Big Data Analytics for Energy Consumption Prediction in Smart Grid Using Genetic Algorithm and Long Short Term Memory

Smart Grids (SG) have smart meters and advance metering infrasturutre (AMI) which generates huge data. This data can be used for predicting energy consumption using big data analytics. A very limited work has been carried out in the literature which shows the utilization of big data in energy consumption prediction. In this paper, the proposed method is based on Genetic Algorithm - Long Short Term Memory (GA-LSTM). LSTM memorises values over an arbitrary interval that manages time series data very effictively while GA is an evolutionary process that is used for optimization. GA combines with LSTM to process hyperparameters such as hidden layers, epochs, data intervals, batchsize and activation functions. Hence, GA creates a new vector for optimum solution that provides minimum error. These methods provide the best performance when compared with existing benchmarks. Moreover, GA-LSTM is used in a multi-threaded environment which increases the speed of convergence. Here, the multi-core platform is operated for solving one-dimensional GA-based inverse scattering problems. The result shows that GA-LSTM provides better convergence as compared to random approach techniques. For validating the results, Pennsylvania-New Jersey-Maryland Interconnection (PJM) energy consumption data has been used while adopting different performance evaluation metrics

How software size influence productivity and project duration

To make a perfect project plan, the software size of the order from the customer is the most important factor. The biggest challenge for the project manager is to estimate the project end date in the beginning of the project i.e. in project planning phase with realistic accuracy. Apart from other major inputs to estimate the project end date, expected team capability (productivity) and estimated software size are the major inputs that may influence the project end date. Software size is one of the most significant independent metric available in the planning phase and project manager has to estimate the other metrics based on the initial estimated software size. There is no direct relationship available between software size and project duration or software size and team productivity, however, there are industry data published by Quantitative Software Management and ISBSG that shows how these metrics influence each other. In this paper, using the data published by ISBSG and Quantitative Software Management, we try to statistically establish how productivity and project duration are influenced by software size. We have done linear regression analysis by generating the secondary data based on the data published by ISBSG and Quantitative Software Management. Linear regression equation validated with the actual project data and experimental results suggest that that productivity is significantly dependent on software size, however, project duration does not significantly depend on software size but may also be dependent on other metrics like team size, apart from software size

Nanomechanical inhomogeneities in CVA-deposited titanium nitride thin films: Nanoindentation and Finite Element Method Investigations

Refractory metals that can withstand at high temperatures and harsh conditions are of utmost importance for solar-thermal and energy storage applications. Thin films of TiN have been deposited using cathodic vacuum arc deposition (CVA) at relatively low temperatures ~ 300 oC using the substrate bias ~ -60V. The nanomechanical properties of these films were investigated using nanoindentation and the spatial fluctuations were observed. The nanoindentation results were simulated using finite element method (FEM) through Johnson-Cook model. We have found the local nitridation plays an important role on nanomechanical properties of TiN thin films and confirms that the nitrogen deficient regions are ductile with low yield stress and hardening modulus. This study further opens the opportunities of modelling the nanoscale system using FEM analysis

Green Tobacco Sickness: A Review

It is still an unknown fact among many that tobacco harvesters are at a potential at a risk of suffering from “Green Tobacco Sickness (GTS)”, with its prevalence seen mostly among Asian and South American tobacco harvesters. These harvesters working in hot, wet conditions are likely to develop GTS,  as in such climatic conditions, the wetness and high humidity causes nicotine to reside on the surfaces of the leaves, while the high ambient temperature increases skin absorption, thereby increasing plasma nicotine concentrations by 30-45%. Patients suffering from GTS report nausea, vomiting, pallor, dizziness, headaches, increased perspiration, chills, abdominal pain, diarrhea, increased salivation, prostration, weakness, cough with or without expectoration, breathlessness and occasional reduction in blood pressure or heart rate. GTS is self-limiting and of short duration and hence treatment is not always necessary and not often sought by the harvesters. This review educates readers about GTS as well as encourages their participation in making tougher regulations in their respective countries for the control of this disease

Russel’s score and diabetes mellitus type 2 “finding the association”: a cross sectional study from one of the districts in Bihar, India

Background: Diabetes has been on the rise since last decade or so with prevalence rate changing as more research is being done. India is one of the most burdened country from diabetes specially type 2. With changing life style other diseases are also on the rise and evidence is being generated to find tangible association. One such disease is periodontitis. As evidence from the State of Bihar for association between periodontitis and diabetes mellitus is negligible this study was conducted to find the same.Methods: A cross sectional observational study in community settings was done for three months on 200 diabetic willing participants. Data was generated using a semi structured pretested questionnaire. Data analysis was done using SPSS version 22.0 and statistical measures of central tendency like mean, mode, median and standard deviation were used.  For establishing association chi square tests were used. P values<0.05 were considered to be statistically significant.Results: 61.5% of the participants were male while 65.5% were from rural area. The study population was on an average on the older side with a mean age of 52±12.15 and on the bulky size with a BMI of 28.85±4.08. Authors got the average Russel’s score to be 1.1±1.79. The mean blood sugar level for fasting was 146.40±59.99 and post prandial levels was 211.59±82.49. A Russel score category of established periodontal disease and terminal disease was present in 77.5% of participants having an altered fasting blood sugar level. Majority of the males had established periodontal disease and terminal disease. Patients with elevated postprandial blood sugar levels had more established periodontal disease and terminal disease.Conclusions: Oral health is definitely associated with diabetes mellitus type 2 and its other factors like duration, glycaemic control, blood sugar levels etc. Public awareness regarding this is minimal. More research and awareness regarding this will help in keeping the diabetic population healthy

Evolution of the bulk properties, structure, magnetic order, and superconductivity with Ni doping in CaFe2-xNixAs2

Magnetization, susceptibility, specific heat, resistivity, neutron and x-ray diffraction have been used to characterize the properties of single crystalline CaFe2-xNixAs2 as a function of Ni doping for x varying from 0 to 0.1. The combined first-order structural and magnetic phase transitions occur together in the undoped system at 172 K, with a small decrease in the area of the a-b plane along with an abrupt increase in the length of the c-axis in the orthorhombic phase. With increasing x the ordered moment and transition temperature decrease, but the transition remains sharp at modest doping while the area of the a-b plane quickly decreases and then saturates. Warming and cooling data in the resistivity and neutron diffraction indicate hysteresis of ~2 K. At larger doping the transition is more rounded, and decreases to zero for x=0.06. The susceptibility is anisotropic for all values of x. Electrical resistivity for x = 0.053 and 0.06 shows a superconducting transition with an onset of nearly 15 K which is further corroborated by substantial diamagnetic susceptibility. For the fully superconducting sample there is no long range magnetic order and the structure remains tetragonal at all temperature, but there is an anomalous increase in the area of the a-b plane in going to low T. Heat capacity data show that the density of states at the Fermi level increases for x > 0.053 as inferred from the value of Sommerfeld coefficient. The regime of superconductivity is quite restrictive, with a maximum TC of 15 K and an upper critical field Hc2=14 T. Superconductivity disappears in the overdoped region.Comment: 14 pages, 12 figures. Submitted to Phys. Rev.

Genetic architecture and population structure of Oat Landraces (Avena sativa L.) using molecular and morphological descriptors

439-450Oat is grown as winter forage in India. It is a self-pollinated crop with less variability. However, the variation for different morphological traits in oat germplasm may be available at genotypic level. The present study was conducted to find out the genetic diversity among 24 oat landraces using 9 morphological traits and 24 SSR primers. Morphological data observed across the 24 landraces showed wide variation and grouped various landraces into two clusters. GFY and DMY were positively and significantly correlated with most of the traits studied. The molecular analysis using 24 SSR primers resulted amplification of 62 polymorphic alleles with an average of 2.58 alleles per primer. Size of amplified alleles ranged from 70 to 480 bp. Mean polymorphic information content was 0.42 showing moderate level of SSR polymorphism. Cluster analysis based on SSR data differentiated 24 oat landraces into three major clusters. Bayesian model-based STRUCTURE analysis assigned landraces into two clusters and showed the extent of admixture within individuals. Clustering pattern of oat landraces based on SSR marker profiles were different from that of morphometric traits. So, based on the pooled analysis at morphological and molecular level, the landraces IG-02-121, IG-02-129 and IG-02-113 were found superior for morphological traits as well as most distant among all the landraces under study. Hence, these landraces could be used in for future breeding programmes for genetic improvement in oats

Genetic architecture and population structure of Oat Landraces (Avena sativa L.) using molecular and morphological descriptors

Oat is grown as winter forage in India. It is a self-pollinated crop with less variability. However, the variation fordifferent morphological traits in oat germplasm may be available at genotypic level. The present study was conducted tofind out the genetic diversity among 24 oat landraces using 9 morphological traits and 24 SSR primers. Morphological dataobserved across the 24 landraces showed wide variation and grouped various landraces into two clusters. GFY and DMYwere positively and significantly correlated with most of the traits studied. The molecular analysis using 24 SSR primersresulted amplification of 62 polymorphic alleles with an average of 2.58 alleles per primer. Size of amplified alleles rangedfrom 70 to 480 bp. Mean polymorphic information content was 0.42 showing moderate level of SSR polymorphism. Clusteranalysis based on SSR data differentiated 24 oat landraces into three major clusters. Bayesian model-based STRUCTUREanalysis assigned landraces into two clusters and showed the extent of admixture within individuals. Clustering pattern ofoat landraces based on SSR marker profiles were different from that of morphometric traits. So, based on the pooledanalysis at morphological and molecular level, the landraces IG-02-121, IG-02-129 and IG-02-113 were found superior formorphological traits as well as most distant among all the landraces under study. Hence, these landraces could be used in forfuture breeding programmes for genetic improvement in oats

High transport spin polarization in the van der Waals ferromagnet Fe4_4GeTe2_2

The challenging task of scaling-down the size of the power saving electronic devices can be accomplished by exploiting the spin degree of freedom of the conduction electrons in van der Waals (vdW) spintronic architectures built with 2D materials. One of the key components of such a device is a near-room temperature 2D ferromagnet with good metallicity that can generate a highly spin-polarized electronic transport current. However, most of the known 2D ferromagnets have either a very low temperature ordering, poor conductivity, or low spin polarization. In this context, the Fe$_n$GeTe$_2$ (with $n\geq3$) family of ferromagnets stand out due to their near-room temperature ferromagnetism and good metallicity. We have performed spin-resolved Andreev reflection spectroscopy on Fe$_4$GeTe$_2$ ($T_{Curie} \sim$ 273 K) and demonstrated that the ferromagnet is capable of generating a very high transport spin polarization, exceeding 50$\%$. This makes Fe$_4$GeTe$_2$ a strong candidate for application in all-vdW power-saving spintronic devices.Comment: Accepted for publication in Physical Review