Search Results (2,150)

Wearable inertial measurement units (IMUs) provide an accessible means of monitoring fatigue-related changes in running biomechanics, yet most existing methods rely on limited feature sets, lack personalization, or fail to generalize across individuals. This study introduces a bioinformatics-inspired stride sequence modeling framework that integrates spectral–entropy features, sample entropy, frequency-domain descriptors, and mixed-effects statistical modeling to detect fatigue using a single lumbar-mounted IMU. Nineteen recreational runners completed non-fatigued and fatigued 400 m runs, from which we extracted stride-level features and evaluated (1) population-level fatigue classification via global leave-one-participant-out (LOPO) models and (2) individualized fatigue detection through supervised participant-specific models and non-fatigued-only anomaly detection. Mixed-effects models revealed robust and multidimensional fatigue effects across key biomechanical features, with large standardized effect sizes (Cohen’s d up to 1.35) and substantial variance uniquely explained by fatigue (partial R² up to 0.31). Global LOPO machine learning models achieved modest accuracy (55%), highlighting strong inter-individual variability. In contrast, personalized supervised Random Forest classifiers achieved near-perfect performance (mean accuracy 97.7%; mean AUC 0.997), and NF-only One-Class SVMs detected fatigue as a deviation from individual baseline patterns (mean AUC 0.967). Entropy and stride-to-stride variability metrics further demonstrated consistent fatigue-linked increases in movement irregularity and reduced neuromuscular control. These findings show that IMU stride sequences contain highly informative, fatigue-sensitive biomechanical signatures, and that combining bioinformatics-inspired sequence analysis with hybrid statistical and personalized AI models enables both robust population-level insights and highly reliable individualized fatigue monitoring. The proposed framework supports future integration into sports analytics platforms, digital coaching systems, and real-time wearable fatigue detection technologies. This highlights the necessity of personalized fatigue-monitoring strategies in wearable systems. Full article

Background/Objectives: Anxiety disorders (ADs) affect up to 20% of mothers in the postpartum period, characterized by psychological symptoms (e.g., emotion dysregulation; ER) and physical symptoms (e.g., disrupted bodily awareness). Although Cognitive Behavioural Therapy effectively reduces anxiety and mood symptoms, it shows limited efficacy in addressing ER difficulties and rarely targets interoceptive dysfunction—both common in postpartum ADs. This study evaluates the effectiveness of a brief mindfulness-based intervention in improving anxiety, ER, and interoception in mothers with postpartum ADs. A secondary aim is to examine changes in brain connectivity associated with these domains. Methods: This protocol describes a proof-of-concept randomized controlled trial involving 50 postpartum mothers with ADs. Participants will be randomized to receive either a 4-week mindfulness intervention plus treatment-as-usual (TAU) or TAU alone. Participants in the mindfulness + TAU group will complete a virtual 4-week group intervention adapted from Mindfulness-Based Cognitive Therapy. The TAU group will receive usual care for 4 weeks and then be offered the mindfulness intervention. Self-report measures of anxiety, ER, and interoception will be collected at baseline, post-intervention, and at a 3-month follow-up. Resting-state functional MRI will be conducted at baseline and post-intervention to assess functional connectivity changes. This trial has been registered on ClinicalTrials.gov (NCT07262801). Results: Improvements in anxiety, ER, and interoception are anticipated, along with decreased default mode network, and increased salience network connectivity post-intervention is hypothesized. Conclusions: This study will be the first to examine the combined psychological and neural effects of mindfulness in postpartum ADs, offering a potentially scalable mind–body treatment. Full article

The flexible positioning platform is a critical structural component in the ultra-high acceleration macro–micro motion platform, enabling precise positioning across multiple scales. However, under high-frequency start–stop cycles and prolonged multi-condition operation, it is prone to fatigue damage induced by random vibrations, which poses a threat to system reliability. This study proposes a method for evaluating and optimizing the platform’s performance under random vibration based on power spectral density (PSD) analysis. In accordance with the IEC 60068-2-64 standard, representative load spectra from Tables A.8 and A.6 were selected as excitation inputs. Frequency-domain analyses of stress, strain, and displacement were conducted using ANSYS Workbench 2022R1 in conjunction with the nCode platform, incorporating the Gaussian three-sigma probability interval. The results reveal that stress and deformation are highly concentrated in the hinge region, indicating a structural vulnerability. Fatigue life predictions were carried out using the Dirlik method and Miner’s linear damage rule under various PSD loading conditions. The findings demonstrate that hinge stiffness is a key factor influencing vibration resistance and service life. This research provides theoretical support for the design optimization of flexible structures operating in complex random vibration environments. Full article

Background: Irisin, an exercise-induced myokine, has emerged as a potent neuroprotective factor, though a systematic synthesis of its role across neurological disorders is lacking. This review systematically evaluates clinical and preclinical evidence on irisin’s association with neurological diseases and its underlying mechanisms. Methods: Following PRISMA 2020 guidelines, a systematic search of PubMed/MEDLINE, Scopus, Web of Science, Embase, and Cochrane Library was conducted. The review protocol was prospectively registered in PROSPERO. Twenty-one studies were included, comprising predominantly preclinical evidence (n = 14), alongside clinical observational studies (n = 6), and a single randomized controlled trial (RCT) investigating irisin in cerebrovascular diseases, Parkinson’s disease (PD), Alzheimer’s disease (AD), and other neurological conditions. Eligible studies were original English-language research on irisin or FNDC5 and their neuroprotective effects, excluding reviews and studies without direct neuronal outcomes. Risk of bias was independently assessed using SYRCLE, the Newcastle–Ottawa Scale, and RoB 2, where disagreements between reviewers were resolved through discussion and consensus. Results were synthesized narratively, integrating mechanistic, pre-clinical, and clinical evidence to highlight consistent neuroprotective patterns of irisin across disease categories. Results: Clinical studies consistently demonstrated that reduced circulating irisin levels predict poorer outcomes. Lower serum irisin was associated with worse functional recovery and post-stroke depression after ischemic stroke, while decreased plasma irisin in PD correlated with greater motor severity, higher α-synuclein, and reduced dopamine uptake. In AD, cerebrospinal fluid irisin levels were significantly correlated with global cognitive efficiency and specific domain performance, and correlation analyses within studies suggested a closer association with amyloid-β pathology than with markers of general neurodegeneration. However, diagnostic accuracy metrics (e.g., AUC, sensitivity, specificity) for irisin as a standalone biomarker are not yet established. Preclinical findings revealed that irisin exerts neuroprotection through multiple mechanisms: modulating microglial polarization from pro-inflammatory M1 to anti-inflammatory M2 phenotype, suppressing NLRP3 inflammasome activation, enhancing autophagy, activating integrin αVβ5/AMPK/SIRT1 signaling, improving mitochondrial function, and reducing neuronal apoptosis. Irisin administration improved outcomes across models of stroke, PD, AD, postoperative cognitive dysfunction, and epilepsy. Conclusions: Irisin represents a critical mediator linking exercise to brain health, with consistent neuroprotective effects across diverse neurological conditions. Its dual ability to combat neuroinflammation and directly protect neurons, demonstrated in preclinical models, positions it as a promising therapeutic candidate for future investigation. Future research must prioritize the resolution of fundamental methodological challenges in irisin measurement, alongside investigating pharmacokinetics and sex-specific effects, to advance irisin toward rigorous clinical evaluation. Full article

Ericksonian hypnotherapy (EH), a client-centered hypnotic approach characterized by indirect suggestion, individualized flexibility, collaboration, and the principle of Utilization, has seen increased interest as a therapeutic modality across diverse clinical settings. This systematic review and meta-analysis aimed to evaluate the efficacy of EH by synthesizing evidence from randomized controlled trials (RCTs) published between 2015 and 2025. Eight eligible RCTs (N = 676) were identified, spanning conditions such as acute pain, depression, grief, irritable bowel syndrome, disordered eating, and alcohol use. EH interventions consistently produced significant symptom reductions compared to waitlists or standard care, with a pooled standardized mean difference of 1.17 (95% CI: 0.70–1.64), indicating a large effect. Moreover, trials comparing EH to active treatments (e.g., CBT, motivational interviewing) revealed comparable efficacy, with pooled estimates supporting non-inferiority. Sensitivity analyses confirmed the robustness of these findings. Notably, some trials suggested that the indirect and personalized nature of EH may confer advantages in domains like grief and hypervigilance. Although evidence remains limited by sample size and heterogeneity, this review provides initial empirical support for EH and supports its inclusion in the evidence-based repertoire for both physical and psychological conditions. Future research should examine mechanisms of change and individual predictors of response to optimize the use of this distinctive hypnotic style. Full article

Speed prediction is fundamental to optimizing energy management strategies. Common evaluation metrics such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) focus primarily on the numerical deviation between predicted and actual speeds. However, when applied to hybrid vehicle energy management strategy optimization, speed prediction models based on these metrics show a random deviation between energy consumption results and the theoretical optimal, indicating that these metrics are not effective in this application domain. To explore a more effective method for evaluating the practical application of speed prediction curves, this study uses multiple metrics to assess numerous speed prediction curves and analyses the correlation between each metric and the deviation from the optimal energy consumption during energy management strategy optimization. The results show that considering acceleration is more aligned with the needs of energy management strategy optimization than merely evaluating the proximity of speed values. Specifically, the standard deviation of the acceleration time ratio deviation performs better than traditional metrics like RMSE and MAE in distinguishing the effectiveness of speed prediction curves. The smaller the standard deviation of the acceleration time ratio deviation between the predicted and actual speed curves, the closer the energy consumption results of energy management based on the predicted speed curve are to the theoretical optimal. Full article

Background/Objectives: Colorectal cancer (CRC) is associated with anorexia–cachexia syndrome, which negatively affects health-related quality of life (HRQoL). This study aimed to evaluate HRQoL and functional status in CRC patients undergoing chemotherapy who were eligible for oral nutritional supplementation (ONS). Methods: In this prospective, randomized study, 72 patients with stage II–IV CRC were enrolled (40 intervention group [IG], 32 control group [CG]). IG received ONS (2 × 125 mL/day, 600 kcal, 36 g protein) for 12 weeks, while CG received dietary counseling only. HRQoL was assessed every 4 weeks with the Functional Assessment of Anorexia/Cachexia Therapy (FAACT, version 4.0). Functional status was evaluated with the Karnofsky scale. Nutritional status was assessed using the Subjective Global Assessment (SGA), Nutritional Risk Screening (NRS-2002), and body mass index (BMI), and appetite was assessed on a visual analogue scale (VAS). Clinical Trial Registration: ClinicalTrials.gov, NCT02848807. Results: Mean FAACT score did not differ significantly between groups over 12 weeks (101.0 ± 22.8, 95% CI: 94.6–107.4 vs. 105.1 ± 21.4, 95% CI: 99.1–111.1; p = 0.06). However, the observed difference corresponded to an effect size at the lower bound of the moderate range. However, minimally important difference (MID) analysis demonstrated that clinically meaningful improvement was significantly more frequent in IG than in CG for global FAACT (32% vs. 8%; p = 0.03, OR = 5.50, 95% CI: 1.10–27.62, φ = 0.29), physical well-being (32% vs. 8%; p = 0.03, OR = 5.50, 95% CI: 1.10–27.62, φ = 0.29), and emotional well-being (38% vs. 4%; p = 0.002, OR = 14.86, 95% CI: 1.79–123.36, φ = 0.40). Functional well-being and anorexia/cachexia concerns showed favorable, but nonsignificant, trends (FWB improvement: 29% vs. 8%, p = 0.05, OR = 4.79, 95% CI: 0.95–24.27, φ = 0.26; ACS deterioration: 3% vs. 20%, p = 0.07, OR = 0.12, 95% CI: 0.01–1.11, φ = 0.28). HRQoL correlated positively with nutritional status, appetite, and functional performance, while Karnofsky scores remained stable in both groups. Conclusions: ONS did not significantly change the mean QoL scores at the group level but increased the proportion of patients achieving clinically meaningful improvement, particularly in the physical and emotional domains. These findings suggest that ONS may benefit selected patients who respond to nutritional interventions, underscoring the clinical relevance of individualized nutrition strategies in oncology. Full article

This paper proposes a grating Moiré fringe signal compensation method based on Variational Mode Decomposition (VMD) to address signal errors in grating encoders. VMD decomposes Moiré fringe signals into multiple amplitude-modulated and frequency-modulated components, and realizes noise compensation through parameter optimization and signal reconstruction. The Multi-Strategy Particle Swarm Optimization (MSPSO) enhances optimization performance via adaptive inertia weight adjustment and chaotic perturbation, solving the problems of mode mixing or over-decomposition caused by blind parameter selection in traditional VMD. A hardware-software co-design test system based on ZYNQ FPGA is developed, which optimally allocates tasks between the Processing System and Programmable Logic, resolving issues of large data volume and long computation time in traditional systems. The compensation scheme provides excellent signal processing performance. The experimental tests on random periodic signals, triangular waves and square waves with different duty cycles have demonstrated the robustness of this scheme. After compensation, the output signal exhibits excellent sinuosity and orthogonality, with harmonic components and noise in the frequency domain almost negligible. It provides a practical solution for high-precision measurement in ultra-precision machining, semiconductor manufacturing, and automated control. Full article

During orbital service, precision aerospace equipment is frequently subjected to harsh vibration environments that can significantly affect reliability and service life. Consequently, the development of effective vibration isolation technologies has become a crucial aspect of aerospace structural design. In this study, random vibration theory and frequency-domain analysis methods were employed to investigate the dynamic response characteristics of a symmetrical frustum-shaped metal rubber (FSMR) isolation device under complex operating conditions. The influence of metal rubber density, spring stiffness, and input vibration level on its isolation performance was systematically examined. This work presents the first systematic experimental investigation into the nonlinear dependencies of the performance of a symmetrical frustum-shaped metal rubber isolator on multiple parameters (density, stiffness, excitation level) under random vibration. The test results show that under identical excitation conditions, the device achieves optimal damping ratio and isolation efficiency (59.71%) when the metal rubber density is 2.0 g/cm³. A moderate increase in spring stiffness reduces the resonance peak and improves stability, with a stiffness of 100 kN/m exhibiting the best overall performance. In addition, higher input vibration levels markedly elevate the acceleration response and the resonant peak amplification factor of the isolator, demonstrating that high-intensity excitation magnifies the vibration response and degrades the isolation efficiency. Full article

Accurate prediction of sensor network data in critical domains such as electric power systems and traffic planning is a core task for ensuring grid stability and enhancing urban operational efficiency. Although deep learning models have achieved significant architectural advancements, their training strategy implicitly assumes that all future events are equally predictable, ignoring that the future evolution of sensor signals intertwines deterministic patterns with stochastic events and that prediction difficulty increases with temporal distance. Forcing a model to fit inherently unpredictable events with a uniform supervision may impair its ability to learn generalizable patterns. To address this, we introduce an Unpredictability Perception loss that dynamically computes a supervision weight. The computation of this weight unifies two assessment dimensions of the intrinsic unpredictability of the forecasting task. The first originates from a posterior analysis of the signal content’s randomness, while the second stems from an a priori consideration of temporal distance. The first dimension, through a complexity-aware weight derived from local spectral entropy, reduces supervision on random segments of the signal. The second dimension, through a temporal decay weight based on exponential decay, lessens supervision for distant future points. Applied to the advanced TimeMixer model, experimental results show that our approach achieves performance improvements across multiple public benchmark datasets. By matching the supervision strength to the intrinsic predictability of the signals, our proposed Unpredictability Perception loss function enhances the forecasting accuracy for sensor network data, providing a more reliable technical foundation for ensuring the stability of critical infrastructures like power grids and optimizing urban traffic systems. Full article

Background: Burnout, a syndrome of emotional exhaustion, cynicism, and reduced personal accomplishment, is a significant concern among dental students because of the intense demands of their academic and clinical training. This study aimed to determine the prevalence of burnout and its related dimensions among dental students at King Khalid University, Abha, Saudi Arabia. Methods: A cross-sectional study was conducted among 300 dental students (147 males, 153 females) from the 4th year to the internship level, selected via simple random sampling. A 12-item survey called the Burnout Clinical Subtype Questionnaire-12-Student Survey (BCSQ-12-SS) was validated for use with students. Burnout was assessed across three domains—Overload, Lack of Development, and Neglect. Descriptive statistics, Mann–Whitney U tests, and Kruskal–Wallis analyses were employed to explore gender- and year-based differences. Results: Overload and Lack of Development were the most prominent burnout dimensions, with more than half of participants reporting excessive academic pressure, personal sacrifices, and dissatisfaction with developmental opportunities. Neglect demonstrated the lowest prevalence. Female students exhibited significantly higher total burnout scores (p = 0.005). Burnout varied across academic years, peaking among fourth-year students (p < 0.001). Internal consistency for all domains was acceptable to excellent (α = 0.62–0.89). Conclusions: Burnout is highly prevalent, particularly in the domains of Overload and Lack of Development. Female and mid-program students represent high-risk groups. Institutional reforms, curricular enhancement, workload redistribution, structured support systems, and early mental-health interventions are crucial to mitigate burnout and promote student well-being. Full article

In the context of the convergence of multiple energy systems, the risk of power quality degradation across different stages of energy generation and distribution has become increasingly significant. Accurate identification of power quality disturbances is crucial for improving power quality and ensuring the stable operation of power grids. However, existing disturbance identification methods struggle to balance accuracy and computational efficiency, limiting their applicability in real-time monitoring scenarios. To address this issue, this paper proposes a novel disturbance recognition framework called ST-mRMR-RF. The method first applies the S-transform to convert the time-domain signal into the time-frequency domain. It then extracts spectrum, low-frequency, mid-frequency, and high-frequency components as frequency-domain features from this domain. These are fused with time-domain features to form a multi-scale feature set. To reduce feature redundancy, the Maximum Relevance Minimum Redundancy (mRMR) algorithm is applied to select the optimal feature subset, ensuring maximum category relevance and minimal redundancy. Based on this foundation, four classifiers—Random Forest (RF), Partial Least Squares (PLS), Extreme Learning Machine (ELM), and Convolutional Neural Network (CNN)—are employed for disturbance identification. Experimental results show that the feature subset selected via mRMR reduces the model’s training time by 88.91%. When tested in a white noise environment containing 21 types of power quality disturbance signals, the ST-mRMR-RF method achieves a recognition accuracy of 99.24% at a 20dB signal-to-noise ratio. Overall, this framework demonstrates outstanding performance in noise resistance, classification accuracy, and computational efficiency. Full article

Background: SGLT2 inhibitors are key therapies in heart failure (HF), but their combined multidomain effects have not been analyzed together. Methods: We conducted a PROSPERO-registered (CRD420251235850) systematic review and meta-analysis of all randomized controlled trials (RCTs) comparing SGLT2i (dapagliflozin, empagliflozin, canagliflozin, sotagliflozin) with placebo in adults with HF, regardless of ejection fraction or diabetes status. We searched PubMed/MEDLINE, Embase, Cochrane CENTRAL, and Web of Science through 1 February 2025. Outcomes were grouped into four domains: (1) clinical events, (2) symptoms/health status (Kansas City Cardiomyopathy Questionnaire, KCCQ), (3) functional capacity (6 min walk distance, peak VO₂), and (4) cardiac remodeling/energetics (cardiac MRI, 31P-MRS). We used random-effects models with Hartung–Knapp adjustment and assessed heterogeneity by I² and prediction intervals. Results: Eleven RCTs with 23,812 patients (HFrEF, HFmrEF, HFpEF, and acute or recently decompensated HF) were included. SGLT2i lowered the risk of cardiovascular death or first HF hospitalization by 23% (HR 0.77, 95% CI 0.72–0.82; p < 0.0001; I² = 28%; prediction interval 0.68–0.87), with similar effects across ejection fraction, diabetes status, and presentation type. All-cause and cardiovascular mortality dropped by 12% (HR 0.88, 95% CI 0.81–0.96) and 14% (HR 0.86, 95% CI 0.78–0.95), respectively. SGLT2i improved KCCQ—Clinical Summary Score by 4.6 points (95% CI 3.4–5.8; p < 0.0001) and increased the odds of a ≥5-point improvement (OR 1.49, 95% CI 1.32–1.68; NNT = 12). Six-minute walk distance increased by 21.8 m (95% CI 9.4–34.2; p = 0.001), and mechanistic trials showed significant reverse remodeling (ΔLVEDV −19.8 mL, ΔLVEF +6.1%; both p < 0.001). No improvement was observed in myocardial PCr/ATP ratio. Safety was favorable, with no excess ketoacidosis or severe hypoglycemia. Conclusions: This multidomain synthesis demonstrates that SGLT2 inhibitors provide consistent, robust reductions in mortality and hospitalizations, while also delivering early, clinically meaningful improvements across multiple patient-centered domains. These results establish SGLT2i as a foundational component of contemporary HF management. Full article

The present research demonstrates the utility of the linear canonical transform (LCT) in constructing the characteristic function of real random variables. We refer to this construction as the linear canonical characteristic function (LCCF). The proposed LCCF aims to address the limitations of the classical characteristic function in both theoretical and applied aspects. Using this approach, we investigate its properties, such as Hermitian symmetry, continuity, convolution, and derivatives, which are generalized forms of the classical characteristic function in the literature. Finally, we implement the obtained results by calculating several probability density functions in the LCCF domains. Full article

Pavement macrotexture, quantified by mean texture depth (MTD) and mean profile depth (MPD), is a critical parameter for road safety and performance. The traditional sand patch test is labor-intensive and slow, creating a bottleneck for modern pavement management systems. Accurately translating the rich point cloud data into reliable MTD values using the 3D scanning method remains a challenge, with current methods often relying on oversimplified correlations. This research addresses this gap by developing and validating a novel machine learning framework to predict MTD and MPD directly from high-resolution 3D laser scans. A comprehensive dataset of 127 pavement samples was created, combining traditional sand patch measurements with detailed 3D point clouds. From these point clouds, 27 distinct surface features spanning statistical, spatial, spectral, and geometric domains were developed. Six machine learning algorithms, consisting of Random Forest, Gradient Boosting, Support Vector Regression, k-Nearest Neighbor, Artificial Neural Networks, and Linear Regression, were implemented. The results demonstrate that the ensemble-based Random Forest model achieved superior performance, predicting MTD with an R² of 0.941 and a mean absolute error (MAE) of 0.067 mm, representing a 56% improvement in accuracy over traditional digital correlation methods. Model interpretation via SHAP analysis identified root mean square height (Sq) and surface skewness (Ssk) as the most influential features. Full article