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This study examines the effectiveness of the countries' health systems in the Horn of Africa region. It also investigates the perspectives of actors who have played an active role in health affairs in Somalia carried out by Türkiye. Using the Data Envelopment Analysis and Malmquist Total Factor Efficiency Analysis, we investigated the effectiveness of the health systems and improvements made throughout the years. In the countries of interest, efficiency levels and average total factor productivity showed positive and/or negative trends between 2000 and 2020. Kenya showed a marked performance in achieving improved average total factor productivity thanks to the effective use of current technology in health, success in integrating new technologies into the health system, and a high potential to produce more output despite insufficient existing inputs. The remaining countries lagged behind in improving their production factors. Since 2014, Türkiye has provided health services in Somalia through health diplomacy and conducted medical examinations for numerous patients in a well-equipped hospital.

Cette étude examine l'efficacité des systèmes de santé des pays de la région de la Corne de l'Afrique. Il étudie également les perspectives des acteurs qui ont joué un rôle actif dans les affaires de santé en Somalie menées par Türkiye. En utilisant l'analyse de l'enveloppe des données et l'analyses d'efficacité des facteurs totales de Malmquist, nous avons étudié l'efficience des systèmes de santé et les améliorations apportées au cours des années. Dans les pays intéressés, les niveaux d'efficacité et la productivité totale moyenne du facteur ont montré des tendances positives et/ou négatives entre 2000 et 2020. Le Kenya a fait preuve d'une performance marquée dans l'amélioration de la productivité totale moyenne du facteur grâce à l'utilisation efficace de la technologie actuelle dans le domaine de la santé, au succès de l'intégration de nouvelles technologies dans le système de santé et au potentiel élevé de produire plus de produits malgré l'insuffisance des produits existants. Les autres pays sont en retard dans l'amélioration de leurs facteurs de production. Depuis 2014, Türkiye a fourni des services de santé en Somalie par le biais de la diplomatie de santé et a effectué des examens médicaux pour de nombreux patients dans un hôpital bien équipé.

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Condition monitoring (CM) is the basis of prognostics and health management (PHM), which is gaining more and more importance in the industrial world. CM, which refers to the tracking of industrial equipment's state of health during operations, plays, in fact, a significant role in the reliability, safety, and efficiency of industrial operations. This paper proposes a data-driven CM approach based on the autoregressive (AR) modeling of the acquired sensor data and their analysis within frequency subbands. The number and size of the bands are determined with negligible human intervention, analyzing only the time-frequency representation of the signal of interest under normal system operating conditions. In particular, the approach exploits the synchrosqueezing transform to improve the signal energy distribution in the time-frequency plane, defining a multidimensional health indicator built on the basis of the AR power spectral density and the symmetric Itakura-Saito spectral distance. The described health indicator proved capable of detecting changes in the signal spectrum due to the occurrence of faults. After the initial definition of the bands and the calculation of the characteristics of the nominal AR spectrum, the procedure requires no further intervention and can be used for online condition monitoring and fault diagnosis. Since it is based on the comparison of spectra under different operating conditions, its applicability depends neither on the nature of the acquired signal nor on a specific system to be monitored. As an example, the effectiveness of the proposed method was favorably tested using real data available in the Case Western Reserve University (CWRU) Bearing Data Center, a widely known and used benchmark.

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Standing and lying are the fundamental behaviours of quadrupedal animals, and the ratio of their durations is a significant indicator of calf health. In this study, we proposed a computer vision method for non-invasively monitoring of calves' behaviours. Cameras were deployed at four viewpoints to monitor six calves on six consecutive days. YOLOv8n was trained to detect standing and lying calves. Daily behavioural budget was then summarised and analysed based on automatic inference on untrained data. The results show a mean average precision of 0.995 and an average inference speed of 333 frames per second. The maximum error in the estimated daily standing and lying time for a total of 8 calf-days is less than 14 min. Calves with diarrhoea had about 2 h more daily lying time (p < 0.002), 2.65 more daily lying bouts (p < 0.049), and 4.3 min less daily lying bout duration (p = 0.5) compared to healthy calves. The proposed method can help in understanding calves' health status based on automatically measured standing and lying time, thereby improving their welfare and management on the farm.

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Background: In Brazil, the Ministry of Health (MH) monitors leprosy using 15 indicators, with the aim of implementing and evaluating evidence-based public policies. However, an excessive number of variables can complicate the definition of objectives and verification of epidemiological goals. Methods: In this paper, we develop the Global Leprosy Assessment Index (GLAI), a composite measure that integrates two key dimensions for the control the disease: epidemiological and operational. Using a confirmatory factor analysis model to examine 2020 state-level data, we have standardized GLAI to a range of 0 to 1. Results: Higher values within this range indicate a greater severity of the disease. The mean value of the GLAI was 0.67, with a standard deviation of 0.22. Roraima has the highest value, followed by Paraíba with 0.88 while Tocantins records the lowest value of the indicator, followed by Mato Grosso with 0.14. The epidemiological and operational indicators have a positive but statistically insignificant correlation (r = 0.25; p-value = 0.20). Conclusions: The development of evidence-based public policies depends on the availability of valid and reliable indicators. The GLAI presented in this paper is easily reproducible and can be used to monitor the disease with disaggregated information. Furthermore, the GLAI has the potential to serve as a more robust parameter for evaluating the impact of actions designed to eradicate leprosy in Brazil.

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Currently, the research on the predictions of remaining useful life (RUL) of rotating machinery mainly focuses on the process of health indicator (HI) construction and the determination of the first prediction time (FPT). In complex industrial environments, the influence of environmental factors such as noise may affect the accuracy of RUL predictions. Accurately estimating the remaining useful life of bearings plays a vital role in reducing costly unscheduled maintenance and increasing machine reliability. To overcome these problems, a health indicator construction and prediction method based on multi-featured factor analysis are proposed. Compared with the existing methods, the advantages of this method are the use of factor analysis, to mine hidden common factors from multiple features, and the construction of health indicators based on the maximization of variance contribution after rotation. A dynamic window rectification method is designed to reduce and weaken the stochastic fluctuations in the health indicators. The first prediction time was determined by the cumulative gradient change in the trajectory of the HI. A regression-based adaptive prediction model is used to learn the evolutionary trend of the HI and estimate the RUL of the bearings. The experimental results of two publicly available bearing datasets show the advantages of the method.

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Afghanistan has been in an active state of conflict and war for twenty continuous years. Social services like health and education have been badly affected, facing issues such as service disruption, brain drain, and generalized instability. Health indices that provide proxy indicators for general population wellness, such as maternal health, child mortality, and immunization coverage, show that the health services available to the Afghan population are sub-optimal. Investment in social service and interventions has increased. The World Bank and the United Nations through its agencies (The World Health Organization (WHO) and United Nations' Children's Fund (UNICEF) are providing social support through targeted and strategic programs. However, the topographic and environmental realities of Afghanistan, with its broad mountain coverage, propensity toward natural disasters, and latent conflict, has made data and information gathering arduous. Since data is essential for measurement and management, the WHO Health Emergencies (WHE) information management unit at WHO Afghanistan has delivered an innovative form of data analysis, specialized and targeted at providing improved information on communities that are not adequately covered by health services. Deploying a geographical information system (GIS) approach, the WHE team has collated primary and secondary data from a combination of datasets to produce a far-reaching piece of analysis. The analysis of underserved communities in hard to reach, remote locations, provides a live, evidence-based information product. This provides a working tool that is essential to primary health programming and intervention in Afghanistan. The estimates show that approximately 9.5 million individuals in 22,181 villages across 34 provinces are underserved by primary health services. This paper is presented to explain the underpinning methodology.

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Electromagnetic coils are indispensable components for energy conversion and transformation in various systems across industries. However, electromagnetic coil insulation failure occurs frequently, which can lead to serious consequences. To facilitate predictive maintenance for industrial systems, it is essential to monitor insulation degradation prior to the formation of turn-to-turn shorts. This paper experimentally investigates coil insulation degradation from both macro and micro perspectives. At the macro level, an evaluation index based on a weighted linear combination of trend, monotonicity and robustness is proposed to construct a degradation-sensitive health indicator (DSHI) based on high-frequency electrical response parameters for precise insulation degradation monitoring. While at the micro level, a coil finite element analysis and twisted pair accelerated degradation test are conducted to obtain the actual turn-to-turn insulation status. The correlation analysis between macroscopic and microscopic effects of insulation degradation is used to verify the proposed DSHI-based method. Further, it helps to determine the threshold of DSHI. This breakthrough opens new possibilities for predictive maintenance for industrial equipment that incorporates coils.

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The prediction of system degradation is very important as it serves as an important basis for the formulation of condition-based maintenance strategies. An effective health indicator (HI) plays a key role in the prediction of system degradation as it enables vital information for critical tasks ranging from fault diagnosis to remaining useful life prediction. To address this issue, a method for monitoring data fusion and health indicator construction based on an autoencoder (AE) and a long short-term memory (LSTM) network is proposed in this study to improve the predictability and effectiveness of health indicators. Firstly, an unsupervised method and overall framework for HI construction is built based on a deep autoencoder and an LSTM neural network. The neural network is trained fully based on the normal operating monitoring data and then the construction error of the AE model is adopted as the health indicator of the system. Secondly, we propose related machine learning techniques for monitoring data processing to overcome the issue of data fusion, such as mutual information for sensor selection and t-distributed stochastic neighbor embedding (T-SNE) for operating condition identification. Thirdly, in order to verify the performance of the proposed method, experiments are conducted based on the CMAPSS dataset and results are compared with algorithms of principal component analysis (PCA) and a vanilla autoencoder model. Result shows that the LSTM-AE model outperforms the PCA and Vanilla-AE model in the metrics of monotonicity, trendability, prognosability, and fitness. Fourthly, in order to analyze the impact of the time step of the LSMT-AE model on HI construction, we construct and analyze the system HI curve under different time steps of 5, 10, 15, 20, and 25 cycles. Finally, the results demonstrate that the proposed method for HI construction can effectively characterize the health state of a system, which is helpful for the development of further failure prognostics and converting the scheduled maintenance into condition-based maintenance.

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Despite an increasing appreciation of the importance of host-microbe interaction in healthy growth, information on gut microbiota changes of the Chinese giant salamander (Andrias davidianus) during growth is still lacking. Moreover, it is interesting to identify gut microbial structure for further monitoring A. davidianus health. This study explored the composition and functional characteristics of gut bacteria in different growth periods, including tadpole stage (ADT), gills internalization stage (ADG), 1 year age (ADY), 2 year age (ADE), and 3 year age (ADS), using high-throughput sequencing. The results showed that significant differences were observed in microbial community composition and abundance among different growth groups. The diversity and abundance of intestinal flora gradually reduced from larvae to adult stages. Overall, the gut microbial communities were mainly composed of Fusobacteriota, Firmicutes, Bacteroidota, and Proteobacteria. More specifically, the Cetobacterium genus was the most dominant, followed by Lactobacillus and Candidatus Amphibiichlamydia. Interestingly, Candidatus Amphibiichlamydia, a special species related to amphibian diseases, could be a promising indicator for healthy monitoring during A. davidianus growth. These results could be an important reference for future research on the relationship between the host and microbiota and also provide basic data for the artificial feeding of A. davidianus.

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BACKGROUND: Anthropogenic disturbance has the potential to negatively affect wildlife health by altering food availability and diet composition, increasing the exposure to agrochemicals, and intensifying the contact with humans, domestic animals, and their pathogens. However, the impact of these factors on the fecal microbiome composition of wildlife hosts and its link to host health modulation remains barely explored. Here we investigated the composition of the fecal bacterial microbiome of the insectivorous bat Kuhl's pipistrelle (Pipistrellus kuhlii) dwelling in four environmental contexts with different levels of anthropogenic pressure. We analyzed their microbiome composition, structure and diversity through full-length 16S rRNA metabarcoding using the nanopore long-read sequencer MinION™. We hypothesized that the bacterial community structure of fecal samples would vary across the different scenarios, showing a decreased diversity and richness in samples from disturbed ecosystems. RESULTS: The fecal microbiomes of 31 bats from 4 scenarios were sequenced. A total of 4,829,302 reads were obtained with a taxonomic assignment percentage of 99.9% at genus level. Most abundant genera across all scenarios were Enterococcus, Escherichia/Shigella, Bacillus and Enterobacter. Alpha diversity varied significantly between the four scenarios (p < 0.05), showing the lowest Shannon index in bats from urban and intensive agriculture landscapes, while the highest alpha diversity value was found in near pristine landscapes. Beta diversity obtained by Bray-Curtis distance showed weak statistical differentiation of bacterial taxonomic profiles among scenarios. Furthermore, core community analysis showed that 1,293 genera were shared among localities. Differential abundance analyses showed that the highest differentially abundant taxa were found in near pristine landscapes, with the exception of the family Alcaligenaceae, which was also overrepresented in urban and intensive agriculture landscapes. CONCLUSIONS: This study suggests that near pristine and undisturbed landscapes could promote a more resilient gut microbiome in wild populations of P. kuhlii. These results highlight the potential of the fecal microbiome as a non-invasive bioindicator to assess insectivorous bats' health and as a key element of landscape conservation strategies.

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Background: Population size and structure have a huge impact on health indicators. In countries with a high proportion of expatriates, there are some limitations in estimating, aggregating and reporting of the health indicators, and corrections may be required in the established estimation methodologies. We review the case of Qatar to see how its specific population characteristics affect its health indicators. Methods: We used routinely collected data and reviewed and calculated a selected list of health indicators for Qatari and non-Qatari populations residing in Qatar. Mortality and cancer incidence rates, stratified by nationality, were used for this purpose. Also, a direct method was used to estimate completeness of the death registry, compared to the mortuary data. Results: Age and sex distribution of Qatari and non-Qatari populations are completely different. Compared to the mortuary data, completeness of death registration for the total population was estimated at 98.9 and 94.3%, with and without considering overseas deaths, respectively. Both estimates were considerably higher than estimates from the indirect methods. Mortality patterns were different even after standardization of age and stratification of sex groups; male age-standardized mortality rates were 502.7 and 242.3 per 100,000 individuals, respectively for Qataris and non-Qataris. The rates were closer in female populations (315.6 and 291.5, respectively). The leading types of cancer incidents were different in Qataris and non-Qataris. Conclusions: Expatriates are a dynamic population with high-turnover, different from Qatari population in their age-sex structure and health status. They are more likely to be young or middle-aged and are less affected by age related diseases and cancers. Also, they might be at higher risks for specific diseases or injuries. Aggregating indicators of Qatari and non-Qatari populations might be mis-leading for policy making purposes, and common estimation correction approaches cannot alleviate the limitations. High-proportion of expatriate population also imposes significant errors to some of the key demographic estimates (such as completeness of death registry). We recommend a standardized approach to consider nationality in addition to age and sex distributions for analysis of health data in countries with a high proportion of expatriates.

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BACKGROUND: There is a need to shift from biomedical and pathogenic approaches to salutogenic approach. OBJECTIVE: To validate the Finnish version of the SHIS by testing its psychometric properties in care workers and to assess the SHIS score over time. METHODS: We first conducted a survey in 2020 (T1) and tested the psychometric properties of SHIS among care workers. We repeated the survey in spring 2022 (T2) among the same subjects. We analyzed the changes in SHIS, self-rated health (SRH), work ability (WAS), sickness absence and occupational calling between T1 and T2. Thereafter, we compared changes between health care sectors' and the other sectors' care workers. RESULTS: The results showed an increase in positive health measured with the SHIS and the occupational calling, a decrease in the SRH, and an increase in the number of sickness-related absences among all the care workers between T1 and T2. There was no change in their WAS. The health care workers had a lower SHIS than the other sectors' care workers in both T1 and T2, but the increase in their SHIS was parallel to that of the other workers. CONCLUSION: SHIS is a useful and reliable measure of positive health and can be used in studies when determining subjective health instead of, or in addition to, diagnoses. It was able to detect the health changes caused by the COVID-19 pandemic. SHIS is capable of capturing the underlying salutogenic approach of health promotive resources.

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Background: The healthcare system of the Islamic Republic of Iran provides special maternal health care services for mothers, regardless of their nationality. Aim: This study, supported by the United Nations Population Fund, was conducted to review available data associated with health indicators of Afghan mothers living in Islamic Republic of Iran. Methods: This descriptive study used data from the electronic registration system of the Maternal Health Office of the Ministry of Health and Medical Education on characteristics, morbidity and mortality among Afghan mothers in the Islamic Republic of Iran from 2017 to 2019. The data were analysed using SPSS version 23.0. Based on the results, we propose interventions to improve health services for vulnerable Afghan mothers. Results: There were 168 488 deliveries over the 3 years of the study (2017-2019). Deliveries by Afghan women increased from 3.4% in 2017 to 5.2% in 2019, and more than 70% of these Afghan women were vulnerable. Ten percent of deliveries among Afghan mothers were performed by traditional birth attendants. The rate of caesarean section among Afghan mothers was 30%. Maternal mortality ratio among the Afghan mothers was 43 per 100 000 for the 3 years. Conclusion: Afghan mothers in the Islamic Republic of Iran use primary health care services provided for mothers in the country. However, healthcare delivery to these mothers is inadequate, although considered better than the care provided to Afghan mothers living in Afghanistan. We recommend targeted interventions to improve the health status of Afghan women living in the Islamic Republic of Iran.

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BACKGROUND: Tobacco smoking is an important public health issue and a core indicator of public health policy worldwide. However, global pandemics and natural disasters have prevented surveys from being conducted. OBJECTIVE: The purpose of this study was to predict smoking prevalence by prefecture and sex in Japan using Internet search trends. METHODS: This study used the infodemiology approach. The outcome variable was smoking prevalence by prefecture, obtained from national surveys. The predictor variables were the search volumes on Yahoo! Japan Search. We collected the search volumes for queries related to terms from the thesaurus of the Japanese medical article database Ichu-shi. Predictor variables were converted to per capita values and standardized as z scores. For smoking prevalence, the values for 2016 and 2019 were used, and for search volume, the values for the April 1 to March 31 fiscal year (FY) 1 year prior to the survey (ie, FY 2015 and FY 2018) were used. Partial correlation coefficients, adjusted for data year, were calculated between smoking prevalence and search volume, and a regression analysis using a generalized linear mixed model with random effects was conducted for each prefecture. Several models were tested, including a model that included all search queries, a variable reduction method, and one that excluded cigarette product names. The best model was selected with the Akaike information criterion corrected (AICC) for small sample size and the Bayesian information criterion (BIC). We compared the predicted and actual smoking prevalence in 2016 and 2019 based on the best model and predicted the smoking prevalence in 2022. RESULTS: The partial correlation coefficients for men showed that 9 search queries had significant correlations with smoking prevalence, including cigarette (r=-0.417, P<.001), cigar in kanji (r=-0.412, P<.001), and cigar in katakana (r=-0.399, P<.001). For women, five search queries had significant correlations, including vape (r=0.335, P=.001), quitting smoking (r=0.288, P=.005), and cigar (r=0.286, P=.006). The models with all search queries were the best models for both AICC and BIC scores. Scatter plots of actual and estimated smoking prevalence in 2016 and 2019 confirmed a relatively high degree of agreement. The average estimated smoking prevalence in 2022 in the 47 prefectures for the total sample was 23.492% (95% CI 21.617%-25.367%), showing an increasing trend, with an average of 29.024% (95% CI 27.218%-30.830%) for men and 8.793% (95% CI 7.531%-10.054%) for women. CONCLUSIONS: This study suggests that the search volume of tobacco-related queries in internet search engines can predict smoking prevalence by prefecture and sex in Japan. These findings will enable the development of low-cost, timely, and crisis-resistant health indicators that will enable the evaluation of health measures and contribute to improved public health.

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Prediction of remaining useful life (RUL) is greatly significant for improving the safety and reliability of manufacturing equipment. However, in real industry, it is difficult for RUL prediction models trained on a small sample of faults to obtain satisfactory accuracy. To overcome this drawback, this paper presents a long short-term memory (LSTM) neural network with transfer learning and ensemble learning and combines it with an unsupervised health indicator (HI) construction method for remaining-useful-life prediction. This study consists of the following parts: (1) utilizing the characteristics of deep belief networks and self-organizing map networks to translate raw sensor data to a synthetic HI that can effectively reflect system health; and (2) introducing transfer learning and ensemble learning to provide the required degradation mechanism for the RUL prediction model based on LSTM to improve the performance of the model. The performance of the proposed method is verified by two bearing datasets collected from experimental data, and the results show that the proposed method obtains better performance than comparable methods.

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BACKGROUND: Educational environments are considered important in strengthening students' health status and knowledge, which are associated with good educational outcomes. It has been suggested to establish healthy universities based on a salutogenic approach - namely, health promotion. The aim of this study was to describe health-promoting resources and factors among first-semester students in higher education in healthcare and social work. METHODS: This cross-sectional study is based on a survey distributed among all students in seven healthcare and social work programmes at six universities in southern Sweden. The survey was carried out in 2018 using a self-reported, web-based questionnaire focussing on general health and well-being, lifestyle factors together with three validated instruments measuring health-promoting factors and processes: the Sense of Coherence (SOC) scale, Salutogenic Health Indicator Scale (SHIS) and Occupational Balance Questionnaire (OBQ). RESULTS: Of 2283 students, 851 (37.3%) completed the survey, of whom 742 (87.1%) were women; 722 (84.8%) were enrolled on healthcare programmes, and 129 (15.2%) were enrolled on social work programmes. Most reported good general health and well-being (88.1% and 83.7%, respectively). The total mean scores for the SOC scale, SHIS and OBQ were, respectively, 59.09 (SD = 11.78), 44.04 (SD = 9.38) and 26.40 (SD = 7.07). Well-being and several healthy lifestyles were related to better general health and higher SOC, SHIS and OBQ scores. Multiple linear and logistic regressions showed that perceived well-being and no sleeping problems significantly predicted higher general health and higher SOC, SHIS and OBQ scores. Being less sedentary and non-smoking habits were significant predictors of higher SOC. CONCLUSIONS: Swedish students in higher education within the healthcare and social work sector report good general health and well-being in the first semester, as well as health-promoting resources (i.e. SOC, SHIS and OBQ), and in some aspects, a healthy lifestyle. High-intensity exercise, no sleeping problems and non-smoking seem to be of importance to both general health and health-promotive resources. This study contributes to knowledge about the health promotive characteristics of students in the healthcare and social work fields, which is of importance for planning universities with a salutogenic approach.

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OBJECTIVES: This study aimed to develop a new chewing problem directory (CPD) and validate it with oral health indicators such as total occlusion force, number of natural and rehabilitated teeth (NRT), NRT posterior, natural teeth, natural teeth posterior, and dental status among Korean elders. BACKGROUND: Chewing problem is the main oral health problem in elders. However, there has been no validated tool using both subjective and objective assessment of chewing problem. SUBJECTS AND METHODS: A total of 537 participants aged 65 years or more were randomly assigned into 2 subsamples: developing sample (n = 260) for developing and internally validating the new CPD as the 1st stage and confirmation sample (n = 277) for confirming validation of CPD as the 2nd stage. CPD was developed using three subjective questionnaires (general eating, chewing nuts, and chewing meat problem) and objective NRT. Periodontitis, age, sex, education, smoking, alcohol drinking, metabolic syndrome, and frailty were considered as confounders. Following the development of CPD, CPD was validated using multiple multivariable logistic regression after controlling for confounders in confirmation sample and total sample. RESULTS: The Cronbach's alpha value for three subjective questionnaires of CPD was 0.87. Among oral health indicators, NRT (0-28) showed the highest impact association with subjective chewing problem score (partial r = - 0.276). The chewing problem from the new CPD was associated with all items of oral health indicators. The prevalence of chewing problems by CPD was 57.7% in developing sample. Elders with NRT ≤ 24, compared with those with NRT ≥ 25, showed the highest impact on chewing problems by new CPD (Odds Ratio = 7.3 in the confirmation sample and 5.04 in the total sample, p < 0.05) among oral health indicators. CONCLUSION: This new CPD was developed as a valid tool to evaluate the chewing problem for Korean elders in dental clinics and community-based settings.

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This paper proposes a new technique for the construction of a concrete-beam health indicator based on the Kullback-Leibler divergence (KLD) and deep learning. Health indicator (HI) construction is a vital part of remaining useful lifetime (RUL) approaches for monitoring the health of concrete structures. Through the construction of a HI, the deterioration process can be processed and portrayed so that it can be forwarded to a prediction module for RUL prognosis. The degradation progression and failure can be identified by predicting the RUL based on the situation of the current specimen; as a result, maintenance can be planned to reduce safety risks, reduce financial costs, and prolong the specimen's useful lifetime. The portrayal of deterioration through HI construction from raw acoustic emission (AE) data is performed using a deep neural network (DNN), whose parameters are obtained by pretraining and fine tuning using a stack autoencoder (SAE). Kullback-Leibler divergence, which is calculated between a reference normal-conditioned signal and a current unknown signal, was used to represent the deterioration process of concrete structures, which has not been investigated for the concrete beams so far. The DNN-based constructor then learns to generate HI from raw data with KLD values as the training label. The HI construction result was evaluated with run-to-fail test data of concrete specimens with two measurements: fitness analysis of the construction result and RUL prognosis. The results confirm the reliability of KLD in portraying the deterioration process, showing a large improvement in comparison to other methods. In addition, this method requires no adept knowledge of the nature of the AE or the system fault, which is more favorable than model-based approaches where this level of expertise is compulsory. Furthermore, AE offers in-service monitoring, allowing the RUL prognosis task to be performed without disrupting the specimen's work.

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OBJECTIVE: To evaluate the association between oral health-related quality of life (OHRQoL) and oral health indicators including dental status, total occlusion force (TOF), number of natural and rehabilitated teeth (NRT), number of natural teeth (NT), and to explore the effect modification on the association by gender among Korean elders. METHODS: A total of 675 participants aged 65 or above recruited by a cluster-based stratified random sampling were included in this cross-sectional study. The 14-items Korean version of the Oral Health Impact Profile (OHIP) was used to measure OHRQoL. The responses about OHIP were dichotomized by the cut-off point of 'fairly often' to determine the 'poor' versus 'fair' OHRQoL. Age, gender, education level, alcohol drinking, smoking, metabolic syndrome, frailty, and periodontitis were considered as confounders. Multiple multivariable logistic regression analyses were applied to assess the adjusted association between oral health indicators and OHRQoL. Gender stratified analysis was also applied to explore the effect modification of the association. RESULTS: The prevalence of poor OHRQoL was 43.0%, which was higher in women, less-educated elders, alcohol non-drinkers and frailty elders (p < 0.05). Elders with poor OHRQoL also showed lower values of oral health indicators than elders with fair OHRQoL (p < 0.05). Those with NRT ≤ 24, NT ≤ 14, and TOF < 330 N increased the risk of poor OHRQoL by 2.3 times (OR = 2.26, confidence interval [CI] 1.54-3.31), 1.5 times (OR = 1.45, CI 1.02-2.07), and 1.5 times (OR = 1.47, CI 1.06-2.04), respectively. In women, the association of NRT ≤ 24 with poor OHRQoL increased from OR of 2.3 to OR of 2.4, while, in men, the association of TOF < 330 N with poor OHRQoL increased from OR of 1.5 to OR of 3.2. CONCLUSION: Oral health indicators consisting of TOF, NRT, and NT were independently associated with poor OHRQoL among Korean elders. Gender modified the association of TOF and NRT. Preventive and/or curative management for keeping natural teeth and the rehabilitation of missing teeth to recover the occlusal force may be essential for reducing poor OHRQoL.

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We focus on a poor region and study the nexuses between health interventions undertaken by a regional authority (RA) and this region's Holling resilience in the presence of a pandemic such as Covid-19. First, we show how a health intervention by the RA probabilistically affects an appropriately defined health indicator. Second, we compute the chance that the health status of this region's population falls below a minimum acceptable level in the presence of the health intervention. Third, we solve an optimization problem in which the RA maximizes the likelihood that the health status of this region's population stays above a minimum acceptable level at a given economic cost. Our analysis demonstrates that there is a connection between a health intervention, a region's health status, and its Holling resilience by presenting two applications. Our analysis reveals that this paper's methodology can be used to compute a region's Holling resilience with a particular health intervention. The main policy implications of our analysis concern the need for a RA to pay attention to (i) a region's health infrastructure and financing, (ii) sufficient engagement with the region's population, (iii) regional heterogeneity, (iv) data collection, and (v) the likelihood that sicker regions are likely to require more health interventions at a higher cost.