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Objective: To investigate the effect of integrated approach of yoga therapy (IAYT) intervention in individual with knee Osteoarthritis.Design: Randomized controlled clincial trail.Participants: Sixty-six individual prediagnosed with knee osteoarthritis aged between 30 and 75 years were randomized into two groups, i.e., Yoga (n = 31) and Control (n = 35). Yoga group received IAYT intervention for 1 week at yoga center of S-VYASA whereas Control group maintained their normal lifestyle.Outcome measures: The Falls Efficacy Scale (FES), Handgrip Strength test (left hand LHGS and right hand RHGS), Timed Up and Go Test (TUG), Sit-to-Stand (STS), and right & left extension and flexion were measured on day 1 and day 7.Results: There were a significant reduction in TUG (p < 0.001), Right (p < 0.001), and Left Flexion (p < 0.001) whereas significant improvements in LHGS (p < 0.01), and right extension (p < 0.05) & left extension (p < 0.001) from baseline in Yoga group.Conclusion: IAYT practice showed an improvement in TUG, STS, HGS, and Goniometer test, which suggest improved muscular strength, flexibility, and functional mobility.CTRI Registration Number: http://ctri.nic.in/Clinicaltrials, identifier CTRI/2017/10/010141.

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Lubricant condition monitoring (LCM) is a preferred condition monitoring (CM) technology for fault diagnosis and prognosis owing to its ability to derive a wide range of information from the system (machine/equipment) state and lubricant state. Given the importance of LCM for maintenance decision support, an accurate and reliable remaining useful life (RUL) prediction framework is necessary. The LCM health information in the form of degradation trends is therefore evaluated using numerous statistical, model-based, and artificial intelligence approaches by various researchers. A multitude of factors widely affects the degradation trends viz. operating conditions, environmental variations, oil replenishments, oil loss, chemical breakdown, etc. These factors increase the complexity of the time-series degradation trends making RUL prediction intractable using several of the standard statistical approaches. Therefore, limited research is available on lubricating oil RUL prediction with these influential factors accounted for. Focusing on the complexity of the degradation trend with oil replenishment effects (ORE), we propose the use of the Gaussian process regression (GPR) model for RUL prediction in this study. The model has an advantage over other data-driven approaches as it is a non-parametric Bayesian method. To exploit prior information and historical data collected, the approach is extended to multi-output GPR (MO-GPR) which effectively defines the correlations between historical degradation trends for similar lubrication systems with the current degradation pattern of a system being monitored in real-time. Three different oil replenishment strategies are considered under MO-GPR to demonstrate the applicability and flexibility of this method.

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Background. Diabetes mellitus (DM) is one of the largest global health emergencies. Prediabetes is an early stage in hyperglycemia continuum where individual is at an increased risk for development of DM. NAFLD represents a range of liver disorders characterized by hepatic steatosis or accumulation of fat in the liver cells in the absence of excessive alcohol consumption, viral or drug related etiologies. However, not many studies have been conducted to study the prevalence of non-alcoholic fatty liver disease (NAFLD) in persons with prediabetes. This study is an endeavor in that direction. Materials and methods. This was a cross-sectional observational study. 100 prediabetic patients, fulfilling the criteria as under, were included in the study over a period from November 2017 to March 2019, after informed consent. Investigations carried out on the patients included baseline biochemical parameters like complete hemogram, fasting plasma glucose, liver function tests, kidney function tests, serum electrolytes and specialized investigations like HbA1c, 2-hour-OGTT and serum insulin levels. Results. The study included 38 males and 62 females, with the median age for the study population being 46 years. The mean BMI was found to be 24.29 ± 3.98 kg/m2, and the mean waist circumference was found to be 81.26 ± 8.71 cm. A significant association was found between the level of fatty echotexture on ultrasound and BMI (p = 0.003), and gender (0.05). 30 % population was found to be insulin sensitive, 22 % was found to be depicting early insulin resistance and 48 % had significant insulin resistance. There was a statistically significant correlation between ultrasound and fibroscan findings. A significant statistical correlation was found between HOMA IR and level of fatty echotexture on ultrasound, as well as median liver stiffness on fibroscan. Conclusions. We found a significant correlation between insulin resistance and presence of NAFLD. Also, significant associations were observed between various demographic characteristics and grade of steatosis. There is a need to undertake further studies on a larger scale, to substantiate the observations of this study. This understanding is expected to go a long way in generating awareness and optimizing public health strategies.

In this study, we present a state-based diagnostic and prognostic methodology for lubricating oil degradation based on a nonparametric Bayesian approach, i.e., sticky hierarchical Dirichlet process–hidden Markov model (HDP-HMM). An accurate health state-space assessment for diagnostics and prognostics has always been unobservable and hypothetical in the past. The lubrication condition monitoring (LCM) data is generally segregated as “healthy or unhealthy”, representing a binary state-based perspective to the problem. This two-state performance-based formulation poses limitations to the precision and accuracy of the diagnosis and prognosis for real data wherein there may be multiple states of discrete performance that are characteristic of the system functionality. In particular, the reversible and nonlinear time-series trends of degradation data increase the complexity of state-based modeling. We propose a multistate diagnostic and prognostic framework for LCM data in the wear-out phase (i.e., the unhealthy portion of degradation data), accounting for irregular oil replenishment and oil change effects (i.e., nonlinearity in the degradation signal). The LCM data is simulated for an elementary mechanical system with four components. The sticky HDP sets the prior for the HMM parameters. The unsupervised learning over infinite observations and emission reveals four discrete health states and helps estimate the associated state transition probabilities. The inferred state sequence provides information relating to the state dynamics, which provides further guidance to maintenance decision making. The decision making is further backed by prognostics based on the conditional reliability function and mean residual life estimation.

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