The Usage of Clouds in Zero-Trust Security Strategy: An Evolving Paradigm

Zero-trust security is a security model that assumes no entity is implicitly trusted, regardless of its origin or scope of access. This approach requires continuous verification of all users, devices, and applications before granting access to resources. Cloud computing is a model for delivering IT resources and applications as a service over the Internet. Cloud computing offers many benefits, including scalability, agility, and cost savings. However, cloud computing also introduces new security challenges. This paper proposes a survey-based research methodology to evaluate the usage of clouds in zero-trust security strategies. The paper identifies different zero-trust security solutions, their key features, and the benefits and challenges of implementing these solutions in the cloud. The paper will also discuss the costs and benefits of different zero-trust security solutions. The findings of this research will be valuable for organizations that are considering implementing a zero-trust security strategy in the cloud. The paper will provide guidance on how to choose the best zero-trust security solution for organizational needs and how to implement it effectively

REBEE- Reusability Based Effort Estimation Technique using Dynamic Neural Network

Software Effort Estimation has been researched for over 25 years but until today no real effective model could be designed that could efficiently gauge the effort required for heterogeneous project data. Reusability factors of software development have been used to design a new effort estimation model called REBEE. This encompasses the usage of Fuzzy Logic and Dynamic Neural Networks. The experimental evaluation of the model depicts efficient effort estimation over varied project types

Myoepithelioma of parotid: an unusual clinical entity with immunohistochemistry

Myoepithelioma was recognized as a histological distinct entity by World Health organization (WHO) in 1991. Only 1% of all salivary gland neoplasms are myoepithelioma. Most commonly affect parotid in approximately 40%. Myoepithelioma is usually a benign tumour arising from neoplastic myoepithelial cells which lack ductal differentiation. The salivary gland tumors in which the ducts comprise less than 5% of the section are classified as myoepitheliomas and in contrast to pleomorphic adenoma myoepithelioma does not show chondroid or osteoid formation. Immunohistochemical analysis can aid in the diagnosis with immunoreactivity to S-100, P63, Calponin, GFAP and myogenic markers. In this report we present a case of myoepithelioma in retroauricular region

The epidemiological and neurological risk factors of Japanese encephalitis virus in the population of Assam, Northeast India

Japanese encephalitis is one of the world's most common public health issues, particularly it is prevalent in the north-eastern Indian states of Assam. This study aimed to find out the risk factors linked to clinical and epidemiological characteristics. A total of 245 cases were found as PCR-positive in Assam. The most common clinical symptoms were fever (87%), seizure (65%), altered sensorium (60%), cold with shivering (74%), vomiting (68%), throat irritation (31%), cough (67%), chest pain (10%), joint pain (18%), mouth ulcer (18%), diarrhea (29%), pain in the abdomen (42.9%), runny nose (64%), redness in eyes (78%), jaundice (25%), and blood in the sputum (25%).   Further, the neurological symptoms included vision problems (66.5%), hearing difficulties (55 %), neck stiffness (62%), limb numbness (65%), dizziness (77%), headaches (75.5%), speaking difficulties (63%), hydrophobia (47%), and abnormal behavior (66%). The epidemiological risk factors included contact with pigs (57%), bats (21%), cattle (32%), and rates (66%). In addition, 24.5% of patients observed the death of animals/birds. The protection measure included window screening, sleeping under a mosquito net, and use of insect repellent while sleeping in open compounds (29%) and floods (63%) are considered important risk factors. JE-positive cases include daily habits like working in agriculture fields (28%), in standing water (16%), swimming in nearby lakes (24%), traveling outside their village (40%), and wearing shirts while working in the field (20%), storing water in open containers in or outside the house (62%). These were the epidemiological factors that affected the abundance of the potential mosquito vectors of the JE infection

Brain Inspired Neural Network Models of Visual Motion Perception and Tracking in Dynamic Scenes

For self-driving vehicles, aerial drones, and autonomous robots to be successfully deployed in the real-world, they must be able to navigate complex environments and track objects. While Artificial Intelligence and Machine Vision have made significant progress in dynamic scene understanding, they are not yet as robust and computationally efficient as humans or other primates in these tasks. For example, the current state-of-the-art visual tracking methods become inaccurate when applied to random test videos. We suggest that ideas from cortical visual processing can inspire real world solutions for motion perception and tracking that are robust and efficient. In this context, the following contributions are made in this dissertation. First, a method for estimating 6DoF ego-motion and pixel-wise object motion is introduced, based on a learned overcomplete motion field basis set. The method uses motion field constraints for training and a novel differentiable sparsity regularizer to achieve state-of-the-art ego and object-motion performances on benchmark datasets. Second, a Convolutional Neural Network (CNN) that learns hidden neural representations analogous to the response characteristics of dorsal Medial Superior Temporal area (MSTd) neurons for optic flow and object motion is presented. The findings suggest that goal driven training of CNNs might automatically result in the MSTd-like response properties of model neurons. Third, a recurrent neural network model of predictive smooth pursuit eye movements is presented that generates similar pursuit initiation and predictive pursuit behaviors as observed in humans. The model provides the computational mechanisms of formation and rapid update of an internal model of target velocity, commonly attributed to zero lag tracking and smooth pursuit of occluded objects. Finally, a spike based stereo depth algorithm is presented that reconstructs dynamic visual scenes at 400 frames-per-second with one watt of power consumption when implemented using the IBM TrueNorth processor. Taken together, the presented models and implementations provide the computations for motion perception in the dorsal visual pathway in the brain and inform ideas for efficient computational vision systems

Surface texturisation for the reduction of light reflection in ZnO/Si heterojunction

In this paper, the impact of pyramidal texture on a silicon substrate in ZnO/p-Si heterojunction was investigated. The texturisation of p-type silicon (100) substrate was obtained using the KOH anisotropic wet chemical etching method for different etching times. The RF magnetron sputtering technique was used to deposit ZnO thin films on textured Si substrates and planar Si substrates to form ZnO/Si heterojunction. The surface morphology was studied with field emission scanning electron microscopy (FE-SEM) and atomic force microscopy (AFM). Optical properties were investigated using UV-Visible spectroscopy and photoluminescence (PL). The results show that the PL intensity in the visible region of the electromagnetic spectrum increases with the etching time, while a significant reduction is observed in the reflectance. Due to impressive anti-reflection response, ZnO/Si (textured silicon-TS) heterojunction can be effective in improving the efficiency of solar cells