Search Results (25,527)

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

In response to the inability to quickly assess wind noise performance during the early stages of automotive styling design, this paper proposes a method for predicting interior wind noise by integrating automotive point cloud models with the Gray Wolf Optimization Residual Network model (GWO-ResNet). Based on wind tunnel test data under typical operating conditions, the point cloud model of the test vehicle is compressed using an auto-encoder and used as input features to construct a nonlinear mapping model between the whole vehicle point cloud and the wind noise level at the driver’s left ear. Through adaptive optimization of key hyperparameters of the ResNet model using the gray wolf optimization algorithm, the accuracy and generalization of the prediction model are improved. The prediction results on the test set indicate that the proposed GWO-ResNet model achieves prediction results that are consistent with the actual measured values for the test samples, thereby validating the effectiveness of the proposed method. A comparative analysis with traditional ResNet models, GWO-LSTM models, and LSTM models revealed that the GWO-ResNet model achieved Mean Absolute Percentage Error (MAPE) and mean squared error (MSE) of 9.72% and 20.96, and 9.88% and 19.69, respectively, on the sedan and SUV test sets, significantly outperforming the other comparison models. The prediction results on the independent validation set also demonstrate good generalization ability and stability (MAPE of 10.14% and 10.15%, MSE of 23.97 and 29.15), further proving the reliability of this model in practical applications. The research results provide an efficient and feasible technical approach for the rapid evaluation of wind noise performance in vehicles and provide a reference for wind noise control in the early design stage of vehicles. At the same time, due to the limitations of the current test data, it is impossible to predict the wind noise during the actual driving of the vehicle. Subsequently, the wind noise during actual driving can be predicted by the test data of multiple working conditions. Full article

(1) Background: Pharmacist interventions are key to optimizing medication therapy and improving patient outcomes. The CLEO multidimensional tool assesses the clinical, economic, and organizational impact of these interventions, though its use in Portuguese hospital settings is limited. This study explored the predicted impact of pharmacist interventions in the Oncology Department of a Portuguese hospital, using CLEO to quantify their potential contribution to patient care and healthcare system efficiency;(2) Methods: A retrospective observational study was conducted at the hospital’s Oncology Outpatient Pharmacy between April and December 2024. Data from 144 pharmacist interventions were analyzed, focusing on drug-related problems, corrective actions, and CLEO scores. Descriptive statistics were used for data analysis; (3) Results: The most frequent drug-related problems were incorrect administration frequency (57.6%), drug interactions (22.2%), and incorrect dosing (10.4%). Nearly half of the interventions (47.2%) resulted in prescription corrections. CLEO analysis demonstrated a predicted positive clinical impact (80% of interventions scored 1C–3C), potential economic benefits (40.3% scored 1E), and organizational improvements (79.9% scored 1O), especially in lung, breast, and colorectal cancer treatments; (4) Conclusions: Pharmacist interventions were predicted to be associated with improvements in clinical, economic, and organizational outcomes in oncology care. These findings suggest that systematic documentation and evaluation of interventions using CLEO may enhance patient safety and healthcare efficiency, although further multicenter and prospective studies are needed to confirm these observations. Full article

Conventional grain monitoring systems often rely on isolated data points (e.g., point-based temperature measurements), limiting holistic condition assessment. This study proposes a novel Mechanism and Data Driven (MDD) framework that integrates physical mechanisms with real-time sensor data. The framework quantitatively analyzes solar radiation and external air temperature effects on silo boundaries and introduces a novel interpolation-optimized model parameter initialization technique to enable comprehensive grain condition perception. Rigorous multidimensional validation confirms the method’s accuracy: The novel initialization technique achieved high precision, demonstrating only 1.89% error in Day-2 low-temperature zone predictions (27.02 m² measured vs. 26.52 m² simulated). Temperature fields were accurately reconstructed (≤0.5 °C deviation in YOZ planes), capturing spatiotemporal dynamics with ≤0.45 m² maximum low-temperature zone deviation. Cloud map comparisons showed superior simulation fidelity (SSIM > 0.97). Further analysis revealed a 22.97% reduction in total low-temperature zone area (XOZ plane), with Zone 1 (near south exterior wall) declining 27.64%, Zone 2 (center) 25.30%, and Zone 3 20.35%. For dynamic evolution patterns, high-temperature zones exhibit low moisture (<14%), while low-temperature zones retain elevated moisture (>14%). A strong positive correlation between temperature and relative humidity fields; temperature homogenization drives humidity uniformity. The framework enables holistic monitoring, providing actionable insights for smart ventilation control, condensation risk warnings, and mold prevention. It establishes a robust foundation for intelligent grain storage management, ultimately reducing post-harvest losses. Full article

Background: The miniaturization of ureterorenoscopes increasingly enables atraumatic primary ureteroscopy, without ureteral dilation or presenting. This study aims to evaluate the feasibility and safety of primary ureteroscopy using the HU30M (6.3 Fr, HugeMed, Shenzhen HugeMed Medical Technical Development Co., Ltd., China), the smallest currently available ureteroscope Methods: We analyzed consecutive patients in whom primary ureteroscopy using the HU30M was performed or attempted, using prospectively collected in-hospital and 30-day follow-up data for retrospective evaluation. The primary outcome was the success rate of primary ostial intubation. Secondary outcomes included the stone-free rate (SFR) in patients with urolithiasis, incidence of in-hospital complications (Clavien–Dindo classification) and 30-day emergency readmission. Additionally, we conducted a propensity score-matched comparative analysis of the HU30M versus a contemporary 7.5 Fr digital single-use ureteroscope (PUSEN PU3033AH, Zhuhai Pusen Medical Technology Co., Ltd., China). Results: Between January and April 2025, primary ureteroscopy using the HU30M was performed or attempted in 34 patients, including four bilateral procedures. Primary ureteroscopy was defined as ureteroscopic access without prior stenting or dilation. Indications were diagnostic evaluation in 15 patients (44%), uretreroscopic stone treatment in 10 patients (29%) and endoscopic combined intrarenal surgery (ECIRS) in 9 patients (27%). Successful primary ostial intubation was achieved in 36 of 38 renal units (95%). Among urolithiasis cases, SFR was 17/19 (90%) in-hospital complications were limited to postoperative fever in two patients (6%) and no procedure-related 30-day emergency readmission occurred. In matched analyses, HU30M demonstrated significantly shorter operative times compared with the 7.5 Fr ureteroscope, while postoperative hemoglobin drop, inflammatory parameters and renal function were comparable. Conclusions: Primary ureteroscopy with HU30M is feasible and safe across diverse indications, achieving high success of atraumatic ostial access. Comparative analyses suggest procedural efficiency advantages and overall safety comparable to the current digital single-use ureteroscope standard. Full article

Background: Philadelphia-like acute lymphoblastic leukemia (Ph-like ALL) is a high-risk subtype of B-cell ALL characterized by a gene expression profile similar to BCR::ABL1-positive leukemia, but lacking the BCR::ABL1 fusion gene. It is frequently associated with kinase-activating alterations, such as CRLF2 rearrangements, JAK-STAT pathway mutations, and ABL-class fusions. Patients with Ph-like ALL typically experience poor outcomes with conventional chemotherapy, underscoring the need for intensified and targeted therapeutic approaches. Methods: This review summarizes current evidence regarding the role of hematopoietic stem cell transplantation (HSCT) in patients with Ph-like ALL. We analyzed retrospective cohort studies, registry data, and ongoing clinical trials, focusing on transplant indications, molecular risk stratification, measurable residual disease (MRD) status, timing of transplant, and post-transplant strategies. Results: Retrospective data suggest that HSCT in first complete remission (CR1) may improve survival in patients with high-risk molecular lesions or MRD positivity at the end of induction. However, the lack of prospective data specific to Ph-like ALL limits definitive conclusions. Post-transplant relapse remains a challenge, and novel strategies, including the use of tyrosine kinase inhibitors or JAK inhibitors as post-HSCT maintenance therapy, are being explored. Emerging immunotherapies, such as chimeric antigen receptor (CAR) T cells, may reshape the therapeutic landscape and potentially alter the indications for transplantation. Conclusions: HSCT remains a crucial therapeutic option for selected patients with Ph-like ALL, particularly those with poor molecular risk features or persistent MRD. However, further prospective studies are needed to evaluate the indication for HSCT in CR1 and the potential integration of transplantation with targeted and immunotherapeutic strategies. Personalized treatment approaches based on genomic profiling and MRD assessment are essential to improve outcomes in this high-risk subset. Full article

Introduction: Gastrointestinal stromal tumors (GISTs) are primarily driven by mutations in KIT (KIT proto-oncogene receptor tyrosine kinase) or PDGFRA (platelet-derived growth factor receptor alpha), but resistance to tyrosine kinase inhibitors (TKIs) such as imatinib remains a major clinical challenge. Alterations in fibroblast growth factor receptor 2 (FGFR2), although rare, are emerging as important contributors to tumor progression and drug resistance. This review evaluates the molecular mechanisms, expression profiles, detection methods, and therapeutic implications of FGFR2 in GIST. Methods: We searched PubMed, Web of Science, Google Scholar, and ClinicalTrials.gov for studies published between January 2010 and June 2025, using combinations of keywords related to FGFR2, gastrointestinal stromal tumor, resistance mechanisms, gene fusion, amplification, polymorphisms, and targeted therapy. Eligible studies were critically assessed to distinguish GIST-specific data from evidence extrapolated from other cancers. Results:FGFR2 is expressed in multiple normal tissues and at variable levels in mesenchymal-derived tumors, including GIST. Its alterations occur in approximately 1–2% of GIST cases, most commonly as gene fusions (e.g., FGFR2::TACC2, <1%) or amplifications (1–2%); point mutations and clinically significant polymorphisms are extremely rare. These alterations activate the MAPK/ERK and PI3K/AKT pathways, contribute to bypass signaling, and enhance DNA damage repair, thereby promoting TKI resistance. Beyond mutations, mechanisms such as amplification, ligand overexpression, and microenvironmental interactions also play roles. FGFR2 alterations appear mutually exclusive with KIT/PDGFRA mutations but occasional co-occurrence has been reported. Current clinical evidence is largely limited to small cohorts, basket trials, or case reports. Conclusions:FGFR2 is an emerging oncogenic driver and biomarker of resistance in a rare subset of GISTs. Although direct evidence remains limited, particularly regarding DNA repair and polymorphisms, FGFR2-targeted therapies (e.g., erdafitinib, pemigatinib) show potential, especially in combination with TKIs or DNA-damaging agents. Future research should prioritize GIST-specific clinical trials, the development of FGFR2-driven models, and standardized molecular diagnostics to validate FGFR2 as a therapeutic target. Full article

Background: This technical note presents a methodological approach to agrichemical inventory documentation. It complements exposure assessments in field settings with time-restricted observational periods. Conducted in Michoacán, Mexico, this method leverages large language model (LLM) capabilities for categorizing agrichemicals from brief video footage. Method: Given time-limited access to a storage shed housing various agrichemicals, a short video was recorded and processed into 31 screenshots. Using OpenAI’s ChatGPT (model: GPT-4o^®), agrichemicals in each image were identified and categorized as fertilizers, herbicides, insecticides, fungicides, or other substances. Results: Human validation revealed that the LLM accurately identified 75% of agrichemicals, with human verification correcting entries. Conclusions: This rapid identification method builds upon behavioral methods of exposure assessment, facilitating initial data collection in contexts where researcher access to hazardous materials may be time limited and would benefit from the efficiency and cross-validation offered by this method. Further refinement of this LLM-assisted approach could optimize accuracy in the identification of agrichemical products and expand its application to complement exposure assessments in field-based research, particularly as LLM technologies rapidly evolve. Most importantly, this Technical Note illustrates how field researchers can strategically harness LLMs under real-world time constraints, opening new possibilities for rapid observational approaches to exposure assessment. Full article

Graduate students often experience difficulties in learning statistics, particularly those who have limited mathematical backgrounds. In recent years, Learning Management Systems (LMS) and Problem-Based Learning (PBL) have been widely adopted to support instruction, yet little research has explored how these tools relate to learning outcomes using mixed methods design. Limited studies have employed machine learning methods such as clustering analysis in Learning Analytics (LA) to explore different behavior of clusters based on students log data. This study followed an explanatory sequential mixed methods design to examine student engagement patterns on Canvas and learning outcomes of students in a graduate-level statistics course. LMS log data and surveys were collected from 31 students, followed by interviews with 19 participants. K-means clustering revealed two groups: a high-performing group with lower LMS engagement and a low-performing group with higher LMS engagement. Six themes emerged from a thematic analysis of interview transcripts: behavioral differences in engagement, the role of assessment, emotional struggle, self-efficacy, knowledge or skill gain, and structured instructional support. Results indicated that low-performing students engaged more frequently and benefited from structured guidance and repeated exposure. High-performing students showed more proactive and consistent engagement habits. These findings highlight the importance of intentional course design that combines PBL with LMS features to support diverse learners. Full article

(1) Background: Pulmonary embolism (PE) is a severe complication of coronavirus disease 2019 (COVID-19), particularly in hospitalized patients. Data from Eastern Europe, including Romania, are limited, despite potential regional differences in demographics, comorbidities, and thromboprophylaxis practices. (2) Methods: This retrospective cohort study included 395 adults hospitalized with RT-PCR-confirmed COVID-19 at the “Victor Babeș” Clinical Hospital of Infectious Diseases and Pneumophthisiology, Timișoara, Romania, from September 2022 to December 2024. Demographic, clinical, laboratory, and imaging data were extracted from medical records. PE was confirmed by computed tomography pulmonary angiography (CTPA). Group comparisons used chi-square and t-tests, with multivariable logistic regression to identify independent PE predictors. (3) Results: PE was diagnosed in 47 patients (11.9%). Compared to those without PE, patients with PE had higher D-dimer (5305.00 ± 1251.00 vs. 537.00 ± 203.00 ng/mL, p < 0.001), fibrinogen (6.33 ± 0.74 vs. 3.51 ± 0.60 g/L, p < 0.001), and PT/INR (1.68 ± 0.21 vs. 1.05 ± 0.09, p < 0.001). Prior venous thromboembolism (VTE; 19.1% vs. 8.3%, p = 0.03) and prolonged immobilization (61.7% vs. 23.0%, p < 0.001) were significant risk factors. Intensive care unit (ICU) transfer occurred in 59.6% of PE cases, with a 25.5% in-hospital mortality rate. All PE patients received anticoagulation; 10.6% underwent thrombolysis. (4) Conclusions: In this Romanian cohort, one of the first large-scale studies in Eastern Europe, PE was prevalent among hospitalized COVID-19 patients, associated with elevated coagulation markers, identifiable risk factors, and high mortality. Early recognition and optimized thromboprophylaxis are critical to improve outcomes. Full article

With the continuous advancement of 3D perception technology, point cloud data has found increasingly widespread application. However, the presence of holes in point cloud data caused by device limitations and environmental interference severely restricts algorithmic performance, making point cloud completion a research topic of high interest. This study observes that most existing mainstream point cloud completion methods primarily focus on capturing global features, while often underrepresenting local structural details. Moreover, the generation process of complete point clouds lacks effective control over fine-grained features, leading to insufficient detail in the completed outputs and reduced data integrity. To address these issues, we propose a Set Combination Multi-Layer Perceptron (SCMP) module that enables the simultaneous extraction of both local and global features, thereby reducing the loss of local detail information. In addition, we introduce the Squeeze Excitation Pooling Network (SEP-Net) module, an informative channel attention mechanism capable of adaptively identifying and enhancing critical channel features, thus improving the overall feature representation capability. Based on these modules, we further design a novel Feature Fusion Point Fractal Network (FFPF-Net), which fuses multi-dimensional point cloud features to enhance representation capacity and progressively refines the missing regions to generate a more complete point cloud. Extensive experiments conducted on the ShapeNet-Part and MVP datasets compared to L-GAN and PCN showed average prediction error improvements of 1.3 and 1.4, respectively. The average completion errors on the ShapeNet-Part and MVP datasets are 0.783 and 0.824, highlighting the improved fine-detail reconstruction capability of our network. These results indicate that the proposed method effectively enhances point cloud completion performance and can further promote the practical application of point cloud data in various real-world scenarios. Full article

Background: Musculoskeletal (MSK) pain is a frequent but under-addressed concern during pregnancy. In Bangladesh, challenges such as limited antenatal care (ANC) access and heavy maternal workloads make this issue particularly urgent for maternal health. This study aimed to determine the prevalence and predictors of MSK pain among pregnant women attending government ANC clinics in Bangladesh. Methods: A facility-based cross-sectional study was conducted among 300 pregnant women recruited from two government hospitals in Dhaka Division. Data were collected using structured interviewer-administered questionnaires covering patient characteristics, pain-related characteristics, and pregnancy-related characteristics. Pain was measured using the Numeric Pain Rating Scale (NPRS; mild <4, moderate 4–7, severe >7), and body mass index (BMI) was calculated based on self-reported height and weight. Descriptive statistics, chi-square tests, and multivariable logistic regression were employed to identify factors independently associated with MSK pain. Results: Overall, 67% of women reported MSK pain, most frequently in the lower back and lower abdomen. Women in later trimesters had about twice the odds of experiencing pain, while those with obesity had nearly six times higher odds compared to women with normal body mass index (BMI). Conclusions: MSK pain is common among pregnant women in Bangladesh and shows associations with later gestational stages and obesity. These findings suggest that integrating routine screening and non-pharmacological management into ANC may help support maternal health and reduce preventable complications in resource-limited settings. Full article

Background: Trelegy Ellipta is a widely prescribed triple inhaler therapy for chronic obstructive pulmonary disease (COPD). Although its clinical efficacy is well established, evidence on sex-specific differences in adverse event (AE) profiles from real-world pharmacovigilance data remains limited. In addition, some AEs may reflect underlying disease characteristics rather than drug exposure, which complicates interpretation of safety signals. Objective: To explore sex-related differences in AEs associated with Trelegy Ellipta using the FDA Adverse Event Reporting System (FAERS). The study aimed to identify potential safety signals while accounting for alternative explanations, including comorbidity burden and disease-related variation. Methods: We retrospectively analyzed FAERS reports from January 2018 to April 2025, identifying 4555 AEs attributed to Trelegy Ellipta. Events were coded by System Organ Class (SOC) and stratified by patient sex. Frequencies were compared between male (n = 1621) and female (n = 2934) patients using chi-square tests, and associations were expressed as reporting odds ratios (RORs) with 95% confidence intervals (CIs). Results: Male patients more frequently reported hypertension (63.4% vs. 47.0%; p = 0.01), pneumonia (87.8% vs. 76.8%; p < 0.001), anxiety (91.0% vs. 66.9%; p < 0.001), sleep disorders (20.1% vs. 6.8%; p < 0.001), and hyperglycemia (92.7% vs. 52.1%; p < 0.001). Female patients more often reported headache (56.7% vs. 32.6%; p < 0.001), depression (33.1% vs. 9.0%; p < 0.001), and osteoporosis (41.7% vs. 2.4%; p < 0.001). Further variation was observed across neurological, musculoskeletal, and respiratory categories, suggesting a multidimensional pattern of sex differences. Conclusions: This FAERS-based analysis indicates distinct sex-specific safety signals for Trelegy Ellipta, particularly in cardiovascular, neuropsychiatric, and steroid-related domains. These findings are hypothesis-generating and highlight the importance of incorporating sex-disaggregated analyses into future pharmacovigilance and clinical studies. Full article

Tableware objects such as plates, bowls, and cups are usually textureless, uniform in color, and vary widely in shape, making it difficult to apply conventional pose estimation methods that rely on texture cues or object-specific CAD models. These limitations present a significant obstacle to robotic manipulation in restaurant environments, where reliable six-degree-of-freedom (6-DoF) pose estimation is essential for autonomous grasping and collection. To address this problem, we propose a model-free and texture-free 6-DoF pose estimation framework based on a transformer encoder architecture. This method uses only geometry-based features extracted from depth images, including surface vertices and rim normals, which provide strong structural priors. The pipeline begins with object detection and segmentation using a pretrained video foundation model, followed by the generation of uniformly partitioned grids from depth data. For each grid cell, centroid positions, and surface normals are computed and processed by a transformer-based model that jointly predicts object rotation and translation. Experiments with ten types of tableware demonstrate that the method achieves an average rotational error of 3.53 degrees and a translational error of 13.56 mm. Real-world deployment on a mobile robot platform with a manipulator further validated its ability to autonomously recognize and collect tableware, highlighting the practicality of the proposed geometry-driven approach for service robotics. Full article

Fractional-order chaotic systems have emerged as powerful tools in secure communications and multimedia protection owing to their memory-dependent dynamics, large key spaces, and high sensitivity to initial conditions. However, most existing fractional-order image encryption schemes rely on fixed-order chaos and conventional solvers, which limit their complexity and reduce unpredictability, while also neglecting the potential of variable fractional-order (VFO) dynamics. Although similar phenomena have been reported in some fractional-order systems, the coexistence of hidden attractors and stable equilibria has not been extensively investigated within VFO frameworks. To address these gaps, this paper introduces a novel discrete variable fractional-order dark matter–dark energy (VFODM-DE) chaotic system. The system is discretized using the piecewise constant argument discretization (PWCAD) method, enabling chaos to emerge at significantly lower fractional orders than previously reported. A comprehensive dynamic analysis is performed, revealing rich behaviors such as multistability, symmetry properties, and hidden attractors coexisting with stable equilibria. Leveraging these enhanced chaotic features, a pseudorandom number generator (PRNG) is constructed from the VFODM-DE system and applied to grayscale image encryption through permutation–diffusion operations. Security evaluations demonstrate that the proposed scheme offers a substantially large key space (approximately $2^{249}$) and exceptional key sensitivity. The scheme generates ciphertexts with nearly uniform histograms, extremely low pixel correlation coefficients (less than 0.04), and high information entropy values (close to 8 bits). Moreover, it demonstrates strong resilience against differential attacks, achieving average NPCR and UACI values of about 99.6% and 33.46%, respectively, while maintaining robustness under data loss conditions. In addition, the proposed framework achieves a high encryption throughput, reaching an average speed of 647.56 Mbps. These results confirm that combining VFO dynamics with PWCAD enriches the chaotic complexity and provides a powerful framework for developing efficient and robust chaos-based image encryption algorithms. Full article