Search Results (82)

To mitigate 3D spatial blockages and channel uncertainty in VHF/low-UHF UAV emergency networks, this paper presents a multimodal environment-aware framework for 3D virtual fluid antenna port scheduling within an Integrated Sensing, Computing, and Communication (ISCCC) architecture. Under rigorously verified spatial resolution and channel stationarity conditions, UAV micro-mobility is mapped onto a discrete 3D virtual port array, transforming continuous flight space into a controllable fluid antenna system (FAS). We define a spatial efficiency metric that quantifies the Pareto trade-off between spatial degrees of freedom and estimation error, parameterized by an error-sensitivity index, and prove the existence of a unique optimal flight scale. Utilizing a joint spatio-temporal channel model, we derive the irreducible entropy lower bound of channel uncertainty, demonstrating that intrinsic environmental randomness constitutes a fundamental predictability limit regardless of port density—a benchmark independent of any specific scheduling strategy. To ensure real-time viability, we introduce an ISCCC-inspired computation-and-caching strategy that leverages pre-calculated stationary probabilities to drive a multidimensional scoring mechanism incorporating channel entropy-based stability, predictive SNR, and load balancing. The suboptimality gap relative to a perfect-CSI oracle is analytically bounded, and proven to narrow significantly under the high temporal correlation inherent in VHF bands. Numerical results confirm that the proposed strategy attains 10.36 bps/Hz effective throughput and 10.5% outage probability, consistently outperforming rule-based, learning-based, and 2D spatial baselines, particularly under prolonged structural obstructions. Full article

We investigated whether bone morphology alone can inform implant selection in shoulder arthroplasty using a hypothesis-driven deep learning framework applied to preoperative computed tomography (CT) scans. The proposed approach extends a previously validated segmentation and pathology-staging pipeline by introducing implant-type prediction and a controlled human–AI comparison. The workflow combines CEL-UNet for 3D bone segmentation with ArthroNet⁺, a multi-task network assessing osteophytes, joint-space narrowing, humeroscapular alignment, and implant type. Trained on a multicenter cohort of 600 patients, CEL-UNet achieved Dice scores of 0.99 for the humerus and 0.98 for the scapula. ArthroNet⁺ achieved high performance in pathology classification (up to 95% for alignment tasks). Under morphology-only conditions, ten orthopedic surgeons achieved 61% accuracy with low inter-rater agreement (Fleiss’ $\mathsf{\kappa }\approx 0.15$), while the model reached 78% agreement with the implant choices observed in the dataset, reflecting the ability to reproduce clinical decision patterns rather than to identify an optimal implant selection. This performance is characterized by a class-dependent asymmetry, with higher recall for reverse implants than for anatomical ones. These findings indicate that bone morphology provides a measurable but incomplete signal for implant selection, and should therefore not be interpreted as reflecting clinical decision-making performance. The framework quantifies the morphology-driven component of surgical decision making under controlled conditions, supporting future integration with multimodal clinical data. Full article

The rapid development of space exploration demands real-time backhaul of massive sensing payload data in space-ground integrated telemetry, tracking, and command (TT&C) networks. However, traditional narrow-band TT&C links suffer from severe congestion during massive data backhaul. Since most TT&C applications are inherently task-oriented and do not require pixel-perfect data reconstruction, we propose a task-oriented joint resource allocation framework based on semantic communications. Specifically, we introduce an adaptive semantic split computing mechanism that extracts and transmits only compact, decision-critical features instead of raw bitstreams, fundamentally mitigating the bandwidth bottleneck. The joint optimization of computation offloading, semantic splitting, and continuous on-board computing allocation is formulated as a stochastic mixed-integer nonlinear programming (MINLP) problem. We propose a decoupled algorithm based on Hierarchical Multi-Agent Proximal Policy Optimization (HMAPPO) to solve it. An outer layer employs multi-agent reinforcement learning (MARL) for distributed discrete decision-making, while an inner layer utilizes a Karush–Kuhn–Tucker (KKT)-based solver for continuous space-based computing allocation. This bi-level architecture overcomes the curse of dimensionality and mathematically guarantees zero-violation of physical capacity constraints. Simulations demonstrate that HMAPPO rapidly converges and sustains a high weighted success rate under heavy traffic congestion, significantly improving system utility compared to state-of-the-art baselines. Full article

Aiming at the problems of inter-row straw congestion, soil accumulation, and consequent uneven seeding depth during high-speed sowing with narrow row spacing under the summer maize no-tillage sowing mode in the Huang-Huai-Hai region, this study proposed a maize seedling belt pre-sowing treatment device suitable for narrow row spacing operation by analyzing the physical properties of straw and soil in the region. Dynamic analysis of the mechanical device was carried out, and the key factors affecting the straw removal effect of the seedling belt and the degree of soil disturbance were identified as machine offset distance, traction speed, and straw-cleaning wheel angle. Discrete element method simulation experiments were conducted via EDEM-ADAMS coupling; the key factors were simulated and optimized, and the optimal parameter combination of the device was determined as follows: machine offset distance of 165 cm (the relative distance between the front and rear positions of the right wheel of adjacent unit cleaning components), traction speed of 11 km/h, and straw-cleaning wheel angle of 44°. Field validation tests of the prototype were performed. The test results showed that the overall straw removal rate of the seedling belt reached 95%, and no large-scale straw and soil accumulation caused by pushing was observed between rows. Compared with the simulation results, the error of straw removal rate was only 0.5%. Sowing comparison tests were conducted, and the results indicated that the device could significantly improve the uniformity of seeding depth and meet the seedling belt quality requirements for high-speed sowing with narrow row spacing of summer maize. This study provides new ideas and methods for the design of straw-cleaning mechanisms in no-till seeding systems. Full article

Background: Arterial hypertension (AH) is a frequent comorbidity in patients with osteoarthritis (OA). Among antihypertensive agents, angiotensin-converting enzyme (ACE) inhibitors, calcium channel blockers (CCBs), angiotensin II receptor blockers (ARBs), and beta-blockers (βBs) have been suggested to influence OA progression and symptomatology. The aim of this study was to assess whether the duration of effectiveness (DE) of viscosupplementation (VS) differs between patients with knee OA who are receiving antihypertensive treatment and those who are not. Methods: This post hoc analysis was conducted using data from a cross-sectional clinical trial (ClinicalTrials.gov Identifier: NCT04988698). The study included consecutive patients with knee OA who came for consultation at the Rheumatology Department and had received intra-articular hyaluronic acid injections within the past three years. The primary outcome was DE, self-reported by patients as the number of weeks of symptom relief. Associations between DE and various factors, including demographics, disease duration, radiographic OA severity (Kellgren–Lawrence grade and affected compartments), comorbidities, OA treatment history, antihypertensive therapy, physical activity level, and prior VS sessions, were analyzed using bivariate and multivariate models. Results: A total of 105 patients (65 women, 149 treated knees) were included. The mean age was 66.1 ± 13.2 years, and the mean body mass index (BMI) was 27.5 kg/m². Thirty-eight percent of patients were receiving antihypertensive treatment (mean number of agents: 1.9; range: 1–4), including CCBs (n = 15), ACE inhibitors (n = 13), ARBs (n = 7), βBs (n = 6), and diuretics (n = 2). The overall mean DE of VS was 48.2 ± 24.8 weeks, with a trend toward longer DE in hypertensive patients compared to non-hypertensive patients (53.1 ± 31.3 vs. 45.4 ± 19.8 weeks, p = 0.06). Bivariate analysis identified significantly longer DE in patients with BMI < 27.5 kg/m² (p = 0.002), Kellgren–Lawrence grade < 4 (p = 0.008), an active lifestyle (p = 0.005), unicompartmental OA (p = 0.01), medial tibiofemoral joint space narrowing (p = 0.046), and fewer than four prior VS sessions (p = 0.02). In multivariate analysis, AH was strongly associated with prolonged DE (p < 0.001), despite AH patients having a higher BMI (29.8 ± 5.5 vs. 25.2 ± 5.2 kg/m², p = 0.001) and being more frequently sedentary (25.5% vs. 13.8%, p = 0.07). A trend toward longer DE was observed in patients treated with βBs and ARBs but not with CCBs or ACE inhibitors. Additional independent predictors of longer DE included BMI < 27.5 kg/m² (p < 0.001), unicompartmental OA (p = 0.02), fewer than four prior VS sessions (p = 0.02), and an active lifestyle (p = 0.027). Conclusions: These findings suggest that antihypertensive treatment may extend the effectiveness of viscosupplementation in knee OA. However, the sample size was insufficient to determine whether specific classes of antihypertensive agents provide greater benefits. Further large-scale, prospective studies are warranted to clarify the potential impact of antihypertensive medications on viscosupplementation outcomes in knee OA. Full article

Background and Objectives: Understanding the structural differences in the sacroiliac joint (SIJ) is essential for distinguishing inflammatory from degenerative disorders. This study aimed to evaluate disease-related morphological patterns and morphometric characteristics of the sacroiliac joint. Additionally, machine learning models were applied to classify inflammatory, degenerative, and control groups based on the morphological and morphometric characteristics of the sacroiliac joint. Materials and Methods: This retrospective study included Magnetic Resonance Imaging (MRI) images of 209 individuals (a total of 418 sacroiliac joints) between the ages of 18 and 75. Participants’ age, sex, disease-related sacroiliac joint morphological features (joint surface type), erosion, sclerosis and inflammation in the joint were determined. Right/left joint space and right/left joint length were measured. According to these anatomical features, machine learning models and a deep neural network were used to classify joints as control, inflammatory, or degenerative. Stratified 5-fold cross-validation was used. Results were reported as mean ± SD with macro averaged precision, recall, and F1-score. Results: The degenerative group was significantly higher than the other groups in terms of mean age (p = 0.001). Both right and left sacroiliac joint spaces were significantly narrower in the inflammatory and degenerative groups than in controls (right SIJ space: p = 0.002; left SIJ space: p = 0.001). Erosion was significantly more frequent in pathological groups (p = 0.001). Although the iliosacral complex was the most common joint type in all groups, no significant difference was observed between the disease groups (right, p = 0.852; left, p = 0.935). In classification, SVM (RBF) and XGBoost achieved the highest accuracy (both: 0.9518 ± 0.0380 and 0.9518 ± 0.0436, respectively) and macro-F1 (0.9509 ± 0.0387 and 0.9506 ± 0.0443). Conclusions: Disease-related morphological and morphometric changes in the sacroiliac joint can be reliably assessed with MRI. These features can then be used in machine learning models to differentiate between inflammatory and degenerative joint disorders. Carefully examining these anatomical features plays a key role in reaching an accurate diagnosis. Machine learning supports this process by helping to interpret the findings in a more consistent and objective way. Full article

Rheumatoid arthritis (RA) frequently affects the joints of the hands, with joint space narrowing (JSN) representing an important early marker of structural damage. The semi-quantitative Sharp/van der Heijde (SvdH) scoring system is widely used in clinical practice but is inherently subjective and susceptible to observer variability. Moreover, the complex anatomy of the wrist and substantial overlap of carpal bones pose challenges for automated quantitative assessment of wrist JSN on routine radiographs. This study aimed to introduce a novel quantitative assessment perspective and to clinically validate an automated, compactness-related quantification framework for evaluating wrist JSN in RA. This study initially enrolled 51 patients with RA. After excluding one case with severe carpal fusion that precluded anatomical differentiation, 50 patients (44 females and 6 males) were included in the final analysis. The cohort had a mean age of 61 years (range: 21–82), a median symptom duration of 9 years (IQR: 1–32), and a median follow-up interval for bilateral hand radiographs of 1.06 years (IQR: 0.82–1.30). To quantify global wrist JSN, 10 compactness-related metrics were computed based on the spatial distribution of bone centroids extracted from carpal segmentation masks. These metrics were validated against the wrist JSN subscore of the SvdH score (SvdH-JSN_wrist) and the total Sharp score (TSS) as gold standards. Several distance-based metrics among the compactness-related metrics showed significant negative correlations with the wrist joint space narrowing subscore of the Sharp/van der Heijde score (SvdH-JSN_wrist). Specifically, mean-pairwise-distance ($MPD$), root-mean-square-radius ($RMSR$), and median-radius ($R50$) showed moderate to strong correlations (r = −0.52 to −0.63, all $p\le 0.0001$) that were consistent at BL and FU. Correlations with TSS were weaker overall, with only $R50$ and its normalized form showing stable negative correlations (r = −0.40 to −0.43, p < 0.01). Longitudinal analyses showed limited correlations between metric changes and clinical score changes. The proposed automated compactness quantification framework enables objective and reliable assessment of wrist JSN on standard radiographs and complements conventional scoring systems by supporting automated and standardized evaluation of RA-related wrist structural changes. Full article

Background/objectives: Structural joint damage remains a major determinant of long-term disability in psoriatic arthritis (PsA). However, its relationship with current disease activity and patient-reported impact in routine clinical practice is not fully understood. We aimed to assess the prevalence and burden of structural joint damage in PsA and to examine its associations with disease activity, patient-reported impact, and clinical characteristics using complementary analytical approaches. Methods: This cross-sectional real-world study included 165 patients with PsA. Structural damage was assessed on conventional radiographs and defined as the presence of at least one joint with erosion, deformity/ankylosis, or joint space narrowing. Damage was analyzed as a binary outcome and as an ordinal burden (0, 1–2, ≥3 affected joints). Disease activity was evaluated using DAPSA, and patient-reported impact using PsAID and the ASAS Health Index (ASAS HI). Multivariable logistics and ordinal regression models were applied. Sensitivity analyses included alternative damage definitions, exclusion of joint space narrowing, restriction to longer disease duration, and adjustment for treatment exposure. Results: Structural damage was present in 26.7% of patients. Disease duration was consistently associated with the presence (OR 1.10 per year; 95% CI 1.05–1.15) and increasing burden of structural damage across all analyses. Distal interphalangeal involvement at presentation was strongly associated with higher damage burden (OR 4.29; 95% CI 1.88–9.78). No significant association was observed between structural damage and current disease activity as assessed by DAPSA, while PsAID showed only a non-significant trend. In contrast, ASAS HI scores were significantly higher in patients with structural damage and increased progressively with greater damage burden (ρ = 0.172; p = 0.027). Findings remained robust across sensitivity analyses, including restriction to erosive damage and exclusion of joint space narrowing. Conclusions: In PsA, structural joint damage is primarily driven by cumulative disease exposure rather than current inflammatory activity. Disease duration and distal interphalangeal involvement identify patients at higher structural risk, while health impact measured by ASAS HI reflects accumulated damage more closely than conventional activity indices. Full article

Background: Knee osteoarthritis (KOA) is characterized by cartilage degradation and joint-space narrowing, resulting in increased friction and observable structural damage. Methods: This study introduces a composite hybrid framework for the automatic classification of KOA severity using anteroposterior knee X-ray images. The methodology applies joint-centered cropping and data augmentation to standardize inputs and uses class weighting to mitigate class imbalance. Deep features extracted from fine-tuned ResNet-101 and EfficientNetB7 models are integrated with handcrafted Gray Level Co-occurrence Matrix (GLCM) texture descriptors, and the final predictions are obtained using a soft-voting ensemble. Results: the proposed ensemble achieves 73% test accuracy (macro-F1 ≈ 0.70; weighted-F1 ≈ 0.73) in a four-class setting (KL-0, KL-2, KL-3, and KL-4). Additional experiments across different classification setups demonstrate consistent performance trends, while Grad-CAM indicates that the model primarily focuses on the joint region. Overall, Conclusions: combining ensemble deep learning with complementary handcrafted texture features provides a reliable and interpretable approach for grading radiographic KOA severity. Full article

Osteoarthritis (OA) is a degenerative joint disease characterized by cartilage degradation, synovial inflammation, osteophyte formation, joint space narrowing, and persistent pain. During OA progression, synovial inflammation triggers the release of pro-inflammatory cytokines, including IL-1β, TNF-α, and IL-6, which activate matrix metalloproteinases (MMPs) and aggrecanases, driving extracellular matrix (ECM) degradation. Emerging evidence indicates that transient receptor potential (TRP) channels, via calcium (Ca²⁺) signaling, function as molecular sensors in joint tissues, including chondrocytes, synoviocytes, sensory neurons, and regulate cartilage homeostasis, synovial inflammation, and OA pain. In cartilage, TRP channels govern chondrocyte survival, mechanotransduction, autophagy, oxidative stress, and ECM turnover, thereby modulating cartilage homeostasis. In synovial tissue, TRP channels regulate inflammatory signaling and cytokine, chemokine, and matrix-degrading enzyme production, leading to synovitis and joint destruction. In sensory neurons innervating the joint, TRP channels respond to mechanical and inflammatory stimuli, increasing nociceptor excitability, neuropeptide release, and pain sensitization, driving OA pain. TRP channel signaling also modulates immune cell infiltration and macrophage-driven inflammation, sustaining chronic pain and tissue damage in OA. This review summarizes emerging evidence on TRP channel functions in OA pathogenesis and highlights their potential as therapeutic targets to alleviate inflammation, protect cartilage, and reduce OA-associated pain. Full article

This study investigates the link between changes in physical activity (PA) measured by wearable accelerometers and the worsening of knee osteoarthritis (KOA) symptoms over two years. Using data from 782 participants in the Osteoarthritis Initiative accelerometer sub-study, PA was tracked with hip-worn ActiGraphs. Participants were classified as “worsening” if their Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) total score increased by >10 points and as “stable” otherwise. PA was categorized into daily counts and minutes spent in various intensity levels, and analyzed in 3 h intervals across the day. Of the participants, 123 (15.7%) experienced worsening symptoms. At baseline, both groups had similar characteristics aside from slower sit-to-stand times in the worsening group. Over two years, the worsening group had a greater decline in total daily activity counts (−18% vs. −10%) and more significant reductions during late afternoon and evening (15:00–21:00; −21% vs. −6%). This group also showed a notable decrease in gait speed, longer sit-to-stand times, and a trend towards greater medial joint space narrowing. These findings suggest that larger declines in PA, especially in activities in the late afternoon and evening, are associated with worsening KOA symptoms, although causality cannot be established. Full article

This paper addresses the issue of safe trajectory tracking for robotic manipulators operating in confined spaces that often exhibit asymmetric geometric structures, such as narrow passages or cluttered obstacle fields. We introduce an innovative model-free control strategy aimed at guaranteeing safe trajectory tracking by constraining joint trajectory tracking errors within prescribed safe bounds. To achieve the desired tracking objective, an error transformation mechanism is developed. Notably, the robustness of this mechanism against model uncertainties eliminates the necessity for knowledge of the system model, enabling model-free control. Additionally, we introduce a Monte Carlo-based search strategy that integrates motion planning, which provides a collision-free feasible reference trajectory, with control, which ensures runtime safety. This framework operates autonomously and guarantees safety at both the planning and control layers. The controller’s robustness, along with its autonomous implementation from planning to control, facilitates practical deployment in complex real-world environments and is effectively validated on a Baxter robotic platform. Full article

The eastern and southwestern sub-basins of the South China Sea (SCS) display starkly contrasting magnetic lineation patterns, yet quantitative 3-D mapping of the subsurface magnetic architecture—essential for deciphering basin evolution—remains challenging due to the dominance of remanent magnetization. We introduce a joint workflow that integrates anomaly separation with Magnetization-Vector Clustering Inversion (MVCI) to resolve this challenge. A low-rank Hankel matrix filter first disentangles co-located seamount and stripe anomalies in the ocean basin; each component is then inverted using MVCI to recover 3-D magnetization intensity and direction without prior orientation constraints, while simultaneously deriving cluster statistics. Synthetic tests replicating the SCS crustal setting demonstrate that seamount-signal removal dramatically enhances inversion fidelity for both anomaly sources. Application to the SCS reveals two distinct vector clusters in the eastern sub-basin, with mean declinations indicating 10–24° counter-clockwise rotation relative to the southwestern sub-basin. Magnetization intensities are slightly stronger in the southwestern sub-basin, where NE-trending magnetic stripes exhibit narrow spacing, whereas the eastern sub-basin shows wider and more variable NE–W to E–W trending stripes. This study provides the first basin-scale quantification of along-strike magnetic heterogeneity, offering new quantitative constraints on late-stage seafloor spreading and the dynamic evolution of the SCS, while delivering a robust, transferable methodology for other remanence-dominated marginal seas. Full article

Snake-like robots, characterized by their high flexibility and multi-joint structure, exhibit exceptional adaptability to complex terrains such as snowfields, jungles, deserts, and underwater environments. Their ability to navigate narrow spaces and circumvent obstacles makes them ideal for operations in confined or rugged environments. However, efficient motion in such conditions requires not only mechanical flexibility but also effective path planning to ensure safety, energy efficiency, and overall task performance. Most existing path planning algorithms for snake-like robots focus primarily on finding the shortest path between the start and target positions while neglecting the optimization of energy consumption during real operations. To address this limitation, this study proposes an energy-efficient path planning method based on an improved A* algorithm enhanced with deep reinforcement learning: Dueling Double-Deep Q-Network (D3QN). An Energy Consumption Estimation Model (ECEM) is first developed to evaluate the energetic cost of snake robot motion in three-dimensional space. This model is then integrated into a new heuristic function to guide the A* search toward energy-optimal trajectories. Simulation experiments were conducted in a 3D environment to assess the performance of the proposed approach. The results demonstrate that the improved A* algorithm effectively reduces the energy consumption of the snake robot compared with conventional algorithms. Specifically, the proposed method achieves an energy consumption of 68.79 J, which is 3.39%, 27.26%, and 5.91% lower than that of the traditional A* algorithm (71.20 J), the bidirectional A* algorithm (94.61 J), and the weighted improved A* algorithm (73.11 J), respectively. These findings confirm that integrating deep reinforcement learning with an adaptive heuristic function significantly enhances both the energy efficiency and practical applicability of snake robot path planning in complex 3D environments. Full article

In this study, a small core diameter single mode fiber laser was applied to weld an 8 mm thick plate of 6061-T6 aluminum alloy. The microstructural evolution and mechanical properties of the laser welded aluminum alloy specimens were investigated in detail. The results indicated that fully penetrated welded specimens, free of welding defects like porosity, melt sagging, and hot cracking could be achieved by optimizing the processing parameters through response surface methodology. The upper part of the fusion zone consisted mainly of fine equiaxed dendrites, with secondary dendrite arm spacing (SDAS) of approximately 3–5 μm. While the lower region of the fusion zone exhibited pronounced microstructural coarsening, made up mostly of coarse columnar grains, along with some localized equiaxed grains, and an SDAS ranging from 8 to 12 μm. Both the fusion zone and heat affected zone (HAZ) were characterized by a “softened” hardness profile. The fusion zone featured a narrow region with the lowest microhardness across the welded joint with the microhardness value reducing to ~72% of the base metal (BM). Meanwhile, the microhardness of the HAZ was ~87.4% of the BM. The ultimate tensile strength of laser welded specimens was ~243.6 MPa, amounting to approximately 78.3% of the base metal. This study provides a fresh approach for welding medium-thick aluminum alloy plate using a high-quality laser beam, even at the kilowatt level with a fiber laser, and it shows a strong promise for applications in light-alloy manufacturing sectors such as automotive, rail transportation, aerospace, and beyond. Full article