Sustainable reference methodology for energy end-use efficiency data in the EU

One of the most important goals of the European energy policy involves the implementation of energy-efficiency measures in large scale so as to promote sustainable development in the European Union (EU) level. The multidimensional character of energy end-use efficiency (EEE) necessitates the collection of a number of related data, apart from the performance and system parameters data, such as socio-economic (e.g., employment, turnover) and Research and Development (R&D) expenditures. Moreover, improved co-ordination of EEE programmes and policies of the community and the member states so as a unified acceptable system to be developed for the monitoring of the EEE data with respect to the existing targets is of significant importance. Even though data-gathering efforts have been implemented, a lot of fragmented data and deduced findings are currently available, which sometimes lack consistency and verification. In this context, the main aim of the paper is to present a sustainable reference methodology for validating EEE data in EU, through the review of existing approaches and methods, defining of most relevant inconsistencies and gaps and provision of recommendations for improvements in EEE data aggregation and statistical interpretation, taking into consideration the related analysis of statisticians, energy technology experts and energy socio-economists.     

Optimal determination of production run and initial settings of process parameters for a deteriorating process

This paper presents a generalized model for the optimal determination of a production run and the initial settings of the process mean and process variance for a deteriorating production process. It is assumed that the process deteriorates due to tool wear-out. The probability that the process deterioration starts at a random point in time follows an exponential distribution. Quality loss from the target values is measured using Taguchi’s quadratic loss function. The time dependent maintenance cost and the salvage value of the equipment are included. The expressions for determining the optimal process mean and process variance are developed. Numerical examples are provided to demonstrate the application of the proposed model.     

A Symbolic Out-of-Core Solution Method for Markov Models

Despite considerable effort, the state-space explosion problem remains an issue in the analysis of Markov models. Given structure, symbolic representations can result in very compact encoding of the models. However, a major obstacle for symbolic methods is the need to store the probability vector(s) explicitly in main memory. In this paper, we present a novel algorithm which relaxes these memory limitations by storing the probability vector on disk. The algorithm has been implemented using an MTBDD-based data structure to store the matrix and an array to store the vector. We report on experimental results for two benchmark models, a Kanban manufacturing system and a flexible manufacturing system, with models as large as 133 million states.     

An overview of protection of privacy in multibiometrics

Biometric authentication systems (BASs) have various advantages over traditional authentication systems such as password based systems. In BASs, information related to individuals’ physical and psychological characteristics is employed for authentication. The leakage of such information will breach individuals’ privacy severely. To simultaneously protect privacy and achieve higher authentication accuracy, privacy preserving multibiometric authentication systems (PPMASs) have been developed. This paper provides a comprehensive review of PPMASs. Specifically, we outline the requirements of PPMASs and discuss the state-of-the-art PPMASs. We also discuss PPMASs in the context of mobile and cloud based applications. Furthermore, we show the drawbacks of the existing PPMASs and point out the future research directions of PPMASs.     

Application of SNPs to improve yield of Pisum sativum L. (pea)

Nanotechnology opens an enormous scope of novel application in the fields of biotechnology and agricultural industries, because nanoparticles (NPs) have unique physicochemical properties, i.e. high surface area, high reactivity, tunable pore size and particle morphology. Present study was carried out to determine the role of silver NPs (SNPs) to improve yield of Pisum sativum L. SNPs (10–100 nm) were synthesised by green method using extract of Berberis lycium Royle. Pea seeds were soaked and seedling were foliage sprayed by 0, 30, 60 and 90 ppm SNPs. The experiment was arranged as split–split plot randomised complete block design with three replicates. The application of SNPs enhanced significantly number of seeds pod⁻¹, number of pods plant⁻¹, hundred seed weight, biological yield and green pod yield over control. The highest yield was found when 60 ppm SNPs were applied. However, exposure to 90 ppm SNPs, the yield of the pea decreased significantly as compared with 30 and 60 ppm. This research shows that SNPs have definite ability to improve growth and yield of crops. Nevertheless, a comprehensive experimentation is needed to establish the most appropriate concentration, size and mode of application of SNPs for higher growth and maximum yield of pea.Inspec keywords: nanobiotechnology, silver, nanoparticles, cropsOther keywords: Ag, foliage spraying, crop yield, crop growth, green pod yield, biological yield, seed weight, pod plant number, seed pod number, pea seeds, Berberis lycium Royle extract, green method, nanotechnology, Pisum sativum L, silver nanoparticles     

Electronic structure,elastic and magnetic properties of the binary intermetallics RFe2 (R = Eu,Gd and Tb)

Journal of Computational Electronics - Laves-phase (C15) binary intermetallics RFe2 (R?=?Eu, Gd and Tb) are studied using various potentials in the domain of density functional theory...     

Smart COVID-3D-SCNN: A Novel Method to Classify X-ray Images of COVID-19

The outbreak of the novel coronavirus has spread worldwide, and millions of people are being infected. Image or detection classification is one of the first application areas of deep learning, which has a significant contribution to medical image analysis. In classification detection, one or more images (detection) are usually used as input, and diagnostic variables (such as whether there is a disease) are used as output. The novel coronavirus has spread across the world, infecting millions of people. Early-stage detection of critical cases of COVID-19 is essential. X-ray scans are used in clinical studies to diagnose COVID-19 and Pneumonia early. For extracting the discriminative features through these modalities, deep convolutional neural networks (CNNs) are used. A siamese convolutional neural network model (COVID-3D-SCNN) is proposed in this study for the automated detection of COVID-19 by utilizing X-ray scans. To extract the useful features, we used three consecutive models working in parallel in the proposed approach. We acquired 575 COVID-19, 1200 non-COVID, and 1400 pneumonia images, which are publicly available. In our framework, augmentation is used to enlarge the dataset. The findings suggest that the proposed method outperforms the results of comparative studies in terms of accuracy 96.70%, specificity 95.55%, and sensitivity 96.62% over (COVID-19 vs. non-COVID19 vs. Pneumonia).