Highly Efficient and Water‐Insensitive Self‐Healing Elastomer for Wet and Underwater Electronics

Integrating self‐healing capabilities into soft electronic devices increases their durability and long‐term reliability. Although some advances have been made, the use of self‐healing electronics in wet and/or (under)water environments has proven to be quite challenging, and has not yet been fully realized. Herein, a new highly water insensitive self‐healing elastomer with high stretchability and mechanical strength that can reach 1100% and ≈6.5 MPa, respectively, is reported. The elastomer exhibits a high (>80%) self‐healing efficiency (after ≈ 24 h) in high humidity and/or different (under)water conditions without the assistance of an external physical and/or chemical triggers. Soft electronic devices made from this elastomer are shown to be highly robust and able to recover their electrical properties after damages in both ambient and aqueous conditions. Moreover, once operated in extreme wet or underwater conditions (e.g., salty sea water), the self‐healing capability leads to the elimination of significant electrical leakage that would be caused by structural damages. This highly efficient self‐healing elastomer can help extend the use of soft electronics outside of the laboratory and allow a wide variety of wet and submarine applications.     

Training radial basis function networks using biogeography-based optimizer

Training artificial neural networks is considered as one of the most challenging machine learning problems. This is mainly due to the presence of a large number of solutions and changes in the search space for different datasets. Conventional training techniques mostly suffer from local optima stagnation and degraded convergence, which make them impractical for datasets with many features. The literature shows that stochastic population-based optimization techniques suit this problem better and are reliably alternative because of high local optima avoidance and flexibility. For the first time, this work proposes a new learning mechanism for radial basis function networks based on biogeography-based optimizer as one of the most well-regarded optimizers in the literature. To prove the efficacy of the proposed methodology, it is employed to solve 12 well-known datasets and compared to 11 current training algorithms including gradient-based and stochastic approaches. The paper considers changing the number of neurons and investigating the performance of algorithms on radial basis function networks with different number of parameters as well. A statistical test is also conducted to judge about the significance of the results. The results show that the biogeography-based optimizer trainer is able to substantially outperform the current training algorithms on all datasets in terms of classification accuracy, speed of convergence, and entrapment in local optima. In addition, the comparison of trainers on radial basis function networks with different neurons size reveal that the biogeography-based optimizer trainer is able to train radial basis function networks with different number of structural parameters effectively.

     

Early detection of Alzheimer’s disease based on the state-of-the-art deep learning approach: a comprehensive survey

Multimedia Tools and Applications - Alzheimer’s disease (AD) is a form of brain disorder that causes functions’ loss in a person’s daily activity. Due to the tremendous progress...     

Effect of Nitrogen Alloying on Sulphur Behaviour during ESR of AISI M41 Steel

In this work, the effects of nitrogen alloying, physical properties and chemical composition of slag used in electro‐slag refining (ESR) on phosphorus and sulphur contents of AISI M41 high speed steel have been studied. The experiments were conducted with two high speed steel grades which were melted in an induction furnace (IF). The first grade is the standard AISI M41 high‐speed steel and the second one is nitrogen alloyed M41 (denoted M41N). The produced ingots were ESR remelted under three grades of calcium fluoride based slag. Results showed that the ESR process has no effect on the phosphorus content in steel but it is a good tool in removing sulphur. This study shows that a high desulphurization rate can be achieved by ESR process by optimizing slag properties where the viscosity and oxidation reactions play an important role in sulphur removal. Nitrogen alloying was found to retard sulphur removal.     

Characterizing multi‐hop localization for Internet of things

Deployments over large geographical areas in the Internet of Things (IoT) pose a major challenge for single‐hop localization techniques, giving rise to applications of multi‐hop localizations. And while many proposals have been made on implementations for multi‐hop localization, a close understanding of its characteristics is yet to be established. Such an understanding is necessary, and is inevitable in extending the reliability of location based services in IoT. In this paper, we study the characteristics of multi‐hop localization and propose a new solution to enhance the performance of multi‐hop localization techniques. We first examine popular assumptions made in simulating multi‐hop localization techniques, and offer rectifications facilitating more realistic simulation models. We identify the introduced errors to follow the Gaussian distribution, and the estimated distance follows the Rayleigh distribution. We next use our simulation model to characterize the effect of the number of hops on localization in both dense and sparse deployments. We find that, contrary to common belief, it is better to use long hops in sparse deployments, while short hops are better in dense deployments – despite the traffic overhead. Finally, we propose a new solution that decreases and manages the overhead generated during the localization process. Copyright © 2016 John Wiley & Sons, Ltd.     

Physical properties of electron beam irradiated poly(vinyl butyral) composites with carbamate,imidazole, and tetrazolium dye

Films of poly(vinyl butyral) composites with triphenyl tetrazolium chloride dye (TTC) and the antioxidant nickel dibutyl dithiocarbamate (NBC) or 2‐mercaptobenzimidazole (MBI) were prepared by the solution casting method using butyl alcohol as a solvent. The effect of various doses of electron beam irradiation (50–200 kGy) on color response, thermal, and mechanical properties were investigated. The color measurements showed that the films of the different composites possessed a high sensitivity to electron beam irradiation, in which the nearly colorless films were changed to deep red color, which can be easily detected by visual observations. In addition, the change in color depends on irradiation dose and the contents of the TTC dye. Moreover, the presence of the antioxidants NBC or MBI has no effect on the development of color. However, PVB/TTC and PVB/TTC/MBI composites showed high regular change in color as a function of irradiation dose. The thermogravimetric analysis used to study the thermal stability indicated that PVB/TTC composites either before or after electron beam irradiation are thermally more stable than neat PVB polymer. The presence of the antioxidants NBC or MBI offered protection to PVB/TTC composites against decomposition or oxidative degradation resulted from irradiation. Blending unirradiated PVB with TTC dye, mixture of TTC and NBC, and mixture of TTC and MBI reduced the tensile strength by 4, 20, and 17% upon blending, respectively. However, the reduction in tensile strength of the entire composite films (～7% based on the initial value) upon exposure to a dose of 50 kGy is acceptable for practical applications. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 101: 4358–4365, 2006     

A multi-variate heart disease optimization and recognition framework

Neural Computing and Applications - Cardiovascular diseases (CVD) are the most widely spread diseases all over the world among the common chronic diseases. CVD represents one of the main causes of...