Search Results (11,116)

Carbon fiber reinforced polymer (CFRP) is extensively employed in aerospace and advanced equipment industries for its outstanding mechanical performance and corrosion resistance. As conventional micro-drilling (CMD) easily induces excessive cutting force, accumulated heat, burrs and matrix thermal damage that degrade hole quality, this paper presents an experimental and theoretical investigation on the cutting force and thermal characteristics of CFRP in ultrasonic-assisted micro-drilling (UAMD). Combined with finite-element method (FEM) simulation and machining experiments, the machinability evolution law of CFRP under different machining methods and parameters is systematically explored. A self-developed high-frequency vibration spindle is adopted to improve the micro-hole machinability of CFRP materials. The paper systematically analyzes the cutting force, cutting heat, tool wear and other experimental results under CMD and UAMD with various machining parameters. To ensure the reliability of the research data, a corresponding finite-element model for CFRP micro-drilling was established and validated through experimental tests. The results demonstrate that UAMD can effectively improve the machining condition and suppress cutting force and cutting heat. Compared with the conventional CMD process, UAMD reduces the cutting force and cutting heat of CFRP micro-drilling by up to 16.3% and 19.6%, respectively. The high-frequency intermittent vibration effect of ultrasonic assistance facilitates heat dissipation, alleviates tool abrasion, and significantly extends tool service life. The proposed UAMD method effectively optimizes the cutting force and thermal characteristics in CFRP micro-drilling, providing a credible theoretical basis and technical reference for high-quality and high-precision micro-hole machining of CFRP materials. Full article

Background: Mucopolysaccharidosis type II (MPS II) is a progressive, rare, X-linked inherited disease with multi-organ involvement and a restricted life expectancy. The disease has two main forms: neuronopathic (severe) and non-neuronopathic (attenuated). Aim: To compare the two main forms of MPS II to identify signs for early recognition of severe disease forms in daily practice. Methods: In this retrospective cohort study, clinical and laboratory data, as well as enzyme replacement therapy data, for approximately 162 patients were extracted and analyzed from the Russian MPS II registry. Patients with insufficient clinical information (23 patients) and children under 5 years of age (15 patients) were excluded. We compared patients with MPS II neuronopathic (n = 82, 66.1%) and non-neuronopathic forms (n = 42, 33.9%) based on the presence of cognitive deficits after age 6 years and on remaining committed to the phenotype throughout follow-up. Results: The patients with both forms had the following symptoms with similar frequency: Hurler phenotype, hernias, hepatosplenomegaly, short neck, orthopedic problems: spinal pathology (curvature), chest deformity, joint stiffness, deformity of the hand, hand contractures, wrist joint deformity and contractures, elbow joint deformity and contractures, shoulder and knee deformity and contractures, hip contractures, cardiomyopathy, obstructive respiratory tract diseases, noisy breathing, hearing loss, carpal tunnel syndrome. Highly specific symptoms (frequency of occurrence above 80%) for the diagnosis of the neuronopathic form of MPS II were: delayed intellectual disability at the age of 1–3 years, epilepsy, and the presence of gross rearrangements in the IDS gene. Highly sensitive symptoms (above 80%) were: psychomotor development delay of up to 1 year of age and delayed intellectual disability at the ages of 1–3 years, heart damage, and myxomatous degeneration of the valves (79.5%). A symptom with high sensitivity and specificity is delayed mental and speech development between the ages of one and three, with respective sensitivities and specificities of 94.8 and 86.5. Conclusions: Identifying symptoms characteristic of neuronopathic forms of MPS is important not only for determining the clinical form of the disease but also for developing various approaches to treating such patients. Early diagnosis of severe forms of MPS II will make it possible to use currently approved methods of intraventricular enzyme replacement therapy, or primarily use of ERT capable of crossing the brain-blood barrier which could improve the prognosis for such patients. Symptoms that predict the risk of developing the neuronopathic form can be used in clinical practice. Full article

Background/Objectives: Stable angina is a significant health issue in Egypt, often showing poor responses to standard treatments. This study examines the effectiveness and safety of early combination therapy with trimetazidine and either a β-blocker or a calcium channel blocker (CCB) among Egyptian patients with stable angina. Methods: This sub-analysis of the Egyptian cohort from the COMBINE Angina study included patients with stable angina (CCS class II–III) who remained symptomatic after 2–4 weeks of β-blocker or CCB monotherapy. Patients received trimetazidine 35 mg modified-release twice daily as an add-on treatment for 4 months. The primary outcome was the Seattle Angina Questionnaire (SAQ-7) summary score, with secondary outcomes including SAQ-7 domain scores, CCS class, frequency of angina attacks, nitrate use, treatment satisfaction, adherence, and safety. Results: In the global COMBINE study involving 578 patients, the Egyptian cohort had 138 participants with an average age of 58.3 years. The SAQ-7 summary score improved from 42.2 at baseline to 76.8 at month 4 (mean change 34.6 points, 95% CI 31.3 to 38.0; p < 0.0001). All 138 patients contributed data at every visit for the SAQ-7 summary score. Patients in CCS class I increased from 0% to 56.5%, weekly angina attacks fell from 5.0 to 1.7 (p < 0.0001), and short-acting nitrate use decreased from 2.9 to 1.0 doses per week (p < 0.0001). Patient satisfaction was about 75%, and physician satisfaction reached 83% by month 4. Medication adherence also improved (MARS-5 score: 20.8 to 22.7), and two non-serious, treatment-related adverse events (nausea and somnolence) were reported in a single patient (0.7%), with no serious or fatal events. Conclusions: In Egyptian patients with stable angina, implementing an early combination strategy incorporating trimetazidine with first-line β-blocker or CCB therapy was associated with clinically meaningful improvements in angina symptoms, functional status, and quality of life, with good tolerability and high satisfaction. As the study was observational and single-arm, these findings are provisional and require confirmation in further long-term controlled studies. Full article

Human factors remain a leading cause of occupational accidents and fatalities in the construction industry. Although human factors are a major contributor to construction accidents, their relative impact across different construction subsectors has not been fully quantified. This study presents a comparative analysis of accident and fatality data from the U.S. Occupational Safety and Health Administration (OSHA) for three major construction subsectors, NAICS 236 (Construction of Buildings), 237 (Heavy and Civil Engineering Construction), and 238 (Specialty Trade Contractors) from January 2010 through June 2025. Seventy human-factor-related keywords were categorized into eight categories, and accident counts, fatality counts, and fatality rates, defined as the proportion of investigated cases that were fatal, were computed. Twenty-four high-frequency keywords (≥60 investigated accidents) were further analyzed across the three subsectors. Descriptive methods, including risk matrices, Pareto analysis, and category-level fatality rate comparisons, were applied to identify the factors contributing most to fatalities. Differences between subsectors were tested using permutation-based chi-square tests with Benjamini–Hochberg correction for multiple comparisons. Results indicate substantial variation in both frequency and severity among keywords. At the category level, cognitive or perceptual errors, procedural violations and unsafe acts, and judgment and awareness failures in equipment use showed significantly higher fatality proportions in Heavy and Civil Engineering Construction than in the other two subsectors, whereas at the keyword level only lost balance and inattention differed significantly once the correction was applied. The findings provide a data-driven basis for prioritizing targeted safety interventions and tailoring human factor mitigation strategies to subsector-specific risks. Full article

In this study, we identified the stiffness, natural frequency, and damping properties of a metallic mesh fabric applied to spaceborne antennas and proposed finite element models (FEMs) based on static and dynamic property tests. Tensile tests along the course and wale directions were performed to analyze the static characteristics of the metallic mesh fabric, and dynamic property tests were conducted to evaluate natural frequencies and damping properties under pre−tensions. By applying pre−tensions of 1, 3, and 5 N to each edge of the mesh fabric, the 1st natural frequencies were derived as approximately 35, 50, and 60 Hz. Furthermore, by applying direct dynamic loads to the fabric, high damping properties (ζ ≈ 1.0) were confirmed. FEMs of the mesh fabric were developed using a 1D beam−based detailed model and a 2D shell−based simplified model. The effectiveness of both models was validated by comparing numerical results with experimental data, showing discrepancies below 5% for static stiffness in the linear elastic region and within 5.4% for the 1st natural frequency. To evaluate dynamic behavior under an agile attitude maneuver, the simplified mesh model was applied to a 4 m deployable antenna model. Under the maneuver profile, dynamic responses rapidly attenuated due to high damping, with residual vibrations showing peak displacements below 0.02 mm decaying to the origin within 0.2 s. For the proposed 4 m deployable antenna with the SUS316L Atlas−Atlas mesh, the post−disturbance dynamic influence was confirmed to be negligible, indicating that post−maneuver residual vibrations do not cause surface accuracy distortions or degradation in the antenna’s electrical RF performance in orbit. Full article

Objective: To address the limitations of existing deep learning methods for brain networks in jointly modeling directional interregional connectivity, multiband information, and short-term dynamic features, this study proposes a multiscale directed temporal graph convolutional network (MSD-STGNN) for the three-class classification of healthy controls (HCs), patients with left temporal lobe epilepsy (lTLE), and patients with right temporal lobe epilepsy (rTLE). Methods: Resting-state magnetoencephalography (MEG) data were obtained from 43 subjects, including 14 HCs, 13 patients with lTLE, and 16 patients with rTLE. Based on 26 predefined default mode network (DMN)-related brain regions, directed effective connectivity networks were constructed in six frequency bands using the directed transfer function (DTF), and indices including information-flow strength, directional preference, and hemispheric asymmetry were used as node features. MSD-STGNN separately modeled incoming and outgoing connectivity information through directed graph convolution, fused frequency-band information using a hierarchical multiband attention mechanism with gated residual correction, and employed gated recurrent units (GRUs) to extract short-term dynamic features from consecutive brain-network slices. Model performance was evaluated using subject-level stratified fivefold cross-validation, with predictions from multiple temporal groups of each subject aggregated to obtain the final subject-level prediction. Frequency-band masking and node-level fusion-weight analyses were further performed to evaluate the model’s dependence on different frequency bands and information from the predefined brain regions. Results: In the subject-level fivefold cross-validation, MSD-STGNN achieved an accuracy of 0.836 ± 0.067, a macro-F1 of 0.830 ± 0.065, and a macro-AUC of 0.900 ± 0.055 using the one-vs-rest strategy, with the highest fivefold mean values across all evaluation metrics among the baseline models and ablation configurations investigated in this study. Post-training frequency-band masking showed that the model exhibited relatively high dependence on the low-gamma (30–80 Hz) and beta (13–30 Hz) bands. Node-level fusion-weight analysis showed that, within the 26 predefined DMN-related brain regions, orbitofrontal, cingulate, medial temporal, and parietal regions exhibited relatively high overall fusion weights across different frequency bands, although the exact top 5 regional rankings varied across folds. Significance: Within a unified framework, MSD-STGNN integrates directional connectivity, multiband information, and short-term dynamic features derived from MEG-based directed brain networks, providing a modeling approach with a certain degree of interpretability for the three-class classification of HCs, patients with lTLE, and patients with rTLE. The current findings are based on internal cross-validation of a small, single-center cohort; therefore, the classification performance and the observed frequency-band and brain-region attention patterns require further validation in larger, independent multicenter datasets. Full article

Real-time environmental monitoring is increasingly supported by Internet of Things (IoT)-based sensing infrastructures and machine learning techniques capable of processing high-frequency urban environmental data. However, many existing solutions remain focused primarily on data acquisition, visualization, or isolated prediction tasks, with limited integration of predictive analytics and human expertise within a unified decision-support framework. This limitation highlights the need for intelligent monitoring approaches that combine continuous sensing, short-term prediction, and Human-in-the-Loop interpretation. Hybrid Human–AI Collaborative Networks (HCNs) are increasingly relevant for supporting environmental monitoring and decision-making in smart cities. This paper proposes an Artificial Intelligence of Things (AIoT)-enabled collaborative framework that integrates distributed uRADMonitor sensors, cloud-based data management, machine learning models, and human stakeholders into a unified monitoring ecosystem. A distributed uRADMonitor sensing network was deployed across multiple locations in Sibiu for continuous environmental monitoring, while the machine learning experiments presented in this study were conducted independently using three sensing-node datasets collected from different neighbourhoods in Sibiu. The same overall modelling and validation methodology was applied to each dataset, while the predictor set reflected the environmental variables available at each sensing node. Several machine learning algorithms were evaluated for Air Quality Index (AQI) prediction, including Linear Regression, Random Forest, and Gradient Boosting. Under a random train–test split, Random Forest was the best-performing contemporaneous model across all three sensing nodes, although predictive performance varied substantially between locations. In contrast, the short-term forecasting experiment provided the operationally relevant predictive component, with Ridge Regression achieving positive skill relative to persistence across forecasting horizons from 5 to 120 min. Full article

To address the urgent need for new satellite constellations for high-speed communication and high-precision orbit determination, this paper designs an intersatellite heterodyne coherent laser mixed-domain integrated communication and ranging system, achieving deep integration of communication and ranging functions. The system combines analog-domain linear equalization with a digital-domain feedback mechanism to establish an integrated processing strategy for clock and data recovery (CDR), along with high-precision ranging. A phase-augmented pseudo-noise ranging dual one-way ranging (PAPR-DOWR) scheme is implemented, which accurately accomplishes intersatellite distance resolution through the synergy between coarse measurements via frame-synchronization matched filtering and fine measurements via an optimized Gardner timing recovery algorithm.Thetest platform of the on-orbit experiment is a medium Earth orbit (MEO) satellite at an altitude of approximately 20,000 km, the two laser terminals under test are separated by approximately 50,000 km, and the Doppler frequency shift is within $\pm 5$ GHz. The experimental results show that at a symbol rate of 1.023 Gsps, the system achieves ranging standard deviations of 4.39 ps (approximately 1.3 mm, $1\sigma$) in a ground-based 4000 s long-term test and 5.69 ps (approximately 1.7 mm, $1\sigma$) in an on-orbit short-term dynamic test, fully demonstrating millimeter-level ranging performance under long-distance, low-SNR intersatellite conditions. Full article

Underwater sensor networks (USNs) are essential for marine exploration and monitoring, yet their performance is limited by transmission reliability in complex underwater environments. Underwater acoustic (UWA) transmission offers a practical solution, with orthogonal frequency division multiplexing (OFDM) extensively adopted for its high data rate and multiple access capability. However, OFDM is extremely vulnerable to Doppler-induced distortions, which degrade demodulation performance. Conventional cross-ambiguity function (CAF) methods estimate Doppler through signal-level correlation, but achieving high accuracy requires long training sequences, thus incurring frame overhead and reducing effective data rates. To address this issue, a novel bit-level correlation (BLC) Doppler estimation algorithm enables accurate estimation with diminished training overhead. A tailored OFDM frame uses the first two OFDM symbols, modulated with M-sequences, as training sequences. Demodulated bits are correlated with a local M-sequence via vector inner-product computation, converting correlation from the signal level to the bit level. Numerical simulations under three Doppler scales demonstrate the effectiveness of the BLC algorithm. By correlating the demodulated training bits with a local M-sequence reference, the proposed algorithm provides a favorable trade-off between training overhead and estimation accuracy under the tested simulation conditions. Full article

High-frequency full-field vibration measurement using three-dimensional digital image correlation (3D-DIC) is limited by the trade-off between camera frame rate, spatial resolution, and measurement noise. This study presents a method to overcome the frame-rate limitation of 3D-DIC vibration measurement by combining stroboscopic equivalent-time sampling with spatiotemporal noise decoupling. Short-pulse stroboscopic illumination freezes structural motion at different vibration phases, allowing a low-frame-rate stereo camera system to reconstruct high-frequency periodic responses through inter-cycle phase sampling. The measured three-dimensional displacement fields are further processed as space–time data, where target-frequency extraction and spatial-frequency filtering are combined to suppress noise and enhance small-amplitude vibration responses. The proposed method was experimentally validated using a plastic plate vibrating at 251.1 Hz. With an actual camera frame rate of approximately 8.34 fps and an equivalent temporal sampling frequency of 2511 Hz, the reconstructed mode shape achieved a Modal Assurance Criterion (MAC) value of 0.9233, comparable to that obtained using a high-speed camera (0.9225), while providing higher spatial resolution and lower hardware cost. A pulse-width experiment on an aluminum plate vibrating at 7154 Hz demonstrated the influence of stroboscopic exposure duration on measurement accuracy. The proposed approach was further integrated into the EMODE-1 full-field vibration measurement system and applied to a Lenovo ThinkPad touchpad vibrating at 1051 Hz, achieving a MAC value of 0.8878 compared with continuous-scanning laser Doppler vibrometry. The results demonstrate the potential of the proposed method for high-frequency full-field vibration sensing of periodic structures using compact and cost-effective imaging systems. Full article

Light resets the mammalian circadian clock, but whether this response is determined solely by integrated light intensity (irradiance) or is also influenced by the spatial distribution of light remains unclear. We tested whether high-contrast or low-spatial-frequency patterns modulate circadian responses in mice independently of scene irradiance. Circadian phase shifts were measured following exposure to spatially patterned illumination, including high-contrast checkerboards and spot stimuli positioned at the horizon or zenith. Across all experiments, redistributing light within the visual field did not measurably alter phase-shift amplitude when irradiance was matched between paired conditions. Circadian responses were well predicted by total scene irradiance whereas a metric based on the region of highest radiance provided a poorer account of the data. These findings are consistent with effective spatial integration of light by the mouse circadian system. Within the range of spatial and irradiance conditions tested, our results support irradiance as a simple and effective metric for predicting circadian responses across light environments with divergent spatial distributions. Full article

This study investigates the micro-cutting mechanism of a 7A09 aluminum matrix composite reinforced with CoCrFeNiAlX (referred to as AlX) high-entropy alloy particles, chosen for their superior wetting properties with aluminum. Using simulation analysis, the research explores how high-entropy alloy particles with varying aluminum content influence the micro-cutting behavior of the aluminum-based composite. The results indicate that cutting force is minimized when the cutting path is above the particle and maximized when below, with a difference of approximately 201 N. Cutting force increases with cutting speed, but the rate of change varies significantly across speeds, with a maximum increase of 67%. The highest cutting temperatures, at 301 °C and 304 °C, are observed when cutting depth and speed are maximized, respectively, with temperature more strongly influenced by cutting speed, leading to a temperature variation of up to 148 °C across different speeds. For composites with different aluminum contents, cutting temperatures follow the order Al₁ > Al_0.6 > Al₀. When the cutting path passes above the particle, thinner chips undergo multiple fractures due to tensile stress; as the path shifts downward, chip breakage frequency increases with cutting depth. At a constant cutting depth, low-speed cutting produces discontinuous chips, while higher speeds yield more continuous chips, achieving the most complete chip formation at 1500 m/min. The surface smoothness, qualitatively reflected by nodal displacement, ranks as follows: path b > path d > path c > path a > path e. This research provides valuable data to enhance machining performance for high-entropy alloy particle-reinforced aluminum matrix composites. Full article

Epilepsy is a chronic neurological disorder with a high global prevalence. In Venezuela, the limited availability of epidemiological and pharmacogenetic data represents an important public health challenge. Antiseizure medications (ASMs) are frequently associated with adverse drug reactions (ADRs), which may be influenced by genetic variability in drug-metabolising enzymes. This study evaluated the association between the CYP2C9*2 and CYP2C9*3 variants and the occurrence of ADRs in patients receiving anticonvulsant treatment in the Venezuelan Andes. An observational, analytical, cross-sectional study was conducted in 72 patients. Genomic DNA was extracted from venous blood, and CYP2C9 genotypes were determined by real-time PCR using TaqMan^® probes. The frequencies of the CYP2C9*2 and CYP2C9*3 alleles were 0.15 and 0.06, respectively. The metaboliser phenotype was significantly associated with the presence of ADRs. The risk of ADRs was elevated in the intermediate/poor metaboliser group (IM/PM: OR = 17.45; 95%CI: 5.09–76.56; p < 0.001) and in patients on polypharmacy (OR = 15.03; 95%CI: 2.73–157.37; p = 0.001), although this latter estimate showed marked imprecision due to the width of its interval. Consistent with this, the ADR rate was higher in PM (100%; 3/3) and IM (84.2%; 16/19) than in normal group (20.9%; 9/43). In the monotherapy subgroup, the type of ASM showed no association with the occurrence of ADR (p = 0.741). Among normal metabolisers receiving polytherapy, regimens containing valproic acid were associated with the highest proportion of ADRs, with dual therapy including lamotrigine accounting for 28.57%. Although the overall analysis indicated differences in ADR occurrence according to polytherapy regimen (p = 0.043), the limited sample size restricts the strength of these findings. These results suggest that CYP2C9*2 and CYP2C9*3 variants may contribute to variability in the safety of anticonvulsant treatment among patients from the Venezuelan Andes by influencing CYP2C9 metaboliser phenotypes, and consequently, drug pharmacokinetics. Further studies in larger and more representative Venezuelan populations are warranted to validate these findings. Full article

The amount of financial information available through regulatory big data repositories, whose scale and update frequency are consistent with the defining characteristics of big data, represents an opportunity for implementing big data analytics and business intelligence in order to support corporate decision-making, although it presents challenges due to the correlated nature of high-dimensional financial indicators. This study proposes a big data analytics model that combines dimensionality reduction with unsupervised machine learning for the classification of financial profiles of firms belonging to Ecuador’s Financial and Insurance sector (sector K). A quantitative and non-experimental approach is employed with a dataset composed of a large panel of 7875 firm-year observations (2019–2024) from 2279 unique firms, extracted from the Superintendencia de Compañías, Valores y Seguros repository and including nine financial indicators related to size, profitability, liquidity, leverage, and firm age, with extreme values addressed through winsorization at the 1st and 99th percentiles rather than case deletion. PCA reduces the dimensionality of the data, while K-means clustering, validated with the elbow method, the Silhouette index, alternative clustering algorithms (PAM, hierarchical (Ward) clustering, and a Gaussian Mixture Model) and alternative values of K, segments 1894 firms that operate in 2024. PCA retains four principal components of the dataset—firm size, financial performance, operating structure, and capital structure and organizational maturity—that explain 76.04% of the total variance. Two interpretable corporate financial profiles with limited statistical separation (average Silhouette coefficient of 0.257) were identified by K-means clustering: a larger, older, less-leveraged profile (Cluster 1) and a smaller, younger, more highly leveraged profile with higher observed returns (Cluster 2). Beyond its regional contribution to the Ecuadorian financial and insurance sector, the study illustrates a fully reproducible big data analytics pipeline—including transparency and robustness diagnostics—that is applicable to other economic sectors and regulatory repositories that are continuously updated. Full article

This paper proposes a hierarchical recurrent neural network framework that leverages a context-gated mechanism and the elastic sorting of time series indicators, addressing the strong dynamic coupling characteristics of aeroengine operational time series data. This framework addresses the coupling of multi-physical field signals and the interleaved cross-scale degradation characteristics of engine time series data. The framework achieves high-precision predictions of residual service life under complex operating conditions by deeply integrating multi-modal feature engineering with hierarchical time series modeling. An elastic ranking algorithm for time series indexes is designed to adaptively fuse multiple sensors’ time domains, frequency domains, and nonlinear characteristics, thereby constructing a dynamic feature space sensitive to load conditions. This approach addresses the limitations of traditional fixed feature combinations in characterizing variable load conditions. Additionally, a multi-scale stack dilation convolution module is introduced to exponentially enlarge the receptive field, allowing for the capture of long-range dependencies in weakly degraded signals. A bidirectional time–frequency converter decomposes the signal into low-frequency trend components and high-frequency anomaly components, enabling coupling characterization through cross-scale feature interactive gating. Furthermore, context-gated cyclic units based on hierarchical cyclic networks are constructed to hierarchically integrate multi-modal features while dynamically adjusting time-dependent weights, thereby enhancing the model’s causal reasoning ability for long sequence degradation patterns. In four typical experiments conducted on the C-MAPSS dataset, the ERCG-HRNN demonstrates significant improvements in RMSE and Score compared to existing methods. Full article