Search Results (2,298)

Background/Objectives: The aim of this study was to identify predictors of temporal window failure (TWF) in transcranial color-coded duplex sonography (TCCS) based on demographical and computed tomography (CT)-based parameters such as temporal bone thickness (TBT), and to define sex-specific thresholds for predicting TWF. Methods: We retrospectively analyzed a series of adult patients who underwent cranial CT and TCCS. Bitemporal TBT was measured in nine standardized regions on CT, and mean TBT per side was calculated. Temporal bone window (TBW) quality was graded with two semiquantitative TCCS scores assessing the visibility of the middle cerebral artery (MCA), contralateral temporal bone, mesencephalon, and ipsilateral sphenoid bone. Associations between TBT, sex, age, and TBW visibility were analyzed by correlation, chi-square tests, and logistic regression. Results: 200 patients (102 men, 98 women; mean age 68 ± 16 years) were enrolled. Mean TBT was 3.1 ± 0.7 mm (right) and 3.2 ± 0.7 mm (left). TBT correlated weakly with age (r = 0.15–0.18, p < 0.05) and was higher in women (p < 0.05). Age and sex influenced TBW visibility (p < 0.05) with small effect sizes. Increased TBT strongly predicted poor TBW (β ≈ –1.7, p < 0.001). Optimal TBT cut-offs predicting adequate TBW were 3.8 mm (men) and 3.3 mm (women), maximizing specificity (men: 0.95, women: 0.85) and negative predictive value (men: 0.87, women: 0.66). Conclusions: Advanced age and female sex were both associated with TWF. CT-assessed TBT represents a robust predictor of TCCS feasibility. Implementation of sex-specific TBT threshold values may facilitate patient pre-selection and improve procedural efficiency in neurosonographic diagnostics. Full article

Down-sampling-based video compression frameworks have shown great potential in improving compression efficiency in modern sensing and imaging systems. However, existing methods ignore critical spatial and temporal redundancy, and treat all frames uniformly during down-sampling. This leads to the loss of important information and impacts compression efficiency. To address these limitations, this paper proposes a temporal down-sampling system, in which only intermediate frames are down-sampled while preserving key frames with high quality for reference. On the decoding side, we employ a frame-recurrent enhancement mechanism to maximize the use of temporal redundancy information. In the fusion of enhancement stage, we design a Multi-scale Temporal-Spatial Attention (MTSA) module. MTSA consists of two components: Multi-Temporal Attention (MTA) and Pyramid Spatial Attention (PSA). MTA performs multi-scale temporal correlation modeling, expanding the receptive field and providing stable cues in compressed regions. PSA integrates local spatial saliency and contextual structure in a progressive and multi-stage manner. Extensive experiments show that our approach achieves consistent BD-rate reductions. Under All-Intra, Low-Delay-P, and Random Access configurations, we observe BD-rate reductions of I, P, and B frames ranging from 14% to 39% compared to VVC, and outperform prior approaches anchored by the standard HEVC. Full article

Objectives: Hormonal changes during the postmenopausal period of life predispose women to changes in fat tissue distribution and the risk of insulin resistance, and may lead to deterioration of bone metabolism. Physical activity plays a significant role in improving metabolic health and may inhibit bone mass decrease. The purpose of this study was to analyze the relationships between bone health, body composition, maximal oxygen uptake (VO₂max), and carbohydrate metabolic indices in non-diabetic postmenopausal women. Methods: Fifty-seven postmenopausal women were included in the study (64.9 ± 4.8 years). The areal bone mineral density (aBMD) of femoral neck and L1–L4, femur strength index (FSI), total fat (FM), lean body mass (LBM), VO₂max, serum insulin, and glucose concentrations were determined. The insulin resistance index (HOMA-IR) was also calculated. The main statistical analyses were performed using hierarchical multiple linear regression models. Results: Body mass index (BMI), FM and LBM positively correlated with aBMD results (p ≤ 0.01) and FM negatively with FSI levels (p < 0.05). VO₂max showed a positive association with FSI and this relationship was confirmed in hierarchical multiple regression analysis (p < 0.05). Regression analysis revealed that the base model including age and BMI explained the variance in the femoral neck aBMD (p ≤ 0.01) and L1–L4 aBMD (p ≤ 0.01), respectively. In the case of the femoral neck aBMD model, adjustment for VO₂max increased the explained variance. Alternative models with carbohydrate metabolic indicators did not increase the explained variance. Conclusion: Our results suggest that aerobic capacity may be related to the level of femur bone strength. Somatic characteristics and carbohydrate metabolic status appear to play a role in the correlations between femur bone health and VO₂max. Full article

To address the instability in extracting key parameters such as time-of-flight (ToF) from ultrasonic echoes due to noise and multi-echo superposition, this paper proposes a robust parameter estimation method based on the secretary bird optimization algorithm (SBOA). The proposed approach adheres to the Gaussian convolution-based echo parameterization and cosine-similarity matching framework, while innovatively introducing SBOA to perform global optimization of model parameters. Consequently, the multi-echo ToF estimation is formulated as a nonlinear optimization problem aimed at maximizing waveform shape consistency. To evaluate the method’s performance, simulations are conducted under multi-echo superposition scenarios. Comparisons are made with representative baseline techniques, including wavelet transform (WT), empirical mode decomposition (EMD), and variational mode decomposition (VMD), using mean squared error (MSE), estimated signal-to-noise ratio (ESNR), and normalized cross-correlation (NCC) as performance metrics. Experimental results demonstrate that, in challenging low-SNR and echo-interference environments, the proposed method achieves overall superiority across all quantitative metrics and exhibits a stronger capability to preserve the main-lobe morphology and structural features of echoes. Validation on semi-synthetic signals further confirms its effectiveness, with practical applicability to be verified by measured datasets in future work. This work provides an effective and robust solution for ultrasonic signal processing in complex field conditions. Full article

This paper proposes a unified analysis of reduplication as the lexical spell-out of a relational part–whole/inclusion predicate (⊆) in morphosyntax. Adopting the framework of Manzini and colleagues, we argue that reduplicative morphology—across diverse languages and domains—encodes a subset relation, whereby an event, individual, or property is interpreted as included in a larger set or continuum of similar instances. We bring evidence from a range of typologically diverse languages (Tagalog, Bikol, Malay, Fulfulde, Italian, and sign languages) to show that reduplication correlates with non-maximality: plural number (members of a set), distributivity (individuals/events taken one by one), iterative aspect (sub-events in a larger event), and evaluative attenuation or intensification (a degree as part of a scale). The analysis is developed in a formal syntactic representation where reduplication is triggered by an elementary inclusion operator (⊆) at the X or XP level. We show that a single semantic primitive (⊆) can account for the varied meanings of reduplication in nominal, verbal, and adjectival domains. We discuss the implications of this unified approach, suggesting that reduplication is not a mere iconic or phonological process, but rather the surface reflex of a fundamental grammatical operation of inclusion. Full article

The high-temperature engine nozzle is a critical component of a rocket motor, and its stability and performance are significantly influenced by internal high-temperature gas radiative heat transfer. Due to the non-gray nature of the nozzle medium and the complexity of the Radiative Transfer Equation (RTE), rapid and accurate simulation of radiative heat transfer is crucial for engineering applications. This paper presents a high-efficiency solution coupling the Full-Spectrum Correlated k-Distribution (FSCK) model with the Null-Collision Monte Carlo Method (NCMCM). To address the inherent computational bottleneck of linear traversal in unstructured grids, a hybrid ray-localization model integrating KD-tree and Bounding Volume Hierarchy (BVH) is proposed. This model shifts the search mechanism from element-wise iteration to spatial topological indexing, achieving logarithmic search complexity and significantly mitigating the sensitivity of computational cost to grid scale. Furthermore, a collaborative MPI–OpenMP parallel framework is established to maximize hardware utilization, where an optimized guided scheduling strategy effectively counteracts the stochastic load imbalances encountered in traditional static schemes. Results indicate that the proposed method reduces the total execution time to approximately 1/4 compared to traditional models. Simulations identify the convergent section as the primary radiation zone, where CO₂ contributes less to the radiative source term than H₂O under high-temperature conditions. Full article

Background: This systematic review examined the relationship between horizontal multi-step jumps and sprint performance, and whether training interventions including these exercises improve sprinting. Methods: A systematic literature search was conducted in SPORTDiscus and PubMed (MEDLINE) and included English-language studies of athletes aged ≥14–15 years that assessed at least one horizontal multi-step jump and reported sprint outcomes over distances up to 100 m. Methodological quality and risk of bias were assessed using design-appropriate critical appraisal tools. Of 316 records identified, 19 studies met the inclusion criteria (10 intervention studies and 9 correlational studies). Results: Across correlational studies, horizontal multi-step jump performance showed associations ranging from weak to very large with sprint performance, with the strongest relationships typically observed during acceleration (≤20–30 m). In trained sprinters, correlations were often large to very large (r ≈ −0.65 to −0.88), whereas team-sport athletes showed more moderate associations, and younger or less specialized populations showed weak or non-significant relationships. Across intervention studies, horizontal multi-step jump training generally improved short-distance sprint performance, with the largest improvements reported for acceleration (up to ~7–12% in some studies), while effects at longer sprint distances and maximal-speed performance were smaller, inconsistent, or not different from comparison training. Conclusions: Overall, the evidence suggests that the association between horizontal multi-step jumps and sprint performance is strongest during the acceleration phase and is influenced by athlete population and training status. Horizontal multi-step jumps appear to be useful for assessing and potentially developing sprint acceleration. However, the findings should be interpreted with caution due to heterogeneity in study design and variable methodological quality, and associations with maximal sprint speed are less consistent across studies. Full article

In contrast to structured urban settings, road networks in post-disaster or unstructured wildland environments are often incomplete or compromised. Navigation in these contexts requires navigating complex terrains and mitigating potential hazards that impede unmanned ground vehicles (UGVs). While high-mobility off-road vehicles are specifically designed to traverse challenging features like ditches and steep slopes, traditional path planning algorithms often fail to exploit these capabilities. These algorithms typically suffer from a binary focus, either relying strictly on road networks or ignoring them altogether, thereby neglecting the synergy between infrastructure and vehicle mobility. This chapter introduces a global path planning method based on traversability analysis and terrain matching to bridge this gap. The methodology incorporates a grid-based traversability evaluation, a road network expansion algorithm for densifying critical segments, and a unified planning strategy. By correlating terrain characteristics with vehicle mobility limits and optimizing the road network density, the proposed framework achieves an integrated on-road and off-road planning solution that maximizes the operational efficiency of high-mobility vehicles in degraded environments. Full article

The growing accumulation of coal beneficiation waste represents a significant environmental and technological challenge while simultaneously creating opportunities for the resource recovery within circular economy frameworks. This study presents the development and process-oriented evaluation of an environmentally safe technology for converting coal beneficiation waste into potassium humate, with the simultaneous recovery of molybdenum compounds via alkaline extraction. The proposed solution is designed to improve resource efficiency, reduce the volume of waste directed to landfilling, and generate a high value-added product for agricultural and technological applications. The process flow includes preliminary characterization and preparation of the waste, determination of moisture, ash, and organic matter content, and the separation of metal-bearing fractions. Alkaline extraction was carried out using potassium hydroxide under controlled temperature and reaction time conditions, followed by purification and concentration of the humate solution. The process management strategy focuses on optimizing key technological parameters, including alkali concentration, solid-to-liquid ratio, temperature, and reaction time, to maximize humate yield while preserving functional groups responsible for biological activity. Comprehensive physicochemical, thermal, and mineralogical analyses confirmed the stability of the aluminosilicate matrix and the suitability of the material for alkaline processing without adverse structural degradation. Biological tests using oat (Avena sativa) demonstrated that potassium humate derived from coal beneficiation waste exhibits higher growth-stimulating effectiveness than a conventional commercial humate. Economic analysis revealed a strong correlation between humic acid content and added value, confirming the feasibility of transforming coal beneficiation waste from an environmental burden into a valuable secondary resource. Full article

The diurnal sea surface temperature variation (DSV) influences atmospheric convection and precipitation through air–sea interactions in the equatorial Pacific. Deep learning-based DSV forecasting has been less explored compared to traditional methods, presenting the potential for a substantial leap in forecast accuracy. In this study, a forecast model is developed for 24 h DSV in the equatorial Pacific using an improved coupled Transformer-CNN (CoTCN-DSV) by incorporating a new loss function including maximal and minimal values. The CoTCN-DSV forecasts diurnal variation in SST at the interval of 3 h based on 3 h SST from the WHOI dataset. The CoTCN-DSV captures DSV well with root mean square error (RMSE) of DSA below 0.03 °C/0.13 °C at 3 h/12 h lead times and maintains high forecast skill with the temporal correlation coefficient (R) of 0.78 at the lead times of 12 h in the equatorial Pacific. The CoTCN-DSV reduces RMSE for daily max/min SST by 10.9% and 12.8% due to replacing the new loss function, then significantly improving DSV forecast. There are systematic SST biases in the WHOI dataset and this leads to relatively large RMSEs when DSV forecasts trained using WHOI are evaluated against TAO observations. Replaced WHOI SST by TAO SST, the forecasted DSA RMSE by CoTCN-DSV is reduced by an average of 43%. This confirms that the CoTCN-DSV has good generalization ability and high-quality data are important to advance the forecast accuracy. These finding show that CoTCN-DSV has the potential to forecast extreme values for different scenarios. Full article

Background: Muscular fitness is an important marker of current and future health in adolescents. However, comparisons are lacking that show which tests are most consistently associated with health-related outcomes during adolescence. Objective: This study aimed to analyze whether performance in the SLJ is more strongly associated with health-related outcomes than maximal isometric handgrip strength (MIHS) in secondary school students in Chile. Methods: A cross-sectional study was conducted in 113 adolescents (77 males and 36 females, mean age 15.90 ± 1.77). Muscular fitness was assessed using the SLJ and MIHS tests. Health-related outcomes included body mass index (BMI), physical activity level, aerobic capacity, sprint performance, flexibility, sleep quality, and psychological well-being. Pearson correlation analyses and multiple linear regression models adjusted for sex and age were performed to examine associations between muscular fitness tests and health-related outcomes. Results: SLJ showed moderate-to-strong associations with several health-related outcomes, including physical activity (r = 0.42, p < 0.001), aerobic capacity (r = 0.60, p < 0.001), sprint performance (r = −0.74, p < 0.001), and body mass index (r = −0.30, p = 0.001). In contrast, handgrip strength demonstrated weaker and less consistent associations. In adjusted regression models, SLJ remained a significant predictor of most outcomes (β range: 0.27–0.56, p < 0.05), whereas handgrip strength provided limited additional explanatory value. Conclusions: SLJ appears to be a more sensitive and consistent indicator of health-related outcomes in adolescents than MIHS. These findings support the use of SLJ as a practical, low-cost, and easily implementable tool for health and fitness screening in school and community settings. Full article

The deployment of Discrete Variable Quantum Key Distribution (DV-QKD) in high-traffic, short-reach environments, such as intra-data center networks, is currently constrained by the saturation of single-photon detectors. While CMOS Single-Photon Avalanche Diodes (SPADs) offer a cost-effective solution, their Secure Key Rate (SKR) is limited by detector dead time. To the best of the authors’ knowledge, this work is the first to derive a generalized detection rate model for SiPMs that addresses the dead-time bottlenecks of gigahertz-rate quantum cryptography. While methods for managing deadtime via active optical switching have been proposed, our model quantifies the benefits of passive spatial multiplexing inherent in standard SiPM arrays. Furthermore, contrasting with models designed to optimize energy resolution or characterize nonlinear charge response to light pulses, our work focuses on maximizing the detection count rate. We derive exact detection rate models for both analog (paralyzable) and digital (non-paralyzable) SiPM architectures, incorporating correlated noise sources such as optical crosstalk and afterpulsing. Simulation results indicate that SiPMs can increase detection rates by over an order of magnitude compared to single SPADs. Full article

Background: Water inertia load training using equipment such as water vests provides unstable resistance that enhances trunk muscle activation. However, practical methods for prescribing exercise intensity without expensive electromyography (EMG) equipment remain limited. This pilot study aimed to develop prediction models for estimating trunk muscle activation using rating of perceived exertion (RPE) during water inertia load exercises. Methods: Seventeen healthy adults (20.45 ± 2.02 years) performed lateral trunk flexion exercises wearing a water vest at five progressive loads (8–16 kg in 2 kg increments). Surface EMG was recorded from four trunk muscles (rectus abdominis, external oblique, internal oblique, erector spinae) and normalized to maximal voluntary isometric contraction (%MVIC). Rating of perceived exertion (RPE) was assessed using the Borg CR-10 scale. Load-dependent changes in muscle activation were examined using repeated-measures ANOVA, and relationships between RPE and EMG were analyzed using regression and linear mixed-effects models. Results: All trunk muscles showed significant increases in activation with increasing load (all p < 0.001, ηp² = 0.381). RPE demonstrated significant positive correlations with all abdominal muscles (r = 0.37–0.46, p < 0.001). Simple regression analyses indicated predictive accuracy (R² = 0.267), representing a 29% increase compared with the strongest individual muscle model. Linear mixed-effects modeling confirmed RPE as a significant predictor after accounting for inter-individual variability. Conclusions: This pilot study provides preliminary evidence that RPE can be used to estimate trunk muscle activation during water inertia load exercise. The proposed composite activation index enhances prescription when EMG measurement is not feasible. Full article

Muscular strength assessment is fundamental for training optimization. While one-repetition maximum (1RM) testing is valid, its practical use is limited, making velocity-based training (VBT) a viable alternative for estimating maximal strength and monitoring performance using submaximal loads. This observational, cross-sectional validity and reliability study aimed to: (i) assess the absolute and relative reliability of movement velocity at submaximal loads in the deadlift and hip thrust, (ii) evaluate the validity and reliability of 1RM estimations using LVP in these exercises, and (iii) compare reliability metrics between sexes. Thirty-two physically active adults voluntarily participated in the study, including sixteen men (age: 25.63 ± 3.79 years; body mass: 75.79 ± 8.64 kg; height: 175.81 ± 7.34 cm) and sixteen women (age: 25.06 ± 5.37 years; body mass: 61.94 ± 4.25 kg; height: 165.06 ± 5.72 cm). Strength-trained participants performed familiarization and two experimental sessions involving progressive load tests at various percentages of 1RM. Movement velocity was monitored using a validated linear velocity transducer. Statistical analyses included two-way ANOVA, Bland–Altman plots and intraclass correlation coefficients (ICCs). The results showed a systematic overestimation of 1RM derived from load–velocity profiles, which was moderate for the deadlift (mean difference ≈ +2.7 kg; p < 0.001) and small-to-moderate for the hip thrust (≈+3.3 kg; p = 0.002). Strong associations were observed between actual and estimated 1RM in both exercises (R² > 0.92). Bland–Altman analyses revealed smaller bias but narrower limits of agreement for the deadlift compared with the hip thrust, indicating greater variability in hip thrust estimations. Test–retest analyses demonstrated good relative reliability of movement velocity in men across all loads (ICC > 0.75; CV < 8.2%), whereas women exhibited lower reliability, particularly at moderate-to-high loads in both exercises. Despite limited validity for precise 1RM estimation, load–velocity profiles showed good reproducibility, supporting their use for longitudinal performance monitoring rather than exact strength prediction. Full article

A significant bottleneck for the practical deployment of fluid antenna systems (FASs) in 6G high-mobility scenarios is the conflicting demands of low outage probability and the high overhead of full port channel estimation. To resolve this problem, a novel “prediction-axiom” dual-driven paradigm is introduced that fundamentally differs from pure data-driven approaches. The core innovation lies in using an enhanced unified adaptive modeling algorithm (UAMA) not for direct decision-making but as a computational foundation to enable information-theoretic axioms under sparse observation conditions (30% of ports). The UAMA predictor, leveraging spatiotemporal correlations, accurately reconstructs the full channel state from limited measurements. This prediction then empowers an information-theoretic scoring mechanism, which synergizes Fisher information, curvature metrics, and port entropy to transform optimal port selection into a tractable maximization problem. Consequently, the system outage probability remains close to the ideal performance limit achievable under full observability. Tests on diverse antenna systems confirm the algorithm’s high accuracy and robust adaptive capability. This work delivers a reliable, low-cost implementation strategy for 6G dynamic networks, effectively bridging the gap between mathematical theory and practical FAS deployment. Full article