UML 2.0 BASED ROUND TRIP ENGINEERING FRAMEWORK FOR THE DEVELOPMENT OF SPF BASED SECURE APPLICATION

This research paper proposes the UML 2.0 based framework for round-trip engineering and use of Security Performance Flexibility model to keep high security in web applications. This model allows system administrators to skip or disable some unnecessary security checks in trusted operating systems through which, they can effectively balance their performance needs without compromising the security of the system. For example, the system admin can tell that video on demand server is allowed to skip only security checks on reading files, while the database server is allowed to skip only security checks on seeking files. Which operation is needed to be skipped and, which operation is not needed to be skipped is very much subjective in nature, this will depend upon the user’s requirement and the particular application’s requirement. The selection of these operations for a particular application is the part of software requirement elicitation process. This UML 2.0 based research work proposes Object-Oriented class-based software development, source code generation in C++ and the integration of security engineering into a model-driven software development. On this source code, Halstead software science measures, etc., can be applied. This helps developers in code restructuring; identify probable bugs or deficiencies for probable improvements and helps from the analysis phase to the maintenance phase

Auto Student Attendance System Using Student ID Card via Wi-Fi

The aim of this project is to eliminate the paper-based attendance with auto attendance by using student ID card. Currently, there is a card based attendance used in companies and web based attendance is used in the universities. In the paper-based attendance the paper is often lost, while in the web based attendance when all the students try to log in at the same time, the server gets down. We came up with the solution of  having a Wi-Fi based attendance where there is no need of paper and login to the server. It can be done with the help of proximity card reader. When a student holds his card in front of the reader, it will read the card and send it to the database with the help of Wi-Fi module. The database will process data based on the time and date of receiving

Heating Oil Level Detection and Assistance Using Amazon Alexa

This work is used to bridge the gap between consumer and oil supplier by building a smart monitoring system with the help of  ESP8266 (Wi-Fi module) and  Alexa (Intelligent AI based Virtual Assistant). In the existing system, many oil tanks are inaccessible or buried under the ground. We came up with the solution  of  having a smart monitoring system where there is no longer need to read the oil level in the heating oil tank manually.  It can be done with the help of an ultrasonic sensor. When the consumer requests to check the oil level, ESP8266  retrieves the oil level from the ultrasonic sensor and sends it to Alexa

All Dogs Go to Prince George’s County: Finding a Home for a Second Animal Services Facility

Final project for URSP 688M: Planning Technologies (Spring 2018). University of Maryland, College Park.As a continuation of the Fall 2017 PALS project, Spring 2018 semester students from the community planning and engineering programs used advanced computer mapping tools (geographic information system, or GIS) to provide Prince George’s County with potential sites to build a second animal shelter. The team attempted to find land that the county already owned, but none of the parcels met the requirements. The team found a solution to this problem by including distressed shopping centers in the site analysis. From these forty shopping centers, eleven were chosen for their location within the county’s Growth Policy Center. We used ArcGIS Online to understand how many potential adopters could reach these facilities within fifteen and thirty minutes. We then chose the five shopping centers closest to the most people and households. We present these to Prince George’s County as potential candidate sites. The link for the county to access the ArcGIS Online website to view the maps and site locations is: http://uofmd.maps.arcgis.com/home/item.html?id=ea8cc7f3ca154064939db517e24b4606.Prince George's Count

Spatial, temporal, and demographic patterns in prevalence of chewing tobacco use in 204 countries and territories, 1990-2019 : a systematic analysis from the Global Burden of Disease Study 2019

Interpretation Chewing tobacco remains a substantial public health problem in several regions of the world, and predominantly in south Asia. We found little change in the prevalence of chewing tobacco use between 1990 and 2019, and that control efforts have had much larger effects on the prevalence of smoking tobacco use than on chewing tobacco use in some countries. Mitigating the health effects of chewing tobacco requires stronger regulations and policies that specifically target use of chewing tobacco, especially in countries with high prevalence. Findings In 2019, 273 center dot 9 million (95% uncertainty interval 258 center dot 5 to 290 center dot 9) people aged 15 years and older used chewing tobacco, and the global age-standardised prevalence of chewing tobacco use was 4 center dot 72% (4 center dot 46 to 5 center dot 01). 228 center dot 2 million (213 center dot 6 to 244 center dot 7; 83 center dot 29% [82 center dot 15 to 84 center dot 42]) chewing tobacco users lived in the south Asia region. Prevalence among young people aged 15-19 years was over 10% in seven locations in 2019. Although global agestandardised prevalence of smoking tobacco use decreased significantly between 1990 and 2019 (annualised rate of change: -1 center dot 21% [-1 center dot 26 to -1 center dot 16]), similar progress was not observed for chewing tobacco (0 center dot 46% [0 center dot 13 to 0 center dot 79]). Among the 12 highest prevalence countries (Bangladesh, Bhutan, Cambodia, India, Madagascar, Marshall Islands, Myanmar, Nepal, Pakistan, Palau, Sri Lanka, and Yemen), only Yemen had a significant decrease in the prevalence of chewing tobacco use, which was among males between 1990 and 2019 (-0 center dot 94% [-1 center dot 72 to -0 center dot 14]), compared with nine of 12 countries that had significant decreases in the prevalence of smoking tobacco. Among females, none of these 12 countries had significant decreases in prevalence of chewing tobacco use, whereas seven of 12 countries had a significant decrease in the prevalence of tobacco smoking use for the period. Summary Background Chewing tobacco and other types of smokeless tobacco use have had less attention from the global health community than smoked tobacco use. However, the practice is popular in many parts of the world and has been linked to several adverse health outcomes. Understanding trends in prevalence with age, over time, and by location and sex is important for policy setting and in relation to monitoring and assessing commitment to the WHO Framework Convention on Tobacco Control. Methods We estimated prevalence of chewing tobacco use as part of the Global Burden of Diseases, Injuries, and Risk Factors Study 2019 using a modelling strategy that used information on multiple types of smokeless tobacco products. We generated a time series of prevalence of chewing tobacco use among individuals aged 15 years and older from 1990 to 2019 in 204 countries and territories, including age-sex specific estimates. We also compared these trends to those of smoked tobacco over the same time period. Findings In 2019, 273 & middot;9 million (95% uncertainty interval 258 & middot;5 to 290 & middot;9) people aged 15 years and older used chewing tobacco, and the global age-standardised prevalence of chewing tobacco use was 4 & middot;72% (4 & middot;46 to 5 & middot;01). 228 & middot;2 million (213 & middot;6 to 244 & middot;7; 83 & middot;29% [82 & middot;15 to 84 & middot;42]) chewing tobacco users lived in the south Asia region. Prevalence among young people aged 15-19 years was over 10% in seven locations in 2019. Although global age standardised prevalence of smoking tobacco use decreased significantly between 1990 and 2019 (annualised rate of change: -1 & middot;21% [-1 & middot;26 to -1 & middot;16]), similar progress was not observed for chewing tobacco (0 & middot;46% [0 & middot;13 to 0 & middot;79]). Among the 12 highest prevalence countries (Bangladesh, Bhutan, Cambodia, India, Madagascar, Marshall Islands, Myanmar, Nepal, Pakistan, Palau, Sri Lanka, and Yemen), only Yemen had a significant decrease in the prevalence of chewing tobacco use, which was among males between 1990 and 2019 (-0 & middot;94% [-1 & middot;72 to -0 & middot;14]), compared with nine of 12 countries that had significant decreases in the prevalence of smoking tobacco. Among females, none of these 12 countries had significant decreases in prevalence of chewing tobacco use, whereas seven of 12 countries had a significant decrease in the prevalence of tobacco smoking use for the period. Interpretation Chewing tobacco remains a substantial public health problem in several regions of the world, and predominantly in south Asia. We found little change in the prevalence of chewing tobacco use between 1990 and 2019, and that control efforts have had much larger effects on the prevalence of smoking tobacco use than on chewing tobacco use in some countries. Mitigating the health effects of chewing tobacco requires stronger regulations and policies that specifically target use of chewing tobacco, especially in countries with high prevalence. Copyright (c) 2021 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Computational Analysis: Unveiling the Quantum Algorithms for Protein Analysis and Predictions

The study of protein-protein interactions (PPIs) and predicting the protein structure plays a critical role in understanding cellular processes and designing therapeutic interventions. In this research, we explore the application of quantum algorithms, specifically Grover’s algorithm, in improving the accuracy and efficiency of PPI prediction. By harnessing the inherent parallelism and quantum search capabilities of Grover’s algorithm, we aim to enhance the identification of interacting protein pairs from large-scale datasets. We demonstrate the effectiveness of using this algorithm through an extensive approach, comparing the performance of Grover’s algorithm with classical machine learning algorithms. Our results reveal that the quantum algorithm offers significant improvements in prediction accuracy, enabling the identification of previously undetected PPIs. Moreover, we discuss the advantages and limitations of using Grover’s algorithm in PPI prediction and provide insights into its potential for accelerating research in protein interaction networks. This research highlights the potential of quantum algorithms in advancing the field of bioinformatics and protein interaction analysis

A Deep Learning-Based Framework for Retinal Disease Classification

This study addresses the problem of the automatic detection of disease states of the retina. In order to solve the abovementioned problem, this study develops an artificially intelligent model. The model is based on a customized 19-layer deep convolutional neural network called VGG-19 architecture. The model (VGG-19 architecture) is empowered by transfer learning. The model is designed so that it can learn from a large set of images taken with optical coherence tomography (OCT) and classify them into four conditions of the retina: (1) choroidal neovascularization, (2) drusen, (3) diabetic macular edema, and (4) normal form. The training datasets (taken from publicly available sources) consist of 84,568 instances of OCT retinal images. The datasets exhibit all four classes of retinal disease mentioned above. The proposed model achieved a 99.17% classification accuracy with 0.995 specificities and 0.99 sensitivity, making it better than the existing models. In addition, the proper statistical evaluation is done on the predictions using such performance measures as (1) area under the receiver operating characteristic curve, (2) Cohen’s kappa parameter, and (3) confusion matrix. Experimental results show that the proposed VGG-19 architecture coupled with transfer learning is an effective technique for automatically detecting the disease state of a retina

Multiexposure laser speckle contrast imaging of the angiogenic microenvironment

We report the novel use of laser speckle contrast imaging (LSCI) at multiple exposure times (meLSCI) for enhanced in vivo imaging of the microvascular changes that accompany angiogenesis. LSCI is an optical imaging technique that can monitor blood vessels and the flow therein at a high spatial resolution without requiring the administration of an exogenous contrast agent. LSCI images are obtained under red (632 nm) laser illumination at seven exposure times (1–7 ms) and combined using a curve-fitting approach to obtain high-resolution meLSCI images of the rat brain vasculature. To evaluate enhancement in in vivo imaging performance, meLSCI images are statistically compared to individual LSCI images obtained at a single exposure time. We find that meLSCI reduced the observed variability in the LSCI-based blood-flow estimates by 30% and improved the contrast-to-noise ratio in regions with high microvessel density by 41%. The ability to better distinguish microvessels, makes meLSCI uniquely suited to longitudinal imaging of changes in the vascular microenvironment induced by pathological angiogenesis. We demonstrate this utility of meLSCI by sequentially monitoring, over days, the microvascular changes that accompany wound healing in a mouse ear model

Brain tumors disrupt the resting-state connectome

Brain tumor patients often experience functional deficits that extend beyond the tumor site. While resting-state functional MRI (rsfMRI) has been used to map such functional connectivity changes in brain tumor patients, the interplay between abnormal tumor vasculature and the rsfMRI signal is still not well understood. Therefore, there is an exigent need for new tools to elucidate how the blood‑oxygenation-level-dependent (BOLD) rsfMRI signal is modulated in brain cancer. In this initial study, we explore the utility of a preclinical model for quantifying brain tumor-induced changes on the rsfMRI signal and resting-state brain connectivity. We demonstrate that brain tumors induce brain-wide alterations of resting-state networks that extend to the contralateral hemisphere, accompanied by global attenuation of the rsfMRI signal. Preliminary histology suggests that some of these alterations in brain connectivity may be attributable to tumor-related remodeling of the neurovasculature. Moreover, this work recapitulates clinical rsfMRI findings from brain tumor patients in terms of the effects of tumor size on the neurovascular microenvironment. Collectively, these results lay the foundation of a preclinical platform for exploring the usefulness of rsfMRI as a potential new biomarker in patients with brain cancer. Keywords: Brain tumor, fMRI, Neurovascular uncoupling, Resting-state, Connectivit