Search Results (68,046)

Crack control and deformation performance are critical to the serviceability of concrete box girders. This study investigates the structural behavior of concrete box girders reinforced with polypropylene fibers through four-point bending tests and clarifies the influence of fiber volume fraction on cracking, deformation, and strain responses. The results show that polypropylene fibers effectively reduce the number of cracks, maximum crack width, and equivalent crack area of the box girders, while improving crack closure. At the service-level load of 0.7P, increasing the fiber volume fraction from 0 to 0.3% reduced the number of cracks from 11 to 4, the maximum crack width from 0.16 mm to 0.12 mm, and the equivalent crack area from 653.97 mm² to 177.50 mm². The initial stiffness of the box girders did not exhibit a monotonic relationship with fiber volume fraction; however, at higher load levels, all fiber-reinforced specimens exhibited greater secant stiffness than the plain-concrete specimen. At a load of 315 kN, compared with the specimen without fibers, the specimen containing 0.2% polypropylene fibers showed an increase in secant stiffness from 15.021 kN/mm to 20.875 kN/mm, accompanied by a reduction in mid-span displacement from 20.971 mm to 15.090 mm. Fiber content also affected the transverse and longitudinal surface strains of the bottom slab at the mid-span section, as well as the longitudinal strains of the webs, with these effects becoming more evident at higher load levels. Overall, a fiber volume fraction of 0.2% provided a favorable balance between crack control and high-load deformation response, whereas 0.3% exhibited the strongest crack-control performance. Full article

Hydrodynamic models underpin Autonomous Underwater Vehicle (AUV) design, motion control, and performance evaluation. Existing methods face two critical bottlenecks: (1) conventional explicit CFD requires predefined trajectories, which fails to capture true motion responses under combined rudder-propeller action and creates a disconnect between simulation and real operations; (2) the widely adopted Standard Submarine Motion Equations (SSME) suffer from high parameter redundancy, while high-precision non-parametric models incur prohibitive computational costs, hindering embedded deployment. To address these gaps, this paper proposes an implicit CFD-driven framework for fully appended AUVs equipped with through-body thrusters. It requires no preset trajectories, directly coupling periodic propeller thrust and rudder angle excitations to achieve 5-degree-of-freedom (5DOF) spatial motion simulations aligned with real navigation states. Parametric and non-parametric models are identified via Least Squares (LS) and Neural Networks (NN), respectively. Sobol global sensitivity analysis reduces SSME dimensionality, yielding a Basic Submarine Motion Equation (BSME) with only 25 key parameters—cutting the parameter count by 55% with negligible accuracy loss. Validation shows the non-parametric NN model reduces prediction error by over 10% compared to its parametric counterpart, while the streamlined BSME enables real-time forecasting in low-power computing scenarios. This approach balances accuracy and efficiency for rapid hydrodynamic prediction during early AUV design and embedded controller deployment. Full article

Salinity is an important environmental factor affecting the physiological homeostasis of freshwater fish, yet the underlying mechanisms in grass carp (Ctenopharyngodon idella) gills remain unclear. Therefore, grass carp were exposed to different salinity levels for 60 days, and gill responses were evaluated using histopathological, ion regulatory, antioxidant, transcriptomic, and metabolomic analyses. Histological observations showed that high salinity (8 g/L) caused marked structural damage to the gill lamellae. Specifically, Na⁺ and Ca²⁺ concentrations and Na⁺/K⁺-ATPase activity significantly decreased, while K⁺ concentration and Ca²⁺-ATPase activity increased, revealing disrupted ion homeostasis. Salinity exposure also led to decreased antioxidant enzyme activities. Integrated omics analysis further demonstrated that a total of 2447 differentially expressed genes and 268 differentially expressed metabolites were identified, with significant enrichment in pathways related to biosynthesis of amino acids, arachidonic acid metabolism, glutathione metabolism, PPAR signaling, and calcium signaling. Notably, the PPAR and calcium signaling pathways showed positive enrichment under salinity stress, suggesting their potential involvement in the regulation of lipid metabolism, energy allocation, and cellular stress responses. Our findings indicated amino acid biosynthesis and arachidonic acid metabolism as key pathways involved in the adaptation of grass carp gills to salinity stress. Overall, chronic salinity exposure caused structural alterations, disrupted ion regulation, altered antioxidant status, and marked transcriptomic and metabolomic changes in grass carp gills, offering new insight into salinity adaptation in freshwater fish. Full article

Consensus molecular subtype 4 (CMS4) colorectal cancer (CRC) is associated with an aggressive clinical course and poor survival, yet the biological basis of heterogeneity within this subtype remains incompletely understood. DNA methylation is an epigenetic mechanism involved in transcriptional regulation, cellular differentiation, and colorectal tumorigenesis. Here, we integrated single-cell RNA sequencing (scRNA-seq), bulk data, and promoter DNA methylation data to characterize CMS4-associated cancer cell states and methylation-related features. Using the scAB algorithm, we integrated scRNA-seq with bulk CMS4 data and identified CMS4-related cells distributed across multiple patients. Single-cell analyses of cell–cell communication and transcriptional regulation revealed a CMS4-related cancer cell population characterized by macrophage migration inhibitory factor (MIF)-centered intercellular communication, enhanced caudal type homeobox 1 (CDX1) and Kruppel-like factor 5 (KLF5) regulon activity, and gene modules enriched in differentiation-related pathways. CytoTRACE analysis further stratified CMS4 cancer cells into poorly and well-differentiated states, yielding 802 differentially expressed genes (DEGs). Linking these differentiation-associated DEGs with bulk expression and promoter methylation data identified 218 methylation-associated DEGs showing significant inverse methylation expression correlations, suggesting a link between differentiation-related heterogeneity and promoter methylation. Univariable Cox regression followed by LASSO regression further prioritized eight genes for construction of the methylation and differentiation-related prognostic model (MeDiff-PM). MeDiff-PM consistently stratified overall survival in the TCGA CMS4 cohort and two independent validation cohorts, with cutoff-independent continuous Cox analyses further supporting its prognostic association across cohorts. And MeDiff-PM remained prognostically significant after adjustment for available clinical variables. High MeDiff-PM risk scores were associated with activation of P53, WNT, and ubiquitin-mediated proteolysis pathways and with consistent predicted drug response differences for compounds across three CMS4 cohorts. While individual in silico knockout analysis suggested links between MeDiff-PM genes and metallothionein-related and immune-associated transcriptional responses. Collectively, these findings indicate that methylation-associated differentiation features represent a molecular dimension of intra-CMS4 heterogeneity and provide a biologically informed framework for prognostic stratification within CMS4 CRC. Full article

This review summarizes recent advances in intelligent spraying systems for precision agriculture. It focuses on the three interconnected components of perception, decision-making, and actuation, and compares the sensing characteristics of 2D planar field crops and three-dimensional orchard canopies. Particular emphasis is placed on evaluating the target recognition capabilities and physical limitations of machine vision, Light Detection and Ranging (LiDAR), ultrasound, and active fluorescence spectroscopy under complex field conditions. At the actuation level, the dynamic response characteristics of Pulse-Width Modulation (PWM), proportional control valves, and adaptive control algorithms are critically reviewed, revealing the influence mechanism of hydraulic transient phenomena, including water hammer effects induced by high-frequency valve switching, on droplet size distribution (DSD). The review further discusses the coupling effects between Unmanned Aerial Vehicle (UAV) airflow fields, Unmanned Ground Vehicle (UGV) motion disturbances, and spray deposition performance, and summarizes reported improvements in pesticide reduction, water conservation, and drift mitigation. Finally, the potential of cyber–physical systems (CPS) and digital twin-based frameworks for developing adaptive and closed-loop intelligent spraying systems is discussed to provide insights into future all-weather and autonomous agricultural operations. Full article

Organophosphate esters (OPEs) are commonly used flame retardants and plasticizers, which can easily be released into the soil environment and have potential hazards such as neurotoxicity and developmental toxicity. Establishing an efficient and sensitive detection method plays a crucial role in soil pollution assessment. Currently, there have been numerous studies on the detection of OPEs in soil using ultrasonic extraction and solid-phase extraction, while relatively few studies have focused on the analysis of multiple OPEs in soil by accelerated solvent extraction combined with d-SPE clean-up and ultra-high performance liquid chromatography–tandem mass spectrometry. The d-SPE purification method eliminates the need for column passage, shortens sample preparation time, and avoids the risk of background contamination from OPEs that may be introduced by SPE. This study developed a liquid chromatography–tandem quadrupole mass spectrometry (LC-MS/MS) method for the determination of 25 OPEs in soil, and systematically optimized the pretreatment and instrumental analysis conditions. Accelerated solvent extraction was used for pretreatment, and dichloromethane–methanol (1:1, V/V) was determined as the optimal extraction solvent. A mixed adsorbent of N-propyl ethylenediamine (PSA) and C18 was selected for dispersive purification, effectively removing matrix interference and improving recovery rates. The mass spectrometry parameters such as collision energy and declustering voltage were optimized, significantly enhancing ion response intensity and detection sensitivity. The method showed good linearity within the concentration range of 1–100 ng/mL, with correlation coefficients all greater than 0.994. The spiked recovery rates ranged from 70.6% to 111% with a relative standard deviation (RSD) of 1.2–11.8%. The precision and accuracy met the requirements for environmental sample analysis. The method was applied to the detection of actual soil samples, and the results were stable and reliable. This method is simple to operate, highly sensitive, and widely applicable, providing reliable technical support for the pollution monitoring, source tracing, and ecological risk assessment of OPEs in soil. Full article

Artificial Intelligence (AI) and Generative Artificial Intelligence (GenAI) increasingly support personalised learning, intelligent assessment, and data-informed educational decision-making. However, persistent misconceptions among gifted students may remain undetected because high achievement can conceal conceptually coherent misunderstandings. This study develops and empirically evaluates a teacher-governed, closed-loop framework integrating Educational Data Mining (EDM), Learning Analytics (LA), ensemble machine learning, Explainable Artificial Intelligence (XAI), GenAI, and differentiated instruction. Educational data from 255 gifted secondary-school learners were transformed into learner profiles and analysed using Random Forest, XGBoost, Boost, Support Vector Machine, Artificial Neural Network, and Logistic Regression, which were then combined via validation-derived weighted probabilistic ensembling. The validation-weight ensemble achieved 96.88% accuracy, 92.00% precision, 100% recall, an F1-score of 95.83%, ROC-AUC of 0.9979, and PR-AUC of 0.9963, outperforming the individual classifiers in overall classification performance. SHAP provided learner-specific explanations, with average response time emerging most frequently as the leading explanatory feature. These explanations informed GenAI-supported personalised interventions, which teachers validated before differentiated delivery. Post-intervention evidence was subsequently incorporated into learner reassessment and profile updating. The principal contribution lies in the operational integration of learner profiling, weight prediction, explainability, evidence-conditioned instructional generation, professional validation, and reassessment within a continuous educational cycle. The findings provide initial empirical evidence for the framework’s computational feasibility and interpretability while supporting further external and longitudinal validation across educational contexts and learner populations. Full article

Background: Children with Down syndrome often have co-occurring physical conditions affecting well-being, and respiratory issues remain a leading cause of hospitalization and death among people with Down syndrome. Parents of children with Down syndrome experience stressors associated with respiratory illness and challenges navigating healthcare access. No prior qualitative study has examined parental perspectives on respiratory health specifically among children with Down syndrome. Identifying parental concerns can help in the development of strategies to mitigate respiratory ailments in children with Down syndrome. Methods: One-hour, semi-structured interviews were conducted among 24 families of children with Down syndrome to understand respiratory concerns. Findings were identified through codebook thematic analysis. Results: The themes identified included persistent concerns about respiratory health, repeated impact from acute and chronic respiratory issues, a rapid and unexpected decline in health during respiratory illness, insurance coverage concerns, and a lack of confidence in assessing air quality which could impact respiratory health. Conclusions: Insights gained from interviews of parents of children with Down syndrome can help policy makers identify opportunities to prevent and mitigate respiratory health problems. Findings underscore the importance of clinician responsiveness to parental concern about rapid deterioration and suggest that insurance coverage policies for this high-risk population warrant further research and policy consideration. Full article

Pixel-level annotation of bi-temporal high-resolution imagery is costly because annotators must distinguish genuine changes from pseudo-changes caused by illumination, seasonality, shadows, and residual misregistration. From a temporal-symmetry perspective, unchanged regions approximately preserve cross-temporal semantic correspondence, whereas genuine building changes introduce localized symmetry breaking between the two acquisition times. This paper presents BSCNet, a semi-supervised framework for binary building change detection that jointly models boundary-sensitive differences and scene-dependent scale preferences. A shared-weight MixTransformer extracts multi-level bi-temporal features. The Edge-Aware Optimization Module suppresses spatially invariant channel responses, enhances residual spatial cues, and predicts a Sobel-supervised edge map. The Parallel Selective Context Module aggregates depthwise-separable branches with different receptive fields and produces an image-level scale distribution. The Multi-scale Edge-Consistent Mean Teacher framework aligns the final prediction, intermediate edge representation, and scale-selection distribution between an exponential-moving-average teacher and the student. Experiments on WHU-CD and LEVIR-CD under 5%, 10%, and 20% labeled-data settings show consistent improvements over RCL, C2F-SemiCD, and CutMix-CD. With 5% labeled data, BSCNet achieves F1/IoU scores of 88.57%/79.49% on WHU-CD and 88.88%/79.98% on LEVIR-CD. An additional UAV-CD evaluation examines transfer to 0.06 m low-altitude UAV imagery containing both building and land changes; under 5% supervision, BSCNet obtains an F1/IoU of 68.07%/51.60%. Progressive ablations confirm complementary gains from the boundary, scale, and consistency components. Full article

Lead contamination remains a significant environmental and public health concern, creating a demand for analytical platforms that combine sensitivity, simplicity, and long-term stability. In this work, a graphene-containing molecular membrane matrix was developed for the fabrication of single-piece all-solid-state potentiometric sensors for Pb(II) detection. The sensing membrane consisted of poly(vinyl chloride), plasticizers, a Pb(II)-selective ionophore, lipophilic ionic sites, and dispersed graphene nanostructures, forming an integrated molecular sensing interface. Within the membrane phase, selective complexation of Pb(II) ions by the ionophore was coupled with graphene-assisted ion-to-electron transduction, enabling efficient signal generation without the need for a separate solid-contact layer. The influence of graphene incorporation and membrane thickness on sensor performance was systematically investigated. Among the tested configurations, a membrane prepared from 40 µL of sensing cocktail provided the best overall performance, combining high electrical capacitance, favorable surface properties, and superior potential stability. SEM imaging revealed a homogeneous membrane morphology without large graphene agglomerates, indicating effective dispersion of graphene within the polymer matrix. The optimized sensor exhibited a near-Nernstian slope of 30.3 mV dec⁻¹, a linear response range from 1.0 × 10⁻⁷ to 1.0 × 10⁻² M, a detection limit of 6.3 × 10⁻⁸ M, and a potential drift of only 0.35 mV h⁻¹. These results demonstrate that direct incorporation of graphene into an ion-selective membrane is an effective strategy for constructing robust and scalable single-piece potentiometric sensors for Pb(II) monitoring and highlight the potential of developed membrane materials for electrochemical sensing applications. Full article

Metal oxide thin-film transistors (MO TFTs) have drawn wide interest in photodetectors and optoelectronic synaptic devices owing to their wide bandgap, low off-state current, high optical transparency, low-temperature processing, and large-area uniformity. Gate modulation in the TFT structure can tune the channel’s initial state and interfacial electric field, enhancing the tunability of photogenerated carrier transport, defect trapping/release, and interfacial charge regulation. This article reviews the progress of MO TFT photodetectors and optoelectronic synaptic devices, and examines the roles of light absorption, carrier transport, defect-related carrier dynamics, interfacial charge control, and persistent photoconductivity in different device functions. For photodetectors, key goals include broadening the response spectrum, reducing dark current, improving spectral selectivity, and enhancing response stability. For optoelectronic synaptic devices, post-illumination conductance retention and slow relaxation enable memory retention and synaptic weight modulation. Thus, rather than being separate, photodetection and optoelectronic synapses are functional extensions of the MO TFT optoelectronic response under different application targets. This article further discusses the synergy between these two functions in array sensing, visual preprocessing, and intelligent vision systems. Future development requires advances in targeted defect engineering, interface and structure optimization, array uniformity, standardized evaluation, and device–circuit–algorithm co-design for low-power, integrable intelligent vision hardware. Full article

This study evaluates the post-earthquake damage of a 17-story plan-irregular reinforced concrete (RC) building damaged during the 2024 Hualien earthquake. A three-dimensional numerical model was constructed in ETABS, utilizing nonlinear component characteristics derived from the mechanics-based Seismic Evaluation of RC Building (SERCB) framework to execute nonlinear dynamic time-history analysis (NDTHA). The actual triaxial ground motion records from the 3 April 2024 earthquake were applied as the seismic input. To correlate analytical outcomes with physical seismic damage, a displacement-based ductility development index (D) was adopted to quantitatively associate simulated plastic hinge responses with field-observed damage states ranging from Slight (DS I) to Collapse (DS V). Post-earthquake reconnaissance revealed that structural damage was primarily concentrated in the perimeter RC shear walls between the 1st and 6th stories. The predicted wall damage states spanned from Moderate (DS II) to Severe (DS IV), whereas the primary beam–column frame exhibited only Slight damage (DS I). Quantitative comparison demonstrates exact damage state match rates of 63.6% (7/11) for Frame 1 and 71.4% (5/7) for Frame 2, with all remaining discrepancies bounded within a minor one-class margin. These analytical results show high consistency with field observations, confirming that NDTHA incorporating SERCB-based plastic hinge modeling can effectively reproduce the nonlinear seismic behavior and localized damage distribution of torsionally irregular RC structures. The findings extend traditional laboratory-scale component validation to full-scale building damage reconnaissance, providing robust empirical evidence for cross-validating physical seismic damage against dynamic simulation predictions. Full article

Integrated nutrient management reduces reliance on chemical fertilizers by combining organic and inorganic inputs. A field experiment was conducted under rainfed conditions in Hungary from 2022 to 2024 to evaluate organic, inorganic and biological fertilizers applied to the high-oleic sunflower hybrid ES Emeric. Seven treatments were compared, namely, an unfertilized control, potassium (K), combined organic and inorganic nitrogen (GOIM), effective microorganisms (EM-1), and the combinations K+GOIM, K+EM-1 and GOIM+EM-1. Seed oil, crude protein and moisture content were determined, together with the fatty acid profile of the oil. Growing season influenced every measured variable far more strongly than fertilization, and all treatment responses were expressed as year-by-treatment interactions. Mean oleic acid content was 69.9% in the dry season of 2022 and 85.3% in 2024, but only 28.9% in the cooler and wetter season of 2023, when linoleic acid reached 59.8%. In 2022, K and K+EM-1 gave the numerically highest oil contents, 48.7% and 48.0%, less than one percentage point above the control, while GOIM+EM-1, GOIM and K+GOIM gave significantly higher protein contents than the remaining treatments. In the same season, EM-1 and K+GOIM raised linoleic and alpha-linolenic acids and, therefore, total polyunsaturated fatty acids, whereas GOIM+EM-1 and K increased oleic acid and total monounsaturated fatty acids. Integrated fertilization can therefore be used to shift the balance between monounsaturated and polyunsaturated fatty acids in sunflower oil, but the size and direction of the shift are governed by the conditions of the growing season. Full article

Precision livestock farming (PLF) integrates sensing technologies, data acquisition (DAQ) systems, and machine learning (ML) frameworks to continuously monitor individual animals and support welfare assessment through physiological and behavioral observations. Advances in infrared thermography, radar sensing, vision-based systems, acoustic monitoring, and wearable technologies have substantially expanded the ability to collect high-resolution data describing animal responses to internal and external stimuli. However, despite considerable technological progress, a persistent gap remains between sensing performance demonstrated under controlled experimental conditions and reliable deployment within commercial livestock environments. This gap is characterized by environmental variability, unrestricted animal movement, and operational constraints within commercial environments. Using a structured review methodology, this review examines sensing modalities, embedded DAQ architectures, communication strategies, ML methodologies, data privacy, farmer adoption, and an illustrative engineering workflow through the lens of welfare-relevant physiological characteristics. Emphasis placed on the distinction between direct sensor measurements and the biological processes they represent. Sensor outputs do not directly quantify welfare, stressors, or management outcomes; rather, they provide measurements of physiological and behavioral responses that require appropriate biological context for meaningful interpretation. As a result, welfare assessment does not depend solely on the ability to acquire data, but also on the ability to accurately relate those data to underlying physiological mechanisms. Within this framework, ML serves as a critical bridge between measurement and interpretation by enabling the analysis of complex, multimodal datasets. Future advancement of welfare-oriented PLF systems will require stronger alignment among sensing methodologies, physiological understanding, and practical deployment realities to generate meaningful, scalable, and biologically grounded welfare assessments. Full article

To improve the threshold accuracy of inertial switches, this study proposes a monolithic metal MEMS inertial switch with nonlinear springs. The switch uses two sets of inclined beams with asymmetric initial angles as suspension springs. Geometric nonlinearity produces low displacement sensitivity away from the design threshold and high sensitivity near the threshold. This response improves threshold discrimination and reduces the deviation between the actual and design thresholds. A nonlinear switch and a linear reference switch are designed with the same static threshold of 27.5 g. Their responses are compared using Abaqus static and explicit dynamic simulations. Both switches are monolithically fabricated from 50 μm thick 304 stainless steel by induction-electrode through-mask electrochemical micromachining (IETMEMM). Key dimensional deviations are below 2.5%. Drop-weight tests show measured nonlinear-switch thresholds of 27.8, 27.8, 26.9, and 25.4 g under half-sine shocks with pulse widths of 4, 6, 8, and 10 ms, respectively. The maximum threshold deviation is 2.1 g. The overall threshold accuracy is 92.4%, substantially higher than the 56.0% of the linear reference switch. This work combines a nonlinear threshold-regulation mechanism with monolithic IETMEMM fabrication and provides a new strategy for metal MEMS inertial switches with high threshold accuracy. Full article