Assessment Of Health Status Research Articles

Adaptive stepsize forward–backward pursuit and acoustic emission-based health state assessment of high-speed train bearings

Compressed sensing (CS) is a promising tool for data compression reconstruction. However, fault diagnosis methods for high-speed train bearings based on CS and acoustic emission (AE) technologies have not been reported yet. Notably, the accuracy and speed of CS two-stage reconstruction methods are affected and restricted by prior initial conditions. Therefore, this article proposes adaptive dynamic thresholds applicable to adaptive stepsize forward–backward pursuit (ASFBP), and bearing health state assessment method. First, the adaptive dynamic thresholds for atom selection and deletion are constructed based on the residual feedback mechanism and the atom quality distribution law, which enables ASFBP to realize high-precision rapid reconstruction of signal without any atom priori initial conditions. Second, the initial dictionary length is improved based on the AE hit characteristics. Furthermore, a damage state comprehensive evaluation index (DSCEI) is established using principal component analysis based on AE time-domain hit parameters and compression-domain energy parameter. Compared with the kurtosis index and permutation entropy index, the DSCEI demonstrates better monotonicity and stability in the quantitative evaluation of high-speed train bearing condition. Finally, the validity and stability of the method are verified by testing under complex test conditions resembling actual high-speed train lines, providing valuable insights for the CS-based data-driven bearing fault diagnosis.

Assessment of a Pilot Program for Remote Support on Mental Health for Young Physicians in Rural Settings in Peru: Mixed Methods Study.

Telemedicine-based interventions show promise in addressing mental health issues among rural populations, yet evidence regarding their impact among the health care workforce in these contexts remains limited. This study aimed to evaluate the characteristics and the responses and perceptions of recently graduated physicians who work in rural areas of Peru as part of the Servicio Rural Urbano Marginal en Salud (Rural-Urban Marginal Health Service [SERUMS], in Spanish) toward a telehealth intervention to provide remote orientation and accompaniment in mental health. A mixed methods study was carried out involving physicians who graduated from the Universidad Nacional Mayor de San Marcos and participated in the Mental Health Accompaniment Program (MHAP) from August 2022 to February 2023. This program included the assessment of mental health conditions via online forms, the dissemination of informational materials through a website, and, for those with moderate or high levels of mental health issues, the provision of personalized follow-up by trained personnel. Quantitative analysis explored the mental health issues identified among physicians, while qualitative analysis, using semistructured interviews, examined their perceptions of the services provided. Of 75 physicians initially enrolled to the MHAP, 30 (41.6%) opted to undergo assessment and use the services. The average age of the participants was 26.8 (SD 1.9) years, with 17 (56.7%) being female. About 11 (36.7%) reported have current or previous mental health issues, 17 (56.7%) indicating some level of depression, 14 (46.7%) indicated some level of anxiety, 5 (16.6%) presenting a suicidal risk, and 2 (6.7%) attempted suicide during the program. Physicians who did not use the program services reported a lack of advertising and related information, reliance on personal mental health resources, or neglect of symptoms. Those who used the program expressed a positive perception regarding the services, including evaluation and follow-up, although some faced challenges accessing the website. The MHAP has been effective in identifying and managing mental health problems among SERUMS physicians in rural Peru, although it faced challenges related to access and participation. The importance of mental health interventions in this context is highlighted, with recommendations to improve accessibility and promote self-care among participants.

Research on Transformer Health Status Assessment and Lifespan Prediction Based on Multidimensional Deterioration Fusion

Research on Transformer Health Status Assessment and Lifespan Prediction Based on Multidimensional Deterioration Fusion

WormCNN-Assisted Establishment and Analysis of Glycation Stress Models in C. elegans: Insights into Disease and Healthy Aging.

Glycation Stress (GS), induced by advanced glycation end-products (AGEs), significantly impacts aging processes. This study introduces a new model of GS of Caenorhabditis elegans by feeding them Escherichia coli OP50 cultured in a glucose-enriched medium, which better simulates human dietary glycation compared to previous single protein-glucose cross-linking methods. Utilizing WormCNN, a deep learning model, we assessed the health status and calculated the Healthy Aging Index (HAI) of worms with or without GS. Our results demonstrated accelerated aging in the GS group, evidenced by increased autofluorescence and altered gene expression of key aging regulators, daf-2 and daf-16. Additionally, we observed elevated pharyngeal pumping rates in AGEs-fed worms, suggesting an addictive response similar to human dietary patterns. This study highlights the profound effects of GS on worm aging and underscores the critical role of computer vision in accurately assessing health status and aiding in the establishment of disease models. The findings provide insights into glycation-induced aging and offer a comprehensive approach to studying the effects of dietary glycation on aging processes.

The peptide regulator Vezusten in the management of overactive bladder syndrome: an efficacy evaluation

Introduction. Overactive bladder (OAB) remains one of the most difficult-to-treat conditions in neurourology. The number of patients seeking help for OAB symptoms does not decrease each year, while a pathogenetic treatment has not yet been developed. This motivates the search for new approaches.Objective. To assess the efficacy of the peptide regulator Vezusten for women with idiopathic OAB based on their health condition and urodynamic assessment of bacterial detrusor overactivity (DO).Materials & methods. A prospective cohort study included 20 patients diagnosed with OAB, which was confirmed by a comprehensive urodynamic study of DO. The patients visited the clinic three times a week. Each patient received ten intramuscular injections of the peptide regulator Vezusten. Within one month after the last injection, the women were given a monitoring voiding diary, a validated Continental Society OAB Symptom Questionnaire, the Overactive Bladder Questionnaire (OAB-q), and a follow-up urodynamic study (UDS).Results. We obtained statistically significant differences in the following indicators: the number of daily urinations according to the urination diary decreased from 14.0 [13.0; 16.3] to 11.0 [8.8; 12.0] (p < 0,001), patient’s episodes of urge incontinence began to be noted for the first time (p = 0.004). At the same time, the urodynamic maximum cystometric capacity (MCC) increased from 267 [158; 332] to 320 [267; 433] ml. Subjectively, participants noted an improvement in the scores of the OAB questionnaire (OAB-q) — the score decreased from 28 [22; 30] to 19 [14; 24] (p = 0.001), and the number of episodes of nocturia decreased from 4 to 2 (p < 0.001). No side effects from therapy were observed in any of the patients.Conclusion. This study demonstrated a statistically significant improvement in subjective symptoms of OAB in DO-women treated with the peptide regulator Vezusten, but further large placebo-controlled studies are required for confirmation.

Health state assessment model for complex systems: Trade-off accuracy and robustness in belief rule base

In complex system, health state assessment can determine the state of the system and identify potential system problems. However, due to the numerous uncertainties and variations present in complex systems, it is difficult to effectively construct assessment models. Belief rule base (BRB) can use data-driven and knowledge-driven methods to effectively address uncertain information, and is widely used for modeling health state assessments of complex systems. The primary modeling and optimization goals of BRB is currently at accuracy, ignoring the impact of robustness on complex systems, and the reliability of the model is reduced. Therefore, this article introduces a novel method to balance the accuracy and robustness of BRB models. This method enhances the performance of the BRB model in assessing complex system health and provides valuable guidance for engineering applications. Firstly, the guidelines for BRB modeling are systematically summarized to address the trade-off between accuracy and robustness. This provides essential guidance for constructing BRB models during the model-building process. Secondly, four feasible domain criteria are proposed to enhance the reliability of the BRB during the model optimization process. A modified multi-objective optimization algorithm is proposed based on the feasible domain criteria. Finally, in the case studies of aerospace relay and lithium-ion battery health assessments, the MSE of the proposed model for aerospace relay health assessment is 0.0015 with a Lipschitz constant of 6.73, while for lithium-ion battery health assessment, the MSE is 0.0013 with a Lipschitz constant of 24.17. The experimental results demonstrate that the proposed model has an advantage in terms of the trade-offs between both robustness and accuracy.

Computer Vision based Greenhouse Fruits and Vegetables Identification – A Review

In recent years, computer vision has emerged as a powerful tool for automating various processes in agriculture, particularly in greenhouse environments. The identification and classification of vegetables and fruits within greenhouses play a crucial role in monitoring crop growth, assessing health status, and optimizing resource utilization. This review study provides a comprehensive overview of recent advancements in computer vision techniques for the identification of vegetables and fruits in greenhouse settings. This study discusses about various methodologies and challenges in this domain, aiming to provide insights for researchers and practitioners interested in leveraging computer vision for greenhouse agriculture.

Assessment of oral health status amongst postal employees of Bhubaneswar city: A cross-sectional study.

Upholding good dental health has wider effects on productivity and performance at work in addition to being crucial for individual well-being. Because it can affect both their personal well-being and productivity at work, post office workers' oral health is therefore a critical issue of concern. Hence, evaluating Bhubaneswar city post office workers' dental health is the study's aim. With the aid of the WHO 2013 oral health assessment form, a cross-sectional study involving 171 regular post office workers was undertaken. Using the Chi-square test and an ANOVA for pertinent variables, the data were analysed using SPSS statistical software version 27.0. Potential confounding variables were adjusted using multiple logistic regression analysis. The survey included a total of 171 post office personnel, of which 76.6% were men and 23.4% were women. The participants' periodontal health revealed that 67.7% had deep periodontal pockets, 18.7% had shallow ones, and 95.3% had bleeding gums. The mean DMFT of the study participants was 4.10 ± 3.149. Among post office workers, tobacco use and oral health conditions represent a crucial junction of variables that call for caution and preventative action.

Assessment of demographic factors and mental health status: initiating a healthy campus program

Mental health issues have been essential in formulating global health policies for 30 years. The campus community is inseparable from the problem of mental health disorders, which can affect the work either students, lecturers, or staff. This research aimed to describe mental health conditions among the campus community based on demographic factors as an opportunity to initiate a healthy campus program. This study used a quantitative method with a cross-sectional design. The population was an academic community at a private campus in Yogyakarta, Indonesia. There are 347 samples taken by accidental sampling. The mental health instrument used the self-reporting questionnaire. There, 60.81% of respondents experienced psychological disorders, and 73.49% of respondents needed to be referred to a mental health professional regarding addiction, psychotic disorders, and post-traumatic stress disorder (PTSD). There was a significant relationship between demographic factors consisting of gender, age, type of work, employment status, education level, faculty, and emotional disorders with a p-value of <0.005. Moreover, age, gender, education level, type of work, and employment status related to mental disorders due to addiction, psychosis, and PTSD with a p-value<0.005. It is concluded that a large proportion of responders need a referral to a mental health expert. To promote a healthy campus, university officials should follow up with health promotion initiatives such as partnering with the local health office and public health center for additional treatment and improving policy support.

Active mussel biomonitoring for the health status assessment of the Western Mediterranean Sea

The Western Mediterranean coast is under the influence of anthropogenic pressures, including land use, increasing amounts of dangerous waste and habitat destruction. In 2021, the French RINBIO network (http://www.ifremer.fr/envlit/) originally dedicated to assess chemical contamination in the region, focused on biological effects produced by contaminants and the interaction with natural variability in mussels using an active caging strategy. Cell and tissue level biomarkers were applied for 17 sampling sites divided in three sub-regions categorized by different environmental conditions. Results provide critical information for ecosystem health assessment using mussels as sentinel species in the Western Mediterranean Sea. The influence of natural and confounding factors (trophic condition, reproductive cycle, caging strategy), on biological responses to mild chemical contamination, was discussed and discriminated for health status assessment. Results provide valuable data available as reference values for the assessment of biomarkers and histopathological alterations for large-scale active biomonitoring campaigns in the Western Mediterranean Sea.

Research on the service life of bearings in the gearbox of rolling mill transmission system under non-steady lubrication state

A rolling bearing plays a key role in the gearbox of the cold rolling mill transmission system. As a result, the degradation and failure of bearings can lead to unplanned shutdowns of the entire rolling mill system. Since on-site cold rolling mills work usually in non-steady lubrication rolling conditions originating form variations of multiple parameters, the uncertainty affecting bearing performance in a cold rolling mill transmission system increases, making it more difficult to assess health status and predict the remaining service life of bearings, Therefore, by establishing a coupling model describing the relationship for the rolling mill and normal /faulty gearboxes under non steady rolling conditions, quantitatively studies the influence of various rolling parameters of a rolling mill on the fatigue service life of bearings in the gearbox of the rolling mill transmission system, and verify the effectiveness of linear cumulative damage theory for bearing fatigue service life through experiments and on-site bearing vibration data. The results indicate that as the thickness of the strip steel inlet, lubricant viscosity, and rolling speed increase, or the thickness of the strip steel outlet and rolling roll radius decrease, the service life of bearings gradually decreases. As the fluctuation amplitude of various rolling parameters in the cold rolling mill increases, the service life of bearings in the gearbox of the rolling mill transmission system gradually shortens. Moreover, when the rolling parameter value or fluctuation amplitude increases to a specific values, the remaining service life of bearings will sharply decrease. Under the same rolling conditions, the service life of bearings in gearboxes with faults decreases more significantly than that in normal gearboxes.

Health status and mental health among elderly in rural, Thailand: a community based cross-sectional study

Background: The transition to an aging society in Thailand over the past several years has had significant impacts across various sectors, include the quality of life for the elderly, health and mental health problems, and the need to expand healthcare services. Therefore, it is essential to investigate the health and mental health status of the elderly in Thai society. Methods: A community based cross-sectional study was conducted to assess health status and mental health among elderly rural areas. A total of 158 samples were collected from the district of U Thong, Suphan Buri province during August and September 2023. Data were collected using the Basic Geriatric Screening (BGS), and Thai Mental Health Indicators version 2007 (TMHI-15) questionnaires. The demographic characteristics questionnaire was also applied. Descriptive statistics were utilized for data analysis. Results: Out of a total of 158 samples, 65.2% were female. The mean age of the participants was 72.2±8.5 years. The prevalence of chronic diseases among the participants was 69.0%. Nutritional status assessment revealed that 57.0% of the participants were overweight or obese based on BMI. Disability data indicated that 10.1% of the participants had hearing impairments, 63.9% had visual impairments, and 71.5% had oral health problems. Overall, 8.2% of the elderly had limitations in activities of daily living, and 19.6% experienced poor mental health. Conclusions: The result showed that important health problems in elderly were non-communicable diseases, disability, and mental health.

A multi-source data fusion driven power field effect transistor health state assessment and remaining useful life prediction method

Abstract The metal oxide semiconductor field effect transistor (MOSFET) is subjected to various stresses due to the external and internal operating environments, leading to ageing and failure of the device. With multiple degradation mechanisms, a single piece of information can no longer fully reflect the health state of MOSFETs, so how to use multi-source data to characterise the state of the device and predict the remaining useful life (RUL) is an issue worth exploring. To address this problem, a method for constructing health indicators (HI) as well as predicting RUL using multi-source data is proposed. In this method, firstly, the features are computed by selecting the appropriate ageing signal from the ageing mechanism. Secondly, the extracted features are filtered using Pearson’s algorithm to find the features that are strongly correlated with longevity. Then, the filtered features are merged by dimensionality reduction using the kernel principal component analysis algorithm and the HI is constructed from the reduced result. Finally, an unsupervised clustering algorithm is used to classify the states based on the data distribution in HI, and the filtered features are used as input to the grey wolf optimisation bidirectional long short-term memory neural network to predict the RUL of the device. In this paper, the proposed method is validated using data from the MOSFET Accelerated Aging Experiment at the NASA Ames Centre of Excellence for Prediction. The results show that the method is able to achieve good results in health state assessment and RUL prediction of MOSFETs. The proposed method is an effective and comprehensive strategy that is more helpful for the operation and maintenance of electronics.

A new aeronautical relay health state assessment method based on generic belief rule base with attribute reliability

As a classical rule-based modeling approach, belief rule base (BRB) expert system can integrate expert knowledge and possesses good interpretability. BRB with attribute reliability (BRB-r), built upon BRB, provides an effective way to deal with the problems of model reliability and environmental disturbances. Moreover, robustness is an important measure of perturbation resistance, and a robust BRB-r can remain reliable and stable in various environments. Therefore, to improve the model's ability to resist perturbations and enhance the model's adaptability, a new generic BRB with attribute reliability (G-BRB-r) is developed. Specifically, the robustness of BRB-r is analyzed in this paper to explore the change of BRB-r robustness under different perturbations. In addition, combining the effects of different factors on robustness, the construction criteria and constraints of robust BRB-r are given to guide modeling. Then, considering the effects of attribute reliability and robustness on modeling performance, a new generic BRB with attribute reliability is developed. Finally, the effectiveness and adaptability of the proposed method are demonstrated through a case study for health state assessment of the aerospace relay.

M4EEG: MATCHING NETWORK-BASED MENTAL HEALTH STATUS ASSESSMENT MODEL USING EEG SIGNALS

Mental health is critical to an individual’s life and social functioning and affects emotions, cognition and behavior. Mental health status assessments can help individuals understand their own psychological status, identify potential problems in real-time and implement effective interventions to promote favorable mental health. In this study, a deep learning approach was used to construct a simple-minded and flexible model for electroencephalogram (EEG)-based mental health status assessment to construct the corresponding M4EEG model. This model is suitable not only for supervised learning tasks containing a large amount of labeled data but also for few-shot classification tasks in special cases. During execution, certain components of a pretrained transformer model are utilized as the model’s foundation. After deriving feature values from different inputs, these features are decoupled by cross-connecting them into the relation module. Finally, the correlation between the outputs and the classification results are determined by a relation score. In experiments, the Database for Emotion Analysis using Physiological Signals (DEAP) and Affective Mood and Interpersonal Goals in the School Environment (AMIGOS) datasets were partitioned into K-Shot files as the input information, and the classification results were derived from the M4EEG model. These results showed that the M4EEG model is capable of assessing mental health status through EEG, and the model can obtain results that cannot be achieved by existing models that do not apply comparable data labeling.

Application of computer vision techniques to damage detection in underwater concrete structures

Underwater concrete structure crack detection and structural health condition assessment based on image processing is a challenging task. The complex underwater environment and severe image degradation seriously affect the accuracy of crack detection. To solve these problems, a monocular vision and image-enhanced fractal-based fractal science based on computer vision and image processing techniques are proposed to carry out a non-contact detection study of underwater concrete cracks. In this study, a four-level structural health condition was established to assist in underwater crack measurement and safety assessment. The box-counting method was used as a practical tool to calculate the fractal dimension. To verify the effective distance of the algorithm, three distances of 0.5 m,0.8 m, and 1.2 m were set. The results show that the method proposed in this study can effectively detect cracks in submerged concrete members within 0.6 m and help managers correctly determine the health of the structure using the fractal dimension.

Mental health problems raise the odds of cognitive impairment in COVID-19 survivors.

COVID-19 survivors around the globe are suffering from mental health issues. While mental health problems can be an early warning sign of dementia, they may also increase the chances of developing the disease. In this study, we examined the mental health of COVID-19 survivors and mapped its associations with cognitive and demographic variables. COVID-19 survivors listed in the databases of three tertiary care hospitals in Kolkata were contacted sequentially. 376 willing patients were interviewed over the telephone. 99 COVID-19 patients and 31 matched controls participated in the in-person interviews that were arranged for a more detailed investigation. The participants were administered standardized tests that are widely used for the assessment of cognitive functioning and mental health status. 64.89% of COVID-19 survivors reported a deterioration in physical functioning. 44.95% reported a decline in mental health, whereas 41.49% reported a drop in cognitive performance. Detailed investigations revealed that they had an increased risk of having depression, anxiety, and poor sleep quality by 91%, 68%, and 140%, respectively. 6.1% of the patients had mild cognitive impairment, and 4% had dementia. COVID-19 patients who had depression and anxiety were 8.6 and 19.4 times more likely to have cognitive decline, respectively. Compared to the matched controls, COVID-19 patients had greater depression (p<.001), anxiety (p<.001), stress (p =.003), and insomnia (p <.001). They also scored significantly lower on Addenbrooke's Cognitive Examination-III (p =.009) and Picture Naming Test (p =.005) and took significantly longer to complete Trail Making Test-A (p =.002). COVID-19 survivors in this study had major mental health issues even one year after contracting the virus. They had significant cognitive deficits that might progress into dementia. Strict monitoring and systematic treatment plans should be implemented as soon as possible.

Development of a Simple Patient-reported Outcome Measurement for Terminally Ill Cancer Patients Receiving Home-based Palliative Care.

To develop a patient-reported outcome measurement for terminally ill cancer patients (PROMs-TCP) receiving home-based palliative care, which is valid, reliable and easy to use by patients or caregivers to indicate urgent needs for assistance from the care team. Three-step approach consisting of literature review, focus groups and questionnaire testing. 169 terminally ill cancer patients who received palliative care at Cancer hospital, tertiary-care hospital and university school of medicine in Thailand. The PROMs-TCP comprised five key questions with a total score of 10 and one supplemental question. PROMs-TCP was tested for content validity, internal consistency and inter-rater reliability, criterion validity, discriminant validity and sensitivity to change. The palliative care outcome scale (POS) was used as an indicator. PROMs-TCP consists of five questions. The item-level content validity index (CVI) ranged from 0.8 to 1, and the scale-level CVI was 0.97. PROMs-TCP correlated well with POS scores, with correlations ranging from -0.7 to -0.8. Internal consistency was good (Cronbach's α = 0.85), while inter-rater agreements between patients and caregivers and between patients and nurses were moderate to good (Cohen's weighted k = 0.69-0.87). The tool could reasonably discriminate terrible days from good days for the patients. It was also responsive to change scores, with effect size scores of 0.36. PROMs-TCP could be used for daily health status assessment of home-based patients with terminally ill cancer, supporting the provision of palliative care in primary care settings.

AI-Based Visual Early Warning System

Facial expressions are a universally recognised means of conveying internal emotional states across diverse human cultural and ethnic groups. Recent advances in understanding people’s emotions expressed through verbal and non-verbal communication are particularly noteworthy in the clinical context for the assessment of patients’ health and well-being. Facial expression recognition (FER) plays an important and vital role in health care, providing communication with a patient’s feelings and allowing the assessment and monitoring of mental and physical health conditions. This paper shows that automatic machine learning methods can predict health deterioration accurately and robustly, independent of human subjective assessment. The prior work of this paper is to discover the early signs of deteriorating health that align with the principles of preventive reactions, improving health outcomes and human survival, and promoting overall health and well-being. Therefore, methods are developed to create a facial database mimicking the underlying muscular structure of the face, whose Action Unit motions can then be transferred to human face images, thus displaying animated expressions of interest. Then, building and developing an automatic system based on convolution neural networks (CNN) and long short-term memory (LSTM) to recognise patterns of facial expressions with a focus on patients at risk of deterioration in hospital wards. This research presents state-of-the-art results on generating and modelling synthetic database and automated deterioration prediction through FEs with 99.89% accuracy. The main contributions to knowledge from this paper can be summarized as (1) the generation of visual datasets mimicking real-life samples of facial expressions indicating health deterioration, (2) improvement of the understanding and communication with patients at risk of deterioration through facial expression analysis, and (3) development of a state-of-the-art model to recognize such facial expressions using a ConvLSTM model.

Monitoring data motivated health condition assessment of cement concrete pavements in field based on FBG sensing technology

Health condition of cement concrete pavements in field is difficult to characterize due to the random action of traffic loads and environmental variations. It is also difficult to accurately get the deformation merely based on theoretical or simulation analysis, for the heterogeneous material and complicated interfacial bonding properties of multilayered concrete pavements. Therefore, in situ health monitoring is particularly significant for structural condition assessment. To contribute this topic, improved fiber Bragg grating sensors have been designed and embedded in the two-layered pavement to measure the strain, deformation, and temperature. Deformation information of the cement concrete pavement under environmental action in about 3 years has been analyzed. Data analysis indicates that the longitudinal and transverse deformations of the pavement have behaved in nonuniform distribution, and local points suffered from the large tensile strains. The vertical measurement data declared that local interfacial micro void defects existed in the soil base and kept stable during the following 3 years. The temperature variation inside the pavement was highly correlated with the environmental temperature, and the variation amplitude was relatively smaller than the environmental temperatures, with the maximum amplitude smaller than 4°C. The load and temperature transfer principles in different layers have also been checked. Generally, the monitoring data in field from the year 2020 to year 2023 have declared that the concrete pavement has good performance to resist the combined action of traffic loads and environmental temperatures. The study can help to understand the actual structural performance of cement concrete pavements in field based on the optical fiber sensing system.