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Background There is a dearth of studies from India evaluating the awareness of patient safety. This study was performed to gain insight into the Indian patients’ awareness about patient safety and evaluate their willingness in promoting the same. Methods In this cross-sectional study, online interactive sessions pertaining to patient safety and patient safety culture were arranged by clinical pharmacologists for 800 urban patients post-discharge from a tertiary care hospital in India. A validated questionnaire was used, and the responses were analysed using descriptive statistics. Results A total of 635 patients [mean age, 43.2 ± 10.3 years; 385 (60.6%) males] responded. A total of 93.4% of the patients were aware of the term “patient safety” and 35.3% faced situations where they felt that patient safety was compromised. Of these patients, 64.3% reported to higher authorities when faced with such situations, while the remaining either ignored the issue or had no idea about how to deal with the same. A total of 99.2% of the patients never participated in any patient safety program; nevertheless, 94.5% of them were willing to participate in the same. Accessibility to information about patient care was deemed essential by 58.3% of the patients. Conclusions Although the overall awareness about patient safety among urban Indian patients is high, there is a lack of awareness about ways of dealing with patient safety issues. Given the high level of interest in participating in patient safety programs, such programs should routinely include patients for optimizing the chances for safer provision of health care.

AimsTo diagnose malnutrition, the nutritional status of each infected patient should be evaluated before starting general treatment. The role of Malnutrition Universal Screening Tool (MUST) in evaluating nutritional status of COVID-19 patients is still unknown. The aim of this study was to evaluate the use of MUST in assessment of nutritional status of COVID-19 patients.MethodsWe retrospectively analyzed the data of hospitalized COVID-19 patients above 18 years of age from July 25th to September 25th, 2020. All COVID-19 patients with a length of hospital stay greater than 24 hours underwent malnutrition screening and nutritional assessment based upon MUST. Demographic data, laboratory parameters and MUST score were retrieved from case files.ResultsOut of 106 COVID-19 patients included in the study, 68 (64%) were male and 38 (36%) were female. Number of deaths due to COVID-19 was 17 (16.03%). A total of 22 (20.75%) patients had MUST score of 2 and above. Analysis between MUST score and age group showed statistically significant result (p=0.012). MUST score according to clinical outcome at the end of hospitalization was also statistically significant (pConclusionOur results highlight a possible role of MUST as screening tool for malnutrition in COVID-19 patients.

Introduction: The Coronavirus Disease-2019 (COVID-19) that originated in China in December 2019 was declared a pandemic by the World Health Organisation (WHO) on 11th March, 2020. It has resulted in extensive lockdown measures in India and in the state of West Bengal. Both Central and State Governments have made considerable efforts to increase awareness about the pandemic among the general public. Aim: This study was conducted to assess the Knowledge, Attitude, and Practice (KAP) among the general public of West Bengal about the pandemic as no such data is available till date. Materials and Methods: The retrospective survey was undertaken among the general public of the state of West Bengal, India, by means of social media platforms (Facebook, Instagram and WhatsApp) through a validated e-questionnaire containing 10 questions during 18th April, 2020 to 3rd May, 2020. Data was captured on Google Forms, which were analysed using appropriate tools. Results: A total of 355 responses were obtained. A 97% of responders were completely aware of the pandemic with 92% practising preventive measures, such as social distancing, washing hands, and wearing a mask. Majority of the participants were young (18-30 years of age) with 51% being males. A 99% of the responders were literate and 86% were from urban dwelling. Total 61% of the participants stated that they have learnt something new after participating in this survey. Conclusion: KAP about COVID-19 pandemic was found to be high in the general population of West Bengal. Involvement of various social media platforms is required for generating mass awareness about COVID-19 in rural West Bengal. Continuous communication and survey by the Government is necessary to spread public awareness as information about COVID-19 is constantly evolving.

Measuring visual quality, as perceived by human observers, is becoming increasingly important in a large number of applications where humans are the ultimate consumers of visual information. Many natural image databases have been developed that contain human subjective ratings of the images. Subjective quality evaluation data is less available for synthetic images, such as those commonly encountered in graphics novels, online games or internet ads. A wide variety of powerful full-reference, reduced-reference and no-reference Image Quality Assessment (IQA) algorithms have been proposed for natural images, but their performance has not been evaluated on synthetic images. In this paper we (1) conduct a series of subjective tests on a new publicly available Embedded Signal Processing Laboratory (ESPL) Synthetic Image Database, which contains 500 distorted images (20 distorted images for each of the 25 original images) in 1920 × 1080 resolution, and (2) evaluate the performance of more than 50 publicly available IQA algorithms on the new database. The synthetic images in the database were processed by post acquisition distortions, including those arising from compression and transmission. We collected 26,000 individual ratings from 64 human subjects which can be used to evaluate full-reference, reduced-reference, and no-reference IQA algorithm performance. We find that IQA models based on scene statistics models can successfully predict the perceptual quality of synthetic scenes. The database is available at: http://signal.ece.utexas.edu/%7Ebevans/synthetic/ .

Measuring digital picture quality, as perceived by human observers, is increasingly important in many applications in which humans are the ultimate consumers of visual information. Standard dynamic range (SDR) images provide 8 b/color/pixel. High dynamic range (HDR) images, usually created from multiple exposures of the same scene, can provide 16 or 32 b/color/pixel, but need to be tonemapped to SDR for display on standard monitors. Multiexposure fusion (MEF) techniques bypass HDR creation by fusing an exposure stack directly to SDR images to achieve aesthetically pleasing luminance and color distributions. Many HDR and MEF databases have a relatively small number of images and human opinion scores, obtained under stringently controlled conditions, thereby limiting realistic viewing. Moreover, many of these databases are intended to compare tone-mapping algorithms, rather than being specialized for developing and comparing image quality assessment models. To overcome these challenges, we conducted a massively crowdsourced online subjective study. The primary contributions described in this paper are: 1) the new ESPL-LIVE HDR Image Database that we created containing diverse images obtained by tone-mapping operators and MEF algorithms, with and without post-processing; 2) a large-scale subjective study that we conducted using a crowdsourced platform to gather more than 300 000 opinion scores on 1811 images from over 5000 unique observers; and 3) a detailed study of the correlation performance of the state-of-the-art no-reference image quality assessment algorithms against human opinion scores of these images. The database is available at http://signal.ece.utexas.edu/%7Edebarati/HDRDatabase.zip .

Background: Medication errors (MEs) can cause significant harm to patients. The MEs identified through reporting processes currently report only a fraction of the actual number of MEs. Data about MEs is limited in India, especially from eastern and north-eastern parts of India. The objective of this study was to analyse the various types of Medication errors reported in a tertiary care private hospital in Eastern India. The aim was to determine the various factors associated with these errors and steps to be taken to reduce the MEs in this healthcare setup.Methods: We carried out a prospective passive surveillance study over the course of 3 years (2016-2018) on 50,822 admitted patients after obtaining approval from the Institutional Ethics Committee. A detailed root-cause analysis was performed for every reported error by a team of healthcare quality professionals and clinical pharmacists along with a clinical pharmacologist followed by appropriate preventive and corrective actions.Results: In our study, a total number of 88 medication errors were reported from a sample size of 50,822 (0.0017%). 61 of the reported MEs were administration errors (69.3%). Higher preponderance of medication errors was seen in male patients (53.1%) in comparison to female patients (46.9%).Conclusions: In this study gross under-reporting of MEs were observed which is in line with previously published studies in India. The reasons reported for gross under-reporting can function as an effective tool to ensure improved reporting of MEs and implementation of mitigation strategies.

Being able to automatically predict digital picture quality, as perceived by human observers, has become important in many applications where humans are the ultimate consumers of displayed visual information. Standard dynamic range (SDR) images provide 8 b/color/pixel. High dynamic range (HDR) images, which are usually created from multiple exposures of the same scene, can provide 16 or 32 b/color/pixel, but must be tonemapped to SDR for display on standard monitors. Multi-exposure fusion techniques bypass HDR creation, by fusing the exposure stack directly to SDR format while aiming for aesthetically pleasing luminance and color distributions. Here, we describe a new no-reference image quality assessment (NR IQA) model for HDR pictures that is based on standard measurements of the bandpass and on newly conceived differential natural scene statistics (NSS) of HDR pictures. We derive an algorithm from the model which we call the HDR IMAGE GRADient-based Evaluator. NSS models have previously been used to devise NR IQA models that effectively predict the subjective quality of SDR images, but they perform significantly worse on tonemapped HDR content. Toward ameliorating this we make here the following contributions: 1) we design HDR picture NR IQA models and algorithms using both standard space-domain NSS features as well as novel HDR-specific gradient-based features that significantly elevate prediction performance; 2) we validate the proposed models on a large-scale crowdsourced HDR image database; and 3) we demonstrate that the proposed models also perform well on legacy natural SDR images. The software is available at: http://live.ece.utexas.edu/research/Quality/higradeRelease.zip .

Particle Swarm Optimization (PSO) is arguably one of the most popular nature-inspired algorithms for real parameter optimization at present. The existing theoretical research on PSO focuses on the issues like stability, convergence, and explosion of the swarm. However, all of them are based on the gbest (global best) communication topology, which usually is susceptible to false or premature convergence over multi-modal fitness landscapes. The present standard PSO (SPSO 2007) uses an lbest (local best) topology, where a particle is stochastically attracted not towards the best position found in the entire swarm, but towards the best position found by any particle in its topological neighborhood. This article presents a first step towards a probabilistic analysis of the particle interaction and information exchange in an lbest PSO with variable random neighborhood topology (as found in SPSO 2007). It addresses issues like the distribution of particles over neighborhoods, the probability distributions of the social and cognitive terms in lbest model, and the explorative power of the lbest PSO. It also presents a state-space model of the lbest PSO and draws important conclusions regarding the stability and convergence of the particle dynamics in the light of control theory.

Invasive Weed Optimization (IWO) was recently proposed as a simple but powerful metaheuristic algorithm for real parameter optimization. IWO draws inspiration from the ecological process of weeds colonization and distribution and is capable of solving general multi-dimensional, linear and nonlinear optimization problems with appreciable efficiency. This article extends the basic IWO for tackling multi-objective optimization problems that aim at achieving two or more objectives (very often conflicting) simultaneously. The concept of fuzzy dominance has been used to sort the promising candidate solutions at each iteration. The new algorithm has been shown to be statistically significantly better than some state of the art existing evolutionary multi-objective algorithms, namely NSGAIILS, DECMOSA-SQP, MOEP, Clustering MOEA, GDE3, and MOEADGM on a 12-function test-suite (including both unconstrained and constrained problems) from the IEEE CEC (Congress on Evolutionary Computation) 2009 competition and special session on multi-objective optimization algorithms. The following performance metrics were considered: IGD, Spacing, and Minimum Spacing. Our experimental results suggest that IWO holds immense promise to appear as an efficient metaheuristic for multi-objective optimization.

Particle Swarm Optimization (PSO) has recently emerged as a nature-inspired algorithm for real parameter optimization. This article describes a method for improving the final accuracy and the convergence speed of PSO by firstly adding a new coefficient (called mobility factor) to the position updating equation and secondly modulating the inertia weight according to the distance between a particle and the globally best position found so far. The two-fold modification tries to balance between the explorative and exploitative tendencies of the swarm with an objective of achieving better search performance. We also mathematically analyze the effect of the modifications on the dynamics of the PSO algorithm. The new algorithm has been shown to be statistically significantly better than the basic PSO and four of its state-of-the-art variants on a twelve-function test-suite in terms of speed, accuracy, and robustness.

The article considers the task of fuzzy clustering in a multi-objective optimization (MO) framework. It compares the relative performance of four recently developedmulti-objective variants of Differential Evolution (DE) on over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions from each algorithm consists of a number of non-dominated solutions, from which the user can choose the most promising ones according to the problem specifications. A real-coded representation for the candidates is used for DE. A comparative study of four DE variants with two most well-known MO clustering techniques, namely the NSGA II (Non Dominated Sorting GA) and MOCK (Multi- Objective Clustering with an unknown number of clusters K) is also undertaken. Experimental results reported for six artificial and four real life datasets (including a microarray dataset of budding yeast) of varying range of complexities indicates that DE can serve as a promising algorithm for devising MO clustering techniques.

Measuring visual quality, as perceived by human observers, is becoming increasingly important in many applications where humans are the ultimate consumers of visual information. Significant progress has been made for assessing the subjective quality of natural images, such as those taken by optical cameras. Natural Scene Statistics (NSS) is an important tool for no-reference visual quality assessment of natural images, where the reference image is not needed for comparison. In this paper, we take an important step towards using NSS to automate visual quality assessment of photorealistic synthetic scenes typically found in video games and animated movies. Our primary contributions are (1) conducting subjective tests on our publicly available ESPL Synthetic Image Database containing 500 distorted images (20 distorted images for each of the 25 original images) in 1920 × 1080 format, and (2) evaluating the performance of 17 no-reference image quality assessment (IQA) algorithms using synthetic scene statistics. We find that similar to natural scenes, synthetic scene statistics can be successfully used for IQA and certain statistical features are good for certain image distortions.

Particle Swarm Optimization (PSO) is arguably one of the most popular nature- inspired algorithms for real parameter optimization at present. The existing theoretical research on PSO is mostly based on the gbest (global best) particle topology, which usually is susceptible to false or premature convergence over multi-modal fitness landscapes. The present standard PSO (SPSO 2007) uses an lbest (local best) topology where a particle is stochastically attracted not towards the best position found in the entire swarm, but towards the best position found by any particle in its topological neighborhood. This paper presents a first step towards a probabilistic analysis of the lbest PSO with variable random neighborhood topology by addressing issues like inter-particle interaction and probabilities of selection based on particle ranks.

Evolutionary optimization algorithms have been recently applied to optimal digital IIR filter design. In this paper, we apply a Bandwidth Adaptive Harmony Search (BAHS) algorithm to the design of 1-dimensional IIR filters. Harmony Search is an evolutionary algorithm, which emulates the improvisation process of musicians. We have modified the algorithm by setting the bandwidth equal to the standard deviation of the vectors in the harmony memory, and by introducing a penalty function to ensure IIR filter stability. The proposed variant has been applied to the design of low-pass, high-pass, band pass and band stop filters, by minimizing the L 1 and L 2 norm approximation errors and the maximum pass band and stop band ripples. The algorithm has been compared with the TIA approach. The harmony search variant has been found to outperform the TIA approach in designing IIR filters having near-desired frequency responses.

This paper describes a method for improving the final accuracy and the convergence speed of Particle Swarm Optimization (PSO) by adapting its inertia factor in the velocity updating equation and also by adding a new coefficient to the position updating equation. These modifications do not impose any serious requirements on the basic algorithm in terms of the number of Function Evaluations (FEs). The new algorithm has been shown to be statistically significantly better than four recent variants of PSO on an eight-function test-suite for the following performance matrices: Quality of the final solution, time to find out the solution, frequency of hitting the optima, and scalability.

Particle Swarm Optimization (PSO) has recently emerged as a nature inspired algorithm for real parameter optimization. This article describes a method for improving the final accuracy and the convergence speed of PSO by adding a new coefficient to the position updating equation and modulating the inertia weight. This work also mathematically analyzes the effect of this modification on the PSO algorithm. The new algorithm has been shown to be statistically significantly better than four recent variants of PSO on a six functions test suite.