New Method for Prediction of the Gurney Energy of High Explosives

An empirical new correlation is used to calculate the Gurney velocity as an important parameter for the estimation of explosive energy output without considering the heat contents of explosives and detonation products. The number of nitrogen molecules as well as the ratios of oxygen to carbon and hydrogen to oxygen are the fundamental factors in the new method. The new correlation may be applied to any C_aH_bN_cO_d explosive, in which b is nonzero, at any loading density. The calculated Gurney velocity for both pure and explosive formulations shows good agreement with respect to measured values. Moreover, there is no need to use any assumed decomposition reaction.     

Fabrication and characterization of electrospun Plantago major seed mucilage/PVA nanofibers

Electrospinning is a well-known technique for producing nanofibers using synthetic and natural polymers like mucilage. In this study, Plantago major Mucilage (PMM) was blended with polyvinyl alcohol (PVA) as a nontoxic adding agent, in order to produce electrospun nanofiber. Electrospinning parameters (voltage, tip-to-collector distance, feed rate, and PMM/PVA ratio) were optimized and solution properties were analyzed. The morphology of nanofibers was investigated using scanning electron microscopy (SEM), Fourier transform infrared (FTIR), X-ray diffraction (XRD), and Brunauer–Emmett–Teller (BET). Mechanical strength of nanofibers was determined, and cell viability on nanofibers was discussed by MTT assay. The results of SEM indicated that the PMM/PVA (50/50) nanofibers obtained with average diameter of 250 nm. Viscosity, electrical conductivity, and surface tension of PMM/PVA solution were 550 Cp, 575 μS/cm, and 47.044 mN/m, respectively. FTIR and XRD results verified the exiting PMM in produced nanofibers and no chemical reaction between PMM and PVA. Improvement in mechanical strength and cell viability of nanofibers by adding PMM to PVA nanofibers indicated the potential application of PMM-based nanofibers for medical and food industries. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47852.     

Microstructure and Optical Bandgap of Cobalt Selenide Nanofilms

Semiconductors - The purpose of this research is to explore the properties of CoSe nanostructured thin films on glass substrates prepared by a chemical solution deposition method. Special attention...     

Supply chain risk management and artificial intelligence: state of the art and future research directions

Supply chain risk management (SCRM) encompasses a wide variety of strategies aiming to identify, assess, mitigate and monitor unexpected events or conditions which might have an impact, mostly adverse, on any part of a supply chain. SCRM strategies often depend on rapid and adaptive decision-making based on potentially large, multidimensional data sources. These characteristics make SCRM a suitable application area for artificial intelligence (AI) techniques. The aim of this paper is to provide a comprehensive review of supply chain literature that addresses problems relevant to SCRM using approaches that fall within the AI spectrum. To that end, an investigation is conducted on the various definitions and classifications of supply chain risk and related notions such as uncertainty. Then, a mapping study is performed to categorise existing literature according to the AI methodology used, ranging from mathematical programming to Machine Learning and Big Data Analytics, and the specific SCRM task they address (identification, assessment or response). Finally, a comprehensive analysis of each category is provided to identify missing aspects and unexplored areas and propose directions for future research at the confluence of SCRM and AI.     

Free convection of water–Fe3O4 nanofluid in an inclined cavity subjected to a magnetic field: CFD modeling,sensitivity analysis

Modeling of natural convection heat transfer in an inclined C-shape cavity is studied in this paper. The enclosure is filled with H₂O-Fe₃O₄ nanofluid under the effect of magnetic field. The operating range of parameters used in this study were Hartmann number (Ha) from 0 to 80, Rayleigh number (Ra) from 1E2 to 1E6, nanoparticles volume fraction (φ) from 0 to 0.1, inclination angle (α) from 0 to 90 deg, and aspect ratio (AR) from 0.2 to 0.8. The employed model is solved using CFD tools based on the finite element method. The comparison with reference experimental data indicated the accuracy and generalization capability of the model. In addition, a novel correlation and an artificial neural network (ANN) model were productively developed for predicting Nu number as a function of aforementioned independent variables. The influence of the model parameters on the Nu number is precisely presented and discussed. It is shown that Ra number and aspect ratio have more impact on Nu than the other variables.     

Semantic Information Extraction from Multi-Corpora Using Deep Learning

Information extraction plays a vital role in natural language processing, to extract named entities and events from unstructured data. Due to the exponential data growth in the agricultural sector, extracting significant information has become a challenging task. Though existing deep learning-based techniques have been applied in smart agriculture for crop cultivation, crop disease detection, weed removal, and yield production, still it is difficult to find the semantics between extracted information due to unswerving effects of weather, soil, pest, and fertilizer data. This paper consists of two parts. An initial phase, which proposes a data preprocessing technique for removal of ambiguity in input corpora, and the second phase proposes a novel deep learning-based long short-term memory with rectification in Adam optimizer and multilayer perceptron to find agricultural-based named entity recognition, events, and relations between them. The proposed algorithm has been trained and tested on four input corpora i.e., agriculture, weather, soil, and pest & fertilizers. The experimental results have been compared with existing techniques and it was observed that the proposed algorithm outperforms Weighted-SOM, LSTM+RAO, PLR-DBN, KNN, and Naïve Bayes on standard parameters like accuracy, sensitivity, and specificity.     

Cognitive radio networks for green wireless communications: an overview

As power consumption results in greenhouse gas emissions and energy costs for operators, analyzing power consumption in wireless networks and portable devices is of crutial importance. Due to environmental effects resulted from energy generation and exploitation as well as the cost of surging energy, energy-aware wireless systems attract unprecedented attention. Cognitive Radio (CR) is one of the optimal solutions that allows for energy savings on both the networks and devices. Thus, cognitive radio contributes to increase spectral and energy efficiency as well as reduction in power consumption. In addition, energy consumption of the CR technologies as intelligent technology should be considered to realize the green networks objective. In this article, we look into energy efficiency of the cognitive wireless network paradigms. Moreover, energy efficiency analysis and modelling in these systems are specifically focused on achieving green communications objectives. However, CRs by altering all elements of wireless data communications are considered in this paper, and the energy-efficient operation and energy efficiency enabler perspectives of CRs are also analyzed.

     

Optimal network restructure via improved whale optimization approach

Latterly, reduction of power loss in distribution system is the objective of many researches due to its impact on total costs and voltage profiles. It can be handled by optimal restructure of radial distribution system (RDS). This article introduces an innovative approach to restructure of RDS by electing the optimal switches combination subject to the system operating constraints, which is improved whale optimization approach (IWOA). The suggested approach combines exploitation of WOA with exploration of differential evolution (DE), and thus, it supplies a promising candidate solution. The suggested approach is tested on IEEE 33 and 69 bus RDS. The superiority of the suggested approach compared with other well‐known approaches is verified through simulation results by observation of total losses, cost, and saving. Also, the impact of alterable loading is investigated to prove the effectiveness of the suggested IWOA.     

An analytical hierarchy process-based approach to solve the multi-objective multiple traveling salesman problem

We consider the problem of assigning a team of autonomous robots to target locations in the context of a disaster management scenario while optimizing several objectives. This problem can be cast as a multiple traveling salesman problem, where several robots must visit designated locations. This paper provides an analytical hierarchy process (AHP)-based approach to this problem, while minimizing three objectives: the total traveled distance, the maximum tour, and the deviation rate. The AHP-based approach involves three phases. In the first phase, we use the AHP process to define a specific weight for each objective. The second phase consists in allocating the available targets, wherein we define and use three approaches: market-based, robot and task mean allocation-based, and balanced-based. Finally, the third phase involves the improvement in the solutions generated in the second phase. To validate the efficiency of the AHP-based approach, we used MATLAB to conduct an extensive comparative simulation study with other algorithms reported in the literature. The performance comparison of the three approaches shows a gap between the market-based approach and the other two approaches of up to 30%. Further, the results show that the AHP-based approach provides a better balance between the objectives, as compared to other state-of-the-art approaches. In particular, we observed an improvement in the total traveled distance when using the AHP-based approach in comparison with the distance traveled when using a clustering-based approach.