Encrypted Color Image Transmission in LDPC Encoded MIMO Wireless Communication System with implementation of MP-WFRFT based

This paper emphasizes on comprehensive study for the performance evaluation of LDPC encoded MIMO wireless communication system under implementation of MP-WFRFT based physical layer security scheme. The 4 5F4; multi antenna configured simulated system under investigation incorporates LDPC channel coding scheme and various types of modulation (QPSK, DQPSK, and 4-QAM) and signal detection (ZF, MMSE, ZF-SIC and MMSE-SIC) techniques. On considering transmission of encrypted color image in a hostile fading channel, it is noticeable from MATLAB based simulation study that the LDPC channel encoded system is very much robust and effective in retrieving color image under utilization of MMSE-SIC signal detection and 4-QAM digital modulation techniques

Amoxyclav Resistance Pattern and Aerobic Bacterial Profile in Diabetic Foot Infection Patients in Bangladesh

Introduction: The goal of this study was to determine the frequency of bacterial isolates cultured from diabetic foot infections and to assess their amoxyclav resistance and susceptibility.Methods: A total of 378 diabetic foot lesions were included in this prospective analysis. The antibiotic susceptibility pattern of bacteria isolated from foot lesions was assessed using the Kirby-Bauer disk diffusion method.Results: The most commonly isolated Gram-positive bacteria were Staphylococcus aureus, followed by Enterococcus spp. and CoNS. The most commonly isolated Gram-negative bacteria were Klebsiella spp., Pseudomonas spp., Proteus spp., Escherichia coli, Enterobacter spp., Citrobacter spp., Citrobacter freundii, and Proteus vulgaris. Amoxyclav was found to be 100.00% resistant against Pseudomonas aeruginosa and followed by Enterococcus spp. (89.50%), Proteus spp. (87.50%), Staphylococcus aureus (84.30%), Escherichia coli (81.50%), Klebsiella spp. (70.50%) and Enterobacter spp. (69.20%).Conclusion: The present study confirmed the prevalence of amoxyclav drug resistant pathogens (84.90%) in diabetic foot ulcers. The diverse bacteria infecting the wound must be evaluated, as well as the antibiotic susceptibility patterns of the isolates from the infected lesion. This information is critical for selecting the right medications, eliminating resistance trends, and lowering healthcare costs. Keywords: Diabetic Foot Infection, Polymicrobial Infections, Amoxyclav DOI: 10.7176/JHMN/94-03 Publication date:October 31st 202

PYROSEQUENCING-PRINCIPLES AND APPLICATIONS

ABSTRACT Pyrosequencing is the first alternative to the conventional Sanger method for de novo DNA sequencing. Pyrosequencing is a DNA sequencing technology based on the sequencing-by-synthesis principle. It employs a series of four enzymes to accurately detect nucleic acid sequences during the synthesis. Pyrosequencing has the potential advantages of accuracy, flexibility, parallel processing, and can be easily automated. Furthermore, the technique dispenses with the need for labeled primers, labeled nucleotides, and gel-electrophoresis. Pyrosequencing has opened up new possibilities for performing sequence-based DNA analysis. The method has been proven highly suitable for single nucleotide polymorphism analysis and sequencing of short stretches of DNA. Pyrosequencing has been successful for both confirmatory sequencing and de novo sequencing. By increasing the read length to higher scores and by shortening the sequence reaction time per base calling, pyrosequencing may take over many broad areas of DNA sequencing applications as the trend is directed to analysis of fewer amounts of specimens and large-scale settings, with higher throughput and lower cost. This article considers key features regarding different aspects of pyrosequencing technology, including the general principles, enzyme properties, sequencing modes, instrumentation, limitations, potential and future applications

A Novel Non-Invasive Estimation of Respiration Rate from Motion Corrupted Photoplethysmograph Signal Using Machine Learning Model

Respiratory ailments such as asthma, chronic obstructive pulmonary disease (COPD), pneumonia, and lung cancer are life-Threatening. Respiration rate (RR) is a vital indicator of the wellness of a patient. Continuous monitoring of RR can provide early indication and thereby save lives. However, a real-Time continuous RR monitoring facility is only available at the intensive care unit (ICU) due to the size and cost of the equipment. Recent researches have proposed Photoplethysmogram (PPG) and/ Electrocardiogram (ECG) signals for RR estimation however, the usage of ECG is limited due to the unavailability of it in wearable devices. Due to the advent of wearable smartwatches with built-in PPG sensors, it is now being considered for continuous monitoring of RR. This paper describes a novel approach for RR estimation using motion artifact correction and machine learning (ML) models with the PPG signal features. Feature selection algorithms were used to reduce computational complexity and the chance of overfitting. The best ML model and the best feature selection algorithm combination were fine-Tuned to optimize its performance using hyperparameter optimization. Gaussian Process Regression (GPR) with Fit a Gaussian process regression model (Fitrgp) feature selection algorithm outperformed all other combinations and exhibits a root mean squared error (RMSE), mean absolute error (MAE), and two-standard deviation (2SD) of 2.63, 1.97, and 5.25 breaths per minute, respectively. Patients would be able to track RR at a lower cost and with less inconvenience if RR can be extracted efficiently and reliably from the PPG signal. 2013 IEEE.Corresponding authors: Muhammad E. H. Chowdhury ([email protected]), Mamun Bin Ibne Reaz ([email protected]), and Md. Shafayet Hossain ([email protected]) This work was supported in part by the Qatar National Research under Grant NPRP12S-0227-190164, and in part by the International Research Collaboration Co-Fund (IRCC) through Qatar University under Grant IRCC-2021-001. The statements made herein are solely the responsibility of the authors.Scopu

Exploring the impact of pumpkin and sweet potato enrichment on the nutritional profile and antioxidant capacity of noodles

Sweet potato and pumpkin are abundant in β-carotene, fiber, and essential minerals. The use of pumpkin and sweet potato with wheat flour to prepare noodles may increase their nutritional value and provide several health benefits. The purpose of the study was to develop pumpkin, sweet potato, and wheat composite flour noodles with improved nutritional and sensory qualities. All of the quality parameters like nutritional value, water activity, color intensity, β-carotene content, antioxidant capacity, and sensory characteristics were analyzed according to the standard procedures. The addition of pumpkin and sweet potato flour to prepared noodles significantly (p < 0.05) increased the β-carotene (18.30 – 713.99 μg/100 g), protein (8.75 – 14.75 g/100 g), crude fiber (0.38 – 3.84 g/100 g), and minerals like potassium, calcium, iron, and zinc contents. In addition, the DPPH scavenging activity, phenolic and flavonoid content of the noodles were significantly (p < 0.05) higher than those of the control noodles. On the contrary, the noodles redness (a*) and yellowness (b*) increased and their lightness (L*) decreased as the incorporation of sweet potato and pumpkin increased. However, sensory assessment results suggested that noodles containing 15 % pumpkin, 10 % sweet potato, and 75 % wheat composite flour were more appealing than other developed noodles. Newly developed noodles will play a crucial role in increasing nutritional diversity and reducing food insecurity among low-income families

Automatic Face Recognition System using P-tree and K-Nearest Neighbor Classifier

Abstract: Face recognition has recently received remarkable attention in both authentication and identification systems due to high acceptability and collectability, regardless its lower circumvention and uniqueness than other biometric verification technologies. The basic approach with face recognition commences with feature set construction from the relevant facial traits of the users, termed enrollment [1]. When a user is to be authenticated (i.e. the user&apos;s identity is to be verified), his/her facial sample is captured and a feature set is created. This feature set is then compared with the enrollment feature set. But feature set search mechanism is time consuming and sometimes exhaustive. In this paper, a very efficient and time saving search mechanism is proposed that exploits the advantages of Peano Count Tree and K- Nearest Neighbor Search techniques

The role of micro-credit on empowerment of women in Bangladesh

The main objective of the study was to know the role of microcredit in income generating activities of rural women and its impact on their socio-economic empowerment and to explore the relationships of the women’s selected characteristics with their empowerment. The study was conducted in South Surma Upazila of Sylhet district in Bangladesh. A total of 100 responses were gathered from a population of 137 and data was analyzed using Statistical Package for Social Science (SPSS). Empowerment was assessed on the basis of 9 factors by using a 4-point rating scale. Pearson’s correlation coefficient (r) was computed to explore the relationship between the extents of rural women empowerment with their selected characteristics. The findings revealed that the majority (50%) of the women had a medium level of empowerment. Based on the results, women must be further empowered by enhancing their skills, awareness, knowledge and technology usage, thereby facilitating the overall development of society as well as the country

Impact of demographics, social capital and participation in income generating activities (IGAs) on economic empowerment of rural women in Bangladesh

The purpose of this paper is to examine the participation, social capital and women‟s economic empowerment in different income generating activities in the rural area of Bangladesh. The paper is based on primarydata collected from randomly selected 100 women in two villages of South SurmaUpazila in Sylhet district of Bangladesh.A structured questionnaire was used for the purpose of data collection. The researcher used descriptive statistics, t-test, ANOVA and correlation and regression analysis. The findings indicate that demographics background (age, education, occupation, communication media exposure, credit received and training received)have positive impact on participation, social capital and economic empowerment of rural women. Pearson correlation showed that there was significant relationship between participation, social capital and economic empowerment of ruralwomen. The mediation effect of social capital on the relationship between participation and economic empowerment was confirmed by regression analysis