Climate is one of the main drivers of hydrological processes, and climate change has caused worldwide effects such as water scarcity, frequent floods and intense droughts. The purpose of this study was to analyze the effects of climate change on the water balance components, high flow and low flow stream conditions in a semi-arid basin in Iran. For this reason, the climate outputs of the CanESM5 model under Shared Socioeconomic Pathways (SSP) scenarios SSP126, SSP245, and SSP585 were spatially downscaled by the Statistical Downscaling Model (SDSM). The hydrological process was simulated by the Soil and Water Assessment Tool (SWAT) model. Key findings include a 74% increase in evapotranspiration, a reduction by up to 9.6% in surface runoff, and variations in discharge by up to 53.6%. The temporal analysis of snow melting changes revealed an increase in the volume of snow melting during winter months and a reduction in the volume during spring. The projected climate change is expected to cause notable variations in high and low flow events, particularly under the SSP585 scenario, which anticipates significant peaks in flow rates. This comprehensive analysis underscores the pressing need for adaptive strategies in water resource management to mitigate the anticipated impacts of climate variability.

Plant leaf litter has a major role in the structure and function of soil ecosystems as it is associated with nutrient release and cycling. The present study is aimed to understand how well the decomposing leaf litter kept soil organic carbon and nitrogen levels stable during an incubation experiment that was carried out in a lab setting under controlled conditions and the results were compared to those from a natural plantation. In natural site soil samples, Anacardium. occidentale showed a higher value of organic carbon at surface (1.14%) and subsurface (0.93%) and Azadirachta. indica exhibited a higher value of total nitrogen at surface (0.28%) and subsurface sample (0.14%). In the incubation experiment, Acacia auriculiformis had the highest organic carbon content initially (5.26%), whereas A. occidentale had the highest nitrogen level on 30th day (0.67%). The overall carbon-nitrogen ratio showed a varied tendency, which may be due to dynamic changes in the complex decomposition cycle. The higher rate of mass loss and decay was observed in A. indica leaf litter, the range of the decay constant is 1.26–2.22. The morphological and chemical changes of soil sample and the vermicast were substantained using scanning electron microscopy (SEM) and Fourier transmission infrared spectroscopy (FT-IR).

This paper presents an advanced approach for structural topology optimization by incorporating fatigue crack propagation analysis. The extended finite element method (X-FEM) is employed to model initial crack propagation, while the Paris model serves as the basis for simulating fatigue crack growth. The proposed methodology aims to optimize the structural design by minimizing compliance while considering volume and fatigue constraints. The proposed method employs the developed bi-directional evolutionary structural optimization (BESO) algorithm. The accuracy of the proposed technique is validated through the solution of benchmark problem and is further demonstrated in its effectiveness and robustness by examining several numerical examples. The optimization process considers various crack conditions, including the absence of cracks, horizontal and vertical cracks of different lengths. The optimized topologies obtained through the proposed algorithm clearly demonstrate the impact of crack presence, crack direction, and crack length on the material distribution. Furthermore, the convergence histories of the objective function, represented by mean compliance, highlight the influence of crack length on the stiffness and converged compliance of the structure. The results demonstrate its ability to adapt the material distribution based on fatigue cracks propagation conditions and achieve optimal topologies that balance structural integrity and performance.

Introduction: We have quantified the healthy life years lost and the costs incurred in 2019 due to the poor health of the Hungarian population aged 30-64 from a societal perspective, but also from the perspective of several other social actors, calculating the indirect costs according to the human capital approach. Objective: The aim of our estimate is to shed light on the radically high losses that Hungary incurs year after year as a result of its inhabitants living significantly shorter and sicker lives than people in similar situations in other countries. Method: In the first part of the analysis, we aggregated the time lost due to illness and disability for the age group 30-64 in 2019. The aggregated Hungarian values for the studied age group were compared with the corresponding values for the Visegrad countries, Austria and the European Union. The second part of the analysis aggregates the social costs of time spent in ill health. A distinction is made between direct costs, which involve the movement of money, and indirect costs, which are embodied in lost income or production. Results: In Hungary, the nearly 4.8 million people aged 30-64 spent a total of 654,000 years on sick leave in 2019, which equated to an average of about 50 days per person. This means 14% of the total number of working days could have been working days in good health. The direct costs, i.e., the expenditure of the Health Insurance Fund and the expenditure financed by patients and voluntary insurance, amounted to HUF 1,446 billion. Indirect costs, i.e., expenditure due to the loss of working years as a result of premature death and illness, represented a further burden of HUF 2,279 billion. Conclusion: In 2019, the direct and indirect expenditure of people aged 30-64 amounted to HUF 3425 billion, or 7.21% of the GDP. It is well known that in developed countries, including Hungary, the greatest health losses are caused by non-communicable chronic diseases, which can be prevented by a healthy lifestyle. Therefore, the promotion of healthy lifestyles and the creation of a conducive physical and social environment are essential for improving the country s competitiveness.

The on-board hydrogen storage of mobile applications is a key area of global industrial transformation to hydrogen technology. The research work provides an overview about the principle of hydrogen fuel cell vehicles, with a focus on the widespread on-board hydrogen storage technologies. In this work, type IV composite pressure vessels in particular are reviewed. The key challenges of polymeric liners are deeply investigated, and liner collapse was identified as a critical failure of type IV vessels. Different factors of liner collapse were categorized and relevant material properties - such as permeability, physical characteristics, and surface properties - were explained in more detail to lay the foundation for further research on high barrier, durable polymeric liner materials.

The Automated Driving Data Analyzer (ADDA) is a modular application written in Python for the synchronized acquisition and analysis of physiological, vehicle, and interface data. It provides a managed data acquisition process and one-click data analysis. It also provides raw data storage and systematic archiving of data sets. ADDA integrates real-time data from a BeamNG vehicle simulation, Pupil Labs eye-tracking system, hand-tracking, and cardiac data. This integration allows the simultaneous recording and analysis of multiple data streams, which can be visualized and controlled through a graphical user interface (GUI) built with Tkinter. The application is designed to help researchers and engineers analyze driving behavior under different conditions, enabling a deeper understanding of the interactions between the driver and automated driving functions.