Computation of Sensitivity Indices with different routines for Latin Hypercube Designs

AbstractComplex mathematical simulations are performed to understand the various phenomena in physics, chemistry, etc. Usually these models consist of several input variables and sensitivity analysis explains the variation of computer code output due to the changes in the input values. We have used the software package, Gaussian Emulation Machine for sensitivity analysis (GEM-SA), (Kennedy and O’Hagan, 2006) for performing sensitivity analysis and chosen Rosenbrock function as a test problem. In this paper we have studied the effect of different available software routines for Latin hypercube designs (LHD) on values of the variance-based sensitive indices, total sensitive indices and roughness parameters for the test problem. We have used MATLAB, R software and GEM-SA modules for LHD

Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics

Value-based reinforcement-learning algorithms provide state-of-the-art results in model-free discrete-action settings, and tend to outperform actor-critic algorithms. We argue that actor-critic algorithms are limited by their need for an on-policy critic. We propose Bootstrapped Dual Policy Iteration (BDPI), a novel model-free reinforcement-learning algorithm for continuous states and discrete actions, with an actor and several off-policy critics. Off-policy critics are compatible with experience replay, ensuring high sample-efficiency, without the need for off-policy corrections. The actor, by slowly imitating the average greedy policy of the critics, leads to high-quality and state-specific exploration, which we compare to Thompson sampling. Because the actor and critics are fully decoupled, BDPI is remarkably stable, and unusually robust to its hyper-parameters. BDPI is significantly more sample-efficient than Bootstrapped DQN, PPO, and ACKTR, on discrete, continuous and pixel-based tasks. Source code: https://github.com/vub-ai-lab/bdpi.Comment: Accepted at the European Conference on Machine Learning 2019 (ECML

Characterizing Safety: Minimal Control Barrier Functions from Scalar Comparison Systems

Verifying set invariance has classical solutions stemming from the seminal work by Nagumo, and defining sets via a smooth barrier function constraint inequality results in computable flow conditions for guaranteeing set invariance. While a majority of these historic results on set invariance consider flow conditions on the boundary, this letter fully characterizes set invariance through minimal barrier functions by directly appealing to a comparison result to define a flow condition over the entire domain of the system. A considerable benefit of this approach is the removal of regularity assumptions of the barrier function. This letter also outlines necessary and sufficient conditions for a valid differential inequality condition, giving the minimum conditions for this type of approach. We also show when minimal barrier functions are necessary and sufficient for set invariance

Clinical approach, diagnosis and medical management of acute pancreatitis among patients attending tertiary care hospital in Prakasam district, Andhra Pradesh, India

Background: There has been an increase in the incidence of acute pancreatitis reported globally and despite of improvements in access to care and interventional techniques, acute pancreatitis continues to be associated with significant morbidity and mortality. The present study was aimed to assess the clinical profile of acute pancreatitis and to assess the efficacy of various severity indices in view of   outcome of patients. Methods: A hospital based prospective cross-sectional study was conducted from October 2022-March 2023 in Gastro and Liver care center in Ongole, Prakasam District, Andhra Pradesh India. All consecutive 72 patients with a diagnosis of acute pancreatitis were included in this study. Results: Out of total acute Pancreatitis cases   61 (84.7%) were males and 11 (15.3%) were females and acute abdominal pain (97.2%) and decreased appetite (95.8%) were the most common presenting complaints, 54.2% cases were due to Alcoholism, followed by hyperlipidemia with 20.8% and Gall stones 13.9%.  All 72 (100%) received pancreatic supplements, 68 (94.4%) were given pain killers, and 65 (90.3%) were taken anti-ulcer agents. Twenty-three (31.9%) patients with 0 to 3 points as per CTSI Score and 4-6 range points were observed in 47 (65.3%) pancreatitis patients. Maximum (40.3%) were improved on 2nd day, 22 (30.6%) were on 3rd day.  Positive correlation noticed between Amylase and in diagnosing acute Pancreatitis, it is significant at 0.05 level. Conclusions: Early assessment of the clinical severity and identification of patients at risk is important for early intensive management and timely intervention and to improve quality of life. So, it is mandatory to assess the clinical severity using different scoring systems. and appropriate treatment based on guidelines

Notes on syphilis vaccine development

The quest for a syphilis vaccine to provide protection from infection or disease began not long after the isolation of the first Treponema pallidum subspecies pallidum (T. pallidum) strain in 1912. Yet, a practical and effective vaccine formulation continues to elude scientists. Over the last few years, however, efforts toward developing a syphilis vaccine have increased thanks to an improved understanding of the repertoire of T. pallidum outer membrane proteins (OMPs), which are the most likely syphilis vaccine candidates. More has been also learned about the molecular mechanisms behind pathogen persistence and immune evasion. Published vaccine formulations based on a subset of the pathogen’s OMPs have conferred only partial protection upon challenge of immunized laboratory animals, primarily rabbits. Nonetheless, those experiments have improved our approach to the choice of immunization regimens, adjuvants, and vaccine target selection, although significant knowledge gaps remain. Herein, we provide a brief overview on current technologies and approaches employed in syphilis vaccinology, and possible future directions to develop a vaccine that could be pivotal to future syphilis control and elimination initiatives

Determination of yolk contamination in liquid egg white using Raman spectroscopy

Purified egg white is an important ingredient in a number of baked and confectionary foods because of its foaming properties. However, yolk contamination in amounts as low as 0.01% can impede the foaming ability of egg white. In this study, we used Raman spectroscopy to evaluate the hypothesis that yolk contamination in egg white could be detected based on its molecular optical properties. Yolk contaminated egg white samples (n = 115) with contamination levels ranging from 0% to 0.25% (on weight basis) were prepared. The samples were excited with a 785 nm laser and Raman spectra from 250 to 3,200 cm−1 were recorded. The Raman spectra were baseline corrected using an optimized piecewise cubic interpolation on each spectrum and then normalized with a standard normal variate transformation. Samples were randomly divided into calibration (n = 77) and validation (n = 38) data sets. A partial least squares regression (PLSR) model was developed to predict yolk contamination levels, based on the Raman spectral fingerprint. Raman spectral peaks, in the spectral region of 1,080 and 1,666 cm−1, had the largest influence on detecting yolk contamination in egg white. The PLSR model was able to correctly predict yolk contamination levels with an R2 = 0.90 in the validation data set. These results demonstrate the capability of Raman spectroscopy for detection of yolk contamination at very low levels in egg white and present a strong case for development of an on-line system to be deployed in egg processing plants