Search Results (30,295)

Large flange shafts are the core load-bearing and connecting components of high-end equipment, and their welding multi-physical fields directly affect the quality and service safety of the components. Traditional experiments and finite element methods suffer from long cycles and low efficiency, which can hardly meet the demand for rapid prediction. Aiming at the fast and accurate prediction of welding temperature, deformation and residual stress, this study combines thermal–mechanical coupled finite element simulation with machine learning to construct and compare a variety of prediction models. A dataset is built based on simulation data from 100 groups of process parameters. Overfitting is reduced through strategies including early stopping and dropout, and models such as MLP, RF, RBF-SVR, TabNet, XGBoost, and FT-Transformer are established and verified through 10-fold cross-validation. The results show that the MLP model performs best in the prediction of temperature, deformation and residual stress, and is in good agreement with the simulation values. The prediction errors of the peak temperature of the weld and base metal are below 5%, and the errors of deformation and residual stress are controlled within 10%. The average error of peak residual stress is about 6 MPa, and the deviation of most samples is less than 5 MPa. The RF model ranks second in accuracy, with an average error of about 6.5 MPa for peak residual stress, showing a satisfactory interpretability and engineering applicability. RBF-SVR and TabNet can meet basic prediction requirements. Under the small-sample condition in this work, XGBoost and FT-Transformer present relatively large errors and a weak generalization ability, making it difficult to achieve high-precision prediction. The MLP model established in this paper can effectively reproduce the evolution of welding multi-physical fields and supports the rapid prediction and process optimization of large flange shaft welding. The generalization ability and practical performance of the model can be further improved by expanding the dataset and experimental verification in the future. Full article

Mechanical harvesting damage is a critical factor constraining potato quality and storage performance. Field curing is a commonly employed pre-treatment prior to mechanical picking of potatoes, which promotes skin suberization and reduces mechanical damage; however, the determination of optimal curing duration lacks a theoretical basis. This study investigated ‘Xisen No. 6’ potatoes at harvest maturity. Curing was performed by field sun-drying (open-air exposure) immediately after mechanical excavation, with five duration gradients (0, 1, 2, 3, and 4 h) established under the recorded meteorological conditions. Twenty-two physical–mechanical and damage parameters were measured, and principal component analysis (PCA) was employed for comprehensive evaluation. The results demonstrated that curing induced a transformation of tubers from “soft-elastic bodies” to “hard-brittle bodies”. This study first revealed the contradictory evolution pattern between skin abrasion damage and tissue impact damage, which exhibited a strong negative correlation (r = −0.89, p < 0.01). PCA indicated that a 3 h curing duration could effectively balance the control of both damage types. These findings provide a quantitative solution to the dilemma of reducing skin damage while controlling impact damage during mechanical potato harvesting, offering significant guidance for optimizing harvesting process parameters and reducing postharvest losses. Full article

Sulfonamide antibiotics (SAs) are ubiquitous and persistent organic contaminants in aquatic and soil ecosystems due to their extensive application and high structural stability, causing rising environmental hazards. Conventional treatment approaches, generally based on physical adsorption or biological processes, remain limited in achieving efficient and stable removal as well as deep molecular modification of SAs. In recent years, modified biochar has developed as a flexible environmental functional material incorporating adsorption and reaction regulation capabilities, owing to its customizable pore structure, surface chemistry, and electronic characteristics. This study comprehensively highlights current achievements in the adsorption and degradation of sulfonamide antibiotics by modified biochar, with specific emphasis on modification techniques, structural modulation, structure–performance connections, and interfacial reaction processes. Through physical activation, heteroatom doping, defect engineering, and metal integration, biochar has developed from a traditional adsorbent into a carbon-based interfacial reactor capable of pollutant adsorption, molecular activation, and directed transformation. Surface-confined reaction interfaces, where π–π interactions, hydrogen bonding, electrostatic interactions, and metal coordination cooperatively control adsorption and transformation processes, are primarily responsible for the elimination of SAs. Moreover, the dual functions of modified biochar in driving both radical and non-radical pathways are explored, showing the vital importance of interfacial electronic structure modulation and electron-transfer mechanisms in influencing reaction efficiency and selectivity. The impact of sulfonamide molecular configurations, ambient circumstances, and concomitant chemicals on removal performance are also explored. Unlike previous reviews that mainly summarize adsorption efficiency or oxidant activation systems separately, this work integrates structural modulation, interfacial electronic regulation, and bond-selective transformation mechanisms into a unified structure–chemistry–reactivity framework. By correlating sulfonamide molecular configuration with biochar electronic structure, this review provides a mechanistic roadmap for the rational design of next-generation catalytic biochar systems. Finally, key challenges related to structural controllability, long-term stability, and engineering scalability are identified, and future research directions are proposed to support the rational design of high-performance biochar materials and the practical control of sulfonamide antibiotic pollution. Full article

Urban road networks (URNs) underpin critical urban functions ranging from public service provision to emergency response. However, URN resilience is commonly assessed using aggregate performance metrics or critical-element identification, which offers limited insight into how disruption reshapes spatial accessibility. This limitation is increasingly salient under stock-based urban development, where opportunities for large-scale physical network reconfiguration and segment-level engineering interventions are constrained, and resilience enhancement increasingly depends on facility-based adaptation. To address this gap, drawing on graph theory and percolation theory, this study proposes a disconnection-pattern-based (DPB) analytical approach for mapping spatial configurations of URN vulnerability. Two generic disconnection patterns derived from topological limits of network redundancy are conceptualized: Local Island Disconnection (LID) and Global Structural Fragmentation (GSF). Corresponding quantitative mapping methods are developed and applied to cities with contrasting URN morphologies. Results show that spatial configurations of connectivity vulnerability can be systematically mapped across heterogeneous URNs, yielding spatially explicit information critical to resilience-oriented facility siting. By treating vulnerability as a spatial configuration rather than a single-state metric, the proposed approach extends URN resilience assessment toward facility-planning strategies that adapt to existing road-network risk configurations under stock-based development. Full article

Background/Objectives: Insect oils have gained attention as sustainable cosmetic ingredients due to their bioactive lipid content. This study aimed to characterize oils from cricket and to evaluate their safety, biological activities, and performance in sunscreen formulations. Methods: Oils were extracted from Gryllus bimaculatus, Teleogryllus mitratus, and Acheta domesticus by cold pressing following hot-air drying. Fatty acid composition was determined using gas chromatography–mass spectrometry. Safety was assessed by cytotoxicity testing in normal human dermal fibroblasts (NHDF) and the hen’s egg chorioallantoic membrane (HET-CAM) assay. Antioxidant and anti-inflammatory activities were evaluated by intracellular reactive oxygen species (ROS) and nitric oxide (NO^•) assays. Based on biological performance, T. mitratus oil (TMO) was incorporated into sunscreen creams containing physical and chemical ultraviolet (UV) filters. Physical stability, viscosity, pH, sun protection factor (SPF), persistent pigment darkening/ultraviolet A protection factor (PPD/UVA-PF), and blue light protection were evaluated. Results: All cricket oils were non-cytotoxic to NHDF cells and were classified as non-irritating in the HET-CAM assay. TMO exhibited the strongest antioxidant activity, reducing intracellular ROS and significantly inhibiting NO^• production in lipopolysaccharide-stimulated cells. Only TMO showed measurable UVA protection (PPD/UVA-PF = 12.1, PA+++). Sunscreen creams formulated with TMO achieved higher photoprotective efficacy than olive oil-based creams, with SPF values up to 40.51 and PPD/UVA-PF up to 39.17. The inclusion of foundation pigments further increased SPF to 43.09 and enhanced blue light protection to 35.1%. Conclusions: TMO is a safe and effective multifunctional ingredient that enhances sunscreen performance and supports sustainable cosmetic formulation. Full article

This study investigates gender differences in conceptual understanding, confidence, and calibration among 369 Turkish university students completing the Force Concept Inventory (FCI). Using accuracy scores, confidence ratings, and bias indices as complementary measures, we examined how male and female students differed in both their conceptual reasoning and their self-evaluative judgments. The results show that male students achieved significantly higher accuracy scores than female students (M = 56.79 vs. 49.96), though the effect size was small, indicating modest conceptual differences. Confidence differences were more pronounced: male students reported substantially higher confidence (M = 68.17) than female students (M = 54.44), representing a moderate effect. Bias scores further revealed that male students exhibited greater overconfidence (M = 11.38), while female students were more likely to underestimate their performance (M = 4.47). Item-level analyses showed that gender differences were concentrated in well-documented areas of conceptual difficulty, including Newton’s first law and gravitation. These patterns align with international findings and suggest that gender differences in physics arise from a combination of conceptual challenges and metacognitive tendencies rather than large performance disparities. The findings highlight the importance of integrating confidence calibration, reflective metacognitive practices, and targeted conceptual support into introductory physics instruction to reduce gender-based differences in learning outcomes. Full article

The present paper provides a human activity-level classification model based on the patient’s lifelog collected from wearable devices. During about two months, the heart rate, step count, and calorie consumption for a total of 182 patients were collected from a wearable device. Using the lifelog data, the machine learning models were developed to classify the physical activity status of patients into five levels. Three types of wearable data with heart rate, step count, and calorie consumption were pre-processed as integrated data in time series. A total of 80% of the integrated data was used as the training dataset, and the remaining 20% was used as the test dataset. Sixteen algorithms were evaluated, including 12 traditional machine learning models (SVM, KNN, RF, etc.) and 4 deep learning models (CNN, RNN, etc.), and cross-validation was performed by dividing the training dataset into 5 folds. By changing the parameters required for training, the models with optimal parameters were derived. The performance of the final models with the new patient lifelog data was evaluated, and it was shown that the classification for human activity level based on heart rate and step count can be performed with high accuracy. Full article

Multi-instrument synergistic observation is vital for studying cloud and precipitation physics. However, using the nearest scan time for matching inevitably introduces temporal mismatches. Here we employ three advection correction methods for temporal matching in weather radar and spaceborne radar observations: Lucas–Kanade (LK), Variational Echo Tracking (VET), and Anisotropic Diffusion (AD). These methods calculate the movement speed of the storms using optical flow methods, and then determine their positions based on the elapsed time between instruments. Next, we conducted a quantitative assessment of the performance of these three methods based on the consistency of storm morphology and rainfall rates. Our results demonstrate that all three advection correction methods effectively reduce the discrepancies in morphology and rainfall rate among multi-source data. Without correction, the Coincidence Rate (CR) and Structural Similarity (SSIM) were 30.96% and 0.689 in the US and 29.44% and 0.670 in China, respectively. In comparison, applying the LK, VET, and AD methods increased those indices to 32.94%, 32.72%, 32.85% and 0.718, 0.715, 0.716 in the US, and 31.34%, 31.17%, 31.24% and 0.696, 0.694, 0.693 in China, respectively. The rainfall rate inconsistencies were also effectively reduced after advection correction. The performances among the three methods were similar. Overall, the LK method performed slightly better than AD, followed by VET. Full article

The grinding of Tremella fuciformis is a critical step for its value-added processing and the efficient utilization of its functional components, significantly impacting product quality and process adaptability. This study investigated ultrafine grinding (UFG) as a mechano-physical strategy to improve product quality, systematically analyzing its impact on physical properties (particle size, powder characteristics, color), extraction efficiency, chemical composition, and rheological behavior compared to conventional grinding (CG). The results revealed that UFG treatment induced an extensive disruption of the matrix, reducing particle size by 91.8% (D90 = 18.18 μm) and significantly increasing specific surface area. Notably, this physical modification directly translated into enhanced processing performance. UFG powder exhibited reduced powder flowability, superior solubility and improved color brightness. This structural degradation proved beneficial for extraction, unlocking a substantially higher yield (60.98–66.48%). Concurrently, the aqueous extracts of UFG powder exhibited more fluid-like rheological characteristics. This study confirms the potential of UFG as an effective pretreatment for the intensive processing of T. fuciformis and indicates its promising application in functional food development and the extraction of bioactive components. Full article

This study presents a SCAPS-1D-based numerical optimization of an organic ultraviolet (UV) photodetector employing an FTO/PTB7/Spiro-OMeTAD/Au device architecture. The novelty of this work lies in a simulation-guided, UV-specific optimization strategy that combines thickness engineering, controlled doping, and contact work-function tuning to achieve intrinsic spectral selectivity without external optical filters. We systematically optimize material and device parameters, including active layer thicknesses, donor and acceptor densities, and the metal electrode work function, to enhance responsivity, detectivity, and spectral performance. Simulations identify optimal thicknesses of 1200 nm for PTB7 and 1000 nm for Spiro-OMeTAD, with donor concentrations of 1 × 10²⁰ cm⁻³ and 1 × 10¹⁸ cm⁻³, respectively. A comparative contact analysis demonstrates that replacing aluminum with gold (Au) forms a near-ohmic back contact, leading to improved hole extraction and suppressed dark current due to favorable energy-level alignment. The optimized device achieves a peak external quantum efficiency of approximately 80% in the 300–400 nm ultraviolet range, with a responsivity up to 0.4 A/W. The UV selectivity originates from the absorption characteristics of PTB7 combined with suppressed long-wavelength charge collection, resulting in a negligible response in the visible–near-infrared region. These results confirm the device’s strong potential for high-sensitivity, solar-blind UV photodetection. By integrating practical material selection with physically consistent SCAPS-1D optoelectronic modeling, this work provides a robust design framework to guide the development of next-generation organic UV photodetectors for environmental sensing, biomedical diagnostics, and wearable optoelectronics. Full article

The component-level line of sight (LOS) angle measurement of spacecraft is much desired during space rendezvous, especially for component-related operations, such as component status evaluation, component repair, etc. However, most existing methods rarely consider the component approaching scenario where a continuous, stable, real-time LOS angle measurement method for the component of interest is needed. In this paper, a continuous robust component-level LOS angle measurement method with high computational efficiency applicable to the approach of the key component is proposed. Firstly, an adaptive gamma correction method is introduced to enhance the image quality in complex and variable lighting environments. Secondly, optimized thresholding that exploits information entropy is proposed to identify the pixels that are supposed to be the target from the background. Region detection is subsequently performed to segment the target region into suspected component regions, which can account for target changes during the approach by seamless parameter adaptation. Then, solar panels are recognized and accurately segmented based on the prior knowledge of their spatial relationship with other components and unique shape features. Finally, the centers of solar panels are localized and their LOS angles are calculated. Extensive experiments are conducted to demonstrate the performance of our proposed method, including the verification of the superiority of the solar panel recognition and segmentation method using both simulated images generated by an image simulator and actual images taken by a camera in a dark-room considering the actual lighting in space, and the validation of the ability of supporting real-time component-level LOS angle measurement by ground semi-physical experiments with a guidance, navigation and control (GNC) system incorporated to simulate an on-line dynamic approach. Full article

This study’s primary objective is to determine how boron-containing wastes from the stripping areas of the Emet–Bigadiç (Türkiye) boron deposits affect the mechanical performance of cement-based mortars and the effectiveness of weak soil improvement. The Bigadiç samples contain colemanite, calcite, dolomite, and quartz minerals, whereas the Emet samples predominantly comprise calcite. The wastes were incorporated into the cement matrix in two different forms: (i) solid-phase cement replacement and (ii) boron waste solution additive. Experimental findings demonstrated that replacing 10% of cement with a 4% Bigadiç-origin boron waste solution resulted in a compressive strength of 55.37 MPa after 7 days of curing, which is higher than that of the reference mixture. Also, the study revealed that the addition of 15% boron waste to weak soils increased the soil density to 1728 kg/m³ by filling micro-voids and enhancing intergranular interlocking. Due to this physical filling and chemical bridging effect, CBR value increased from an initial 4 to 6, providing a significant improvement in the soil’s deformation modulus and bearing capacity. Consequently, used boron wastes not only provide high mechanical performance in cement-based systems but also offer potential as an alternative additive material for sustainable and cost-effective soil stabilization applications. Full article

This study proposed an innovative assembled blast-resistant composite structure integrating ultra-high performance concrete plates and ceramic foam layers, designed to enhance blast protection for a power valve hall hole blocking system. Based on the full-scale blast test and numerical simulation, the dynamic response of the structure under blast load was revealed. The parametric studies showed that when the thickness of the UHPC ribbed plate was increased from 30 mm to 40 mm, the maximum displacement at the edge of the hole was reduced by 60.9%. However, a further increase in thickness to 50 mm led to an increase in the inertia effect due to the high stiffness, resulting in a reduction in the maximum displacement value by only 8.61%. In addition, a machine learning framework combining generative adversarial networks (GANs) and Extremely Randomized Trees (ERT) model was constructed to predict the maximum displacement of the structure under blast loading. Furthermore, interpretability analysis by the (SHapley Additive exPlanations) SHAP algorithm verified the consistency of the decision logic of the ERT model with the physical mechanism of the explosion. This study established a full-chain design framework of structural design, mechanism research and intelligent prediction, which provided theoretical support and an intelligent tool system for protection engineering. Full article

To address the issue of inefficient soil water utilization in dryland wheat fields, caused by a mismatch between summer fallow precipitation and crop growth periods, implementing fallow-period tillage was crucial for conserving water and enhancing yield. However, there was a lack of comprehensive evaluations of the impact of different tillage practices on soil functional quality based on multidimensional indicators, and the relationship between yield and soil functional quality remained unclear. This study established three treatments during the summer fallow period: no tillage (FNT), subsoiling tillage (FST) and plowing tillage (FPT). We determined the soil water-stable aggregates particle size distribution and stability, aggregate organic carbon (AOC) content, soil organic carbon (SOC) content and soil organic carbon storage (SOCs), as well as winter wheat yield. Using the Z-score method, we integrated the soil’s physical and chemical indicators to perform a comprehensive evaluation of different tillage practices. The results showed that FNT significantly enhanced soil aggregate stability in the 0–30 cm soil depths compared to FST and FPT (p < 0.05), which was primarily attributed to a substantial increase in the content of >2 mm aggregates. Meanwhile, FNT resulted in significantly higher SOCs within the 0–50 cm profile, with increases of 8.1% and 5.8% compared to FST and FPT (p < 0.05), respectively. This was primarily due to elevated SOC content and higher AOC contents within the 2–0.25 mm and >2 mm aggregates in the topsoil layer. In contrast, FST significantly increased grain yield compared to FNT and FPT, by 16.7% and 15.0% (p < 0.05), respectively, which was associated with higher ear number and ear grains. A comprehensive evaluation using the Z-score method revealed that FNT achieved the highest soil functional quality score across the five layers. Therefore, no tillage during the summer fallow can enhance soil functional quality, primarily due to its positive impact on soil structure and carbon sequestration, but may not immediately increase crop yield. Full article

Background: Fibromyalgia (FM) is a chronic widespread pain condition implicated in accelerated aging, functional decline, and physical frailty. Objective: This study examined differences in performance-based and self-reported physical frailty phenotypes among middle-aged and older adults with and without FM. Materials and Methods: A cross-sectional sample of 234 community-dwelling middle-aged and older adults with (59.0%) or without FM was analyzed. Physical frailty was defined as weakness, low physical activity, exhaustion, and slowness, assessed using validated performance-based (Fullerton Advanced Balance Scale [FAB], 8-foot up and go test [8FUPGT], 30-second chair stand [30SCS], 6-minute walk [6MWT], 30-foot walk [30FW]) and self-report measures (Rapid Assessment of Physical Activity [RAPA], fatigue numeric rating scale). Principal component analysis (PCA) evaluated the underlying structure of physical frailty indicators, yielding performance-based and self-reported components. Standardized factor scores were used as outcomes in regression analyses examining associations with pain intensity. Results: PCA supported a two-component frailty structure explaining 61% of the variance. After adjusting for age, gender, depressive symptoms, and body mass index, greater pain intensity was associated with worse performance-based (B = −0.10, p < 0.001; adjusted R² = 0.36) and self-reported (B = −0.10, p < 0.001; adjusted R² = 0.39) frailty. Discussion: Findings suggest that pain intensity is associated with frailty risk among aging adults, supporting the clinical utility of both performance-based and self-reported physical frailty assessments in FM. Full article