Structure and role of neutrophil cytosol factor 1 (NCF1) gene in various diseases

The neutrophil cytosol factor 1 (NCF1) gene consists of 11 exons and is found in two forms; one is wild type gene and the other is pseudogene. It has more than 98% homology. Both genes occupy the same chromosome region. The mutation in this gene leads to various types of diseases such as chronic granulomatous disease, multiple sclerosis, arthritis and parasitic infection. The common mutation of this gene in most diseases is GT deletion at the start of exon 2. The NCF1 gene interact with other subunits of nicotinamide adenine dinucleotide phosphate-oxidase (NADPH) and play an important role in innate immunity and produce reactive oxygen species and reduce the severity and duration of parasitic infection and autoimmune disease. NCF1 also has a role in T cell activation.Keywords: Neutrophil cytosol factor 1 (NCF1) gene, exons, T cell activatio

Cyber-threat detection system using a hybrid approach of transfer learning and multi-model image representation

Currently, Android apps are easily targeted by malicious network traffic because of their constant network access. These threats have the potential to steal vital information and disrupt the commerce, social system, and banking markets. In this paper, we present a malware detection system based on word2vec-based transfer learning and multi-model image representation. The proposed method combines the textual and texture features of network traffic to leverage the advantages of both types. Initially, the transfer learning method is used to extract trained vocab from network traffic. Then, the malware-to-image algorithm visualizes network bytes for visual analysis of data traffic. Next, the texture features are extracted from malware images using a combination of scale-invariant feature transforms (SIFTs) and oriented fast and rotated brief transforms (ORBs). Moreover, a convolutional neural network (CNN) is designed to extract deep features from a set of trained vocab and texture features. Finally, an ensemble model is designed to classify and detect malware based on the combination of textual and texture features. The proposed method is tested using two standard datasets, CIC-AAGM2017 and CICMalDroid 2020, which comprise a total of 10.2K malware and 3.2K benign samples. Furthermore, an explainable AI experiment is performed to interpret the proposed approach

Creation and Implementation of Process FMEA with Focus on Risk Reduction for Packaging Process

A Twin Cities electronic device manufacturer, with its increasing customers in medical device industry, decided to get certified for ISO 13485:2003 and ISO 14971. As a result of this the company is implementing risk based approach to different process to fulfill the requirement of ISO 13485 and ISO 14971.This capstone project focuses on studying the packaging process and conducting risk analysis on this process. The project includes creating process flow chart, and calculating and managing risk using FMEA for packaging process. FMEA which stands for Failure mode and effect analysis is a proactive tool developed to identify, evaluate and prevent product and/or process failures. The project studies the packaging process and helps identifying different failure modes (FM) for each of the process input, determining effect of each of the FM, identifying causes for the FM, analyzing severity, quantifying occurrences and detectability to each of the FM, calculating risk priority number, assessing risk and mitigating risk according to Risk Management Plan for the company. This includes conducting risk-benefit analysis as well

GIFT: Gesture-Based Interaction by Fingers Tracking, an Interaction Technique for Virtual Environment

Three Dimensional (3D) interaction is the plausible human interaction inside a Virtual Environment (VE). The rise of the Virtual Reality (VR) applications in various domains demands for a feasible 3D interface. Ensuring immersivity in a virtual space, this paper presents an interaction technique where manipulation is performed by the perceptive gestures of the two dominant fingers; thumb and index. The two fingertip-thimbles made of paper are used to trace states and positions of the fingers by an ordinary camera. Based on the positions of the fingers, the basic interaction tasks; selection, scaling, rotation, translation and navigation are performed by intuitive gestures of the fingers. Without keeping a gestural database, the features-free detection of the fingers guarantees speedier interactions. Moreover, the system is user-independent and depends neither on the size nor on the color of the users’ hand. With a case-study project; Interactions by the Gestures of Fingers (IGF) the technique is implemented for evaluation. The IGF application traces gestures of the fingers using the libraries of OpenCV at the back-end. At the front-end, the objects of the VE are rendered accordingly using the Open Graphics Library; OpenGL. The system is assessed in a moderate lighting condition by a group of 15 users. Furthermore, usability of the technique is investigated in games. Outcomes of the evaluations revealed that the approach is suitable for VR applications both in terms of cost and accuracy

EVEN-VE: Eyes Visibility Based Egocentric Navigation for Virtual Environments

Navigation is one of the 3D interactions often needed to interact with a synthetic world. The latest advancements in image processing have made possible gesture based interaction with a virtual world. However, the speed with which a 3D virtual world responds to a user’s gesture is far greater than posing of the gesture itself. To incorporate faster and natural postures in the realm of Virtual Environment (VE), this paper presents a novel eyes-based interaction technique for navigation and panning. Dynamic wavering and positioning of eyes are deemed as interaction instructions by the system. The opening of eyes preceded by closing for a distinct time-threshold, activates forward or backward navigation. Supporting 2-Degree of Freedom head’s gestures (Rolling and Pitching) panning is performed over the xy-plane. The proposed technique was implemented in a case-study project; EWI (Eyes Wavering based Interaction). With EWI, real time detection and tracking of eyes are performed by the libraries of OpenCV at the backend. To interactively follow trajectory of both the eyes, dynamic mapping is performed in OpenGL. The technique was evaluated in two separate sessions by a total of 28 users to assess accuracy, speed and suitability of the system in Virtual Reality (VR). Using an ordinary camera, an average accuracy of 91% was achieved. However, assessment made by using a high quality camera testified that accuracy of the system could be raised to a higher level besides increase in navigation speed. Results of the unbiased statistical evaluations suggest/demonstrate applicability of the system in the emerging domains of virtual and augmented realities

Cyber-Threat Detection System Using a Hybrid Approach of Transfer Learning and Multi-Model Image Representation

Currently, Android apps are easily targeted by malicious network traffic because of their constant network access. These threats have the potential to steal vital information and disrupt the commerce, social system, and banking markets. In this paper, we present a malware detection system based on word2vec-based transfer learning and multi-model image representation. The proposed method combines the textual and texture features of network traffic to leverage the advantages of both types. Initially, the transfer learning method is used to extract trained vocab from network traffic. Then, the malware-to-image algorithm visualizes network bytes for visual analysis of data traffic. Next, the texture features are extracted from malware images using a combination of scale-invariant feature transforms (SIFTs) and oriented fast and rotated brief transforms (ORBs). Moreover, a convolutional neural network (CNN) is designed to extract deep features from a set of trained vocab and texture features. Finally, an ensemble model is designed to classify and detect malware based on the combination of textual and texture features. The proposed method is tested using two standard datasets, CIC-AAGM2017 and CICMalDroid 2020, which comprise a total of 10.2K malware and 3.2K benign samples. Furthermore, an explainable AI experiment is performed to interpret the proposed approach

Design and analysis of a photonic crystal based planar antenna for thz applications

Modern advancements in wearable smart devices and ultra-high-speed terahertz (THz) communication systems require low cost, low profile, and highly efficient antenna design with high directionality to address the propagation loss at the THz range. For this purpose, a novel shape, high gain antenna for THz frequency range applications is presented in this work. The proposed antenna is based on a photonic bandgap (PBG)-based crystal polyimide substrate which gives optimum performance in terms of gain (9.45 dB), directivity (9.99 dBi), and highly satisfactory VSWR (<1) at 0.63 THz. The performance of the antenna is studied on PBGs of different geometrical configurations and the results are compared with the antenna based on the homogeneous polyimide-based substrate. The effects of variations in the dimensions of the PBG unit cells are also studied to achieve a -10 dB bandwidth of 28.97 GHz (0.616 to 0.64 THz)