Background Breast cancer is a leading cause of mortality and morbidity among females worldwide. As part of the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023, we provided an updated comprehensive assessment of the epidemiological trends, disease burden, and risk factors associated with breast cancer globally, regionally, and nationally from 1990 to 2023. Methods Breast cancer incidence, mortality, prevalence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) were estimated by age and sex for 204 countries and territories from 1990 to 2023. Mortality estimates were generated using GBD Cause of Death Ensemble models, leveraging data from population-based cancer registration systems, vital registration systems, and verbal autopsies. Mortality-to-incidence ratios were calculated to derive both mortality and incidence estimates. Prevalence was calculated by combining incidence and modelled survival estimates. YLLs were established by multiplying age-specific deaths with the GBD standard life expectancy at the age of death. YLDs were estimated by applying disability weights to prevalence estimates. The sum of YLLs and YLDs equalled the number of DALYs. Breast cancer burden attributable to seven risk factors was examined through the comparative risk assessment framework. The GBD forecasting framework was used to forecast breast cancer incidence and mortality from 2024 to 2050. Age-standardised rates were calculated for each metric using the GBD 2023 world standard population. Findings In 2023, there were an estimated 2·30 million (95% uncertainty interval [UI] 2·01 to 2·61) breast cancer incident cases, 764 000 deaths (672 000 to 854 000), and 24·1 million (21·3 to 27·5) DALYs among females globally. In the World Bank low-income group, where a low age-standardised incidence rate (ASIR) was estimated (44·2 per 100 000 person-years [31·2 to 58·4]), the age-standardised mortality rate (ASMR) was the highest (24·1 per 100 000 [16·8 to 31·9]). The highest ASIR was in the high-income group (75·7 per 100 000 [67·1 to 84·0]), and the lowest ASMR was in the upper-middle-income group (11·2 per 100 000 [10·2 to 12·3]). Between 1990 and 2023, the ASIR in the low-income group increased by 147·2% (38·1 to 271·7), compared with a 1·2% (–11·5 to 17·2) change in the high-income group. The ASMR decreased in the high-income group, changing by –29·9% (–33·6 to –25·9), but increased by 99·3% (12·5 to 202·9) in the low-income group. The increase in age-standardised DALY rates followed that of ASMRs. Risk factors such as dietary risks, tobacco use, and high fasting plasma glucose contributed to 28·3% (16·6 to 38·9) of breast cancer DALYs in 2023. The risk factors with a decrease in attributable DALYs between 1990 and 2023 were high alcohol use and tobacco. By 2050, the global incident cases of breast cancer among females were forecast to reach 3·56 million (2·29 to 4·83), with 1·37 million (0·841 to 2·02) deaths. Interpretation The stable incidence and declining mortality rates of female breast cancer in high-income nations reflect success in screening, diagnosis, and treatment. In contrast, the concurrent rise in incidence and mortality in other regions signals health system deficits. Without effective interventions, many countries will fall short of the WHO Global Breast Cancer Initiative's ambitious target of achieving an annual reduction of 2·5% in age-standardised mortality rates by 2040. The mounting breast cancer burden, disproportionately affecting some of the world's most vulnerable populations, will further exacerbate health inequalities across the globe without decisive immediate action. Funding Gates Foundation, St Jude Children's Research Hospital.

A communication-focused drama-based program was delivered over a period of 1 year to 10- to 11-year-olds in Hungary. The aim of the program was to develop participants’ social problem-solving, coping strategies, and assertive communication. Outcomes were measured by the Assertiveness Questionnaire (Gaumer Erickson et al., 2016), the Social Problem-Solving Inventory–Revised (D’Zurilla et al., 2002), and the Ways of Coping Questionnaire (Folkman & Lazarus, 1988). N = 18 children received the intervention, and N = 28 children formed a control group. The 1-year program consisted of 18 sessions, each lasting for 90 min; they took place every 2 weeks and resulted in significant changes across three areas in the intervention group. Contrary to our hypothesis, rationality was not strengthened, but impulsivity and avoidance-escape were significantly reduced, and confrontation increased in frequency. In all three areas, assertive communication has significant explanatory power. This program was a suitable way to address some of the problem-solving styles and coping strategies that the research (e.g., Zsolnai, 2013) suggest may cause a range of life management difficulties (e.g., conflict, managing emotions) in adolescence.

Abiotic stresses including drought, salinity, heat, cold, and heavy metal toxicity severely constrain plant productivity worldwide. Nitrogen (N), beyond its fundamental nutritional role, has emerged as a central regulator of plant stress responses through its involvement in metabolic reprogramming, osmotic adjustment, antioxidant defense, and hormonal signaling. This review synthesizes current advances in understanding how nitrogen availability and form influence plant tolerance to major abiotic stresses. Particular emphasis is placed on nitrogen-mediated modulation of reactive oxygen species (ROS) scavenging systems, nitrogen–carbon metabolic coordination, phytohormonal crosstalk, osmoprotectant biosynthesis, and regulation of stress-responsive gene expression. Recent molecular insights highlight the role of nitrogen transporters, nitrate signaling pathways, and nitrogen-use efficiency in stress adaptation mechanisms. Furthermore, agronomic and biotechnological strategies aimed at optimizing nitrogen management to enhance stress resilience are discussed, including precision fertilization, integrated nutrient management, and genetic approaches targeting nitrogen-responsive regulatory networks. By integrating physiological, biochemical, and molecular perspectives, this review provides a comprehensive framework for understanding nitrogen-driven mitigation strategies under abiotic stress conditions and outlines future research directions for sustainable crop production in changing environments.

The risk of exercise addiction, characterized by an uncontrollable urge to engage in physical activity, poses significant health risks yet lacks clinical diagnostic criteria. The need for its assessment is increasing in research and applied settings. The present study evaluated the psychometric properties and reliability of the Expanded Exercise Addiction Inventory (EAI-3) within a Danish population. The present study involved 392 Danish adults who were all regular exercisers. Participants completed the EAI-3, the Exercise Dependence Scale-Revised (EDS-R), the SCOFF Questionnaire for eating disorders, the Obsessive–Compulsive Inventory-Revised (OCI-R), and the Ten-Item Personality Inventory (TIPI). Confirmatory factor analysis (CFA) and measurement invariance testing were performed to assess the factor structure and reliability of the EAI-3 across biological sex. The results indicated strong reliability and validity for the EAI-3, with good fit indices across models (CFI =.981, RMSEA =.054). The scale scores demonstrated configural, metric, and scalar invariance, indicating consistent performance across male and female exercisers. Reliability analyses yielded high internal consistency (α =.85, ω =.88), and ROC analysis established a cut-off score of 33.5 for potential exercise addiction risk, with high specificity (.856) and sensitivity (.889). Similar good results emerged from the bifactor model, but the original structure was still preferable. The present study supports the EAI-3 as a valid and reliable tool for screening the risk of exercise addiction among Danish adults, facilitating early identification and potential intervention. Further research should focus on longitudinal studies and clinical validations to enhance the understanding and management of exercise addiction.

Romania joined the UNESCO Convention in 1990. The fortified church of Biertan was inscribed on the World Heritage List in 1993, followed by six additional Transylvanian fortified church villages in 1999. An interesting feature of this heritage landscape is that settlements with different demographic and development trajectories share the same World Heritage designation. In our research, we collected demographic and tourism data from these seven municipalities. Subsequently, a standard questionnaire was sent to municipal decision-makers (mayors) in 2023 to map tourism development in their municipalities. The communication activities of the municipalities were analysed using a content analysis method, which was observation-based and based only on online content. In our experience, there is no common strategy to turn this heritage into a tourist attraction; each of the seven municipalities has faced this challenge separately. The main result of the research was to explore how heritage tourism works in municipalities with different demographic, linguistic-cultural heritage and with different levels of management.

Electric Vehicles (EVs) play a pivotal role in advancing environmental sustainability and accelerating the transition toward clean energy systems. However, large-scale EV adoption poses significant operational challenges, particularly when charging and discharging activities are uncoordinated, potentially leading to elevated peak demand and increased grid stress. Effective scheduling techniques are therefore essential to ensure reliable integration of EVs into modern power systems. This study provides a rigorous comparative evaluation of two metaheuristic optimization paradigms for EV charging and discharging scheduling: the traditional Particle Swarm Optimization (PSO) algorithm and the more recent Transit Search Optimization (TSO) algorithm. Using an identical system configuration and EV dataset, the study assesses the performance of both approaches based on peak power reduction, cost minimization, and overall system efficiency. Results demonstrate that while enhanced PSO scenarios exhibit noticeable improvements over earlier literature, TSO consistently achieves superior outcomes due to its stronger exploration-exploitation balance. In particular, TSO attains a 46.23 % reduction in average EV charging cost and achieves the lowest power-loss levels across all tested scenarios. Relative to the best previously published benchmarks, TSO further improves peak power consumption by 1.6 % and total charging cost by 6.1 %. These findings highlight TSO’s strong potential as a high-efficiency scheduling tool for large-scale EV integration in future smart grid environments.

The progressive financialization of oil, in tandem with the advancement of economic globalization, has led to a sharp increase in oil prices. The growing volatility in the global economic and financial landscape has had some impact on the green bond market. Emerging markets, such as China, are particularly interesting due to their rapid evolution. This paper empirically analyzes the dynamic impact of oil market price uncertainty on China's Green Bond (GB) using the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroscedasticity (DCC-GARCH) model. The empirical findings indicate that the uncertainty of oil has a remarkable time-varying influence on China's green bonds. Specifically, when oil prices rise, the yields on green bonds decrease. Dynamic correlation analysis reveals that oil market uncertainty exhibits a negative correlation with green bonds, with a more pronounced impact during the COVID-19 pandemic. Furthermore, an impulse response analysis shows that long-term interactions between oil prices and green bonds gradually stabilize, and short-term fluctuations are frequent and complex due to market factors. These fluctuations were more pronounced during the COVID-19 pandemic, consistent with the above conclusions. Oil market uncertainty increases risk levels in the overall financial market, which may affect investors' perceptions of green bonds. Drawing on the research outcomes, this study presents targeted policy recommendations aimed at promoting the stable and sustainable development of China's GB market. These measures are designed to bolster the nation's transition toward a green economy and align with its long-term sustainability goals.

Groundwater modeling in semi-arid regions presents significant challenges due to complex aquifer dynamics, limited data availability, and heterogeneous hydrogeological conditions. This study presents a comprehensive comparative analysis of three fuzzy expert system strategies for monthly groundwater level forecasting in the Al-Hsain Basin, Syria: localized models based on hydrogeographical grouping, a unified basin-wide approach, and an innovative behavioral clustering methodology. Using synchronized rainfall and temperature data from 35 monitoring wells over four years (2020–2024), we developed and evaluated fuzzy inference systems’ directional classification accuracy as the primary performance metric, categorizing groundwater level changes into rise, stable, and decline states rather than predicting continuous values. This choice reflects the qualitative nature of fuzzy expert systems and their suitability for groundwater management under data-limited conditions. The behavioral clustering approach achieved excellent overall performance with a mean accuracy of 0.74, outperforming localized models (0.71) and unified models (0.67). Behavioral clustering demonstrated effectiveness in 66% of wells, with individual accuracy improvements reaching up to 0.23, while reducing model complexity from five group-specific systems to three behaviorally coherent clusters. Localized models achieved optimal performance in 29% of wells where hydrogeological conditions aligned with spatial assumptions, whereas unified models provided consistent moderate performance across 89% of locations. The incorporation of lagged variables and seasonal indices in behavioral clustering models proved essential for capturing temporal complexity in semi-arid groundwater responses. Statistical analysis revealed lower intra-group variability in behavioral clusters (standard deviation 0.06–0.09) than in geographical groupings (0.08–0.14), confirming improved functional homogeneity through response-based organization. These findings indicate that fuzzy modeling strategy selection should be context-dependent, with behavioral clustering offering an effective balance between accuracy, interpretability, and generalization for regional groundwater management applications. The novelty of this work lies in isolating the effect of fuzzy system organization logic (localized, unified, and behavioral) on forecasting performance, robustness, and transferability, evaluated under an identical inference and time-series validation framework.

The optimal power flow (OPF) problem is a highly nonlinear and complex multi-dimension optimization problem, especially with the increased penetration of uncertain renewable energies (RES). In this line, this paper presents the Hybrid Brown-Bear and Hippopotamus Optimization Algorithms with Quasi-Opposition-Based Learning (HBOA-QOBL) to enhance multi-dimension OPF solution. The algorithm combines the strengths of Brown-Bear optimizer, which excels in exploration and adaptive search mechanisms, and the Hippopotamus optimizer, known for its social behavior modeling and localized search strategies. By integrating QOBL, the HBOA-QOBL improves exploration through the generation of quasi-opposite solutions, allowing for a wider search of the solution space and reducing the risk of premature convergence. Adaptive search mechanisms embedded in HBOA-QOBL enhance exploitation by dynamically adjusting search behaviors during iterative power dispatch tuning, enabling improved fine-tuning of generation schedules and voltage profiles. The effectiveness of the proposed method is evaluated on the IEEE 30-bus, 57-bus, and 118-bus test systems for multiple dimension OPF objectives, including fuel cost minimization, emission reduction, power loss reduction, voltage deviation minimization, reactive power loss reduction and the voltage stability indicator (L-index). Simulation results indicate faster convergence compared to conventional techniques, achieving near-optimal solutions within 200 iterations, with a standard deviation of 63.8%, demonstrating superior technical and economic performance relative to previous research. Key convergence parameters such as population size, maximum iterations, and learning factor are explicitly tuned to enhance both exploration and exploitation. Simulation results confirm that HBOA-QOBL outperforms conventional optimization techniques in terms of solution quality, convergence speed, and stability, establishing significant improvement in the technical and economic issues.