Electrically modulated photoluminescence in ferroelectric liquid crystal

Electrical modulation and switching of photoluminescence (PL) have been demonstrated in pure deformed helix ferroelectric liquid crystal (DHFLC) material. The PL intensity increases and peak position shifts towards lower wavelength above a threshold voltage which continues up to a saturation voltage. This is attributed to the helix unwinding phenomenon in the DHFLC on the application of an electric field. Moreover, the PL intensity could be switched between high intensity (field-on) and low intensity (field-off) positions. These studies would add a new dimension to ferroelectric liquid crystal's application in the area of optical devices.Comment: 4 figure

Performance Analysis of Disaster Management Using WSN Technology

AbstractIn this research paper we propose a model of Wireless Sensor Networksused for pre-detection of disasters. Here we have discussed the basic architecture of WSNs and how these can be used in disaster management. The major reasons for mass destruction are Earthquake and Tsunami. Millions of lives are lost owing to these. Disaster, be it natural or man-made has a catastrophic impact on lives, money and infrastructure. We do not have a sensitive system yet which provides pre detection of these calamities. Therefore we need to take serious measures to ensure our safety from these disasters. WSNs are a new technology which can be helpful in these situations. The paper also throws light on the future scope of the topic. The information derived can be stored and used for future reference to predict climate of the area at a particular time period

A hybrid supervised machine learning classifier system for breast cancer prognosis using feature selection and data imbalance handling approaches

Nowadays, breast cancer is the most frequent cancer among women. Early detection is a critical issue that can be effectively achieved by machine learning (ML) techniques. Thus in this article, the methods to improve the accuracy of ML classification models for the prognosis of breast cancer are investigated. Wrapper-based feature selection approach along with nature-inspired algorithms such as Particle Swarm Optimization, Genetic Search, and Greedy Stepwise has been used to identify the important features. On these selected features popular machine learning classifiers Support Vector Machine, J48 (C4.5 Decision Tree Algorithm), Multilayer-Perceptron (a feed-forward ANN) were used in the system. The methodology of the proposed system is structured into five stages which include (1) Data Pre-processing; (2) Data imbalance handling; (3) Feature Selection; (4) Machine Learning Classifiers; (5) classifier's performance evaluation. The dataset under this research experimentation is referred from the UCI Machine Learning Repository, named Breast Cancer Wisconsin (Diagnostic) Data Set. This article indicated that the J48 decision tree classifier is the appropriate machine learning-based classifier for optimum breast cancer prognosis. Support Vector Machine with Particle Swarm Optimization algorithm for feature selection achieves the accuracy of 98.24%, MCC = 0.961, Sensitivity = 99.11%, Specificity = 96.54%, and Kappa statistics of 0.9606. It is also observed that the J48 Decision Tree classifier with the Genetic Search algorithm for feature selection achieves the accuracy of 98.83%, MCC = 0.974, Sensitivity = 98.95%, Specificity = 98.58%, and Kappa statistics of 0.9735. Furthermore, Multilayer Perceptron ANN classifier with Genetic Search algorithm for feature selection achieves the accuracy of 98.59%, MCC = 0.968, Sensitivity = 98.6%, Specificity = 98.57%, and Kappa statistics of 0.9682.Web of Science106art. no. 69

Deep phenotyping and genomic data from a nationally representative study on dementia in India

The Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI-DAD) is a nationally representative in-depth study of cognitive aging and dementia. We present a publicly available dataset of harmonized cognitive measures of 4,096 adults 60 years of age and older in India, collected across 18 states and union territories. Blood samples were obtained to carry out whole blood and serum-based assays. Results are included in a venous blood specimen datafile that can be linked to the Harmonized LASI-DAD dataset. A global screening array of 960 LASI-DAD respondents is also publicly available for download, in addition to neuroimaging data on 137 LASI-DAD participants. Altogether, these datasets provide comprehensive information on older adults in India that allow researchers to further understand risk factors associated with cognitive impairment and dementia.Peer reviewe

Risk factors for Coronavirus disease 2019 (Covid-19) death in a population cohort study from the Western Cape province, South Africa

Risk factors for coronavirus disease 2019 (COVID-19) death in sub-Saharan Africa and the effects of human immunodeficiency virus (HIV) and tuberculosis on COVID-19 outcomes are unknown. We conducted a population cohort study using linked data from adults attending public-sector health facilities in the Western Cape, South Africa. We used Cox proportional hazards models, adjusted for age, sex, location, and comorbidities, to examine the associations between HIV, tuberculosis, and COVID-19 death from 1 March to 9 June 2020 among (1) public-sector “active patients” (≥1 visit in the 3 years before March 2020); (2) laboratory-diagnosed COVID-19 cases; and (3) hospitalized COVID-19 cases. We calculated the standardized mortality ratio (SMR) for COVID-19, comparing adults living with and without HIV using modeled population estimates.Among 3 460 932 patients (16% living with HIV), 22 308 were diagnosed with COVID-19, of whom 625 died. COVID19 death was associated with male sex, increasing age, diabetes, hypertension, and chronic kidney disease. HIV was associated with COVID-19 mortality (adjusted hazard ratio [aHR], 2.14; 95% confidence interval [CI], 1.70–2.70), with similar risks across strata of viral loads and immunosuppression. Current and previous diagnoses of tuberculosis were associated with COVID-19 death (aHR, 2.70 [95% CI, 1.81–4.04] and 1.51 [95% CI, 1.18–1.93], respectively). The SMR for COVID-19 death associated with HIV was 2.39 (95% CI, 1.96–2.86); population attributable fraction 8.5% (95% CI, 6.1–11.1)

GWLBC: Gray Wolf Optimization Based Load Balanced Clustering for Sustainable WSNs in Smart City Environment

In a smart city environment, with increased demand for energy efficiency, information exchange and communication through wireless sensor networks (WSNs) plays an important role. In WSNs, the sensors are usually operating in clusters, and they are allowed to restructure for effective communication over a large area and for a long time. In this scenario, load-balanced clustering is the cost-effective means of improving the system performance. Although clustering is a discrete problem, the computational intelligence techniques are more suitable for load balancing and minimizing energy consumption with different operating constraints. The literature reveals that the swarm intelligence-inspired computational approaches give excellent results among population-based meta-heuristic approaches because of their more remarkable exploration ability. Conversely, in this work, load-balanced clustering for sustainable WSNs is presented using improved gray wolf optimization (IGWO). In a smart city environment, the significant parameters of energy-efficient load-balanced clustering involve the network lifetime, dead cluster heads, dead gateways, dead sensor nodes, and energy consumption while ensuring information exchange and communication among the sensors and cluster heads. Therefore, based on the above parameters, the proposed IGWO is compared with the existing GWO and several other techniques. Moreover, the convergence characteristics of the proposed algorithm are demonstrated for an extensive network in a smart city environment, which consists of 500 sensors and 50 cluster heads deployed in an area of 500 × 500 m2, and it was found to be significantly improved

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Graphene functionalized with 3-mercatopropionic acid capped zinc peroxide nanoparticles: A potential ferromagnetic material at room-temperature

The literature reveals that ferromagnetism in zinc peroxide (ZnO2) is due to the exchange interaction between localized electron spin moments resulting from oxygen vacancies at the surface of nanoparticles, while in graphene may be due to existence of various defects. However ZnO2 show paramagnetic behaviour, whereas graphene exhibits very low magnetic intensity (0.0004 emu g(-1)). To enhance magnetization, graphene was treated with 3-mercatopropionic acid followed by coating with polyvinylpyrrolidone (PVP) capped ZnO2 nanoparticles. Interestingly coating of graphene over ZnO2 does not enhance magnetization, whereas coating of 15-20% ZnO2 nanoparticles over graphene enhances magnetization more than 30 times, which gradually decreases on increasing concentration of ZnO2 nanoparticles. Such coated graphene shows highest saturation of magnetization at room temperature ever reported in graphene (130 memu g(-1)). The Magnetic measurements studies of ZnO2 nanoparticles coated graphene indicates excellent room temperature ferromagnetic behaviour, which has been further confirmed by Electron Paramagnetic Resonance and Magnetic Force Microscopy studies. A comparative study was also done with ZnO nanoparticles with graphene and only 60 memu g(-1) magnetization has been observed. It has been concluded that higher magnetization in graphene coated with ZnO2 than ZnO is due to more oxygen vacancies in ZnO2 nanoparticles

Smanjenje troškova pri prskanju soje (Glycine Max L.) optimizacijom radnih parametara

Spraying of plant protection chemicals is widely practised for minimizing the losses occurred due to attack of insect pests, and occurrence of diseases. However, indiscriminate and improper use of pesticide has brought up many environmental issues especially the soil pollution. Application of pesticide and its deposition at the target surface has drawn much attention to improve the efficacy of spray. A number of factors affect the deposition of pesticide on the plant surface and its loss to soil. These factors include both morphological characteristics of leaf and operational parameters of spraying. Among the operational parameters type of nozzle, pressure, droplet size, travel speed, etc. are some of the factors responsible for efficacy of spray on plant and losses to soil. Crop growth stage also affects the efficacy of the spray. The study established that spraying of insecticides with a suitable hydraulic sprayer fitted with HCN-80250 nozzle having provision for air supply at an advanced stage of crop growth with a travel speed of 3.5 km per hour ensure minimum spray losses to soil in soybean crop with considerably higher coverage area by the droplets on both sides of the leaves.Pskanje hemijskih sredstava za zaštitu bilja se široko primenjuje za smanjenje gubitaka koje uzrokuju insekti, štetočine i bolesti. Ipak, nepravilna upotreba pesticida ugrozila je životnu sredinu, posebno zagađenjem zemljišta. Primena pesticide i njihovo taloženje na ciljnoj površini privuklo je mnogo pažnje na unapređenje efikasnosti prskanja. Veliki broj faktora utiče na taloženje pesticide na površinu biljke i njihove gubitke u zemljištu. Ovi faktori uključuju, kako morfološke karakteristike lista, tako i radne parametre prskanja. Među radnim parametrima, na efikasnost prskanja i gubitke u zemljište utiču: tip mlaznice, pritisak, dimenzije kapljice, brzina kretanja itd. Stanje porasta useva takođe utiče na efikasnost prskanja. Ovim istraživanjem je utvrđeno da je prskanje insekticida odgovoarajućim hidrauličkim rasprskivačem sa mlaznicama HCN-80250, uz dodatno snabdevanje vazduhom, u naprednom stanju porasta useva i pri radnoj brzini od 3.5 km•h-1 obezbedilo minimalne gubitke kroz zemljište pri zaštiti soje. Uz to, postignuta je značajno veća površina pokrivena kapljicama, na obe strane lista

Anomalous Low Frequency Dielectric Relaxation in Nanoparticles/Isotropic Fluid Mixed Ferroelectric Liquid Crystals

The collective dielectric relaxation studies have been carried out on nanoparticles/isotropic fluid mixed ferroelectric liquid crystals (FLCs). It has been observed from the dielectric loss spectra that the mixing of nanoparticles (Graphene Oxide and ZnO) and isotropic fluid (Water) into FLCs possesses two dielectric relaxations in low frequency range (i.e. Goldstone and associated novel relaxation). The behavior of novel relaxation peak is found to be anomalous in terms of its strong dependence on type of nanoparticles/fluids, external electric field and temperature variation. The effective dipolar contribution of nanoparticles/fluids into FLC dipole moment could be the probable reason for such relaxation