Search Results (14,346)

The 1.9 billion metric tons of steel globally manufactured in 2023 justify the steel industry’s pivotal role in modern society’s growth. Considering the rapid development of countries that have not fully taken part in the global market, such as Africa, steel production is expected to increase in the next decade. However, the environmental burden associated with steel manufacturing must be mitigated to achieve sustainable production, which would align with the European Green Deal pathway. Such a burden is associated both with the GHG emissions and with the solid residues arising from steel manufacturing, considering both the integrated and electrical routes. The valorisation of the main steel residues from the electrical steelmaking is the central theme of this work, referring to the steel electric manufacturing in the Dalmine case study. The investigation was carried out from two different points of view, comprising the action of a plasma electric reactor and a RecoDust unit to optimize the recovery of iron and zinc, respectively, being the two main technologies envisioned in the EU-funded research project ReMFra. This work focuses on those preliminary steps required to detect the optimal recipes to consider for such industrial units, such as thermodynamic modelling, testing the mechanical properties of the briquettes produced, and the smelting trials carried out at pilot scale. However, tests for the usability of the dusty feedstock for RecoDust are carried out, and, with the results, some recommendations for pretreatment can be made. The outcomes show the high potential of these streams for metal and mineral recovery. Full article

Background: Treatment compliant with the Global Initiative for Asthma (GINA) can promote more effective disease control. Single-inhaler triple therapy (SITT) is one method that is used to optimize therapy in this context, but TRIPLE therapy is still employed by physicians to a limited extent. Objective: This study aimed to describe the factors influencing challenges in optimizing asthma therapy. Methods: A 19-question survey, created via the CATI system, was distributed among pulmonologists, allergologists, general practitioners, and internal medicine specialists in Poland, Greece, Sweden, Slovenia, and Austria. Results: Statistically significant percentage differences in the use of TRIPLE therapy in the context of asthma management were observed among countries as well as between pulmonologists, allergists, and other specialists. Overuse of oral corticosteroids (OCSs) to treat nonsevere and severe asthma in the absence of an approach that focuses on optimizing inhalation therapy among asthma patients receiving TRIPLE therapy was observed in different countries as well as among physicians with different specialties. Twenty elements associated with the challenges involved in diagnosing and managing difficult-to-treat and severe asthma were identified. Six clinical categories for the optimization of asthma therapy via SITT were highlighted. The degree of therapeutic underestimation observed among severe asthma patients was assessed by comparing actual treatment with the recommendations of the GINA 2023 guidelines. Conclusions: Physicians of various specialties in Europe are subject to therapeutic inertia in terms of their compliance with the GINA 2023 guidelines. Full article

Coarse-grained soils serve as essential fill materials in earth–rock dam engineering, where their mechanical properties critically influence dam deformation and stability, directly impacting project safety. Artificial intelligence (AI) techniques are emerging as powerful tools for predicting the mechanical properties of coarse-grained soils. However, AI-based prediction models for these properties face persistent challenges, particularly in parameter tuning—a process requiring substantial computational resources, extensive time, and specialized expertise. To address these limitations, this study proposes a novel prediction model that integrates Adaptive Simulated Annealing (ASA) with an improved Particle Swarm Optimization (PSO) algorithm to optimize the Elman Neural Network (ENN). The methodology encompasses three key aspects: First, the standard PSO algorithm is enhanced by dynamically adjusting its inertial weight and learning factors. The ASA algorithm is then employed to optimize the Adaptive PSO (APSO), effectively mitigating premature convergence and local optima entrapment during training, thereby ensuring convergence to the global optimum. Second, the refined PSO algorithm optimizes the ENN, overcoming its inherent limitations of slow convergence and susceptibility to local minima. Finally, validation through real-world engineering case studies demonstrates that the ASA-PSO-optimized ENN model achieves high accuracy in predicting the mechanical properties of coarse-grained soils. This model provides reliable constitutive parameters for stress–strain analysis in earth–rock dam engineering applications. Full article

Root phenology significantly influences ecosystem processes yet remains poorly characterized across biomes. This study synthesized data from 59 studies spanning Arctic to tropical ecosystems to identify woody plants root phenological patterns and their environmental drivers. The analysis revealed distinct climate-specific patterns. Arctic regions had a short growing season with remarkably low temperature threshold for initiation of root growth (0.5–1 °C). Temperate forests displayed pronounced spring-summer growth patterns with root growth initiation occurring at 1–9 °C. Mediterranean ecosystems showed bimodal patterns optimized around moisture availability, and tropical regions demonstrate seasonality primarily driven by precipitation. Root-shoot coordination varies predictably across biomes, with humid continental ecosystems showing the highest synchronous above- and belowground activity (57%), temperate regions exhibiting leaf-before-root emergence (55%), and Mediterranean regions consistently showing root-before-leaf patterns (100%). Winter root growth is more widespread than previously recognized (35% of studies), primarily in tropical and Mediterranean regions. Temperature thresholds for phenological transitions vary with climate region, suggesting adaptations to environmental conditions. These findings provide a critical, region-specific framework for improving models of terrestrial ecosystem responses to climate change. While our synthesis clarifies distinct phenological strategies, its conclusions are drawn from data focused primarily on Northern Hemisphere woody plants, highlighting significant geographic gaps in our current understanding. Bridging these knowledge gaps is essential for accurately forecasting how belowground dynamics will influence global carbon sequestration, nutrient cycling, and ecosystem resilience under changing climatic regimes. Full article

The increasing penetration of renewable energy into power systems has intensified transient power quality (PQ) disturbances, demanding efficient detection and classification methods to enable timely operational decisions. This paper introduces a hybrid framework combining an Improved Adaptive S-Transform (IAST) with a Random Forest (RF) classifier to address these challenges. The IAST employs a globally adaptive Gaussian window as its kernel function, which automatically adjusts window length and spectral resolution based on real-time frequency characteristics, thereby enhancing time–frequency localization accuracy while reducing algorithmic complexity. To optimize computational efficiency, window parameters are determined through an energy concentration maximization criterion, enabling rapid extraction of discriminative features from diverse PQ disturbances (e.g., voltage sags and transient interruptions). These features are then fed into an RF classifier, which simultaneously mitigates model variance and bias, achieving robust classification. Experimental results show that the proposed IAST–RF method achieves a classification accuracy of 99.73%, demonstrating its potential for real-time PQ monitoring in modern grids with high renewable energy penetration. Full article

Hydrogen production is increasingly vital for global decarbonization but remains a water- and energy-intensive process, especially in arid regions. Despite growing attention to its climate benefits, limited research has addressed the environmental impacts of water sourcing. This study employs a life cycle assessment (LCA) approach to evaluate three water supply strategies for hydrogen production: (1) seawater desalination without brine treatment (BT), (2) desalination with partial BT, and (3) freshwater purification. Scenarios are modeled for the United Arab Emirates (UAE), Australia, and Spain, representing diverse electricity mixes and water stress conditions. Both electrolysis and steam methane reforming (SMR) are evaluated as hydrogen production methods. Results show that desalination scenarios contribute substantially to human health and ecosystem impacts due to high energy use and brine discharge. Although partial BT aims to reduce direct marine discharge impacts, its substantial energy demand can offset these benefits by increasing other environmental burdens, such as marine eutrophication, especially in regions reliant on carbon-intensive electricity grids. Freshwater scenarios offer lower environmental impact overall but raise water availability concerns. Across all regions, feedwater for SMR shows nearly 50% lower impacts than for electrolysis. This study focuses solely on the environmental impacts associated with water sourcing and treatment for hydrogen production, excluding the downstream impacts of the hydrogen generation process itself. This study highlights the trade-offs between water sourcing, brine treatment, and freshwater purification for hydrogen production, offering insights for optimizing sustainable hydrogen systems in water-stressed regions. Full article

Traditional algorithms such as Dijkstra and APF rely on complete environmental information for path planning, which results in numerous constraints during modeling. This not only increases the complexity of the algorithms but also reduces the efficiency and reliability of the planning. Swarm intelligence algorithms possess strong data processing and search capabilities, enabling them to efficiently solve path planning problems in different environments and generate approximately optimal paths. However, swarm intelligence algorithms suffer from issues like premature convergence and a tendency to fall into local optima during the search process. Thus, an improved Artificial Bee Colony-Beetle Antennae Search (IABCBAS) algorithm is proposed. Firstly, Tent chaos and non-uniform variation are introduced into the bee algorithm to enhance population diversity and spatial searchability. Secondly, the stochastic reverse learning mechanism and greedy strategy are incorporated into the beetle antennae search algorithm to improve direction-finding ability and the capacity to escape local optima, respectively. Finally, the weights of the two algorithms are adaptively adjusted to balance global search and local refinement. Results of experiments using nine benchmark functions and four comparative algorithms show that the improved algorithm exhibits superior path point search performance and high stability in both high- and low-dimensional environments, as well as in unimodal and multimodal environments. Ablation experiment results indicate that the optimization strategies introduced in the algorithm effectively improve convergence accuracy and speed during path planning. Results of the path planning experiments show that compared with the comparison algorithms, the average path planning distance of the improved algorithm is reduced by 23.83% in the 2D multi-obstacle environment, and the average planning time is shortened by 27.97% in the 3D surface environment. The improvement in path planning efficiency makes this algorithm of certain value in engineering applications. Full article

Biochar, a carbon-rich material derived from biomass, presents a sustainable solution to several pressing challenges in U.S. agriculture, including soil degradation, carbon emissions, and waste management. Despite global advancements, the U.S. biochar market remains underexplored in terms of economic viability, adoption potential, and sector-specific applications. This narrative review synthesizes two decades of literature to examine biochar’s applications, production methods, and market dynamics, with a focus on its economic and environmental role within the United States. The review identifies biochar’s multifunctional benefits: enhancing soil fertility and crop productivity, sequestering carbon, reducing greenhouse gas emissions, and improving water quality. Recent empirical studies also highlight biochar’s economic feasibility across global contexts, with yield increases of up to 294% and net returns exceeding USD 5000 per hectare in optimized systems. Economically, the global biochar market grew from USD 156.4 million in 2021 to USD 610.3 million in 2023, with U.S. production reaching ~50,000 metric tons annually and a market value of USD 203.4 million in 2022. Forecasts project U.S. market growth at a CAGR of 11.3%, reaching USD 478.5 million by 2030. California leads domestic adoption due to favorable policy and biomass availability. However, barriers such as inconsistent quality standards, limited awareness, high costs, and policy gaps constrain growth. This study goes beyond the existing literature by integrating market analysis, SWOT assessment, cost–benefit findings, and production technologies to highlight strategies for scaling biochar adoption. It concludes that with supportive legislation, investment in research, and enhanced supply chain transparency, biochar could become a pivotal tool for sustainable development in the U.S. agricultural and environmental sectors. Full article

The Kuroshio Current’s flow velocity imposes exacting requirements on underwater vehicle propulsive systems. Ecological preservation necessitates low-noise propeller designs to mitigate operational disturbances. As technological evolution advances toward greater intelligence and system integration, intelligent unmanned systems are positioning themselves as a critical frontier in marine innovation. In recent years, the global research community has increased its efforts towards the development of high-maneuverability underwater vehicles. However, propeller design optimization ignores the key balance between acoustic performance and hydrodynamic efficiency, as well as the appropriate speed threshold for blade rotation. In order to solve this problem, the propeller design of the NACA 65A010 airfoil is optimized by using OpenProp v3.3.4 and XFlow 2022 software, aiming at innovating the propulsion system of shallow water agile submersibles. The study presents an integrated design framework combining lattice Boltzmann method (LBM) simulations synergized with fully Lagrangian-LES modeling, implementing rotational speed thresholds to detect cavitation inception, followed by advanced acoustic propagation analysis. Through rigorous comparative assessment of hydrodynamic metrics, we establish an optimization protocol for propeller selection tailored to littoral zone operational demands. Studies have shown that increasing the number of propeller blades can reduce the single-blade load and delay cavitation, but too many blades will aggravate the complexity of the flow field, resulting in reduced efficiency and noise rebound. It is concluded that the propeller with five blades, a diameter of 234 mm, and a speed of 500 RPM exhibits the best performance. Under these conditions, the water efficiency is 69.01%, and the noise is the lowest, which basically realizes the balance between hydrodynamic efficiency and acoustic performance. This paradigm-shifting research carries substantial implications for next-generation marine vehicles, particularly in optimizing operational stealth and energy efficiency through intelligent propulsion architecture. Full article

Heart failure (HF) poses a substantial global burden due to its high morbidity, mortality, and healthcare costs. Accurate prognostication is crucial for optimizing treatment, resource allocation, and patient counseling. Prognostic tools range from simple clinical scores such as ADHERE and MAGGIC to more complex models incorporating biomarkers (e.g., NT-proBNP, sST2), imaging, and artificial intelligence techniques. In acute HF, models like EHMRG and STRATIFY aid early triage, while in chronic HF, tools like SHFM and BCN Bio-HF support long-term management decisions. Despite their utility, most models are limited by poor generalizability, reliance on static inputs, lack of integration into electronic health records, and underuse in clinical practice. Novel approaches involving machine learning, multi-omics profiling, and remote monitoring hold promise for dynamic and individualized risk assessment. However, these innovations face challenges regarding interpretability, validation, and ethical implementation. For prognostic models to transition from theoretical promise to practical impact, they must be continuously updated, externally validated, and seamlessly embedded into clinical workflows. This review emphasizes the potential of prognostic models to transform HF care but cautions against uncritical adoption without robust evidence and practical integration. In the evolving landscape of HF management, prognostic models represent a hopeful avenue, provided their limitations are acknowledged and addressed through interdisciplinary collaboration and patient-centered innovation. Full article

Landslide displacement prediction is crucial for disaster mitigation, yet traditional methods often fail to capture the complex, non-stationary spatiotemporal dynamics of slope evolution. This study introduces an enhanced prediction framework that integrates multi-scale signal processing with dynamic, geology-aware graph modeling. The proposed methodology first employs the Maximum Overlap Discrete Wavelet Transform (MODWT) to denoise raw Global Navigation Satellite System (GNSS)-monitored displacement time series data, enhancing the underlying deformation features. Subsequently, a geology-aware graph is constructed, using the temporal correlation of displacement series as a practical proxy for physical relatedness between monitoring nodes. The framework’s core innovation lies in a dynamic graph optimization model with low-rank constraints, which adaptively refines the graph topology to reflect time-varying inter-sensor dependencies driven by factors like mining activities. Experiments conducted on a real-world dataset from an active open-pit mine demonstrate the framework’s superior performance. The DCRNN-proposed model achieved the highest accuracy among eight competing models, recording a Root Mean Square Error (RMSE) of 2.773 mm in the Vertical direction, a 39.1% reduction compared to its baseline. This study validates that the proposed dynamic graph optimization approach provides a robust and significantly more accurate solution for landslide prediction in complex, real-world engineering environments. Full article

The highly stochastic nature of rainfall presents significant challenges for the accurate prediction of its time series. To enhance the prediction performance of non-stationary rainfall time series, this study proposes a hybrid deep learning forecasting framework—VMD-IWMA-BiLSTM—that integrates Variational Mode Decomposition (VMD), Improved Whale Migration Algorithm (IWMA), and Bidirectional Long Short-Term Memory network (BiLSTM). Firstly, VMD is employed to decompose the original rainfall series into multiple modes, extracting Intrinsic Mode Functions (IMFs) with more stable frequency characteristics. Secondly, IWMA is utilized to globally optimize multiple hyperparameters of the BiLSTM model, enhancing its ability to capture complex nonlinear relationships and long-term dependencies. Finally, experimental validation is conducted using daily rainfall data from 2020 to 2024 at the Xinzheng National Meteorological Observatory. The results demonstrate that the proposed framework outperforms traditional models such as LSTM, ARIMA, SVM, and LSSVM in terms of prediction accuracy. This research provides new insights and effective technical pathways for improving rainfall time series prediction accuracy and addressing the challenges posed by high randomness. Full article

Unmanned platforms such as unmanned aerial vehicles, unmanned ground vehicles, and autonomous underwater vehicles often face challenges of data, device, and model heterogeneity when performing collaborative data processing tasks. Existing research does not simultaneously address issues from these three aspects. To address this issue, this study designs an unmanned platform cluster architecture inspired by the cloud-edge-end model. This architecture integrates federated learning for privacy protection, leverages the advantages of distributed model training, and utilizes edge computing’s near-source data processing capabilities. Additionally, this paper proposes a federated edge intelligence method (DSIA-FEI), which comprises two key components. Based on traditional federated learning, a data sharing mechanism is introduced, in which data is extracted from edge-side platforms and placed into a data sharing platform to form a public dataset. At the beginning of model training, random sampling is conducted from the public dataset and distributed to each unmanned platform, so as to mitigate the impact of data distribution heterogeneity and class imbalance during collaborative data processing in unmanned platforms. Moreover, an intelligent model aggregation strategy based on similarity measurement and loss gradient is developed. This strategy maps heterogeneous model parameters to a unified space via hierarchical parameter alignment, and evaluates the similarity between local and global models of edge devices in real-time, along with the loss gradient, to select the optimal model for global aggregation, reducing the influence of device and model heterogeneity on cooperative learning of unmanned platform swarms. This study carried out extensive validation on multiple datasets, and the experimental results showed that the accuracy of the DSIA-FEI proposed in this paper reaches 0.91, 0.91, 0.88, and 0.87 on the FEMNIST, FEAIR, EuroSAT, and RSSCN7 datasets, respectively, which is more than 10% higher than the baseline method. In addition, the number of communication rounds is reduced by more than 40%, which is better than the existing mainstream methods, and the effectiveness of the proposed method is verified. Full article

Background and Objectives: The management of type 2 diabetes (T2D) extends beyond glycemic control, requiring a more global strategy that includes optimization of body composition, even more so in the context of sarcopenia and visceral adiposity, as they contribute to poor outcomes. Past reviews have typically been focused on weight reduction or glycemic effectiveness, with limited inclusion of new therapies’ effects on muscle and fat distribution. In addition, the emergence of incretin-based therapies and dual agonists such as tirzepatide requires an updated synthesis of their impacts on body composition. This review attempts to bridge the gap by taking a systematic approach to how current blood-glucose lowering therapies affect lean body mass, fat mass, and the risk of sarcopenia in T2D patients. Materials and Methods: Between January 2015 and March 2025, we conducted a narrative review by searching the PubMed, Scopus, and Web of Science databases for English-language articles. The keywords were combinations of the following: “type 2 diabetes,” “lean body mass,” “fat mass,” “body composition,” “sarcopenia,” “GLP-1 receptor agonists,” “SGLT2 inhibitors,” “tirzepatide,” and “antidiabetic pharmacotherapy.” Reference lists were searched manually as well. The highest precedence was assigned to studies that aimed at adult type 2 diabetic subjects and reported body composition results. Inclusion criteria for studies were: (1) type 2 diabetic mellitus adult patients and (2) reporting measures of body composition (e.g., lean body mass, fat mass, or muscle function). We prioritized randomized controlled trials and large observational studies and excluded mixed diabetic populations, non-pharmacological interventions only, and poor reporting of body composition. Results: Metformin was widely found to be weight-neutral with minimal effects on muscle mass. Insulin therapy, being an anabolic hormone, often leads to fat mass accumulation and increases the risk of sarcopenic obesity. Incretin-based therapies induced substantial weight loss, mostly from fat mass. Notable results were observed in studies with tirzepatide, demonstrating superior reduction not only in fat mass, but also in visceral fat. Sodium-glucose cotransporter 2 inhibitors (SGLT2 inhibitors) promote fat loss but are associated with a small yet significant decrease in lean muscle mass. Conclusions: Blood-glucose lowering therapies demonstrated clinically relevant effects on body composition. Treatment should be personalized, balancing glycemic control, cardiovascular, and renal benefits, together with optimal impact on muscle mass along with glycemic, cardiovascular, and renal benefits. Full article

Flocculants are indispensable in water and wastewater treatment, enabling the aggregation and removal of suspended particles, colloids, and emulsions. However, the conventional development and application of flocculants rely heavily on empirical methods, which are time-consuming, resource-intensive, and environmentally problematic due to issues such as sludge production and chemical residues. Recent advances in machine learning (ML) have opened transformative avenues for the design, optimization, and intelligent application of flocculants. This review systematically examines the integration of ML into flocculant research, covering algorithmic approaches, data-driven structure–property modeling, high-throughput formulation screening, and smart process control. ML models—including random forests, neural networks, and Gaussian processes—have successfully predicted flocculation performance, guided synthesis optimization, and enabled real-time dosing control. Applications extend to both synthetic and bioflocculants, with ML facilitating strain engineering, fermentation yield prediction, and polymer degradability assessments. Furthermore, the convergence of ML with IoT, digital twins, and life cycle assessment tools has accelerated the transition toward sustainable, adaptive, and low-impact treatment technologies. Despite its potential, challenges remain in data standardization, model interpretability, and real-world implementation. This review concludes by outlining strategic pathways for future research, including the development of open datasets, hybrid physics–ML frameworks, and interdisciplinary collaborations. By leveraging ML, the next generation of flocculant systems can be more effective, environmentally benign, and intelligently controlled, contributing to global water sustainability goals. Full article