at an early stage. Further research will be conducted on subject-independent validation and multimodal neuro-motor data integration to improve on its generalizability.

The rapid development of innovative learning technologies in recent years has served as a basis for the transition from traditional teaching methods to interactive, learner-centered approaches. Among such methods, gamification is recognized as a promising pedagogical tool aimed at increasing students’ interest, motivation and engagement in learning. This article proposes a systematic literature review (SLR) of research on the use of gamification in school geography teaching. This review followed the PRISMA protocol and PICOS criteria to ensure transparency and reproducibility. The purpose of the study-Evaluating the effectiveness and directions of gamification in geography lessons and identifying problems arising in the process of its implementation in practice. The research was based on the PRISMA methodology. The search in the international databases Scopus and Google Scholar initially resulted in 22,712 documents. After refining the search, duplicates and irrelevant papers were excluded and 40 scientific articles published between 2019 and 2024 were analyzed in depth. VOSviewer software was used to visualize bibliometric connections and thematic clusters. The results showed that gamification has a positive effect on students’ cognitive development, motivation and spatial reasoning. However, the use of subject-specific digital tools, adapting content to match students’ learning styles, and assessing long-term changes in motivation have not been sufficiently explored. Experience with interactive maps, mobile applications and VR/AR technologies, especially in geography, is rare. Infrastructure, teacher training and challenges of adaptation in rural school settings also require attention. The results show that gamification has a positive effect on students’ motivation and cognitive development, but the limitations are related to the small number of empirical studies and insufficient representation of developing countries. Future research should evaluate the long-term effects of gamification and the possibilities of its wider application in geography teaching.

Diagnosis of concealed internal faults within power transformer is a key for high grid reliability to ensure continuity of power supply to customers. One of the urgent situations of power transformer is the faults under CT saturation and the operation under inrush currents that lead to huge failure of fault identification of the power transformer. In this paper, a fault identification scheme is designed using details and approximate coefficients obtained by discreet wavelet transform applied to a differential current signal under different situations. Also, this paper considers the impact of transformer internal faults such as turn to earth and turn to turn faults, external faults, and inrush currents. The signature of processing differential current is employed for identifying these fault conditions since such fault has a distinct differential current signature. The simulation tests are performed on a 115/22 kV power transformer using ATP-EMTP real-time simulator. Different wavelet families are assessed to show that the optimum mother wavelet, db1, has high fault detection and classification performance. The proposed scheme is verified for transformer energization conditions, and the influence of CT saturation is also considered in this study. Moreover, one of the most important proposed scheme features is simplicity with high lights aspects toward all fault conditions and fault types at different fault location and different fault resistances. Intensive simulation results are obtained to prove the improved selectivity and sensitivity of the proposed scheme for identifying internal transformer faults. Furthermore, sensitivity analysis is not only conducted in terms of transformer loading and fault resistance variation, but transformer scalability study is also verified. Finally, to evaluate the performance of the proposed scheme, an assessment study is adopted to show the accuracy and reliability of differential protection scheme.

This paper proposes the data-driven tuning of low-cost Proportional-Integral (PI) fuzzy control of the payload position of tower crane systems using Iterative Feedback Tuning (IFT). A back-calculation and tracking anti-windup diagram is included to prohibit integrator windup and compensate for the process's dead-zone and saturation nonlinearity. A two-stage tuning strategy is proposed. In the first stage, the parameters of the fuzzy logic part of the PI fuzzy controller and the anti-windup tracking gain are optimally tuned in a model-based manner using an original version of the hybrid Particle Filter-Particle Swarm Optimization algorithm, improved by adding an information feedback model. In the second stage, the parameters of the linear part of the PI fuzzy controller are tuned in a data-driven manner using IFT, and then mapped to the remaining PI fuzzy controller parameters using the modal equivalence principle. The efficacy of the new data-driven fuzzy control strategy is demonstrated through experimental results, and the comparison shows the performance improvement over other strategies designed using competing optimization algorithms.