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Evaluation of the quality of rural human settlement in Yunnan-Guizhou-Guangxi adjoining areas and study on pluralistic governance
A neural poly-vector based non-orthogonal frame field generation method for quad meshing
Assessing the feasibility of near-infrared spectroscopy for evaluating physiological exercise thresholds
Abstract To examine the feasibility of using near-infrared spectroscopy (NIRS) for physiological threshold detection and whether NIRS-derived parameters differ between highly-trained and less-trained cyclists. Twenty-seven male cyclists were divided into: highly trained endurance cyclists (EA) and recreational cyclists (RA). Participants performed a step-incremental cycling test to exhaustion. Ventilatory thresholds (VT1 and VT2) were determined using gas-exchange variables. NIRS sensor was placed on the vastus lateralis muscle to identify breakpoints corresponding to ventilatory thresholds. No significant differences were observed between NIRS-derived thresholds, compared to VT1 and VT2 (F = 1.04–1.33, p = 0.26–0.36). Moderate to strong correlations were found between NIRS-derived thresholds and ventilatory thresholds (r = 0.65–0.9, p < 0.01). A moderate correlation was found between maximal oxygen uptake and minimal tissue saturation index (TSI) value during the test (r = − 0.411, p = 0.037). EA group showed tendency towards lower minimal TSI values compared to RA group (MD = 5.46% TSI, p = 0.081). NIRS is a feasible tool for non-invasive assessment of ventilatory thresholds during incremental exercise. TSI, in particular, showed lower variability compared to other NIRS-derived parameters, and may therefore be more suitable for practical applications in sport science. Highly trained athletes demonstrated distinct physiological responses compared to recreational athletes, suggesting enhanced peripheral oxygen extraction.
A spatial decision making framework using neutrosophic VIKOR for wind energy investment in Turkey
Abstract The growing demand for clean energy and the urgency of reducing carbon emissions have made wind power a key element of Turkey’s renewable energy strategy. However, identifying optimal regions for wind energy investment remains a complex task due to the interplay of technical, spatial, and economic factors, all of which are characterized by varying degrees of uncertainty. Although GIS-based site selection and multi-criteria decision-making (MCDM) methods are widely used, few approaches integrate expert judgment and spatial analysis within an uncertainty-aware national planning framework. This study proposes a novel investment prioritization model that combines Geographic Information Systems (GIS) with the Neutrosophic-VIKOR method to assess regional wind energy potential in Turkey. The model considers five core criteria: wind potential, land cost, energy consumption based on population density, presence of existing wind farms, and expert judgment. Expert input is represented using Single-Valued Neutrosophic linguistic scales. A similarity-based weighting method is used to determine the relative influence of each expert. The resulting Priority Index (PI) highlights Balıkesir, Çanakkale, and İzmir as the top three investment regions due to their wind characteristics and energy demand. Istanbul and Samsun also rank highly, supported by existing infrastructure and consumption levels. The proposed framework offers a replicable, uncertainty-aware tool for supporting national wind energy planning. By combining expert-based neutrosophic modeling with spatial analysis, the study addresses existing methodological gaps and provides actionable insights for investors and policymakers pursuing efficient and balanced renewable energy development.
Assessing pesticide handling practices and predictors among farm workers in Awi zonal administration using health belief model
Nomogram for predicting the success rate of sedation with intranasal dexmedetomidine in paediatric nonpainful diagnostic procedures: a retrospective study
Research on temperature prediction method for rail transit train inverters based on spatial and timing improving Transformer
Abstract Inverter overheating is a critical fault factor in rail transit systems. To address the challenges of sparse low-voltage data and high-dimensional input features, we propose a hybrid prediction framework for inverter temperature. The Random Masked Dual DCGAN (RTDG) model is introduced to enhance low-voltage data diversity, while a Gaussian Markov Random Field (GMRF) method performs dimensionality reduction by identifying key variables. To capture spatio-temporal dependencies, an enhanced Transformer architecture (STTr) is constructed, integrating state space modeling and temporal normalization. These components are fused using a weighted stacking strategy. The model is trained and validated on real-world rail transit datasets. Performance is evaluated using MSE, RMSE, and MAE metrics. Experimental results show that the proposed model outperforms conventional approaches, achieving a 4.93% improvement over single models and a 9.73% gain compared to non-augmented training. This framework supports intelligent fault prevention and contributes to the safe, efficient operation of modern rail systems.
Mimed speech as an intermediary state between overt and imagined speech production in an electrocorticography study
Dietary patterns associated with the new onset of chronic kidney disease using clustering algorithm
Correlation between the number and pattern of lateral pterygoid muscle attachments and pathologic changes of the temporomandibular joint according to Hegab stages based on MRI findings of 510 joints
Abstract The correlation between the lateral pterygoid muscle attachment type to the disc-condyle complex and temporomandibular joint (TMJ) dysfunction has rarely been discussed and remains unclear. The study aimed to assess the correlation between the number and pattern of LPM attachment and the pathologic findings of the temporomandibular joint based on MR imaging findings. The study population comprised consecutive TMD patients. They were included if they had TMD requiring MRI examination for evaluation of internal derangement. Patients with either TMJ clicking, TMJ locking, restricted movement of the jaw, or pain in the TMJ region were included in the study. Patients with rheumatoid arthritis, condylar hyperplasia, and congenital craniofacial syndrome, and those who had undergone previous TMJ surgery were excluded from this study. Variations of the number of heads and the attachment pattern of the LPM to DCC was evaluated using MRI in the oblique sagittal and coronal images. The variation of the LPM heads and attachment patterns was correlated with pathologic changes of the TMJ. The sample size calculation was performed using G*Power version 3.1.9.2. The significance level was set at 0.05. The data were analysed using Instat statistical software (GraphPad Software, Inc., La Jolla, CA). A total of 255 patients (510 joints) were enrolled in the study. Of these, 52 (104 joints) were male and 203 (406 joints) were female, with ages ranging from 18 to 67 (mean age 32.05). Patients with internal derangement of TMJ were included. According to the data obtained from MRI examinations, LPM attachments to the disc condyle complex were categorized into four different types. The most common variation (type II-B) was shown to be two heads with the upper head attached to the disc and condyle, and the lower to the condyle. There was a statistical correlation between the type of LPM attachment and the pathological changes within the joint regarding disc displacement, osteoarthritis, joint effusion, disc degeneration, and condylar translation (P = 0.0003, r = -0.87, P < 0.0001& r = 0.29, P = 0.0002 & r = -0.93, P = 0.0061 & r = -0.98, and P = 0.0004, r = -0.54 respectively). The current study shows a statistically significant direct correlation between LPM attachment and TMJ osteoarthritis, while the disc-condyle relationship, joint effusion, disc degeneration, and condylar translation shown significant inverse correlations with LPM attachment patterns.
Elucidation of the treatment mechanism of pulsed radiofrequency based on its antiinflammatory effects
Effect and acceptability of different exercise modes on adult patients with clinically diagnosed depression: a network meta-analysis
Red blood cell distribution width and mortality in chronic obstructive pulmonary disease patients with acute respiratory failure: a retrospective study
A study on intuitionistic fuzzy generating function using T-Norm, T-Conorm operators to enhance night-time images for autonomous driving system
Abstract Enhancing night-time images is crucial for improving the performance of autonomous driving systems, which rely on high-quality visual input for accurate decision-making. This study explores the application of intuitionistic fuzzy generator in combination with T-Norm and T-Conorm operators to enhance low-visibility night-time images. Unlike traditional image processing methods, intuitionistic fuzzy set (IFS) incorporates both the degree of belonging and non-belonging aspects of an image allowing for a more detailed representation of uncertainty in image enhancement. The proposed method analyzes various aggregation operators, out of which Einstein’s T-Norm, Hamacher’s T-Conorm, Weber’s T-Conorm and W-probabilistic T-Conorm operators refine contrast, suppress noise and enhance illumination while preserving critical visual details. Extensive experiments on night-time driving datasets in contrast with existing state-of-the-art methods demonstrate that the recommended approach significantly improves image clarity via standard image quality metrics like SSIM, PSNR and correlation coefficient. Additionally, a sensitivity analysis conducted to assess the robustness and stability of the IFS components and aggregation operators with respect to the parameter $$\Upsilon$$ in validating its effectiveness in diverse low-light conditions. The findings indicate that integrating IFS with some particular T-Norm and T-Conorm operations is an innovative strategy to improve the autonomous vehicle’s perception in low-light conditions.
The masking effects of self-efficacy and marital satisfaction on the association between childhood trauma and postpartum depressive symptoms: a cross-sectional study
Unsupervised electric signal separation for linking behavior and electrocommunication in Gnathonemus petersii
Abstract The transfer of information between individuals is fundamental to living systems and requires comprehensive research in various species. Weakly electric fish, Gnathonemus petersii, provides a unique model organism for such investigations due to its advanced electrocommunication via electric organ discharges (EODs). As separating EODs from multiple individuals remains challenging, we developed an unsupervised approach for EOD separation in two free-swimming individuals. Using continuous wavelet transform, t-distributed Stochastic Neighbor Embedding, and hierarchical clustering, we achieved accurate discrimination of EODs without the necessity of any training data. This approach overcomes the supervised algorithms based on previously published methods in accuracy and computational efficiency, simplifies experimental procedures, and supports animal well-being by reducing the number of required measurements. We applied our separation approach in a dyadic fish model, where ketamine was used to induce schizophrenia-like behavior in one fish. We confirmed the ketamine-induced alteration of the intrinsic relationship between locomotion and EOD signaling. Moreover, while ketamine-induced changes in locomotion were socially transferred, correlated changes in EOD signaling were not observed between dyad members, which may be interpreted as a communication deficit. Additionally, we introduced two techniques for EOD sonification, facilitating exploratory analysis of EOD sequences. These advancements lay the groundwork for future studies of EOD-based communication, highlighting the potential of Gnathonemus petersii in neuroethological, psychopharmacological, and translational research.