Search Results (16,318)

Cavitation bubble collapse generates impact loads that can initiate surface damage and accelerate material degradation in hydraulic and fluid-handling systems. Predicting these loads remains challenging because the collapse response depends on nonlinear interactions among bubble dynamics, stand-off distance, and polymer mechanical, acoustic, and viscoelastic properties. This study developed machine-learning regression models to predict impact loads from laser-induced single-bubble collapse on polyethylene, polytetrafluoroethylene (PTFE), polyamide, and antistatic polyethylene terephthalate (antistatic PET). Experiments were performed using a pulsed Nd:YAG laser at energies of $12.5$, 25, and $50\mathrm{mJ}$ and normalized stand-off distances of $\gamma =1$–5, producing 180 measurements. Impact loads were measured using a force-calibrated PVDF sensor with material-specific calibration equations. Bubble radius was obtained from high-speed imaging, whereas collapse time was determined from the PVDF voltage-time response. Thirteen input variables were used to train linear, ridge, LASSO, multilayer perceptron, and random forest regressors with Bayesian optimization and grouped five-fold cross-validation. Random forest regression achieved the best performance, with $\mathrm{RMSE}=0.6820\mathrm{N}$, $\mathrm{MAE}=0.5339\mathrm{N}$, $R^2=0.9168$, and $\mathrm{MAPE}=12.44\%$. SHAP analysis identified collapse time, acoustic impedance, and loss modulus as dominant predictors. The model is therefore suitable for trend prediction within the tested experimental domain. Full article

Cucumber target spot disease, caused by Corynespora cassiicola, is a major constraint on greenhouse cucumber production. Accurate pixel-level segmentation and automated severity assessment remain challenging because early lesions are small, lesion boundaries are gradual, and field images often contain complex illumination and background interference. To address these issues, this study proposes a small-lesion and boundary-aware Mask2Former framework with a ResNet-50 (R50) backbone for cucumber target spot segmentation. Specifically, a Small Target Enhancement Module (ST) is designed by integrating atrous spatial pyramid pooling (ASPP), a high-resolution feature retention branch, and a small-target-weighted loss to improve sensitivity to early micro-lesions. In addition, a Boundary Segmentation Module (BS) is introduced to enhance boundary localization through boundary attention and explicit supervision with Dice and Focal losses. A field dataset containing 2559 cucumber leaf images was annotated at the pixel level and split into training, validation, and test sets at a ratio of 7:1:2. Disease severity was categorized into four grades based on the lesion-to-leaf-area ratio. On the test set, the proposed model achieved an mIoU of 85.05%, a Dice coefficient of 91.94%, and a precision of 92.30%, outperforming the Mask2Former baseline by 4.95, 4.72, and 4.58 percentage points, respectively. Moreover, severity assessment based on segmentation results reached an overall grading accuracy of 91.6% (Cohen’s Kappa = 0.89), with misclassifications predominantly confined to adjacent severity grades and no large cross-category errors observed. These results indicate that the proposed method has practical potential for automated disease monitoring and precision management in greenhouse cucumber production. Full article

Visible-infrared (RGB-IR) object detection leverages multimodal information to ensure reliable perception in complex environments. However, dynamic scenes pose significant challenges due to the frequent inconsistency between scene-level modality contributions and local spatial reliability. Furthermore, standard feature extraction progressively attenuates boundary-sensitive structural cues, and unified fusion strategies often fail to capture spatially varying cross-modal complementarity. To overcome these limitations, we propose a Boundary-Guided Dual-Perspective Cross-Modal Fusion Network (BDPNet) to explicitly preserve shallow geometric structures and decouple deep semantic fusion into macroscopic and microscopic perspectives. Specifically, a Geometric Boundary Enhancement Module (GBEM) embeds Sobel-based high-frequency priors into shallow dual-modal features via residual spatial modulation, preventing the loss of crucial localization cues during downsampling. In the deep semantic space, a Hybrid Dual-Perspective Adaptive Fusion Module (HDAM) employs an illumination-aware branch for global modality weighting and a spatial confidence-driven branch for local cross-modal rectification. A spatial gating mechanism then dynamically reconciles these macro-environmental and micro-signal features. Extensive experiments on M3FD, LLVIP, and DroneVehicle demonstrate the effectiveness of BDPNet. Compared with state-of-the-art methods, BDPNet improves mAP${}_{50-95}$ by 0.8% and 1.0% on M3FD and LLVIP, respectively, and improves mAP${}_{50}$ by 0.6% on DroneVehicle, while using substantially fewer parameters and lower computational cost. Full article

Compared to conventional fuels, hydrogen fuel offers advantages such as high specific heat capacity, low boiling point, and zero carbon emissions, demonstrating significant potential for green energy conservation and sustainable development in the aviation field. This paper reviewed the latest advances, technical challenges, research hotspots, and future development directions related to the response and regulation of various components within the hydrogen transportation and thermal management systems for aeroengines, filling a gap in the existing literature. (1) As to the fuel of aeroengines, the heat exchanger efficiency of the heat exchanger employed for intercooling while utilizing hydrogen fuel can reach 10.63 times that of kerosene, and the turbine inlet temperature is significantly reduced under sea-level takeoff conditions. Under high-altitude supersonic flight conditions, its specific fuel consumption is approximately 0.33–0.40 times that of kerosene. However, aeroengines also confront challenges such as the requirement for high-efficiency thermal insulation and the control of cold energy losses. (2) When the pressure regulation accuracy of hydrogen storage containers, hydrogen supply stability, and thermal management coordination are ensured, the fuel weight index can be optimized to 0.62 during hydrogen transportation, significantly reducing the impact of the hydrogen storage system on the payload capacity of aircraft models. Nevertheless, crucial components involved in hydrogen transportation, such as cryogenic liquid hydrogen tanks, are vulnerable to significant temperature fluctuations, which can cause pressure oscillations, response delays, and seal failures, thereby affecting the stability of the hydrogen fuel supply. (3) In the thermal management system of hydrogen-fueled aeroengines, the fuel consumption and transportation cost of the engine compared with the unoptimized baseline system are reduced by 14.54% and 11.74% through regulating important component parameters such as heat exchanger power. However, the thermal management system confronts challenges during the heat exchange among hydrogen fuel, high-temperature airflow, and residual heat, including strong coupling among multiple components and insufficient real-time sensing capability for dynamic thermal loads. Future development should shift from “passive adaptation” to “active regulation and control,” aiming to achieve dynamic decoupling of temperature, pressure, and stress fields under strongly coupled multi-heat source operating conditions, along with coordinated regulation and matching of multi-component dynamic responses. Full article

Background/Objectives: Mixed dentition is critical for identifying developing malocclusions that may benefit from preventive or interceptive orthodontic management. This study aimed to assess IPION-defined preventive/interceptive orthodontic treatment need using the Index of Preventive and Interceptive Orthodontic Needs (IPION), compare early and late mixed dentition, and evaluate associated clinical findings. Methods: This single-center cross-sectional study included 200 children attending their first examination at the university Pediatric Dentistry and/or Orthodontics clinics: 100 aged 6 and 100 aged 9 years. IPION-defined treatment need was assessed using the age-specific IPION-6 and IPION-9 subsystems. Dental, developmental, and occlusal findings were recorded using standardized criteria. Treatment-need categories and clinical findings common to both subsystems were compared between groups, while total IPION scores were summarized separately by age. Relationships of caries and premature primary tooth loss with IPION scores were evaluated. Results: Definite treatment need predominated (58.0% vs. 51.0%), and treatment-need distributions differed between age groups (p = 0.047). Dental caries, molar relationship, overjet, and overbite showed significant differences. In 6-year-olds, carious and prematurely lost tooth counts were positively correlated with IPION scores, with the strongest relationship for premature tooth loss (ρ = 0.609; p < 0.001). Relationships were weaker in 9-year-olds. Conclusions: IPION-defined preventive/interceptive orthodontic treatment need was common within this university-based clinical sample across mixed dentition stages. Differences in treatment-need distribution and clinical findings indicated distinct preventive and interceptive orthodontic profiles across mixed dentition stages. Age-specific IPION assessment may support identification of children who may benefit from further orthodontic evaluation by integrating dental, developmental, and occlusal findings. Full article

IDH-mutant gliomas show variable clinical behavior that may reflect transcriptional programs organized within tumor tissue. We evaluated two curated expression scores: a differentiation-loss/Stem-NPC score (Axis1) and a mesenchymal–hypoxia score (Axis2). Public single-cell RNA-sequencing data were used to characterize malignant-cell states, and the scores were evaluated in bulk clinical cohorts using portable within-sample ranks and covariate-adjusted Cox models. Axis2 was separated into non-overlapping mesenchymal-only and hypoxia-only components. Spatial organization was tested in Visium sections from 11 patients using patient-level inference and constrained null models. The mesenchymal-only component was associated with shorter overall survival in CGGA-693 (hazard ratio per standard deviation = 1.73, 95% confidence interval 1.43–2.11) and CGGA-325 (1.41, 1.10–1.79), although molecular adjustment attenuated the association in CGGA-325. It remained spatially autocorrelated (mean Moran’s I = 0.297; p = 0.001). Removing gene overlap reduced its patient-level correlation with hypoxia from 0.798 to 0.254; residual co-organization was supported across all patients but not within either IDH subgroup. Axis1 showed no portable survival association. Thus, the mesenchymal component is outcome-associated and spatially organized, whereas the data do not establish hypoxia-driven organization, causality, or malignant-cell specificity. Full article

Reliable estimation of the air change rate from CO₂ decay measurements is essential for ventilation assessment, but results may strongly depend on the assumed background concentration and the selected analysis window. This study compares the ISO 12569 two-point and multi-point methods with a study-specific nonlinear least-squares (NLSQ) exponential-fitting approach developed for systematic window-sweep analysis. A synthetic benchmark with known decay parameters was used to quantify the sensitivity of the estimated air change rate N to background-concentration error and time-window selection. The framework was then applied to CO₂ decay measurements from four rooms and to an OpenFOAM-generated CFD decay case using dense N(t_beg,t_end) stability maps and automatic plateau selection. The benchmark showed that admissible windows shrink as background uncertainty increases, while late, tail-dominated intervals are particularly prone to bias. The two ISO methods produced similar results, whereas the NLSQ approach generally preserved stable solutions over longer windows when fitting started sufficiently early. For three measured rooms, stable regions yielded effective N values of approximately 0.14, 0.75, and 1.24 1/h, while one room remained non-stationary. To assess energy implications, N was propagated into the ventilation heat-loss coefficient H_vent = 0.34 NV. Illustrative late-tail scenarios showed that background-sensitive window selection may translate into substantial over- or underestimation of ventilation-related heat-loss indicators. Full article

Silicosis is one of the most prevalent occupational diseases worldwide, resulting from prolonged exposure to crystalline silica (SiO₂). This exposure leads to persistent inflammation, lung fibrosis, and ultimately significant loss of lung function and death. Although the precise mechanisms remain unclear, the primary molecular pathogenesis of silicosis—ranging from SiO₂-induced chronic inflammation to irreversible pulmonary fibrosis—shares considerable overlap with fibrosis mechanisms in various organs, including the secretion of inflammatory cytokines, the activation of fibrosis-related signaling pathways, and ferroptosis. Currently, there is no cure for silicosis; anti-fibrotic treatments are recommended to slow disease progression. Bicyclol, a hepatoprotective agent commonly used to manage elevated aminotransferases due to chronic hepatitis, has garnered attention in recent years for its anti-inflammatory and anti-fibrotic properties in organs such as the liver, heart, and kidney. It significantly reduces extracellular matrix (ECM) synthesis and deposition in these organs, thereby decelerating fibrosis progression. In the context of silicosis, bicyclol has demonstrated promising effects on both chronic inflammation and fibrosis, indicating its potential as a clinical treatment for silicosis. This review summarizes the effects of bicyclol during the chronic inflammation and fibrosis stages of silicosis progression, providing both direct and indirect evidence for its potential therapeutic application in silicosis management. However, it is worth noting that there is limited direct evidence specific to silicosis, and the current prospects mainly stem from indirect evidence of anti-fibrotic effects in other organs. There is still a long way to go before bicyclol truly becomes an approved drug for silicosis. Full article

With the increasing number of end devices and the growing heterogeneity of application demands, task orchestration in dynamic cloud–edge environments faces challenges arising from network fluctuations, resource contention, and decision uncertainty. To address these challenges, this paper proposes a Cost-Optimized Adaptive Three-Way Decision (COATWD) method for cloud–edge task orchestration. Candidate execution costs for Local Edge, Remote Edge, and Cloud are evaluated by jointly considering network transmission, task queuing, task execution, and QoS constraints, while historical prediction residuals provide empirical evidence for estimating candidate-superiority probabilities. Based on the current task characteristics and candidate resource states, decision losses are dynamically constructed, and adaptive dual thresholds are generated according to the effectiveness of historical refinement, thereby partitioning candidate relations into positive, boundary, and negative regions. Candidate relations assigned to the boundary region are further refined by matching historical information to the task type, candidate execution role, and specific Edge host. Observed execution outcomes continuously update prediction residuals and task success and failure statistics, thereby forming a closed-loop, online, adaptive orchestration process. Experimental results obtained with EdgeCloudSim 4.0 show that COATWD effectively reduces the task failure rate, average service time, and average processing time under different device scales and application workloads. Full article

As exhaust emission standards continue to tighten, evaporative emissions have attracted increasing attention, while information on motorcycles, particularly older models, remains limited. In this study, seven China II and China III motorcycles with engine displacement between 50 and 150 mL were tested using a sealed housing for evaporative determination (SHED), including standardized hot soak and 1 h diurnal breathing loss (DBL) measurements; an additional non-replicated 12 h laboratory-reproduced temperature-profile test was conducted as an exploratory comparison. Across the displacement-matched China II and China III motorcycles, DBL decreased from 6.17–11.12 g to 0.33–0.57 g, corresponding to pairwise reductions of approximately 91–97%, while hot soak loss (HSL) decreased from approximately 1.2–1.6 g to 0.07–0.29 g, corresponding to reductions of approximately 82–95%. These differences coincided with multiple evaporative-emission-control upgrades introduced with China III, including activated-carbon canisters, improved fuel-system sealing, and closed-crankcase ventilation; their individual contributions could not be isolated. In a vehicle-specific comparison between two 150 China III motorcycles, the electronic fuel injection (EFI) equipped motorcycle exhibited 66.9% lower DBL than the carburetor-equipped motorcycle, although this single comparison cannot establish a general EFI effect. The exploratory 12 h test also yielded higher cumulative DBL than the standardized 1 h procedure; however, because test duration, temperature amplitude, and temperature-change rate differed simultaneously, the individual contribution of these factors could not be separated. Full article

Soil salinization poses a significant threat to sustainable agricultural development and ecological security worldwide, resulting in considerable crop losses annually. The advent of drone-based hyperspectral remote sensing offers promising solutions for monitoring soil salinity, due to its high spatial resolution and versatile data acquisition capabilities. However, soil moisture alters both the scattering and absorption characteristics of electromagnetic radiation, thereby modifying soil spectral reflectance and masking salinity-related diagnostic spectral features, which can reduce the accuracy of conventional salinity estimation models. This study evaluates six spectral transformation methods—raw reflectance data (Ref), first derivative (FDR), Piecewise Direct Standardization (PDS), Orthogonal Signal Correction (OSC), FDR + PDS, and FDR + OSC—in conjunction with three machine learning algorithms: K-Nearest Neighbors (KNN), Support Vector Regression (SVR), and Multi-Layer Perceptron (MLP). A Stacking ensemble model integrating these base learners was further developed to improve soil salinity inversion under moisture interference. The results demonstrated that the Stacking model achieved the highest accuracy and stability among the evaluated models. Additional comparisons with XGBoost and Random Forest (RF) further confirmed the competitive performance of the proposed Stacking framework. The FDR + OSC–Stacking combination achieved the best validation performance, with $R_p^2$ = 0.87, RMSEP = 0.67 mS·cm⁻¹, and RPD = 2.93. Compared with the Ref–Stacking model, $R_p^2$ increased by 0.32 (from 0.55 to 0.87), while RMSEP decreased by 0.58 mS·cm⁻¹ (from 1.25 to 0.67 mS·cm⁻¹). The results showed that PDS had limited effectiveness in correcting moisture-related spectral variation, whereas OSC more effectively mitigated moisture interference while preserving spectral information relevant to salinity estimation. Among the machine learning models evaluated, the Stacking ensemble model achieved better predictive performance than MLP, SVR, and KNN. Furthermore, the FDR + OSC–Stacking combination provided the best performance among the evaluated modeling frameworks and was successfully applied to UAV hyperspectral imagery for spatial mapping of EC_1:5. These findings demonstrate the potential of combining appropriate spectral correction with Stacking for UAV-based soil salinity assessment and provide useful technical support for site-specific salinity management in precision agriculture. Full article

The management of sludge generated during drinking water treatment poses an environmental challenge due to the large volumes produced and the associated disposal costs. This study evaluates the potential use of sludge from the El Bosque Drinking Water Treatment Plant (Cartagena, Colombia) as a partial replacement of fine aggregate in concrete production. The sludge was characterized through physical, chemical, mineralogical, and microstructural analyses, including particle size distribution, Atterberg limits, specific gravity, X-ray diffraction (XRD), X-ray fluorescence (XRF), scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM-EDS), and petrographic examination. Five concrete mixtures were produced by replacing the fine aggregate with sludge at 0%, 5%, 10%, 15%, and 20% by mass. Mechanical performance was assessed through compressive strength and splitting tensile strength tests at curing ages of 7, 14, and 28 days. The results showed that increasing sludge content led to a progressive reduction in mechanical strength. Nevertheless, the mixture containing 5% sludge achieved a 28-day compressive strength of 26.15 MPa, corresponding to 96.9% of the design strength and comparable to the control mixture. Microstructural analyses revealed that higher sludge incorporation levels increased porosity and microcracking while reducing the relative proportion of cementitious matrix, explaining the observed decline in mechanical performance. These findings demonstrate the technical feasibility of incorporating DWTS from the El Bosque Drinking Water Treatment Plant as a limited replacement for fine aggregate without substantial losses in mechanical performance. The study provides site-specific physicochemical, mineralogical, and microstructural information that supports the sustainable valorization of this locally generated residue within circular economy strategies for the construction sector. Full article

Sepsis induces profound alterations in liver function that contribute to disease progression. We previously demonstrated that peritoneal sepsis is associated with marked disruption of hepatic transcriptional programs, including loss-of-function of Hepatocyte Nuclear Factor 4 alpha (HNF4α), a master regulator of hepatic identity and metabolism. Using hepatocyte-specific HNF4α knockout mice, we further showed that loss of HNF4α is detrimental during peritoneal sepsis. Here, we investigated whether this response is conserved in pneumonia-induced sepsis and assessed its functional significance. Bulk liver RNA sequencing revealed that suppression of hepatic metabolic pathways and HNF4α target genes are conserved features of both peritoneal and Streptococcus pneumoniae-induced sepsis. Unexpectedly, hepatocyte-specific HNF4α deficiency was associated with lower bacterial burden and increased survival during pneumococcal sepsis. Mechanistically, this protective effect was dependent on neutrophils and not macrophages. These findings demonstrate that sepsis-induced hepatic HNF4α dysregulation is conserved across distinct infectious etiologies but exerts context-dependent effects on disease outcome. Full article

Exercise recognition and movement quality assessment remain challenging in supervised exercise training, particularly under viewpoint changes and self-occlusion. Vision-based methods provide spatial posture information but are susceptible to keypoint loss, whereas inertial sensing is less affected by occlusion but provides limited information about global posture geometry. This study presents a dual-stream prototype that combines a nine-axis inertial measurement unit (IMU) with vision-based pose estimation. A 1DCNN-LSTM branch models inertial dynamics, a custom keypoint temporal branch models normalized pose sequences, and a confidence-gated rule adjusts their contributions according to visual keypoint reliability. Owing to the absence of a public synchronized multi-view IMU–vision exercise dataset with the required protocol, we constructed IMV-Exercise, comprising 10 participants, three exercises, and 900 repetition-level samples with side-, front-, and posterior-view recordings. The system achieved 96.0% exercise recognition accuracy under leave-one-subject-out cross-validation. In a separate viewpoint-specific evaluation, the fused output achieved 91.2% action-window accuracy under posterior viewing. Across 50 online trials, the reported recognition accuracy was 96.0%, the mean end-to-end latency was 195 ms, and the recorded maximum was below 210 ms. These results establish feasibility within the studied cohort and exercises; broader generalization and feedback effectiveness require larger, independently controlled evaluations. Full article

Predictive accuracy is often treated as a sufficient condition for profitable algorithmic trading, yet whether machine learning forecasts translate into trading performance that holds up across different market regimes remains contested. This study develops and stress-tests a prediction-driven, dynamically optimized trading framework across twelve instruments spanning equities, commodities, foreign exchange, and cryptocurrencies, and three structurally distinct regimes: a calm market (2018), the COVID-19 crisis (2020), and the Russian–Ukrainian geopolitical shock (2022). Daily price forecasts from RNN, LSTM, GRU, and hybrid architectures feed a rule-based framework that opens long or short positions from the divergence between predicted and observed prices, applies volatility-adjusted stop-loss and take-profit thresholds optimized by Sharpe-ratio grid search, and is extended with a rolling-MAPE confidence filter and an error-based dynamic position-sizing rule. Across the resulting 36 asset-period combinations, the prediction-based strategies outperformed the buy-and-hold benchmark in 86–92% of cases on cumulative return and 92% of cases on the Sharpe ratio; a one-sided binomial sign test rejects the null of no systematic advantage at p < 0.001 for every strategy variant, and the pattern is stable when each of the three regimes is examined separately (10–11 of 12 assets per period). Excluding crude oil—the only instrument where active strategies persistently underperformed, plausibly reflecting structural breaks such as the negative 2020 futures prices—raises the win rate to roughly 97%. Median outperformance reached about 15 percentage points in cumulative return and close to two Sharpe-ratio points, with maximum drawdowns falling in almost every case. The rolling-MAPE filter was the most effective mechanism for containing losses in turbulent regimes, while the position-sizing variant delivered the strongest average risk-adjusted returns. These results indicate that the value of machine learning forecasts in trading depends less on raw predictive accuracy than on the risk-management logic wrapped around it, and that this advantage is statistically robust across assets, regimes, and specification choices rather than an artifact of a single favorable sample. Full article