Evacuation Plans and Simulations for Crowd Egress – A Review

Due to growing population density and increasing complexity and congestion of human habitat evacuation planning is essential to avoid major loss of life during a natural or un-natural disaster. The paper provides a review of existing evacuation systems and strategies and also points out the possible research directions. Intelligent Evacuation Management System coupled with evolutionary and machine learning techniques such as PSO,ACO , k-mean clustering is a promising solution to ensure safe and jostle free egress of people.

The influence of joint line position on functional outcome in primary total knee arthroplasty

Background: To find out the cut off for position of joint line which will provide a good functional outcome.Methods: 58 patients underwent total knee arthroplasty using standard medial parapatellar approach were included in this study. Pre-operative and post-operative radiological joint line were assessed. Pre-operative and post-operative functional assessment were done using oxford knee score and International knee society score. functional outcome was assessed at 1, 3 and 6 months. Influence of post-operative joint line change on functional outcome was assessed on follow up visit in 6th month.Results: Of 58 patients studied, mean age ± SD was 69.0±7.5 years. Joint line elevation of 36, 16, 8 and 2 patients were between 0-2, 2-4 4-8 and>8 mm respectively with mean ± SD of joint line elevation of 2.24±2.07 mm in the group. Mean OKS-knee score at 6 months follow-up of study group was 36.60 with mean IKSS-knee score of 38.17 at 6 months follow up of group<4 mm which was significant (p<0.05) when compared to mean IKSS-knee score at 6 months follow up of group>4 mm (28.40).Conclusions: It was concluded that joint line restoration is necessary for getting a good functional outcome after primary total knee arthroplasty. The functional outcome of patients with joint line elevation between 4-8mm showed inferior outcomes in comparison to the patients with joint line elevation within 4 mm

Fault Detection and Classification in Transmission Line Using Wavelet Transform and ANN

Recent years, there is an increased interest in fault classification algorithms. The reason, behind this interest is the escalating power demand and multiple interconnections of utilities in grid. This paper presents an application of wavelet transforms to detect the faults and further to perform classification by supervised learning paradigm. Different architectures of ANN aretested with the statistical attributes of a wavelet transform of a voltage signal as input features and binary digits as outputs. The proposed supervised learning module is tested on a transmission network. It is observed that ANN architecture performs satisfactorily when it is compared with the simulation results. The transmission network is simulated on Matlab. The performance indices Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Sum Square Error (SSE) are used to determine the efficacy of the neural network

Fault Detection and Classification in Transmission Line Using Wavelet Transform and ANN

In recent years, there is an increased interest in fault classification algorithms. The reason, behind this interest is the escalating power demand and multiple interconnections of utilities in grid. This paper presents an application of wavelet transforms to detect the faults and further to perform classification by supervised learning paradigm. Different architectures of ANN are tested with the statistical attributes of a wavelet transform of a voltage signal as input features and binary digits as outputs. The proposed supervised learning module is tested on a transmission network. It is observed the Layer Recurrent Neural Network (LRNN) architecture performs satisfactorily when it is compared with the simulation results. The transmission network is simulated on Matlab. The performance indices Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Sum Square Error (SSE) are used to determine the efficacy of the neural network

Ambient Air Quality Classification by Grey Wolf Optimizer Based Support Vector Machine

With the development of society along with an escalating population, the concerns regarding public health have cropped up. The quality of air becomes primary concern regarding constant increase in the number of vehicles and industrial development. With this concern, several indices have been proposed to indicate the pollutant concentrations. In this paper, we present a mathematical framework to formulate a Cumulative Index (CI) on the basis of an individual concentration of four major pollutants (SO2, NO2, PM2.5, and PM10). Further, a supervised learning algorithm based classifier is proposed. This classifier employs support vector machine (SVM) to classify air quality into two types, that is, good or harmful. The potential inputs for this classifier are the calculated values of CIs. The efficacy of the classifier is tested on the real data of three locations: Kolkata, Delhi, and Bhopal. It is observed that the classifier performs well to classify the quality of air

Fault Detection and Classification in Transmission Line Using Wavelet Transform and ANN

Recent years, there is an increased interest in fault classification algorithms. The reason, behind this interest is the escalating power demand and multiple interconnections of utilities in grid. This paper presents an application of wavelet transforms to detect the faults and further to perform classification by supervised learning paradigm. Different architectures of ANN aretested with the statistical attributes of a wavelet transform of a voltage signal as input features and binary digits as outputs. The proposed supervised learning module is tested on a transmission network. It is observed that ANN architecture performs satisfactorily when it is compared with the simulation results. The transmission network is simulated on Matlab. The performance indices Mean Square Error (MSE), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Sum Square Error (SSE) are used to determine the efficacy of the neural network

Recent trends in the antimicrobial susceptibility patterns of urinary pathogens in type II diabetes mellitus

Background: Diabetes mellitus is one of the most frequently encountered diseases in clinical practice and since the diabetic patients are at an increased risk of infections specially those of the urinary tract it is imperative for a physician to be aware of the prevalence and antibiotic susceptibility patterns of urinary pathogens. Thus, in this study we assess the recent trends in antimicrobial susceptibility patterns of urinary pathogens in type II diabetes mellitus.Methods: Ninety-three eligible type II diabetes mellitus cases without genitourinary symptoms or abnormalities along with 93 non-diabetic healthy controls were recruited. Mid-stream urine was collected after taking informed consent and each sample tested using the dipstick, microscopy and culture techniques. Isolates were identified using standard biochemical tests.Results: Prevalence of asymptomatic bacteriuria (ASB) in our study was found to be 34.4% among cases of type II diabetes mellitus while it was 6.45% among non-diabetic healthy controls. E. coli was the most common urinary pathogen isolated. E. coli susceptibility towards amikacin was 85.71%, towards ceftriaxone and nitrofurantoin was 71.73% and for meropenem and doxycycline 66.67% susceptibility was observed. In the one case where pseudomonas was cultured, it was susceptible to meropenem, gentamycin, cefoperazone-sulbactum and cefuroxime. In an isolated case where Proteus species was grown, it showed susceptibility to meropenem, norfloxacin, levofloxacin and co-trimoxazole. Enterobacter species which was grown, showed susceptibility to meropenem, vancomycin, amikacin, nitrofurantoin, norfloxacin, levofloxacin and co-trimoxazole. Gram positive bacteria mainly showed susceptibility to ceftriaxone, teicoplanin, vancomycin and doxycycline.Conclusions: The prevalence of bacteriuria is significantly higher in diabetics as compared to non-diabetics and with the recent trends suggestive of emerging resistance among urinary pathogens to some of the commonly used anti-microbials it is of utmost importance to carry out regular surveillance of bacterial profile and their anti-microbial susceptibilities to formulate updated guidelines for effectively treating urinary infections in diabetic patients

Performance Evaluation of Ingenious Crow Search Optimization Algorithm for Protein Structure Prediction

Protein structure prediction is one of the important aspects while dealing with critical diseases. An early prediction of protein folding helps in clinical diagnosis. In recent years, applications of metaheuristic algorithms have been substantially increased due to the fact that this problem is computationally complex and time-consuming. Metaheuristics are proven to be an adequate tool for dealing with complex problems with higher computational efficiency than conventional tools. The work presented in this paper is the development and testing of the Ingenious Crow Search Algorithm (ICSA). First, the algorithm is tested on standard mathematical functions with known properties. Then, the application of newly developed ICSA is explored on protein structure prediction. The efficacy of this algorithm is tested on a bench of artificial proteins and real proteins of medium length. The comparative analysis of the optimization performance is carried out with some of the leading variants of the crow search algorithm (CSA). The statistical comparison of the results shows the supremacy of the ICSA for almost all protein sequences

Enhancing Semantic Segmentation: Design and Analysis of Improved U-Net Based Deep Convolutional Neural Networks

In this research, we provide a state-of-the-art method for semantic segmentation that makes use of a modified version of the U-Net architecture, which is itself based on deep convolutional neural networks (CNNs). This research delves into the ins and outs of this cutting-edge approach to semantic segmentation in an effort to boost its precision and productivity. To perform semantic segmentation, a crucial operation in computer vision, each pixel in an image must be assigned to one of many predefined item classes. The proposed Improved U-Net architecture makes use of deep CNNs to efficiently capture complex spatial characteristics while preserving associated context. The study illustrates the efficacy of the Improved U-Net in a variety of real-world circumstances through thorough experimentation and assessment. Intricate feature extraction, down-sampling, and up-sampling are all part of the network's design in order to produce high-quality segmentation results. The study demonstrates comparative evaluations against classic U-Net and other state-of-the-art models and emphasizes the significance of hyperparameter fine-tuning. The suggested architecture shows excellent performance in terms of accuracy and generalization, demonstrating its promise for a variety of applications. Finally, the problem of semantic segmentation is addressed in a novel way. The experimental findings validate the relevance of the architecture's design decisions and demonstrate its potential to boost computer vision by enhancing segmentation precision and efficiency

Flat Gain on C-band using Raman-EDFA Hybrid Optical Amplifier for DWDM System

An efficient optical link which provides flat gain over C-band  has been designed using a raman fibre amplifier and an Erbium doped fibre amplifier (EDFA) in a hybrid configuration for dense wave division multiplexing  system (DWDM). With an input power of 3 mW, gain of  > 40 dB is obtained  across the range of 1530 nm to 1565 nm with a gain variation of <1.2 dB without using any gain flattening techniques