Oil fouling in double‐pipe heat exchanger under liquid‐liquid dispersion and the influence of copper oxide nanofluid

Dispersions of oil in water are encountered in a variety of industrial processes leading to a reduction in the performance of the heat exchangers when thermally treating such two phase fluids. This reduction is mainly due to changes in the thermal and hydrodynamical behavior of the two phase fluid. In the present work, an experimental investigation was performed to study the effects of light oil fouling on the heat transfer coefficient in a double‐pipe heat exchanger under turbulent flow conditions. The effects of different operating conditions on the fouling rate were investigated including: hot fluid Reynolds number (the dispersion), cold fluid Reynolds number, and time. The oil fouling rate was analyzed by determining the growth of fouling resistance with time and through pressure drop measurements. The influence of copper oxide (CuO) nanofluid on the fouling rate in the dispersion was also determined. It was found that the presence of dispersed oil causes a reduction in heat transfer coefficient by percentages depending on the Reynolds number of both cold and hot fluids and the concentration of oil. In addition, the time history of fouling resistance exhibited different trends with the flow rates of both fluids and its trend was influenced appreciably by the presence of CuO nanofluid.     

Statistical Analysis with Dingo Optimizer Enabled Routing for Wireless Sensor Networks

Security is a vital parameter to conserve energy in wireless sensor networks (WSN). Trust management in the WSN is a crucial process as trust is utilized when collaboration is important for accomplishing trustworthy data transmission. But the available routing techniques do not involve security in the design of routing techniques. This study develops a novel statistical analysis with dingo optimizer enabled reliable routing scheme (SADO-RRS) for WSN. The proposed SADO-RRS technique aims to detect the existence of attacks and optimal routes in WSN. In addition, the presented SADO-RRS technique derives a new statistics based linear discriminant analysis (LDA) for attack detection, Moreover, a trust based dingo optimizer (TBDO) algorithm is applied for optimal route selection in the WSN and accomplishes secure data transmission in WSN. Besides, the TBDO algorithm involves the derivation of the fitness function involving different input variables of WSN. For demonstrating the enhanced outcomes of the SADO-RRS technique, a wide range of simulations was carried out and the outcomes demonstrated the enhanced outcomes of the SADO-RRS technique.     

Novel trust-aware intrusion detection and prevention system for 5G MANET–Cloud

International Journal of Information Security - 5G-based mobile ad hoc networks with cloud are a new paradigm that combines several real-world applications. Routing and security are current issues...     

Detection of low-abundance protein phosphorylation by selective (18)o labeling and mass spectrometry

Reversible phosphorylation regulates the majority of intracellular networking and pathways. The study of this widely explored post-translational modification is usually challenged by low stoichiometric levels of modification. Many approaches have been developed to overcome this problem and to achieve rigorous characterization of protein phosphorylation. We describe a method for enhanced detection of low-abundance protein phosphorylation that uses selective introduction of (18)O label into phosphorylation sites with H(2)(18)O and mass spectrometric detection. The method was applied to introduce (18)O label into bacterially expressed Aurora A kinase phosphorylation sites and resulted in the representation of phosphorylated peptides as doublets or triplets according to the number of phosphate groups. A total of 28 phosphopeptides were observed by this method.     

Suggestion Mining from Opinionated Text of Big Social Media Data

Social media data are rapidly increasing and constitute a source of user opinions and tips on a wide range of products and services. The increasing availability of such big data on biased reviews and blogs creates challenges for customers and businesses in reviewing all content in their decision-making process. To overcome this challenge, extracting suggestions from opinionated text is a possible solution. In this study, the characteristics of suggestions are analyzed and a suggestion mining extraction process is presented for classifying suggestive sentences from online customers’ reviews. A classification using a word-embedding approach is used via the XGBoost classifier. The two datasets used in this experiment relate to online hotel reviews and Microsoft Windows App Studio discussion reviews. F1, precision, recall, and accuracy scores are calculated. The results demonstrated that the XGBoost classifier outperforms—with an accuracy of more than 80%. Moreover, the results revealed that suggestion keywords and phrases are the predominant features for suggestion extraction. Thus, this study contributes to knowledge and practice by comparing feature extraction classifiers and identifying XGBoost as a better suggestion mining process for identifying online reviews.     

Structural,morphological and optoelectronic properties of porous silicon combined alumina coating film deposited by PLD

Pulsed laser deposition of Al₂O₃ onto porous silicon (PS) is shown to provide excellent passivation of multi-crystalline silicon surfaces intended for solar cells applications. Surface passivation and reflectivity are investigated before and after the deposition of various nominal thicknesses of Al₂O₃ ranging from isolated nanoparticles to ~80 nm-thick films. The level of surface passivation is determined by techniques based on photoconductance and FTIR. As a result, the effective minority carrier lifetime increase from 1 to 130 μs at a minority carrier density (Δn) of 1?×?10¹³ cm^?3. However, passivation scheme provide a significant decrease in the reflectivity; it’s reduced from 28% to about 5% after Al₂O₃/PS coating.     

Modified Bat Algorithm for Optimal VM's in Cloud Computing

All task scheduling applications need to ensure that resources are optimally used, performance is enhanced, and costs are minimized. The purpose of this paper is to discuss how to Fitness Calculate Values (FCVs) to provide application software with a reliable solution during the initial stages of load balancing. The cloud computing environment is the subject of this study. It consists of both physical and logical components (most notably cloud infrastructure and cloud storage) (in particular cloud services and cloud platforms). This intricate structure is interconnected to provide services to users and improve the overall system's performance. This case study is one of the most important segments of cloud computing, i.e., Load Balancing. This paper aims to introduce a new approach to balance the load among Virtual Machines (VM's) of the cloud computing environment. The proposed method led to the proposal and implementation of an algorithm inspired by the Bat Algorithm (BA). This proposed Modified Bat Algorithm (MBA) allows balancing the load among virtual machines. The proposed algorithm works in two variants: MBA with Overloaded Optimal Virtual Machine (MBA-OOVM) and Modified Bat Algorithm with Balanced Virtual Machine (MBA-BVM). MBA generates cost-effective solutions and the strengths of MBA are finally validated by comparing it with Bat Algorithm.