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Selecting a good mate is a decision with important fitness consequences. For this reason, mate choice has promoted the evolution of sexual ornaments signaling the quality of an individual. In fossorial animals, inhabiting visually restricted underground environments, chemical senses should be very important for mate choice. We examined whether sexual chemical signals (substrate scent marks) produced by males of the Iberian worm lizard, Blanus cinereus, a strictly fossorial blind amphisbaenian, provide information to females on morphological traits and health state. We administered corticosterone (CORT) to males simulating a continuous stressor affecting their health. Females preferred settling at sites scent‐marked by males in comparison with similar sites with female scent or unmarked sites, but the attractiveness of males' scent differed between individuals. Females preferred scent marks of larger/older males and with a higher immune response, while their body condition and CORT treatment were unrelated to female preferences. Chemical analyses showed that proportions of some compounds in precloacal secretions of males (used to produce scent marks) were correlated with the morphological (body size) and health state (immune response and body condition, but not CORT treatment) of these males. These results suggest that females may make site‐selection decisions based on assessing the chemical characteristics of males' scent marks, which were reliably related to some of the traits of the male that produced the scent. Therefore, females might use chemical senses to increase the opportunities to find and mate with males of high quality, coping with the restrictions of the subterranean environment. [ABSTRACT FROM AUTHOR]

The stability, reliability, and lifespan of proton exchange membrane fuel cells (PEMFCs) are closely related to power dynamic response. Therefore, it is necessary to study in depth the relationship between the power operation state and typical characteristics. In this paper, the fuel cell system (FCS) power dynamic response based on constant power change amplitude and various power change amplitude is analyzed. Then, three typical power characteristics of the FCS, namely, low single-cell voltage, drastic voltage uniformity change, voltage undershoot and voltage overshoot, are investigated. Furthermore, to solve the high-dimensional datasets problem, a quick evaluation method based on data dimensionality reduction (DDR) is proposed. The innovation of this method is based on principal component analysis to extract typical characteristic relationships of power operation levels. The results demonstrate that the extracted new one-dimensional (1-D) eigenvectors index exhibits a positive correlation with power levels. Meanwhile, the power operation state empirical model is established through data regression. The Pearson coefficients of the empirical model are all greater than 0.9981. Thus, the new index and proposed method offer novel insights for improving the power adaptive control of fuel cells and quickly assessing their health state. • The effects of different loading amplitudes on power output are investigated. • Typical characteristics of power operation state are analyzed. • A quick evaluation method for power operation state is proposed. [ABSTRACT FROM AUTHOR]

Background: Duchenne muscular dystrophy (DMD) is a genetic disease resulting in progressive muscle weakness, loss of ambulation, and cardiorespiratory complications. Direct estimation of health-related quality of life for patients with DMD is challenging, highlighting the need for proxy measures. This study aims to catalog and compare existing published health state utility estimates for DMD and related conditions. Methods: Using two search strategies, relevant utilities were extracted from the Tufts Cost-Effectiveness Analysis Registry, including health states, utility estimates, and study and patient characteristics. Analysis One identified health states with comparable utility estimates to a set of published US patient population utility estimates for DMD. A minimal clinically important difference of ± 0.03 was applied to each DMD utility estimate to establish a range, and the registry was searched to identify other health states with associated utilities that fell within each range. Analysis Two used pre-defined search terms to identify health states clinically similar to DMD. Mapping was based on the degree of clinical similarity. Results: Analysis One identified 4,308 unique utilities across 2,322 cost-effectiveness publications. The health states captured a wide range of acute and chronic conditions; 34% of utility records were extrapolated for US populations (n = 1,451); 1% were related to pediatric populations (n = 61). Analysis Two identified 153 utilities with health states clinically similar to DMD. The median utility estimates varied among identified health states. Health states similar to the early non-ambulatory DMD phase exhibited the greatest difference between the median estimate of the sample (0.39) and the existing estimate from published literature (0.21). Conclusions: When available estimates are limited, using novel search strategies to identify utilities of clinically similar conditions could be an approach for overcoming the information gap. However, it requires careful evaluation of the utility instruments, tariffs, and raters (proxy or self). [ABSTRACT FROM AUTHOR]

Abstract Background Duchenne muscular dystrophy (DMD) is a genetic disease resulting in progressive muscle weakness, loss of ambulation, and cardiorespiratory complications. Direct estimation of health-related quality of life for patients with DMD is challenging, highlighting the need for proxy measures. This study aims to catalog and compare existing published health state utility estimates for DMD and related conditions. Methods Using two search strategies, relevant utilities were extracted from the Tufts Cost-Effectiveness Analysis Registry, including health states, utility estimates, and study and patient characteristics. Analysis One identified health states with comparable utility estimates to a set of published US patient population utility estimates for DMD. A minimal clinically important difference of ± 0.03 was applied to each DMD utility estimate to establish a range, and the registry was searched to identify other health states with associated utilities that fell within each range. Analysis Two used pre-defined search terms to identify health states clinically similar to DMD. Mapping was based on the degree of clinical similarity. Results Analysis One identified 4,308 unique utilities across 2,322 cost-effectiveness publications. The health states captured a wide range of acute and chronic conditions; 34% of utility records were extrapolated for US populations (n = 1,451); 1% were related to pediatric populations (n = 61). Analysis Two identified 153 utilities with health states clinically similar to DMD. The median utility estimates varied among identified health states. Health states similar to the early non-ambulatory DMD phase exhibited the greatest difference between the median estimate of the sample (0.39) and the existing estimate from published literature (0.21). Conclusions When available estimates are limited, using novel search strategies to identify utilities of clinically similar conditions could be an approach for overcoming the information gap. However, it requires careful evaluation of the utility instruments, tariffs, and raters (proxy or self).

To assess the health state utilities of ovarian cancer patients, clinicians, and non-cancer controls regarding surgical complications in ovarian cancer. Utilities for 14 surgical complications were assessed from patients with recently diagnosed or recurrent ovarian cancer, clinicians, and non-cancer controls using the visual analog scale (VAS) and time trade-off (TTO) methods. Health state utilities were converted to a 0-to-1 scale, where 0 represents the least favorable outcome and 1 represents the most favorable outcome. Fifty patients, 50 clinicians, and 50 controls participated. Median VAS scores were lower than TTO scores across all groups (p < 0.01). Patients viewed 'bleeding requiring transfusion' most favorably (VAS utility 0.75), followed in order by less favorable utility scores for hernia, thromboembolism, pleural effusion, abscess, ileus/bowel obstruction, wound infection, bowel obstruction requiring surgery, anastomotic leak requiring drain, temporary ostomy, anastomotic leak requiring surgery, genito-urinary fistula, permanent ostomy, and genito-intestinal fistula (VAS utility 0.2). Overall, clinicians perceived complications more favorably than patients by VAS (overall utility score 0.49 vs 0.43, p < 0.01), but not by the TTO. There were no differences in overall utility scores between patients and controls. Patients who had not experienced certain surgical complications had less favorable scores than patients who did (utility score for ostomy = 0.2 for patients without ostomy vs. 0.7 for patients with ostomy, p = 0.02). This study establishes health state utilities for surgical complications associated with ovarian cancer. These utilities can be used in future cost-effectiveness evaluations to determine quality-adjusted outcomes and may help in counseling patients during the shared decision-making process. • Individuals who have experienced a surgical complication perceived them more favorably than those who did not • Differing views of trade-offs between survival and toxicity by patients and clinicians may affect shared decision making. • Understanding perceptions of surgical morbidity is critical to providing appropriately tailored counseling for patients [ABSTRACT FROM AUTHOR]

We summarized the biological characteristics, nutritional composition of earthworm and its application in aquatic feed. The research of earthworm meal, fresh earthworm and other earthworm products in different aquatic animals was briefly described. This review also summarized adding appropriate level of earthworm products in aquatic animal feed could promote aquatic animals′ growth and health. The problems existing in the process of earthworm used as aquatic feed were put forward in order to provide reference for the aquatic feed development and production. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Technology (Natural Science) is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: To provide EuroQoL EQ-5D-5L French population norms based on a sample of 15,000 responders. Methods: Based on the National Health and Wellness Survey, an international, annual, selfadministered Internet-based survey, this study extracted data from France for 2018 involving a sample of 15,000 respondents stratified by age and gender. Responses to the EQ-5D-5L questionnaire and the Visual Analog Scale (VAS) score, together with sociodemographic, health behavior, and disease status variables were collected. VAS, value indexes, the level sum score, and the distribution of levels per dimension were described. Multivariate regression analyses were performed to identify covariates with a statistically significant effect on the five dimensions and the three scores. Results: The mean [standard deviation (SD)] VAS was 73.4 (22.2) for the entire sample (male 74.8 vs female 72.2, p < 0.0001). The Mean SD value index was 0.905 (0.158) (male 0.915 vs female 0.895, p < 0.0001). The mean SD level sum score was 7.6 (3.0) (7.4 for males vs. 7.9 for females p < 0.0001). Health state 11,111 (no problem in any dimension) represented 25.1% of all responses. "No problem" responses' proportions were Self Care (91.3%), Usual Activities (74.2%), Mobility (72.4%), Anxiety/Depression (52.6%) and Pain/Discomfort (37.7%). Multivariate regressions revealed a significant relationship for health states, value indexes, and VAS with age, income, employment, marital status, smoking and alcohol consumption, obesity, and the presence of one or more health problems. Conclusion: Based on a large sample, this study is the first to report EQ-5D-5L population norms for France. [ABSTRACT FROM AUTHOR]

Copyright of Machine Tool & Hydraulics is the property of Guangzhou Mechanical Engineering Research Institute (GMERI) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Recently, modern intelligent fault diagnosis algorithms based on deep learning have been widely used to recognize the health state of rolling bearings. However, the constantly varying load in real industry leads to unsatisfactory diagnosis results. How to make the models effectively diagnose the health state of rolling bearings under varying loads is a key issue. In this paper, an Adaptive Attention Network (AANet) is proposed to resolve the issue. That the interference is introduced by the Multi-scale Convolution Module with wide kernels (MCM) at the head of the AANet is the premise for extending the model to other loads. And the Adaptive Attention Modules (AAMs) embedded in the AANet distinguishe state-related features and unrelated features, which enhances the diagnostic ability of the model across loads. In order to verify the effectiveness of the algorithm, experiments have been performed on a public data set. Experimental results show that the average accuracy of this algorithm achieves 0.976, which can effectively recognize the health state of rolling bearings under varying loads, compared to other algorithms. [ABSTRACT FROM AUTHOR]

Abstract Background Measurement of the Chinese burden of disease with disability-adjusted life-years (DALYs) requires disability weight (DW) that quantify health losses for all non-fatal consequences of disease and injury. The Global Burden of Disease (GBD) 2013 DW study indicates that it is limited by lack of geographic variation in DW data and by the current measurement methodology. We aim to estimate DW for a set of health states from major diseases in the Wuhan population. Methods We conducted the DW measurement study for 206 health states through a household survey with computer-assisted face-to-face interviews and a web-based survey. Based on GBD 2013 DW study, paired comparison (PC) and Population health equivalence (PHE) method was used and different PC/PHE questions were randomly assigned to each respondent. In statistical analysis, the PC data was analyzed by probit regression. The probit regression results will be anchored by results from the PHE data analyzed by interval regression on the DW scale units between 0 (no loss of health) and 1 (loss equivalent to death). Results A total of 2610 and 3140 individuals were included in the household and web-based survey, respectively. The results from the total pooled data showed health state “mild anemia” (DW = 0.005, 95% UI 0.000–0.027) or “allergic rhinitis (hay fever)” (0.005, 95% UI 0.000–0.029) had the lowest DW and “heroin and other opioid dependence, severe” had the highest DW (0.699, 95% UI 0.579–0.827). A high correlation coefficient (Pearson’s r = 0.876; P

Abstract Background A universal set of disability weights(DWs) is mainly based on the survey of North America, Australia and Europe, whereas the participants in Asia was limited. The debate hasn’t yet settled whether a universal DW is desirable or useful.The focus of the debate is its representativenes-s.After all, the DWs come from people's subjective evaluation of pain, and they may vary according to cultural background.The differences of the DWs could have implications for the magnitude or ranking of disease burdens.The DWs of Anhui Province has not been completely presented.This paper aims to obtain the DWs suitable for the general population of Anhui Province of China, and attempts to explore the differences between different DWs by comparing the DWs with the similar-cultural background and the DWs with cross-cultural background. Methods A web-based survey was conducted to estimate the DWs for 206 health states of Anhui province in 2020. Paired comparison (PC) data were analyzed and anchored by probit regression and fitting loess model. We compared the DWs in Anhui with other provinces in China and those in Global burden of disease (GBD) and Japan. Results Compared with Anhui province, the proportion of health states which showed 2 times or more differences ranged from 1.94% (Henan) to 11.17% (Sichuan) in China and domestic provinces. It was 19.88% in Japan and 21.51% in GBD 2013 respectively. In Asian countries or regions, most of the health states with top 15 DWs belonged to the category of mental, behavioral, and substance use disorders. But in GBD, most were infectious diseases and cancer. The differences of DWs in neighboring provinces were smaller than other geographically distant provinces or countries. Conclusion PC responses were largely consistent across very distinct settings,but the exceptions do need to be faced squarely.The differences of DWs among similar-cultural regions were smaller than cross-cultural regions. There is an urgent need for relevant gold standards.

Achieving accurate retired battery state of health (SOH) and state of charge (SOC) estimation is a safe prerequisite for securing the battery secondary utilization and thus effectively improving the energy utilization efficiency. The data-driven approach is efficient and accurate, and does not rely on accurate battery models, which is a hot direction in battery state estimation research. However, the huge number of retired batteries and obvious consistency differences bring bottleneck problems such as long learning time and low model updating efficiency to the traditional data-driven algorithm. In view of this, this paper proposes an integrated learning algorithm based on AdaBoost. Rt-LSTM to realize the joint estimation of SOC and SOH of retired lithium batteries, which relies on the LSTM neural network model and completes the correlation adaption in the spatio-temporal dimension of the whole life cycle sample data. The LSTM model is used as the base learner to construct the AdaBoost. Rt-LSTM strong learning model. The LSTM weak predictor is combined with weights to form a strong predictor, which greatly solves the problem of low accuracy of state estimation due to the large number and variability of retired batteries. Simulation and experimental comparison show that the integrated algorithm proposed in this paper is suitable for improving the SOC and SOH prediction accuracy and the generalization performance of the model. [ABSTRACT FROM AUTHOR]

To examine how disease status and current health state influence treatment preferences of patients with multiple myeloma (MM). Participants with MM from France, Germany, and the United Kingdom completed a web-based survey that included a discrete choice experiment (DCE) and EQ-5D assessment. The DCE elicited preferences for 8 attributes: increased life expectancy, increased time to relapse, pain, fatigue, risk of infection, administration (route and duration), frequency of administration, and monitoring. Multinomial logit models were used to analyze DCE preference data and to calculate life expectancy trade-offs. Three hundred participants with MM (newly diagnosed, transplant eligible, n = 108; newly diagnosed, transplant ineligible, n = 105; relapsed-refractory, n = 87) completed the survey. The most valued attributes were pain, fatigue, and increased life expectancy. Participants would want an additional 2.7 years of life expectancy (95% confidence interval [CI] 2.4-3.1 years) to tolerate extreme pain and an additional 2.0 years of life expectancy (95% CI 1.6-2.3 years) to tolerate constant fatigue. Participants in a better health state (third EQ-5D score quartile [0.897]) required less additional life expectancy than participants with a worse health state (first EQ-5D score quartile [0.662]) to tolerate extreme pain (2.3 years [95% CI 1.9-2.6 years] vs 3.0 years [95% CI 2.6-3.4 years]; P =.007). There was little difference in treatment preferences between newly diagnosed and relapsed-refractory patients for pain, fatigue, and increased life expectancy. Current health state influenced treatment preferences of patients with MM more than disease status and should be considered when making treatment decisions. • Past studies on the preferences of patients with multiple myeloma (MM) have not addressed how preferences may vary with disease status or current health state. • Patients with MM are willing to accept significantly lower life expectancy for improvements in pain or fatigue. Patients with a worse current health state would want greater increases in life expectancy than those with better current health state to tolerate higher levels of pain. • Current health state should be considered when analyzing preferences and making treatment decisions in MM. [ABSTRACT FROM AUTHOR]

To perform indicator selection and verification for the on-orbit fault reconstruction of a giant satellite swarm, a hybrid multi-objective fault reconstruction algorithm is proposed and then verified by Monte Carlo analysis. First, according to the on-orbit failure analysis of the satellite swarm, several optimization indicators, such as the health state of the satellite swarm, the total energy consumption of reconstruction, and the balance of fuel consumption, are proposed. Then, a hybrid multi-objective fitness function is constructed, and a hybrid multi-objective genetic algorithm is used to optimize the objective function to obtain the optimal reconstruction strategy. Finally, the algorithm is statistically verified by Monte Carlo analysis. The simulation results not only show the algorithm's validity but also reveal the relationship between the number of satellite faults and the health of the satellite swarm. From this, the maximum number of faulty satellites allowed in the giant satellite swarm is calculated, which is significant for assessing the swarm's health. [ABSTRACT FROM AUTHOR]

Background and Aims: To determine how the health state of ulcerative colitis patients is impacted by their disease, different health state questionnaires are deployed. This study examines to what extent these health state questionnaires determine the same underlying health state concept and to what extent the complementary use of the health state questionnaires has added value for physicians. Methods: In total, 307 patients were enrolled in this cross-sectional multicenter cohort study. Medical, psychological, economic, and composite health state questionnaires were administered to determine reliability, convergent validity, and explained variance. Reliability was determined using Cronbach’s alpha. Convergent validity was measured using Spearman’s correlation coefficients. Explained variance was interpreted using R-squared coefficients. Results: All questionnaires can be considered reliable. The medical, psychological, and economic health state questionnaires show weak to moderate convergent validity with each other. The medical, psychological, and economic health state questionnaires also explain limited variance in each other’s outcomes. The composite health state questionnaire shows moderate to strong convergent validity with the other health state questionnaires. The composite health state questionnaire further explains considerable variance in the outcomes of the other health state questionnaires. Conclusion: Deploying divergent medical, psychological, and economic health state questionnaires may have added value as they provide a multiperspective holistic insight into patients’ health states. Deploying the composite health state questionnaire combined with other health state questionnaires may have added value as it provides additional understanding of their outcomes. Deploying an independent psychological health state questionnaire may have added value as it shows particularly limited convergent validity and explained variance regarding other health state questionnaires.

Background: Measurement of the Chinese burden of disease with disability-adjusted life-years (DALYs) requires disability weight (DW) that quantify health losses for all non-fatal consequences of disease and injury. The Global Burden of Disease (GBD) 2013 DW study indicates that it is limited by lack of geographic variation in DW data and by the current measurement methodology. We aim to estimate DW for a set of health states from major diseases in the Wuhan population. Methods: We conducted the DW measurement study for 206 health states through a household survey with computer-assisted face-to-face interviews and a web-based survey. Based on GBD 2013 DW study, paired comparison (PC) and Population health equivalence (PHE) method was used and different PC/PHE questions were randomly assigned to each respondent. In statistical analysis, the PC data was analyzed by probit regression. The probit regression results will be anchored by results from the PHE data analyzed by interval regression on the DW scale units between 0 (no loss of health) and 1 (loss equivalent to death). Results: A total of 2610 and 3140 individuals were included in the household and web-based survey, respectively. The results from the total pooled data showed health state "mild anemia" (DW = 0.005, 95% UI 0.000–0.027) or "allergic rhinitis (hay fever)" (0.005, 95% UI 0.000–0.029) had the lowest DW and "heroin and other opioid dependence, severe" had the highest DW (0.699, 95% UI 0.579–0.827). A high correlation coefficient (Pearson's r = 0.876; P < 0.001) for DWs of same health states was observed between Wuhan's survey and GBD 2013 DW survey. Health states referred to mental symptom, fatigue, and the residual category of other physical symptoms were statistically significantly associated with a lower Wuhan's DWs than the GBD's DWs. Health states with disfigurement and substance use symptom had a higher DW in Wuhan population than the GBD 2013 study. Conclusions: This set of DWs could be used to calculate local diseases burden for health policy-decision in Wuhan population. The DW differences between the GBD's survey and Wuhan's survey suggest that there might be some contextual or culture factors influencing assessment on the severity of diseases. [ABSTRACT FROM AUTHOR]

Background: A universal set of disability weights(DWs) is mainly based on the survey of North America, Australia and Europe, whereas the participants in Asia was limited. The debate hasn't yet settled whether a universal DW is desirable or useful.The focus of the debate is its representativenes-s.After all, the DWs come from people's subjective evaluation of pain, and they may vary according to cultural background.The differences of the DWs could have implications for the magnitude or ranking of disease burdens.The DWs of Anhui Province has not been completely presented.This paper aims to obtain the DWs suitable for the general population of Anhui Province of China, and attempts to explore the differences between different DWs by comparing the DWs with the similar-cultural background and the DWs with cross-cultural background. Methods: A web-based survey was conducted to estimate the DWs for 206 health states of Anhui province in 2020. Paired comparison (PC) data were analyzed and anchored by probit regression and fitting loess model. We compared the DWs in Anhui with other provinces in China and those in Global burden of disease (GBD) and Japan. Results: Compared with Anhui province, the proportion of health states which showed 2 times or more differences ranged from 1.94% (Henan) to 11.17% (Sichuan) in China and domestic provinces. It was 19.88% in Japan and 21.51% in GBD 2013 respectively. In Asian countries or regions, most of the health states with top 15 DWs belonged to the category of mental, behavioral, and substance use disorders. But in GBD, most were infectious diseases and cancer. The differences of DWs in neighboring provinces were smaller than other geographically distant provinces or countries. Conclusion: PC responses were largely consistent across very distinct settings,but the exceptions do need to be faced squarely.The differences of DWs among similar-cultural regions were smaller than cross-cultural regions. There is an urgent need for relevant gold standards. [ABSTRACT FROM AUTHOR]

In order to make sure a safe use of electric vehicles, it is of great significance to accurately estimate the health of its battery and predict the remaining useful battery life. However, it is difficult to ensure an accurate prediction of the remaining useful life of lithium-ion batteries under complex working conditions during daily life, since these two related variables, namely battery capacity and internal resistance, are easily affected by the environment. In addition, it is not easy to obtain accurate battery capacity and internal resistance in practical applications, which brings great difficulties to obtain the health of the battery. In order to solve these problems, a new remaining useful life prediction method of lithium-ion batteries based on multi-feature fusion of charging and discharging is proposed, which provides a different perspective to solve the problem of battery life prediction. Firstly, through an analysis of battery cycle aging data from NASA Prognostics Center of Excellence, the change trend characteristics of the charging curve and the discharge curve in different cycles are compared and studied. Based on this, four characteristics in the process of charging and discharging are extracted, including equal voltage drop discharging time, equal time discharge voltage drop, constant voltage charging time and charging outage voltage drop. Then, the correlation between the characteristics and the capacity fading caused by the cycle is analyzed through Pearson correlation coefficient calculation between each feature ad the remaining capacity. The average correlation coefficient reaches above 0.8, which means that the four features extracted have strong capacity correlation. In order to find out the relationship between indexes and health state of the battery, an improved gray correlation analysis method based on information entropy is proposed to process the four extracted indexes so that the fused health index is established. Based on battery charging and discharging data, the fused health index not only reflects battery operating conditions, but also replaces the traditional heath state of the battery. The constructed fusion index is used to represent the health state of the battery, and its mean square root error is 0.0213. Meanwhile, the fusion index considering both the charging and discharging processes gets a smaller estimation error than the ones in any single charging or single discharging process. This proves accuracy and effectiveness of the established fusion index, which lays a foundation for its further application. After that, in order to combine the established fusion health index with the goal of battery remaining useful life prediction, a deep convolution neural network model is established, in which the measured data during both charge and discharge cycles are taken as the network input, including voltage, current data and other data. Besides, the established fused health characteristic index is taken as the network output, thus establishing a complete battery life prediction model is. The training and verification results of the public battery data set show that the established remaining useful life prediction model can reach a life prediction error of about 0.089, while the stability error is about 0.016. These results show that the proposed battery remaining useful life prediction method based on charge-discharge data fusion health characteristic indexes and the deep convolution neural network has high prediction accuracy and stability. [ABSTRACT FROM AUTHOR]

The notion of predictive maintenance is perceived as a breakthrough in the manufacturing and other industrial sectors. The recent developments in this field can be attributed to the amalgamation of Artificial Intelligence- and Machine Learning (ML)-based solutions in predicting the health state of the machines. Most of the existing machine learning models are a hybridization of common ML algorithms that require extensive feature engineering. However, the real time deployment of these models demands a lower computational effort with higher accuracy. The proposed Multi-labeled Context-based Multilayered Bayesian Inferential (M-CMBI) predictive analytic classification framework is a novel approach that uses a cognitive approach by mimicking the brain's activity, termed MisMatch Negativity (MMN), to classify the faults. This adaptive model aims to classify the faults into multiple classes based on the estimated fault magnitude. This model is tested for efficacy on the Pump dataset which contains 52 items of raw sensor data to predict the class into normal, broken and recovering. Not all sensor data will contribute to the quality of prediction. Hence, the nature of the sensor data is analyzed using Exploratory Data Analysis (EDA) to prioritize the significance of the sensors and the faults are classified based on their fault magnitude. The results of the classification are validated on metrics such as accuracy, F1-Score, Precision and Recall against state of art techniques. Thus, the proposed model can yield promising results without time-consuming feature engineering and complex signal processing tasks, making it highly favorable to be deployed in real-time applications. [ABSTRACT FROM AUTHOR]

High-speed shaft bearing (HSSB) failures are exorbitant since they lead electrical energy generation to halt suddenly. In order to identify the health condition of the wind turbine and preserve the sustainability of energy production, a nonlinear vibration-based monitoring technique based on kernel principal component analysis (KPCA) has been developed. After extracting degradation characteristics from the time, frequency, and time-frequency domains. The most sensitive features are then fused using KPCA to capture the monitored bearing's operating conditions; this method demonstrated its efficiency in dealing with the nonlinearity of the system. To detect flaws in HSSB and assess whether it is healthy, degraded, or broken, T 2 , and SPE charts have been used. Real run-to-failure data from a wind turbine HSSB is used to validate the proposed technique. The suggested strategy caught the nonlinear relationship in the process variables more successfully than existing techniques, including linear PCA, and demonstrated enhanced process monitoring performance. [ABSTRACT FROM AUTHOR]

Individuals in many observational studies and clinical trials for chronic diseases are enrolled well after onset or diagnosis of their disease. Times to events of interest after enrollment are therefore residual or left-truncated event times. Individuals entering the studies have disease that has advanced to varying extents. Moreover, enrollment usually entails probability sampling of the study population. Finally, event times over a short to moderate time horizon are often of interest in these investigations, rather than more speculative and remote happenings that lie beyond the study period. This research report looks at the issue of delayed entry into these kinds of studies and trials. Time to event for an individual is modelled as a first hitting time of an event threshold by a latent disease process, which is taken to be a Wiener process. It is emphasized that recruitment into these studies often involves length-biased sampling. The requisite mathematics for this kind of sampling and delayed entry are presented, including explicit formulas needed for estimation and inference. Restricted mean survival time (RMST) is taken as the clinically relevant outcome measure. Exact parametric formulas for this measure are derived and presented. The results are extended to settings that involve study covariates using threshold regression methods. Methods adapted for clinical trials are presented. An extensive case illustration for a clinical trial setting is then presented to demonstrate the methods, the interpretation of results, and the harvesting of useful insights. The closing discussion covers a number of important issues and concepts. [ABSTRACT FROM AUTHOR]

A feeding trial was conducted to investigate the effects of glycerol monolaurate (GML) on growth performance, lipid metabolism, inflammation, and related gene expression in common carp fed a high lipid diet. Juvenile common carp were distributed into 18 cages and fed one of six isonitrogenous diets: a normal lipid diet (control diet, CT), a high lipid diet (HL), and high lipid diets supplemented with 0.5, 1, 2, and 4 g kg -1 GML (designated as GML-0.5, GML-1, GML-2, and GML-4, respectively), with three replicates per treatment. After 56 days of feeding, the results indicated that the final body weight (FBW) and specific growth rate (SGR) in the GML-1 and GML-2 groups were significantly higher than those observed in the CT, HL, and GML-4 groups (P < 0.05). The crude lipid content in the hepatopancreas of the GML-1 and GML-2 groups was significantly lower than that in the HL group (P < 0.05). Morphological analysis of the hepatopancreas revealed a reduction in vacuole presence with GML supplementation (P < 0.05). Additionally, GML supplementation significantly enhanced the development of intestinal structures of common carp. The inclusion of GML significantly influenced the quality of the fillet, as evidenced by notable increases in hardness, gumminess, chewiness, and shear force compared to the HL group (P < 0.05). Additionally, the dripping loss of raw fillets in the GML groups decreased than that observed in the HL group (P < 0.05). Furthermore, GML-1 and GML-2 groups exhibiting the lowest serum TG levels among all groups (P < 0.05). Conversely, serum high density lipoprotein cholesterol (HDL) levels significantly increased with GML supplementation, with the GML-2 group demonstrating the highest HDL content (P < 0.05). Key genes of lipid synthesis in the hepatopancreas were down-regulated, whereas genes involved in lipolysis were up-regulated in the GML-1 and GML-2 groups relative to the HL group (P < 0.05). KEGG functional annotation analysis of differentially expressed genes in the hepatopancreas of fish fed GML-supplemented diets revealed significant alterations in the PPAR signaling pathway. GML effectively enhanced the antioxidant enzyme activities of hepatopancreas, intestine, spleen, kidney, and serum following high lipid feeding accompanied with the significant up-regulation of antioxidant genes in the hepatopancreas and intestine of the GML-1 and GML-2 groups. Simultaneously, pro-inflammatory factors in these tissues were significantly down-regulated, while anti-inflammatory factors were markedly up-regulated in the GML-1 and GML-2 groups compared to the HL group (P < 0.05). In summary, common carp fed high lipid diets supplemented with 1-2 g kg -1 GML exhibited improved growth performance, enhanced fillet quality, regulated lipid metabolism, promoted intestinal structural development, and bolstered both antioxidant and immune capacities., (Copyright © 2024. Published by Elsevier Ltd.)

Objective: This study aims to address the challenge of selecting optimal drug delivery strategies for tumor patients by introducing a novel theoretical framework., Methods: We propose a fuzzy logic-based framework for quantitatively assessing Health States (HS) in tumor patients. This framework integrates quantified HS assessments with causality strength analyses, offering a comprehensive understanding of various drug delivery schemes' effectiveness from pharmacokinetic and pharmacodynamic perspectives., Results: The efficacy of our approach is demonstrated through a series of real-world patient case studies, highlighting its potential to enhance the evaluation and selection of targeted drug delivery strategies., Conclusion: Our work contributes to the field by showcasing practical applications of fuzzy logic in targeted drug delivery systems (TDDs) and establishing a new benchmark for precision in drug delivery strategy selection., Significance: This study has significant implications for developing personalized medical treatments, potentially revolutionizing the field with a more nuanced and scientifically rigorous method for evaluating and selecting drug delivery protocols., Contributions: Development of a fuzzy logic framework for precise quantification of health states in tumor patients. Innovative integration of a causal system for comprehensively evaluating targeted drug delivery strategies., Competing Interests: Declaration of competing interest We declare that all the authors have no financial or personal relationships with other people or organizations that can inappropriately influence our work. There is no professional or other personal interest of any nature or kind in any product, service, and/or company that could be construed as influencing the position presented in, or the review of, the manuscript., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Summary: Background: The disability weight (DW) quantifies the severity of health states from disease sequela and is a pivotal parameter for disease burden calculation. We conducted a national and subnational DW measurement in China. Methods: In 2020–2021, we conducted a web-based survey to assess DWs for 206 health states in 31 Chinese provinces targeting health workers via professional networks. We fielded questions of paired comparison (PC) and population health equivalence (PHE). The PC data were analysed by probit regression analysis, and the regression results were anchored by results from the PHE responses on the DW scale between 0 (no loss of health) and 1 (health loss equivalent to death). Findings: We used PC responses from 468,541 respondents to estimate DWs of health states. Eight of 11 domains of health had significantly negative coefficients in the regression of the difference between Chinese and Global Burden of Disease (GBD) DWs, suggesting lower DW values for health states with mention of these domains in their lay description. We noted considerable heterogeneity within domains, however. After applying these Chinese DWs to the 2019 GBD estimates for China, total years lived with disability (YLDs) increased by 14·9% to 177 million despite lower estimates for musculoskeletal disorders, cardiovascular diseases, mental disorders, diabetes and chronic kidney disease. The lower estimates of YLDs for these conditions were more than offset by higher estimates of common, low-severity conditions. Interpretation: The differences between the GBD and Chinese DWs suggest that there might be some contextual factors influencing the valuation of health states. While the reduced estimates for mental disorders, alcohol use disorder, and dementia could hint at a culturally different valuation of these conditions in China, the much greater shifts in YLDs from low-severity conditions more likely reflects methodological difficulty to distinguish between health states that vary a little in absolute DW value but a lot in relative terms. Funding: This work was supported by the National Natural Science Foundation of China [grant number 82173626], the National Key Research and Development Program of China [grant numbers 2018YFC1315302], Wuhan Medical Research Program of Joint Fund of Hubei Health Committee [grant number WJ2019H304], and Ningxia Natural Science Foundation Project [grant number 2020AAC03436].

Achieving accurate retired battery state of health (SOH) and state of charge (SOC) estimation is a safe prerequisite for securing the battery secondary utilization and thus effectively improving the energy utilization efficiency. The data-driven approach is efficient and accurate, and does not rely on accurate battery models, which is a hot direction in battery state estimation research. However, the huge number of retired batteries and obvious consistency differences bring bottleneck problems such as long learning time and low model updating efficiency to the traditional data-driven algorithm. In view of this, this paper proposes an integrated learning algorithm based on AdaBoost. Rt-LSTM to realize the joint estimation of SOC and SOH of retired lithium batteries, which relies on the LSTM neural network model and completes the correlation adaption in the spatio-temporal dimension of the whole life cycle sample data. The LSTM model is used as the base learner to construct the AdaBoost. Rt-LSTM strong learning model. The LSTM weak predictor is combined with weights to form a strong predictor, which greatly solves the problem of low accuracy of state estimation due to the large number and variability of retired batteries. Simulation and experimental comparison show that the integrated algorithm proposed in this paper is suitable for improving the SOC and SOH prediction accuracy and the generalization performance of the model.

Health state estimation can quantitatively evaluate the current degradation state of equipment, and remaining life prediction can quantitatively predict the remaining service time of equipment. These two technologies can provide a basis for condition-based maintenance and predictive maintenance of equipment, respectively. In recent years, a large amount of research has been implemented in these two technologies. However, there is not any systematic review that covers these two technologies, and their engineering applications, comprehensively. To fill the gap, this paper makes a comparative analysis of existing health state estimation and remaining life prediction methods, and details the characteristics and limitations of various methods. The engineering applications of these two methods are summarized, and their applicable objects are briefly given. Finally, these two methods are summarized, and their feasibility for engineering application is discussed. This work provides guidance for the selection of industrial equipment health assessment and remaining life prediction methods. [ABSTRACT FROM AUTHOR]

Maintenance is important for the service of products and it is different from repair because repair focuses on the time node when the products break down, which is a qualitative problem, but maintenance pays more attention to the continuity of machine's work, and thus it is not a qualitative problem. Nevertheless, almost all the study methods are of qualitative methods because they only qualitatively divided the health state of machines into several levels, which are not fit to comprehensively explore the degradation principle of machines and the relationship between degradation and maintenance. To discover the degradation principle of machines influenced by maintenance, a quantitative study method is proposed by calculating the Health Index (HI) based on fuzzy analytic hierarchy process (FAHP) and convolutional neural network (CNN). Finally, a case study is used to demonstrate the implementation and potential applications of the proposed method, in which two major maintenance methods in prognostic and health management (PHM), i.e. time-based maintenance (TBM) and condition-based maintenance (CBM) are studied. The results show that the application of the proposed method leads to a significant increase in the life of machines. This study puts forward a new method to study the degradation principle of machines and will lead to the development of PHM using the HI. [ABSTRACT FROM AUTHOR]

To perform indicator selection and verification for the on-orbit fault reconstruction of a giant satellite swarm, a hybrid multi-objective fault reconstruction algorithm is proposed and then verified by Monte Carlo analysis. First, according to the on-orbit failure analysis of the satellite swarm, several optimization indicators, such as the health state of the satellite swarm, the total energy consumption of reconstruction, and the balance of fuel consumption, are proposed. Then, a hybrid multi-objective fitness function is constructed, and a hybrid multi-objective genetic algorithm is used to optimize the objective function to obtain the optimal reconstruction strategy. Finally, the algorithm is statistically verified by Monte Carlo analysis. The simulation results not only show the algorithm’s validity but also reveal the relationship between the number of satellite faults and the health of the satellite swarm. From this, the maximum number of faulty satellites allowed in the giant satellite swarm is calculated, which is significant for assessing the swarm’s health.

• Health state assessment and remaining useful life prognostic of hydrocyclone • Augmenting degradation sequences by fusing mechanism modeling and TimeGAN learning • A unified framework for constructing and clustering degradation health indicators • Quantifying prediction uncertainty of remaining useful life of equipment Deploying health state (HS) assessment and remaining useful life (RUL) prognostic on the industrial equipment can effectively avoid shutdown and reduce productivity losses. This paper proposes a temporal self-clustering (TSC) and time-series generative adversarial network (TimeGAN) based uncertainty-aware health prognostic algorithm for the hydrocyclone, a critical machine in the mineral processing. To label the HS of degradation data, an autoencoder based TSC algorithm is proposed. It integrates the extraction and clustering of health indicators into a unified framework, so that the correlation information between feature extraction and clustering can be captured for cluster analysis. A Benders decomposition based optimization approach is designed to synthetically optimize network weights and clustering centers, thereby extracting and labeling data features more reasonably. For quantifying prediction uncertainty, an uncertainty-aware prognostic algorithm is proposed to predict the RUL distribution, and the uncertainty can be quantified as the standard deviation (Std) of the distribution. Furthermore, a mechanism and TimeGAN fusion temporal sequence generation algorithm is proposed to augment the degradation process sequences of the equipment. Verification experiments using the actual data show that the proposed algorithm can effectively evaluate the HS of the equipment and provide a sensible and convincing RUL prediction. [ABSTRACT FROM AUTHOR]

The site of Sphinx (SBK.W-60), located about 80 km north of Khartoum in the western part of Jebel Sabaloka, has been investigated since 2011 by the Charles University Sabaloka Expedition. Eleven trenches contained settlement deposits relating to occupation by hunter-fisher-gatherers of the Khartoum Mesolithic (or the Early Khartoum culture) dated at the site between 8750 and 4750 cal BC. In addition, five of the trenches yielded also human skeletal remains in primary and secondary deposits, with the minimal number of individuals estimated following anthropological standards at 45 individuals. The demographic structure of the burial site shows no selection based on sex, but immature individuals, especially those under the age of one year, seem to be markedly underrepresented, which is not unusual in pre-Neolithic funeral assemblages. Dental diseases, especially periapical lesions and inflammatory changes, predominate at Sphinx; however, a very low incidence of dental caries was recorded. Healed traumatic lesions or fractures possibly of accidental origin occurred in only six individuals. One identified case of perimortem trauma on the right scapula indicates an act of inter-personal violence. Nevertheless, it is possible to perceive the population buried at Sphinx as relatively healthy and peaceful. [ABSTRACT FROM AUTHOR]

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A few instruments that identify and valuate health states are based on the International Classification of Functioning, Disability and Health States of the World Health Organization. One of them is the Health State Description (HSD) questionnaire first used in the World Health Survey (WHS) initiative (HSD-WHS), whose psychometric properties have not been evaluated in Chile. Additionally, the use of latent variables for the valuation process of health states has been scarcely investigated in the context of population health metrics. We aim to evaluate the psychometric properties and factorial structure of the HSD-WHS for Chile and describe a latent variable method for valuating health states associated with diseases. We used data from the second Chilean National Health population-based survey from 2009 to 2010 (N = 5293). We explored the factorial structure of the HSD-WHS through exploratory and confirmatory factor analyses, the reliability, and the discriminant validity of the latent variable of disability. Disability weights for diseases were calculated using a linear regression model. We found an adequate goodness of fit for a second-order model with 9 factors corresponding to disability domains (Tucker–Lewis index = 0.99, comparative fit index = 0.99, root mean square error of approximation = 0.060), and good reliability estimates (standardized α = 0.91). The rescaled (between 0 and 100) latent variable of disability showed significant difference according to the explored variables. We estimated disability weights for the following: (1) depressive episode, 13.6 (12.1-15.2), (2) hypertension, 1.6 (0.0-3.3), and (3) diabetes, 5.0 (2.5-7.4). This study supports the use of the HSD-WHS questionnaire in the Chilean population and a latent variable approach for valuating health states associated with diseases. • The psychometric properties of the Health State Description-World Health Survey (HSD-WHS) questionnaire have been scarcely studied in the context of the WHS initiative. • The use of latent variable methods for valuating health states associated with diseases has been poorly studied in the context of population health metrics. • The HSD-WHS shows adequate psychometric properties and factorial structure in the Chilean population. • The latent variable approach seems to be a practical way for valuating health states associated with diseases, especially in the analysis of National Health Surveys. [ABSTRACT FROM AUTHOR]

Purpose of the study. The goal is to conduct a comparative analysis of the correlation of learning success with self-assessment of physical and mental status and objective health status, taking into account the influence of demographic and socio-economic variables in senior students of medical and humanitarian specialties Materials and methods. The study was conducted from 01.12. 2017 to 01.03.2019 at the Far Eastern Federal University and the Pacific State Medical University. The sample size was 410 students (207 (50.6%) female, 203 (49.4%) male, average age 21.5 (1.2) years), who at the time of the study were studying in 4–6 courses. Information on demographic parameters, social conditions of study, residence, subjective data on morbidity and appeal was collected using: a comprehensive questionnaire on student status of a university (Pozdeeva, 2008; with additions of authors). The objective state of health and the comorbidity index were investigated when studying the medical documentation of medical institutions attached: Form 025 / y; 001–1 / y; 062 / y. In the study of quality of life associated with health, we used the international questionnaire “A short form of self-assessment of quality of life related to health MOS SF-36”. Results. The total score of self-esteem of physical well-being among students of humanities was 66.1 points, among students of medical specialties 56.4 points. Academic performance was higher among students of both directions, marking their learning conditions as satisfactory and good (r = 0.034; r = 0.048 / r = 0.045; r = 0.065). Student performance was also higher for students in both areas, evaluating the quality of education as high (r = 0.032; r = 0.046 / r = 0.033; r = 0.043), but lower for students with a high comorbidity index (r = –0.038 / r = –0.036). We also found that an increase in the number of HRQoL points is accompanied by a significant increase in academic performance in students of both directions (r = 0.035; r = 0.045 / r = 0.033; r = 0.050). An analysis of the linear regression model describing the association of academic performance with variables in the pooled sample revealed that this indicator strongly correlates with the comorbidity index (r = 8.27), HRQoL (r = 7.28), quality of education, and family income (5.55 / 5.06). At the same time, academic performance had an unreliable relationship with age, individual income, and weakly correlated with the conditions of study and residence. Conclusion. Chronic morbidity among senior medical and humanitarian students is high (comorbidity index 1.5–1.7), but the total self-assessment of the quality of life associated with health is 59–62 points and falls within the normal range of values. Thus, despite the high level of chronic somatic pathology, students generally positively assess their physical and psychological status.

Data-driven intelligent models have received more and more attention in liquid rocket engine (LRE) state recognition. However, due to practical limitations, the fault data are rare for model training, which results in poor testing results. To better recognize the state of LRE, we proposed a supervised recognition framework without fault samples. First, we proposed a negative sample generation method. Then, fusion recurrent convolutional neural network (FRCNN) is constructed for data fusion and feature extraction. Finally, we evaluated the recognition results with several quantization indicators. The proposed method is verified with the real monitoring data, which is obtained from the static firing tests of a certain type of LRE. Results show that the FRCNN could recognize the state of LRE with accuracy over 93% under the confidence of 96%, which shows the effectiveness of the proposed framework. And the comparison with the literature method shows the superiority of the proposed framework. In addition, it could also perform real-time monitoring for LRE, and recognize the abnormal at the initial stage of the steady state, which has practical potential in monitoring task. At last, we further discuss and analyze the generated negative sample and the proposed framework. [ABSTRACT FROM AUTHOR]

Background: The study aimed to analyze major underlying factors of the subhealth condition evaluation and find the optimization model. Methods: Selected 524 cases of health state and 453 cases of subhealth state from the research objects. A genetic algorithm was applied to discover the optimization model. The decision tree algorithm was used to find the main performance in the areas of physical, psychological, and social adaptation in the two populations which were the health state and the subhealth state. Conclusions: To establish the optimization model, the author would set up a curve-fitting equation between reduction of health self-assessment score (S-G1) and white blood cell (WBC) value in routine blood, so as to establish the relationship between S-G1 and WBC and found the approximate minimum solution of each equation. Besides, the author would analyze the differences between two populations in WBC examination to seize the difference. Revealed the differences of two populations in the areas of physical, psychological, and social adaptation by data mining and got the result that WBC of “the health state population” was higher than that of “the sub-health state population” in the index changes of WBC. The problem in social adaptation area of the subhealth state population was more serious in degree than the health state population; the reason was complex. If WBC of the health state population was near or below 5.5079, the object may be in the state of subhealth. However, if WBC was near or below 4.35, it is possible to enter the “morbid state.”

Abstract Reviews on the cost/outcome of donor human milk (DHM) for infants requiring care in the neonatal intensive care unit (NICU) setting have been undertaken. However, the cost‐effectiveness evidence is unclear. Therefore, we conducted a systematic review of published full economic evaluations of DHM versus standard feeding in infants in neonatal care with the aim of undertaking a narrative synthesis of the cost‐effectiveness evidence and critical appraisal of the methods used. MEDLINE, EMBASE, Web of Science, Cochrane Library, Centre for Reviews and Dissemination (CRD) and PROSPERO databases were searched. Studies were included if they were full economic evaluations (model‐based or trial‐based), the participants were infants in neonatal units requiring nutritional support, the intervention was DHM and the comparator was any standard feeding option. There were no restrictions on outcome measures. Two authors independently assessed eligibility, extracted data, assessed quality and cross‐checked results, with disagreements resolved by consensus. Information extracted focused on study context, and economic evaluation methods and results. Of 2861 studies, seven were included. Six (86%) studies originated from high‐income countries. Four (57%) of the studies were model‐based. Although we could not directly compare the different studies, due to the heterogenous nature of health and economic parameters used in the studies, all DHM interventions indicated cost‐effective or cost saving results. This review suggests that economic evaluation of DHM interventions is an expanding area of research. Although these interventions show promise, future economic evaluations of DHM interventions need to explicitly provide more details on long‐term costs and consequences.

Reviews on the cost/outcome of donor human milk (DHM) for infants requiring care in the neonatal intensive care unit (NICU) setting have been undertaken. However, the cost‐effectiveness evidence is unclear. Therefore, we conducted a systematic review of published full economic evaluations of DHM versus standard feeding in infants in neonatal care with the aim of undertaking a narrative synthesis of the cost‐effectiveness evidence and critical appraisal of the methods used. MEDLINE, EMBASE, Web of Science, Cochrane Library, Centre for Reviews and Dissemination (CRD) and PROSPERO databases were searched. Studies were included if they were full economic evaluations (model‐based or trial‐based), the participants were infants in neonatal units requiring nutritional support, the intervention was DHM and the comparator was any standard feeding option. There were no restrictions on outcome measures. Two authors independently assessed eligibility, extracted data, assessed quality and cross‐checked results, with disagreements resolved by consensus. Information extracted focused on study context, and economic evaluation methods and results. Of 2861 studies, seven were included. Six (86%) studies originated from high‐income countries. Four (57%) of the studies were model‐based. Although we could not directly compare the different studies, due to the heterogenous nature of health and economic parameters used in the studies, all DHM interventions indicated cost‐effective or cost saving results. This review suggests that economic evaluation of DHM interventions is an expanding area of research. Although these interventions show promise, future economic evaluations of DHM interventions need to explicitly provide more details on long‐term costs and consequences. [ABSTRACT FROM AUTHOR]

Health state estimation can quantitatively evaluate the current degradation state of equipment, and remaining life prediction can quantitatively predict the remaining service time of equipment. These two technologies can provide a basis for condition-based maintenance and predictive maintenance of equipment, respectively. In recent years, a large amount of research has been implemented in these two technologies. However, there is not any systematic review that covers these two technologies, and their engineering applications, comprehensively. To fill the gap, this paper makes a comparative analysis of existing health state estimation and remaining life prediction methods, and details the characteristics and limitations of various methods. The engineering applications of these two methods are summarized, and their applicable objects are briefly given. Finally, these two methods are summarized, and their feasibility for engineering application is discussed. This work provides guidance for the selection of industrial equipment health assessment and remaining life prediction methods.

In economic evaluations of health technologies, health outcomes are commonly measured in terms of quality‐adjusted life years (QALYs). QALYs are the product of time and health‐related quality of life. Health‐related quality of life, in turn, is determined by a social tariff, which is supposed to reflect the public's preference over health states. This study argues that, because of the tariff's role in the societal decision‐making process, it should not be understood as merely an operational (statistical) definition of health, but as a major instrument of democratic participation. I outline what implications this might have for both the method used to aggregate individual preferences, and the set of individuals whose preferences should count. Alternative tariff specifications and decision rules are explored, and future research directions are proposed. [ABSTRACT FROM AUTHOR]

Aim: To describe the experience of nonsense mutation Duchenne muscular dystrophy (DMD) across different health states in the DMD natural history model. Materials and methods: Qualitative interviews were conducted with UK caregivers (n = 10) of individuals with nonsense mutation DMD treated with ataluren. Results: Interviews were conducted with caregivers of individuals in the early ambulatory (n = 6), late ambulatory (n = 3) and transfers (n = 3) health states. Increasing health state severity was related to decreased physical function, which impacted daily and social activities, emotional well-being and caregiver burden. Conclusion: Declining physical function can impair daily life and impact the health-related quality of life of individuals and their caregivers. Delaying progression between health states has the potential to prevent loss of function and decline in health-related quality of life. This study aimed to understand the experience of living with nonsense mutation Duchenne muscular dystrophy (DMD) from the perspective of caregivers and how this varies depending on an individual's ability to move around (motor function). Caregivers of individuals with nonsense mutation DMD were interviewed about the impact of DMD on their child and on themselves. Responses were grouped according to the individual's level of motor function. Ten interviews were conducted. The worse the level of motor function, the more difficulties with daily and social activities and emotional wellbeing, and the higher the level of caregiver burden. Declining motor function can impair daily life and impact the health-related quality of life of individuals and their caregivers, highlighting the importance of delaying disease progression. [ABSTRACT FROM AUTHOR]

Objective: To examine changes in quality of life (QoL) after an eight-week period of robotic exoskeleton training in a homogeneous group of patients with chronic complete spinal cord injury (SCI)., Design: Prospective single-group pre-post study., Setting: Rehabilitation center., Participants: Patients with a chronic (>6 months) motor complete SCI (T1-L1)., Intervention: Twenty-four training sessions with the ReWalk exoskeleton over an eight-week period., Main Outcome Measure: QoL, assessed with the sum score of the Short Form-36 with Walk Wheel modification (SF-36ww). Secondary outcome measures were the eight SF-36ww subdomains, satisfaction with bladder and bowel management, lower extremity joint passive range of motion (pROM), and lower extremity spasticity., Results: Twenty-one participants completed the training. QoL significantly improved after the training period (average SF-36 sum score 621 ± 90) compared to baseline (571 ± 133) ( t (20)=-2.5, P =.02). Improvements were seen on the SF-36ww subdomains for pain ( P =.003), social functioning ( P =.03), mental health ( P =.02), and general health perception ( P =.01). Satisfaction with bladder management (range 1-5) improved from median 3 at baseline to 4 after exoskeleton training ( P =0.01). No changes in satisfaction with bowel management ( P =.11), pROM (hip-extension ( P =.49), knee-extension ( P =.36), ankle dorsiflexion ( P =.69)), or spasticity ( P =.94) were found., Conclusion: Even in patients with chronic motor complete SCI and a relatively high level of QoL at baseline, a short-term exoskeleton training improved their QoL, pain and satisfaction with bladder management; findings that warrant further controlled studies in this specific SCI population.

Background: Hypoglycemia threatens cognitive function and driving safety. Previous research investigated in-vehicle voice assistants as hypoglycemia warnings. However, they could startle drivers. To address this, we combine voice warnings with ambient LEDs., Objective: The study assesses the effect of in-vehicle multimodal warning on emotional reaction and technology acceptance among drivers with type 1 diabetes., Methods: Two studies were conducted, one in simulated driving and the other in real-world driving. A quasi-experimental design included 2 independent variables (blood glucose phase and warning modality) and 1 main dependent variable (emotional reaction). Blood glucose was manipulated via intravenous catheters, and warning modality was manipulated by combining a tablet voice warning app and LEDs. Emotional reaction was measured physiologically via skin conductance response and subjectively with the Affective Slider and tested with a mixed-effect linear model. Secondary outcomes included self-reported technology acceptance. Participants were recruited from Bern University Hospital, Switzerland., Results: The simulated and real-world driving studies involved 9 and 10 participants with type 1 diabetes, respectively. Both studies showed significant results in self-reported emotional reactions (P<.001). In simulated driving, neither warning modality nor blood glucose phase significantly affected self-reported arousal, but in real-world driving, both did (F 2,68 =4.3; P<.05 and F 2,76 =4.1; P=.03). Warning modality affected self-reported valence in simulated driving (F 2,68 =3.9; P<.05), while blood glucose phase affected it in real-world driving (F 2,76 =9.3; P<.001). Skin conductance response did not yield significant results neither in the simulated driving study (modality: F 2,68 =2.46; P=.09, blood glucose phase: F 2,68 =0.3; P=.74), nor in the real-world driving study (modality: F 2,76 =0.8; P=.47, blood glucose phase: F 2,76 =0.7; P=.5). In both simulated and real-world driving studies, the voice+LED warning modality was the most effective (simulated: mean 3.38, SD 1.06 and real-world: mean 3.5, SD 0.71) and urgent (simulated: mean 3.12, SD 0.64 and real-world: mean 3.6, SD 0.52). Annoyance varied across settings. The standard warning modality was the least effective (simulated: mean 2.25, SD 1.16 and real-world: mean 3.3, SD 1.06) and urgent (simulated: mean 1.88, SD 1.55 and real-world: mean 2.6, SD 1.26) and the most annoying (simulated: mean 2.25, SD 1.16 and real-world: mean 1.7, SD 0.95). In terms of preference, the voice warning modality outperformed the standard warning modality. In simulated driving, the voice+LED warning modality (mean rank 1.5, SD rank 0.82) was preferred over the voice (mean rank 2.2, SD rank 0.6) and standard (mean rank 2.4, SD rank 0.81) warning modalities, while in real-world driving, the voice+LED and voice warning modalities were equally preferred (mean rank 1.8, SD rank 0.79) to the standard warning modality (mean rank 2.4, SD rank 0.84)., Conclusions: Despite the mixed results, this paper highlights the potential of implementing voice assistant-based health warnings in cars and advocates for multimodal alerts to enhance hypoglycemia management while driving., Trial Registration: ClinicalTrials.gov NCT05183191; https://classic.clinicaltrials.gov/ct2/show/NCT05183191, ClinicalTrials.gov NCT05308095; https://classic.clinicaltrials.gov/ct2/show/NCT05308095., (©Caterina Bérubé, Martin Maritsch, Vera Franziska Lehmann, Mathias Kraus, Stefan Feuerriegel, Thomas Züger, Felix Wortmann, Christoph Stettler, Elgar Fleisch, A Baki Kocaballi, Tobias Kowatsch. Originally published in JMIR Human Factors (https://humanfactors.jmir.org), 18.04.2024.)