Search Results (1,043)

In this paper, a joint Matching Field Processing (MFP) Algorithm based on horizontal uniform circular array (UCA) is proposed for three-dimensional position of underwater wrecked targets. Firstly, a Marine search and rescue position model based on Minimum Variance Distortionless Response (MVDR) and matching field quadratic joint Algorithm was proposed. Secondly, an MVDR beamforming method based on pre-Kalman filtering is designed to refine the real-time DOA estimation of the desired signal and the interference source, and the sound source azimuth is determined for prepositioning. The antenna array weights are dynamically adjusted according to the filtered DOA information. Finally, the Adaptive Matching Field Algorithm (AMFP) used the DOA information to calculate the range and depth of the lost target, and obtained the range and depth estimates. Thus, the 3D position of the lost underwater target is jointly estimated. This method alleviates the angle ambiguity problem and does not require a computationally intensive 2D spectral search. The simulation results show that the proposed method can better realise underwater three-dimensional positioning under certain signal-to-noise ratio conditions. When there is no error in the sensor coordinates, the positioning error is smaller than that of the baseline method as the SNR increases. When the SNR is 0 dB, with the increase in the sensor coordinate error, the target location error increases but is smaller than the error amplitude of the benchmark Algorithm. The experimental results verify the robustness of the proposed framework in the hierarchical ocean environment, which provides a practical basis for the deployment of rapid response underwater positioning systems in maritime search and rescue scenarios. Full article

In high-dynamic bipedal locomotion control, robotic systems are often constrained by motor torque limitations, particularly during explosive tasks such as jumping. One of the key challenges in reinforcement learning lies in bridging the sim-to-real gap, which mainly stems from both inaccuracies in simulation models and the limitations of motor torque output, ultimately leading to the failure of deploying learned policies in real-world systems. Traditional RL methods usually focus on peak torque limits but ignore that motor torque changes with speed. By only limiting peak torque, they prevent the torque from adjusting dynamically based on velocity, which can reduce the system’s efficiency and performance in high-speed tasks. To address these issues, this paper proposes a reinforcement learning jump-control framework tailored for tendon-driven bipedal robots, which integrates dynamic torque boundary constraints and torque error-compensation modeling. First, we developed a torque transmission coefficient model based on the tendon-driven mechanism, taking into account tendon elasticity and motor-control errors, which significantly improves the modeling accuracy. Building on this, we derived a dynamic joint torque limit that adapts to joint velocity, and designed a torque-aware reward function within the reinforcement learning environment, aimed at encouraging the policy to implicitly learn and comply with physical constraints during training, effectively bridging the gap between simulation and real-world performance. Hardware experimental results demonstrate that the proposed method effectively satisfies actuator safety limits while achieving more efficient and stable jumping behavior. This work provides a general and scalable modeling and control framework for learning high-dynamic bipedal motion under complex physical constraints. Full article

Background and Objectives: Emerging evidence indicates that individuals exposed to adverse childhood experiences (ACEs) face elevated risks for various chronic illnesses. However, the association between ACEs and osteoporosis risk remains underexplored, particularly regarding potential modifications by genetic susceptibility. This prospective cohort study aims to examine the relationship of ACEs with incident osteoporosis and investigate interactions with polygenic risk score (PRS). Materials and Methods: This study analyzed 124,789 UK Biobank participants initially free of osteoporosis. Cumulative ACE burden (emotional neglect, emotional abuse, physical neglect, physical abuse, sexual abuse) was ascertained through validated questionnaires. Multivariable-adjusted Cox proportional hazards models assessed osteoporosis risk during a median follow-up of 12.8 years. Moderation analysis examined genetic susceptibility interactions using a standardized PRS incorporating osteoporosis-related SNPs. Results: Among 2474 incident osteoporosis cases, cumulative ACEs showed dose–response associations with osteoporosis risk (adjusted hazard ratio [HR]_{per one-unit increase} = 1.07, 95% confidence interval [CI] 1.04–1.11; high ACEs [≥3 types] vs. none: HR = 1.26, 1.10–1.43). Specifically, emotional neglect (HR = 1.14, 1.04–1.25), emotional abuse (HR = 1.14, 1.03–1.27), physical abuse (HR = 1.17, 1.05–1.30), and sexual abuse (HR = 1.15, 1.01–1.31) demonstrated comparable effect sizes. Sex-stratified analysis revealed stronger associations in women. Joint exposure to high ACEs/high PRS tripled osteoporosis risk (HR = 3.04, 2.46–3.76 vs. low ACEs/low PRS) although G × E interaction was nonsignificant (P-interaction = 0.10). Conclusions: These results suggest that ACEs conferred incremental osteoporosis risk independent of genetic predisposition. These findings support the inclusion of ACE screening in osteoporosis prevention strategies and highlight the need for targeted bone health interventions for youth exposed to ACEs. Full article

Unmanned aerial vehicles (UAVs) have emerged as a promising platform for integrated sensing and communication (ISAC) due to their mobility and deployment flexibility. By adaptively adjusting their flight trajectories, UAVs can maintain favorable line-of-sight (LoS) communication links and sensing angles, thus enhancing overall system performance in dynamic and complex environments. However, ensuring physical layer security (PLS) in such UAV-assisted ISAC systems remains a significant challenge, particularly in the presence of mobile users and potential eavesdroppers. This manuscript proposes a joint optimization framework that simultaneously designs robust transmit beamforming and UAV trajectories to secure downlink communication for multiple ground users. At each time slot, the UAV predicts user positions and maximizes the secrecy sum-rate, subject to constraints on total transmit power, multi-target sensing quality, and UAV mobility. To tackle this non-convex problem, we develop an efficient optimization algorithm based on successive convex approximation (SCA) and constrained optimization by linear approximations (COBYLA). Numerical simulations validate that the proposed framework effectively enhances the secrecy performance while maintaining high-quality sensing, achieving near-optimal performance under realistic system constraints. Full article

The equator’s unique combination of high humidity and temperature renders optical satellite imagery highly susceptible to persistent cloud cover. In contrast, synthetic aperture radar (SAR) offers a robust alternative due to its ability to penetrate clouds with microwave imaging. This study addresses the challenges of cloud-induced data gaps and cross-sensor geometric biases by proposing an advanced optical and SAR image-matching framework specifically designed for cloud-prone equatorial regions. We use a prompt-driven visual segmentation model with automatic prompt point generation to produce cloud masks that guide cross-modal feature-matching and joint adjustment of optical and SAR data. This process results in a comprehensive digital orthophoto map (DOM) with high geometric consistency, retaining the fine spatial detail of optical data and the all-weather reliability of SAR. We validate our approach across four equatorial regions using five satellite platforms with varying spatial resolutions and revisit intervals. Even in areas with more than 50 percent cloud cover, our method maintains sub-pixel edging accuracy under manual check points and delivers comprehensive DOM products, establishing a reliable foundation for downstream environmental monitoring and ecosystem analysis. Full article

Objective: Physical joint examination is fundamental in rheumatoid arthritis (RA) assessment. This study evaluated the diagnostic accuracy and agreement between standardized and non-standardized physical joint examinations in RA patients using musculoskeletal ultrasound as the reference standard. Methods: We assessed the joints for tenderness and swelling, calculating sensitivity, specificity, and predictive values. Musculoskeletal ultrasound was used as the reference standard, with adjustment for imperfect reference bias. Agreement between the methods was evaluated using the average kappa coefficient. Results: A total of 1496 joints were evaluated. Without adjustment for imperfect reference bias, standardized examination showed higher sensitivity for detecting pain and swelling than non-standardized examination. Specificity was similar for pain but higher for swelling in standardized examination. After bias adjustment, standardized examination sensitivity improved for pain (93.8% vs. 77.3%; 95% CI: 0.14–0.19) and swelling (91.9% vs. 60.0%; 95% CI: 0.29–0.34). Tenderness specificity remained comparable (standardized examination: 75.4%, non-standardized examination: 76.3%), while the non-standardized examination maintained superior swelling specificity (85.7% vs. 77.1%). Standardized joint examination demonstrated significantly higher concordance than non-standardized assessment in evaluating joint tenderness; standardized assessment yielded significantly greater average kappa coefficients under both false-positive-prioritized (0.44 vs. 0.37; p = 0.01) and false-negative-prioritized scenarios (0.59 vs. 0.45; p < 0.0001). For joint swelling, standardized evaluation showed significantly higher concordance when false negatives were considered more critical (0.59 vs. 0.37; p < 0.0001), whereas differences under false-positive prioritization were not statistically significant. Conclusions: Standardization of the physical joint examination significantly improves diagnostic accuracy and agreement in detecting joint tenderness and swelling in patients with rheumatoid arthritis. Implementing a standardized physical examination protocol may enhance disease activity diagnosis and optimize clinical management of RA. Full article

Driven by the rapid development of e-commerce and intelligent logistics, the volume of express delivery services has surged, making the efficient and accurate identification of shipping information a core requirement for automatic sorting systems. However, traditional Optical Character Recognition (OCR) technology struggles to meet the accuracy and real-time demands of complex logistics scenarios due to challenges such as image distortion, uneven illumination, and field overlap. This paper proposes a three-level collaborative recognition method based on deep learning that facilitates structured information extraction through regional normalization, dual-path parallel extraction, and a dynamic matching mechanism. First, the geometric distortion associated with contour detection and the lightweight direction classification model has been improved. Second, by integrating the enhanced YOLOv5s for key area localization with the upgraded PaddleOCR for full-text character extraction, a dual-path parallel architecture for positioning and recognition has been constructed. Finally, a dynamic space–semantic joint matching module has been designed that incorporates anti-offset IoU metrics and hierarchical semantic regularization constraints, thereby enhancing matching robustness through density-adaptive weight adjustment. Experimental results indicate that the accuracy of this method on a self-constructed dataset is 89.5%, with an F1 score of 90.1%, representing a 24.2% improvement over traditional OCR methods. The dynamic matching mechanism elevates the average accuracy of YOLOv5s from 78.5% to 89.7%, surpassing the Faster R-CNN benchmark model while maintaining a real-time processing efficiency of 76 FPS. This study offers a lightweight and highly robust solution for the efficient extraction of order information in complex logistics scenarios, significantly advancing the intelligent upgrading of sorting systems. Full article

This paper proposes a novel Twin Delayed Deep Deterministic Policy Gradient (TDDPG)-based joint optimization algorithm for hybrid reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) systems in Internet of Vehicles (IoV) scenarios. The proposed system model achieves deep integration of sensing and communication by superimposing the communication and sensing signals within the same waveform. To decouple the complex joint design problem, a dual-DDPG architecture is introduced, in which one agent optimizes the transmit beamforming vector and the other adjusts the RIS phase shift matrix. Both agents share a unified reward function that comprehensively considers multi-user interference (MUI), total transmit power, RIS noise power, and sensing accuracy via the CRLB constraint. Simulation results demonstrate that the proposed TDDPG algorithm significantly outperforms conventional DDPG in terms of sum rate and interference suppression. Moreover, the adoption of a hybrid RIS enables an effective trade-off between communication performance and system energy efficiency, highlighting its practical deployment potential in dynamic IoV environments. Full article

Background: Periprosthetic joint infections (PJIs) are considered as one of the most serious complications after total joint arthroplasty. Aim of this study was to evaluate the prevalence of PJI caused by difficult-to-treat (DTT) pathogens as well as PJIs with a superinfection with a DTT pathogen in the course of the infection and assess the risk factors leading to this emergence. Methods: Data of 169 consecutive patients with a PJI was analyzed in this retrospective observational single-center study, and cases were categorized into PJIs with initial DTT pathogens, PJIs with DTT pathogen superinfection, non-DTT PJIs, and PJIs with superinfection. Recorded parameters comprised age, gender, side, body mass index (BMI), preoperative anticoagulation, and serum level of C-reactive protein (CRP) at admission, as well as preoperative patient status using the ASA (American Society of Anesthesiologists) score and the age-adjusted form of the Charlson comorbidity index (CCI). Furthermore, the infecting microorganism and the type of infection as well as the chosen operative treatment regime, duration of the antibiotics interval, and the outcome were recorded. Results: In total, 46.2% of cases were DTT PJIs, and 30.8% of them were superinfections. Elevated serum CRP levels at admission (≥92.1 mg/L) were linked to a nearly 7-fold increased likelihood of a DTT PJI (OR 6.981, CI [1.367–35.63], p = 0.001), compared to patients with a non-DTT PJI. Hip joint involvement was also associated with a 3.5-fold higher risk compared to knee joints (OR 3.478, CI [0.361–33.538], p = 0.0225). Furthermore, patients undergoing ≥3 revision surgeries demonstrated a significantly 1.3-fold increased risk of developing a DTT superinfection (OR 1.288, CI [1.100–1.508], p < 0.0001). Chronic PJIs were similarly associated with a markedly 3.5-fold higher likelihood of superinfection by DTT pathogens (OR 3.449, CI [1.159–10.262], p = 0.0387). Remaining parameters did not significantly affect the rate of a DTT PJI or a PJI with DTT superinfection. Conclusions: These findings underscore the importance of early identification of high-risk patients and highlight the need for tailored preventive and therapeutic strategies in managing DTT PJIs. Full article

To address the challenges of high complexity in modeling the correlation of multi-dimensional stochastic variables and the difficulty of solving long-term scheduling models in continuous action spaces in multi-energy complementary systems, this paper proposes a long-term optimization scheduling method based on Deep Deterministic Policy Gradient (DDPG). First, an improved C-Vine Copula model is used to construct the multi-dimensional joint probability distribution of water, wind, and solar energy, and Latin Hypercube Sampling (LHS) is employed to generate a large number of water–wind–solar coupling scenarios, effectively reducing the model’s complexity. Then, a long-term optimization scheduling model is established with the goal of maximizing the absorption of clean energy, and it is converted into a Markov Decision Process (MDP). Next, the DDPG algorithm is employed with a noise dynamic adjustment mechanism to optimize the policy in continuous action spaces, yielding the optimal long-term scheduling strategy for the water–wind–solar multi-energy complementary system. Finally, using a water–wind–solar integrated energy base as a case study, comparative analysis demonstrates that the proposed method can improve the renewable energy absorption capacity and the system’s power generation efficiency by accurately quantifying the uncertainties of water, wind, and solar energy and precisely controlling the continuous action space during the scheduling process. Full article

Background: Hearing loss is increasingly prevalent and poses a significant public health concern. While both aging and occupational noise exposure are recognized contributors, their interactive effects and gender-specific patterns remain underexplored. Methods: This cross-sectional study analyzed data from 135,251 employees in Jiangsu Province, China. Demographic information, noise exposure metrics, and hearing thresholds were obtained through field measurements, questionnaires, and audiometric testing. Multivariate logistic regression, restricted cubic spline modeling, and interaction analyses were conducted. Machine learning models were employed to assess feature importance. Results: A nonlinear relationship between age and high-frequency hearing loss (HFHL) was identified, with a critical inflection point at 37.8 years. Noise exposure significantly amplified HFHL risk, particularly in older adults (OR = 2.564; 95% CI: 2.456–2.677, p < 0.001), with consistent findings across genders. Men exhibited greater susceptibility at high frequencies, even after adjusting for age and co-exposures. Aging and noise exposure have a joint association with hearing loss (OR = 2.564; 95% CI: 2.456–2.677, p < 0.001) and an interactive association (additive interaction: RERI = 2.075, AP = 0.502, SI = 2.967; multiplicative interaction: OR = 1.265; 95% CI: 1.176–1.36, p < 0.001). And machine learning also confirmed age, gender, and noise exposure as key predictors. Conclusions: Aging and occupational noise exert synergistic effects on auditory decline, with distinct gender disparities. These findings highlight the need for integrated, demographically tailored occupational health strategies. Machine learning approaches further validate key risk factors and support targeted screening for hearing loss prevention. Full article

This study examined the dynamic interconnectedness and portfolio implications within the cryptocurrency ecosystem, focusing on five representative digital assets across the core functional categories: Layer 1 cryptocurrencies (Bitcoin (BTC) and Ethereum (ETH)), decentralized finance (Uniswap (UNI)), stablecoins (Dai), and crypto infrastructure tokens (Maker (MKR)). Using the Extended Joint Connectedness Approach within a Time-Varying Parameter VAR framework, the analysis captured time-varying spillovers of return shocks and revealed a heterogeneous structure of systemic roles. Stablecoins consistently acted as net absorbers of shocks, reinforcing their defensive profile, while governance tokens, such as MKR, emerged as persistent net transmitters of systemic risk. Foundational assets like BTC and ETH predominantly absorbed shocks, contrary to their perceived dominance. These systemic roles were further translated into portfolio design, where connectedness-aware strategies, particularly the Minimum Connectedness Portfolio, demonstrated superior performance relative to traditional variance-based allocations, delivering enhanced risk-adjusted returns and resilience during stress periods. By linking return-based systemic interdependencies with practical asset allocation, the study offers a unified framework for understanding and managing crypto network risk. The findings carry practical relevance for portfolio managers, algorithmic strategy developers, and policymakers concerned with financial stability in digital asset markets. Full article

(1) Background: This study used latent profile analysis (LPA) to investigate family patterns of paternal and maternal responses to adolescents’ discrete emotions (happiness, sadness, and anger) and examined the relationship between these profiles and demographic factors, as well as adolescents’ emotion adjustment (emotion regulation and depressive symptoms). (2) Methods: A sample of 666 adolescents reported parental responses and their emotional adjustment; their mothers provided family information. (3) Results: (a) The LPA identified four profiles for adolescent happiness, including high enhancing but low dampening and neglect from both parents (Consistent Supportive); low enhancing but high dampening and neglect from both parents (Consistent Unsupportive); low to moderate scores on each response from both parents (Consistent Disengaging); and high maternal dampening and neglect but relatively low scores on the paternal response (Inconsistent). There were two profiles for sadness (Consistent Supportive, Consistent Unsupportive) and three for anger (Consistent Supportive, Consistent Unsupportive, Consistent Disengaging). (b) Parents with boys, higher incomes, better education, and greater marital satisfaction were likely to be classified into the Consistent Supportive profile across emotions. (c) When adolescents perceived their parents with the Consistent Supportive profile, they would show the best emotional adjustment; while for parents with the Inconsistent profile (for happiness) and the Consistent Unsupportive profile, the adolescents had the poorest outcome. Interestingly, adolescents who perceived their parents as fitting the Consistent Disengaging profile (especially for anger) exhibited comparatively less adverse adjustment. (4) Implications: A person-centered approach highlights different patterns of emotion socialization, underscores the importance of fostering parental cooperation and supportive responses to adolescents’ happiness, and suggests that joint disengagement from anger may promote healthier emotional development. Full article

In recent years, the rapid advancement of deep learning techniques has significantly propelled the development of face forgery methods, drawing considerable attention to face forgery detection. However, existing detection methods still struggle with generalization across different datasets and forgery techniques. In this work, we address this challenge by leveraging both local texture cues and global frequency domain information in a complementary manner to enhance the robustness of face forgery detection. Specifically, we introduce a local texture mining and enhancement module. The input image is segmented into patches and a subset is strategically masked, then texture enhanced. This joint masking and enhancement strategy forces the model to focus on generalizable localized texture traces, mitigates overfitting to specific identity features and enabling the model to capture more meaningful subtle traces of forgery. Additionally, we extract multi-scale frequency domain features from the face image using wavelet transform, thereby preserving various frequency domain characteristics of the image. And we propose an innovative frequency-domain processing strategy to adjust the contributions of different frequency-domain components through frequency-domain selection and dynamic weighting. This Facilitates the model’s ability to uncover frequency-domain inconsistencies across various global frequency layers. Furthermore, we propose an integrated framework that combines these two feature modalities, enhanced with spatial attention and channel attention mechanisms, to foster a synergistic effect. Extensive experiments conducted on several benchmark datasets demonstrate that the proposed technique demonstrates superior performance and generalization capabilities compared to existing methods. Full article

During the construction of large-span steel truss arch bridges, challenges such as complex control calculations, frequent adjustments of the cantilever structure, and deviations in the closure state often arise in the process of the assembly and closure of arch ribs. Based on the stress-free state control theory, this paper proposes a precise assembly control method for steel truss arch bridges, which takes the minimization of structural deformation energy and the maintenance of the stress-free dimensions of the closure wedge as the control objectives. By establishing a mathematical relationship between temporary buckle cables and the spatial position of the closure section, as well as adopting the influence matrix method and the quadprog function to determine the optimal parameters of temporary buckle cables (i.e., size, position, and orientation) conforming to actual construction constraints, the automatic approaching of bridge alignment to the target alignment can be achieved. Combined with the practical engineering case of Muping Xiangjiang River Bridge, a numerical calculation study of the precise assembly and closure of steel truss arch bridges was conducted. The calculated results demonstrate that, under the specified construction scheme, the proposed method can determine the optimal combination for temporary buckle cable tension. Considering the actual construction risk and the economic cost, the precise matching of closure joints can be achieved by selectively trimming the size of the closure wedge by a minimal amount. The calculated maximum stress of the structural rods in the construction process is 42% of the allowable value of steel, verifying the feasibility and practicality of the proposed method. The precise assembly method of steel truss arch bridges based on stress-free state control can significantly provide guidance and reference for the design and construction of bridges of this type. Full article