Health Assessment Model Research Articles

Health Risk of Heavy Metals in Drinking Water Sources of Water-Carrying Lakes Affected by Retreating Polder: A Case Study of Luoma Lake

Heavy metal pollution is a critical issue affecting the safety of drinking water sources. However, the impact of human activities on heavy metal risk levels in water-carrying lakes remains unclear. This study aims to explore the risk mechanisms of heavy metals in Luoma Lake, an important water-carrying lake for the South-to-North Water Diversion Project. We explored the spatial and temporal differences in the distribution of heavy metals in Lake Luoma using methods such as the heavy metal pollution index (HPI) and assessed the risk variations using a health assessment model. The results indicated that heavy metal concentrations in water-carrying lakes generally decreased during the dry season, with Mn and Zn levels decreasing by 89.3% and 56.2%, respectively. The comprehensive score of HPI decreased by 13.16% following the retreating polder compared to the control area (Non-retreating polder area). Furthermore, the HPI at the drinking water intake was lower, which is closely associated with the elevated dissolved oxygen (DO) and oxidation–reduction potential (ORP) resulting from water diversion. The annual average health risk across the entire lake was not significant, with higher levels observed in the control area. The annual non-carcinogenic risk levels of Mn, Ni, Cu, Zn, and Pb range from 10−13 to 10−9, which are considered negligible risk levels. Notably, the carcinogenic risk posed by arsenic (As) through the drinking pathway reached 10−5 a−1, exceeding the maximum levels recommended by certain organizations. These findings provide a critical foundation for managing heavy metals in water-carrying drinking water sources.

Lithium-Ion Battery Health Assessment Method Based on Double Optimization Belief Rule Base with Interpretability

Health assessment is necessary to ensure that lithium-ion batteries operate safely and dependably. Nonetheless, there are the following two common problems with the health assessment models for lithium-ion batteries that are currently in use: inability to comprehend the assessment results and the uncertainty around the chemical reactions occurring inside the battery. A rule-based modeling strategy that can handle ambiguous data in health state evaluation is the belief rule base (BRB). In existing BRB studies, experts often provide parameters such as the initial belief degree, but the parameters may not match the current data. In addition, random global optimization methods may undermine the interpretability of expert knowledge. Therefore, this paper proposes a lithium-ion battery health assessment method based on the double optimization belief rule base with interpretability (DO-BRB-I). First, the belief degree is optimized according to the data distribution. Then, to increase accuracy, belief degrees and other parameters are further optimized using the projection covariance matrix adaptive evolution strategy (P-CMA-ES). At the same time, four interpretability constraint strategies are suggested based on the features of lithium-ion batteries to preserve interpretability throughout the optimization process. Finally, to confirm the efficacy of the suggested approach, a sample of the health status assessment of the B0006 lithium-ion battery is provided.

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.

Urban Lake Health Assessment Based on the Synergistic Perspective of Water Environment and Social Service Functions.

Urban lakes serve as vital ecological and recreational anchors within built environments, essential for enhancing urban resilience. Evaluating lake health predominantly focuses on water quality, assessing indicators such as nutrient levels, toxicity, pH balance, and water clarity to monitor changes. This study proposes a comprehensive evaluation framework that systematically describes specific spatiotemporal manifestations and periodic exogenous regulation characteristics across five dimensions: physical structure, water quality, shoreline dynamics, external regulation, and social service. Furthermore, it introduces an urban lake health assessment model based on synergistic development to evaluate the integrated development and interaction between water environments and social services. This model is applied across urban lakes in various developmental stages in China. Key findings include: 1) Urban development often impacts lake health disparately, with varying degrees of synergy observed between water environments and social services across different urban lakes. However, shifts in urban ideologies and improvements in governance, along with protective policies and project implementations, have contributed to improving water quality to some extent. 2) Engineering interventions do not consistently correspond with improvements in water quality, and governance measures sometimes yield mixed outcomes, underscoring the necessity for systematic solutions to lake health. Restoring hydrological processes emerges as crucial for enhancing sustainability.

A Multitime-Scale Deep Learning Model for Lithium-ion Battery Health Assessment Using Soft Parameter-sharing Mechanism

A Multitime-Scale Deep Learning Model for Lithium-ion Battery Health Assessment Using Soft Parameter-sharing Mechanism

Assessing reproductive effects and epigenetic responses in Austrolebias charrua exposed to Roundup Transorb®: Insights from miRNA profiling and molecular interaction analysis

This study examines the effects of Roundup Transorb® (RDT) exposure on reproductive functions and ovarian miRNA expression in Austrolebias charrua. Exposure to RDT (at 0.065 or 5 mg. L−1 for 96 h) significantly disrupts fertility, evidenced by changes in fertilization rates and egg diameter. Profiling of ovarian miRNAs identified a total 205 miRNAs in A. charrua. Among these, three miRNAs were upregulated (miR-10b-5p, miR-132–3p, miR-100–5p), while ten miRNAs were downregulated (miR-499–5p, miR-375, miR-205–5p, miR-206–3p, miR-203a-3p, miR-133b-3p, miR-203b-5p, miR-184, miR-133a-3p, miR-2188–5p) compared to non-exposed fish. This study reveals that differentially expressed miRNAs are linked to molecular pathways such as steroid hormone biosynthesis, lipid and carbohydrate metabolism, bioenergetics, and antioxidant defense. It also analyzes molecular interactions between miRNAs and target genes during RDT exposure in annual killifish, providing insights into biomarkers in ecotoxicology. Moreover, it provides scope for developing environmental health assessment models based on epigenomic endpoints, supporting the protection of biodiversity and ecosystem services through the quantification of stress responses in living organisms exposed to pesticides.

Registered general nurses' health assessment practices in a tertiary hospital: A focused ethnography study.

To explore the assumptions and values that influence nursing health assessment practices among registered general nurses in general medical and surgical wards. The study was designed as a focused ethnography. A semi-structured interview guide was used to explore prevailing nursing health assessment practices of 13 registered general nurses in an attempt to explore the assumptions and values influencing health assessment practices in the study setting. Data were analysed inductively using an interpretive qualitative content analysis method. Nursing health assessment practices, and underlying assumptions and values were underpinned by a central theme of a culture of low expectation relating to nursing health assessment. The culture of low expectation was highlighted in five themes: (1) Unsystematic Assessment of Health Status, (2) Purpose of Nursing Health Assessment, (3) The Role of Nursing Educational and Regulatory Institutions, (4) Ward Ethos and (5) The Role of Organizational and Ward Leadership. The adoption of a holistic nursing health assessment framework with a clearly defined purpose of aiding nursing diagnoses can guide patient-centred care delivery and facilitate early recognition of physiological deterioration. Thirteen registered general nurses were interviewed, and the initial findings returned to them for validation. The potential contribution of nursing health assessment to nursing practice and patient outcomes may not be fully realized if nursing health assessment is not situated within a holistic health assessment model with a clearly defined purpose for nursing practice.

Establishing a telehealth model addressing paediatric sleep health in remote and rural Northern Territory Australia: Overcoming the distance barrier.

This study examined the outcomes of a telehealth model for sleep health assessment among Indigenous and non-Indigenous children residing in remote and regional communities at the Top End Northern Territory (NT) of Australia. Video telehealth consultation, that included clinical history and relevant physical findings assessed virtually with an interstate paediatric sleep physician was conducted remotely. Polysomnography (PSG) and therapeutic interventions were carried out locally at Darwin, NT. The study participants were children referred between 2015 and 2020. Of the total 812 children referred for sleep assessment, 699 underwent a diagnostic PSG. The majority of patients were female (63%), non-Indigenous (81%) and resided in outer regional areas (88%). Indigenous children were significantly older and resided in remote or very remote locations (22% vs. 10%). Referral patterns differed according to locality and Indigenous status - (non-Indigenous via private (53%), Indigenous via public system (35%)). Receipt of referrals to initial consultation was a median of 16 days and 4 weeks from consult to PSG. Remote children had slightly longer time delay between the referral and initial consult (32 vs. 15 days). Fifty one percent were diagnosed to have OSA, 27% underwent adenotonsillectomy and 2% were prescribed with CPAP therapy. This study has demonstrated that a telehealth model can be an effective way in overcoming logistical barriers and in providing sleep health services to children in remote and regional Australia. Further innovative efforts are needed to improve the service model and expand the reach for vulnerable children in very remote communities.

Simulation of health assessment model for thermal management system of lithium-ion battery based on fuzzy clustering algorithm

Considering the inadequate assessment of the factor decline index in the thermal management system for lithium-ion batteries, which has a negative impact on their efficient utilization, a fuzzy clustering algorithm is suggested to create a simulation model for evaluating the health of the lithium-ion battery thermal management system. In the process of restoring characteristic data by the fuzzy clustering algorithm, according to the characteristic parameters of energy signal and power signal, the decay characteristic results of the lithium-ion battery thermal management system are extracted. Taking the minimum attenuation as a reference, the evaluation function is calculated, the health trend of the lithium-ion battery thermal management system is evaluated, the health grade is established, and the health evaluation model of the lithium-ion battery thermal management system is constructed. The experimental results show that the prediction of the remaining service life of five kinds of lithium-ion batteries by this model is consistent with the actual value, and the average absolute error is always within 0.02, which is superior in health assessment.

Optimal Maintenance Decision Method for a Sensor Network Based on Belief Rule Base considering Attribute Correlation

Optimal maintenance decision for a sensor network aims to intelligently determine the optimal repair time. The accuracy of the optimal maintenance decision method directly affects the reliability and safety of the sensor network. This paper develops a new optimal maintenance decision method based on belief rule base considering attribute correlation (BRB-c), which is designed to address three challenges: the lack of observation data, complex system mechanisms, and characteristic correlation. This method consists of two sections: the health state assessment model and the health state prediction model. Firstly, the former is accomplished through a BRB-c-based health assessment model that considers characteristic correlation. Subsequently, based on the current health state, a Wiener process is used to predict the health state of the sensor network. After predicting the health state, experts are then required to establish the minimum threshold, which in turn determines the optimal maintenance time. To demonstrate the proposed method is effective, a case study for the wireless sensor network (WSN) of oil storage tank was conducted. The experimental data were collected from an actual storage tank sensor network in Hainan Province, China. The experimental results validate the accuracy of the developed optimal maintenance decision model, confirming its capability to efficiently predict the optimal maintenance time.

A Co-Created Assessment Framework to Measure Inclusive Health and Wellbeing in a Vulnerable Context in the South of Europe.

Rapid urbanisation exacerbates health and wellbeing disparities in vulnerable contexts and underscores the imperative need to develop innovative and participatory co-creation approaches to understand and address the specificities of these contexts. This paper presents a method to develop an assessment framework that integrates top-down dimensions with bottom-up perspectives to monitor the impact of inclusive health and wellbeing interventions tailored to the neighbourhood's needs in Las Palmeras, a vulnerable neighbourhood in Cordoba (Spain). Drawing upon studies in the literature examining urban health and wellbeing trends, it delineates a participatory and inclusive framework, emphasising the need for context-specific indicators and assessment tools. Involving diverse stakeholders, including residents and professionals, it enriches the process and identifies key indicators and assessment methods. This approach provides valuable insights for managing innovative solutions, aligning them with local expectations, and measuring their impact. It contributes to the discourse on inclusive urban health by advocating for participatory, context-specific strategies and interdisciplinary collaboration. While not universally applicable, the framework offers a model for health assessment in vulnerable contexts, encouraging further development of community-based tools for promoting inclusive wellbeing.

Multiple Soil Health Assessment Methods for Evaluating Effects of Organic Fertilization in Farmland Soil of Agro-Pastoral Ecotone

The incorporation of organic fertilizer is an important practice to improve the sustainability and productivity of crop production and decrease environmental pollution from crop-livestock systems in global agriculture. However, establishing an evaluation dataset is still the main challenge for quickly and effectively assessing the effect of management measures on farmland soil health. Hereby, we developed a minimum dataset (MDS) using three methods (network analysis (NA), random forest analysis (RF), and principal component analysis (PCA)). Based on MDS and two scoring functions (nonlinear (NL) and linear scoring curve (L)), the SHI (soil health index framework) was constructed to assess soil health conditions under four fertilization treatments (no fertilization, CK; only chemical fertilizer, NPK; only cow manure, MF; 50% chemical fertilizer + 50% cow manure, NPKM) in the northern ecotone of China. The results showed that the MDS-based on SHIs were positively correlated with each other and SHI-TDS (total dataset), verifying the consistency of soil health assessment models. Higher R2 was observed in the fitting of SHIs based on NA and TDS, which suggested that nMDS (minimum dataset based on network analysis) could represent most of the information in the TDS. The SHI-NL-nMDS (based on network analysis and nonlinear scoring curve) has the highest ability of sensitivity and accuracy, which indicates that compared with PCA and RF, the SHI based on NA can better embody farmland sol ecosystem functions. In addition, crop yield was significantly positive relative to SHI (soil health index). The incorporation of cow manure and chemical fertilizer improved soil health and increased crop yield. These results indicate that network analysis was a reliable technology for determining the minimum dataset in the evaluation of farmland soil health, and incorporating livestock manure could improve soil health and crop yield in this study area.

Constructing an Efficient Health Assessment Model for Drainage Network to Evaluate the Drainage Network in Zone A of Zhenjiang City

Aiming at the problem of low pollutant concentration in the sewage treatment plant due to external water intrusion into the sewage collection system, which in turn leads to low pollutant reduction efficiency. A sewage system in Zhenjiang City is taken as an example. Analyze the situation of external water intrusion in the sewage pipe network and determine the external water intrusion proportion based on the water quality and quantity method. First, the dry season flow rate of the sewage pipe is obtained according to the monitoring data of the flowmeter. Then, the key research areas are screened out based on the changes in the concentration of water quality characteristic factors. Furthermore, chemical oxygen demand and electrical conductivity are used as water quality characteristic indicators to characterize shallow groundwater and river water. In addition, the proportions of groundwater and river water intrusion in the sewage pipe network are quantitatively analyzed based on the chemical mass balance equation. At the same time, the dredging detection method is used to assist in the investigation, and finally, the engineering rectification of the problems found in the drainage is carried out. The results show that the water quality and quantity method can effectively identify the types of external water and analyze the proportion of external water intrusion, which is of exemplary significance for the evaluation of sewage collection systems.

Health assessment of an electro-hydraulic servo pump control system for servomotor based on LGA deep neural network

Due to its advantages of having a high power-to-weight ratio and being energy-efficient, the electro-hydraulic servo pump control system (abbreviated as EHSPCS) is frequently employed in the industrial field, such as the electro-hydraulic servo pump control (EHSPC) servomotor for steam turbine valve regulation control. However, the EHSPCS has strong nonlinearity and time-varying features, and the factors that cause system performance degradation are complex. Once a system failure occurs, it may lead to serious accidents, causing serious casualties and economic losses. To address the above issues, a system health assessment method based on LSTM-GRNN-ANN (LGA) deep neural network is proposed in this paper. Firstly, with oil volume gas content, servo motor air-gap flux density, and system leakage coefficient as the health assessment performance indicators, a health assessment performance index system for the EHSPCS is built, Furthermore, the system performance index threshold is set. Secondly, an LGA deep neural network is constructed by combining LSTM, GRNN and ANN, and a deep neural network based on the LGA is used to create an EHSPCS health assessment model. Subsequently, system feature parameter extraction, algorithm design, and parameter debugging are carried out. Finally, an EHSPCS experimental platform is established, typical system failure simulation experiments are designed, and comparative experimental analysis is conducted. The experimental findings demonstrate that the average accuracy of the system health assessment model based on the LGA deep neural network suggested in this paper is 96.37%, compared to 89.84%, 87.99% for LSTM and GRNN, which validates the accuracy of the system health assessment model based on the LGA deep neural network.

Reliability enhancement of state of health assessment model of lithium-ion battery considering the uncertainty with quantile distribution of deep features

Lithium-ion batteries (LIBs) are widely used in many fields, such as electric vehicles and energy storage, and directly impact the device performance and safety. Therefore, the state of health (SOH) assessment is critical for LIB usage. However, most of the existing data-driven SOH modeling methods overlook the inherent uncertainty in battery health prediction, which decreases the reliability of the model. To address this issue, this paper proposes a novel SOH assessment model based on the deep learning framework. The SOH results are derived from the quantile distribution of deep features, giving the SOH values with associated confidence intervals. This enhances the reliability and generalization of SOH assessment results. Additionally, to complete the optimization of the deep model, a Wasserstein distance-based quantile Huber (QH) loss function is developed. This function integrates Huber loss and quantile regression loss, enabling the model to be optimized based on a distribution output. The proposed method is validated using the NASA dataset, and the results confirm that the proposed method can effectively estimate the SOH of LIB while accounting for uncertainty. The incorporation of SOH distribution enhances the reliability and generalization ability of the SOH assessment model.

Spatial-temporal Evolution of Ecosystem Health and Its Influencing Factors in Beijing-Tianjin-Hebei Region

Exploring ecosystem health and its influencing factors is of great significance for promoting regional sustainable development. An ecosystem health assessment model was constructed, and the spatial-temporal evolution characteristics of ecosystem health in the Beijing-Tianjin-Hebei Region in 2000, 2010, and 2020 were analyzed. The geographical detector and GWR were used to identify the dominant factors affecting ecosystem health. The main conclusions were as follows:during the study period, the index of ecosystem natural health in the Beijing-Tianjin-Hebei Region was generally better in the north and west than that in the southeast, and it showed an overall upward trend. The index of ecosystem services in the Beijing-Tianjin-Hebei Region presented as a spatial differentiation pattern of high in the north and low in the south, and it showed a downward trend. The ecosystem health level in the Beijing-Tianjin-Hebei Region showed a trend of rising first and then declining, showing significant heterogeneity in spatial distribution. The ecological health level in the central urban area of large cities was mostly poor, and the ecosystem health level in the Yanshan and Taihang Mountains and Bohai Rim was better. During the study period, the spatial pattern of ecosystem health in the Beijing-Tianjin-Hebei Region remained relatively stable. The hot spots and sub-hot spots were mainly distributed in the northern mountainous areas of Hebei Province and the Taihang Mountains, and the cold spots and sub-cold spots were mainly distributed in the southeast plain and the surrounding areas of some big cities. Population density, annual average temperature, per capita cultivated land area, and urbanization level were the dominant factors of ecosystem health in the Beijing-Tianjin-Hebei Region, and they were all negatively correlated with ecosystem health.

A predictive maintenance model for health assessment of an assembly robot based on machine learning in the context of smart plant

A predictive maintenance model for health assessment of an assembly robot based on machine learning in the context of smart plant

Deep Convolutional Neural Network with Symbiotic Organism Search-Based Human Activity Recognition for Cognitive Health Assessment.

Cognitive assessment plays a vital role in clinical care and research fields related to cognitive aging and cognitive health. Lately, researchers have worked towards providing resolutions to measure individual cognitive health; however, it is still difficult to use those resolutions from the real world, and therefore using deep neural networks to evaluate cognitive health is becoming a hot research topic. Deep learning and human activity recognition are two domains that have received attention for the past few years. The former is for its relevance in application fields like health monitoring or ambient assisted living, and the latter is due to their excellent performance and recent achievements in various fields of application, namely, speech and image recognition. This research develops a novel Symbiotic Organism Search with a Deep Convolutional Neural Network-based Human Activity Recognition (SOSDCNN-HAR) model for Cognitive Health Assessment. The goal of the SOSDCNN-HAR model is to recognize human activities in an end-to-end way. For the noise elimination process, the presented SOSDCNN-HAR model involves the Wiener filtering (WF) technique. In addition, the presented SOSDCNN-HAR model follows a RetinaNet-based feature extractor for automated extraction of features. Moreover, the SOS procedure is exploited as a hyperparameter optimizing tool to enhance recognition efficiency. Furthermore, a gated recurrent unit (GRU) prototype can be employed as a categorizer to allot proper class labels. The performance validation of the SOSDCNN-HAR prototype is examined using a set of benchmark datasets. A far-reaching experimental examination reported the betterment of the SOSDCNN-HAR prototype over current approaches with enhanced precision of 86.51% and 89.50% on Penn Action and NW-UCLA datasets, respectively.

Comprehensive Health Assessment for Children in Out-of-Home Care: An Exploratory Study of Service Needs and Mental Health in a Norwegian Population.

A comprehensive model for routine multi-disciplinary health assessment for children in out-of-home care was piloted in a Norwegian region. This paper reports on identified service needs and mental disorders among 196 children (0-17 years) receiving the assessment. Cross-sectional data was extracted from assessment reports. Results show needs across a range of services, with a mean of 2.8 recommended services for children aged 0-6 and 3.3 for children aged 7-17. Mental disorders were identified in 50% of younger children, and 70% of older children. For all children, overall service need was associated with mental disorders, in addition to male gender among younger children. Need for specialized mental health services was associated with mental disorders among younger children and increasing age among older children. The high frequency of service needs and mental disorders illustrate the importance of offering comprehensive health assessments routinely to this high-risk child population and necessitates coordinated service delivery.

Building The Health Monitoring and Fault Diagnosis Models For Stamping Press

A stamping press is widely used for the metal forming process. To achieve continuous automation and high precision forming, monitoring the press's health and diagnosing faults during stamping is necessary. The three primary types of faults that may occur in the stamping press are lack of lubrication oil, quality variation of lubrication oil, and clearance variation, which can lead to a decline in workpiece quality and reduced lifespan of the dies and presses. This study adopted the Prognostics and Health Management (PHM) technique to implement a predictive maintenance system for the stamping press. To extract relevant data, the National Instrument (NI) DAQ was used to acquire the three-phase currents and X, Y, and Z vibration signals. Six signals provided a total of seventy-two features, and the top three key features were selected for building a health assessment model using the Logistic regression and PCA algorithms. An early warning is triggered when the health indicator drops below the threshold, alerting the operators. Additionally, fault diagnosis was achieved using classification algorithms such as Support Vector Machine (SVM), K Nearest Neighbors (K-NN), and eXtreme Gradient Boosting (XGBoost). The fault diagnosis model achieved high accuracies of up to 99%.