Prediction of Contact Fatigue Performance Degradation Trends Based on Multi-Domain Features and Temporal Convolutional Networks.

Contact fatigue is one of the most common failure forms of typical basic components such as bearings and gears. Accurate prediction of contact fatigue performance degradation trends in components is conducive to the scientific formulation of maintenance strategies and health management of equipment, which is of great significance for industrial production. In this paper, to realize the performance degradation trend prediction accurately, a prediction method based on multi-domain features and temporal convolutional networks (TCNs) was proposed. Firstly, a multi-domain and high-dimensional feature set of vibration signals was constructed, and performance degradation indexes with good sensitivity and strong trends were initially screened using comprehensive evaluation indexes. Secondly, the kernel principal component analysis (KPCA) method was used to eliminate redundant information among multi-domain features and construct health indexes (HIs) based on a convolutional autoencoder (CAE) network. Then, the performance degradation trend prediction model based on TCN was constructed, and the degradation trend prediction for the monitored object was realized using direct multi-step prediction. On this basis, the effectiveness of the proposed method was verified using a bearing common-use data set, and it was successfully applied to performance degradation trend prediction for rolling contact fatigue specimens. The results show that using KPCA can reduce the feature set from 14 dimensions to 4 dimensions and retain 98.33% of the information in the original preferred feature set. The method of constructing the HI based on CAE is effective, and change processes versus time of the constructed HI can truly reflect the degradation process of rolling contact fatigue specimen performance; this method has obvious advantages over the two commonly used methods for constructing HIs including auto-encoding (AE) networks and gaussian mixture models (GMMs). The model based on TCN can accurately predict the performance degradation of rolling contact fatigue specimens. Compared with prediction models based on long short-term memory (LSTM) networks and gating recurrent units (GRUs), the model based on TCN has better performance and higher prediction accuracy. The RMS error and average absolute error for a prediction step of 3 are 0.0146 and 0.0105, respectively. Overall, the proposed method has universal significance and can be applied to predict the performance degradation trend of other mechanical equipment/parts.

Deep Learning to Unveil Correlations between Urban Landscape and Population Health.

The global healthcare landscape is continuously changing throughout the world as technology advances, leading to a gradual change in lifestyle. Several diseases such as asthma and cardiovascular conditions are becoming more diffuse, due to a rise in pollution exposure and a more sedentary lifestyle. Healthcare providers deal with increasing new challenges, and thanks to fast-developing big data technologies, they can be faced with systems that provide direct support to citizens. In this context, within the EU-funded Participatory Urban Living for Sustainable Environments (PULSE) project, we are implementing a data analytic platform designed to provide public health decision makers with advanced approaches, to jointly analyze maps and geospatial information with healthcare and air pollution data. In this paper we describe a component of such platforms, which couples deep learning analysis of urban geospatial images with healthcare indexes collected by the 500 Cities project. By applying a pre-learned deep Neural Network architecture, satellite images of New York City are analyzed and latent feature variables are extracted. These features are used to derive clusters, which are correlated with healthcare indicators by means of a multivariate classification model. Thanks to this pipeline, it is possible to show that, in New York City, health care indexes are significantly correlated to the urban landscape. This pipeline can serve as a basis to ease urban planning, since the same interventions can be organized on similar areas, even if geographically distant.

Identification and Prioritization of Health Indexes in Nomadic Tribespeople by Fuzzy Delphi Method: An Ecological Study.

The migratory lifestyle of nomadic communities, combined with the lack of a suitable health-related organizational structure, has made it difficult to provide health care services that can improve their health status. To achieve the concept of justice in health and sustainable development, it is imperative to improve the health status of all citizens in Iran, which consists of the nomadic communities, and urban and rural populations. In this ecological study national health indexes in nomadic tribespeople was Identified and prioritized by expert panel and fuzzy Delphi method. In the first step, the national health indexes were extracted from the literature, and then indexes that can be measured, evaluated and representative of the nomadic communities were extracted and prioritized by using fuzzy Delphi and TOPSIS methods, Questionnaire options were analyzed according to 3 criteria of economic efficiency, measurability, and simplicity in the form of 13 components and their indicators. The analysis of the results of the fuzzy Delphi method shows that the mental health component has the lowest real score in the criteria of measurability, simplicity and economic efficiency. The child care component has the highest real score in terms of economic efficiency and the vaccination component has the highest real score based on the criteria of measurability and simplicity in nomadic communities. The results of the TOPSIS method show that the components of vaccination, maternal care and child care have the highest priority for attention and investigation of their indicators in this segment of the population. In general, by designing and implementing systems to record the information of priority indexes extracted from the present study, it is possible for responsible organizations to make effective decisions and plans for the improvement of the health status of nomadic communities.

Health-related quality of life in kidney transplant patients with diabetes.

OBJECTIVE: We sought to assess the disutility associated with diabetes in the kidney transplant population. METHODS: We enrolled 233 kidney transplant recipients age 18-74 from a Midwestern hospital outpatient department. Recipients with multiple or multi-organ transplants, those with laboratory evidence that suggests acute cellular damage (creatinine-kinase > 200 U/L), or a diagnosis of acute renal failure or acute rejection were excluded from the analysis (n = 33). Participants health-related quality of life (HRQOL) were evaluated using the Euro-QoL-5 Dimension (EQ-5D), Health Utility Index Mark III (HUI-III), and the Short Form-6D (SF-6D), which was calculated from the generic section (SF-12) of the Kidney Disease Quality of Life 36 (KDQOL-36). We estimated health utilities associated with diabetes using general linear modeling after adjusting for demographic, socioeconomic, and clinical characteristics. RESULTS: The adjusted health disutilities associated with diabetes were clinically and statistically significant: EQ-5D (Δ = 0.05; p < 0.01), HUI-III (Δ = 0.09; p < 0.01), and SF-6D (Δ = 0.04, p < 0.01). There was no difference between diabetic patients with good glycemic control (mean serum glucose <126 mg/dL in the three months prior to enrollment) and patients with poor glycemic control. CONCLUSIONS: Among kidney transplant patients between the ages of 18-74, non-diabetics have significantly higher HRQOL scores on the EQ-5D, HUI-III, and SF-6D compared with patients with diabetes.

Social, economic, and health indexes affecting epidemiology of preterm birth: A cross-sectional study.

Background and Aim: Preterm birth (PTB) accompanies with morbidities and mortality among newborns. This study aimed to show that different factors such as economic (adjusted net national income and gross domestic product [GDP] per capita), human developmental (human developmental index), and health (overall health performance, pregnancy prenatal care rate, and modeled estimated maternal mortality rate) indexes might influence the prevalence of PTB. Methods: To this, the top 10 countries with the highest and lowest prevalence of PTB were extracted from the Global Burden of Disease report for PTB. Then, we have gathered some common indexes for economic (adjusted net national income and GDP per capita), health-related (overall health performance, pregnancy prenatal care rate, and modeled estimated maternal mortality rate), and combined developmental (human developmental index) factors from different resources including World Bank and United Nations for those countries. The truncated Bayesian linear regression model, decision tree, and k-means clustering algorithms were used for data analysis. Results: The results showed Pregnancy Prenatal Care Rate index has a significant effect on the PTB rate in the top 10 countries with the highest prevalence of PTB. Also, for the top 10 less prevalent countries for PTB, it was shown that modeled estimated maternal mortality rate, human developmental index rank, and pregnancy prenatal care rate have significant effects on PTB rate. In addition, the clustering based on the similarities in socioeconomic, human developmental, and health indexes were approximately like with the clustering of the countries based on the PTB rates (Rand index = 0.823). Conclusion: The results showed studies on the epidemiology of PTB (either worldwide or nation-based) should consider these confounder factors to obtain accurate results.

Sexual behavior and sexual health of transgender women and men before treatment: Similarities and differences.

BACKGROUND: There is a lack of research on the sexual health of transgender individuals, as well as a paucity of data about overall sexual health indexes before treatment. AIMS: This study aims to analyze the main issues involved in transgender individuals' sexual behavior (with and without a partner), overall sexual health indexes, and potential predictors of sexual health, comparing trans men and trans women on all the variables assessed. METHODS: 260 trans people were recruited at a transgender health clinic in Spain. Participants completed the Sexual Behavior Questionnaire for transgender people before receiving any treatment. RESULTS: Overall results show that trans women have a more ego-dystonic and problematic experience of their sexuality. Regarding masturbation, there are no differences in its frequency between trans women and trans men, although there are differences in the reasons they do not masturbate. Regarding sexual behavior with a partner, trans women experience more difficulties than trans men, with significant differences in several areas (e.g., the percentage of transgender individuals who never allow their partners to touch their genitals). Regarding sexual health, we found differences between groups, with lower sexual desire, sexual arousal without orgasm, and overall sexual health in trans women. Likewise, different variables such as pain during sex, fantasies, and time with a steady partner seem to be associated with the different sexual health indexes. CONCLUSION: Our results show a clear difference between trans women and trans men in most of the sexual aspects assessed, revealing greater gender dysphoria in trans women. We would recommend taking these data into account, as well as variables that may be associated with different sexual health indexes, when designing interventions for transgender people.