Search Results (11,039)

This study presents an artificial intelligence-based predictive framework as an efficient alternative to conventional analytical procedures for evaluating elastic–plastic thermal stresses in long functionally graded solid cylinders (FGSCs) subjected to uniform internal heat generation. A hybrid artificial neural network-based fuzzy logic (ANNBFL) model is developed to estimate dimensionless thermal load parameters at both the cylinder center and outer surface by learning from validated analytical reference solutions. The material properties, including yield strength, elastic modulus, thermal conductivity, and thermal expansion coefficient, are assumed to vary radially following a parabolic gradation law. Eight influential material parameters are incorporated as input variables to describe the coupled thermo-mechanical behavior of the FGSC. Multiple ANNBFL subnetworks are trained using analytically generated datasets and subsequently integrated into a unified prediction framework, enabling rapid and accurate stress field estimation without repeated analytical calculations. Model performance is systematically assessed by direct comparison with analytical solutions, demonstrating an overall prediction consistency of approximately 98.2%. The results confirm that the proposed ANNBFL approach provides a reliable, computationally efficient surrogate modeling tool for parametric evaluation and optimum material design of functionally graded cylindrical structures under thermal loading. Full article

Sandstone-hosted uranium deposits represent one of the most critical global uranium resources suitable for in situ recovery, with their formation closely associated with organic matter (OM). We conducted a systematic literature review to synthesize over 100 published studies sourced from authoritative databases (Elsevier, Google Scholar, Web of Science, Scopus, CNKI, etc.). This study systematically summarizes the types and geological characteristics of OM in sandstone reservoirs and thoroughly analyzes the geochemical mechanisms by which OM regulates the transport and precipitation of aqueous uranium. By integrating case studies of representative sandstone uranium deposits globally, three major OM-related metallogenic models are proposed with distinct core characteristics: the humic-dominated model is driven by the complexation and direct reduction of uranium by humic substances/coal-derived OM; the roll-front model relies on reactions between oxidized uranium-bearing fluids and scattered OM, as well as microbially generated sulfides at the migration front; and the seepage-related model is fueled by upward-migrating deep hydrocarbon fluids (petroleum, methane) that act as both uranium carriers and reductants. Furthermore, this review explores the spatial coupling relationships between OM distribution and uranium mineralization in typical geological settings, evaluates the guiding significance of OM for uranium exploration, and outlines key unresolved scientific issues. The findings refine the genetic theoretical framework of sandstone-hosted uranium deposits and provide important technical support and theoretical guidance for future uranium exploration deployment and resource potential evaluation. Full article

Background: Coronary artery disease (CAD) is a multifactorial atherosclerotic disorder. Silicon (Si) is a trace mineral with potential antioxidant, anti-inflammatory, and lipid-modulating effects, but its clinical relevance in cardiovascular disease remains unclear. This study evaluated whether hair Si concentration—reflecting long-term exposure—is associated with CAD severity, clinical phenotype, risk factors, and systemic inflammation. Methods: A total of 130 patients with angiographically confirmed CAD (N = 36, 28% women) who met the inclusion criteria were enrolled. Disease severity was quantified using the Coronary Artery Surgery Study Score (CASSS) and SYNTAX score. Hair Si concentration was determined by inductively coupled plasma optical emission spectrometry (ICP-OES). Associations with demographic, clinical, biochemical, and inflammatory parameters were analyzed using non-parametric tests and multivariable ordinal logistic regression. Results: Median hair Si concentration was 21.3 ppm (range: 0.7–211.0). Hair Si levels showed no significant differences across CAD severity assessed by CASSS (H = 2.51; p = 0.47) or SYNTAX score (r = 0.079; p = 0.37). Similarly, no differences were observed between patients with stable angina and those presenting with acute coronary syndrome (p = 0.57) or between individuals with and without prior myocardial infarction. Hair Si concentration was unrelated to age, BMI, cardiovascular risk factors, lipid profile, or systemic inflammatory indices (all p > 0.2). Conclusions: Hair silicon concentration was not associated with CAD severity, phenotype, or systemic inflammation, suggesting that long-term Si exposure is metabolically neutral in advanced atherosclerosis. Unlike other minerals, silicon appears unlikely to serve as a diagnostic or prognostic biomarker in CAD, although its relevance may be confined to early vascular remodeling and primary prevention. Full article

Accurate analysis of trace elements in particulate matter (PM) emitted by brake systems critically depends on the filter selection and handling processes, which can significantly impact analytical results due to contamination and elemental interference from filter elemental composition. This study systematically evaluated two widely used filter types, EMFAB (borosilicate glass microfiber reinforced with PTFE) and Teflon (PTFE), for their suitability in the trace element determination of brake-wear PM₁₀ collected using a tribometer set-up. A total of twenty-three PM₁₀ samples were analyzed, encompassing two different friction materials, to thoroughly assess the performance and analytical implications of each filter type. Filters were tested for their chemical background, handling practicality and potential contamination risk through extensive elemental analysis by inductively coupled plasma–optical emission spectrometry (ICP-OES) and inductively coupled plasma-mass spectrometry (ICP-MS). Additionally, morphological characterization of both filter types was conducted via scanning electron microscopy (SEM) coupled with energy-dispersive X-ray spectroscopy (EDS) to elucidate structural features affecting particle capture and subsequent analytical performance. Significant differences emerged between the two filters regarding elemental interferences: EMFAB filters exhibited substantial background contribution, particularly for alkali and alkaline earth metals (Ca, Na, Mg and K), complicating accurate quantification at trace levels. Conversely, Teflon filters demonstrated considerably lower background but required careful manipulation due to their structural fragility and the necessity to remove supporting rings, potentially introducing analytical variability. Statistical analysis confirmed that the filter material significantly affects elemental quantification, particularly when the collected PM₁₀ mass is limited, highlighting the importance of careful filter selection and handling procedures. Recommendations for optimal analytical practices are provided to minimize contamination risks and enhance reliability in trace element analysis of PM₁₀ emissions. These findings contribute to refining analytical methodologies essential for accurate environmental monitoring and regulatory assessments of vehicular non-exhaust emissions. Full article

Evaluating the potential chronic health risks posed by pesticides to consumers is essential for ensuring food safety and protecting public health. An ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) method coupled with modified QuEChERS extraction was developed to simultaneously determine 26 pesticide residues in wheat grain and bran. Samples were extracted with acetonitrile with 2% (v/v) acetic acid and cleaned up using C₁₈ sorbent. Method validation demonstrated excellent linearity, accuracy, and precision. When applied to 48 wheat grain and 24 bran samples collected from major wheat-growing regions in China, 12 and 21 pesticides were detected at concentrations ranging from <0.005 to 1.785 mg kg⁻¹ and <0.01 to 2.188 mg kg⁻¹, respectively. Chronic hazard quotients (HQc) and acute hazard quotients (HQa) for all pesticides for grain and bran were far below the safety threshold of 100%. These results indicate that pesticide residues in wheat grain and bran present negligible chronic dietary risks to consumers across all age groups. Full article

Decarbonizing building energy use requires efficient heat pumps and low-impact geothermal exchangers. A novel standalone TRNSYS Type was developed for a patented shallow horizontal ground heat exchanger (HGHE), called flat-panel (FP), designed at the University of Ferrara. Beyond simulating the FP in isolation, the Type enables coupling with other components within heat-pump configurations, allowing performance assessments that reflect realistic operating conditions. The Type was implemented in TRNSYS models of a ground-source heat pump (GSHP) and of a dual air and ground source heat pump (DSHP) to verify Type reliability and evaluate potential DSHP advantages over GSHP in terms of efficiency and ground-loop downsizing. The performance of the system was analyzed under varying HGHE lengths and DSHP control strategies, which were based on onset temperature differential $DT$. The results highlighted that shorter HGHE lines yielded higher specific HGHE performance, while higher $DT$ reduced HGHE operating time. Concurrently, the total energy extracted from the ground decreased with increasing $DT$ and reduced length, thus supporting long-term thermal preservation and allowing HGHE to operate under more favorable conditions. Exploiting air as an alternative or supplemental source to the ground allows significant reduction of the HGHE length and the related installation costs, without compromising the system performance. Full article

Against the backdrop of the global “Bamboo as a Substitute for Plastic” initiative, China’s bamboo processing industry has expanded rapidly, generating large amounts of residues annually. To achieve high-value utilization of this biomass, this study optimized the fluidized bed pyrolysis process using Response Surface Methodology (RSM). Bamboo residue served as the feedstock, with particle size (8–28 mesh), pyrolysis temperature (400–700 °C), and N₂ flow rate (25–30 L/min) as independent variables. The yields of pyrolytic char, pyrolytic oil, and total product were targeted for optimization. Interaction effects between each pair of variables—such as particle size and temperature, etc.—were systematically evaluated, revealing significant coupling influences on product distribution. Optimal conditions were identified as 10–12 mesh, 577 °C, and 27.5 L/min N₂ flow, yielding 28.65% char and 43.50% bio-oil, with a total yield of 72.15%, consistent with RSM predictions. This study confirms the effectiveness of RSM in optimizing bamboo pyrolysis and offers valuable insights for industrial-scale valorization of bamboo residues into biochar and bio-oil. Full article

Accurate and rapid estimation of aboveground biomass (AGB) in cotton is crucial for precise agricultural management. However, current AGB estimation methods are limited by data monotony and insufficient model accuracy, which fail to comprehensively reflect the cotton growth status. This study introduces a novel approach by coupling cotton canopy Soil and Plant Analyzer Development (SPAD) values with multispectral (MS) data to achieve precise estimation of cotton AGB. Two experimental treatments, involving varied nitrogen fertilizer rates and organic manure applications, were conducted from 2022 to 2023. MS data from UAVs were collected across multiple cotton growth stages, while AGB and canopy SPAD values were synchronously measured. Using the coefficient of variation method, SPAD values were coupled with existing vegetation indices to develop a novel vegetation index (CGSI_VI). Moreover, the applicability of various machine learning algorithms—including Random Forest Regressor (RFR), eXtreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Particle Swarm Optimization-XGBoost (PSO-XGBoost), and Particle Swarm Optimization-CatBoost (PSO-CatBoost)—was evaluated for inverting cotton AGB. The results indicated that, compared to the original vegetation indices, the correlation between the improved vegetation index (CGSI_VI) and AGB was enhanced by 13.60% overall, with the CGSI_CIre exhibiting the highest correlation with cotton AGB (R² = 0.87). The overall AGB estimation accuracy across different growth stages, spanning the entire growth period, ranged from 0.768 to 0.949, peaking during the full-bloom stage. Furthermore, when the CGSI_VI was used as an input parameter in comparisons of different machine learning algorithms, the PSO-XGBoost algorithm demonstrated superior estimation accuracy across the entire growth stage and within individual growth stages. This high-throughput crop phenology analysis method is rapid and precise. It reveals the spatial heterogeneity of cotton growth status, thereby providing a powerful tool for accurately identifying growth differences in the field. Full article

This study presents a scenario-based modeling framework to evaluate potential decarbonization pathways for Portugal’s road transport sector. The model simulates the evolution of a light-duty vehicle (LDV) fleet under varying degrees of electrification and biofuel integration, accounting for energy consumption, CO₂ emissions and market shares of alternative propulsion technologies. Coupled with projected energy mix trajectories, the framework estimates final energy demand and well-to-wheel (WTW) emissions for each scenario, benchmarking outcomes against national and European climate targets. A key structural limitation identified is the long vehicle survival rate—averaging 14 years—which constrains fleet renewal and delays the transition to full electrification. Diesel-powered light commercial vehicles exhibit even slower replacement dynamics, rendering the Portuguese targets of full electrification by 2030 highly improbable without targeted scrappage and incentive programs. Scenario analysis indicates that even with accelerated electric vehicle (EV) uptake, battery electric vehicles (BEVs) would comprise only 12% of the fleet by 2030 and 77% by 2050. Electrification scenario raises electricity demand fortyfold by 2050, stressing generation and infrastructure. Scenarios that consider diversification of energy sources reduce this strain but require triple electricity for large-scale green hydrogen and synthetic fuel production. Full article

This study presents an integrated stochastic framework for assessing and forecasting drought dynamics in the western Bhagirathi–Hooghly River Basin, encompassing the districts of Bankura, Birbhum, Burdwan, Medinipur, and Purulia. Employing multiple probabilistic and statistical techniques, including the gamma-based standardized precipitation index (SPI), effective drought index (EDI), rainfall anomaly index (RAI), and the auto-regressive integrated moving average (ARIMA) model, the research quantifies spatio-temporal variability and projects drought risk under non-stationary climatic conditions. The analysis of century-long rainfall records (1905–2023), coupled with LANDSAT-derived vegetation and moisture indices, reveals escalating drought frequency and severity, particularly in Purulia, where recurrent droughts occur at roughly four-year intervals. Stochastic evaluation of rainfall anomalies and SPI distributions indicates significant inter-annual variability and complex temporal dependencies across all districts. ARIMA-based forecasts (2025–2045) suggest persistent negative SPI trends, with Bankura and Purulia exhibiting heightened drought probability and reduced predictability at longer timescales. The integration of remote sensing and time-series modelling enhances the robustness of drought prediction by combining climatic stochasticity with land-surface responses. The findings demonstrate that a hybrid stochastic modelling approach effectively captures uncertainty in drought evolution and supports climate-resilient water resource management. This research contributes a novel, region-specific stochastic framework that advances risk-based drought assessment, aligning with the broader goal of developing adaptive and probabilistic environmental management strategies under changing climatic regimes. Full article

Johne’s disease (JD) is a chronic infection of cattle that undermines herd productivity and profitability. While test-and-cull programs are commonly proposed for control, their effectiveness and economic feasibility remain uncertain in beef production systems. This study used an updated agent-based model (ABM) to simulate JD transmission in a representative 300-cow Western Canadian beef herd, coupled with a partial budget model to evaluate net present value (NPV) over a 10-year time horizon. Seven diagnostic test-and-cull strategies were compared, varying in test type (ELISA, individual PCR, and pooled PCR), sampling frequency (6, 12, or 24 mo), and risk-based sampling protocols. Results showed that, under baseline assumptions (6% starting prevalence; 1% prevalence in purchased stock), all strategies reduced JD prevalence relative to no testing, and six of seven yielded higher NPVs. Annual individual PCR testing provided the best balance between prevalence reduction and profitability, whereas semi-annual PCR most effectively reduced prevalence but at greater economic cost. Failure to implement control measures resulted in increasing prevalence and long-term economic losses. Sensitivity analyses demonstrated that strategy performance was consistent across variations in market conditions, cost of production, and replacement female management, although profitability declined substantially when JD prevalence in externally sourced stock was high (i.e., 10%). Collectively, these findings indicate that JD can be controlled economically in beef herds, with long-term application of various test-and-cull strategies offering robust options adaptable to management preferences. Full article

Unmanned Aerial Vehicles (UAVs) are promising nodes for Integrated Sensing and Communication (ISAC), but accurate Direction-of-Arrival (DoA) estimation on a small airframe is challenged by platform loading, motion, attitude, and multipath. Traditionally, DoA algorithms have been developed and evaluated for stationary, ground-based (or otherwise mechanically stable) antenna arrays. Extending them to UAVs violates these assumptions. This work designs a six-element Uniform Circular Array (UCA) at 2.4 GHz (radius $\approx 0.5\lambda$) for a quadrotor and introduces a Pose-Aware MUSIC (MUltiple SIgnal Classification) estimator for DoA. The novelty is a MUSIC formulation that (i) applies pose correction using the drone’s instantaneous roll–pitch–yaw (pose correction) and (ii) applies a Doppler correction that accounts for platform velocity. Performance is assessed using data synthesized from embedded-element patterns obtained by electromagnetic characterization of the installed array, with additional channel/hardware effects modeled in post-processing (Rician LOS/NLOS mixing, mutual coupling, per-element gain/phase errors, and element–position jitter). Results with the six-element UCA show that pose and Doppler compensation preserve high-resolution DoA estimates and reduce bias under realistic flight and platform conditions while also revealing how coupling and jitter set practical error floors. The contribution is a practical PA-MUSIC approach for UAV ISAC, combining UCA design with motion-aware signal processing, and an evaluation that quantifies accuracy and offers clear guidance for calibration and field deployment in GNSS-denied scenarios. The results show that, across 0–25 dB SNR, the proposed hybrid DoA estimator achieves <$0.5^{\circ }$ RMSE in azimuth and elevation for ideal conditions and ≈$5^{\circ }$–$6^{\circ }$ RMSE when full platform coupling is considered, demonstrating robust performance for UAV ISAC tracking. Full article

Bolter miners have been widely used in coal mining or excavation industries. Its efficiency is closely related to the performance of its cutting reducer, which is literally determined by the thermal behavior of the planetary gear set. Thus, this study conducts experimental investigation on the thermal behavior of a cutting reducer (produced by Zhengzhou Machinery Research Institute Transmission Technology Co., Ltd., rated input power 170 kW, transmission ratio 3.06), where the results show the high temperature rise around the intermediate shaft for unloaded condition and significant influence of the torque for loaded conditions. Then, the Finite Element Method (FEM) is used to analyze the temperature field and thermal–structural coupling of the planetary gear set. The thermal stress and deformation increase by 11.5% and 38.4%, respectively, indicating high risk of gear damage. Moreover, the load spectrum imitating the actual industrial condition is added to the KISSsoft to evaluate the reliability and contact of the planetary gear set. The findings including low safety factors of the sun gear tooth surface and planetary gear root, slipping during the sun gear and planetary gear meshing, and uneven contact fluctuations can benefit planetary gear set optimization. Full article

This paper introduces an adaptive impedance control strategy for robotic manipulators, developed through the extremum seeking technique. A model-based disturbance observer (DOB) is employed to estimate contact forces, removing the dependency on torque sensors. An impedance vector is constructed to correct the errors arising from motor uncertainties and unknown couplings, without considering the threshold value of the control parameters. Joint tracking errors and fluctuations in contact force are incorporated into the cost function. For various tasks, suitable control parameters are adaptively optimized in real time using an extremum seeking approach, which continuously evaluates the cost function. A rigorous analysis is conducted on the stability of the proposed controller. Compared to conventional approaches, the proposed adaptive impedance control offers a more streamlined design for adjusting the manipulator’s contact impedance. Experimental results confirm that the extremum seeking strategy successfully tuned the controller parameters online according to variations in the cost function. Full article

As data centers become essential large-scale infrastructures for data processing and intelligent computing, the efficiency of their internal scheduling systems is critical for both service quality and energy consumption. The performance of these scheduling systems significantly impacts the quality of computing services and overall energy usage. However, the rapid increase in task volume, coupled with the diversity of computing resources, poses substantial challenges to traditional scheduling approaches. Conventional container scheduling approaches typically focus on either minimizing task execution time or reducing energy consumption independently, often neglecting the importance of balancing these two objectives simultaneously. In this study, a container scheduling algorithm based on the Soft Actor–Critic framework, called SAC-CS, is proposed. This algorithm aims to enhance container execution efficiency while concurrently reducing energy consumption in data centers. It employs a maximum entropy reinforcement learning approach, enabling a flexible trade-off between energy use and task completion times. Experimental evaluations on both synthetic workloads and Alibaba cluster datasets demonstrate that the SAC-CS algorithm effectively achieves joint optimization of efficiency and energy consumption, outperforming heuristic methods and alternative reinforcement learning techniques. Full article