Simulated annealing algorithm for combined economic and emission power dispatch using max/max price penalty factor

A new recurrent neural model for crack growth process of aluminium alloy is developed in this work. It is shown that a recurrent neural network with the feedback loops at the output layer is constructed to model the dynamic relationship between the crack growth and cyclic stress excitations of aluminium alloy. The output feedback loops in the neural model play the role of capturing the fine changes of crack growth dynamics. The Extreme Learning Machine is then used to uniformly randomly assign the input weights in a proper range and globally optimize both the output weights and feedback parameters, to ensure that the dynamics of crack growth under variable-amplitude loading can be accurately modeled. The simulation results with the averaged experimental data of the 2024-T351 aluminium alloy show that the excellent modeling and prediction performance of the recurrent neural model can be achieved for fatigue crack growth of aluminium alloys.     

Photo‐induced hydrogen on iron hexagonal mesoporous silica (Fe‐HMS) photo‐catalyst

The iron hexagonal mesoporous silica (Fe‐HMS)‐n photocatalyst, where n is the molar ratio Si/Fe in the precursor gel (=50), has been successfully synthesized at an ambient temperature using hexadecylamine as template agent. The material was characterized by X‐ray diffraction, N₂ adsorption measurement Brunauer, Emmet, Taller (BET) and Barrett–Joyner–Halenda theory, UV–Vis spectroscopy, high‐resolution transmission electron microscopy and electron spin resonance. The electrical conductivity follows an Arrhenius‐type law with activation energy of 0.04 eV. Fe₂O₃ is uniformly dispersed on the HMS surface; it works synergistically to make Fe‐HMS photoelectrochemically active. The flat band potential (?0.75 V_SCE) is higher than the potential of hydrogen generation (?0.50 V_SCE at pH~7), and the material shows high efficiency for hydrogen evolution upon visible light. The photoactivity in Na₂SO₄ and Na₂SO₃ (0.1 M) solution was found to be 136 and 175 µmol g^‐1 min^‐1, respectively under full light (29 mW cm^‐2). The tendency to saturation, observed only in SO₃^2‐ electrolyte, is ascribed to the competitive reduction of the end product, namely S₂O₆^2‐ with the water reduction. Copyright © 2011 John Wiley & Sons, Ltd.     

Selective access to novel aryl alkyl disulfonates via photosulfochlorination process: An eco-benign approach to a highly active class of surfactants

In this work we report the synthesis of a new family of surfactants based on aryl alkyl disulfonates; the latter compounds were derived from a mixture of linear alkylbenzene with the resulting disulfonate groups residing on the linear alkyl side-chain rather than the usual aromatic ring structure. The Reed sulfochlorination reaction was successfully utilized to selectively promote the formation of disulfonates on the alkyl group. A number of analytical methods in the form of liquid chromatography–mass spectrometry (LC–MS), gas chromatography–mass spectrometry (GC–MS), Fourier-transform infrared spectroscopy (FTIR) and nuclear magnetic resonance (NMR) were used to characterize the resulting compounds and intermediates. The critical micelle concentration, the surface tension at the critical micelle concentration, the surface excess concentration at surface tension, and the area per molecule were measured at different concentrations and temperatures. Thermodynamic parameters and Krafft temperature were also determined. The obtained results were compared to those of commercial n-dodecylbenzene sulfonates and linear alkylbenzene sulfonates surfactants, showing that these new synthesized surfactants present good surface properties.     

Ab initio Insight of the Electronic,Structural, Mechanical and Optical Properties of X $$_3$$ 3 P $$_2$$ 2 (X= Mg,Ca) from GGA and Hybrid Functional (HSE06)

Journal of Superconductivity and Novel Magnetism - In this paper, the structural, mechanical and optoelectronic properties of X $$_3$$ P $$_2$$ (X= Mg, Ca) have been investigated by using the...     

Transfer Learning for Chest X-rays Diagnosis Using Dipper ThroatedAlgorithm

Most children and elderly people worldwide die from pneumonia, which is a contagious illness that causes lung ulcers. For diagnosing pneumonia from chest X-ray images, many deep learning models have been put forth. The goal of this research is to develop an effective and strong approach for detecting and categorizing pneumonia cases. By varying the deep learning approach, three pre-trained models, GoogLeNet, ResNet18, and DenseNet121, are employed in this research to extract the main features of pneumonia and normal cases. In addition, the binary dipper throated optimization (DTO) algorithm is utilized to select the most significant features, which are then fed to the K-nearest neighbor (KNN) classifier for getting the final classification decision. To guarantee the best performance of KNN, its main parameter (K) is optimized using the continuous DTO algorithm. To test the proposed approach, six evaluation metrics were employed namely, positive and negative predictive values, accuracy, specificity, sensitivity, and F1-score. Moreover, the proposed approach is compared with other traditional approaches, and the findings confirmed the superiority of the proposed approach in terms of all the evaluation metrics. The minimum accuracy achieved by the proposed approach is (98.5%), and the maximum accuracy is (99.8%) when different test cases are included in the evaluation experiments.     

Evolutionary Algorithsm with Machine Learning Based Epileptic Seizure Detection Model

Machine learning (ML) becomes a familiar topic among decision makers in several domains, particularly healthcare. Effective design of ML models assists to detect and classify the occurrence of diseases using healthcare data. Besides, the parameter tuning of the ML models is also essential to accomplish effective classification results. This article develops a novel red colobuses monkey optimization with kernel extreme learning machine (RCMO-KELM) technique for epileptic seizure detection and classification. The proposed RCMO-KELM technique initially extracts the chaotic, time, and frequency domain features in the actual EEG signals. In addition, the min-max normalization approach is employed for the pre-processing of the EEG signals. Moreover, KELM model is used for the detection and classification of epileptic seizures utilizing EEG signal. Furthermore, the RCMO technique was utilized for the optimal parameter tuning of the KELM technique in such a way that the overall detection outcomes can be considerably enhanced. The experimental result analysis of the RCMO-KELM technique has been examined using benchmark dataset and the results are inspected under several aspects. The comparative result analysis reported the better outcomes of the RCMO-KELM technique over the recent approaches with the of 0.956.     

The Effects of Physical Layer on the Routing Wireless Protocol

Mobile ad hoc networks (MANETs) are characterized by multiple entities, a frequently changing network topology and the need for efficient dynamic routing protocols. In MANETs, nodes are usually powered by batteries. Power control is tightly coupled with both the physical and medium access layers (MACs). However, if we increase the transmission power, at the same time we increase the interference to other nodes which diminish the transport capacity of wireless systems. Thus, the routing protocols based on hop count metric suffer from performance degradation when they operate over MANET. Routing in ad hoc wireless networks is not only a problem of finding a route with shortest length, but it is also a problem of finding a stable and good quality communication route in order to avoid any unnecessary packet loss. Cross-layer design of ad hoc wireless networks has been receiving increasing attention recently. Part of these researches suggests that routing should take into account physical layer characteristics. The goal of this paper is to improve the routing reliability in MANET and to reduce power consumption through cross-layer approach among physical, MAC and network layers. The proposed cross-layer approach is based on signal to interference plus noise ratio (SINR) and received signal strength indication (RSSI) coming from the physical layer. This solution performs in one hand the ad hoc on-demand distance vector routing protocol by choosing reliable routes with less interferences using SINR metric and in another hand; it permits to reduce the power transmission when sending the data packets by using RSSI metric.     

Meta-heuristics for Feature Selection and Classification in Diagnostic BreastCancer

One of the most common kinds of cancer is breast cancer. The early detection of it may help lower its overall rates of mortality. In this paper, we robustly propose a novel approach for detecting and classifying breast cancer regions in thermal images. The proposed approach starts with data preprocessing the input images and segmenting the significant regions of interest. In addition, to properly train the machine learning models, data augmentation is applied to increase the number of segmented regions using various scaling ratios. On the other hand, to extract the relevant features from the breast cancer cases, a set of deep neural networks (VGGNet, ResNet-50, AlexNet, and GoogLeNet) are employed. The resulting set of features is processed using the binary dipper throated algorithm to select the most effective features that can realize high classification accuracy. The selected features are used to train a neural network to finally classify the thermal images of breast cancer. To achieve accurate classification, the parameters of the employed neural network are optimized using the continuous dipper throated optimization algorithm. Experimental results show the effectiveness of the proposed approach in classifying the breast cancer cases when compared to other recent approaches in the literature. Moreover, several experiments were conducted to compare the performance of the proposed approach with the other approaches. The results of these experiments emphasized the superiority of the proposed approach.