Search Results (3,878)

On September 7, 2024, a deep convection event was observed in Northern and Central Greece, and based on radar data analysis, three supercells were identified. One of these, the most intense with maximum radar reflectivity of 68 dBZ, had a lifetime of almost 7 h and covered a distance of more than 200 km, producing damaging winds and large hail along its track. The goal of this study was to analyze this case using radar data and to evaluate the predictability of such a high-impact event using a numerical weather prediction model. The Weather Research and Forecasting (ARW-WRF) model was used to perform an array of simulations, and using multiple initialization times, the influence of lead time was examined. Furthermore, the dependence of the results on the choice of parameterization scheme used in the model is assessed below. The model performed satisfactorily in predicting intense storm activity, without reaching the extreme values observed by the radar. Full article

The diesel particulate filter (DPF) is among the most effective measures for controlling particulate emissions from diesel vehicles. Therefore, resource-efficient DPF design and operation are critical to sustainable deployment. In practical engineering, the pursuit of high filtration efficiency inevitably leads to excessively high pressure drop, which in turn impairs the fuel economy and operational reliability of the engine. To address this pair of conflicting objectives, this study introduces a hybrid GRA-MLR-WOA approach, with the initial filtration efficiency and pressure drop at an 80 g soot capture amount as the optimization objectives, to optimize the structural parameters of the DPF. Firstly, based on a computational fluid dynamics (CFD) model and orthogonal experimental design, combined with grey relational analysis (GRA), the effects of key structural parameters on filtration efficiency and pressure drop were evaluated. Secondly, Box–Behnken Design (BBD) was integrated with multiple linear regression (MLR) to establish mathematical regression models describing the relationships between structural parameters, filtration efficiency, and pressure drop. Finally, the whale optimization algorithm (WOA) was employed to obtain the Pareto frontier of the regression models. Through screening with the goal of maximizing initial filtration efficiency, the optimized DPF achieved a 46.85% increase in initial filtration efficiency and a 34.88% reduction in pressure drop compared to the original model. This study targets sustainable filtration design and proposes an optimization framework that jointly optimizes pressure drop and the initial filtration efficiency. The results provide a robust empirical basis for engineering practice and demonstrate strong reproducibility. Full article

Hidradenitis suppurativa (HS) is a chronic, recurrent, and highly debilitating inflammatory disorder of the pilosebaceous unit. Its pathogenesis is considered multifactorial, involving genetic, environmental, hormonal, lifestyle, and microbiome-related factors. The microbiota, defined as the collection of microorganisms, their genomes, and their interactions within a given environment, colonizes multiple sites of the healthy human body, which include the skin and gut, where it contributes to the maintenance of homeostasis. In HS, both skin and gut microbiota exhibit disruptions in composition and diversity, a state referred to as dysbiosis. Alterations in the expression of antimicrobial peptides in HS further implicate the microbiome in disease pathophysiology. In addition, chronic inflammation, bacterial biofilm formation, and dysbiosis are thought to contribute to the severity and recurrence of HS. Although the precise role of dysbiosis in HS pathogenesis remains unclear, several studies have demonstrated a reduction in cutaneous microbial diversity in HS patients, distinguished by an increased abundance of anaerobic and opportunistic bacteria and a reduction in commensal species. The intestinal microbiome has been even less thoroughly investigated, but available evidence suggests decreased overall diversity and richness, with enrichment of pro-inflammatory and depletion of anti-inflammatory bacterial taxa. This review aims to provide an overview of the current knowledge regarding the role of the microbiome in HS, with the goal of informing the direction of future research, including the potential utility of the microbiome as a biomarker for diagnosis and severity stratification in HS. Full article

Understanding trait relationships is fundamental in soybean breeding because the goal is to maximize simultaneous gains. Standard multi-trait genome-wide association studies (MT-GWAS) identify variants linked to multiple traits but fail to capture phenotypic structures or interrelations. Structural Equation Models (SEM) account for covariances and recursion, enabling the decomposition of single nucleotide polymorphism (SNP) effects into direct or indirect components and identifying pleiotropic regions. We applied SEM to analyze morphology (pod thickness, PT) and yield traits (number of pods, NP; number of grains, NG; hundred-grain weight, HGW). The dataset comprised 96 soybean individuals genotyped with 4070 SNP markers. The phenotypic network was constructed using the hill-climbing algorithm, a class of score-based methods commonly applied to learn the structure of Bayesian networks, and structural coefficients were estimated with SEM. According to coefficient signs, we identified negative interrelationships between NG and HGW, and positive ones between NP and NG, and HGW and PT. NG, HGW, and PT showed indirect SNP effects. We also found loci jointly controlling traits. In total, 46 candidate genes were identified: 7 associated exclusively with NP and 4 associated with NG. An additional 15 genes were common to NP and NG, 3 were common to NP and HGW, 6 were common to NG and HGW, and 11 were common to NP, NG, and HGW. In summary, SEM-GWAS revealed novel relationships among soybean traits, including PT, supporting breeding programs. Full article

Advancements in material science have allowed us to exploit the potential of new era for aircraft production. High-performance composites and alloys have allowed us to improve the performance and durability of aircraft, but they have become more and more precious with time. These materials can provide significant advantages in use but are costly, energy-intensive to produce, and their recovery and reuse has become a critical step to be addressed. Accordingly, a new approach in which end-of-life aircrafts represent unconventional mines rather than a disposal challenge is becoming increasingly relevant, providing access to high-value strategic raw materials and aligning with circular economy principles including European Green Deal and the United Nations Sustainable Development Goals. The complexity of dismantling and processing hybrid structures composed of metal alloys, ceramics, and advanced composites requires multiple approaches able to integrate chemical, mechanical, and thermal recovery routes. Accordingly, this review critically discusses the state of the art of the routes of end-of-life aircraft treatments, evaluating the connections between technology and regulation, and positions material recycling and reuse as central pillars for advancing sustainability in aerospace. Furthermore, this review provides a comprehensive reference for addressing the technical, economic, and policy challenges of waste management in aviation, contributing to broader goals of resource circularity and environmental preservation set forth by international sustainability agendas. Full article

Recipes available on the web often lack nutritional transparency and clear indicators of dietary suitability. While searching by title is straightforward, exploring recipes that meet combined dietary needs, nutritional goals, and ingredient-level preferences remains challenging. Most existing recipe search systems do not effectively support flexible multi-dietary reasoning in combination with user preferences and restrictions. For example, users may seek gluten-free and dairy-free dinners with suitable substitutions, or compound goals such as vegan and low-fat desserts. Recent systematic reviews report that most food recommender systems are content-based and often non-personalized, with limited support for dietary restrictions, ingredient-level exclusions, and multi-criteria nutrition goals. This paper introduces DietQA, an end-to-end, language-adaptable chatbot system that integrates a Knowledge Graph (KG), Retrieval-Augmented Generation (RAG), and a Large Language Model (LLM) to support personalized, dietary-aware recipe search and question answering. DietQA crawls Greek-language recipe websites to extract structured information such as titles, ingredients, and quantities. Nutritional values are calculated using validated food composition databases, and dietary tags are inferred automatically based on ingredient composition. All information is stored in a Neo4j-based knowledge graph, enabling flexible querying via Cypher. Users interact with the system through a natural language chatbot friendly interface, where they can express preferences for ingredients, nutrients, dishes, and diets, and filter recipes based on multiple factors such as ingredient availability, exclusions, and nutritional goals. DietQA supports multi-diet recipe search by retrieving both compliant recipes and those adaptable via ingredient substitutions, explaining how each result aligns with user preferences and constraints. An LLM extracts intents and entities from user queries to support rule-based Cypher retrieval, while the RAG pipeline generates contextualized responses using the user query and preferences, retrieved recipes, statistical summaries, and substitution logic. The system integrates real-time updates of recipe and nutritional data, supporting up-to-date, relevant, and personalized recommendations. It is designed for language-adaptable deployment and has been developed and evaluated using Greek-language content. DietQA provides a scalable framework for transparent and adaptive dietary recommendation systems powered by conversational AI. Full article

Human activities have accelerated the extinction of species, driving biodiversity loss and ecosystem degradation. Establishing protected areas (PAs) that encompass habitats of endangered species is essential for achieving biodiversity conservation and ecosystem protection goals. This study aimed to identify and prioritize critical conservation areas for the endangered long-tailed goral (Naemorhedus caudatus) in five regions of Gangwon and Gyeongbuk Provinces, South Korea. The MaxEnt model was applied to predict the potential habitat of the species, considering key environmental factors such as topographic, distance-related, vegetation, and land cover variables. The InVEST Habitat Risk Assessment (HRA) model was used to quantitatively assess cumulative risks within the habitat from the impacts of forest development and anthropogenic pressures. Subsequently, the Zonation software was employed for spatial prioritization by integrating the outputs of the models, and core conservation areas (CCAs) with high ecological value were identified through overlap analysis with 1st-grade areas from the Ecological and Nature Map (ENM). Results indicated that suitable habitats for the long-tailed goral were mainly located in forested regions, and areas subjected to multiple stressors faced elevated habitat risk. High-priority areas (HPAs) were primarily forested zones with high habitat suitability. The overlap analysis emphasized the need to implement conservation measures targeting CCAs while also managing additional HPAs outside CCAs, which are not designated as ENM. This study provides a methodological framework and baseline data to support systematic conservation planning for the long-tailed goral, offering practical guidance for future research and policy development. Full article

Under the background of energy transition, the Integrated Energy System (IES) of the park has become a key carrier for enhancing the consumption capacity of renewable energy due to its multi-energy complementary characteristics. However, the high proportion of wind and solar resource access and the fluctuation of diverse loads have led to the system facing dual uncertainty challenges, and traditional optimization methods are difficult to adapt to the dynamic and complex dispatching requirements. To this end, this paper proposes a new dynamic energy management method based on Deep Reinforcement Learning (DRL) and constructs an IES hybrid integer nonlinear programming model including wind power, photovoltaic, combined heat and power generation, and storage of electric heat energy, with the goal of minimizing the operating cost of the system. By expressing the dispatching process as a Markov decision process, a state space covering wind and solar output, multiple loads and energy storage states is defined, a continuous action space for unit output and energy storage control is constructed, and a reward function integrating economic cost and the penalty for renewable energy consumption is designed. The Deep Deterministic Policy Gradient (DDPG) and Deep Q-Network (DQN) algorithms were adopted to achieve policy optimization. This study is based on simulation rather than experimental validation, which aligns with the exploratory scope of this research. The simulation results show that the DDPG algorithm achieves an average weekly operating cost of 532,424 yuan in the continuous action space scheduling, which is 8.6% lower than that of the DQN algorithm, and the standard deviation of the cost is reduced by 19.5%, indicating better robustness. Under the fluctuation of 10% to 30% on the source-load side, the DQN algorithm still maintains a cost fluctuation of less than 4.5%, highlighting the strong adaptability of DRL to uncertain environments. Therefore, this method has significant theoretical and practical value for promoting the intelligent transformation of the energy system. Full article

The study presents a spectral assessment and analysis of various data and methods for snow cover analysis in different ranges of the electromagnetic spectrum through a differentiated approach applied to the territory of Bansko, Pirin Mountain. The aim of the presented research is to assess the effectiveness and accuracy of satellite observations together with field (in situ) measurements and to create a model of an integrated methodology. To achieve this goal, several indices, such as land surface temperature (LST), optical indices, Tasseled Cap Transformation (TCT) with wetness component (TCW), High-Resolution (HR) imagery, and Synthetic Aperture Radar (SAR) measurements, were analyzed. The results of the analysis proved that combining satellite and field data through a mobile thermal camera provides an accurate and comprehensive picture of snow conditions in high mountain regions for powder, hard-packed and wet snow. As the most important, there is the verification and validation of the results through the so-called regression analysis of the different data types, through which multiple correlations (over 10) were established, both in data from Sentinel 1SAR, Sentinel 2MSI, Sentinel 3 SLSTR, and PlanetScope. The results showed the effectiveness of optical indices for hard and fresh snow and radar and LST data for wet snow. The results can be used to improve snow surveys, event prediction (e.g., avalanches), and the interpretation of spectral analysis of snow. The study does not aim to perform a temporal analysis; all satellite data is from the temporal period 30 December 2024–5 January 2025. Full article

Food and water security are essential components for Panama’s advancement toward the Sustainable Development Goals. This study aimed to quantify the prevalence of household food insecurity and water insecurity, and to explore the association between them using standardized measurement tools. A cross-sectional survey was conducted between January and June 2024 using an online questionnaire administered via Google Forms. The survey collected sociodemographic data and applied the Food Insecurity Experience Scale (FIES) and the Household Water Insecurity Experiences (HWISE) scale to assess water and food insecurity, respectively. A total of 222 adult household heads were included (66.2% female), with a median age of 31.4 years. The prevalence of moderate and severe food insecurity was 29.7% (95% CI: 24.8–34.6%) and 6.1% severe food insecurity (95% CI: 3.7–8.4%), while water insecurity affected 27% of households (10.4% high; 16.7% moderate). Multiple linear regression showed that moderate to severe food insecurity was significantly associated with water insecurity (β = 0.19; 95% CI: 0.08–0.31) and lower income levels. Specifically, food insecurity was associated with households reporting no income (β = 0.25; 95% CI: 0.05–0.44) and those with monthly income between 501 and 1000 USD (β = 0.11; 95% CI: 0.01–0.22), compared to households with income above 1000 USD. The results suggest that food insecurity is significantly associated with water insecurity, supporting the need for integrated approaches in public policy to address basic resource access in vulnerable populations. Full article

First-line options for chronic lymphocytic leukemia (CLL) are evolving, recently returning to a fixed-duration (FD) approach incorporating regimens such as venetoclax + obinutuzumab, ibrutinib + venetoclax, and soon acalabrutinib + venetoclax ± obinutuzumab. Five Canadian hematologists convened to share perspectives regarding the attributes of these options and considerations for clinically appropriate integration within Canada’s publicly funded healthcare system. The hematologists underscored the importance of shared decision-making with patients, family members, and caregivers involving careful consideration of disease profile and patient characteristics, preferences, and values. They indicated that although a role persists for continuous therapy with approved covalent Bruton’s tyrosine kinase inhibitors (typically in high-risk disease), newer FD regimens offer multiple benefits related to the treatment-free period, quality of life, safety, re-treatment, healthcare resource utilization, and costs. The hematologists highlighted the appeal of all-oral FD combinations given their convenience and impact on treatment equity, factors especially compelling given Canada’s vast geography and large segment of rural populations. In closing, they emphasized the quickly evolving therapeutic setting of CLL in the 1L and beyond, underscoring the need for ongoing patient involvement in decision-making to support optimal treatment selection based on patient goals and within the confines of provincial funding. Full article

Thorough investigation into Tesla valve (TV) design was conducted across a large design of experiments (DOE) consisting of four varying geometric parameters and six different Reynolds number regimes in order to develop an optimized pressure drop device utilizing machine learning (ML) methods. A non-standard TV design was geometrically parameterized, and an automation suite was created to cycle through numerous combinations of parameters. Data were collected from completed computational fluid dynamics (CFD) simulations. TV designs were tested in the restricted flow direction for overall differential pressure, and overall minimum pressure with consideration to the onset of cavitation. Qualitative observations were made on the effects of each geometric parameter on the overall valve performance, and particular parameters showed greater influence on the pressure drop compared to classically optimized parameters used in previous TV studies. The overall minimum pressure demonstrated required system pressure for a valve to be utilized such that onset to cavitation would not occur. Data were utilized to train an ML model, and an optimized geometry was selected for maximized pressure drop. Multiple optimization efforts were made to meet design pressure drop goals versus traditional diodicity metrics, and two geometries were selected to develop a final design tool for overall pressure drop component development. Future work includes experimental validation of the large dataset, as well as further validation of the design tool for use in industry. Full article

The increasing carbon footprint of civil aviation has made the use of Sustainable Aviation Fuel (SAF) a strategic necessity in line with the sector’s sustainability goals. This study evaluates the existing SAF types based on environmental, economic, technical and social criteria, determines the criteria weights with Fuzzy-Step-Wise Weight Assessment Ratio Analysis (F-SWARA) and selects the most suitable alternative through Spherical Fuzzy-Multi Objective Optimization on the basis of Ratio Analysis plus full MULTIplicative form (SF-MULTIMOORA) method. The alternative evaluation process was carried out on a Python-based online platform and sensitivity analysis was performed on five different scenarios. According to the findings, the Hydroprocessed Esters and Fatty Acids (HEFA-SPK) alternative stands out as the most suitable option in all scenarios, followed by the Fischer-Tropsch Synthetic Paraffinic Kerosene (FT-SPK) alternative. In contrast, Alcohol-to-Jet (ATJ-SPK) and Power-to-Liquid (PtL) options seem to be more variable and less stable. The study provides methodological contributions for the evaluation of SAF alternatives with fuzzy multi-criteria decision making (MCDM) methods and provides strategic implications for manufacturers and airlines in achieving the low-carbon targets of the aviation sector. Full article

Multi-target optimization management of reservoirs plays a crucial role in balancing multiple scheduling objectives, thereby contributing to watershed sustainability. In this study, a model was developed for the multi-target optimization scheduling of water–sediment, ecological, and socioeconomic objectives of reservoirs with multi-dimensional scheduling needs, including flood control, sediment discharge, ecological protection, and socio-economic development. After obtaining the Pareto solution set by solving the optimization model, a decision model based on cumulative prospect theory (CPT) was constructed to select optimal scheduling schemes, resulting in the development of a multi-target decision framework for reservoirs. The proposed framework not only mitigates multi-target conflicts among water–sediment, ecological, and socioeconomic objectives but also quantifies the different preferences of decision-makers. The framework was then applied to six cascade reservoirs (Longyangxia, Liujiaxia, Haibowan, Wanjiazhai, Sanmenxia, and Xiaolangdi) in the Yellow River basin of China. A whole-river multi-target decision model was developed for water–sediment, ecological, and socioeconomic objectives, and the cooperation–competition dynamics among multiple objectives and decision schemes were analyzed for wet, normal, and dry years. The results demonstrated the following: (1) sediment discharge goals and ecological goals were somewhat competitive, and sediment discharge goals and power generation goals were highly competitive, while ecological goals and power generation goals were cooperative, and cooperation–competition relationships among the three objectives was particularly pronounced in dry years; (2) the decision plans for abundant, normal, and low water years were S293, S241, and S386, respectively, and all are consistent with actual dispatch conditions; (3) compared to local models, the whole-river multi-target scheduling model achieved increases of 71.01 × 10⁶ t in maximum sediment discharge, 0.72% in maximum satisfaction rate of suitable ecological flow, and 0.20 × 10⁹ kW·h in maximum power generation; and (4) compared to conventional decision methods, the CPT-based approach yielded rational results with substantially enhanced sensitivity, indicating its suitability for selecting and decision-making of various schemes. This study provides insights into the establishment of multi-target dispatching models for reservoirs and decision-making processes for scheduling schemes. Full article

China possesses abundant solar energy resources and remains heavily dependent on agriculture. The integration of photovoltaic (PV) power generation with agricultural production has emerged as a strategic pathway to advance China’s ecological transition and dual carbon goals. By 2023, PV power generation represented 21% of the nation’s total installed capacity. The cumulative capacity was projected to reach approximately 887 GW by 2024. The novelty of this study lies in offering a systematic and integrative review of PV agriculture in China. This paper used a combination of field research, case studies, policy analysis, and a comparative evaluation of diverse “PV+” development models. The findings reveal a pronounced spatial imbalance. Western China possesses 42% of the country’s solar energy resources, whereas the eastern provinces of Jiangsu, Zhejiang, and Anhui collectively comprise 37.8% of all PV agricultural projects. Three dominant “PV+” models are identified and categorized as follows: “PV + ecological restoration”, “PV + agriculture, forestry, animal husbandry, and fisheries,” and “PV + facility agriculture.” These models provide multiple benefits. They enhance land use efficiency, stimulate local economic development, and contribute to food security by expanding the supply of essential agricultural products. Based on these insights, the study highlights future priorities in technological innovation, ecological evaluation, intelligent equipment, digitalization, and region-specific policy support. Overall, this research fills a key gap in systematically and comprehensively describing the current development status of photovoltaic agriculture in China. It also offers transferable lessons for sustainable agriculture and global energy transitions. Full article