Solving Target Coverage Problem in Wireless Sensor Network Using Genetic Algorithm

The past few years have seen tremendous increase of interest in the field of wireless sensor network. These wireless sensor network comprise numerous small sensor nodes distributed in an area and collect specific data from that area. The nodes comprising a network are mostly battery driven and hence have a limited amount of energy. The target coverage deals with the surveillance of the area under consideration taking into account the energy constraint associated with nodes. In nutshell, the lifetime of the network is to be maximized while ensuring that all the targets are monitored. The approach of segregating the nodes into various covers is used such that each cover can monitor all the targets while other nodes in remaining covers are in sleep state. The covers are scheduled to operate in turn thereby ensuring that the targets are monitored all the time and the lifetime of the network is also maximized. The segregation method is based on Maximum Set Cover (MSC) problem which is transformed into Maximum Disjoint Set Cover problem (MDSC). This problem of finding Maximum Disjoint Set Cover falls under the category of NP-Complete problem. Hence, two heuristics based approach are discussed in this work; first Greedy Heuristic is implemented to be used as baseline. Then a Genetic Algorithm based approach is proposed that can solve this problem by evolutionary global search technique. The existing and proposed algorithms are coded and functionality verified using MATLAB R2010b and performance evaluation and comparisons are made in terms of number of sensors and sensing range

A Novel IDS Security Scheme for Multicast Communication in DTN

This DTN routing should naturally support unicast and multicast routing strategies. A network node can register itself to any receiver group by setting the corresponding destination. In this research we proposed a new security algorithm with multi cast routing against malicious packet dropping attack in DTN. The proposed security method of finding attacker is based on the link detection method for data forwarding in between sender to receiver. The packet dropping on link through node is detected and prevented by IDS security system. This method not only identified the black hole and grey hole but also prevent from routing misbehavior of malicious nodes. The attacker is identified by data dropping of packets in excessive quantity and their prevention is possible by selecting the next possible route where attacker does not exist in connected link between senders to receivers. The intermediate nodes are identified the attacker through confirm positive reply of malicious node or nodes in dynamic network. The proposed secure IDS (Intrusion Detection and prevention) is securing the DTN and improves the network performance after blocking black hole and grey hole in network. The network performance in presence of attack and secure IDS is measures through performance metrics like throughput, routing packets flooding and proposed secures routing is improves data receiving and minimizes dropping data network

Renal involvement in COVID-19: a review report

COVID-19 is recent emerging pandemic caused by SARS-CoV-2 (severe acute respiratory syndrome- Coronavirus). It is seen mainly affecting lungs, but many recent studies have shown involvement of hematological, kidney, gastrointestinal and other systems. In kidneys it mainly affects the tubules and interstitial areas. The main pathology behind involvement of renal system in COVID-19 is due to presence of ACE 2 receptors in proximal tubules. These receptors are same like that found in lungs and they form binding sites for coronavirus and hence causing the disease. Therefore, patients presenting with raised serum urea and creatinine should be checked for potential renal damage caused by virus and their urine samples should also be tested for presence of coronavirus. Effective testing and prompt management will prevent this virus from being transmitted in community

Decoding the learning curve of non-descent vaginal hysterectomy in the era of laparoscopy- experience at a Zonal Hospital

Background: Despite of the increasing popularity of laparoscopic hysterectomy, vaginal route still stays pertinent. Non descent vaginal hysterectomy (NDVH) involves d steep learning curve and hence, should be a fundamental part of every Gynaecology residency program. Objective of the study was to assess the learning curve of NDVH surgery skill at a Military Zonal Hospital by a single Specialist over a period of two years.Methods: Retrospective study conducted at Military Hospital, Agra between June 2015 to June 2017 on 30 patients who underwent NDVH for benign gynaecological conditions.Results: The average blood loss was noted to reduce from a mean of 285ml (±108.94) in the first 20 cases (Group 1) to 227ml (±110.89) in the next 10 cases (Group 2) despite of the average uterine size increasing from 8.5 (±1.43) weeks in Group 1 to 10.2 (±2.39) weeks in Group 2. The average time taken in minutes was also seen to reduce from 89.75 (±12.62) in Group 1 to 70.5 (±16.50) in Group 2 indicating an improvement of surgical skills. The average 24 hr post-operative haemoglobin fall of 0.8gm% was similar between the two groups.Conclusions: Acquiring NDVH skills is a slow but rewarding process. NDVH involves no incisions, no elaborate set-up, avoids complications of general anaesthesia and pneumo-peritoneum and displays similar results as of laparoscopy. In limited resource countries vaginal route may be the only available minimally invasive option for hysterectomy. Hence, it’s pertinent that Gynecologists are trained in the same.

Secrecy Performance of Cooperative Cognitive AF Relaying Networks With Direct Links Over Mixed Rayleigh and Double-Rayleigh Fading Channels

This paper investigates the secrecy performance of an underlay cooperative cognitive relaying network, wherein a secondary source vehicle communicates with a fixed secondary destination terminal via a direct link and with the assistance of a secondary amplify-and-forward relay vehicle in the presence of a passive secondary eavesdropper vehicle, taking into consideration of interference at the primary user. We assume that the eavesdropper vehicle takes the advantages of both the relay link and direct link. We consider that vehicle-to-vehicle links are modeled as double-Rayleigh fading, while vehicle-to-fixed infrastructure links are modeled as Rayleigh fading. Such a scenario finds it relevancy in vehicle-to-vehicle communication and/or vehicle-to-infrastructure communication under spectrum sharing heterogeneous cooperative vehicular networks. For such a realistic scenario, in particular, we derive a tight lower bound expression of the secrecy outage probability under mixed Rayleigh and double-Rayleigh fading channels. We also present an effective secrecy diversity order analysis and show that the considered system can achieve a secrecy diversity order of 2 for infinitely large average channel gain values of the main links. Finally, we demonstrate the accuracy of our analytical findings via numerical and simulation results and show the impact of channel conditions, primary interference constraints, and direct links on the secrecy performance of the considered syste

Enhancing healthcare recommendation: transfer learning in deep convolutional neural networks for Alzheimer disease detection

Neurodegenerative disorders such as Alzheimer’s Disease (AD) and Mild Cognitive Impairment (MCI) significantly impact brain function and cognition. Advanced neuroimaging techniques, particularly Magnetic Resonance Imaging (MRI), play a crucial role in diagnosing these conditions by detecting structural abnormalities. This study leverages the ADNI and OASIS datasets, renowned for their extensive MRI data, to develop effective models for detecting AD and MCI. The research conducted three sets of tests, comparing multiple groups: multi-class classification (AD vs. Cognitively Normal (CN) vs. MCI), binary classification (AD vs. CN, and MCI vs. CN), to evaluate the performance of models trained on ADNI and OASIS datasets. Key preprocessing techniques such as Gaussian filtering, contrast enhancement, and resizing were applied to both datasets. Additionally, skull stripping using U-Net was utilized to extract features by removing the skull. Several prominent deep learning architectures including DenseNet-201, EfficientNet-B0, ResNet-50, ResNet-101, and ResNet-152 were investigated to identify subtle patterns associated with AD and MCI. Transfer learning techniques were employed to enhance model performance, leveraging pre-trained datasets for improved Alzheimer’s MCI detection. ResNet-101 exhibited superior performance compared to other models, achieving 98.21% accuracy on the ADNI dataset and 97.45% accuracy on the OASIS dataset in multi-class classification tasks encompassing AD, CN, and MCI. It also performed well in binary classification tasks distinguishing AD from CN. ResNet-152 excelled particularly in binary classification between MCI and CN on the OASIS dataset. These findings underscore the utility of deep learning models in accurately identifying and distinguishing neurodegenerative diseases, showcasing their potential for enhancing clinical diagnosis and treatment monitoring

Measurements of the pp → ZZ production cross section and the Z → 4ℓ branching fraction, and constraints on anomalous triple gauge couplings at √s = 13 TeV

Four-lepton production in proton-proton collisions, pp -> (Z/gamma*)(Z/gamma*) -> 4l, where l = e or mu, is studied at a center-of-mass energy of 13 TeV with the CMS detector at the LHC. The data sample corresponds to an integrated luminosity of 35.9 fb(-1). The ZZ production cross section, sigma(pp -> ZZ) = 17.2 +/- 0.5 (stat) +/- 0.7 (syst) +/- 0.4 (theo) +/- 0.4 (lumi) pb, measured using events with two opposite-sign, same-flavor lepton pairs produced in the mass region 60 4l) = 4.83(-0.22)(+0.23) (stat)(-0.29)(+0.32) (syst) +/- 0.08 (theo) +/- 0.12(lumi) x 10(-6) for events with a four-lepton invariant mass in the range 80 4GeV for all opposite-sign, same-flavor lepton pairs. The results agree with standard model predictions. The invariant mass distribution of the four-lepton system is used to set limits on anomalous ZZZ and ZZ. couplings at 95% confidence level: -0.0012 < f(4)(Z) < 0.0010, -0.0010 < f(5)(Z) < 0.0013, -0.0012 < f(4)(gamma) < 0.0013, -0.0012 < f(5)(gamma) < 0.0013

CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods

Background: The Critical Assessment of Genome Interpretation (CAGI) aims to advance the state-of-the-art for computational prediction of genetic variant impact, particularly where relevant to disease. The five complete editions of the CAGI community experiment comprised 50 challenges, in which participants made blind predictions of phenotypes from genetic data, and these were evaluated by independent assessors. // Results: Performance was particularly strong for clinical pathogenic variants, including some difficult-to-diagnose cases, and extends to interpretation of cancer-related variants. Missense variant interpretation methods were able to estimate biochemical effects with increasing accuracy. Assessment of methods for regulatory variants and complex trait disease risk was less definitive and indicates performance potentially suitable for auxiliary use in the clinic. // Conclusions: Results show that while current methods are imperfect, they have major utility for research and clinical applications. Emerging methods and increasingly large, robust datasets for training and assessment promise further progress ahead

Measurement of the t(t)over-barb(b)over-bar production cross section in the all-jet final state in pp collisions at root s=13 TeV

A measurement of the production cross section of top quark pairs in association with two b jets (t (t) over barb (b) over bar) is presented using data collected in proton-proton collisions at root s=13 TeV by the CMS detector at the LHC corresponding to an integrated luminosity of 35.9 fb(-1). The cross section is measured in the all-jet decay channel of the top quark pair by selecting events containing at least eight jets, of which at least two are identified as originating from the hadronization of b quarks. A combination of multivariate analysis techniques is used to reduce the large background from multijet events not containing a top quark pair, and to help discriminate between jets originating from top quark decays and other additional jets. The cross section is determined for the total phase space to be 5.5 +/- 0.3 (stat)(-1.3)(+)(1.6) (syst)pb and also measured for two fiducial t (t) over barb (b) over bar, definitions. The measured cross sections are found to be larger than theoretical predictions by a factor of 1.5-2.4, corresponding to 1-2 standard deviations. (C) 2020 The Author. Published by Elsevier B.V.Peer reviewe