Health Assessment Model Research Articles

Multi-Sensor Graph Transfer Network for Health Assessment of High-Speed Rail Suspension Systems

Suspension systems are significant for safe and comfort operation of high-speed trains. Health assessment is a useful tool to schedule maintenance plans of suspension systems, and furthermore ensure safety operation of high-speed railway transportation. In real operating condition, two problems, i.e. data imbalance and shortage of labelled data, result in difficult for health assessment of the suspension system using deep learning. In this work, a multi-sensor information fusion method, called as multi-sensor graph transfer network (MSGTN), is proposed in basis of deep transfer learning and graph neural network. In the proposed method, a domain-share multi-sensor graph neural network (MSGNN) is firstly proposed to extract features from vibration signals collected from three different positions in train vehicles. A graph-based fusion layer in MSGNN is proposed to fuse multi-sensor information by combining frequency response curves of the suspension system. The MSGTN mainly includes two parts in source and target domains respectively. In the source domain, a simple physical dynamic model of high-speed rail suspension system is built to generate labelled simulation datasets to pre-train MSGNN. In the target domain, the initial hyper-parameters of MSGNN are that of the pre-train model in the source domain. The labelled data in the target domain is fed to fine-tune MSGNN and then the final model for health assessment can be obtained by minimizing the loss function. The effectiveness of the proposed method was verified using real-work operation data.

Multi-Parameter Health Assessment of Jujube Trees Based on Unmanned Aerial Vehicle Hyperspectral Remote Sensing

To address the current difficult problem of scientifically assessing the health status of date palm trees due to a single parameter for date palm health assessment, an imperfect index system, and low precision. In this paper, using jujube trees in 224 regiment of the 14th division of Xinjiang Production and Construction Corps “Kunyu city” as the research object, we carried out the inversion study of various physicochemical parameters of jujube trees (canopy chlorophyll content, leaf area index (LAI), tree height, canopy area) using the unmanned aerial vehicle (UAV) hyperspectral imagery of jujube trees during the period of fruit expansion, and put forward a model for assessing the health of jujube trees based on multiple physicochemical parameters. First, we calculated six spectral indices for inversion of chlorophyll content and four spectral index for inversion of LAI, analyzed the spectral index with high correlation with chlorophyll content and LAI of jujube trees canopy, and constructed the inversion models of chlorophyll content and LAI. Second, the Mask R-CNN model was used to achieve jujube trees’ canopy segmentation and area extraction, and the segmented canopy was matched with the Canopy Height Model (CHM) for jujube trees’ height extraction. Finally, based on the four physicochemical parameters of inversion, we construct four jujube trees’ health assessment models, namely, Partial Least Squares Regression Analysis (PLSR), Random Forest (RF), Support Vector Machines (SVM), and Decision Tree (DT). The results showed that the R2 of the PLSR tree health assessment model constructed based on the multi-physical and chemical parameters of chlorophyll content, LAI, tree height, and canopy area was 0.853, and the RMSE was 0.3. Compared with the jujube trees’ health assessment models constructed by RF, SVM, and DT, the R2 increased by 0.127, 0.386, and 0.165, and the RMSE decreased by 0.04, 0.175, and 0.063, respectively. This paper can achieve rapid and accurate inversion of multi-physical and chemical parameters of jujube trees with the help of UAV hyperspectral images, and the PLSR model constructed based on multi-physical and chemical parameters can accurately assess the health status of jujube trees and provide a reference for a scientific and reasonable assessment of jujube trees’ health.

Cost-benefits analysis of ultra-low emissions standard on air quality and health impact in thermal power plants in China

As China's largest energy infrastructure, thermal power plant consumed approximately half of China's coal over the past decade and threatened air quality, human health and socioeconomic development. Thus, a series of control policies have been implemented to alleviate those impacts in China. Particularly, China has witnessed unprecedented declines in air pollutant emissions from thermal power plants since the ultra-low emissions (ULE) standards were implemented. In contrast, the effect of the ULE policy on air quality, health and cost benefits remains poorly understood. Therefore, this study estimates the improved air quality and associated health and economic benefits under the ULE standards in the thermal power sector by using a measure-specific approach, combining a bottom-up emission inventory, an atmospheric model, a health assessment model and a cost analysis model. The results show that all the control measures lead to reduced air pollution, and renovating pre-existing units (RPU) is the most effective. Compared to without implementing the ULE policy, the population-weighted average PM2.5 and O3 concentrations decreased by 1.50 μg/m3 and 0.87 ppm, and 67,831 premature deaths could be avoided nationally. Furthermore, the results also show the net economic benefits of combining health benefits and costs due to control measures are 109.92 billion Yuan (in 2015 value) in China. The comprehensive results reveal that the health benefits outweigh the direct policy. Based on these empirical findings and the specific circumstances of China, we suggest that RPU should be further promoted to the entire of China, and if necessary, establish a long-term compensation mechanism for inter-provincial interests and institute and enforce comprehensive policies that carefully consider the health impacts of policies. This study provides strong arguments for China's policy-making and considering tightening emission standards for thermal power plants worldwide.

Research on Safety Operation and Maintenance Management and Health Status Assessment for Lithium Battery Energy Storage System

The safe operation and maintenance of lithium batteries not only needs to monitor the working status of lithium batteries timely and accurately, but also needs to evaluate its health status and automatically switch the backup battery when safety hazards are found. However, research on the safe operation and maintenance of lithium batteries is still lacking. In light of this, this paper constructs a safe operation and maintenance mechanism by monitoring the voltage and surface temperature of the lithium battery. In addition, a novel online health assessment model based on GRU-CNN is proposed to find out the potential safety hazards of lithium batteries timely. The working voltage, current and ambient temperature are used as the input features of the model to predict the degradation of lithium batteries in advance. In addition, a security operation and maintenance controller is designed to quickly switch the backup battery pack when a lithium battery pack appears to have performance degradation or security risks. The system realizes the real-time monitoring of the safe operation of lithium batteries and the rapid assessment of health status, providing technical guarantees for the reliable, stable, and safe operation of the lithium battery system. The experimental comparison verified that the proposed assessment model of the status of health (SOH for short) has less prediction error.

System Health Assessment Model Based on Anomaly Detection

Accurately assessing system health and taking timely interventions play a crucial role in promoting intelligent system operation. Traditional system health assessment techniques depend on human-set thresholds, which are only appropriate for tracking a few indicators and do not accurately represent the system’s health overall. Therefore, keeping track of a large amount of data and a diverse variety of indicators will be extremely challenging. To address this issue, we develop the AD_Select anomaly detection algorithm and suggest a system health assessment model based on indicator anomaly detection. By fusing anomaly detection with system health assessment, this algorithm increases the scientific rigor and precision of health assessment. The experiments of AD_Select anomaly detection algorithm and healthiness assessment model are conducted on the 2018AIOps dataset and industrial dataset respectively, and the results show that both the AD_Select algorithm and the healthiness assessment model proposed in this paper achieve good performance.

Res-HSA: Residual hybrid network with self-attention mechanism for RUL prediction of rotating machinery

Remaining useful life (RUL) prediction is of great significance for improving maintenance efficiency and ensuring the reliability of rotating machinery. In recent years, there are a large number of deep-learning-based methods for RUL prediction of rotating machinery. However, the effect of conventional end-to-end RUL prediction methods relies on the distribution consistency of training data and test data, and conventional health indicator extrapolation RUL prediction methods are susceptible to interference from abnormal fluctuations in the health curve. To overcome the problem, this paper proposes a new RUL prediction method for rotating machinery using health indicators constructed by the residual hybrid network with self-attention mechanism (Res-HSA). First of all, we propose the residual hybrid network combined with self-attention mechanism to extract the high-level degenerate feature. Then, the health assessment model based on Res-HSA is proposed to generate the health indicators of the equipment. To assist in network training, the segmented data labels based on the degradation rule are applied to optimize the labels of training sets. Finally, to address the problem of abnormal fluctuations in the health curve, a fitting interval selection method is used to optimize conventional curve fitting schemes to calculate RUL. Two public datasets, IEEE-PHM-2012-challenge datasets and C-MAPSS datasets, are used to verify the effectiveness of the proposed method. The experiment results on two public datasets show that the RUL prediction method proposed in this paper has good prediction performance. Compared to the state-of-the-art method, the method proposed in this article reaches the most advanced level in some test projects, while the rest of the projects can be very close to the most advanced method.

Health assessment of natural larch forest in arxan guided by forestry remote sensing integrated with canopy feature analysis

This work aims to propose a more accurate assessment method for forest health in natural larch pine forests of the Arxan by integrating remote sensing technology with tree crown feature analysis. Currently, forest health assessment of natural Larch pine forests relies mainly on ground surveys, and there is a gap in the application of remote sensing technology in this field. This work introduces deep learning technology and proposes a spectral-Gabor space discrimination and classification model to analyze multi-spectral remote sensing image features. Additionally, quantitative indicators, such as tree crown features, are incorporated into the forest health assessment system. The health status of natural Larch pine forests is evaluated using forest resource survey data. The results show that the health levels of natural Larch pine forests in different areas vary and are closely related to factors such as canopy density, community structure, age group, and slope. Both quantitative and qualitative indicators are used in the analysis. The introduction of this innovative method enhances the accuracy and efficiency of forest health assessment, providing significant support for forest protection and management. In addition, the classification accuracy of the health assessment model suggested that the maximum statistical values of average classification accuracy, average classification effectiveness, overall classification accuracy, and Kappa were 74.19%, 61.91%, 63.18%, and 57.63%, respectively. This demonstrates that the model can accurately identify the health status of natural larch forests. This work can effectively assess the health status of the natural larch forest in the Arxan and provide relevant suggestions based on the assessment results to offer a reference for the sustainable development of the forest system.

Establishment and application of ecological health evaluation system for urban and rural rivers in Yangtze Estuary

The assessment of river ecosystem health is crucial for improving river resilience, achieving ecological protection and rational utilization in the Yangtze Estuary region where there is high utilization of rivers and a high demand for quality rivers by Shanghai, the world's largest modern city. To assess the ecological health status of Yangtze Estuary rivers, this study established a river health assessment model consisting of five dimensions: water quality, river landscape, aquatic organisms, river hydrology, and human interference, and a total of ten indicators based on the ecological survey results in the summer and autumn of six river channels in Chongming Island in the Yangtze Estuary. The evaluation results reveal that the health status of rural rivers in the northwest and east of Chongming Island (S2, S3) is the best, reaching an excellent level, while the small river in the central part of Chongming Island (S6) is the worst, reaching a somewhat inferior level. Compared with rural rivers, the comprehensive evaluation results of urban rivers are good or ordinary level. The high proportion of building area on both sides of the river and the low vegetation cover are the main factors that restrict their scoring results. In contrast, rural rivers need to focus on the area of buffer zones such as forests and vegetation on both sides of the river, river connectivity, appropriate widening of narrow rivers, regular cleaning and dredging of rivers, as well as reducing human interference with the rivers. Regarding seasonal changes, the health assessment results of Chongming Island rivers in summer are better than those in autumn, and the differences between sites in summer are slightly greater than those in autumn. The seasonal differences between sites are mainly due to changes in indicators of the diversity of zooplankton, phytoplankton, and macrobenthos. To further improve the ecological health of rivers, measures of ecological restoration could be adjusted based on regular health assessment and health weakness analysis.

Research on the application of digital music in mental health education in colleges and universities

Abstract The interdisciplinary study of mental health education and digital music in colleges and universities is an indispensable part of China’s education discipline system. To solve the current problems of mental health education in colleges and universities, this paper analyzes the promotion role of digital music and the development of mental health in colleges and universities. The hierarchical analysis method is used to build a mental health assessment index system in colleges and universities. Using the VPMCD method, based on the intrinsic relationship between different index characteristics, a digital music-based psychological health assessment model for colleges and universities is established to assess the psychological health problems of college students. According to the psychological assessment results, the listening psychological intervention method is used to psychologically intervene with students. And by calculating the main melody of digital music materials, the type of music intervention materials used was determined. Experimental results: The listening psychological intervention method successfully led to the slow recovery of students with severe and moderate mental health problems and the complete recovery of students with mild psychological problems. 11 students (9.2% of the total) were completely cured of their psychological problems among the freshmen students in college A. The number of students who reduced their psychological problems was 86, accounting for 81.1% of the total. 26 students, accounting for 32.3% of the total, were completely cured of their psychological problems in their sophomore year at College A. The number of students who reduced their psychological problems was 56, accounting for 51.3% of the total. It proves that: digital music plays a supplementary role in college mental health education and proposes a new way of implementation for college mental health education. It promotes the diversified development of mental health education in colleges and universities as well as the cultivation of positive psychological qualities of college students and guides them to grow up positively and healthily.

An intelligence expert system for quality health monitoring of power transformers using testing attributes

Power transformers are very vital components in electrical power network. Deterioration of transformer’s insulation has significant impacts on its health condition. Hence, detailed analysis of insulation testing attributes is very important in diagnosing the transformer. It is possible with an effective decision-making system. In the present paper, a new grey relational-based fuzzy logic expert system is proposed for continuous health monitoring of power transformers. In this approach, ranking-based qualitative relational analysis is initially carried out in order to reduce large number of input attributes into three input health grades. These input grades are further integrated with a technically designed expert fuzzy rule base. Outputs of the proposed model are validated with the outputs of multi-criterion based fuzzy logic model presented in [Mharakurwa E T and Goboza R 2019 Multiparameter-based fuzzy logic health index assessment for oil-immersed power transformers Adv. Fuzzy Syst. 2019]. Testing data of 200 in-service transformers are utilised in the validation of both the models. It is observed from the comparison that similarity index of the proposed grey-fuzzy system is 92 percent. Several issues exist in regular transformer health assessment models such as the large number of rules, uncertainties, requirement of large training data, complexity issues are overcome by integrating the grey relational analysis with the fuzzy logic system. The proposed model can be easily implemented by all utilities and diagnostic experts of transformers.

Reconstructing the ecological restoration pattern from the perspective of ecosystem health assessment in a typical black soil region of Northeast China

Assessments on the spatial pattern of ecological restoration potential provide insights into the current status of ecosystem health, facilitate the identification of hotspots of ecosystem degradation, and further serves as a link that bridges regional ecological planning with specific restoration efforts. The goal of this study was to reconstruct the ecological restoration pattern, through establishing an ecosystem health assessment model encompassing four elements of vigor, organization, resilience and service of an ecosystem. Siping City, a typical black soil region located in central Jilin Province of Northeast China, was selected as the study area, and ecological protection and restoration units were identified based on temporal changes in ecosystem health levels from 2000 to 2020. Getis-Ord Gi* analysis was adopted to classify the restoration zones and depict the spatial pattern of ecological restoration. From 2000 to 2020, the ecosystem health index of Siping City generally increased from 0.49 to 0.51 due to increases in forestland area and precipitation, but a fraction of suburban areas also experienced decreases. Four different restoration zones were delineated, including the windbreak and sand-fixing zone in the western part, the black soil protection and reclamation zone in the middle part, the Daheishan ecological shelter zone in the central and eastern part, and the water restoration zone in the eastern part, which account for 28.12%, 37.05%, 16.53% and 18.29% of the total area. The protection units covered an area of 1,189.83 km2 and the restoration units covered an area of 2009.83 km2, which could be further classified into 10 types and accounted for 31.24% of the total area. It was suggested that the restoration methods applied to different units should be tailored towards specific restoration objectives. This proposed methodological framework serves as a basis for reconstructing ecological restoration in the black soil region, and a reference for making restoration plans elsewhere.

College students’ mental health evaluation model based on tensor fusion network with multimodal data during the COVID-19 pandemic

ABSTRACT The COVID-19 pandemic has caused a series of effects on the mental health of college students, especially long-term home isolation or online learning, which has caused college students to have both academic pressure and employment pressure. How to accurately and effectively assess the mental health status of college students has become a research hotspot. Traditional methods based on questionnaires such as Self-Rating Depression Scale (SDS) and Self-Rating Anxiety Scale (SAS) are difficult to collect data and have poor evaluation accuracy. This paper analyzes the psychological state through text-images of multi-modal data with tensor fusion networks and constructs a mental health assessment model for college students. First, the validity of the model is verified through the MVSA (Multi-View Sentiment Analysis) dataset. Second, the psychological state of college students under the epidemic is analyzed using the collected text-images dataset. The results show that the TFN-MDA (Tensor Fusion Network-Multimodal Data Analysis) based mental health assessment model constructed in this paper can effectively assess the mental health status of college students, with an average accuracy of more than 70%.

A Method for Satellite Component Health Assessment Based on Multiparametric Data Distribution Characteristics

This research presents a novel data-based multi-parameter health assessment method to meet the growing need for the in-orbit health assessment of satellite components. This method analyzed changes in component health status by calculating distribution deviations and variation similarities in real-time operational data. Firstly, a single-parameter health state description method based on data distribution characteristics was presented. Secondly, the main health characteristic parameters were selected by mechanistic analysis and expert experience. The CRITIC method and the entropy weighting method were fused to assign reasonable weights and establish a multi-parameter component health assessment model. Then, the feasibility of a component health assessment algorithm based on data distribution characteristics was verified using real telemetry data from satellites. Finally, to verify the rationality of the presented health assessment algorithm, the results were compared with the pre-processed original data using empirical mode decomposition. The experimental results show that the method can accurately describe the change trend of the health status of the components. It proves that the method can be effectively used for the real-time health condition assessment and monitoring of satellite components.

Lithium-ion battery health assessment method based on belief rule base with interpretability

Health assessment is important for the safe and reliable operation of lithium-ion battery. However, there exist two typical issues in existing health assessment models for lithium-ion battery: uncertainty of the chemical reaction inside the battery and lack of interpretability of the evaluation results. The belief rule base (BRB) is a rule-based modelling approach and can deal with uncertain information in health state assessment. However, the interpretability of the lithium-ion battery health state assessment model based on BRB needs to be preserved effectively. Thus, a new lithium-ion battery health state assessment model based on belief rule base with interpretability (BRB-I) is proposed in this paper. In the BRB-I model, both structural interpretability and optimization interpretability are constructed through the mechanism analysis of lithium-ion batteries. Moreover, a new interpretability evaluation criterion is defined as the objective function to achieve a balanced optimization between interpretability and accuracy. In addition, an improved optimization method based on the whale optimization algorithm (WOA) is proposed to enhance the interpretability of the model and the optimization process. A case study on the health state assessment of B0006 lithium-ion battery is conducted to illustrate the effectiveness of the proposed method.

A cable health assessment method based on multi-agent and matter-element extension model

It is of great significance for the stable operation of the power system to accurately evaluate the health status of the cable, and then provide a reference for the implementation of condition maintenance. Based on the matter-element extension model, this paper divides different cable health levels, selects the important state quantity of the cable as the evaluation index, assigns different weights to it, and finally obtains the health level of the cable evaluation. The multi-agent plays its role to make the cable health assessment system run well. The calculation example results show that the health assessment model in this paper can effectively assess the health status of cables in different stages of operation, and the assessment results can be used as a reference for the formulation of on-site maintenance strategies. Compared with other assessment methods, the assessment results are more comprehensive and more reliable.

ASSESSMENT OF KNOWLEDGE AND PRACTICE OF CHILD CARE AMONG MOTHERS OF ONE TO THREE YEARS CHILDREN IN A SELECTED RURAL AND URBAN COMMUNITY. KOLKATA, WEST BENGAL

The investigator conducted a comparative descriptive study to assess Knowledge and Practice of Childcare among Mothers of One to Three years children in a selected rural and urban community. Kolkata, West Bengal. She selected 50 rural mothers and 50 urban mothers using non-probability purposive sampling technique. The conceptual framework is based on Barnard Child health assessment model. Semi Structured and structured interview schedule used for data collection. Result showed that mean knowledge score between rural mothers and urban mothers statically significant as evidenced by calculated‘t’ value 11.42 at 0.05 level of significance . The difference of mean Practice score between rural mothers and urban mothers statically significant as evidenced by calculated‘t’ value 13.75 at 0.05 level of significance. There was relationship between knowledge and practice (0<0.67<1) and (0<0.71<1), in rural and urban community, respectively.The findings of study have implications in the field of Community Health Nursing, Nursing Administration and Nursing Research.

A New Health Analysis Method for Lithium-Ion Batteries Based on the Evidential Reasoning Rule Considering Perturbation

Lithium-ion batteries are widely used in energy storage, small electronic devices and other fields due to their advantages of high energy density and long life cycles, as well as causing less damage to the environment than alternatives. For safety, it is essential to propose reasonable methods to assess batteries’ health statuses. Therefore, a health assessment model based on the evidential reasoning (ER) rule is proposed in this article. Firstly, the voltage rise time and the current fall time are taken as observation indicators, which contain information about the health status of lithium-ion batteries. Secondly, the information of various indicators is integrated into a belief structure, and the indicator reliability and indicator weights are adequately considered in the assessment model. Thirdly, there are some perturbations that will affect the operating status of batteries and cause the batteries’ reliability to fluctuate, so we use perturbation analysis to determine the adaptability of batteries to perturbations. We set two bounded parameters, the perturbation coefficient and the maximum perturbation error, to assess the reliability of lithium-ion batteries when experiencing perturbations. Finally, on the basis of the whole-life open data set of lithium-ion batteries from the National Aeronautics and Space Administration’s Prognostics Center of Excellence, the validity of the health assessment model and perturbation analysis is demonstrated.

Management for stroke intelligent early warning empowered by big data

Global aging population, especially with the global pandemic outbreak of the Corona Virus Disease 2019 (COVID-19), has endangered human health security. Digital information technology through big data empowerment and intelligent application is widely considered a key element to solve the problems. Stroke is a life-threaten disorder. We studied individual health management and disease risk perception using human health assessment model and make full use of wearable wireless sensor, Internet of Things, big data, and Artificial Intelligence for potential risk monitoring and real-time stroke warning. We proposed an effective method of monitoring, early warning and rescue to improve the stroke treatment. The result shows that the health management empowered by big data can generate new opportunities and ideas to solve early detection and warning of stroke.

Pollution Status, Source Apportionment, Ecological and Human Health Risks of Potentially (Eco)toxic Element-Laden Dusts from Urban Roads, Highways and Pedestrian Bridges in Uganda

The occurrence of potentially (eco)toxic elements (PTEs) in street, indoor and roadside dusts have been associated with potential human health risks. For the first time, the pollution levels of PTEs—copper (Cu), nickel (Ni), manganese (Mn), zinc (Zn), lead (Pb), cadmium (Cd) and chromium (Cr)—were investigated in 24 dust samples from eight selected sampling sites on urban roads, highways and pedestrian bridges constructed over River Manafwa, a flood-prone river in Eastern Uganda. Concentration of PTEs in the sample digests were quantified by atomic absorption spectrometry. Multivariate geostatistical (Pearson’s Correlation, Principal Components and Hierarchical Cluster) analyses were used to apportion sources of the contaminants. Contamination, ecological and human health assessment indices and models were employed to establish any potential risks the elements could pose to the environment and humans. The study revealed that there is severe PTE pollution of dusts from roads, highways and pedestrian bridges in Eastern Uganda when compared with their crustal averages, except for Cu, Ni and Cr. The mean concentrations (mg kg−1) of Cu (11.4–23.2), Ni (0.20–23.20), Mn (465.0–2630.0), Zn (26.8–199.0), Pb (185.0–244.0), Cd (0.178–1.994) and Cr (5.40–56.60) were highest in samples obtained near high-traffic areas. Source apportionment studies suggested that Cu, Ni, Mn and Cr are from combustion processes and vehicular traffic, whereas Pb, Zn and Cd came from traffic and geogenic contributions. Assessment using the pollution load index indicated that only dust from Zikoye–Bushika road, the junction of Zikoye–Bushika and Bududa–Manafwa roads and Manafwa town were substantially polluted as the indices were greater than 1. Further assessment of pollution degree of the dust samples using index of geo-accumulation revealed that the dusts were practically uncontaminated to medium-to-strongly contaminated. Health risk assessment showed that there are non-carcinogenic health risks that could emanate from direct ingestion of PTEs in dusts by children. This study therefore opens the lead for investigation of the contamination levels and the health risks of PTEs in dusts from industrial areas as well as busy Ugandan cities such as Kampala, Jinja, Mbarara and Gulu.

Construction of a three-dimensional point cloud model for health assessment of outdoor infrastructure by combining LiDAR point cloud and SfM point cloud

Construction of a three-dimensional point cloud model for health assessment of outdoor infrastructure by combining LiDAR point cloud and SfM point cloud