Search Results (50,467)

This paper contains an analysis of methods for person classification based on signals from wearable IMU sensors during sports. While this problem has been investigated in prior work, existing approaches have not addressed it within the context of few-shot or minimal-data scenarios. A few-shot scenario is especially useful as the main use case for person identification in sports systems is to be integrated into personalised biofeedback systems in sports. Such systems should provide personalised feedback that helps athletes learn faster. When introducing a new user, it is impractical to expect them to first collect many recordings. We demonstrate that the problem can be solved with over 90% accuracy in both open-set and closed-set scenarios using established methods. However, the challenge arises when applying few-shot methods, which do not require retraining the model to recognise new people. Most few-shot methods perform poorly due to feature extractors that learn dataset-specific representations, limiting their generalizability. To overcome this, we propose a combination of an unsupervised feature extractor and a prototypical network. This approach achieves 91.8% accuracy in the five-shot closed-set setting and 81.5% accuracy in the open-set setting, with a 99.6% rejection rate for unknown athletes. Full article

Background: Personalizing adjuvant chemotherapy (ACT) after curative resection in early-stage NSCLC remains unmet because prior ACT-biomarker findings rarely reproduce across studies. Key barriers are platform and preprocessing heterogeneity, dominant batch effects, and incomplete ACT annotations. As a result, many signatures that perform well in a single cohort fail during external validation. We created an open, harmonized meta-database linking gene expression with curated ACT exposure and survival to enable fair benchmarking and modeling. Methods: A PRISMA-guided search of 999 GEO studies (through January 2025) used LLM-assisted triage of titles, clinical tables, and free text to identify datasets with explicit ACT status and patient-level survival. Eight Affymetrix microarray cohorts (GPL570/GPL96) met eligibility. Raw CEL files underwent robust multi-array average; probes were re-annotated to Entrez IDs and collapsed by median. Covariate-preserving ComBat adjusted platform/study while retaining several clinical factors. Batch structure was quantified by principal-component analysis (PCA) variance, silhouette width, and UMAP. Two quality-control (QC) filters, median M-score deviation and PCA leverage, flagged and removed technical outliers. Results: The final meta-database comprises 1340 patients (223 (16.6%) ACT; 1117 (83.4%) observation), 13,039 intersecting genes, and 594 overall-survival events. Batch-associated variance (PC1 + PC2) decreased from 63.1% to 20.1%, and mean silhouette width shifted from 0.82 to −0.19 post-correction. Seven arrays (0.5%) were excluded by QC. Event depth supports high-dimensional survival and heterogeneity-of-treatment modeling, and the multi-cohort design enables internal–external validation. Conclusions: This first open, rigorously harmonized NSCLC transcriptomic database provides the sample size, demographic diversity, and technical consistency required to benchmark ACT-benefit markers. By making these data openly available, it will accelerate equitable precision-oncology research and enable data-driven treatment decisions in early-stage NSCLC. Full article

Background: Patients with adolescent idiopathic scoliosis (AIS) require effective bracing to control curve progression. However, most traditional spinal braces commonly pose challenges in terms of undesired bulkiness and restricted mobility. Recent advancements have focused on innovative brace designs, utilizing novel materials and structural configurations to improve wearability and functionality. However, it remains unclear how effective these next-generation braces are biomechanically compared to traditional braces. Objectives: This review aimed to analyze the design features of next-generation AIS braces and assess their biomechanical effectiveness via reviewing contemporary studies. Methods: Studies on newly designed scoliosis braces over the past decade were searched in databases, including Web of Science, PubMed, ScienceDirect, Wiley, EBCOHost and SpringerLink. The Joanna Briggs Institute Critical Appraisal Checklist for Cohort Studies was adopted to evaluate the quality of the included studies. The data extracted for biomechanical effect analysis included brace components/materials, design principle, interfacial pressure, morphological changes, and intercomparison parameters. Results: A total of 19 studies encompassing 12 different kinds of braces met the inclusion/exclusion criteria. Clinical effectiveness was reported in 14 studies, with an average short-term Cobb angle correction of 25.4% (range: 12.41–34.3%) and long-term correction of 18.22% (range: 15.79–19.3%). This result aligned broadly with the previously reported efficacy of the traditional braces in short-term cases (range: 12.36–31.33%), but was lower than the long-term ones (range: 23.02–33.6%). Two included studies reported an interface pressure range between 6.0 kPa and 24.4 kPa for novel braces, which was comparable to that of the traditional braces (4.8–30.0 kPa). Additionally, five of six studies reported the trunk asymmetric parameters and demonstrated improvement in trunk alignment. Conclusions: This study demonstrates that most newly designed scoliosis braces could achieve comparable biomechanical efficacy to the conventional designs, particularly in interface pressure management and Cobb angle correction. However, future clinical adoption of these novel braces requires further improvements of ergonomic design and three-dimensional correction, as well as more investigation and rigorous evidence on the long-term treatment outcomes and cost-effectiveness. Full article

Deep brain stimulation (DBS) is an established neurosurgical treatment for Parkinson’s disease (PD), mainly targeting motor symptoms resistant to pharmacological therapy. This review examines strategies to optimize DBS using advanced anatomical, functional, and imaging approaches. The subthalamic nucleus (STN) remains the principal target for alleviating bradykinesia and rigidity, while recent evidence highlights the dentato-rubro-thalamic tract (DRTt) as an additional promising target, especially for tremor control. Clinical data demonstrate that co-stimulation of both STN and DRTt via electrode electric fields results in superior motor outcomes, including greater reductions in UPDRS-III scores and lower levodopa requirements. The review highlights the use of high-resolution MRI and diffusion tensor imaging tractography in visualizing STN and DRTt with high precision. These methods support accurate targeting and individualized treatment planning. Electric field modelling is discussed as a tool to quantify stimulation overlap with target structures and predict clinical efficacy. Anatomical variability in DRTt positioning relative to the STN is emphasized, supporting the need for patient-specific DBS approaches. Alternative and emerging DBS targets—including the pedunculopontine nucleus, zona incerta, globus pallidus internus, and nucleus basalis of Meynert—are discussed for their potential in treating axial and cognitive symptoms. The review concludes with a forward-looking discussion on network-based DBS paradigms, the integration of adaptive stimulation technologies, and the potential of multimodal imaging and electrophysiological biomarkers to guide therapy. Together, these advances support a paradigm shift from focal to network-based neuromodulation in PD management. Full article

Background/Objectives: The U.S. Centers for Disease Control and Prevention recommends some vaccinations using shared clinical decision-making (SCDM). SCDM recommendations are made when not every individual within a given age or risk group would benefit from vaccination, requiring collaborative discussions between patients and providers to assess risks and benefits. Pharmacists play a key role in implementing this recommendation and have frequent opportunities to engage with patients who may be eligible for SCDM-based vaccines. Because SCDM requires provider discussions to assess each patient’s eligibility for the vaccines under SCDM, trust may play a central role in the process. Trust has been suggested to affect patient’s participation in their care and their decision making preferences; however, the nature of this relationship in the context of SCDM vaccines and willingness to engage with pharmacists has yet to be investigated. As the CDC continues to expand the SCDM vaccine category, there is need to assess these. This study aimed to examine relationships between patient characteristics, trust in pharmacists, willingness to engage in SCDM, and vaccination decision control preference. Methods: Using quota sampling, cross-sectional data were collected from Alabama residents aged 18+ between February and March 2024 via a validated online questionnaire. Bivariate and multivariable logistic regression analyses were used to determine the association between trust, patient characteristics and willingness. Structural equation modeling was used to assess the direct and indirect relationships between trust, willingness and vaccination decision control preference. Statistical significance was set at p < 0.05. Results: A substantial portion (45.8%) of participants were unaware that certain vaccinations were based on SCDM. Multivariable logistic regression showed that race (Black vs. White, p = 0.001), age (25–34 vs. 18–24, p = 0.029), highest degree obtained (high school diploma or graduate equivalency degree vs. less than high school, p = 0.001; associate degree or vocational certificate vs. less than high school, p = 0.000; bachelor’s degree or higher vs. less than high school, p = 0.001), political affiliation (Democrat vs. Republican, p = 0.002), confidence in understanding health-related information (high vs. low, p =.029); moderate vs. low, p = 0.002), and patients’ trust in community pharmacists’ communication skills (p = 0.045) and benevolence (p = 0.001) towards their patients were significantly associated with patients’ willingness to engage in SCDM. Trust had a significant direct (p = 0.001) and indirect relationship (p = 0.000) with decision control preference through the willingness variable. Conclusions: Educational interventions are recommended to improve awareness and knowledge of SCDM vaccines among patients. Given their trusted role, pharmacists should actively build and maintain trust with patients, as this may help foster collaborative environments for discussion, encourage patient engagement in SCDM, and support more informed vaccination choices. Full article

The poultry industry requires sustainable strategies to improve gut health and nutrient utilization while reducing antibiotic use. This study assessed the effects of dietary supplementation with a microencapsulated phytosynbiotic from Yanang (Tiliacora triandra) leaf extract fermented with Pediococcus acidilactici V202 (YEP) on broiler ileal digestibility, microbial viability, and cecal fermentation using an in vitro gastrointestinal simulation model. Six YEP inclusion levels (0–2.5%) were tested. Results revealed significant improvements in ileal dry matter and gross energy digestibility and enhanced survival and proliferation of beneficial lactic acid bacteria in the ileum. Increased gas production, lactic acid, and volatile fatty acid concentrations, including acetate, propionate, and butyrate, indicated that cecal fermentation was enhanced in a dose-dependent manner. Moderate YEP levels optimized fermentation speed and butyrate synthesis, while higher levels enhanced total gas and acetate production. YEP also shifted the cecal microbiota toward a healthier profile, enhancing Lactobacillaceae counts and the Lactobacillaceae-to-Enterobacteriaceae ratio. Overall, protective microencapsulation, synergistic phytochemical interactions, and balanced nutrient supply had positive effects at the gut level. Thus, the data highlight YEP as a promising synbiotic feed additive that can enhance nutrient utilization, microbial balance, and gut health in broilers, warranting future in vivo validation. Full article

Autonomous landing capability is crucial for fully autonomous UAV flight. Currently, most solutions use either color imaging from a camera pointed down, lidar sensors, dedicated landing spots, beacons, or a combination of these approaches. Classical strategies can be limited by either no color data when lidar is used, limited obstacle perception when only color imaging is used, a low field of view from a single RGB-D sensor, or the requirement for the landing spot to be prepared in advance. In this paper, a new approach is proposed where an RGB-D camera mounted on a gimbal is used. The gimbal is actively actuated to counteract the limited field of view while color images and depth information are provided by the RGB-D camera. Furthermore, a combined UAV-and-gimbal-motion strategy is proposed to counteract the low maximum range of depth perception to provide static obstacle detection and avoidance, while preserving safe operating conditions for low-altitude flight, near potential obstacles. The system is developed using a PX4 flight stack, CubeOrange flight controller, and Jetson nano onboard computer. The system was flight-tested in simulation conditions and statically tested on a real vehicle. Results show the correctness of the system architecture and possibility of deployment in real conditions. Full article

Accurate assessment of a crop’s water requirement is essential for optimising irrigation scheduling and increasing the sustainability of water use. The crop coefficient (K_c) is a dimensionless factor that converts reference evapotranspiration (ET₀) into actual crop evapotranspiration (ET_c) and is widely used for irrigation scheduling. The K_c reflects canopy cover, phenology, and crop type/variety, but is difficult to measure directly in heterogeneous perennial systems, such as vineyards. Remote sensing (RS) products, especially open-source satellite imagery, offer a cost-effective solution at moderate spatial and temporal scales, although their application in vineyards has been relatively limited due to the large pixel size (~100 m²) relative to vine canopy size (~2 m²). This study aimed to improve grapevine K_c predictions using vegetation indices derived from harmonised Sentinel-2 imagery in combination with spectral unmixing, with ground data obtained from canopy light interception measurements in three winegrape cultivars (Shiraz, Cabernet Sauvignon, and Chardonnay) in the Barossa and Eden Valleys, South Australia. A linear spectral mixture analysis approach was taken, which required estimation of vine canopy cover through beta regression models to improve the accuracy of vegetation indices that were used to build the K_c prediction models. Unmixing improved the prediction of seasonal K_c values in Shiraz (R² of 0.625, RMSE = 0.078, MAE = 0.063), Cabernet Sauvignon (R² = 0.686, RMSE = 0.072, MAE = 0.055) and Chardonnay (R² = 0.814, RMSE = 0.075, MAE = 0.059) compared to unmixed pixels. Furthermore, unmixing improved predictions during the early and late canopy growth stages when pixel variability was greater. Our findings demonstrate that integrating open-source satellite data with machine learning models and spectral unmixing can accurately reproduce the temporal dynamics of K_c values in vineyards. This approach was also shown to be transferable across cultivars and regions, providing a practical tool for crop monitoring and irrigation management in support of sustainable viticulture. Full article

Cycle slips, abrupt discontinuities in carrier-phase measurements, pose a significant challenge for single-frequency GNSS receivers, particularly in real-time applications where rapid detection is critical. Unlike dual-frequency approaches, these receivers cannot rely on redundant combinations to isolate slips from other errors. This study proposes a real-time cycle slip detection algorithm for single-frequency GNSS receivers based solely on short-term differencing of pseudorange and carrier-phase observables. The method employs a two-step logic: first-order differencing of code-minus-carrier and second-order differencing of carrier phase. Both steps employ satellite elevation-dependent adaptive thresholds, enabling robust detection under diverse signal conditions. The method requires no user position, receiver-generated tracking flags, or additional sensor data. Experimental results reveal that the algorithm achieves over 98% detection accuracy for slips exceeding 10 cycles, with no false positives in artificial slip testing, and 87.93% agreement with Loss of Lock Indicators (LLI) during periods in which the receiver indicated signal instability. Full article

In this paper, we apply an information-theoretic method proposed by Ryabko and Savina (therefore called the RS-method), based on the use of data compression, to recognize the individual author’s style of a writer across four languages from different language groups and families. In this paper, the presented method was used to study fiction texts in Russian (East Slavic group of languages of the Indo-European language family), Amharic (South Ethiosemitic group of the Semitic language family), Chinese (Sinitic group of the Sino-Tibetan language family) and English (West Germanic language group of the Indo-European language family). It was found that the amount of data necessary for recognizing an author’s style is almost the same for all four languages, i.e., the amount of data is invariant across different language groups. The results obtained are of interest to computer science, literary studies, linguistics and, in particular, computational linguistics. Full article

In massive multiple-input and multiple-output (mMIMO) systems, superimposed channel state information (CSI) feedback is developed to improve the occupation of uplink bandwidth resources. Nevertheless, the interference from this superimposed mode degrades the recovery performance of both downlink CSI and uplink data sequences. Although machine learning (ML)-based methods effectively mitigate superimposed interference by leveraging the multi-domain features of downlink CSI, the complex interactions among network model parameters cause a significant burden on system resources. To address these issues, inspired by sensing-assisted communication, we propose a novel superimposed CSI feedback method assisted by inactive sensing information that previously existed but was not utilized at the base station (BS). To the best of our knowledge, this is the first time that inactive sensing information is utilized to enhance superimposed CSI feedback. In this method, a new type of modal data, different from communication data, is developed to aid in interference suppression without requiring additional hardware at the BS. Specifically, the proposed method utilizes location, speed, and path information extracted from sensing devices to derive prior information. Then, based on the derived prior information, denoising processing is applied to both the delay and Doppler dimensions of downlink CSI in the delay—Doppler (DD) domain, significantly enhancing the recovery accuracy. Simulation results demonstrate the performance improvement of downlink CSI and uplink data sequences when compared to both classic and novel superimposed CSI feedback methods. Moreover, against parameter variations, simulation results also validate the robustness of the proposed method. Full article

Background: The aim of our study is to assess the risk factors for the development of coronary artery stent thrombosis and restenosis, as well as the main localization of these processes in patients who underwent repeated coronary revascularization during the COVID-19 pandemic. Materials and Methods: Data were retrospectively analyzed from 490 patients who underwent coronary angiography and required repeat revascularization from May 2020 to May 2023. The prevalence and anatomical distribution of coronary stenosis, restenosis, and stent thrombosis were assessed. Results: Coronary artery stenosis was detected in 46.9% of patients. The most affected arteries were the left anterior descending (13.7%), right coronary artery (15.1%), and circumflex branch (9.4%). In-stent restenosis was observed in 19.0% of cases. Coronary thrombosis occurred in 22.8% of patients, while stent thrombosis was found in 11.2%. Multivariate regression revealed that leukocyte count (OR = 1.18, p < 0.05), activated partial thromboplastin time (APTP) (OR = 1.021, p = 0.025), low-density lipoproteins (LDL) (OR = 1.421, p = 0.042), and prior COVID-19 infection (OR = 2.05, p = 0.038) were significant predictors of stent thrombosis. The left ventricular ejection fraction (LVEF) (OR = 0.959, p = 0.017) and hemoglobin levels (OR = 0.975, p = 0.014) have inverse association with risk of stent thrombosis. Conclusion: COVID-19 history is a strong independent risk factor for coronary stent thrombosis, alongside inflammatory and coagulation markers. Full article

Immune checkpoint inhibitors (ICIs) improve lung cancer prognosis but are associated with immune-related adverse events, most commonly thyroid dysfunction. While prior studies and guidelines have focused on thyroid dysfunction during ICI therapy, data on hypothyroidism and its monitoring after ICI therapy remain limited. We aimed to investigate hypothyroidism incidence and implementation of thyroid function monitoring after ICI therapy completion in patients with lung cancer. We conducted a retrospective observational study using the DeSC claims database of approximately 12 million individuals in Japan. Patients with lung cancer who received ICI therapy between April 2014 and August 2023 were included; those with a history of thyroid hormone replacement or insufficient follow-up were excluded. Among 6883 eligible patients, 277 (4.0%) developed hypothyroidism requiring hormone replacement post-ICI therapy completion (median onset, 67.0 d). Risk factors included ICI plus bevacizumab therapy and a history of myasthenia gravis, while steroid use for ≥28 d during ICI therapy lowered the risk. Post-ICI therapy completion thyroid monitoring was performed in 73.7% of patients, with test date distribution showing a median of 126.0 d and mode of 21.0 d. Hypothyroidism was frequently found to develop within 2 months post-ICI therapy completion, highlighting the need for continued thyroid monitoring and prospective studies to establish optimal surveillance strategies. Full article

Coastal debris is a global environmental issue that requires systematic monitoring strategies based on reliable statistical data. Recent advances in remote sensing and deep learning-based object detection have enabled the development of efficient coastal debris monitoring systems. In this study, two state-of-the-art object detection models—RT-DETR and YOLOv10—were applied to UAV-acquired images for coastal debris detection. Their false positive characteristics were analyzed to provide guidance on model selection under different coastal environmental conditions. Quantitative evaluation using mean average precision (mAP@0.5) showed comparable performance between the two models (RT-DETR: 0.945, YOLOv10: 0.957). However, bounding box label accuracy revealed a significant gap, with RT-DETR achieving 80.18% and YOLOv10 only 53.74%. Class-specific analysis indicated that both models failed to detect Metal and Glass and showed low accuracy for fragmented debris, while buoy-type objects with high structural integrity (Styrofoam Buoy, Plastic Buoy) were consistently identified. Error analysis further revealed that RT-DETR tended to overgeneralize by misclassifying untrained objects as similar classes, whereas YOLOv10 exhibited pronounced intra-class confusion in fragment-type objects. These findings demonstrate that mAP alone is insufficient to evaluate model performance in real-world coastal monitoring. Instead, model assessment should account for training data balance, coastal environmental characteristics, and UAV imaging conditions. Future studies should incorporate diverse coastal environments and apply dataset augmentation to establish statistically robust and standardized monitoring protocols for coastal debris. Full article

The construction of large-scale infrastructure, such as power facilities, requires extensive use of reinforced concrete. The durability degradation of reinforced concrete structures in chloride environments involves multi-physics coupling effects, chloride ion diffusion, rebar corrosion, and concrete damage. Existing models neglect the coupling mechanisms among these processes and the influence of mesoscale structural characteristics. Therefore, this study proposes a diffusive–mechanical coupled phase field by integrating the phase field, chloride ion diffusion, and mechanical equivalence for rebar corrosion, establishing a multi-physics coupling analysis framework at the mesoscale. The model incorporates heterogeneous meso-structure of concrete and constructs a dynamic coupling function between the phase field damage variable and chloride diffusion coefficient, enabling full-process simulation of corrosion-induced cracking under chloride erosion. Numerical results demonstrate that mesoscale heterogeneity significantly affects crack propagation paths, with increased aggregate content delaying the initiation of rebar corrosion. Moreover, the case with corner-positioned rebar exhibits earlier cracking compared to the case with centrally located rebar. Furthermore, larger clear spacing delays delamination failure. Comparisons with the damage mechanics model and experimental data confirm that the proposed model more accurately captures tortuous crack propagation behavior, especially suitable for evaluating the durability of reinforced concrete components in facilities such as transmission tower foundations, substation structures, and marine power facilities. This research provides a highly accurate numerical tool for predicting the service life of reinforced concrete power infrastructure in chloride environments. Full article