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

Population and fertility by age and sex for 195 countries and territories, 1950–2017: a systematic analysis for the Global Burden of Disease Study 2017

Background: Population estimates underpin demographic and epidemiological research and are used to track progress on numerous international indicators of health and development. To date, internationally available estimates of population and fertility, although useful, have not been produced with transparent and replicable methods and do not use standardised estimates of mortality. We present single-calendar year and single-year of age estimates of fertility and population by sex with standardised and replicable methods. Methods: We estimated population in 195 locations by single year of age and single calendar year from 1950 to 2017 with standardised and replicable methods. We based the estimates on the demographic balancing equation, with inputs of fertility, mortality, population, and migration data. Fertility data came from 7817 location-years of vital registration data, 429 surveys reporting complete birth histories, and 977 surveys and censuses reporting summary birth histories. We estimated age-specific fertility rates (ASFRs; the annual number of livebirths to women of a specified age group per 1000 women in that age group) by use of spatiotemporal Gaussian process regression and used the ASFRs to estimate total fertility rates (TFRs; the average number of children a woman would bear if she survived through the end of the reproductive age span [age 10–54 years] and experienced at each age a particular set of ASFRs observed in the year of interest). Because of sparse data, fertility at ages 10–14 years and 50–54 years was estimated from data on fertility in women aged 15–19 years and 45–49 years, through use of linear regression. Age-specific mortality data came from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2017 estimates. Data on population came from 1257 censuses and 761 population registry location-years and were adjusted for underenumeration and age misreporting with standard demographic methods. Migration was estimated with the GBD Bayesian demographic balancing model, after incorporating information about refugee migration into the model prior. Final population estimates used the cohort-component method of population projection, with inputs of fertility, mortality, and migration data. Population uncertainty was estimated by use of out-of-sample predictive validity testing. With these data, we estimated the trends in population by age and sex and in fertility by age between 1950 and 2017 in 195 countries and territories. Findings: From 1950 to 2017, TFRs decreased by 49\ub74% (95% uncertainty interval [UI] 46\ub74–52\ub70). The TFR decreased from 4\ub77 livebirths (4\ub75–4\ub79) to 2\ub74 livebirths (2\ub72–2\ub75), and the ASFR of mothers aged 10–19 years decreased from 37 livebirths (34–40) to 22 livebirths (19–24) per 1000 women. Despite reductions in the TFR, the global population has been increasing by an average of 83\ub78 million people per year since 1985. The global population increased by 197\ub72% (193\ub73–200\ub78) since 1950, from 2\ub76 billion (2\ub75–2\ub76) to 7\ub76 billion (7\ub74–7\ub79) people in 2017; much of this increase was in the proportion of the global population in south Asia and sub-Saharan Africa. The global annual rate of population growth increased between 1950 and 1964, when it peaked at 2\ub70%; this rate then remained nearly constant until 1970 and then decreased to 1\ub71% in 2017. Population growth rates in the southeast Asia, east Asia, and Oceania GBD super-region decreased from 2\ub75% in 1963 to 0\ub77% in 2017, whereas in sub-Saharan Africa, population growth rates were almost at the highest reported levels ever in 2017, when they were at 2\ub77%. The global average age increased from 26\ub76 years in 1950 to 32\ub71 years in 2017, and the proportion of the population that is of working age (age 15–64 years) increased from 59\ub79% to 65\ub73%. At the national level, the TFR decreased in all countries and territories between 1950 and 2017; in 2017, TFRs ranged from a low of 1\ub70 livebirths (95% UI 0\ub79–1\ub72) in Cyprus to a high of 7\ub71 livebirths (6\ub78–7\ub74) in Niger. The TFR under age 25 years (TFU25; number of livebirths expected by age 25 years for a hypothetical woman who survived the age group and was exposed to current ASFRs) in 2017 ranged from 0\ub708 livebirths (0\ub707–0\ub709) in South Korea to 2\ub74 livebirths (2\ub72–2\ub76) in Niger, and the TFR over age 30 years (TFO30; number of livebirths expected for a hypothetical woman ageing from 30 to 54 years who survived the age group and was exposed to current ASFRs) ranged from a low of 0\ub73 livebirths (0\ub73–0\ub74) in Puerto Rico to a high of 3\ub71 livebirths (3\ub70–3\ub72) in Niger. TFO30 was higher than TFU25 in 145 countries and territories in 2017. 33 countries had a negative population growth rate from 2010 to 2017, most of which were located in central, eastern, and western Europe, whereas population growth rates of more than 2\ub70% were seen in 33 of 46 countries in sub-Saharan Africa. In 2017, less than 65% of the national population was of working age in 12 of 34 high-income countries, and less than 50% of the national population was of working age in Mali, Chad, and Niger. Interpretation: Population trends create demographic dividends and headwinds (ie, economic benefits and detriments) that affect national economies and determine national planning needs. Although TFRs are decreasing, the global population continues to grow as mortality declines, with diverse patterns at the national level and across age groups. To our knowledge, this is the first study to provide transparent and replicable estimates of population and fertility, which can be used to inform decision making and to monitor progress. Funding: Bill &amp; Melinda Gates Foundation