Search Results (2,159)

Linear velocity is commonly used to estimate 1-repetition maximum (1RM) from a load–velocity profile (LVP), as well as prescribe training intensity. However, no study has assessed angular velocity, which may be more representative of joint motion. The purpose of this study was to compare the prediction of 1RM from linear velocity (1RM_linear) and angular velocity (1RM_angular) LVPs in men and women. Fourteen recreationally trained college-aged subjects (7 males, 7 females) completed 1RM testing on day 1, then a randomized submaximal (30–90% 1RM) squat protocol on day 2. Linear velocity was measured with a linear position transducer, while angular velocity was recorded using an accelerometer affixed to the thigh. 1RM_angular was not significantly different from actual 1RM (p = 0.951), with a trivial effect size (d = 0.02), and nearly perfect correlation with actual 1RM (r = 0.984). 1RM_linear had a near perfect correlation with actual 1RM (r = 0.991) but was significantly different than actual 1RM (p < 0.001) with a large effect size (d = 1.56). Additionally, 1RM_angular had a significantly (p = 0.020) lower absolute error (6.7 ± 5.3 kg) than 1RM_linear (12.9 ± 8.2 kg). Regardless of prediction method, males (12.9 ± 8.2 kg) had a greater absolute error in 1RM prediction than females (6.7 ± 5.2 kg). During submaximal loads, a significant load × gender interaction was observed for linear velocity (p < 0.001), with men showing faster velocities at 30% (p = 0.009) and 40% (p = 0.044) 1RM, with no significant interaction (p = 0.304) of main effect of gender (p = 0.116). Angular velocity may provide strength and conditioning coaches a more accurate 1RM prediction during submaximal sets of back squat than using linear velocity; however, neither meet all criteria to be considered highly valid. Lastly, the gender differences in linear velocity at submaximal exercises suggest gender-specific considerations in velocity-based training particularly at lighter loads. Full article

Low-Earth-Orbit Satellite Internet (LEO SI), with its capability for seamless global coverage, is a key solution for connecting IoT devices in areas beyond terrestrial network reach, playing a vital role in building a future ubiquitous IoT system. Inspired by the IEEE 802.15.4 Improved Adaptive Backoff Algorithm (I-ABA), this paper proposes an Attack-Aware Adaptive Backoff Indicator (AA-BI) mechanism to enhance the security and robustness of the two-step random access process in LEO SI. The mechanism constructs a composite threat intensity indicator that incorporates collision probability, Denial-of-Service (DoS) attack strength, and replay attack intensity. This quantified threat level is smoothly mapped to a dynamic backoff window to achieve adaptive backoff adjustment. Simulation results demonstrate that, with 200 pieces of user equipment (UE), the AA-BI mechanism significantly improves the access success rate (ASR) and jamming resistance rate (JRR) under various attack scenarios compared to the I-ABA and Binary Exponential Backoff (BEB) algorithms. Notably, under high-attack conditions, AA-BI improves ASR by up to 25.1% and 56.6% over I-ABA and BEB, respectively. Moreover, under high-load conditions with 800 users, AA-BI still maintains superior performance, achieving an ASR of 0.42 and a JRR of 0.68, thereby effectively ensuring the access performance and reliability of satellite Internet in malicious environments. Full article

With the high penetration of renewable energy integrated into the power grid, the system exhibits strong randomness and volatility. To balance these uncertainties, a large amount of flexible regulating resources is required. This paper proposes an optimization method based on a Seq2Seq Transformer model, which takes stochastic renewable energy and load data as inputs and outputs the allocation ratios of various regulating resources. The method considers renewable energy stochasticity, power flow constraints, and adjustment characteristics of different regulating resources, while constructing a multi-objective loss function that integrates ramping response matching and cost minimization for comprehensive optimization. Furthermore, a multi-feature perception attention mechanism for stochastic renewable energy is introduced, enabling better coordination among resources and improved ramping speed adaptation during both model training and result generation. A multi-solution optimization framework with Pareto-optimal filtering is designed, where the Decoder outputs multiple sets of diverse and balanced allocation ratio combinations. Simulation studies based on a regional power grid demonstrate that the proposed method effectively addresses the problem of regulating resource capacity optimization in new-type power systems. Full article

Early and accurate detection of distributed bearing faults is essential to prevent equipment failures and reduce downtime in industrial environments. This study explores the optimal selection of input signal sources for high-accuracy distributed fault classification, employing wavelet transform and machine learning algorithms. The primary contribution of this work is to demonstrate that robust distributed bearing fault diagnosis can be achieved through optimal sensor fusion and wavelet-based feature engineering, without the need for deep learning or high-dimensional inputs. This approach provides interpretable, computationally efficient, and generalizable fault classification, setting it apart from most existing studies that rely on larger models or more extensive data. All experiments were conducted in a controlled laboratory environment across multiple loads and speeds. A comprehensive dataset, including three-axis vibration, stray magnetic flux, and two-phase current signals, was used to diagnose six distinct bearing fault conditions. The wavelet transform is applied to extract frequency-domain features, capturing intricate fault signatures. To identify the most effective input signal combinations, we systematically evaluated Random Forest, XGBoost, and Support Vector Machine (SVM) models. The analysis reveals that specific signal pairs significantly enhance classification accuracy. Notably, combining vibration signals with stray magnetic flux consistently achieved the highest performance across models, with Random Forest reaching perfect test accuracy (100%) and SVM showing robust results. These findings underscore the importance of optimal source selection and wavelet-transformed features for improving machine learning model performance in bearing fault classification tasks. While the results are promising, validation in real-world industrial settings is needed to fully assess the method’s practical reliability and impact on predictive maintenance systems. Full article

In the context of the new power system, high-current switchgear is prone to various faults due to complex operation environments and severe load fluctuations. Among them, an abnormal temperature rise can lead to contact oxidation, insulation aging, and even equipment failure, posing a serious threat to the safety of the distribution system. The operation risk assessment of high-current switchgear has thus become a key to ensuring the safety of the distribution system. Ensemble learning, which integrates the advantages of multiple models, provides an effective approach for accurate and intelligent risk assessment. However, existing ensemble learning methods have shortcomings in feature extraction, time-series modeling, and generalization ability. Therefore, this paper first preprocesses and reduces the dimensionality of multi-source data, such as historical load and equipment operation status. Secondly, we propose an operation risk assessment method for high-current switchgear based on ensemble learning: in the first layer, an improved random forest (RF) is used to optimize feature extraction; in the second layer, an improved long short-term memory (LSTM) network with an attention mechanism is adopted to capture time-series dependent features; in the third layer, an adaptive back propagation neural network (ABPNN) model fused with an adaptive genetic algorithm is utilized to correct the previous results, improving the stability of the assessment. Simulation results show that in temperature rise prediction, the proposed algorithm significantly improves the goodness-of-fit indicator with increases of 15.4%, 4.9%, and 24.8% compared to three baseline algorithms, respectively. It can accurately assess the operation risk of switchgear, providing technical support for intelligent equipment operation and maintenance, and safe operation of the system. Full article

This study aims to investigate the syntactic complexity in individuals with Alzheimer’s disease (AD) by conducting a comprehensive analysis that incorporates mean dependency distance (MDD), fine-grained grammatical metrics, and dependency network structures. A total of 150 adults with AD and 150 healthy controls (HC) responded in English to interview prompts based on the Cookie Theft picture description task, and the results were compared. The key findings are as follows: (1) The primary syntactic change is a strategic shift from hierarchical, clause-based constructions to linear, phrase-based ones, a direct consequence of working memory deficits designed to minimize cognitive load. (2) This shift is executed via a resource reallocation, where costly, long-distance clausal dependencies are systematically avoided in favor of a compensatory reliance on local dependencies, such as intra-phrasal modification and simple predicate structures. (3) This strategic reallocation leads to a systemic reorganization of the syntactic network, transforming it from a flexible, distributed system into a rigid, centralized one that becomes critically dependent on the over-leveraged structural role of function words to maintain basic connectivity. (4) The overall syntactic profile is the result of a functional balance governed by the principle of cognitive economy, where expressive richness and grammatical depth are sacrificed to preserve core communicative functions. These findings suggest that the syntactic signature of AD is not a random degradation of linguistic competence but a profound and systematic grammatical adaptation, where the entire linguistic system restructures itself to function under the severe constraints of diminished cognitive resources. Full article

Background: Hamstring tendon autografts are frequently used for anterior cruciate ligament reconstruction (ACLR), but they are associated with persistent hamstring strength deficits and delayed functional recovery. Current rehabilitation guidelines often delay open kinetic chain (OKC) hamstring exercises due to safety concerns, despite the limited supporting evidence. This uncontrolled, underpowered, and exploratory study aimed to evaluate the safety and effectiveness of introducing OKC hamstring strengthening exercises as early as three weeks after ACLR. Methods: An exploratory retrospective observational study was conducted at a single physiotherapy center on 13 patients (aged 18–35) who underwent primary ACLR with semitendinosus–gracilis grafts. Participants followed a standardized rehabilitation program including isometric leg curls at 60° and 90° knee flexion and long-lever glute bridges twice weekly, starting from postoperative week 3. Safety was assessed through predefined “safety flags” (pain > 4/10, hematoma, clinical hamstring strain). Strength outcomes, including isometric knee flexion strength at 60° and 90°, limb symmetry index (LSI), and endurance tests, were assessed at 6 and 12 weeks. Results: All participants completed the program without major adverse events. Pain remained consistently low (median 2.5/10), with only one transient episode exceeding the threshold. No other complications were recorded. Isometric knee flexion strength significantly improved between week 6 and week 12 at both 60° (p = 0.018) and 90° (p = 0.003), with large effect sizes. LSI at 90° also increased significantly (p = 0.006), whereas improvements at 60° did not reach significance. Endurance testing showed functional gains as early as 6 weeks. Conclusions: The early introduction of OKC hamstring strengthening exercises three weeks after ACLR with hamstring autografts appears safe and promotes clinically meaningful improvements in strength and endurance. These findings, while from a small uncontrolled study, challenge conservative rehabilitation protocols and support the reconsideration of early hamstring loading. Given the retrospective, uncontrolled, and underpowered design, these findings are hypothesis-generating and not generalizable beyond young adults with hamstring autografts; larger randomized trials are required. Full article

Surgical trauma triggers oxidative and inflammatory responses that contribute to postoperative complications. Although the antioxidant and anti-inflammatory effects of ketamine have been reported, the impact of anesthesia duration on these mechanisms remains unclear. Forty-two male Wistar rats were randomized into healthy control (HG), ketamine only (KET; 60 mg/kg, i.p.), or laparotomy plus ketamine with 0–4 additional ketamine doses at 20 min intervals (KET + L, KET + L1–L4). At 24 h, levels of MDA, tGSH, SOD, CAT, IL-1β, IL-6, TNF-α, adrenaline and noradrenaline were measured in tail-vein blood. One-way ANOVA with Tukey’s post hoc test was used. Laparotomy under single-dose ketamine increased MDA and pro-inflammatory cytokines and decreased tGSH, SOD, CAT, ADR, and NDR versus HG and KET (all p < 0.001). After laparotomy, repeated ketamine dosing produced graded decreases in MDA and cytokines and increases in tGSH, SOD, CAT, ADR, and NDR toward control levels; effects were most pronounced in KET + L4 (all p < 0.001). Ketamine alone did not differ significantly from HG. In rats, ketamine modulates postoperative biological stress in a duration-dependent manner; prolonging anesthesia reduces oxidative–inflammatory load and restores catecholaminergic tone. These findings strongly support revisiting dose–duration protocols and underscore the need for mechanistic and clinical studies. Full article

Nonintrusive load monitoring (NILM) relies on high-resolution sensor data to disaggregate total building energy into end-use load components, for example HVAC, ventilation, and appliances. On the ADRENALIN corpus, simple NaN handling with forward fill and mean substitution reduced average NMAE from 0.82 to 0.76 for the Bayesian baseline, from 0.71 to 0.64 for BI-LSTM, and from 0.59 to 0.53 for the Time–Frequency Mask (TFM) model, across nine buildings and four temporal resolutions. However, many NILM models still show degraded accuracy due to unresolved data-quality issues, especially missing values, timestamp irregularities, and sensor inconsistencies, a limitation underexplored in current benchmarks. This paper presents a fully automated data-quality assurance pipeline for time-series energy datasets. The pipeline performs multivariate profiling, statistical analysis, and threshold-based diagnostics to compute standardized quality metrics, which are aggregated into an interpretable Building Quality Score (BQS) that predicts NILM performance and supports dataset ranking and selection. Explainability is provided by SHAP and a lightweight large language model, which turns visual diagnostics into concise, actionable narratives. The study evaluates practical quality improvement through systematic handling of missing values, linking metric changes to downstream error reduction. Using random-forest surrogates, SHAP identifies missingness and timestamp irregularity as dominant drivers of error across models. Core contributions include the definition and validation of BQS, an interpretable scoring and explanation framework for time-series quality, and an end-to-end evaluation of how quality diagnostics affect NILM performance at scale. Full article

In the process of digital transformation, the performance diagnosis of server systems is crucial for ensuring service continuity and enhancing user experience. Addressing the issues of invasiveness, poor universality, and difficulty in precisely locating abnormal bottlenecks in service requests with traditional performance analysis methods, this paper proposes a nonintrusive diagnosis method named Cross-Environment Server Diagnosis with Fusion (CSDF), which is based on the fusion of network traffic and Application Performance Management (APM) metrics. This CSDF method uses a traffic replay tool to reproduce real service requests captured via network cards in a production environment at a 1:1 ratio in a replay environment, comparing performance differences between the two environments to identify abnormal bottlenecks. By integrating Key Performance Indicator (KPI) metrics collected from APM systems, a correlation model between metrics and bottlenecks is established using the Random Forest algorithm within CSDF to pinpoint the root cause at the host resource layer. Simultaneously, it supplements network layer bottleneck analysis by parsing network transmission characteristics of data packets as an important part of CSDF. Experimental results demonstrate that this CSDF method can effectively identify abnormal bottlenecks in specific service requests, verifying its effectiveness in China Tower’s production system—the correlation coefficient between 1 min average load and latency reached 0.87, and the optimization effect was significant. This study provides a general framework for the precise diagnosis and optimization of server systems via CSDF, possessing strong practical value and promising application prospects. Full article

This study investigates the primary frequency regulation dynamic characteristics of industrial steam extraction turbine units under deep peak regulation conditions. A high-fidelity integrated dynamic model was established, incorporating the governor system, steam turbine with extraction modules, and interconnected pipeline dynamics. Through comparative simulations and experimental validation, the model demonstrates high accuracy in replicating real-unit responses to frequency disturbances. For the power grid system in this study, the frequency disturbance mainly comes from three aspects: first, the power imbalance formed by the random mutation of the load side and the intermittence of new energy power generation; second, transformation of the energy structure directly reduces the available frequency modulation resources; third, the system-equivalent inertia collapse effect caused by the integration of high permeability new energy; the rotational inertia provided by the traditional synchronous unit is significantly reduced. In the cogeneration unit and its control system in Guangxi involved in this article, key findings reveal that increased peak regulation depth (30~50% rated power) exacerbates nonlinear fluctuations. This is due to boiler combustion stability thresholds and steam pressure variations. Key parameters—dead band, power limit, and droop coefficient—have coupled effects on performance. Specifically, too much dead band (>0.10 Hz) reduces sensitivity; likewise, too high a power limit (>4.44%) leads to overshoot and slow recovery. The robustness of parameter configurations is further validated under source-load random-intermittent coupling disturbances, highlighting enhanced anti-interference capability. By constructing a coordinated control model of primary frequency modulation, the regulation strategy of boiler and steam turbine linkage is studied, and the optimization interval of frequency modulation dead zone, adjustment coefficient, and frequency modulation limit parameters are quantified. Based on the sensitivity theory, the dynamic influence mechanism of the key control parameters in the main module is analyzed, and the degree of influence of each parameter on the frequency modulation performance is clarified. This research provides theoretical guidance for optimizing frequency regulation strategies in coal-fired units integrated with renewable energy systems. Full article

This work suggests a new use for the Non-Monopolize Optimization (NO) method to improve the dynamic stability and robustness of PID and PIDA controllers in Automatic Voltage Regulator (AVR) systems when there are load disruptions. The NO algorithm is a new search method that does not use metaphors and only looks for one answer. It utilizes adaptive dimension modifications to strike a balance between exploration and exploitation. Its addition to AVR control makes parameter tweaking more efficient, without relying on random metaphors or population-based heuristics. MATLAB/Simulink R2025a runs full simulations to check how well the system works in both the time domain (step response, root locus) and the frequency domain (Bode plot). We compare the results to those of well-known optimizers like WOA, TLBO, ARO, GOA, and GA. The suggested NO-based PID and PIDA controllers always show less overshoot, faster rise and settling periods, and higher phase and gain margins, which proves that they are more stable and responsive. A robustness test with a load change of ±50% shows that NO-tuned controllers are even more reliable. The results show that using NO to tune different controllers could be a good choice for real-time AVR controller tuning in modern power systems because it is lightweight and works well. Full article

Background/Objectives: Skeletal muscle functions as an endocrine organ by secreting myokines in response to exercise, with interleukin-6 (IL-6) recognized as a representative intensity-dependent biomarker that rapidly increases immediately after exercise and is strongly dependent on exercise intensity. However, it is unclear how changes in mechanical stress affect the response of myokines after exercise. This randomized crossover study aimed to investigate the effect of mechanical stress on acute myokine secretion during matched metabolic exercise under different mechanical stress. Methods: Ten healthy adult males performed 30 min of cycling at 60% of peak $\stackrel{·}{\mathrm{V}}$O₂ in both semi-recumbent position and side-lying positions. Blood samples were collected before, immediately after, and at 30 and 60 min post-exercise to evaluate IL-6, brain-derived neurotrophic factor (BDNF), and lactate. Results: BDNF and lactate levels peaked immediately after exercise, and IL-6 reached its peak at 30 min post-exercise in both the semi-recumbent position and side-lying positions. All markers showed significant elevations in response to exercise. However, no significant differences were found between the two postures in any of the measured variables. Conclusions: These findings suggest that reduced mechanical load does not impair endocrine responses when the intensity of metabolic stress is maintained. This study provides scientific evidence that, regardless of posture or environment, sufficient exercise intensity can induce adequate IL-6 and BDNF secretion, through which the beneficial effects of exercise may be expected. Full article

Autoimmune diseases are characterized by immune response dysregulation against self-components, leading to chronic inflammation and tissue damage. Vitamin C (VitC), a water-soluble vitamin with established functions in antioxidant defence and collagen synthesis, has also been of interest based on its potential immunomodulatory effects. This review discusses the role of VitC in the course and progression of (A) autoimmune diseases (multiple sclerosis, rheumatoid arthritis, Sjögren’s disease, type 1 diabetes, Hashimoto’s thyroiditis, pernicious anaemia, antiphospholipid syndrome), (B) other immune-mediated diseases (Crohn’s disease, periodontitis), and (C) Alzheimer’s disease, a neurodegenerative disorder with autoimmune features. Results from clinical, observational, and experimental trials show that VitC deficiency is common in many of these diseases and may contribute to increased oxidative stress and immune disequilibrium. Supplementation has been associated with improved antioxidant levels, control of inflammatory mediators, and, in some cases, clinical outcomes like disease activity decrease or symptom load. Although findings vary across conditions and few large, randomized trials are available, the overall evidence indicates that maintaining good VitC status can be useful in maintaining immune homeostasis and reducing inflammation. VitC should be viewed as an adjunct to be employed safely, perhaps and ideally within larger treatment regimens, but not in place of effective therapies. Further research, including large-scale clinical trials, will be required to determine more clearly optimal dosing, timing of treatment, and patient population most likely to benefit. By integration of current knowledge, this review recognizes both promise in VitC for treatment of autoimmune/immune-mediated disease and promise in its potential use within future treatment regimens. Full article

Low-voltage series arc faults pose a significant threat to power system safety due to their random, nonlinear, and non-stationary characteristics. Traditional detection methods often suffer from low sensitivity and poor robustness under complex load conditions. To address these challenges, this paper proposes a novel detection framework based on Random Forest (RF) feature selection, Adaptive Boosting (Adaboost) classification, and SHapley Additive exPlanations (SHAP) interpretability. First, RF is employed to rank and select the most discriminative features from arc fault current signals. Then, the selected features are input into an Adaboost classifier to enhance the detection accuracy and generalization capability. Finally, SHAP values are introduced to quantify the contribution of each feature, improving the transparency and interpretability of the model. Experimental results on a self-built arc fault dataset demonstrate that the proposed method achieves an accuracy of 97.1%, outperforming five widely used traditional classifiers. The integration of SHAP further reveals the physical relevance of key features, providing valuable insights for practical applications. This study confirms that the proposed RF-Adaboost-SHAP framework offers both high accuracy and interpretability, making it suitable for real-time arc fault detection in complex load scenarios. Full article