Search Results (7,386)

We surveyed the air quality conditions in 18 homes with gas stoves for PM_2.5, CO₂, NO₂ and CO using calibrated low-cost sensors. In each home, participants were asked to cook as usual, but to record their cooking activities and mitigation efforts (windows, ventilation fans, etc.). All homes showed enhanced pollutants during, and immediately after, times of cooking or stove use. For each home, we quantified the minutes per day and minutes per minute of cooking over known health thresholds for each pollutant. On average, homes exhibited 38 min per day over one or more of these thresholds, with PM_2.5 and NO₂ being the pollutants of greatest concern. Six homes had much higher occurrences over the health thresholds, averaging 73 min per day. We found an average of 1.0 min over one or more of the health thresholds per minute of cooking when no mitigation was used, whereas when mitigation was used (filtration or vent fan), this value was reduced by 34%. We further investigated several mitigation methods including natural diffusion, a commercial HEPA filter unit, a commercial O₃ scrubber and a ventilation fan. We found that the HEPA unit was highly effective for PM_2.5 but had no impact on any of the gaseous pollutants. The O₃ scrubber was moderately effective for NO₂ but had little impact on the other pollutants. The ventilation fan was highly effective for all pollutants and reduced the average pollutant lifetime significantly. Under controlled test conditions, the pollutant lifetime (or time to reach 37% of the original concentration), was reduced from an average of 45 min (with no ventilation) to 7 min. While no commercial filter showed efficacy for both PM_2.5 and NO₂, the fact that each could be removed individually suggests that a combined filter for both pollutants could be developed, which would significantly reduce health impacts in homes with gas stoves. Full article

Clean and safe air quality is essential for public health, yet particulate matter (PM) significantly degrades air quality and poses serious health risks. The Gulf Cooperation Council (GCC) countries are particularly vulnerable to frequent and intense dust storms due to their vast desert landscapes. This study presents the first health risk assessment of carcinogenic and non-carcinogenic risks associated with exposure to PM2.5 and PM10 bound heavy metals and polycyclic aromatic hydrocarbons (PAHs) based on air quality data collected during the years of 2016–2018 near Dubai International Airport and Abu Dhabi International Airport. The results reveal no significant carcinogenic risks for lead (Pb), cobalt (Co), nickel (Ni), and chromium (Cr). Additionally, AI-based regression analysis was applied to time-series dust monitoring data to enhance predictive capabilities in environmental monitoring systems. The estimated incremental lifetime cancer risk (ILCR) from PAH exposure exceeded the acceptable threshold (10⁻⁶) in several samples at both locations. The relationship between visibility and key environmental variables—PM1, PM2.5, PM10, total suspended particles (TSPs), wind speed, air pressure, and air temperature—was modeled using three machine learning algorithms: linear regression, support vector machine (SVM) with a radial basis function (RBF) kernel, and artificial neural networks (ANNs). Among these, SVM with an RBF kernel showed the highest accuracy in predicting visibility, effectively integrating meteorological data and particulate matter variables. These findings highlight the potential of machine learning models for environmental monitoring and the need for continued assessments of air quality and its health implications in the region. Full article

This study conducted uniaxial creep tests on coal samples under both natural and water-saturated conditions for durations of about 180 days per sample to study the stability of coal pillar dams of the Daliuta Coal Mine underground reservoir. Combined with synchronized acoustic emission (AE) monitoring, the research systematically revealed the time-dependent deformation mechanisms and damage evolution laws of coal under prolonged water immersion and natural conditions. The results indicate that water-immersed coal exhibits a unique negative creep phenomenon at the initial stage, with the strain rate down to −0.00086%/d, attributed to non-uniform pore compaction and elastic rebound effects. During the steady-state creep phase, the creep rates under water-immersed and natural conditions were comparable. However, water immersion led to an 11.4% attenuation in elastic modulus, decreasing from 2300 MPa to 2037 MPa. Water immersion would also suppress AE activity, leading to the average daily AE events of 128, which is only 25% of that under natural conditions. In the accelerating creep stage, the AE event rate surged abruptly, validating its potential as an early warning indicator for coal pillar instability. Based on the identified long-term strength of the coal sample, it is recommended to maintain operational loads below the threshold of 9 MPa. This research provides crucial theoretical foundations and experimental data for optimizing the design and safety monitoring of coal pillar dams in CMURs. Full article

The increasing incorporation of renewable energy into power grids has significantly reduced system inertia and damping, posing challenges to frequency stability and power quality. To address this issue, an adaptive virtual synchronous generator (VSG) control strategy is proposed, which dynamically adjusts virtual inertia and damping in response to real-time frequency variations. Virtual inertia is modulated by an exponential function according to the frequency variation rate, while damping is regulated via a hyperbolic tangent function, enabling minor support during small disturbances and robust compensation during severe events. Control parameters are optimized using an enhanced particle swarm optimization (PSO) algorithm based on a composite performance index that accounts for frequency deviation, overshoot, settling time, and power tracking error. Simulation results in MATLAB/Simulink under step changes, load fluctuations, and single-phase faults demonstrate that the proposed method reduces the frequency deviation by over 26.15% compared to fixed-parameter and threshold-based adaptive VSG methods, effectively suppresses power overshoot, and eliminates secondary oscillations. The proposed approach significantly enhances grid transient stability and demonstrates strong potential for application in power systems with high levels of renewable energy integration. Full article

Chronic neck pain is a multifactorial condition involving physical, psychological, and neurological dimensions. This case report describes the clinical course of a 25-year-old female with chronic neck pain and recurrent headaches who underwent a 6-week integrative intervention consisting of manual therapy and pain neuroscience-based sensorimotor retraining, administered three times per week. Outcome measures included the Headache Impact Test-6 (HIT-6), Neck Pain and Disability Scale (NPDS), Pain Catastrophizing Scale (PCS), Fear-Avoidance Beliefs Questionnaire (FABQ), pressure pain threshold (PPT), cervical range of motion (CROM), and functional near-infrared spectroscopy (fNIRS) to assess brain activity. Following the intervention, the patient demonstrated marked reductions in pain and psychological distress: HIT-6 decreased from 63 to 24 (61.9%), NPDS from 31 to 4 (87.1%), FABQ from 24 to 0 (100%), and PCS from 19 to 2 (89.5%). Improvements in PPT and CROM were also observed. fNIRS revealed decreased dorsolateral prefrontal cortex (DLPFC) activation during pain stimulation and movement tasks, suggesting a possible reduction in central sensitization burden. These findings illustrate that an integrative approach targeting biopsychosocial pain mechanisms may be beneficial in managing chronic neck pain, improving function, and modulating cortical responses. This report provides preliminary evidence in support of the clinical relevance of combining manual therapy with neurocognitive retraining in similar patients. Full article

Background: This study aims to evaluate the cost-effectiveness of providing the 23-valent pneumococcal polysaccharide vaccine (PPV23) free of charge versus self-paying vaccination among adults aged 60 years and older in Nanning, Guangxi, China. Methods: A decision tree–Markov model was developed to compare three strategies (government-funded free vaccination, self-funded vaccination, and no vaccination) over a 5-year time horizon. The model incorporated local epidemiological data and cost parameters, applying a 3% discount rate. Sensitivity analyses were conducted on key parameters, including vaccine effectiveness against pneumonia and pneumonia treatment costs. Results: The benefit–cost ratios for free and self-funded vaccination were 0.075 and 0.015, respectively, both below the cost-effectiveness threshold of 1. However, the free vaccination strategy resulted in a higher net benefit (USD 399,651.32) compared to the self-funded strategy (USD 222,594.14), along with a lower Incremental Cost-Effectiveness Ratio (ICER) (USD 1.47 per USD 0.14 of avoided disease cost). Although both strategies yielded benefit–cost ratios far below the conventional threshold of 1, the free strategy demonstrated relatively greater economic efficiency. Sensitivity analyses confirmed that vaccine effectiveness against pneumonia and treatment costs were key drivers of economic outcomes. Conclusions: While neither vaccination strategy achieved conventional cost-effectiveness benchmarks in this setting, the free PPV23 vaccination program demonstrated relatively greater economic efficiency compared to the self-funded approach; although neither strategy met the conventional cost-effectiveness thresholds, they should be considered for inclusion in regional health policy for older adults. Full article

The vibration and noise of friction pairs are severe when cutting titanium alloy with cemented carbide tools, and the surface micro-texture can significantly reduce noise and friction. Therefore, it is very important to clarify the correlation mechanism between friction noise and friction force for processing quality control. Consequently, investigating the underlying mechanisms that link friction noise and friction is of considerable importance. This study focuses on the friction and wear acoustic signals generated by micro-textured cemented carbide–titanium alloy. A friction testing platform specifically designed for the micro-textured cemented carbide grinding of titanium alloy has been established. Acoustic sensors are employed to capture the acoustic signals, while ultra-depth-of-field microscopy and scanning electron microscopy are utilized for surface analysis. A novel approach utilizing the dung beetle algorithm (DBO) is proposed to optimize the parameters of variational mode decomposition (VMD), which is subsequently combined with wavelet packet threshold denoising (WPT) to enhance the quality of the original signal. Continuous wavelet transform (CWT) is applied for time–frequency analysis, facilitating a discussion on the underlying mechanisms of micro-texture. Additionally, features are extracted from the time domain, frequency domain, wavelet packet, and entropy. The Relief-F algorithm is employed to identify 19 significant features, leading to the development of a hybrid model that integrates Bayesian optimization (BO) and Transformer-LSTM for predicting friction. Experimental results indicate that the model achieves an R² value of 0.9835, a root mean square error (RMSE) of 0.2271, a mean absolute error (MAE) of 0.1880, and a mean bias error (MBE) of 0.1410 on the test dataset. The predictive performance and stability of this model are markedly superior to those of the BO-LSTM, LSTM–Attention, and CNN–LSTM–Attention models. This research presents a robust methodology for predicting friction in the context of friction and wear of cemented carbide–titanium alloys. Full article

Obesity is a chronic, relapsing disease with multifactorial origins and significant global health implications. Historically, bariatric surgery has been the most effective intervention for achieving sustained weight loss and metabolic improvement, especially in individuals with moderate to severe obesity. However, the therapeutic landscape is rapidly evolving. Recent advances in pharmacotherapy—including GLP-1 receptor agonists, dual and triple incretin agonists, and amylin-based combination therapies—have demonstrated unprecedented efficacy, with some agents inducing 15–25% weight loss, approaching outcomes once exclusive to surgical intervention. These developments challenge the continued applicability of existing bariatric surgery criteria, which were established in an era of limited medical alternatives. In this narrative review, we examine the evolution of surgical eligibility thresholds and critically assess the potential role of novel pharmacotherapies in redefining treatment algorithms. By comparing the efficacy, safety, metabolic benefits, and cost-effectiveness of surgery versus next-generation drugs, we explore whether a more stepwise, pharmacotherapy-first approach may now be justified, particularly in patients with BMI 30–40 kg/m². We also discuss future directions in obesity management, including personalized treatment strategies, perioperative drug use, and the integration of pharmacologic agents into long-term care pathways. As the field advances, a paradigm shift toward individualized, minimally invasive interventions appears inevitable—necessitating a timely re-evaluation of current bariatric surgery guidelines to reflect the expanding potential of medical therapy. Full article

As a key component in the hydraulic brake system of automobiles, the brake joint directly affects the braking performance and driving safety of the vehicle. Therefore, improving the quality of brake joints is crucial. During the processing, due to the complexity of the welding material and welding process, the weld seam is prone to various defects such as cracks, pores, undercutting, and incomplete fusion, which can weaken the joint and even lead to product failure. Traditional weld seam detection methods include destructive testing and non-destructive testing; however, destructive testing has high costs and long cycles, and non-destructive testing, such as radiographic testing and ultrasonic testing, also have problems such as high consumable costs, slow detection speed, or high requirements for operator experience. In response to these challenges, this article proposes a defect detection and classification method for laser welding seams of automotive brake joints based on machine vision inspection technology. Laser-welded automotive brake joints are subjected to weld defect detection and classification, and image processing algorithms are optimized to improve the accuracy of detection and failure analysis by utilizing the high efficiency, low cost, flexibility, and automation advantages of machine vision technology. This article first analyzes the common types of weld defects in laser welding of automotive brake joints, including craters, holes, and nibbling, and explores the causes and characteristics of these defects. Then, an image processing algorithm suitable for laser welding of automotive brake joints was studied, including pre-processing steps such as image smoothing, image enhancement, threshold segmentation, and morphological processing, to extract feature parameters of weld defects. On this basis, a welding seam defect detection and classification system based on the cascade classifier and AdaBoost algorithm was designed, and efficient recognition and classification of welding seam defects were achieved by training the cascade classifier. The results show that the system can accurately identify and distinguish pits, holes, and undercutting defects in welds, with an average classification accuracy of over 90%. The detection and recognition rate of pit defects reaches 100%, and the detection accuracy of undercutting defects is 92.6%. And the overall missed detection rate is less than 3%, with both the missed detection rate and false detection rate for pit defects being 0%. The average detection time for each image is 0.24 s, meeting the real-time requirements of industrial automation. Compared with infrared and ultrasonic detection methods, the proposed machine-vision-based detection system has significant advantages in detection speed, surface defect recognition accuracy, and industrial adaptability. This provides an efficient and accurate solution for laser welding defect detection of automotive brake joints. Full article

Background/Objectives: The optimal treatment for femoral neck fractures (FNFs) in the elderly remains unclear. Internal fixation, bipolar hip hemiarthroplasty (BH), standard total hip arthroplasty (THA), or dual mobility (DM-THA) cups have been employed, each presenting various advantages and disadvantages. This systematic review and meta-analysis evaluated comparative studies of BH and DM-THA in FNFs among the elderly, aiming to ascertain differences in outcomes, including functional recovery, patient-reported outcome measures, implant survival, complications, and mortality rates. Methods: This meta-analysis followed PRISMA 2020 guidelines with a pre-registered PROSPERO protocol (CRD420251065762). A comprehensive search of electronic databases and grey literature included only comparative studies of BH and DM-THA in patients over 65 years with FNFs. Results: Sixteen studies were eligible, comprising four randomised controlled trials and twelve retrospective comparative studies involving 11,460 patients (10,036 BH; 1424 DM-THA). Patients with DM-THA exhibited a higher postoperative Harris Hip Score (4.55, p < 0.0001), alongside a lower dislocation risk ([OR] 2.77, p < 0.0001), a reduced revision rate ([OR] 2.36, p < 0.0001), and decreased mortality ([OR] 1.94, p < 0.0001). The operative time was somewhat longer in the DM-THA group, by 12.71 min, and blood loss was greater by 121 mL, indicating significant heterogeneity across the studies. Conclusions: DM-THA for FNFs in elderly patients results in improved functional recovery and lower dislocation, reoperation, and mortality risk. However, longer operative times and increased blood loss remain significant considerations. Further, well-designed comparative studies are required to evaluate overall cost-effectiveness and define the optimal age threshold, beyond which the limitations of DM-THA may outweigh its benefits. Full article

Large turbine generators rely on shaft earthing brushes to safely divert harmful shaft currents to ground, protecting bearings from electrical damage. This paper presents a novel deep learning-based diagnostic framework to detect and classify faults in shaft earthing brushes of large turbine generators. A key innovation lies in the use of FFT-derived spectrograms from both voltage and current waveforms as dual-channel inputs to the CNN, enabling automatic feature extraction of time–frequency patterns associated with different SEB fault types. The proposed framework combines advanced signal processing and convolutional neural networks (CNNs) to automatically recognize fault-related patterns in shaft grounding current and voltage signals. In the approach, raw time-domain signals are converted into informative time–frequency representations, which serve as input to a CNN model trained to distinguish normal and faulty conditions. The framework was evaluated using data from a fleet of large-scale generators under various brush fault scenarios (e.g., increased brush contact resistance, loss of brush contact, worn out brushes, and brush contamination). Experimental results demonstrate high fault detection accuracy (exceeding 98%) and the reliable identification of different fault types, outperforming conventional threshold-based monitoring techniques. The proposed deep learning framework offers a novel intelligent monitoring solution for predictive maintenance of turbine generators. The contributions include the following: (1) the development of a specialized deep learning model for shaft earthing brush fault diagnosis, (2) a systematic methodology for feature extraction from shaft current signals, and (3) the validation of the framework on real-world fault data. This work enables the early detection of brush degradation, thereby reducing unplanned downtime and maintenance costs in power generation facilities. Full article

Background: Hypotension following epidural labor analgesia (ELA) is its most common complication, affecting approximately 20% of patients and posing risks to both maternal and fetal health. As digital tools and predictive analytics increasingly shape perioperative and obstetric anesthesia practices, real-world implementation data are needed to guide their integration into clinical care. Current monitoring practices rely on intermittent non-invasive blood pressure (NIBP) measurements, which may delay recognition and treatment of hypotension. The Hypotension Prediction Index (HPI) algorithm uses continuous arterial waveform monitoring to predict hypotension for potentially earlier intervention. This clinical trial evaluated the feasibility, acceptability, and efficacy of continuous HPI-guided treatment in reducing time-to-treatment for ELA-associated hypotension and improving maternal hemodynamics. Methods: This was a prospective randomized controlled trial design involving healthy pregnant individuals receiving ELA. Participants were randomized into two groups: Group CM (conventional monitoring with NIBP) and Group HPI (continuous noninvasive blood pressure monitoring). In Group HPI, hypotension treatment was guided by HPI output; in Group CM, treatment was based on NIBP readings. Feasibility, appropriateness, and acceptability outcomes were assessed among subjects and their bedside nurse using the Acceptability of Intervention Measure (AIM), Intervention Appropriateness Measure (IAM), and Feasibility of Intervention Measure (FIM) instruments. The primary efficacy outcome was time-to-treatment of hypotension, defined as the duration between onset of hypotension and administration of a vasopressor or fluid therapy. This outcome was chosen to evaluate the clinical responsiveness enabled by HPI monitoring. Hypotension is defined as a mean arterial pressure (MAP) < 65 mmHg for more than 1 min in Group CM and an HPI threshold < 75 for more than 1 min in Group HPI. Secondary outcomes included total time in hypotension, vasopressor doses, and hemodynamic parameters. Results: There were 30 patients (Group HPI, n = 16; Group CM, n = 14) included in the final analysis. Subjects and clinicians alike rated the acceptability, appropriateness, and feasibility of the continuous monitoring device highly, with median scores ≥ 4 across all domains, indicating favorable perceptions of the intervention. The cumulative probability of time-to-treatment of hypotension was lower by 75 min after ELA initiation in Group HPI (65%) than Group CM (71%), although this difference was not statistically significant (log-rank p = 0.66). Mixed models indicated trends that Group HPI had higher cardiac output (β = 0.58, 95% confidence interval −0.18 to 1.34, p = 0.13) and lower systemic vascular resistance (β = −97.22, 95% confidence interval −200.84 to 6.40, p = 0.07) throughout the monitoring period. No differences were found in total vasopressor use or intravenous fluid administration. Conclusions: Continuous monitoring and precision hypotension treatment is feasible, appropriate, and acceptable to both patients and clinicians in a labor and delivery setting. These hypothesis-generating results support that HPI-guided treatment may be associated with hemodynamic trends that warrant further investigation to determine definitive efficacy in labor analgesia contexts. Full article

This study aimed to investigate the differences in proprioceptive changes at different time points (Pre vs. Post vs. 90 min vs. 24 h) before and after ischemic preconditioning. It followed a within-subject, self-controlled design, and a total of 21 trained male participants were assessed using two-point discrimination threshold tests on thigh and knee joint position sense testing. The results demonstrated that ischemic preconditioning effectively improved proprioceptive accuracy (two-point discrimination, right lower limb, p < 0.001; two-point discrimination, left lower limb, p < 0.001; knee position sense, right lower limb, p = 0.001; knee position sense, left lower limb, p = 0.014) and stability (two-point discrimination, right lower limb, p < 0.001; two-point discrimination, left lower limb, p = 0.002; knee position sense, right lower limb, p < 0.001; knee position sense, left lower limb, p = 0.003), with the optimal time point for enhancement identified at 90 min. This research suggests administering IPC 90 min before warm-up or competition to enhance athletic performance. Full article

Background/Objectives: Bruxism has been associated with dysregulation of the autonomic nervous system (ANS). Visceral manual therapy (VMT) has shown beneficial effects on the vagal tone and modulation of ANS activity. This study aimed to evaluate the immediate and short-term effects of VMT in individuals with clinically based bruxism. Methods: A single-blind randomized controlled trial was conducted including 24 individuals with clinically based bruxism. Participants received two sessions of either VMT or a sham placebo technique. Outcome measures included heart rate variability (HRV), both normal-to-normal intervals (HRV-SDNN), and the root mean square of successive normal-to-normal intervals (HRV-RMSSD), as well as muscle tone and stiffness and pressure pain thresholds (PPTs). Measurements were made at T1 (baseline), T2 (post-first intervention), T3 (pre-second intervention), T4 (post-second intervention), and T5 (4-week follow-up). Results: A significant time*group interaction was observed for HRV-SDNN (p = 0.04, η² = 0.12). No significant changes were found for muscle tone or stiffness. PPTs significantly increased at C4 after the second session (p = 0.049, η² = 0.16) and at the left temporalis muscle after the first session (p = 0.01, η² = 0.07). Conclusions: The findings suggest that two sessions of VMT may lead to significant improvements in HRV-SDNN compared to the placebo, suggesting a modulatory effect on autonomic function. No consistent changes were observed for the viscoelastic properties of the masticatory muscles. Isolated improvements in pressure pain sensitivity were found at C4 and the left temporalis muscle. Further research with larger sample sizes and long-term follow-up is needed to determine the clinical relevance of VMT in the management of signs and symptoms in individuals with bruxism. Full article

Fault prediction of hydropower station is crucial for the stable operation of generator set equipment, but the traditional method struggles to deal with data with an imbalanced distribution and untrustworthiness. This paper proposes a fault detection method based on a convolutional neural network (CNNs) and long short-term memory network (LSTM) with a generative adversarial network (GAN). Firstly, a reliability mechanism based on principal component analysis (PCA) is designed to solve the problem of data bias caused by multiple monitoring devices. Then, the CNN-LSTM network is used to predict time series data, and the GAN is used to expand fault data samples to solve the problem of an unbalanced data distribution. Meanwhile, a multi-scale feature extraction network with time–frequency information is designed to improve the accuracy of fault detection. Finally, a dynamic multi-task training algorithm is proposed to ensure the convergence and training efficiency of the deep models. Experimental results show that compared with RNN, GRU, SVM, and threshold detection algorithms, the proposed fault prediction method improves the accuracy performance by $5.5\%$, $4.8\%$, $7.8\%$, and $9.3\%$, with at least a $160\%$ improvement in the fault recall rate. Full article