Church Hill Activities & Tutoring

Problem Statement: Church Hill Activities and Tutoring ( CHAT ), a non-profit organization which invests in the lives of Richmond’s most at-risk children by establishing strong connections through one-on one tutoring, mentorship, and enrichment activities , is facing difficulty in managing consistent communication with parents and volunteers. Each of these audiences requires different channels and information, and currently, sending paper to the home through students has become the primary channel for communicating with parents. The reliability of this practice is limited but continues because parents’ contact information, such as phone number or email, changes frequently. Rationale: Since CHAT is facing difficulties in communicating with parents and volunteers, we as a team are creating a mobile solution to share important and timely information with parents and volunteers. Users will receive notifications and regular updates to schedules and announcements. Users will also be able to update their contact information directly through the app so that the CHAT staff does not have difficulties reaching them. Approach: We divided the application in to three groups, students, parents and volunteers. Student page will contain the announcements or schedule relating to student events, parent’s page will contain the announcements, an option to change/update their contact information, and an option to submit questions/suggestions to CHAT, and finally the volunteer’s page will contain the same thing as the parent’s page except it will have its own announcements. Interim Results and Conclusions: For this semester, we are tackling an android application, and so far we have designed a layout for the app. We have also designed a SQL database to store student’s, parent’s and volunteer’s information. Anticipated Results and Conclusions: By the end of the next semester, we will have completed both the Android and IOS application, and hope to have it available for download for android and IOS users.https://scholarscompass.vcu.edu/capstone/1018/thumbnail.jp

Cloud Based Mobile Application Testing

Abstract -In today's world all applications are available on mobile and therefore there is a certain need to develop all applications on mobile as well. Since there is huge demand for mobile applications these application needs to be tested thoroughly for its correctness. Testing of mobile application is the most difficult task due to its varieties and different operating systems. Although there are simulators and emulators available but they only simulate the working of operating system and cannot test the core functionalities for the mobile device. In order to overcome above testing issues cloud testing provides major solution to the problems faced during mobile testing. Making use of cloud infrastructure in which the service provider does the software testing activities of a given mobile application in a cloud infrastructure for customers as a service based on their requirements

A systematic review of decentralized finance protocols

Decentralized finance (DeFi) has revolutionized the financial industry in recent years. Industries such as banking, insurance, and investment are experiencing a significant shift due to the growth of DeFi. The decentralized finance market is expanding exponentially, emphasizing the potential of digital currencies and decentralized platforms in providing an alternative to the traditional finance paradigm. It eliminates the need for costly intermediaries, reduces transaction fees, and increases accessibility to financial services for everyone, regardless of their geographic location or economic status. With the enormous increase in cryptocurrency investment, individuals and institutions have started to use DeFi. In this context, understanding the architecture and working mechanisms of different DeFi protocols becomes crucial in creating new and innovative products. This review paper explores various DeFi protocols, including derivatives, decentralized exchanges (DEX), lending and borrowing, asset management, and stablecoins. It analyses their internal structure and composability, providing insights into how these protocols can be combined to create new and innovative DeFi products and explore the potential of DeFi in providing an alternative to the traditional finance paradigm

Deep Learning-Based Malicious Smart Contract and Intrusion Detection System for IoT Environment

The Internet of Things (IoT) is a key enabler technology that recently received significant attention from the scientific community across the globe. It helps transform everyone’s life by connecting physical and virtual devices with each other to offer staggering benefits, such as automation and control, higher productivity, real-time information access, and improved efficiency. However, IoT devices and their accumulated data are susceptible to various security threats and vulnerabilities, such as data integrity, denial-of-service, interception, and information disclosure attacks. In recent years, the IoT with blockchain technology has seen rapid growth, where smart contracts play an essential role in validating IoT data. However, these smart contracts can be vulnerable and degrade the performance of IoT applications. Hence, besides offering indispensable features to ease human lives, there is also a need to confront IoT environment security attacks, especially data integrity attacks. Toward this aim, this paper proposed an artificial intelligence-based system model with a dual objective. It first detects the malicious user trying to compromise the IoT environment using a binary classification problem. Further, blockchain technology is utilized to offer tamper-proof storage to store non-malicious IoT data. However, a malicious user can exploit the blockchain-based smart contract to deteriorate the performance IoT environment. For that, this paper utilizes deep learning algorithms to classify malicious and non-malicious smart contracts. The proposed system model offers an end-to-end security pipeline through which the IoT data are disseminated to the recipient. Lastly, the proposed system model is evaluated by considering different assessment measures that comprise the training accuracy, training loss, classification measures (precision, recall, and F1 score), and receiver operating characteristic (ROC) curve

Sentinel Lymph Node Biopsy in Patients with Thick Primary Cutaneous Melanoma: Patterns of Use and Underuse Utilizing a Population-Based Model

Background. Sentinel lymph node biopsy (SLNB) for thick cutaneous melanoma is supported by national guidelines. We report on factors associated with the use and underuse of SLNB for thick primary cutaneous melanoma. Methods. The Surveillance, Epidemiology, and End Results database was queried for patients who underwent surgery for thick primary cutaneous melanoma from 2004 to 2008. We used multivariate logistic regression models to predict use of SLNB. Results. Among 1,981 patients, 833 (41.8%) did not undergo SLNB. Patients with primary melanomas of the arm (OR 2.07, CI 1.56-2.75; P < 0.001), leg (OR 2.40, CI 1.70-3.40; P < 0.001), and trunk (OR 1.82, CI 1.38-2.40; P < 0.001) had an increased likelihood of receiving a SLNB, as did those with desmoplastic histology (OR 1.47, CI 1.11-1.96; P = 0.008). A decreased likelihood of receiving SLNB was noted for advancing age ≥ 60 years (age 60 to 69: OR 0.58, CI 0.33-0.99, P = 0.047; age 70 to 79: OR 0.32, CI 0.19-0.54, P < 0.001; age 80 or more: OR 0.10, CI 0.06-0.16, P < 0.001) and unknown race/ethnicity (OR 0.21, CI 0.07-0.62; P = 0.005). Conclusions. In particular, elderly patients are less likely to receive SLNB. Further research is needed to assess whether use of SLNB in this population is detrimental or beneficial

Predictors of residual disease after unplanned excision of soft tissue sarcomas

BackgroundUnplanned excision of soft tissue sarcomas (STS) is an important quality of care issue given the morbidity related to tumor bed excision. Since not all patients harbor residual disease at the time of reexcision, we sought to determine predictors of residual STS following unplanned excision.MethodsWe identified 76 patients from a prospective database (January 1, 2008-September 30, 2014) who received a diagnosis of primary STS following unplanned excision on the trunk or extremities. We used univariable and multivariable analyses to evaluate predictors of residual STS as the primary endpoint. We calculated the sensitivity, specificity, and accuracy of interval magnetic resonance imaging (MRI) to predict residual sarcoma at reexcision.ResultsMean age was 52 y, and 63.2% were male. 50% had fragmented unplanned excision. Among patients undergoing reexcision, residual STS was identified in 70%. On univariable analysis, MRI showing gross disease and fragmented excision were significant predictors of residual STS (odds ratio, 10.59; 95% CI, 2.14-52.49; P = 0.004 and odds ratio, 3.61; 95% CI, 1.09-11.94; P = 0.035, respectively). On multivariable analysis, tumor size predicted distant recurrence and overall survival. When we combined equivocal and positive MRI, the sensitivity and specificity of MRI for predicting residual STS were 86.7% (95% CI, 73.2%-95.0%) and 57.9% (95% CI, 33.5%-79.8%), with an overall accuracy of 78.1% (95% CI, 66.0%-87.5%).ConclusionsAbout 70% of patients undergoing repeat excision after unplanned excision of STS harbor residual sarcoma. Although interval MRI and fragmented excision appear to be the most significant predictors of residual STS, the accuracy of MRI remains modest, especially given the incidence of equivocal MRI