Search Results (2,321)

Interior Permanent Magnet Synchronous Motors (IPMSMs) are widely used in the electrification sector; however, reliance on rare-earth magnets imposes constraints stemming from supply instability and mining-related environmental impacts, raising sustainability concerns. To address these issues, this study investigates an IPMSM employing a consequent pole (CP) structure, in which one permanent magnet pole is replaced by iron. Because flux asymmetry in CP IPMSMs can cause torque ripple and associated vibration and noise, we propose an Intersect Consequent Pole (ICP) rotor geometry and evaluate it against a conventional IPMSM under identical stator conditions. The proposed ICP topology reduces permanent magnet usage and provides a rare-earth-reduced design alternative that addresses the vibration/noise trade-off, with a particular focus on electric power steering (EPS) applications. Electromagnetic characteristics and performance were analyzed using finite element analysis (FEA) and verified via FEA-based comparisons. Full article

This paper proposes an intelligent hierarchical fuzzy decision-making framework for threat detection and identification in Counter-Unmanned Aerial Vehicle (Counter-UAV) systems, based on the fusion of heterogeneous sensor data. To address the increasing complexity and ambiguity in modern UAV threats, this study introduces a novel three-stage fuzzy inference architecture that supports adaptive sensor evaluation and optimal pairing. The proposed methodology consists of three-layered Fuzzy Inference Systems (FIS): FIS-A quantifies sensor effectiveness based on UAV flight altitude and detection probability; FIS-B assesses operational suitability using sensor range and cost; and FIS-C synthesizes both outputs, along with sensor capability overlap, to determine the composite suitability of sensor pairs. This hierarchical structure enables detailed analysis and system-level optimization, reflecting real-world constraints and performance trade-offs. Simulation-based evaluation using diverse sensor modalities (EO/IR, Radar, Acoustic, RF), supported by empirical data and literature, demonstrates the framework’s ability to handle uncertainty, enhance detection reliability, and support cost-effective sensor deployment in Counter-UAV operations. The framework’s modularity, scalability, and interpretability represent significant advancements in intelligent Counter-UAV system design, offering a transferable methodology for dynamic threat environments. Full article

Data preprocessing and feature engineering play key roles in data mining initiatives, as they have a significant impact on the accuracy, reproducibility, and interpretability of analytical results. This review presents an analysis of state-of-the-art techniques and tools that can be used in data input preparation and data manipulation to be processed by mining tasks in diverse application scenarios. Additionally, basic preprocessing techniques are discussed, including data cleaning, normalisation, and encoding, as well as more sophisticated approaches regarding feature construction, selection, and dimensionality reduction. This work considers manual and automated methods, highlighting their integration in reproducible, large-scale pipelines by leveraging modern libraries. We also discuss assessment methods of preprocessing effects on precision, stability, and bias–variance trade-offs for models, as well as pipeline integrity monitoring, when operating environments vary. We focus on emerging issues regarding scalability, fairness, and interpretability, as well as future directions involving adaptive preprocessing and automation guided by ethically sound design philosophies. This work aims to benefit both professionals and researchers by shedding light on best practices, while acknowledging existing research questions and innovation opportunities. Full article

Background/Objectives: The attainment of a stone-free (SF) condition is a fundamental indicator of successful outcomes after flexible ureteroscopy (fURS) for urinary stone disease. External confirmations of preoperative scores remain limited. We externally validated the T.O.HO. and S.T.O.N.E. scores in an independent Japanese cohort and examined calibration, decision curve utility, and threshold-guided use to support personalized planning. Methods: We retrospectively analyzed 361 consecutive patients treated with fURS from March 2018 to August 2023. Postoperative SF status was defined as the absence of residual calculi greater than 2 mm on non-contrast computed tomography performed within three months of surgery. Independent determinants of SF were identified using multivariable logistic regression, predictive performance was quantified by receiver operating characteristic analyses with DeLong’s test, and model calibration and decision curve analysis were additionally assessed. Results: Among the 361 patients, 255 (70.6%) achieved an SF state. A larger stone diameter, the presence of lower-pole calculi, and preoperative pyuria (positive urine WBC) were significant independent predictors of residual fragments. T.O.HO. demonstrated superior discrimination (AUC 0.86) compared with S.T.O.N.E. (AUC 0.77; p < 0.01) and surpassed individual predictors. Both scores showed acceptable calibration. Decision curve analysis demonstrated higher net benefit for T.O.HO. across clinically relevant thresholds. We provide clinically useful cut-offs (e.g., T.O.HO. ≤5: high SF probability; 6: trade-off discussion; ≥7: higher residual risk) to align actions with patient priorities. Conclusions: Beyond discrimination, a calibrated, threshold-aware use of T.O.HO. enables personalized preoperative counseling and shared decision-making, potentially reducing unnecessary staging and enhancing routine fURS planning. Full article

The integration of miniaturized magnetometers with Unmanned Aerial Vehicles (UAVs) has revolutionized magnetic surveying, offering flexible, high-resolution, and cost-effective solutions for geophysical applications also in remote areas. This study presents a comparative analysis of two configurations using UAV-borne scalar magnetometers through several surveys conducted in the Altopiano di Verteglia (Southern Italy), chosen as a test-site since buried pipes are present. The two systems differ significantly in sensor–platform arrangement, noise sensitivity, and flight configuration. Specifically, the first employs the MagNimbus magnetometer with two sensors rigidly attached on two masts at fixed distances, respectively, above and below the UAV, enabling the vertical gradient estimation while increasing noise due to proximity to the platform. The second involves the use of the MagArrow magnetometer suspended at 3 m below the UAV through non-rigid ropes, which benefits from minimal electromagnetic interference but suffers from oscillation-related instability. The retrieved magnetic anomaly maps effectively indicate the location and orientation of buried pipes within the studied area. Our comparative analysis emphasizes the trade-offs between the two systems: the MagNimbus-based configuration offers greater stability and operational efficiency, whereas the MagArrow-based one provides cleaner signals, which deteriorate with the vertical gradient computation. This work underscores the need to optimize UAV-magnetometer configurations based on environmental, operational, and survey-specific constraints to maximize data quality in drone-borne magnetic investigations. Full article

The delayed coking unit (DCU) is a critical technology in Mexican refineries for upgrading heavy crude oil into lighter, high-value products. Despite its economic relevance, the process is energy-intensive, generates substantial emissions, and produces significant coke, challenging its sustainability. This study proposes a multi-objective optimization framework to enhance DCU performance by integrating Aspen Plus^® v.12.1 simulations with sustainability metrics. Five key indicators were considered: Global Warming Potential (GWP), Specific Energy Intensity (SEI), Mass Intensity (MI), Reaction Mass Efficiency (RME), and Product Yield. A validated Aspen Plus^® model was combined with sensitivity analysis to identify critical decision variables, which were optimized through the $ϵ$-constraint method. Strategic adjustments in reflux flows, split ratios, and column operating conditions improved separation efficiency and reduced energy demand. Results show GWP reductions of 15–25% and SEI improvements of 5–18% for light and heavy gas oils, with smaller gains in MI and trade-offs in RME. Product yield was preserved under optimized conditions, ensuring economic feasibility. A key limitation is that this study did not model coking reactions; instead, optimization focused on the separation network, using reactor effluent as a fixed input. Despite this constraint, the methodology demonstrates a replicable path to improve refining sustainability. Full article

In the ecologically fragile western Shanxi Loess region, stand density regulation of artificial Robinia pseudoacacia L. forests plays a crucial role in sustaining the water regulation functions of the litter-soil system, yet multi-scale mechanistic analyses remain scarce. To address this gap, we established six stand density classes (ranging from 1200 to 3200 stems/ha) and quantified litter water-holding traits and soil physicochemical properties. We then applied principal component analysis (PCA) and structural equation modeling (SEM) to examine density-litter-soil relationships. Low-density stands (≤2000 stems/ha) exhibited significantly higher litter accumulation (6.08–6.37 t/ha) and greater litter water-holding capacity (maximum 20.58 t/ha) than the high-density stands (p < 0.05). Soil capillary water-holding capacity decreased with increasing density (4702.63–4863.28 t/ha overall), while non-capillary porosity (5.26–6.21%) and soil organic carbon (~12.5 g/kg) were higher in high-density stands (≥2800 stems/ha), reflecting a structural-carbon optimization trade-off. PCA revealed a primary hydrological function axis with low-density stands clustering in the positive quadrant, while high-density stands shifted toward nutrient-conservation traits. SEM confirmed that stand density affected soil capillary water-holding capacity indirectly through litter accumulation (significant indirect path; non-significant direct path), highlighting the central role of litter quantity. When density exceeded ~2400 stems/ha, litter decomposition rate decreased by ~56%, coinciding with capillary porosity falling below ~47%, a threshold linked to impaired balance between water storage and infiltration. These findings identify 1200–1600 stems/ha as the optimal density range; in this range, soil capillary water-holding capacity reached 4788–4863 t/ha, and available phosphorus remained ≥2.1 mg/kg, providing a density-centered, near-natural management paradigm for constructing “water-conservation vegetation” on the Loess Plateau. Full article

This study investigates the fabrication and performance of a novel composite material by infiltrating SnSb11Cu6 babbitt alloy into an open-cell AlSn6Cu-SiC matrix. The composites, produced via a multi-stage liquid-state processing route, were comprehensively characterized for their microstructural, mechanical, and tribological properties. The inclusion of 5 wt.% silicon carbide reinforcement resulted in a significant improvement in tribological performance under dry-sliding conditions. Specifically, the reinforced composite exhibited a 24.8% reduction in wear and a 10.8% reduction in the coefficient of friction compared to its unreinforced counterpart. Crucially, this enhancement in wear resistance was achieved while the bulk compressive mechanical properties and ductile deformation behavior remained virtually identical to the unreinforced material. Microstructural analysis confirmed that the high-hardness SiC particles act as primary load-bearing agents, shielding the softer metallic matrix from severe wear. These findings demonstrate the successful development of a high-performance composite with enhanced tribological durability without a mechanical trade-off, making it a promising candidate for advanced bearing applications. Full article

Climate change poses serious challenges to Mediterranean crops such as the olive tree (Olea europaea L. subsp. europaea), underscoring the need for cultivars with improved drought tolerance and disease resistance. This study investigates variability in leaf and wood traits among Moroccan and introduced olive cultivars and their crossbreed genotypes grown under similar conditions. Specifically, we assessed (1) variation in key functional traits, (2) the effects of crossbreeding combinations, and (3) trait syndromes shaped by selection. Results showed substantial intraspecific variation in leaf traits, including specific leaf area (SLA), specific leaf water content (SLWC), stomatal size (SS), and density (SD), indicating diverse strategies for resource use and plasticity. Crossbreed genotypes generally displayed higher SLWC and lower SLA, reflecting adaptation to water stress. Wood traits, particularly vessel size (SVS) and number (NVS), also varied, revealing trade-offs between hydraulic efficiency and safety. Notably, an increase in vessel size and hydraulic conductivity was correlated with oil content (OC%), while OC% increased with higher vessel and stomatal densities. Larger stomata increased conductance and fruit growth, while lower SLA was linked to higher yield. Multivariate analysis distinguished two genotype groups, consistent with parental combinations. Overall, crossbreeding generated novel functional diversity that may enhance adaptive potential. These findings highlight the value of integrating functional and anatomical traits into olive breeding programs to improve resilience and productivity under climate change. Full article

The management of municipal solid waste (MSW) plays a crucial role in advancing sustainable development and circular economy goals across the European Union. In Italy, despite improvements in separate collection, significant regional disparities in MSW performance and costs persist. This study assesses the eco-efficiency of MSW services in 5516 Italian municipalities to uncover performance gaps and their underlying drivers. Eco-efficiency is measured using a Data Envelopment Analysis (DEA) model based on the Generalized Directional Distance Function (GDDF). This model incorporates per capita cost as an input, sorted waste as a desirable output, and residual waste as an undesirable output. A second-stage quantile regression is then utilized to explore how contextual factors influence eco-efficiency across various performance levels. The results reveal significant territorial disparities, with only 0.13% of municipalities achieving full eco-efficiency. Paradoxically, higher levels of separate waste collection—typically a policy goal—are associated with increased costs, especially in more efficient municipalities, suggesting a trade-off between environmental performance and economic sustainability. Similarly, population density negatively affects eco-efficiency but may facilitate economies of scale in collection systems. These findings highlight a tension between achieving optimal sorting rates and maintaining cost-effectiveness. Policy interventions should consider these trade-offs, prioritizing basic performance in lagging areas while promoting cost-control strategies in high-performing municipalities. Full article

Oases in arid regions are crucial for sustaining agricultural production and ecological stability, yet few studies have simultaneously examined the coupled dynamics of land use/cover change (LUCC), carbon emissions, and ecosystem service value (ESV) at the oasis–agricultural scale. This gap limits our understanding of how different land use trajectories shape trade-offs between carbon processes and ecosystem services in fragile arid ecosystems. This study examines the spatiotemporal interactions between land use carbon emissions and ESV from 1990 to 2020 in the Wensu Oasis, Northwest China, and predicts their future trajectories under four development scenarios. Multi-period remote sensing data, combined with the carbon emission coefficient method, modified equivalent factor method, spatial autocorrelation analysis, the coupling coordination degree model, and the PLUS model, were employed to quantify LUCC patterns, carbon emission intensity, ESV, and its coupling relationships. The results indicated that (1) cultivated land, construction land, and unused land expanded continuously (by 974.56, 66.77, and 1899.36 km²), while grassland, forests, and water bodies declined (by 1363.93, 77.92, and 1498.83 km²), with the most pronounced changes occurring between 2000 and 2010; (2) carbon emission intensity increased steadily—from 23.90 × 10⁴ t in 1990 to 169.17 × 10⁴ t in 2020—primarily driven by construction land expansion—whereas total ESV declined by 46.37%, with water and grassland losses contributing substantially; (3) carbon emission intensity and ESV exhibited a significant negative spatial correlation, and the coupling coordination degree remained low, following a “high in the north, low in the south” distribution; and (4) scenario simulations for 2030–2050 suggested that this negative correlation and low coordination will persist, with only the ecological protection scenario (EPS) showing potential to enhance both carbon sequestration and ESV. Based on spatial clustering patterns and scenario outcomes, we recommend spatially differentiated land use regulation and prioritizing EPS measures, including glacier and wetland conservation, adoption of water-saving irrigation technologies, development of agroforestry systems, and renewable energy utilization on unused land. By explicitly linking LUCC-driven carbon–ESV interactions with scenario-based prediction and evaluation, this study provides new insights into oasis sustainability, offers a scientific basis for balancing agricultural production with ecological protection in the oasis of the arid region, and informs China’s dual-carbon strategy, as well as the Sustainable Development Goals. Full article

A significant problem in cybersecurity is to accurately detect malicious network activities in real-time by analyzing patterns in socket-level packet transmissions. This challenge involves distinguishing between legitimate and adversarial behaviors while optimizing detection strategies to minimize false alarms and resource costs under intelligent, adaptive attacks. This paper presents a comprehensive framework for network security by modeling socket-level packet transmissions and extracting key features for temporal analysis. A long short-term memory (LSTM)-based anomaly detection system predicts normal traffic behavior and identifies significant deviations as potential cyber threats. Integrating this with a zero trust signaling game, the model updates beliefs about agent legitimacy based on observed signals and anomaly scores. The interaction between defender and attacker is formulated as a Stackelberg game, where the defender optimizes detection strategies anticipating attacker responses. This unified approach combines machine learning and game theory to enable robust, adaptive cybersecurity policies that effectively balance detection performance and resource costs in adversarial environments. Two baselines are considered for comparison. The static baseline applies fixed transmission and defense policies, ignoring anomalies and environmental feedback, and thus serves as a control case of non-reactive behavior. In contrast, the adaptive non-strategic baseline introduces simple threshold-based heuristics that adjust to anomaly scores, allowing limited adaptability without strategic reasoning. The proposed fully adaptive Stackelberg strategy outperforms both partial and discrete adaptive baselines, achieving higher robustness across trust thresholds, superior attacker–defender utility trade-offs, and more effective anomaly mitigation under varying strategic conditions. Full article

This paper introduces a power-driven systems engineering methodology for the early-phase design of highly miniaturized satellites: PlanarSats. We derive an analytical framework linking power requirements, contingency policies, solar-cell performance, and subsystem integration to determine the absolute minimum satellite size. Through idealized and detailed case studies, we explore the trade-offs inherent in subsystem selection and integration constraints. Sensitivity analysis identifies critical factors affecting minimum area and operational envelopes. Our framework provides a clear tool for balancing functionality, reliability, and physical limits in next-generation ultra-small satellite missions. Full article

Different strategies are used in organic and conventional cultivation, which can significantly influence crop yield, greenhouse gas (GHG) emissions, and soil quality. However, the relative efficiency of these fertilization practices has not been systematically compared. The objective of this study was to evaluate the impacts of organic, conventional, and semi-organic fertilization systems on soil properties, crop productivity, and GHG emissions through a comprehensive meta-analysis. The analysis showed that conventional systems had the highest increase in nitrous oxide (N₂O) emissions (+62%), followed by semi-organic (+55%) and organic (+21%). Soil texture strongly influenced methane (CH₄) and carbon dioxide (CO₂) fluxes, with clay soils showing the highest CH₄ response (+50%). Cropping practices such as intercropping and crop rotation enhanced soil nitrate availability (+18%), while vegetable and cereal systems improved crop yield by +29% and +19%, respectively. Importantly, semi-organic systems increased yield (+25%) while reducing greenhouse gas intensity (+13%), especially in cereals under intercropping. Integrating organic inputs into semi-organic systems, especially in cereal cultivation under intercropping practices, appears to reduce the carbon intensity per unit yield while maintaining productivity. These findings underscore the importance of context-specific management strategies to optimize agronomic performance and mitigate environmental impacts. Full article

This paper presents the development and characteristics of geopolymer foams modified with paraffin-based phase change materials (PCMs) encapsulated in diatomite. The aim was to increase both the thermal insulation and heat storage capacity of the foams while maintaining sufficient mechanical strength for construction applications. Eleven variants of composites with different PCM fractions (5–10% by mass) and grain sizes (<1.6 mm to >2.5 mm) were synthesized and tested. The inclusion of PCM encapsulated in diatomite modified the porous structure: the total porosity increased from 6.6% in the reference sample to 19.6% for the 1.6–1.8 mm_10% wt. variant, with pore diameters ranging from ~4 to 280 µm. Thermal conductivity (λ) ranged between 0.090–0.129 W/m·K, with the lowest values observed for composites 2.0–2.5 mm_5–10% wt. (≈0.090–0.091 W/m·K), which also showed high thermal resistance (R ≈ 0.287–0.289 m²·K/W). The specific heat (Cp) increased from 1.28 kJ/kg·K (reference value) to a maximum value of 1.87 kJ/kg·K for the 2.0–2.5 mm_10% mass variant, confirming the effective energy storage capacity of PCM-modified foams. Mechanical tests showed compressive strength values in the range of 0.7–3.1 MPa. The best structural performance was obtained for the 1.6–1.8 mm_10% wt. variant (3.1 MPa), albeit with a higher λ (≈0.129 W/m·K), illustrating the classic trade-off between porosity-based insulation and mechanical strength. SEM microstructural analysis and mercury porosimetry confirmed the presence of mesopores, which determine both thermal and mechanical properties. The results show that medium-sized PCM fractions (1.6–2.0 mm) with moderate content (≈10% by weight) offer the most favorable compromise between insulation and strength, while thicker fractions (2.0–2.5 mm) maximize thermal energy storage capacity. These findings confirm the possibility of incorporating natural PCMs into geopolymer foams to create multifunctional materials for sustainable and energy-efficient building applications. A unique contribution to this work is the use of diatomite as a natural PCM carrier, ensuring stability, compatibility, and environmental friendliness compared to conventional encapsulation methods. Full article