A Multidimensional Health Indicator Based on Autoregressive Power Spectral Density for Machine Condition Monitoring.

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.

Comparative analysis of healthcare systems in the Horn of Africa and the evaluation of Türkiye's diplomatic health studies conducted in Somalia.

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.

An Unsupervised Machine Learning Approach for Monitoring Data Fusion and Health Indicator Construction.

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.

A Multi-Featured Factor Analysis and Dynamic Window Rectification Method for Remaining Useful Life Prognosis of Rolling Bearings.

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.

Degradation-Sensitive Health Indicator Construction for Precise Insulation Degradation Monitoring of Electromagnetic Coils.

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.

IR-Based Novel Device for Real-Time Online Acquisition of Fish Heart ECG Signals.

We developed an infrared (IR)-based real-time online monitoring device (US Patent No: US 10,571,448 B2) to quantify heart electrocardiogram (ECG) signals to assess the water quality based on physiological changes in fish. The device is compact, allowing us to monitor cardiac function for an extended period (from 7 to 30 days depending on the rechargeable battery capacity) without function injury and disturbance of swimming activity. The electrode samples and the biopotential amplifier and microcontroller process the cardiac-electrical signals. An infrared transceiver transmits denoised electrocardiac signals to complete the signal transmission. The infrared receiver array and biomedical acquisition signal processing system send signals to the computer. The software in the computer processes the data in real time. We quantified ECG indexes (P-wave, Q-wave, R-wave, S-wave, T-wave, PR-interval, QRS-complex, and QT-interval) of carp precisely and incessantly under the different experimental setup (CuSO4 and deltamethrin). The ECG cue responses were chemical-specific based on CuSO4 and deltamethrin exposures. This study provides an additional technology for noninvasive water quality surveillance.

Health-promoting factors among students in higher education within health care and social work: a cross-sectional analysis of baseline data in a multicentre longitudinal study.

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.

Predicting Smoking Prevalence in Japan Using Search Volumes in an Internet Search Engine: Infodemiology Study.

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.

Long Short-Term Memory Neural Network with Transfer Learning and Ensemble Learning for Remaining Useful Life Prediction.

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.

A Deep-Learning-Based Health Indicator Constructor Using Kullback-Leibler Divergence for Predicting the Remaining Useful Life of Concrete Structures.

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.

Gender modified association of oral health indicators with oral health-related quality of life among Korean elders.

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.

Development a new chewing problem directory and its validation for Korean elders.

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.

[The characteristics of medical demographic indices in the Jewish autonomous oblast].

The research in the field of regional trends and risk factors related to population morbidity is considered as background of management decision-making in health care. The article presents the results of analysis of medical demographic indices and population health status in the Jewish Autonomous Oblast in 2000-2019. It is established that in in the Jewish Autonomous Oblast population size reduced by 35 100 people to 2019. The negative migration dispositions facilitate losses of population size from 0.5% to 1.23% annually. The gradual increase of number of the elderly brought to ratio working/non-working people 100 to 128. The infant mortality rate decreased from 20.2 in 2000 to 9.2 per 1000 newborns in 2019 and continues to decrease. The total morbidity increase (diagnosed for the first time) of cardiovascular diseases and neoplasms is probably related to preventive measures efficiency. In the structure of malignant neoplasms leading positions are taken by tumors of bronchopulmonary system, skin affections and breast cancer. In the Jewish Autonomous Oblast, mortality of cardiovascular diseases comprised 807.32±22.87 per 100 000 of population. In the last decade, this most important indicator decreased up to 2.12%. The mortality of malignant neoplasms made up to 217.31±15.25 per 100 000 of population. The mortality due to consolidated causes "Traumas, poisonings and some other consequences of external causes exposure" made up to 16.19±3.28 per 100 000 of population. In 2019, the average life interval made up to 68.8 years, having increased by 6.2 years since 2000. The evaluation of medical demographic indices and population health status demonstrate necessity of adjusting medical organizational measures targeting to improve medical care of population considering regional characteristics.

Research on the Establishment and Application of the Environmental Health Indicator System of Atmospheric Pollution in China.

To understand the health impact represented by exposure to current atmospheric pollution in China, an environmental health indicators (EHIs) system of atmospheric pollution was established. The EHIs were based on comprehensive consideration of environment, population, economy and diseases associated with atmospheric pollution. An EHIs evaluation system of atmospheric pollution, based on corresponding EHIs data collection and weighting coefficients determined using principal component analysis, was applied to major provinces and regions in China to evaluate the environmental health status. Results showed that the EHIs of atmospheric pollution in Central and East China were low, indicating a serious environmental health condition. Prevention and management of atmospheric pollution in these regions should be strengthened and protective measures taken to improve human health. Compared with other methods, the EHIs evaluation system was more intuitive, which facilitated users to identify the environmental health status and provided support for health management and pollution prevention.

Intelligent Online Monitoring of Rolling Bearing: Diagnosis and Prognosis.

This paper suggests a new method to predict the Remaining Useful Life (RUL) of rolling bearings based on Long Short Term Memory (LSTM), in order to obtain the degradation condition of the rolling bearings and realize the predictive maintenance. The approach is divided into three parts: the first part is the clustering to detect the damage state by the density-based spatial clustering of applications with noise. The second one is the health indicator construction which could give a better reflection of the bearing degradation tendency and is selected as the input for the prediction model. In the third part of the RUL prediction, the LSTM approach is employed to improve the accuracy of the prediction. The rationale of this work is to combine the two methods-the density-based spatial clustering of applications with noise and LSTM-to identify the abnormal state in rolling bearings, then estimate the RUL. The suggested method is confirmed by experimental data of bearing life cycle, and the RUL prediction results of the model LSTM are compared with the nonlinear au-regressive model with exogenous input model. In addition, the constructed health indicator is compared with the spectral kurtosis feature. The results demonstrated that the suggested method is more appropriate than the nonlinear au-regressive model with exogenous input model for the prediction of bearing RUL.

Assessing the utility of leukocyte differential cell counts for predicting morbidity, mortality, and growth in a grain-fed veal facility: A prospective single cohort study.

Selective antimicrobial treatment strategies present a means to reduce antimicrobial use at the time of arrival at a veal or dairy beef operation. On-farm machine leukocyte differential cell counts (DCC) that can be acquired quickly may be useful to augment calf risk identification protocols. The objective of this study was to assess the utility of DCC taken at the time of arrival at a grain-fed veal facility and 72 h postarrival for determining morbidity risk, mortality risk, and growth during the production cycle. Data were collected between June and October 2018 from 240 calves upon arrival and from a subset of 160 calves 72 h postarrival at a commercial grain-fed veal facility in Ontario, Canada. Blood samples were evaluated using the QScout BLD test for leukocyte DCC (Advanced Animal Diagnostic, Morrisville, NC). All calves were screened using a standardized health examination, and a blood sample was collected to evaluate serum total protein and DCC. Cox proportional hazards models were constructed for both morbidity and mortality outcomes. Mixed linear regression models were constructed to evaluate average daily gain. Results from data collected at the time of arrival suggest that total protein values ≥5.2 g/dL reduced the hazard of mortality and that a rectal temperature >39.6°C was associated with an increased hazard of morbidity. Calves that were dehydrated gained less, whereas calves with an increased lymphocyte count had a higher rate of growth. Results from DCC collected 72 h postarrival suggest that lymphocyte counts between 4.8 and 5.8 × 109 cells/L decreased the hazard of mortality and counts >7.0 × 109 cells/L decreased the hazard of morbidity, whereas neutrophil counts >6.0 × 109 cells/L increased the hazard of mortality. This study demonstrates that machine DCC at the time of arrival and 72 h after arrival has potential for use in identifying high-risk calves that might require treatment, as part of selective antimicrobial therapy protocols, with the purpose of reducing antimicrobial use without sacrificing animal health in veal facilities.

Validation of an automated cell counter to determine leukocyte differential counts in neonatal Holstein calves.

Recent advances in the understanding of risk factors and biomarkers in calves entering rearing facilities show promise for identifying high-risk calves on arrival at veal and dairy beef operations. Rapid automated leukocyte differential cell counts may be a good addition for augmenting or refining calf risk identification on-farm. The objective of this study was to validate an automated leukocyte cell counter, the QScout BLD test (Advanced Animal Diagnostics, Morrisville, NC), for its ability to determine leukocyte differential cell counts in neonatal Holstein calves. From June to July 2018, blood samples collected in EDTA anticoagulant from 235 calves upon arrival at an independent veal research facility in Ontario, Canada, were evaluated using the QScout BLD test and manually by microscopy. We compared these leukocyte differential counts using Lin's concordance correlation coefficient (ρ) and found very good agreement between tests for neutrophil counts (ρ = 0.83); fair agreement for lymphocyte counts (ρ = 0.32); fair agreement for the ratio of neutrophils to lymphocytes (ρ = 0.36); slight agreement for monocyte counts (ρ = 0.14); and slight agreement for eosinophil counts (ρ = 0.20). We further examined test results to determine if they differed in their classification of samples as being above, within, or below reported 95% reference intervals for neonatal Holstein calves. Classification between tests resulted in very good agreement for neutrophils and lymphocytes, with only 4.2% and 5.8% disagreement in classification, respectively. We observed moderate agreement for monocytes, with 23.3% classified differently, and poor agreement for eosinophils, with 70.3% classified differently. Further study is required to determine the role of leukocyte profiling in the risk assessment of calves arriving at calf-rearing facilities.

Enhancing Sustainability in Healthcare Delivery-A Challenge to the New Malaysia.

There have been substantial improvements in the health indicators since Malaysia achieved independence. These were accomplished through strong primary healthcare services addressing maternal and paediatric health, as well as the successful control of communicable diseases. The rate of decline in the mortality statistics has been at a virtual standstill, or at best, almost plateaued since 2000. However, with the plethora of national health issues at both the policy and delivery levels, we cannot continue on with 'business as usual'. Therefore, we must strategise effective and practical approaches to a renewed and revamped national healthcare services for a modern 'New Malaysia' that are compatible with our quest toward the status of a 'truly developed' nation.

Analysis of conditions sensitive to primary care in a successful experience of primary healthcare expansion in Brazil, 1998-2015.

OBJECTIVE: To analyze trends in expansion of coverage of the family health strategy and hospitalization for conditions sensitive to primary care (CSPC) in a successful experience of primary healthcare expansion in Brazil. STUDY DESIGN: Ecological study with data from the Brazilian National Health Information System. METHODS: CSPC were analyzed between 1998 and 2015 in Rio de Janeiro, Brazil, by cause groups. Trends, variation, and correlation between indicators in the period were evaluated. RESULTS: Most of the cause groups showed a reduction in hospitalization rate, particularly cardiovascular diseases and asthma, but an increase was seen for obstetric causes. The main causes of hospitalization were heart failure, cerebrovascular diseases, and bacterial pneumonia. The contribution of vaccine-preventable diseases, cardiovascular diseases, diabetes, nutritional deficiencies, and chronic lung diseases to the total number of hospitalizations was seen to decrease. CONCLUSIONS: Analysis demonstrates that the family health strategy, as access to the healthcare system, decreases the majority of CSPC hospitalization rates.

A Reliable Health Indicator for Fault Prognosis of Bearings.

Estimation of the remaining useful life (RUL) of bearings is important to avoid abrupt shutdowns in rotary machines. An important task in RUL estimation is the construction of a suitable health indicator (HI) to infer the bearing condition. Conventional health indicators rely on features of the vibration acceleration signal and are predominantly calculated without considering its non-stationary nature. This often results in an HI with a trend that is difficult to model, as well as random fluctuations and poor correlation with bearing degradation. Therefore, this paper presents a method for constructing a bearing's HI by considering the non-stationarity of the vibration acceleration signals. The proposed method employs the discrete wavelet packet transform (DWPT) to decompose the raw signal into different sub-bands. The HI is extracted from each sub-band signal, smoothened using locally weighted regression, and evaluated using a gradient-based method. The HIs showing the best trends among all the sub-bands are iteratively accumulated to construct an HI with the best trend over the entire life of the bearing. The proposed method is tested on two benchmark bearing datasets. The results show that the proposed method yields an HI that correlates well with bearing degradation and is relatively easy to model.