Past, Present, And Future: Regional Perceptions Of Cancer And How They Affect Treatment Availability

For my thesis, I will be looking at the history of cancer and its treatments, and the influence of the public’s perception about cancer on modern day treatments based on regional differences. The history of cancer involves analyzing when and where cancer was first “discovered” and observed, and how treatments of the illness began. Based on this, different regions of the world have developed different methods for analyzing and treating cancer, potentially based on cultural, social, and technological differences and perspectives. After analyzing and compiling a history of cancer and its treatment, I will analyze how different regions perceive cancer today based on this history and investigate whether these perceptions among different groups were based on a region’s infrastructure, or an individual’s socioeconomic status, education, and potentially gender in that region. Next, I analyzed and interpreted how cancer treatments vary in different regions, and whether the difference in treatments was based on the different perceptions among different groups. I hope to also answer the question about why treatments differ based on analysis of these perceptions, affordability, availability and environmental differences. I also wanted to determine if perception of cancer and its treatment among individuals affects compliance to these treatments

Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review

Human gesture detection, obstacle detection, collision avoidance, parking aids, automotive driving, medical, meteorological, industrial, agriculture, defense, space, and other relevant fields have all benefited from recent advancements in mmWave radar sensor technology. A mmWave radar has several advantages that set it apart from other types of sensors. A mmWave radar can operate in bright, dazzling, or no-light conditions. A mmWave radar has better antenna miniaturization than other traditional radars, and it has better range resolution. However, as more data sets have been made available, there has been a significant increase in the potential for incorporating radar data into different machine learning methods for various applications. This review focuses on key performance metrics in mmWave-radar-based sensing, detailed applications, and machine learning techniques used with mmWave radar for a variety of tasks. This article starts out with a discussion of the various working bands of mmWave radars, then moves on to various types of mmWave radars and their key specifications, mmWave radar data interpretation, vast applications in various domains, and, in the end, a discussion of machine learning algorithms applied with radar data for various applications. Our review serves as a practical reference for beginners developing mmWave-radar-based applications by utilizing machine learning techniques.publishedVersio

An overview on Chronic Kidney Disease allied risk factors and complications

Chronic kidney disease is a long term condition characterized by the gradual loss of kidney function at least a period of 3 months or more. About two-thirds of the cases are mainly caused due to HTN and DM. The symptom load plays a crucial role in the patient's disease experience and among the main signs of CKD are troubling physical and psychological symptoms. The evaluation of the symptom burden of CKD patients is of the utmost importance in clinical management. The risk factors include age, sex, race and ethnicity, family history, drug use, smoking, and socioeconomic status; and other comorbidities, such as hypertension and diabetes. Some risk factors can be modified and prevent or slow down the progression to ESRD. CKD progression is associated with serious complications such as cardiovascular risk, dyslipidemia, anemia, nutritional issues, and mineral and bone disorders

Reinforcement Learning based Fault-Tolerant Routing Algorithm for Mesh based NoC and its FPGA Implementation

Network-on-Chip (NoC) has emerged as the most promising on-chip interconnection framework in Multi-Processor System-on-Chips (MPSoCs) due to its efficiency and scalability. In the deep submicron level, NoCs are vulnerable to faults, which leads to the failure of network components such as links and routers. Failures in NoC components diminish system efficiency and reliability. This paper proposes a Reinforcement Learning based Fault-Tolerant Routing (RL-FTR) algorithm to tackle the routing issues caused by link and router faults in the mesh-based NoC architecture. The efficiency of the proposed RL-FTR algorithm is examined using System-C based cycle-accurate NoC simulator. Simulations are carried out by increasing the number of links and router faults in various sizes of mesh. Followed by simulations, real-time functioning of the proposed RL-FTR algorithm is observed using the FPGA implementation. Results of the simulation and hardware shows that the proposed RL-FTR algorithm provides an optimal routing path from the source router to the destination router.publishedVersio

DRUG INTERACTION INDUCED PHENYTOIN TOXICITY: A CASE REPORT

Phenytoin is a narrow therapeutic indexed antiepileptic drug. Many drugs competitively inhibit isoenzymes responsible for its metabolism when concurrently administered and increases the phenytoin plasma concentration leading to serious adverse effects. One such case is being reported with phenytoin toxicity due to concurrent administration of phenytoin and Isoniazid

DESIGN AND DEVELOPMENT OF ORAL SUSTAINED RELEASE MATRIX TABLETS OF DIDANOSINE

Objectives: In present study, an attempt was made to design sustained-release tablets containing Didanosine using natural gums like Xanthan gum, Guar gum and Karaya gum. Methods: The sustained-release tablets containing Didanosine prepared by using natural gums by wet granulation method. Influence of natural polymer on Didanosine was studied. The prepared tablets were selected for DSC and FTIR studies.Results and Discussions: The tablets were selected for DSC and FTIR studies did not show any chemical interaction between drug and polymer. The prepared formulations were evaluated for Hardness, Thickness, Friability, Weight variation, drug content estimation, Swelling index, in-vitro drug release are within the acceptable standard. In-vitro release profile was check for 8 hrs to evaluate the SR matrix tablet of Didanosine. The optimized tablets were carried out according to ICH guidelines at 40 ± 2º C/ 75 ± 5percent RH for three months. All the prepared tablets were stable at room temperature. The values of pre-compression parameters of prepared granules were evaluated the results were within prescribed limits and indicated good free flowing property. The prepared tablets were subjected to all the quality control tests they were within the official pharmacopoeial limits. Friability is less than 1percent, indicated that tablets had a good mechanical resistance. Weight variation test revealed that the tablets were within the range of pharmacopoeial limit. Thickness, hardness and drug content were within the range of pharmacopoeial limit. The evaluation parameters were within acceptable range for all the formulations. The in-vitro release of Didanosine was conducted for 8 hrs. The optimized formulations WGX3, WGG5 and WGK9 sustained the release up to 8hr. Hence Didanosine along with Xanthan gum, Guar gum and Karaya Gum could be used to prepared sustained released matrix tablets. The in-vitro release obeyed zero order kinetics with mechanism of release was erosion followed by non-fickian diffusion.Conclusion: Among all the formulations WGK9 is the best shows excellent release around 99percent after 8 hrs. The prepared matrix tablets of Didanosine were stable. So, it may be concluded that sustained release matrix tablets would improve the patient compliance and bioavailability may be improved. Keywords:  Didanosine, Xanthan gum, Guar gum and Karaya gum

Flexible Spare Core Placement in Torus Topology based NoCs and its validation on an FPGA

In the nano-scale era, Network-on-Chip (NoC) interconnection paradigm has gained importance to abide by the communication challenges in Chip Multi-Processors (CMPs). With increased integration density on CMPs, NoC components namely cores, routers, and links are susceptible to failures. Therefore, to improve system reliability, there is a need for efficient fault-tolerant techniques that mitigate permanent faults in NoC based CMPs. There exists several fault-tolerant techniques that address the permanent faults in application cores while placing the spare cores onto NoC topologies. However, these techniques are limited to Mesh topology based NoCs. There are few approaches that have realized the fault-tolerant solutions on an FPGA, but the study on architectural aspects of NoC is limited. This paper presents the flexible placement of spare core onto Torus topology-based NoC design by considering core faults and validating it on an FPGA. In the first phase, a mathematical formulation based on Integer Linear Programming (ILP) and meta-heuristic based Particle Swarm Optimization (PSO) have been proposed for the placement of spare core. In the second phase, we have implemented NoC router addressing scheme, routing algorithm, run-time fault injection model, and fault-tolerant placement of spare core onto Torus topology using an FPGA. Experiments have been done by taking different multimedia and synthetic application benchmarks. This has been done in both static and dynamic simulation environments followed by hardware implementation. In the static simulation environment, the experimentations are carried out by scaling the network size and router faults in the network. The results obtained from our approach outperform the methods such as Fault-tolerant Spare Core Mapping (FSCM), Simulated Annealing (SA), and Genetic Algorithm (GA) proposed in the literature. For the experiments carried out by scaling the network size, our proposed methodology shows an average improvement of 18.83%, 4.55%, 12.12% in communication cost over the approaches FSCM, SA, and GA, respectively. For the experiments carried out by scaling the router faults in the network, our approach shows an improvement of 34.27%, 26.26%, and 30.41% over the approaches FSCM, SA, and GA, respectively. For the dynamic simulations, our approach shows an average improvement of 5.67%, 0.44%, and 3.69%, over the approaches FSCM, SA, and GA, respectively. In the hardware implementation, our approach shows an average improvement of 5.38%, 7.45%, 27.10% in terms of application runtime over the approaches SA, GA, and FSCM, respectively. This shows the superiority of the proposed approach over the approaches presented in the literature.publishedVersio

Design and Implementation of Deep Learning Based Contactless Authentication System Using Hand Gestures

Hand gestures based sign language digits have several contactless applications. Applications include communication for impaired people, such as elderly and disabled people, health-care applications, automotive user interfaces, and security and surveillance. This work presents the design and implementation of a complete end-to-end deep learning based edge computing system that can verify a user contactlessly using ‘authentication code’. The ‘authentication code’ is an ‘n’ digit numeric code and the digits are hand gestures of sign language digits. We propose a memory-efficient deep learning model to classify the hand gestures of the sign language digits. The proposed deep learning model is based on the bottleneck module which is inspired by the deep residual networks. The model achieves classification accuracy of 99.1% on the publicly available sign language digits dataset. The model is deployed on a Raspberry pi 4 Model B edge computing system to serve as an edge device for user verification. The edge computing system consists of two steps, it first takes input from the camera attached to it in real-time and stores it in the buffer. In the second step, the model classifies the digit with the inference rate of 280 ms, by taking the first image in the buffer as input.publishedVersio