Assessment Of Health Status Research Articles

Assessment of Saliva Oxidative Stress Biomarkers and Gingival Health Status in a Sample of High-Functioning Autistic Children in Erbil City

Assessment of Saliva Oxidative Stress Biomarkers and Gingival Health Status in a Sample of High-Functioning Autistic Children in Erbil City

Effect of acupuncture on quality of life in atrial fibrillation: study protocol for a randomised controlled trial

IntroductionAtrial fibrillation (AF) is the prevalent cardiac arrhythmia and can significantly impair the quality of life (QoL). Although catheter ablation (CA) is an established treatment for AF,post-procedural complications or perceived...

A generalized health index: automated thoracic CT-derived biomarkers predict life expectancy.

To identify image biomarkers associated with overall life expectancy from low-dose computed tomography and integrate them as an index for assessing an individual's health. Two categories of CT image features, body composition tissues and cardiopulmonary vasculature characteristics, were quantified from LDCT scans in the Pittsburgh Lung Screening Study cohort(n = 3,635). Cox proportional-hazards models identified significant image features which were integrated with subject demographics to predict the subject's overall hazard. Subjects were stratified using composite model predictions and feature-specific risk stratification thresholds. The model's performance was validated extensively, including 5-fold cross-validation on PLuSS baseline, PLuSS follow-up examinations, and the National Lung Screening Trial (NLST). The composite model had significantly improved prognostic ability compared to the baseline model (p < 0.01) with AUCs of 0.774 (95% CI: 0.757-0.792) on PLuSS, 0.723 (95% CI: 0.703-0.744) on PLuSS follow-up, and 0.681 (95% CI: 0.651-0.710) on the NLST cohort. The identified high-risk stratum were several times more likely to die, with mortality rates of 79.34% on PLuSS, 76.47% on PLuSS follow-up, and 46.74% on NLST. Two cardiopulmonary structures (intrapulmonary artery vein ratio, intrapulmonary vein density) and two body composition tissues (SM density, bone density) identified high-risk patients. Body composition and pulmonary vasculatures are predictive of an individual's health risk; their integrations with subject demographics facilitate the assessment of an individual's overall health status or susceptibility to disease. CT-computed body composition and vasculature biomarkers provide improved prognostic value. The integration of CT biomarkers and patient demographic information improves subject risk stratification.

Impact of Consumer Wearables Data on Pediatric Surgery Clinicians' Management: Multi-Institutional Scenario-Based Usability Study.

At present, parents lack objective methods to evaluate their child's postoperative recovery following discharge from the hospital. As a result, clinicians are dependent upon a parent's subjective assessment of the child's health status and the child's ability to communicate their symptoms. This subjective nature of home monitoring contributes to unnecessary emergency department (ED) use as well as delays in treatment. However, the integration of data remotely collected using a consumer wearable device has the potential to provide clinicians with objective metrics for postoperative patients to facilitate informed longitudinal, remote assessment. This multi-institutional study aimed to evaluate the impact of adding actual and simulated objective recovery data that were collected remotely using a consumer wearable device to simulated postoperative telephone encounters on clinicians' management. In total, 3 simulated telephone scenarios of patients after an appendectomy were presented to clinicians at 5 children's hospitals. Each scenario was then supplemented with wearable data concerning or reassuring against a postoperative complication. Clinicians rated their likelihood of ED referral before and after the addition of wearable data to evaluate if it changed their recommendation. Clinicians reported confidence in their decision-making. In total, 34 clinicians participated. Compared with the scenario alone, the addition of reassuring wearable data resulted in a decreased likelihood of ED referral for all 3 scenarios (P<.01). When presented with concerning wearable data, there was an increased likelihood of ED referral for 1 of 3 scenarios (P=.72, P=.17, and P<.001). At the institutional level, there was no difference between the 5 institutions in how the wearable data changed the likelihood of ED referral for all 3 scenarios. With the addition of wearable data, 76% (19/25) to 88% (21/24 and 22/25) of clinicians reported increased confidence in their recommendations. The addition of wearable data to simulated telephone scenarios for postdischarge patients who underwent pediatric surgery impacted clinicians' remote patient management at 5 pediatric institutions and increased clinician confidence. Wearable devices are capable of providing real-time measures of recovery, which can be used as a postoperative monitoring tool to reduce delays in care and avoidable health care use.

Mental Health Identification System

In an era where technology's reach extends to the intricacies of human well- being, "Mental Health Identification" emerges as a thought-provoking narrative that explores the convergence of innovation, ethics, and mental health. Relationships strain under the weight of newfound possibilities, underscoring. Developing an AI-Enabled Mental Health Identification for Early Intervention This project introduces an innovative AI-enabled mental health identification designed to revolutionize the early identification of mental health conditions. The project’s goal is to create a valuable tool that contributes to improved mental health awareness and support. The abstract provides a concise overview of the project. It highlights the development of an artificial intelligence-based mental health identification that utilizes cutting- edge machine learning algorithms to analyze face patterns, facial expressions, and textual inputs. The project’s focus is on creating an automated and non-intrusive tool for early detection and assessment of mental health conditions, aiming to contribute to improved mental health awareness and support. Mental health disorders have become one of the leading causes of disability worldwide, affecting millions of individuals. The early identification of mental health conditions is crucial for effective treatment and prevention. This paper presents a Mental Health Identification System that utilizes machine learning algorithms to predict and identify common mental health disorders based on a questionnaire dataset. The model is designed to focus on anxiety, depression, and stress disorders, using classification techniques to analyze user inputs

Prediction of Fragility Fractures and Mortality in a Cohort of Geriatric Patients.

Risk factors of refracture after fragility fractures include osteoporosis, female gender and advanced age among others. We hypothesized that the assessment of functionality, muscle health and nutrition status contribute to the risk prediction for further fractures and death. We assessed 334 patients admitted to the department of acute geriatrics for sociodemographic data, bone fragility, selected laboratory tests, body composition and data on functionality using the comprehensive geriatric assessment. Patients had follow-ups until the occurrence of further fractures or death. Dual-energy X-ray absorptiometry and pulse echo measurements were performed to assess bone mineral density. Fracture risk was assessed using the FRAX score and muscle strength according to published guidelines on sarcopenia. The mean age was 81 years (70-95), and 82.3% (275/334) were women. An incidence of 10.4% (35/334) new fragility fractures was observed within 24 months, and the mortality rate was 12.2% (41/334). A significantly higher rate of further fractures was associated with lower BMI (body mass index) (HR 0.925, CI 0.872-0.98; p = 0.009), lower parathyroid hormone levels (HR 0.986, CI 0.973-0.998; p = 0.026) and with the diagnosis of osteoporosis (HR 2.546, CI 1.192-5.438; p = 0.016). No significant associations were present in patients with previous fractures, with higher age, higher FRAX scores, sarcopenia, in women, sarcopenic obesity, frail patients, lower grip strength, lower walking speed, lower Barthel index or lower DI (density index) values. The predictive power for further fractures was 10.7% higher adding osteosarcopenia, BMI and parathyroid hormone levels to standard assessment parameters osteoporosis, age and the status of previous fractures. Mortality was significantly higher with advanced age (HR 1.101, CI 1.052-1.151; p < 0.001), in men (HR 6.464, CI 3.141-13.305; p < 0.001), in smokers (p = 0.002), higher FRAX score (HR 1.039, CI 1.009-1.070; p = 0.010), lower renal function (HR 0.987, CI 0.976-0.997; p = 0.010), lower Tinetti test scores (HR 0.943, CI 0.900-0.987; p = 0.012), lower walking speed (HR 0.084, CI 0.018-0.382; p = 0.001), lower hand grip (HR 0.876, CI 0.836-0.919; p < 0.001) and lower Barthel index scores (HR 0.984, CI 0.971-0.997; p = 0.015). In a cohort of geriatric patients, the addition of BMI, low parathyroid hormone levels and osteosarcopenia increases the predictive power for further fractures by 10.7%. These parameters are a valuable addition to the standard assessment parameters age and history of sustained fractures. Mortality is partly associated with potentially treatable functional parameters.

Improving health outcomes among older adults in India: effectiveness and implementability of a novel comprehensive geriatric assessment based intervention

Background There is significant evidence on the benefits of comprehensive assessment in older adults. But this evidence is primarily from western countries and in secondary care settings. National policies in India recognize this need and envision community-based screening and facility-based assessment programs integrated into the care pathways for the elderly. However, this is yet to translate into specific interventions, primarily due to lack of complex interventions necessary and evidence of their effectiveness. This study aims to design and pilot an integrated (Community + Facility) Elderly Health Status Assessment and Screening (EHSAS) intervention to improve health outcomes of older adults and assess its feasibility for implementation in Indian rural settings. Methods We propose a hybrid design where we will build the complex intervention, develop and validate the tools needed, pilot it using an exploratory cluster randomized trial and evaluate its implementatbility using the Exploration-Preparation-Implementation-Sustainment (EPIS) framework. Conclusions This study will fill critical gaps in evidence regarding the effectiveness of geriatric screening and assessment in community and primary care settings in low-middle income countries and provide validated tools and implementation models for adoption into national programs. Registration CTRI/2023/07/055661

Metabolite and protein associations with general health in the population-based CHRIS study

Identifying biomarkers able to discriminate individuals on different health trajectories is crucial to understand the molecular basis of age-related morbidity. We investigated multi-omics signatures of general health and organ-specific morbidity, as well as their interconnectivity. We examined cross-sectional metabolome and proteome data from 3,142 adults of the Cooperative Health Research in South Tyrol (CHRIS) study, an Alpine population study designed to investigate how human biology, environment, and lifestyle factors contribute to people’s health over time. We had 174 metabolites and 148 proteins quantified from fasting serum and plasma samples. We used the Cumulative Illness Rating Scale (CIRS) Comorbidity Index (CMI), which considers morbidity in 14 organ systems, to assess health status (any morbidity vs. healthy). Omics-signatures for health status were identified using random forest (RF) classifiers. Linear regression models were fitted to assess directionality of omics markers and health status associations, as well as to identify omics markers related to organ-specific morbidity. Next to age, we identified 21 metabolites and 10 proteins as relevant predictors of health status and results confirmed associations for serotonin and glutamate to be age-independent. Considering organ-specific morbidity, several metabolites and proteins were jointly related to endocrine, cardiovascular, and renal morbidity. To conclude, circulating serotonin was identified as a potential novel predictor for overall morbidity.

Wind turbine blade health state assessment based on fuzzy evaluation

Abstract Due to the health state of wind turbine blade being affected by many factors and the mechanism being unclear, a fuzzy evaluation method using SCADA information is proposed. Firstly, the index structure of blade health state assessment is determined by correlation analysis of SCADA data. Secondly, entropy weight is used to allocate weights to all levels of parameters, and then the index data degradation function is optimized by the 3σ criterion. Thirdly, the ridge distribution function is utilized to establish the membership function of the health indexs. Finally, a mathematical model is created for the fuzzy comprehensive assessment of the blade health status. The described techniques is employed to assess the blade health state in the Qingdao wind farm. The “healthy” and “dangerous” states of the blade can be distinguished. The assessment result aligns with reality, verifying that the method in this paper is applicable to the blade health state assessment of wind farms.

A Plantar Pressure Detection and Gait Analysis System Based on Flexible Triboelectric Pressure Sensor Array and Deep Learning.

Gait detection is essential for the assessment of human health status and early diagnosis of diseases. The current gait analysis systems are bulky, limited in the scope of use, and cause interference with the movement of the measured person. Hence, it is necessary to develop a wearable gait detection system that is soft, breathable, lightweight, and self-powered. Here, a plantar pressure sensor array and gait analysis system based on a flexible triboelectric pressure sensor (FTPS) array is developed. Soft, breathable, and wearable electrospinning nanofiber film with excellent triboelectric properties is used as the plantar pressure sensor, achieving a high sensitivity of 45.1 mVkPa-1 in the range of 40-200 kPa and 19.4 mVkPa-1 in the range of 200-400 kpa. 32 FTPSs are integrated into an intelligent insole, which has the characteristics of soft, easy production, good air permeability, long-time stability, no external power supply, and etc. Based on the long short-term memory artificial neural network deep learning model, the accuracy of gait judgment can reach 94.23%. This work provides a feasible solution for real-time gait detection, which will have potential applications in human health assessment and early diagnosis of disease.

A new reliability health status assessment model for complex systems based on belief rule base

In complex systems, health status assessment identifies system conditions and potential issues. However, large uncertainties and variations make efficient model construction challenging. The belief rule base (BRB), which addresses uncertainty through data-driven and knowledge-driven methods, is widely used for health status assessment of complex systems. Current BRB modeling methods focus primarily on accuracy, leaving a gap in research on reliability. Therefore, a reliable BRB (RE-BRB), which enables effective modeling for complex system health assessment under high reliability requirements, is proposed in this paper. First, a systematic reliability analysis of the BRB is performed, and the reliability criteria are defined. Second, the model parameters of the RE-BRB are optimized via the nondominated sorting whale optimization algorithm with reliability constraints (NSWOA), and the reliability of the model is ensured. In addition, a perturbation analysis of the RE-BRB model is conducted to identify the perturbation thresholds. The perturbation thresholds acceptable to the model provide guidance for managers in making decisions. Last, using the WD615 diesel engine and flywheel bearing as examples, this method achieves reliable system health status assessment by accurately assessing system status, incorporating the ability to address external perturbations and providing an easily interpretable output process.

Chronic Coronary Syndromes with/without obstructive coronary arteries: are there differences in ongoing chest pain?

Abstract Background Stable coronary artery disease (CAD) is the archetype chronic coronary syndrome, clinically manifesting as angina with obstructive CAD on angiography and managed via evidence-based guidelines. In contrast, patients with Angina with Non-Obstructive Coronary Arteries (ANOCA) are often diagnosed with ‘non-specific chest pain’ and not appropriately managed. Purpose To compare the Seattle Angina Questionnaire (SAQ) responses and 3-year rates of chest pain hospital presentations in patients with ANOCA compared to Stable CAD. Methods Consecutive angina patients undergoing elective coronary angiography enrolled in the CADOSA (Coronary Angiogram Database of South Australia) Registry between 2012-18 were included. Consenting patients participated in a longitudinal health status assessment utilising the SAQ. In-hospital clinical data was extracted from CADOSA and mortality and hospitalisation data over 12-months was extracted via administrative records. Results Of 6,372 consecutive patients undergoing elective angiography for the investigation of chest pain 2,287 had ANOCA and 4,085 Stable CAD. ANOCA patients were younger (61±11 vs 66±11, p&amp;lt;0.05) and more often female (59% vs 27%, p&amp;lt;0.05) compared to Stable CAD. The SAQ health status assessment was completed at baseline, 1 and 12-months for 377 patients (ANOCA n=156, Stable CAD n=221). At 12-month follow-up, there were no differences in SAQ scores amongst all domains (physical limitation- 92±16 vs 92±15, angina stability- 64±25 vs 62 ±24, angina frequency- 91±17 vs 92±16, treatment satisfaction- 94±13 vs 94±13 and quality of life- 78±22 vs 81±21) between ANOCA and Stable CAD, all p&amp;gt;0.05. However, in the 12-month follow-up, ANOCA patients were less likely to have a chest pain hospital emergency department presentation (10% vs 15%, p&amp;lt;0.001) or admission (6% vs 12%, p&amp;lt;0.005) compared to the Stable CAD cohort. Conclusion Although patient-reported outcomes were identical between Stable CAD and ANOCA patients, the latter were less likely to present to emergency department or be admitted to hospital than their Stable CAD counterparts, reflecting the clinical nihilism encountered in the ANOCA patient journey.

Prognostic role of remnant cholesterol and traditional cardiovascular risk factors in young adults: results of long-term follow-up of a cohort of 30-year-old norwegians

Abstract Background There are limited data on the prevalence and impact of traditional cardiovascular (CV) risk factors among young adults. In addition, more knowledge on novel CV risk factors is needed to explain the residual risk beyond that of the traditional risk factors. Increasing evidence suggest a causal role of remnant cholesterol (RC) in the development of atherosclerotic cardiovascular disease (CVD) and subsequent clinical events. However, the role of RC in young adults is unresolved. Purpose We aimed to evaluate the prevalence of traditional risk factors and the respective sex-differences in young adults, as well as their association with long-term CV events. Further, we hypothesized that higher RC concentrations are associated with impaired CV prognosis. Methods In year 2000, all 30-year-old inhabitants of our city were invited to a cross-sectional study including assessment of health status, anthropometric measurements, and non-fasting blood samples. RC was calculated as total cholesterol minus (LDL-C + HDL-C). Long-term clinical follow-up was performed through linkage with mandatory national registries, 22 years after inclusion. The primary outcome was a composite of cardiovascular death, non-fatal myocardial infarction, non-fatal ischemic stroke, hospitalization for unstable angina and coronary revascularization. Results A total of 5950 individuals were included (56% women). At baseline, men displayed significantly higher systolic and diastolic blood pressure, total cholesterol, LDL-C and triglycerides, as compared with women (Table 1). Also, men had higher levels of RC (Table 1). During a mean follow-up time of 22 years, 108 events (1.8%) occurred. In multivariable Cox regression analysis, non-West-European ancestry (adjusted [a] HR 1.76, 95% confidence interval [CI] 1.03-2.99), diabetes mellitus (aHR 6.39, 95% CI 2.52-16.2), systolic blood pressure (aHR 1.03, 95% CI 1.01-1.04), and LDL-cholesterol (aHR 1.37, 95% CI 1.11-1.68) were associated with an increased risk of CV events during long-term follow-up. Female gender was associated with a reduced risk of CV events (aHR 0.38, 95% CI 0.23-0.64), whereas smoking status did not show any significant association. RC was identified as a significant predictor of CV events (aHR 1.55, 95% CI 1.11-2.18, per 1.0 mmol/L increase). The highest RC quartile (mean RC: 1.1 mmol/L), had the highest event rate (Figure 1, p&amp;lt;0.001 with log-rank test). Conclusion This study uncovers significant sex-differences in modifiable CV risk factors among young adults at the age of 30, which are associated with increased risk for long-term CV events. Interestingly, increasing concentrations of RC at the age of 30 were strongly associated with impaired long-term CV outcome and should be accounted for in early risk prediction.Baseline characteristics.Remnant cholesterol and CVD events.

Association between physical activity based on wearables and self-reported health status among adolescents: NHANES 2011–2014

BackgroundPhysical activity (PA) was strongly associated with health status, with fewer related studies in adolescents (12–19 years). Most current studies using questionnaires to assess PA levels were not objective enough. So, this study used a wearable device to assess PA levels quantitatively and focused on the association between PA levels and self-reported 4 health status among adolescents.MethodsData were obtained from adolescents (2241) with both PA and health status assessments from two cycles of NHANES, 2011–2014, using a wearable accelerometer-based device to assess PA levels quantitatively, MIMS-units as the metric, averaging over all valid days of wear (MIMS-units average). There were 4 health statuses, including (1) whether or not flu, pneumonia, or ear infection, (2) whether or not stomach or intestinal illness, (3) whether or not head cold or chest cold, and (4) general health condition, from the past 30 days self-reported. Weighted multiple logistic regression was used to analyze the association between the 4 health statuses and the MIMS-units average and MIMS-units average quartiles respectively. Subgroup analyses were also conducted on age, sex, BMI, and race.ResultsAfter controlling confounding factors, there was no significant correlation between the MIMS-units average and the four physical health conditions in Table 3 model 3. While MIMS-units average quartiles results showed that compared to lower PA levels (Q1), higher PA levels (Q4) were linked to a lower incidence of “flu”(OR = 0.94, 95% CI [0.91, 0.99], P < 0.001). Furthermore, Q3 was linked to a decrease in the incidence of “head cold” (OR = 0.91, 95% CI [0.85, 0.96], P < 0.001), while Q2 showed a decrease in “stomach illness” (OR = 0.95, 95% CI [0.92, 0.99], P = 0.003) in Table 3 model 3. Subgroup analyses showed that higher PA levels significantly reduced “flu” risk among 12–15 year-olds, girls, obese, and Non-Hispanic White; decreased “head cold” risk among 16–19 year-olds, boys, obese, and Non-Hispanic White; significantly lower risk of “stomach illness” in girls.ConclusionsEnhancing PA levels can help prevent flu, head cold, and stomach or intestinal illness in US adolescents. Further studies are needed to determine whether adolescents in other regions have similar results, and high-quality cohort studies are needed to further validate the causal association.

Trypanosomiasis in Introduced Southern White Rhinoceros (Ceratotherium simum simum) Gifts to Ex Situ Habitat in Aitong, Kenya.

During the opening of diplomatic relations in the 1990s, South Africa gifted 20 southern white rhinoceros (Ceratotherium simum simum) to Kenya. The species is not indigenous to Kenya, and management of the introduction was not clearly addressed in the legislation. Responsibility was left to the private sector and local authorities. Ten of the animals were introduced to land contiguous with the Maasai Mara National Reserve, an area with tsetse-trypanosomiasis challenges, and with rare cases of human sleeping sickness. Mortalities had been previously documented when indigenous naïve black rhinoceros were introduced to areas with tsetse; hence there was no consensus on the management of this introduction. Feasibility was only explored once before with the introduction of two animals in a monitored and managed translocation from Lewa Downs, Laikipia in 1992-1994. Ultimately, Kenyan experts were co-opted to address risk after trypanosomiasis occurred in many animals. Unfortunately, this finding was followed by gradual mortalities of most rhinoceros with only a few being saved by removal to highland private sanctuaries. This event was complicated by many factors. Samples were only sporadically collected, and mainly from sick animals. With no clear responsibility by government agencies, a collaboration between veterinarians and researchers resulted in characterization of the disease challenge, and when invited, assessment of health status. Laboratory diagnostics revealed common and sometimes severe infections with Trypanosoma brucei, a normally infrequent trypanosome. Infection was associated with disturbances in erythropoiesis, especially anemia. Symptoms varied from sudden death associated with intestinal atony, to a semiparalyzed animal that was partially responsive to treatment for trypanosomes. This event should be used as a caution to future movements of this species that are planned or ongoing in Africa, for conservation or other purposes.

Health status assessment of pump station units based on spatio-temporal fusion and uncertainty information

An effective health status assessment (HSA) for pump station units (PSUs) is crucial for accurately determining their real status and providing technical support for safe operational decisions. Due to the limitations of existing data-driven HSA methods, which primarily focus on the temporal dependencies of monitoring signals and fail to explore the complex interconnections among signals comprehensively. Moreover, when constructing performance degradation indices based on linear differences, these methods do not effectively integrate heterogeneous signals, resulting in an incomplete and inaccurate assessment of the overall system degradation. This paper proposes a real-time comprehensive HSA method for PSUs based on multi-source heterogeneous uncertainty information. Initially, a health benchmark model (HBM) is built using CrossGNN, which possesses cross-scale and cross-variable interaction capabilities, to precisely capture the temporal dependencies and dynamic relationships among variables in monitoring signals. Subsequently, key measurement points that reflect the operational status of the PSUs are identified through correlation analysis to establish multi-source evaluation indices. Then, considering the uncertainty in signal changes, a novel health degradation index (HDI) is developed using Mahalanobis distance (MD) and the Gaussian Cloud Model (GCM) to analyze changes in unit status. Furthermore, a weighting calculation method based on the non-dominated sorting genetic algorithm (NSGA-II) is proposed to establish a real-time comprehensive health index (RCHDI) for a thorough assessment of PSUs status. Finally, the effectiveness of the proposed method is validated through a case study using data from a pump station in the South-to-North Water Diversion Project in China. The results show that, compared to other studies, the proposed method significantly improves the stability and smoothness of the state assessment curve, with increases of 21.5% and 47.1% respectively, providing a new perspective for comprehensively assessing the health status of PSUs.V

Automotive workers: the role of coordinative and conditional abilities as effectiveness wellness indicator.

Noncommunicable diseases are on the rise due to sedentary and unhealthy lifestyles. The World Health Organization (WHO) and the American College of Sports Medicine (ACSM) recommend maintaining a healthy diet and engaging in regular physical activity, particularly in the workplace. Prolonged and repetitive work tasks can result in extended sick leave and decreased productivity while at work. Therefore, it is important to identify predictive factors that can act as indicators of the health status of employees. Utilizing motor abilities assessment tests can help to identify health issues at an early stage. Promoting preventive health measures is crucial for addressing chronic diseases and enhancing overall occupational well-being. The purpose of the study was to characterize workers' motor abilities and fitness levels and identify potential indicators. A total of 605 workers participated in this study, including 529 male participants with a height of 176 ± 0.09 cm and a body mass of 75.9 ± 14.1 kg and 77 female participants with a height of 162 ± 0.11 cm and an average body mass of 58.9 ± 11.1 kg. These individuals were enrolled during an Italian automotive corporation's mandatory annual medical check-up. The participants were categorized into three groups based on their occupational roles: blue-collar, manager, and white-collar groups. The participants underwent motor abilities assessment tests for upper-limb strength, trunk flexibility, and reaction time. The blue-collar group reported the best results in upper-limb strength (93.3 kgf ± 18.60), but had the worst results in flexibility (21.7 cm ± 7.90), total reaction time (58.8 s ± 4.74); and average intermedium (0.68 ms ± 0.11). The white-collar group reported the best result in flexibility (23.7 cm ± 8.94) and reaction time (48.5 s ± 4.38; 0.64 ms ± 0.09). Despite being frequently used to assess health status, handgrip measurements may not provide accurate differentiations because of the common use of blue-collar workers for tasks that require upper-limb strength. In contrast, reaction time metrics appear more reliable and discriminative in evaluating a worker's physical fitness.

The intelligent operation and maintenance method and experimental research of CNC machine tool bearings

As the core components of high-precision rotary machine tools, machine tool bearings are prone to failure or damage, and their running state directly affects the safety and reliability of the entire equipment. Therefore, the paper proposes an intelligent operation and health management method for machine tool bearings based on Bayes-VMD-KPCA, Bayes-CNN-SVM, and health status evaluation index. Aiming at the complex working environment and high nonlinear degree of fault features of machine tool bearings, the Bayes-VMD-KPCA algorithm is used to perform variational mode decomposition of the original signal of the bearing fault, which avoids the influence of background noise and solves the problem of high dimension of multi-domain fault feature set. Second, the CNN-SVM fault diagnosis model uses a convolutional neural network (CNN) to deeply mine the extracted multi-domain fault features, and a support vector machine (SVM) can be used as a fault classifier to complete the diagnosis of various fault types of machine tool bearings. At the same time, the Bayes algorithm is introduced to tune the hyperparameters in the CNN-SVM model to further improve the prediction performance of the CNC machine tool-bearing fault diagnosis model. The results show that the intelligent operation and maintenance and health management method of CNC machine tool bearings proposed in this paper can effectively complete the fault type diagnosis and health status assessment of bearings, and its accuracy index reaches 99.33%. Compared with the single SVM model, Bayes-CNN model, Bayes-SVM model, and CNN-SVM model, the health effect is evaluated. The accuracy is increased by 6.33%, 4.33%, 2.66%, and 1%, respectively. It can be seen that the proposed method can more effectively realize the monitoring and health management of bearing fault.

The health status assessment method and practice of emergency diesel generator system in nuclear power plant based on deterioration degree

Abstract Due to the complexity of the emergency diesel generator (EDG) system in nuclear power plants and the lack of detailed equipment-level health status assessment models, this article introduces a deterioration-based system health status assessment method. Taking the large and complex EDG system as a pilot, the method combines monitoring data and maintenance information of the EDG to achieve dynamic assessment of the health status of the EDG system at the equipment level. This provides guidance for predicting and repairing faults of EDGs in nuclear power plants. Additionally, this method can be extended to other large and complex systems.

Hydro-steel structure digital twins: Application in structural health monitoring and maintenance of large-scale reservoir

In the context of frequent accidents during hydro-steel structures (HSS) operations due to harsh environments and extended service conditions, a novel approach is proposed to reduce the frequency of structural failure incidents and ensure safe and reliable operation. The approach begins with introducing a comprehensive DT modeling framework. Subsequently, detailed DT modeling and DT-based SHM methods are developed. Finally, a platform with perception, interaction, analysis, and decision-making for intelligent health monitoring and maintenance of HSS is constructed and validated in China’s large-scale reservoir project, Luhun Reservoir. The platform includes functions of condition monitoring, fault feature recognition, health status assessment, and maintenance strategies optimization. The integration of DT technology has led to significant improvements in health monitoring and maintenance quality, which includes data collection, model optimization, comprehensive evaluation, and decision-making. This approach has also demonstrated its effectiveness by reducing the operation and maintenance response time and enhancing the overall efficiency and reliability.