Search Results (3,003)

Objective: This study aimed to investigate whether the subjective assessments of strong and weak muscles in the Functional Myodiagnosis muscle test (FMD-MT) can be objectively and reproducibly verified using physically measurable parameters. Additionally, we sought to evaluate the reliability of the manual muscle test in order to reinforce the scientific evidence supporting this accepted, yet not widely adopted, complementary medicine method. Methods: In a crossover observational study, three experienced medical practitioners conducted the FMD-MT of the rectus femoris muscle on 24 healthy participants using a specially designed therapy bench, with all measurements recorded via an oscillogram. The study investigated the force–time integral, joint angle change, additional force load, mean force turning point 1, as well as the interrater reliability and validity of both examiner assessments and instrumental analyses for the two muscle reaction variants: strong and weak. Results: A significant difference between the response pattern of strong and weak muscles was identified for the force–time integral (p = 0.005), the change in joint angle (p < 0.001), and the additional force load (p = 0.001). No difference between strong and weak muscles could be detected regarding the force turning point 1 (p = 0.972). The examiners demonstrated 100% accuracy in identifying weak muscle reactions as weak, and 99.2% accuracy in identifying strong muscle reactions as strong (p = 0.316). The overall intra-class correlation coefficient was 0.984. The oscillogram correctly visualized weak muscle reactions in weak muscles with an accuracy of 81.7%, and strong muscle reactions in strong muscles with an accuracy of 86.7% (p = 0.289). Conclusions: The Functional Myodiagnosis muscle test (FMD-MT) enables a clear and objective differentiation between strong and weak muscles, with statistically significant differences observed in the force–time integral, additional force load, and joint angle changes. Under rigorously standardized testing conditions, the FMD-MT of the rectus femoris muscle demonstrates a validity rate of 99.6% and an excellent reliability (ICC 0.984). Consequently, the FMD muscle test proves to be a reliable, reproducible, and objective diagnostic method. Trial registration: German Register of Clinical Studies U1111-1212-6622. Full article

As large language models (LLMs) increasingly enter specialized domains, inference without external resources often leads to knowledge gaps, opaque reasoning, and hallucinations. To address these challenges in power equipment defect grading, we propose a zero-shot question-answering framework that requires no task-specific examples. Our system performs two-stage retrieval—first using a Sentence-BERT model fine-tuned on power equipment maintenance texts for coarse filtering, then combining TF-IDF and semantic re-ranking for fine-grained selection of the most relevant knowledge snippets. We embed both the user query and the retrieved evidence into a Chain-of-Thought (CoT) prompt, guiding the pre-trained LLM through multi-step reasoning with self-validation and without any model fine-tuning. Experimental results show that on a held-out test set of 218 inspection records, our method achieves a grading accuracy of 54.2%, which is 6.0 percentage points higher than the fine-tuned BERT baseline at 48.2%; an Explanation Coherence Score (ECS) of 4.2 compared to 3.1 for the baseline; a mean retrieval latency of 28.3 ms; and an average LLM inference time of 5.46 s. Ablation and sensitivity analyses demonstrate that a fine-stage retrieval pool size of k = 30 offers the optimal trade-off between accuracy and latency; human expert evaluation by six senior engineers yields average Usefulness and Trustworthiness scores of 4.1 and 4.3, respectively. Case studies across representative defect scenarios further highlight the system’s robust zero-shot performance. Full article

The a priori procedure (APP) is concerned with determining appropriate sample sizes to ensure that sample statistics to be obtained are likely to be good estimators of corresponding population parameters. Previous researchers have shown how to compute a priori confidence interval means or locations for normal and skew-normal distributions. However, two critical limitations persist in the literature: (1) While numerous skewed models have been proposed, the APP equations for location parameters have only been formally established for the basic skew-normal distributions. (2) Even within this fundamental framework, the APPs for sample size determinations in estimating locations are constructed on samples of specifically dependent observations having multivariate skew-normal distributions jointly. Our work addresses these limitations by extending a priori reasoning to the more comprehensive unified skew-normal (SUN) distribution. The SUN family not only encompasses multiple existing skew-normal models as special cases but also enables broader practical applications through its capacity to model mixed skewness patterns and diverse tail behaviors. In this paper, we establish APP equations for determining the required sample sizes and set up confidence intervals for the location parameter in the one-sample case, as well as for the difference in locations in matched pairs and two independent samples, assuming independent observations from the SUN family. This extension addresses a critical gap in the literature and offers a valuable contribution to the field. Simulation studies support the equations presented, and two applications involve real data sets for illustrations of our main results. Full article

Accurate and timely estimation of soybean emergence at the plot scale using unmanned aerial vehicle (UAV) remote sensing imagery is essential for germplasm evaluation in breeding programs, where breeders prioritize overall plot-scale emergence rates over subimage-based counts. This study proposes PlotCounter, a deep learning regression model based on the TasselNetV2++ architecture, designed for plot-scale soybean seedling counting. It employs a patch-based training strategy combined with full-plot validation to achieve reliable performance with limited breeding plot data. To incorporate additional agronomic information, PlotCounter is extended into a multitask learning framework (MTL-PlotCounter) that integrates sowing metadata such as variety, number of seeds per hole, and sowing density as auxiliary classification tasks. RGB images of 54 breeding plots were captured in 2023 using a DJI Mavic 2 Pro UAV and processed into an orthomosaic for model development and evaluation, showing effective performance. PlotCounter achieves a root mean square error (RMSE) of 6.98 and a relative RMSE (rRMSE) of 6.93%. The variety-integrated MTL-PlotCounter, V-MTL-PlotCounter, performs the best, with relative reductions of 8.74% in RMSE and 3.03% in rRMSE compared to PlotCounter, and outperforms representative YOLO-based models. Additionally, both PlotCounter and V-MTL-PlotCounter are deployed on a web-based platform, enabling users to upload images via an interactive interface, automatically count seedlings, and analyze plot-scale emergence, powered by a multimodal large language model. This study highlights the potential of integrating UAV remote sensing, agronomic metadata, specialized deep learning models, and multimodal large language models for advanced crop monitoring. Full article

Glioblastoma (GBM) is a highly aggressive and heterogeneous brain tumor. Glioma stem-like cells (GSCs) play a central role in tumor progression, therapeutic resistance, and recurrence. Although immune cells are known to shape the GBM microenvironment, the impact of antigen-presenting-cell (APC) signature genes on tumor-intrinsic phenotypes remains underexplored. We analyzed both bulk- and single-cell RNA sequencing datasets of GBM to investigate the association between APC gene expression and tumor-cell states, including stemness and metabolic reprogramming. Signature scores were computed using curated gene sets related to APC activity, KEGG metabolic pathways, and cancer hallmark pathways. Protein–protein interaction (PPI) networks were constructed to examine the links between immune regulators and metabolic programs. The high expression of APC-related genes, such as HLA-DRA, CD74, CD80, CD86, and CIITA, was associated with lower stemness signatures and enhanced inflammatory signaling. These APC-high states (mean difference = –0.43, adjusted p < 0.001) also showed a shift in metabolic activity, with decreased oxidative phosphorylation and increased lipid and steroid metabolism. This pattern suggests coordinated changes in immune activity and metabolic status. Furthermore, TNF-α and other inflammatory markers were more highly expressed in the less stem-like tumor cells, indicating a possible role of inflammation in promoting differentiation. Our findings revealed that elevated APC gene signatures are associated with more differentiated and metabolically specialized GBM cell states. These transcriptional features may also reflect greater immunogenicity and inflammation sensitivity. The APC metabolic signature may serve as a useful biomarker to identify GBM subpopulations with reduced stemness and increased immune engagement, offering potential therapeutic implications. Full article

Background/Objectives: Educational background is an aspect of socio-economic status, that may be associated with higher caries risk. This study aimed to investigate differences in caries prevalence between different school types for older adolescents in Greifswald, Germany. Methods: Cross-sectional data were collected as part of compulsory dental school examinations between 2020 and 2023. Oral health status was assessed according to WHO criteria by six calibrated examiners and reported as mean D₃MFT (D₃: dentin caries, M: missing, F: filled, SD/±: standard deviation). To compare educational backgrounds, the adolescents were divided into two groups according to their age and type of school (11–15 and 16–18 years old). Results: The study included 5816 adolescents (48.7% females) with a mean D₃MFT of 0.65 (Q1–Q3: 0–1); 73.8% were clinically caries-free, having D₃MFT = 0, confirming the polarization in caries experience with 2.5 ± 2.13 SaC index. The logistic regression model showed a significantly increased Odds Ratio for having caries in relation to age, being male, having plaque or gingivitis (p < 0.005). There were significant differences in caries experience and prevalence between school types, where high schools had the lowest D₃MFT values in both age groups (0.39 ± 1.17 and 0.64 ± 1.49, respectively). The highest D₃MFT values were in schools for special educational needs in younger adolescents (1.12 ± 1.9) and in vocational schools in older adolescents (1.63 ± 2.55). Conclusions: In a low-caries-risk population, there were significant differences in caries experience and prevalence among adolescents in different school types. Prevention strategies should aim to reduce the polarization in caries across different educational backgrounds in late adolescence. Full article

Health literacy improves informal caregivers’ knowledge and ability to provide care. The aim of this study is to analyze the impact of a group-based intervention on the health literacy of informal caregivers of children with special health needs (SHNs). The intervention focuses on movement, hydrotherapy, walking, and relaxation, with three evaluation stages. Participants included 34 informal caregivers of children with SHNs, recruited at a Physical Medicine and Rehabilitation Service. We collected sociodemographic data of the participants and measured their health literacy through the short-form version of the European Health Literacy Survey Questionnaire (HLS-EU-Q16). Around 70% of the participants were female, with a mean age of 41.06 ± 4.98 years, and nearly 85% were married or in a de facto union. About 26% were unemployed, and more than 65% had completed secondary education or higher. The mean scores of the HLS-EU-Q16 were statistically significantly higher throughout the intervention, with differences over the evaluation stages [F(2,56) = 75.55; p < 0.05]. A structured, dynamic, and group-based intervention plan showed improvements in the health literacy of the participants, with an increase in the percentage of participants with sufficient and excellent levels of health literacy at the end of the intervention. Full article

Timely and accurate diagnosis of gastrointestinal diseases (GIDs) remains a critical bottleneck in clinical endoscopy, particularly due to the limited contrast and sensitivity of conventional white light imaging (WLI) in detecting early-stage mucosal abnormalities. To overcome this, this research presents Spectrum Aided Vision Enhancer (SAVE), an innovative, software-driven framework that transforms standard WLI into high-fidelity hyperspectral imaging (HSI) and simulated narrow-band imaging (NBI) without any hardware modification. SAVE leverages advanced spectral reconstruction techniques, including Macbeth Color Checker-based calibration, principal component analysis (PCA), and multivariate polynomial regression, achieving a root mean square error (RMSE) of 0.056 and structural similarity index (SSIM) exceeding 90%. Trained and validated on the Kvasir v2 dataset (n = 6490) using deep learning models like ResNet-50, ResNet-101, EfficientNet-B2, both EfficientNet-B5 and EfficientNetV2-B0 were used to assess diagnostic performance across six key GI conditions. Results demonstrated that SAVE enhanced imagery and consistently outperformed raw WLI across precision, recall, and F1-score metrics, with EfficientNet-B2 and EfficientNetV2-B0 achieving the highest classification accuracy. Notably, this performance gain was achieved without the need for specialized imaging hardware. These findings highlight SAVE as a transformative solution for augmenting GI diagnostics, with the potential to significantly improve early detection, streamline clinical workflows, and broaden access to advanced imaging especially in resource constrained settings. Full article

To address the wear life prediction challenge of Guide Cones in passive compliant connectors under dynamic loads within specialized equipment, this study proposes a dynamic wear modeling and life assessment method based on the improved Archard model. Through integrated theoretical modeling, finite element simulation, and experimental validation, we establish a bidirectional coupling framework analyzing dynamic contact mechanics and wear evolution. By developing phased contact state identification criteria and geometric constraints, a transient load calculation model is established, revealing dynamic load characteristics with peak contact forces reaching 206.34 N. A dynamic contact stress integration algorithm is proposed by combining Archard’s theory with ABAQUS finite element simulation and ALE adaptive meshing technology, enabling real-time iterative updates of wear morphology and contact stress. This approach constructs an exponential model correlating cumulative wear depth with docking cycles (R² = 0.997). Prototype experiments demonstrate a mean absolute percentage error (MAPE) of 14.6% between simulated and measured wear depths, confirming model validity. With a critical wear threshold of 0.8 mm, the predicted service life reaches 45,270 cycles, meeting 50-year operational requirements (safety margin: 50.9%). This research provides theoretical frameworks and engineering guidelines for wear-resistant design, material selection, and life evaluation in high-reliability automatic docking systems. Full article

Chronic pain (CP) markedly impairs quality of life (QoL) and contributes to physical, psychological, and social dysfunction. In Saudi Arabia, limited research exists on CP and its impact on QoL. This cross-sectional questionnaire-based study conducted at King Abdullah Medical City, Makkah, over 3 months aimed to comparatively assess CP severity and the QoL between individuals who attended specialized pain clinics and those who did not. Data were collected from 250 participants by using a five-part questionnaire, including the RAND 36-Item Health Survey 1.0, to assess QoL. Statistical analysis included descriptive statistics and comparative analysis using the Statistical Package for the Social Sciences v22.0, with p-values of <0.05 considered significant. Most of the participants were female (56%), aged > 18 years (94.4%), Saudi nationals (88%), and married (72%). The most frequently reported pain site was the knee (33.6%). The mean scores for QoL domains were low, especially physical functioning (18.5), emotional well-being (38.4), and social functioning (38.8). Pain severity and poor general health were significantly associated with nationality, educational level, and clinic type. In conclusion, CP profoundly diminished QoL, particularly in terms of physical, emotional, and social aspects. Outcomes were influenced by factors such as educational level, employment, nationality, and clinical care settings. Full article

Films of a Ni/Al-layered double hydroxide intercalated with reduced graphene oxide were deposited, by means of a simple and rapid electrochemical synthesis, on Pt electrodes previously submitted to a special cleaning procedure. The aim of the research was to determine whether the better electrocatalytic properties of the Ni(III)/Ni(II) couple, due to the presence of the carbon nanomaterial, as compared to the Ni/Al-LDH alone, could allow glucose detection at physiological pHs, as normally LDHs work as redox mediators in basic solutions. Chronoamperometric experiments were carried out by applying a potential of 1.0 V vs. SCE to the electrode soaked in solutions buffered at pHs from 5.0 to 9.0 to which glucose was continuously added. The steady-state currents increased as the pH solution increased, but at pH = 7.0 the modified electrode exhibited a fast and rather sensitive response, which was linear up to 10.0 mM glucose, with a sensitivity of 0.56 A M⁻¹ cm⁻² and a limit of detection of 0.05 mM. Our results suggest the potential application of Ni/Al-LDH(ERGO) composite for the non-enzymatic detection of glucose or other oxidizable analytes under biological conditions. Full article

Extreme surface winds and wave heights of tropical cyclones (TCs)—pose serious threats to coastal community, infrastructure and environments. In recent decades, progress in numerical wave modeling has significantly enhanced the ability to reconstruct and predict wave behavior. This review offers an in-depth overview of TC-related wave modeling utilizing different computational schemes, with a special attention to WAVEWATCH III (WW3) and Simulating Waves Nearshore (SWAN). Due to the complex air–sea interactions during TCs, it is challenging to obtain accurate wind input data and optimize the parameterizations. Substantial spatial and temporal variations in water levels and current patterns occurs when coastal circulation is modulated by varying underwater topography. To explore their influence on waves, this study employs a coupled SWAN and Finite-Volume Community Ocean Model (FVCOM) modeling approach. Additionally, the interplay between wave and sea surface temperature (SST) is investigated by incorporating four key wave-induced forcing through breaking and non-breaking waves, radiation stress, and Stokes drift from WW3 into the Stony Brook Parallel Ocean Model (sbPOM). 20 TC events were analyzed to evaluate the performance of the selected parameterizations of external forcings in WW3 and SWAN. Among different nonlinear wave interaction schemes, Generalized Multiple Discrete Interaction Approximation (GMD) Discrete Interaction Approximation (DIA) and the computationally expensive Wave-Ray Tracing (WRT) A refined drag coefficient (C_d) equation, applied within an upgraded ST6 configuration, reduce significant wave height (SWH) prediction errors and the root mean square error (RMSE) for both SWAN and WW3 wave models. Surface currents and sea level variations notably altered the wave energy and wave height distributions, especially in the area with strong TC-induced oceanic current. Finally, coupling four wave-induced forcings into sbPOM enhanced SST simulation by refining heat flux estimates and promoting vertical mixing. Validation against Argo data showed that the updated sbPOM model achieved an RMSE as low as 1.39 m, with correlation coefficients nearing 0.9881. Full article

Maintaining the required relative humidity values in the vehicle cabin is an important HVAC task, along with considerations related to the temperature, velocity, air pressure and noise. Deviation from the optimal values worsens the psycho-physiological state of the driver and affects the energy efficiency of the train. In this study, a model of liquid film formation on and removal from various cabin surfaces was constructed using the fundamental Navier–Stokes hydrodynamic equations. A special transport model based on the liquid vapor diffusion equation was used to simulate the air environment inside the cabin. The evaporation and condensation of surface films were simulated using the Euler film model, which directly considers liquid–gas and gas–liquid transitions. Numerical results were obtained using the RANS equations and a turbulence model by means of the finite volume method in Ansys CFD. Conjugate fields of temperature, velocity and moisture concentration were constructed for various time intervals, and the dependence values for the film thicknesses on various surfaces relative to time were determined. The verification was conducted in comparison with the experimental data, based on the protocol for measuring the microclimate indicators in workplaces, as applied to the train cabin: the average ranges encompassed temperature changes from 11% to 18%, and relative humidity ranges from 16% to 26%. Comparison with the results of other studies, without considering the phase transition and condensation, shows that, for the warm mode, the average air temperature in the cabin with condensation is 12.5% lower than without condensation, which is related to the process of liquid evaporation from the heated walls. The difference in temperature values for the model with and without condensation ranged from −12.5% to +4.9%. We demonstrate that, with an effective mode of removing condensate film from the window surface, including recirculation modes, the energy consumption of the climate control system improves significantly, but this requires a more accurate consideration of thermodynamic parameters and relative humidity. Thus, considering the moisture condensation model reveals that this variable can significantly affect other parameters of the microclimate in cabins: in particular, the temperature. This means that it should be considered in the numerical modeling, along with the basic heat transfer equations. Full article

This study explores the potential of craft-based activities in the context of special education, focusing on a papier mâché sculpting workshop implemented at the Special Kindergarten of Komotini, Greece, as part of the Horizon Europe Craeft project. The initiative aimed to assess how such creative activities could enhance the learning experience of children with intellectual and motor impairments, foster socialization, and develop fine motor skills. With reference to literature in art therapy, craft education, and inclusive pedagogy, the study applied a mixed-methods approach combining observation, visual analysis, and a survey. The findings indicate that, despite varied levels of participation based on individual needs, all students engaged meaningfully with the materials and activities. School professionals observed increased student engagement, emotional comfort, and communication, while also identifying the activity as well adapted and replicable in similar contexts. The results highlight the value of crafts in special education, not only as a sensory and cognitive stimulus but also as a means of fostering inclusion and self-expression. The study concludes with a call for further research into the role of tactile materials and hand gestures in relation to specific impairments. Full article

Bruising in ‘Korla’ pears represents a prevalent phenomenon that leads to progressive fruit decay and substantial economic losses. The detection of early-stage bruising proves challenging due to the absence of visible external characteristics, and existing deep learning models have limitations in weak feature extraction under complex optical interference. To address the postharvest latent damage detection challenges in ‘Korla’ pears, this study proposes a collaborative detection framework integrating structured-illumination reflectance imaging (SIRI) with multi-order gated attention mechanisms. Initially, an SIRI optical system was constructed, employing 150 cycles·m⁻¹ spatial frequency modulation and a three-phase demodulation algorithm to extract subtle interference signal variations, thereby generating RT (Relative Transmission) images with significantly enhanced contrast in subsurface damage regions. To improve the detection accuracy of latent damage areas, the MOGA-UNet model was developed with three key innovations: 1. Integrate the lightweight VGG16 encoder structure into the feature extraction network to improve computational efficiency while retaining details. 2. Add a multi-order gated aggregation module at the end of the encoder to realize the fusion of features at different scales through a special convolution method. 3. Embed the channel attention mechanism in the decoding stage to dynamically enhance the weight of feature channels related to damage. Experimental results demonstrate that the proposed model achieves 94.38% mean Intersection over Union (mIoU) and 97.02% Dice coefficient on RT images, outperforming the baseline UNet model by 2.80% with superior segmentation accuracy and boundary localization capabilities compared with mainstream models. This approach provides an efficient and reliable technical solution for intelligent postharvest agricultural product sorting. Full article