Multiple architecture system for wind speed prediction

A new approach based on multiple architecture system (MAS) for the prediction of wind speed is proposed. The motivation behind the proposed approach is to combine the complementary predictive powers of multiple models in order to improve the performance of the prediction process. The proposed MAS can be implemented by associating the predictions obtained from the different regression algorithms (MLR, MLP, RBF and SVM) making up the ensemble by three fusion strategies (simple, weighted and non-linear). The efficiency of the proposed approach has been assessed on a real data set recorded from seven locations in Algeria during a period of 10 years. The experimental results point out that the proposed MAS approach is capable of improving the precision of the wind speed prediction compared to the traditional prediction methods.     

Advanced Characterization of Precipitation and Microstructure Heterogeneity in X70 Steel

Metallurgical and Materials Transactions A - The spatial distribution of precipitates and microstructure heterogeneity in a vacuum induction melted X70 steel rolled at a pilot-scale facility were...     

Interfacial Interactions in Particle-Induced Abnormal Grain Growth

The presence of secondary particles to polycrystalline alloys results in kinetic stabilization of the grain boundaries, which maintains desirable fine microstructures. In some instances, secondary particles trigger abnormal grain growth. The mechanisms influencing abnormal grain growth are still a subject of conjecture. As dispersed fine particles can contribute to abnormal grain growth, it is necessary to clarify the governing mechanism by which this occurs. The current work employs a multiphase field modeling approach to shed light onto abnormal grain growth. Particular attention is placed on understanding the role of grain boundary–particle interactions on abnormal grain growth. The results show that, in the presence of particles, normal grain growth occurs until a pinned state is achieved. In the pinned state, some grains overcome the pinning pressure exerted by some particles by piercing through the particles, which results in abnormal grain growth. The piercing events appear to be entirely random and not related to the size of the interacting particles. None-the-less, a bimodal particle size distribution is observed to lead to abnormal grain growth. A pinning parameter is introduced as a metric to identify the transition from normal to abnormal grain growth.     

A green approach for modification and functionalization of wool fabric using bio- and nano-technologies

In the present work, we propose a green and sustainable strategy for eco-friendly surface modification of wool structure using biosynthesized kerationlytic proteases, from C4-ITA-EGY, Streptomyces harbinensis S11-ITA-EGY and Streptomyces carpaticus S33-ITA-EGY, followed by subsequent environmentally sound functionalization of the bio-treated substrates using ZnONPs, ZrO₂NPs, ascorbic acid and vanillin, individually, to provide durable antibacterial as well as UV-protection properties. Both surface modification changes and the extent of functionalization of the final products were characterized by SEM, EDX, antibacterial efficacy, UV-blocking ability, loss in weight, nitrogen content and durability to washing analysis. The obtained data reveal that the developed green wool fabrics exhibit outstanding durable antibacterial activity and UV-blocking ability for fabricating multi-functional textile products that can be utilized in a wide range of sustainable protective textiles, irrespective of the used post-finishing formulation ingredients. The results also show that both modification and functionalization processes are governed by the type of enzyme and kind of active material respectively. Moreover, the biosynthesized kerationlytic proteases could be accessibly used to remove protein-based stains like blood and egg.

Graphical abstract

     

Multi‐agent‐based sharing power economy for a smart community

In this paper, a multi‐agent‐based locally administrated power distribution hub (PDH) for social welfare is proposed that optimizes energy consumption, allocation, and management of battery energy storage systems (ESSs) for a smart community. Initially, formulation regarding optimum selection of a power storage system for a home (in terms of storage capacity) is presented. Afterwards, the concept of sharing economy is inducted in the community by demonstrating PDH. PDH is composed of multiple small‐scale battery ESSs (each owned by community users), which are connected together to form a unified‐ESS. Proposed PDH offers a localized switching mechanism that takes decision of whether to buy electricity from utility or use unified‐ESS. This decision is based on the price of electricity at ‘time of use’ and ‘state of charge’ of unified‐ESS. In response to power use or share, electricity bills are created for individual smart homes by incrementing or decrementing respective submeters. There is no buying or selling of power from PDH; there is power sharing with the concept of ‘no profit, no loss’. The objective of the proposed PDH is to limit the purchase of electricity on ‘high priced’ hours from the utility. This not only benefits the utility at crucial hours but also provides effective use of power at the demand side. The proposed multi‐agent system depicts the concept of sharing power economy within a community. Finally, the proposed model is analyzed analytically, considering on‐peak, off‐peak, and mid‐level (mid‐peak) prices of a real‐time price signal during 24 h of a day. Results clearly show vital financial benefits of ‘sharing power economy’ for end users and efficient use of power within the smart community. Copyright © 2017 John Wiley & Sons, Ltd.     

A bigraphical model for specifying cloud-based elastic systems and their behaviour

The few recent years have witnessed the appearance of a new kind of self-adaptive systems called cloud based-elastic systems. These systems are particularly appealing for their ability to maintain a decent quality of service and reduce a system’s operating cost at the same time. They achieve this by dynamically adjusting resources allocation in terms of elasticity. Meanwhile, complexity of structural and behavioural aspects related to cloud-based elastic systems increase the difficulty of designing and developing such systems. In this paper, we address this challenge by proposing a formal approach based on bigraphical reactive systems for modelling both structural and behavioural aspects of cloud-based elastic systems. In particular, we represent their behaviour in terms of client/application interactions and elasticity methods at different levels using bigraphical reaction rules. The feasibility of the proposed approach is illustrated through a motivating example running on the top of an Amazon Elastic Compute Cloud (EC2) infrastructure.