Predicting the dissolution kinetics of silicate glasses using machine learning

Predicting the dissolution rates of silicate glasses in aqueous conditions is a complex task as the underlying mechanism(s) remain poorly understood and the dissolution kinetics can depend on a large number of intrinsic and extrinsic factors. Here, we assess the potential of data-driven models based on machine learning to predict the dissolution rates of various aluminosilicate glasses exposed to a wide range of solution pH values, from acidic to caustic conditions. Four classes of machine learning methods are investigated, namely, linear regression, support vector machine regression, random forest, and artificial neural network. We observe that, although linear methods all fail to describe the dissolution kinetics, the artificial neural network approach offers excellent predictions, thanks to its inherent ability to handle non-linear data. Overall, we suggest that a more extensive use of machine learning approaches could significantly accelerate the design of novel glasses with tailored properties

AYURVEDIC MANAGEMENT OF UNEXPLAINED INFERTILITY- A CASE STUDY

Infertility is a condition in which successful pregnancy has not occurred, despite normal intercourse over 12 months. The cause of female infertility is multifactorial. Ayurveda assures normal pregnancy by proper maintenance of Garbha Samgraha samagris and normalcy of mind. All the causes of female infertility come under the imbalance of Garbha Samgraha Samagri and mind factors. In this case report patient suffered from primary infertility since six years, after allopathic consultation came for ayurvedic treatment. From detailed history involvement of vitiated Vatha, Agnimandhya and stressful mind was noticed. She was treated with Chiruvilwadi kashayam, Dhanwantharam gulika, Jeerakarishtam, Kumaryasavam and Manasamithravatakam for one month.  Took follicular study on next cycle and revealed post ovulatory status on 16th day of cycle. Advised Phalasarpis, Dhanwantharam Gulika and Manasamithravatakam for two weeks. Patient came with positive urine pregnancy test after one week of missed period. The line of treatment followed in this case was to maintain Agni, normalize Vatha and assure proper health to mind. During the second half of the cycle Garbhasthapana medicines were administered. Patient took Dhanwantharam gulika and phalasarpis throughout the first trimester along with regular ante natal check up. Continued Dhanwantharam gulika up to 36 weeks and started Sukhaprasavagritham upto delivery from 36 weeks onwards. She delivered a female baby on 06.05.2018

Non-normality and nonlinearity in thermoacoustic instabilities

Analysis of thermoacoustic instabilities were dominated by modal (eigenvalue) analysis for many decades. Recent progress in nonmodal stability analysis allows us to study the problem from a different perspective, by quantitatively describing the short-term behavior of disturbances. The short-term evolution has a bearing on subcritical transition to instability, known popularly as triggering instability in thermoacoustic parlance. We provide a review of the recent developments in the context of triggering instability. A tutorial for nonmodal stability analysis is provided. The applicability of the tools from nonmodal stability analysis are demonstrated with the help of a simple model of a Rjike tube. The article closes with a brief description of how to characterize bifurcations in thermoacoustic systems

A comparative evaluation of clinical and radiological scoring systems in the early prediction of severity in acute pancreatitis

BACKGROUND: Acute Pancreatitis is non bacterial inflammation of Pancreatic Gland by activation and digestion of the gland by its own enzymes. Acute pancreatitis embodies a large spectrum of diseases which ranges from mild pancreatitis comprising of parenchymal edema to severe necrotizing pancreatitis. Identification of patients at risk for severe disease early in the course of acute pancreatitis is an important step to guiding management and improving outcomes. Several scoring systems are used to assess the severity and predict the outcome and prognosis of acute pancreatitis. An improved outcome in the severe form of acute pancreatitis is based on early identification of disease severity and subsequent focused management of the high risk patients. There is a need to evaluate the efficacy of clinical scoring system versus CT severity index to triage the patient into intensive care. The present study is designed to examine the effect of using BISAP score on patient outcome and its value in comparison with MCTSI. STUDY DESIGN: Diagnostic Test Evaluation. MATERIALS AND METHOD: All patients with Acute Pancreatitis presenting to the Department of General Surgery who fit the inclusion criteria were included in the study after obtaining informed consent. Extensive demographic, radiographic and laboratory data which includes complete haemogram, serum electrolytes, renal function test, liver function test, serum amylase, lipid profile, chest X-ray, USG abdomen etc were collected. BISAP score was calculated using data from the first 24 hours from admission. A score of 1 is given for each criteria for a maximum score of 5. MCTSI was calculated from CECT within 48 hours. Patients were closely monitored during the entire stay in hospital and evidence of organ failure documented. Patients were classified as mild acute pancreatitis and severe acute pancreatitis based on the presence of organ failure that persist for more than 48 hours. Pancreatic necrosis was assessed from CECT. Pancreatic necrosis is defined as lack of enhancement of pancreatic parenchyma with contrast. Comparison of prediction of severity of acute pancreatitis by BISAP and MCTSI score is the primary outcome of interest and comparison of prediction of mortality and pancreatic necrosis by both scores is the secondary outcome of interest. SAMPLE SIZE: n = 100. Inclusion Criteria: All patients admitted with the diagnosis of Acute Pancreatitis based on the presence of at least two of the following three criteria: 1. Characteristic epigastric abdominal pain , with or without radiation to the back. 2. Serum amylase or lipase levels elevated to at least three times the upper limit of normal. 3. Characteristic finding of Acute Pancreatitis on abdominal CT scan. Exclusion Criteria: Patients with pre existing Chronic Kidney Disease (CKD) which may be associated with elevated Blood Urea Nitrogen values were excluded from the study as they may result in high BISAP score. OUTCOME: 1. Organ Failure, 2. Mortality, 3. Pancreatic Necrosis. RESULTS: The study compares BISAP score which is a clinical scoring system with MCTSI, which is a radiological score in predicting severity, mortality and necrosis in 100 patients with acute pancreatitis. Mean age of patients presenting with acute pancreatitis is 39 years. Males were 97% and females were 3%. Alcohol is the most common etiological agent contributing 46% followed by gallstones contributing 27%. This may be attributed to the difference in dietary, social, genetic and cultural factors between Indian population and Western population. 29 out of 100 patients (29%) developed severe acute pancreatitis. The AUC for prediction of severity by BISAP and MCTSI score are 0.917 ( 95% CI 0.864 – 0.970) and 0.853 (95% CI 0.777 – 0.928) respectively. The in-hospital mortality rate is 8%. Patients with BISAP ≥3 had thirty eight times more chance of ending up in death compared to those with BISAP < 3. MCTSI was found to have higher sensitivity and positive predictive value in predicting pancreatic necrosis..Patients with MCTSI ≥ 4 had 23 times chance of having pancreatic necrosis than MCTSI < 4. CONCLUSION: To classify patients with acute pancreatitis into mild and severe groups, BISAP is a reliable prognostic tool. The components of BISAP are clinically relevant and easy to obtain. The sensitivity of BISAP score ≥ 3 in predicting severe acute pancreatitis was found to be 65.52%. AUC concludes that BISAP score is an ideal tool in predicting severity in Acute Pancreatitis

Abdominal Compartment Syndrome: What Is New?

Intra-abdominal hypertension (IAH) and abdominal compartment syndrome (ACS) are continuation of the same pathological and physiological processes that are largely unrecognized in critical patients. From an era of indistinct definitions and recommendations, this condition has been studied extensively and experts have come forward with clear definitions and recommendations for management. IAH is graded in four grades and ACS is IAH above 20 cm H2O with new organ dysfunction. IAH/ACS can present as acute, hyperacute, or chronic and aetiologically can be classified into primary, secondary and tertiary. It affects various body systems including respiratory, cardiovascular, central nervous, gastrointestinal, renal and hepatic systems adversely and results in deleterious consequences. Management of IAH/ACS is based on the evacuation of intra-luminal and extra-luminal contents, improving the abdominal wall compliance. There are various surgical techniques recommended for preventing the development of IAH/ACS and mitigating the negative consequences. New medical therapies such as octreotide, tissue plasminogen activator, melatonin and vitamin C are being investigated and non-pharmacological methods such as continuous negative abdominal pressure (CNAP) have been introduced recently but are still experimental and not recommended for routine use

A novel approach for analyzing student interaction with educational systems

© 2017 IEEE. The data in higher educational institutions come from the interaction of students with the various online systems, such as learning management, registration, advising and email. Research in the field of educational data mining is concerned with the collection and analysis of such data to discover new insights about student behavior, learning style and success factors. Since different departments in an institution manage different IT systems, collecting data from all of these departments requires collaboration. The data has to be extracted from many systems, which uses different data formats. Therefore, a typical research work in this field analyzes data extracted from one system. This paper, on the other hand, analyzes the network traffic that students generate while on-campus. This approach provides us with a better view of the student interaction with the educational systems, compared to the single view achieved by analyzing data from one system. We anonymize student personally identifiable information to protect student privacy. Further, we propose the use of fog computing to enhance student privacy and reduce network load

Effective Patient Similarity Computation for Clinical Decision Support Using Time Series and Static Data

© 2020 ACM. This paper presents a technique for computing patient similarity using time series data effectively combined with static data. Time series data of inpatients, such as heart rate, blood pressure, Oxygen saturation, respiration are measured at regular intervals, especially for inpatients in intensive care unit (ICU). The static data are mainly patient background and demographic data, including age, weight, height and gender. The similarity computation is done in unsupervised way. It is therefore free from data labeling requirement. However, such patient similarity can be very useful in developing various clinical decision support systems including treatment, medication, hospital admission and diagnosis. Our proposed technique works in three main steps. First, patient similarity is computed for each individual time series. Second, patients are grouped by clustering the static data. Finally, similarities from individual time series are combined and effectively blended with the patient group information to create a nearest neighborhood model. This model consists of a collection of the nearest neighbors for a given patient. We encounter several challenges for this task, including dealing with multi-variate time series data, variable sampling quantities and rates, missing values, and combining time-series with static data. We evaluate the proposed technique on a real patient database on two target features, namely, \u27diagnosis\u27 and \u27admission type\u27. Notable performance is recorded for both targets, achieving f1-score as high as 0.8. We believe this technique can effectively combine different types of clinical data and develop an efficient unsupervised framework for computing patient similarity to be utilized for clinical decision support systems

Torsional Behavior of Hollow-Core FRP-Concrete-Steel Bridge Columns

This paper presents the behavior of hollow-core fiber reinforced polymer-concrete-steel (HC-FCS) column under pure torsion loading with constant axial load. The HCFCS consists of outer FRP tube and inner steel tube with concrete shell sandwiched between the two tubes. The FRP tube was stopped at the surface of the footing and provided confinement to the concrete shell from outer direction. The steel tube was embedded into the footing to a length of 1.8 times to the diameter of the steel tube. The longitudinal and transversal reinforcements of the column were provided by the steel tube only. A large-scale HC-FCS column with a diameter of 610 mm and height of applied load of 2,438 mm with aspect ratio of 4 was investigated during this study. The study revealed that the torsional behavior of HC-FCS column mainly depended on the stiffness of the steel tube and the interactions among the column components (concrete shell, steel tube, and FRP tube)