Search Results (11,300)

In air jet looms, relay nozzles are critical components in governing airflow velocity and air consumption during the weft insertion process. Although computational fluid dynamics (CFD) offers high-fidelity simulation for aerodynamic analysis, its computational burden hinders its practicality in iterative aerodynamic design of relay nozzles. To address the challenge, this study proposes a data-driven framework integrating a Chebyshev polynomial Kolmogorov–Arnold Network (Chebyshev KAN) surrogate model with an Improved Multi-objective Red-billed Blue Magpie Optimizer (IMORBMO). The accuracy of the Chebyshev KAN model was benchmarked against conventional multilayer perceptrons (MLP), convolutional neural networks (CNN), and the standard Kolmogorov–Arnold Network (KAN). Experimental results demonstrate that the Chebyshev KAN model achieves the lowest mean absolute error (MAE) of 0.103 for airflow velocity and 0.115 for air consumption. Building upon the non-dominated sorting and crowding distance strategies, IMORBMO was developed, incorporating an adaptive mutation mechanism by information entropy for improvement of convergence, diversity, and uniformity of the Pareto-optimal solutions. Comprehensive evaluations on the ZDT and WFG benchmark suites confirm that the IMORBMO consistently attains the best and highly competitive performance, yielding the lowest generation distance (GD), inverted generational distance (IGD) values and the highest hypervolume (HV). Applied to the aerodynamic optimization of a relay nozzle, the proposed framework delivers an optimal aerodynamic design that increases airflow velocity by 10.5% while reducing air consumption by 15.4%, as verified by CFD simulation. The steady-state flow field was simulated by solving the Reynolds-Average NavierStokes equations with the k−ω turbulent model, utilizing Fluent 2025.R2. No-slip wall, inlet pressure and outlet pressures are boundary conditions to the relay nozzle surfaces. This work establishes a computationally efficient and accurate optimization paradigm that holds significant promise for aerodynamic design and other complex real-world engineering applications. Full article

The running reliability of rolling bearings depends on the effective lubrication state, and poor lubrication will induce abnormal vibration. Therefore, vibration-based fault diagnosis is an important means to evaluate the health of bearings through vibration characteristics. However, the lack of fault samples in actual working conditions seriously restricts the generalization ability and accuracy of an intelligent diagnosis model. A novel few-shot diagnosis method integrating multi-layer feature fusion and adaptive similarity measurement is proposed. This method adopts a meta-learning framework to simulate sample scarcity through numerous N-way K-shot diagnostic tasks. An efficient feature extractor with a cross-task feature stitching mechanism is designed to fuse features from support and query sets. To overcome the limitation of fixed-distance metrics in existing meta-learners, a learnable similarity scheduler adaptively generates optimal pseudo-distance functions. In particular, a multi-layer feature fusion strategy is introduced to compute adaptive similarities at multiple network depths, which significantly enhances feature robustness against operational variations. Experimental results demonstrate the method achieves stable diagnostic accuracy above 90% under extremely few-shot conditions and maintains over 90% accuracy when transferring from laboratory-simulated faults to natural operational faults, validating its strong potential for practical industrial applications where annotated fault data is scarce. Full article

Background: Standard spirometry fundamentally overlooks the mechanical dynamics of forced expiration. This study derived novel biomechanical parameters to establish functional phenotypes and predict clinical respiratory impairments. Methods: Utilizing 16,596 acceptable spirometry records from NHANES (2007 to 2012), parameters reflecting kinetic power, mass constraint, and airway instability were mathematically derived. Principal component analysis, K-means clustering, and a Multilayer Perceptron neural network were sequentially applied. Results: Three distinct biomechanical phenotypes emerged: Load-Constrained (45.4%), Mechanically Efficient (23.5%), and Dynamic Collapse (31.0%). Aging significantly degraded kinetic power, demonstrating a steeper functional decline in males (p < 0.001). The neural network achieved 93.2% testing accuracy in classifying spirometric abnormalities. Crucially, Dynamic Airway Collapse Ratio (100% normalized importance), BMI (89.4%), and kinetic power (86.2%) fundamentally outperformed traditional demographic predictors such as chronological age (20.4%) and biological sex (7.1%). Conclusions: Structural and dynamic kinetic factors drive pulmonary dysfunction far more accurately than conventional demographics. Classifying these mechanical phenotypes facilitates highly targeted precision cardiopulmonary rehabilitation. Full article

Background: Active middle ear implants (AMEIs) provide an alternative auditory rehabilitation strategy for patients who cannot tolerate conventional hearing aids. However, clinical data regarding the outcomes of Vibrant Soundbridge (VSB) implantation using the incus short process (SP) coupler in older adults remain limited. Objective: This study aimed to evaluate the audiological outcomes, patient-reported hearing benefits, tinnitus improvement, and surgical safety of VSB implantation using the SP coupler in older adults with bilateral sloping sensorineural hearing loss. Methods: This retrospective study included 45 older male veterans (mean age 76.1 ± 5.3 years) with bilateral sloping sensorineural hearing loss who underwent unilateral VSB implantation with the SP coupler between 2019 and 2023. Functional hearing gain was assessed using preoperative and postoperative sound-field pure-tone thresholds. Patient-reported outcomes were evaluated using the Speech, Spatial and Qualities of Hearing Scale (SSQ) and the Tinnitus Handicap Inventory (THI). Operative characteristics and postoperative complications were also analyzed. Results: Mean operative time was 40.2 ± 8.7 min. Functional hearing gain increased progressively across speech-critical frequencies, reaching +20 dB at 2 kHz and +30 dB at 4 kHz. The mean four-frequency pure tone average improved from 57.4 ± 8.3 dB HL preoperatively to 35.6 ± 6.9 dB HL postoperatively (p < 0.001). All SSQ subdomains showed significant improvement (p < 0.001). THI scores decreased significantly from 43.2 ± 8.4 to 17.1 ± 6.2 (p < 0.0001), with clinically meaningful tinnitus improvement observed in 75.6% of patients. No major surgical complications occurred. Conclusions: Vibrant Soundbridge implantation using the incus short process coupler provides effective auditory rehabilitation for older adults with sloping sensorineural hearing loss. The procedure yields meaningful high-frequency hearing gain, improved hearing-related quality of life, and significant tinnitus reduction while maintaining a favorable surgical safety profile. Restoration of auditory input through active middle ear implantation may also contribute to improved central auditory processing in older adults. Full article

This study presents a reproducible simulation-based framework for visual-comfort and energy-optimised lighting design in UK residential buildings using DIALux Evo. Circadian and biophilic principles inform the conceptual approach, specifically colour temperature selection aligned with occupant comfort—but the study measures only photopic illuminance (lux) and electrical energy consumption (kWh), explicitly excluding biological circadian metrics, dynamic controls, and daylight harvesting. A controlled comparative design evaluates twenty matched lighting scenes in one-bedroom flats, compliant with EN 12464-1 and CIBSE LG9. The DIALux-optimised designs, incorporating LED luminaires in place of CFL luminaires used in existing manual designs, reduced mean energy consumption from 10.25 kWh to 8.68 kWh—a statistically significant reduction of 15.3% (t = 5.12, p = 1.2 × 10⁻⁵, d = 1.61)—while increasing mean illuminance from 165.86 lux to 205.14 lux (t = 3.084, p = 1.0 × 10⁻⁶, d = 0.81), improving CIBSE LG9 compliance across scenes. The framework offers a standards-aligned reproducible methodology with direct relevance to UK Net Zero objectives, Part L compliance, and residential retrofit policy, providing actionable guidance for architects, engineers, and policymakers. It is acknowledged that the observed gains reflect the combined benefit of an integrated LED-plus-simulation workflow; the absence of a same-technology comparison condition is identified as the primary design limitation. Full article

Addressing the lack of high-fidelity test cycles for fuel cell light-duty trucks, this paper proposes an automated driving cycle construction method that integrates unsupervised clustering and reinforcement learning. Firstly, based on large-sample real-world driving data, four libraries of typical driving pattern segments are extracted through dimensionality reduction via Principal Component Analysis (PCA) and K-means clustering. Subsequently, the cycle construction process is formulated as a sequential decision-making problem, and a framework based on the Proximal Policy Optimization (PPO) algorithm, incorporating an action masking mechanism, is designed. This framework innovatively injects macro-level time budget allocation as a hard constraint into the agent’s policy space via action masking, while utilizing micro-level Markov transition probabilities as a soft guide. This dual approach drives the agent to learn an optimal segment concatenation strategy, thereby simultaneously ensuring both the macro-level statistical representativeness and the micro-level driving logic coherence of the synthesized cycle. Validation results demonstrate that the cycle constructed by the proposed method achieves an average relative error of only 7.53% in key characteristic parameters, and its joint speed-acceleration distribution exhibits a similarity as high as 0.9886 with the original data, significantly outperforming traditional methods such as the clustering method, the Markov chain method, and standard driving cycles. This study provides an effective tool for generating high-fidelity driving cycles and testing energy management strategies for fuel cell commercial vehicles. Full article

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD), metabolic dysfunction and alcohol-associated liver disease (MetALD), and related fibrosis are increasingly prevalent conditions. The relation of the American Heart Association’s (AHA) cardiovascular health (CVH) metric Life’s Essential 8 (LE8) with MASLD, MetALD, and hepatic fibrosis remains unclear. We aimed to investigate the associations of CVH with MASLD, MetALD, and hepatic fibrosis. Methods: We defined significant hepatic fibrosis as a liver stiffness ≥8.2 kPa measured by vibration-controlled transient elastography. MASLD was defined as steatosis (controlled attenuation parameter of ≥274 dB/m) with ≥1 cardiometabolic risk factor and mild alcohol intake (≤140 g/week [women]; ≤210 g/week [men]). MetALD was defined as steatosis with ≥1 cardiometabolic risk factor and moderate alcohol intake (141–350 g/week [women]; 211–420 g/week [men]). Data from 2962 participants in the Framingham Heart Study (mean age 59 years, 57% women) were used in multivariable-adjusted logistic regression models, accounting for demographic and clinical covariates to relate CVH and liver outcomes. Results: Our study included 2704 participants with mild and 258 with moderate alcohol use. MASLD and MetALD prevalence was 34% and 40%, respectively, and 9% had significant hepatic fibrosis. Each 10-point increase in LE4 score (composite of diet, sleep health, physical activity, and smoking) was associated with 16% lower odds of MASLD (Odds Ratio [OR] 0.84; 95% CI: 0.80–0.90; p < 0.001) but not MetALD. Each 10-point increase in LE8 score was associated with 17% lower odds of hepatic fibrosis (OR 0.83; 95% CI: 0.78–0.89; p < 0.001). Conclusions: Better CVH is related to lower odds of MASLD and significant hepatic fibrosis. Full article

Background and Objectives: Endoscopic ultrasound point shear-wave elastography (EUS-pSWE) bypasses subcutaneous fat and may provide weight-independent liver stiffness measurements; however, data on reproducibility and quality criteria remain limited. This study aimed to evaluate the intra-observer reproducibility and short-term variability of EUS-pSWE. Materials and Methods: In this single-center prospective cohort study (December 2024–February 2025), 120 consecutive adults undergoing diagnostic EUS were enrolled. For each hepatic lobe, 10 consecutive measurements were obtained and grouped into two sequential blocks of five measurements without scope repositioning. Intra-observer reproducibility was assessed using intraclass correlation coefficients (ICC3,1). The agreement between acquisition runs and determinants of short-term variability was also evaluated. Same-day vibration-controlled transient elastography (VCTE) served as an external comparator. Results: Forty-six participants were obese (BMI ≥ 30 kg/m²). The mean VCTE stiffness was 6.24 kPa, while the mean EUS-pSWE stiffness was 9.40 ± 5.64 kPa. Among examinations meeting IQR/Median < 30% quality criteria, reproducibility was excellent (left ICC 0.97 [0.95–0.98]; right ICC 0.92 [0.86–0.95]) and consistent across BMI strata. EUS-pSWE correlated strongly with VCTE (r = 0.81, p < 0.001). In contrast, agreement between consecutive acquisition runs was low, indicating increased short-term variability. EUS-pSWE quality pass rates based on IQR/Median criteria were modest (left 56.7%, right 41.7%, both lobes 23.3%), although all measurements fulfilled device-specific validity criteria (VSN > 60%). Age and BMI were not significant predictors of variability. Conclusions: EUS-pSWE demonstrates excellent intra-observer reproducibility under quality-controlled conditions and shows a strong correlation with VCTE. However, short-term variability between acquisition runs and limited feasibility based on conventional quality thresholds should be considered. EUS-pSWE appears to be a promising modality for liver stiffness assessment, warranting further validation of quality criteria and clinical thresholds. Full article

Mn₅₄Al₄₆ alloys with τ-phase as their main component were successfully obtained in a reproducible processing window combining melt-spinning, annealing at intermediate temperatures (450 °C) and low-energy milling. The complete ε → τ phase transformation was driven by thermal decomposition of ε-phase and favored by high grain boundary density inherent to the melt-spun microstructure. An improved magnetic response of the melt-spun Mn₅₄Al₄₆ alloys was observed, as they exhibited saturation magnetization values between 80 and 90 emu/g, together with intrinsic coercivities around 2000 Oe and Curie temperatures between 640 and 648 K. Significant coercivity enhancement over 6000 Oe was predicted, by means of micromagnetic calculations, for alloys with grain size refinement below 100 nm. The efficient, single-step experimental phase transformation with no additional stabilizers for the τ-phase was explained in terms of microstructural features, whereas magnetic enhancement was attributed to lattice distortions promoted by the milling process. This integrated approach introduces a pathway to achieve τ-phase Mn-Al with tunable magnetic performance useful for applications. Full article

Non-Newtonian weighted fracturing fluids are used to carry out hydraulic fracturing operations into the deep and ultra-deep earth for oil and gas extraction, though their flow and friction drag characteristics are largely unknown. This study aims to understand the abovementioned characteristics. An engineering-oriented cost-effective numerical scheme is deployed, incorporating LES with a generalized Newtonian fluid constitutive equation, for predicting the non-Newtonian pipe flow and friction drag coefficient C_f. The weighted fracturing fluid is described as a power-law fluid, i.e., viscosity $\mu \left(\dot{\gamma }\right)=K{\dot{\gamma }}^{n-1}$, where both K and n are coefficients related to fluid rheology, and $\dot{\gamma }$ is the shear rate. The influences of fluid density ρ, mean velocity U and pipe diameter D, as well as K and n on C_f were documented and compared with a water pipe flow. It was found that C_f = f₁ (K, n, ρ, U, D) may be reduced to C_f = f₂ (Re_g), where the scaling factor Re_g = ρU²⁻ⁿDⁿ/(K8ⁿ⁻¹) is the generalized Reynolds number. This scaling law can reasonably well predict the friction drag variation in the pipe flow of non-Newtonian weighted fracturing fluids throughout a range of interests and engineering applications. Full article

This study examines the influence of misinformation on vaccination decision-making and the perception of social security in Romania in the context of potential future pandemics. Using a survey-based design, data were collected through an online questionnaire administered to a sample of 1005 respondents. The analysis employed descriptive and inferential statistical methods, including chi-square tests, ANOVA, Kruskal–Wallis tests, principal component analysis (PCA), K-means clustering, random forest models, and Spearman correlations. The results indicate statistically significant associations between belief in misinformation and vaccination attitudes (p < 0.001), with moderate effect sizes. Effect size estimates indicated small-to-moderate associations (e.g., Cramér’s V up to 0.371 for key demographic differences, and Kendall’s W = 0.273 for the increase in willingness across the three severity scenarios). Individuals with higher levels of education, urban residence, and younger age were more likely to report higher willingness to vaccinate, whereas respondents from rural areas and those with lower educational levels showed greater susceptibility to misinformation. In addition, risk perception was significantly associated with vaccination intention, which increased as the severity of hypothetical pandemic scenarios intensified. Predictive modeling identified specific misinformation beliefs—particularly those related to vaccine safety and natural immunity—as key factors associated with vaccination decisions. These findings suggest that misinformation is strongly associated with both individual vaccination behavior and broader perceptions of social security. Full article

Accurate measurement of atmospheric temperature profiles in the mesopause region is crucial for understanding the atmospheric dynamics and climate processes. To address this challenge, a sodium Doppler lidar based on the resonance fluorescence scattering mechanism was recently developed to precisely detect atmospheric temperatures in the mesopause region in Qingdao (36.1°N, 120.1°E), China. For the first time, high-resolution observations of atmospheric temperature in the mesopause region (80–105 km) were achieved by the self-developed Na Doppler lidar in Qingdao under the complex atmospheric conditions of the mid-latitude coastal zone. A systematic cross-validation between the self-developed lidar and SABER satellite observations was conducted, and the temperature bias between the two detection methods in the mesopause region and its altitude-dependent characteristics were quantitatively assessed. The temperature profiles measured by lidar exhibited good agreement when compared with the satellite data yielding estimations of RMSE and mean absolute deviation of 9.2 K and 7.3 K, respectively, from 80 km to 100 km altitudes. A correlation analysis conducted between the lidar temperature data and satellite data showed that the closer the satellite passed over Qingdao, the better the correlation demonstrated by the data. The correlation coefficient of the closer comparison data can reach 0.86, which means that the self-developed lidar system in Qingdao has a good ability to detect temperature profiles in the middle and upper atmosphere. The nocturnal evolution details and short-period fluctuations of the temperature field in the mesopause region over Qingdao were observed, revealing the local temperature structural characteristics under the complex atmospheric conditions at the land–sea interface in the Qingdao area. Full article

Background: Postoperative neurocognitive disorders (PND) are frequent complications in the elderly surgical patients, with aging recognized as a major risk factor. This study aimed to identify electrophysiological markers and establish an exploratory machine learning framework for PND-related vulnerability prediction using anesthetic electroencephalography (EEG) features in aged mice. Methods: Young and aged mice underwent laparotomy under isoflurane anesthesia with EEG recording. Neurocognitive performance was quantified by 16 standardized behavioral fractions. A semi-supervised K-means algorithm, anchored on young-surgery mice, stratified aged-surgery mice into PND and non-PND clusters. EEG dynamics during anesthesia maintenance and emergence were analyzed, and machine learning models were trained to predict PND from EEG features. Results: At baseline, neurocognitive function was comparable across groups. After anesthesia/surgery, aged mice exhibited selective spatial and contextual memory impairments, with two-thirds classified as PND. During emergence, PND mice displayed elevated δ power and reduced α and β ratios. A Multi-layer Perceptron classifier showed discriminatory performance for PND classification in one evaluation setting (AUC = 0.94). Conclusions: This study identifies emergence-related EEG features associated with postoperative neurocognitive vulnerability in aged mice and provides an exploratory machine learning framework for preclinical risk stratification. These findings support further mechanistic investigation and warrant future validation in human perioperative EEG datasets. Full article

Background: Upper limb functional performance depends on the interaction of strength, mobility, and neuromuscular control, while inter-limb asymmetries may increase injury risk. However, comprehensive analyses integrating these factors remain limited. This study aimed to evaluate sex differences and identify functional phenotypes in young adults using a multidimensional assessment approach. Methods: Forty-six healthy young adults (23 women, 23 men) underwent a comprehensive battery of upper limb assessments, including anthropometric measurements, maximal handgrip strength, isometric elbow flexion and extension torque, postural stability via the Fall Risk Index (FRI), and functional reach using the Upper Quarter Y-Balance Test (YBT-UQ). Inter-limb symmetry was calculated using the Limb Symmetry Index (LSI). K-means clustering was applied to standardized variables to identify latent functional phenotypes. Results: Men demonstrated significantly greater body mass, height, limb length, and absolute strength (p < 0.01), while functional performance (YBT-UQ composite scores) and inter-limb symmetry were similar between sexes. Strength asymmetry was most prevalent for elbow flexion and handgrip strength (up to 89%), whereas stability asymmetry was less frequent (≈54%). Three functional clusters were identified: Cluster 1—high strength and moderate stability, Cluster 2—lower anthropometry and strength, Cluster 3—high strength but reduced stability and increased asymmetry. Despite phenotypic differences, composite functional performance was comparable across clusters. Conclusions: Upper limb function reflects the interaction of morphological and neuromuscular factors rather than strength alone. Observed asymmetries should be interpreted within a functional context, as moderate asymmetries may represent normal variation in motor control, while larger asymmetries may indicate potential functional imbalance; however, due to the cross-sectional design of this study, no causal inferences regarding injury risk can be made. Functional phenotyping provides a framework for individualized training, screening, and rehabilitation strategies. Full article

Surface electromyography (sEMG) signals are important for assessing muscle activity, neuromuscular behavior, and movement stability. sEMG signals are widely used in athlete performance monitoring and human–machine interface applications. However, existing methods have limitations in classification, accuracy and generalization across users. In this study, a real-world dataset was generated from 30 professional wrestlers using an 8-channel system based on 10 physical movements and technical elements. Nine time-domain and energy features, mean absolute value (MAV), integrated EMG (IEMG), root mean square (RMS), simple square integral (SSI), fourth power (4POW), wavelength (WL), difference absolute standard deviation (DASDV), variance (VAR), and average amplitude change (AAC), were systematically evaluated separately and in combination. Five classifiers were compared: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), k-Nearest Neighbor (KNN), and Neural Networks (NNs). The models were evaluated for accuracy, sensitivity, specificity, positive predictive value, and F1-score. The generalization ability was analyzed through cross-subject (24/6) and cross-session validation protocols. The nine feature combinations achieved the highest classification accuracy of 97.8% with the RF algorithm. The proposed approach can serve as a practical basis for real-time muscle activity monitoring, movement classification, and rehabilitation systems. Full article