Search Results (1,651)

Face milling of aluminum 7075 hybrid metal matrix composites with 10 wt.% TiO₂ and 3 wt.% graphite (HMMCs) are needed to improve performance and sustainability. This study focuses on optimizing the milling process for Al7075 HMMCs using the desirability approach and advanced multi-criteria decision-making (MCDM) methodologies, including the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and the Combined Distance-based Assessment (CODAS). Surface roughness (SR), cutting force (CF), carbon emissions (CE), and energy consumption (EC) were systematically evaluated and ranked using the L₁₈ Taguchi Orthogonal Array. Minimum Quantity Lubrication (MQL) and cryogenic CO₂ cooling techniques were used to achieve a superior surface finish and reduce friction at the tool-workpiece interface, thereby minimizing scratches and thermal damage. Desirability evaluation results showed the optimal machining conditions for milling of Al7075 (HMMCs) occurred at a cutting speed (V_c) of 200 m/min, a feed rate (f) of 0.02 mm/rev, and a depth of cut (a_p) of 0.3 mm, proving the potential of integrating MCDM tools with effective cooling strategies. The desirability method favored a balanced compromise, while entropy-weighted TOPSIS/CODAS emphasized energy and carbon-related responses. Improvements of 6% in cutting force, 7% in surface roughness, and a 7% reduction in energy consumption, along with 8% lower carbon emissions, were achieved, demonstrating the effectiveness of hybrid cooling strategies in promoting eco-friendly and resource-efficient processes. Full article

Background/Objectives: Pediatric obesity is closely associated with metabolic syndrome (MetS), a condition linked to increased cardiometabolic risk. Early identification of high-risk individuals remains challenging. This study aimed to evaluate the diagnostic performance of selected anthropometric, metabolic dysfunction and insulin resistance indexes for identifying MetS in children and adolescents with obesity. Methods: In this retrospective, cross-sectional, single-center study, 758 children and adolescents with obesity (mean age 14.8 ± 2.1 years; 59.9% females) hospitalized for a body weight-reduction program were included. MetS was defined according to International Diabetes Federation criteria, in which central obesity is a mandatory diagnostic component. The triglyceride–glucose (TyG), TyG–waist circumference (TyG-WC), body adiposity index (BAI), fasting glucose-to-insulin ratio (FGIR), and quantitative insulin sensitivity check index (QUICKI) were calculated. Receiver operating characteristic curve analysis was used to assess their discriminative ability. Results: The prevalence of MetS was 27.8% and was significantly higher in males than females (34.9% vs. 23.1%, p < 0.0001). TyG and TyG-WC showed the best discriminative performance (AUC 0.75 and 0.76, respectively), although with only moderate sensitivity and specificity. FGIR and QUICKI demonstrated lower accuracy (AUC 0.64 and 0.63), whereas BAI showed no discriminative ability (AUC 0.48). These findings were consistent across sexes, although sex-specific differences in both MetS prevalence and optimal cut-off values were observed. Correlation analyses confirmed moderate associations between TyG-based indexes and MetS, whereas other indexes showed weaker relationships. Conclusions: In the present cohort of children and adolescents with obesity, TyG and TyG-WC showed the best performance in identifying MetS compared with the other evaluated indexes. However, their performance remained moderate, and the proposed cut-off values require validation in independent populations. These indexes may represent simple supportive screening and risk-stratification tools but should be used alongside comprehensive clinical assessment and established diagnostic criteria rather than as stand-alone diagnostic measures. Full article

Real-time precise point positioning (PPP) is increasingly supported by open satellite-broadcast state-space representation (SSR) services, yet standalone operation with a single service remains vulnerable to limited constellation support, correction outages, latency variations, and service-dependent modeling inconsistencies. In the Asia-Pacific region, MADOCA-PPP and PPP-B2b provide two publicly accessible and complementary SSR sources, but their consistent fusion before user-level PPP estimation remains insufficiently investigated. This paper proposes a correction-domain fusion framework that combines MADOCA-PPP and PPP-B2b orbit and clock corrections before PPP estimation, rather than merging final positioning solutions. Inter-service discrepancies and unknown cross-correlations are handled by a bias-state-aware structured covariance intersection strategy, in which the relative weighting is derived from the respective correction information (inverse variance), preserving statistical consistency and avoiding overconfident fusion. A unified multi-GNSS PPP scheme further supports signal-priority harmonization, broadcast-ephemeris adaptation, correction-age control, and GLONASS inter-frequency and differential code bias handling. Static-station per-epoch (pseudo-kinematic) and offshore kinematic experiments validate the framework. In the static-station test, fusion raised the mean number of valid satellites from 21.98 and 14.98 to 26.56 and improved the horizontal RMS to 0.033 m—better than either standalone service (0.037 m, 0.079 m)—confirming a genuine combination rather than source selection, while the 3D RMS (0.068 m) matched the best standalone service (0.066 m). In the offshore test, fusion achieved the best overall accuracy (0.232 m horizontal, 0.290 m 3D, versus 0.332 m and 0.313 m for the standalone services) and the most satellites (25.4). It also degraded most slowly with increasing elevation cut-off, outperforming both services about threefold at 40°. A normalized-innovation-squared check confirmed the fused covariance is consistent and not overconfident (median ≈ 1.1; within the 99% bound in 100% of epochs). Under single-service outages from 30 s to 600 s, fusion maintained 100.0% availability, confirming its advantage in redundancy, continuity, and resilience. Full article

This study focuses on the design and development of the Double Purpose Bench Vise to address safety, efficiency, and adaptability challenges in welding and fabrication environments. The project responds to limitations of conventional vises that restrict precision and increase the risk of strain-related injuries when handling heavy, irregular, or vertically oriented workpieces. Through an engineering-based development approach involving analysis, design, fabrication, and performance evaluation, the study introduces a Double Jaw Vertical Bench Vise equipped with a dual-clamping system and an integrated hydraulic jack mechanism for precise vertical adjustment with minimal physical effort. The device is designed to securely hold various materials, including metal bars, pipes, and wooden components, during cutting, grinding, shaping, welding, and assembly operations. Evaluation results from functional testing and user feedback indicate improved clamping stability, alignment accuracy, and ergonomic performance compared to traditional models, although refinements in structural optimization, weight distribution, and user interface components are recommended. The study suggests further prototype enhancement, extended field testing, and integration of advanced ergonomic and safety features to maximize durability, usability, and overall productivity in professional workshops and technical training laboratories. Full article

Background/Objectives: Accurate bone lesion classification on radiographs is critical for clinical decision-making and forensic identification. Existing deep learning approaches treat radiographs as whole images, neglecting available spatial annotations and clinical metadata. To develop an ROI-guided deep learning framework integrating clinical metadata for three-class (Normal, Benign, Malignant) bone lesion classification and to assess its clinical safety profile. Methods: Using the BTXRD (3746 radiographs: 1879 Normal, 1525 Benign, 342 Malignant), an EfficientNetV2-S backbone was combined with an 11-dimensional metadata MLP trained on ROI-cropped regions. Training employed Focal Loss with adaptive class weighting, Mixup/CutMix augmentations, Stochastic Weight Averaging, and Test-Time Augmentation. Five-fold stratified cross-validation with bootstrap confidence intervals (n = 2000) and probability calibration metrics were used. Results: The framework achieved 96.05% accuracy (95% CI: 95.41–96.66%), 93.94% balanced accuracy, 92.62% macro F1-score, and 99.21% macro-AUC (95% CI: 98.89–99.42%). Critically, near-zero Malignant-to-Normal misclassifications occurred (1/342, 0.29%; 95% Clopper–Pearson CI: 0.01–1.62%) across all 3746 predictions. The minority Malignant class attained F1 = 83.53% despite comprising only 9.1% of the dataset. Conclusions: ROI-guided deep learning with metadata fusion achieves state-of-the-art bone lesion classification with clinically safe error patterns and probability outputs whose calibration was explicitly quantified, supporting its potential as a decision support tool in diagnostic radiology and forensic anthropology, pending external validation on independent cohorts. Full article

Background/Objectives: Brain magnetic resonance imaging (MRI) is an important imaging modality for assessing neurological disorders. However, automatic multi-class MRI classification remains challenging because of visual similarity between disease categories, heterogeneous pathological patterns, class imbalance, and the need for reliable confidence estimation. This study aims to develop a comprehensive and well-calibrated deep learning framework for image-level brain MRI classification across multiple neurological categories. Methods: This paper introduces a new deep learning framework, MCND-ComputeNet++, for brain MRI classification into eight image-level categories using the MCND dataset, which comprises 16,400 two-dimensional brain MRI images belonging to eight diagnostic categories: AD-MildDemented, AD-ModerateDemented, AD-VeryMildDemented, BT-glioma, BT-meningioma, BT-pituitary, MS, and Normal. The proposed model uses a single pretrained EfficientNetV2-S backbone to extract hierarchical feature maps from three intermediate stages. These multi-level features are projected into a common latent space, spatially aligned, adaptively fused through learnable gated multi-scale fusion, further refined using convolutional processing, and aggregated using spatial attention pooling before classification. The training strategy combines class-balanced focal loss with label smoothing, MixUp/CutMix regularization, exponential moving average weight smoothing, warmup cosine learning-rate scheduling, temperature scaling, and test-time augmentation to improve generalization and calibration. The framework was evaluated using accuracy, precision, recall, macro-F1, macro-AUC, macro-average precision, expected calibration error, Brier score, bootstrap confidence intervals, ablation analysis, McNemar testing, and comparisons against standard pretrained baseline models. Results: MCND-ComputeNet++ achieved mean accuracy, macro-F1, macro-AUC, and macro-average precision values of 0.9738, 0.9771, 0.9993, and 0.9971, respectively, with narrow bootstrap confidence intervals indicating stable image-level performance. These findings should be interpreted as image-level/slice-level performance on MCND, because patient-level identifiers and subject-wise splitting were not available. These results outperformed most evaluated baselines, including ResNet50, DenseNet121, EfficientNetB0, EfficientNetV2-S with a standard classifier, Swin-Tiny, and ConvNeXt-Tiny, across several discrimination and calibration metrics. Compared with ConvNeXt-Tiny, the proposed model achieved higher macro-AUC and macro-average precision, together with a lower ECE and Brier score, suggesting improved image-level discrimination and confidence reliability. Compared with the EfficientNetV2-S standard classifier, accuracy increased from 0.9308 to 0.9738, while the Brier score decreased from 0.1045 to 0.0400. Conclusions: The results suggest that MCND-ComputeNet++ is a promising image-level brain MRI classification framework for the eight MCND categories. The proposed model integrates hierarchical feature extraction, shared latent projection, gated multi-scale fusion, convolutional refinement, spatial attention pooling, and calibrated inference within a unified architecture. However, because the current evaluation was conducted at the image/slice level without available patient-level identifiers, the findings should not be interpreted as patient-level clinical diagnostic validation. Further studies using subject-wise splitting, external multi-center datasets, 3D volumetric modeling, and multimodal clinical information are required to assess generalizability and potential clinical decision-support applicability. Full article

Effective water management in plug trays is critical for high-quality young plant production. While soil moisture sensor-based irrigation can improve irrigation efficiency, its application to young plants is constrained by the small size and high density of plug tray cells, which hinder reliable moisture sensing. We developed a load cell-based automated irrigation system that estimates substrate volumetric water content (VWC) from plug tray weight dynamics and evaluated its applicability for plug production. The system continuously monitored tray weight and estimated VWC relative to a saturated reference tray weight, which was updated after each irrigation event to account for plant growth. Estimated VWC closely matched oven-dry measurements (R² = 0.9888, RMSE = 0.029 m³·m⁻³). In a trial with torenia, irrigation was regulated using three VWC thresholds (0.30, 0.45, and 0.60 m³·m⁻³) and compared with a daily irrigation regime. The system regulated irrigation according to the target thresholds, with the 0.45 m³·m⁻³ threshold achieving the best balance between plant growth and irrigation efficiency, indicating it as the optimal irrigation threshold for torenia cutting propagation. This approach provides a practical decision-support tool for precision irrigation and for determining crop-specific VWC thresholds, supporting growers in improving water-use efficiency while ensuring high propagation performance in plug production. Full article

The main aim of the airborne early warning (AEW) system is to search the potential targets in a large surveillance area. The underlying assumption is that the desired target signals only exist in a few range cells for space-time adaptive processing (STAP), i.e., targets (with certain look direction and Doppler) are sparsely distributed in the entire range cells and most of the range cells are target-free. By utilizing the sparsity of the target distribution, we propose a new STAP method by minimizing the l1-norm of the output magnitude. Unlike conventional STAP methods, which exclude the cell under test from the training samples to avoid target self-nulling, our method processes the cell under test (CUT) and the training samples simultaneously without sample selection. Moreover, to achieve robustness against target steering vector mismatch, we constrain the l1-modulus of the response of any steering vector within a rhombus uncertainty set to exceed unity. Additionally, based on a new definition of the l1-norm of a complex-valued vector, the original nonlinear programming problem can be transformed into a linear programming problem. On the other hand, unlike the slide window processor (SWP) whose weights need to be updated for each range cell, the adaptive weight of our method for a block of samples requires no updating. Consequently, the computational complexity of the proposed method is much lower than that of conventional STAP methods. Finally, since the CUT is used to compute the STAP weights, our method can also suppress the discrete interference. The robustness, computational effectiveness and superiority of the proposed STAP method are verified based on simulated data and the MCARM data. Full article

Extreme opinion propagation in social information networks often appears as a low-frequency but high-impact process, in which abnormal activity becomes synchronized across structurally related users or communities during crisis periods. Conventional correlation-based methods mainly describe average co-movement and may therefore miss dependence patterns that emerge only in the tail regime. To address this issue, this paper proposes a community-structured extremal dependence framework for social opinion propagation risk analysis. A tail pairwise dependence matrix (TPDM) is used to construct a weighted extremal dependence network, on which node-level risk scoring, community detection, and community-level intervention analyses are performed. The proposed risk score integrates degree centrality, betweenness centrality, tail exposure, and community embedding strength, while the intervention component is formulated as a minimum cut problem on the induced community graph. The framework is evaluated on a controlled synthetic social discussion network with 100 nodes. The experiment is intended as a methodological proof of concept rather than as a real-platform empirical validation. The results show that the TPDM-based network produces a structured representation with two dominant coupled communities, several peripheral singleton nodes, concentrated high-risk nodes, and one principal source–target interface in the community graph. These findings indicate that extremal dependence can provide a useful representation of candidate risk-coupling structures under the synthetic setting. However, the inferred edges should not be interpreted as causal propagation paths, and the minimum cut result should be understood as a candidate intervention interface rather than as a guarantee of complete diffusion blockage. Future work should validate the framework on real social media traces, incorporate temporal causal information, and examine robustness under multi-channel diffusion and adaptive user behavior. Full article

Cable routing is a pivotal design component for electrical systems and safety-critical engineering fields, such as nuclear propulsion systems, nuclear power plants and aircraft. Scientific and optimized routing schemes are essential for efficient and safe power and signal transmission and for mitigating system failure risks. Previous studies have adopted heuristic search and swarm intelligence optimization algorithms for cable path planning; however, these methods tend to converge to local optima under complex constraints and cannot theoretically guarantee global optimality, failing to address multi-constraint, high-dimensional optimization challenges of confined-space cable routing. This paper proposes a mathematical programming-based systematic optimization model: it first discretizes continuous three-dimensional space into a grid coordinate system and constructs a composite cost field integrating geometric distance and thermal interference, then formulates a multi-objective optimization model considering path length, thermal impact and routing feasibility, which is converted into a single-objective problem via normalized weighting coefficients and solved by exact mathematical programming techniques, yielding a best feasible solution together with a provable lower bound and an optimality gap. When the solver converges within the time limit, global optimality for the discretized model can be certified. Simulation results show the proposed method reduces overall path cost by an average of 31.8% compared with classical algorithms like the A* algorithm, Dijkstra’s algorithm, Rapidly-exploring Random Tree (RRT), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Furthermore, it cuts decision variables by an average of 70% (up to 82% in complex scenarios) against the 0–1 Integer Linear Programming (ILP) model and the graph-theoretic Multi-Commodity Flow (MCF) model with multi-cost considerations. These results preliminarily validate the favorable solution quality, computational efficiency and engineering applicability of the proposed model for confined-space cable routing optimization. Full article

Accurate detection of initial contact (IC) during side-cutting is essential for evaluating m. semitendinosus (ST) pre-activity, a protective mechanism against ACL injury in team sport athletes. This study developed two insole pressure sensor (IPS) algorithms—a body weight-based and a criteria-based algorithm—for IC detection and evaluated their agreement with force-plate-derived IC based on vertical ground reaction forces (vGRF). Twenty-six adult female athletes performed sport-specific side-cutting while IPS, vGRF, and ST electromyography were recorded. IPS-derived IC events were compared with vGRF-derived IC, and ST pre-activity within 50 ms prior to IC was compared between methods. Agreement and limits of agreement (LoA) were evaluated using Bland–Altman analysis. The body weight-based IPS algorithm showed a systematic delay in IC detection of 9.2 ms (LoA: 4.1 to 14.3 ms) and a −3.5 percentage point bias in ST pre-activity (LoA: −8.9 to 1.9% of MVC). In contrast, the criteria-based IPS algorithm, demonstrated minimal bias in IC detection (−0.1 ms; LoA: −3.5 to 3.4 ms) and ST pre-activity (−0.1% MVC; LoA: −1.9 to 1.7% of MVC). These findings suggest the criteria-based IPS algorithm enables accurate IC detection, supporting its potential for practical monitoring of ST pre-activity during sports-specific side-cutting outside laboratory environments. Full article

Background: Risk stratification in aortic stenosis (AS) remains challenging, particularly in patients with preserved left ventricular ejection fraction or inconclusive symptom status, as conventional parameters primarily reflect valvular obstruction and may underestimate the extent of cardiac dysfunction. Left atrial reservoir strain (LASr) has emerged as a promising and potentially more comprehensive marker of atrial function and diastolic burden, with potential prognostic implications. Methods: A systematic review was conducted in accordance with PRISMA guidelines. PubMed, Scopus, and EMBASE were searched from inception to April 2026. Studies including adult patients with moderate or severe AS and evaluating LASr using different imaging modalities (speckle-tracking echocardiography, cardiac computed tomography, or cardiac magnetic resonance) were considered eligible if clinical outcomes were reported. Data were qualitatively synthesized, and continuous variables were summarized as weighted medians with interquartile ranges. Results: Twenty-one studies were included, encompassing a large and clinically heterogeneous population. During follow-up, a substantial proportion of patients experienced adverse events, including mortality, heart failure hospitalization, arrhythmic events, and composite cardiovascular outcomes. Across studies, reduced LASr consistently emerged as a significant predictor of adverse outcomes. This association was observed both when LASr was analyzed as a continuous variable and when defined using study-specific cut-off values, which generally clustered within a relatively narrow range. Importantly, LASr demonstrated incremental prognostic value beyond conventional echocardiographic parameters, including left atrial size, left ventricular ejection fraction, global longitudinal strain, and indices of diastolic dysfunction. The prognostic relevance of LASr was consistent across different imaging modalities, including both echocardiography and cardiac computed tomography (with no eligible studies using cardiac magnetic resonance). Conclusions: LASr is a robust and reproducible marker of adverse prognosis in patients with AS, reflecting the cumulative burden of left-sided pressure overload and atrial remodeling. Its integration into multiparametric assessment may enhance risk stratification and support more individualized clinical decision-making. Further prospective studies are warranted to standardize measurement techniques and define clinically actionable thresholds. Full article

Private cloud object stores provide infrastructure isolation but leave application-layer data exposed to insider threats and compromised credentials. This paper presents an engineering integration of an Add-Rotate-XOR (ARX) block cipher and multi-bit Least Significant Bit (LSB) steganography into an end-to-end pipeline for private MinIO object storage. The cipher, KREA v2, is a SPECK-64/128 derived ARX construction with three application-driven choices: CRC32 key whitening, byte-aligned rotations ($\alpha =7$, $\beta =2$), and deterministic CTR-mode nonces. Mixed Integer Linear Programming (MILP) trail analysis matches SPECK-64/128’s minimum-trail weights through rounds 1–4. KREA v2 ciphertext meets standard keystream-quality preconditions (NIST SP 800-22 battery, 49.98% mean avalanche, Shannon entropy 7.9992–7.9998 bits/byte across realistic XML, JSON, video, and HTTP/2 payloads). Modified LSB (MLSB) embeds 3 bits per RGB channel with an XOR watermark at 37–38 dB Peak Signal-to-Noise Ratio (PSNR), providing $3\times$ standard-LSB capacity. Steganalysis uses chi-square and RS detectors plus a Convolutional Neural Network (CNN) detector (Yedroudj-Net) trained on 8000 BOSSBase-1.01 cover/stego pairs; CNN area under the ROC curve is ≥0.999 against the watermarked variant. The MinIO pipeline runs at 355.1 ms (68.6% network I/O) with 100% message fidelity. The XOR watermark increases RS detectability above 75% capacity; a 200-image ablation cuts median RS detection (0.289 to 0.000) and mean (0.342 to 0.130) in a sparse-keystream variant, prioritised for follow-on full-scale evaluation. The architecture is offered as a documented engineering integration with explicit security caveats and threat-model boundaries, not as a production-hardened cryptographic primitive. Full article

Carbon-fiber-reinforced polymer (CFRP) components commonly require milling to achieve final dimensional accuracy and surface integrity, yet tool selection remains a trade-off between surface quality, process load, and cost. This study compared two industrial tool concepts for CFRP side milling under matched cutting conditions: a WC-Co compression-type end mill and a PCD end mill. A two-factor central composite design with 13 parameter sets was used, and tool effects were evaluated through paired differences in Ra_mean, Rz_mean, and F_xy,RMS. The PCD tool significantly improved surface quality, with mean paired differences of −2.00 µm for Ra_mean and −6.67 µm for Rz_mean, while increasing F_xy,RMS by 14.86 N relative to WC-Co. Response-surface analysis showed that the roughness advantage of PCD was broadly stable across the investigated process window, whereas the force penalty was nonlinear and was best described by a second-order CCD model (R² = 0.820, model p = 0.015), with a significant quadratic cutting-speed term. Scenario-based decision analysis further showed that PCD was preferred in 12 of 13 DOE points under quality-driven weighting, whereas WC-Co was preferred in all 13 points under cost-driven weighting. The results indicate that PCD is the preferred quality-oriented solution for CFRP side milling, while WC-Co remains advantageous when lower load or lower cost is prioritized. Full article

The presence of multiple uncertainties in integrated electricity–heat energy systems (E-HIES) poses significant challenges to system dispatch. To achieve an effective balance between economy and robustness, this paper proposes an optimal scheduling method based on fuzzy chance-constrained Information Gap Decision Theory (Fuzzy-IGDT), accounting for uncertainties in wind power output, photovoltaic output, electrical load, and thermal load. The method employs trapezoidal fuzzy numbers to model the four types of uncertain variables and constructs a fuzzy robust model (F-RM) for conservative decision-makers and a fuzzy opportunity model (F-OM) for aggressive decision-makers. An Adaptive Step Ratio (ASR) optimization method is then developed to solve the proposed models. Case studies demonstrate the effectiveness of the proposed methodology. Results show that: compared with conventional IGDT, pure fuzzy and stochastic programming, Fuzzy-IGDT simultaneously optimizes economy, stability and reliability: daily operating cost is reduced by 12.7%, the standard deviation of cost volatility shrinks by 34.5%, and the loss-of-load probability is only 0.3%. Relative to the traditional Weighted Offset Coefficient (WOC) method, ASR directly coordinates the deviation ratios of multiple variables through its step-ratio mechanism, cutting system risk cost by 21.3%, raising solution efficiency by 42%, and improving convergence stability by a factor of 3.8. This research provides new theoretical support and practical tools for optimal scheduling of E-HIES under multiple uncertainties. Full article