Search results for: neural substrates

Authors: Yasaman Mohammadi

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Emotion regulation is a complex process that allows individuals to manage and modulate their emotional responses in order to adaptively respond to environmental demands. As individuals age, emotion regulation abilities may decline, leading to an increased vulnerability to mood disorders and other negative health outcomes. Advances in neuroimaging techniques have greatly enhanced our understanding of the neural substrates underlying emotion regulation and age-related changes in these neural systems. Additionally, genetic research has identified several candidate genes that may influence age-related changes in emotion regulation. In this paper, we review recent findings from neuroimaging and genetic research on age-related changes in the neural substrates of emotion regulation, highlighting the mechanisms and consequences of these changes. We also discuss potential interventions, including cognitive and behavioral approaches, that may be effective in mitigating age-related declines in emotion regulation. We propose that a better understanding of the mechanisms underlying age-related changes in emotion regulation may lead to the development of more targeted interventions aimed at promoting healthy emotional functioning in older adults. Overall, this paper highlights the importance of studying age-related changes in emotion regulation and provides a roadmap for future research in this field.

Keywords: emotion regulation, aging, neural substrates, neuroimaging, emotional functioning, healthy aging

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Authors: Sungpyo Kim, Changseok Oh, Ga-Young Lee, Hyun-Man Kim

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Intercellular junction of keratinocytes is crucial for epithelia to build an epithelial barrier. Junctional epithelium (JE) seals the interfaces between tooth and gingival tissue. Keratinocytes of JE attach to surfaces roughened by abrasion or erosion with aging. Thus behavior of oral keratinocytes on the rough substrates may help understand the epithelial seal of JE of which major intercellular junction is E-cadherin junction (ECJ). The present study investigated the influence of various substrate roughnesses on the development of ECJ between normal human gingival epithelial keratinocytes, HOK-16B cells. HOK-16B cells were slow in the development of ECJ on the rough substrates compared to on the smooth substrates. Furthermore, oral keratinocytes on the substrates of higher roughnesses were delayed in the development of E-cadherin junction than on the substrates of lower roughnesses. Delayed development of E-cadherin junction on the rough substrates was ascribed to the impaired spreading of cells and its higher JNK activity. Cells on the smooth substrates rapidly spread wide cytoplasmic extensions around cells. However, cells on the rough substrates slowly extended narrow cytoplasmic extensions of which number was limited due to the substrate irregularity. As these cytoplasmic extensions formed ECJ when met with the extensions of neighboring cells, thus, the present study demonstrated that a limited chance of contacts between cytoplasmic extensions due to the limited number of cytoplasmic extensions and slow development of cytoplasmic extensions brought about a delayed development of ECJ in oral keratinocytes on the rougher substrates. Sealing between cells was not complete because only part of cell membrane contributes to the formation of intercellular junction between cells on the substrates of higher roughnesses. Interestingly, inhibition of JNK activity promoted the development of ECJ on the rough substrates, of which mechanism remains to be studied further.

Keywords: substrate roughness, E-cadherin junction, oral keratinocyte, cell spreading, JNK

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Authors: Daniel Li

Abstract:

We present a novel application of neural rendering methods to confocal microscopy. Neural rendering and implicit neural representations have developed at a remarkable pace, and are prevalent in modern 3D computer vision literature. However, they have not yet been applied to optical microscopy, an important imaging field where 3D volume information may be heavily sought after. In this paper, we employ neural rendering on confocal microscopy focus stack data and share the results. We highlight the benefits and potential of adding neural rendering to the toolkit of microscopy image processing techniques.

Keywords: neural rendering, implicit neural representations, confocal microscopy, medical image processing

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Authors: Ogbonnaya Nwokoro, Florence O. Anya

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Nutritional factors relating to the production of linamarase from Lactobacillus delbrueckii NRRL B–763 were investigated. The microorganism was cultivated in a medium containing 1% linamarin. Enzyme was produced using a variety of carbon substrates but the highest enzyme activity was detected in the presence of salicin (522 U/ml) after 48 h while the lowest yield was observed with CM cellulose (38 U/ml) after 72 h. Enzyme was not produced in the presence of cellobiose. Among a variety of nitrogen substrates tested, peptone supported maximum enzyme production (412 U/ml) after 48 h. Lowest enzyme production was observed with urea (40 U/ml). Organic nitrogen substrates generally supported higher enzyme productivity than inorganic nitrogen substrates. Enzyme activity was observed in the presence of Mn2+ (% relative activity = 216) while Hg2+ was inhibitory (% relative activity = 28). Locally-formulated media were comparable to MRS broth in supporting linamarase production by the bacterium. Higher enzyme activity was produced in media with surfactant than in media without surfactant. The enzyme may be useful in enhanced degradation of cassava cyanide.

Keywords: linamarase, locally formulated media, carbon substrates, nitrogen substrates, metal ions

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Authors: José Luis Carrillo-Medina, Roberto Latorre

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Experimental evidence has revealed that different living neural systems can sign their output signals with some specific neural signature. Although experimental and modeling results suggest that neural signatures can have an important role in the activity of neural networks in order to identify the source of the information or to contextualize a message, the functional meaning of these neural fingerprints is still unclear. The existence of cellular mechanisms to identify the origin of individual neural signals can be a powerful information processing strategy for the nervous system. We have recently built different models to study the ability of a neural network to process information based on the emission and recognition of specific neural fingerprints. In this paper we further analyze the features that can influence on the information processing ability of this kind of networks. In particular, we focus on the role that the duration of a refractory period in each neuron after emitting a signed message can play in the network collective dynamics.

Keywords: neural signature, neural fingerprint, processing based on signal identification, self-organizing neural network

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Authors: Ga-Young Lee, Hyun-Man Kim

Abstract:

The modulation of the cell-cell junction is critical in epithelial-mesenchymal transition during tumorigenesis. As RhoA activity is known to be up-regulated to dissociate cell-cell junction by contracting acto-myosin complex in various cancer cells, the present study investigated if RhoA activity was also associated with the disruption of the cell-cell junction of oral cancer cells. We studied SCC-25 cells which are established from oral squamous cell carcinoma if their E-cadherin junction (ECJ) was under control of RhoA. Interestingly, development of ECJ of SCC-25 cells depended on the amount of fibronectin (FN) coated on the culture dishes. Seeded cells promptly aggregated to develop ECJ on the substrates coated with a low amount of FN, whereas they were retarded in the development of ECJ on the substrates coated with a high amount of FN. However, it was an unexpected finding that total RhoA activity was lower in the dissociated cells on the substrates of high FN than in the aggregated cells on the substrates of low FN. Treating the dissociated cells on the substrates of high FN with LPA, a RhoA activator, promoted the development to ECJ. In contrast, treating the aggregated cells on the substrates of low FN with Clostridium botulinum C3, a toxin decreasing RhoA activity, dissociated cells concomitant with the disruption of ECJ. Genetical knockdown of RhoA expression by transfecting RhoA siRNA also down-regulated the development of ECJ in SCC-25 cells. Furthermore, PMA, an activator of protein kinase C (PKC), down-regulated the development of ECJ junction of SCC-25 cells on the substrates coated with low FN. In contrast, GO6976, a PKC inhibitor, up-regulated the development of ECJ of SCC-25 cells with the activation of RhoA on the substrates coated with high FN. In conclusion, in the present study, we demonstrated unexpected results that the activation of RhoA promotes the development of ECJ, whereas the inhibition of RhoA retards the development of ECJ in SCC-25 cells.

Keywords: E-cadherin junction, oral squamous cell carcinoma, PKC, RhoA, SCC-25

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Authors: Saha Saradindu, Das Payel, Somdeb BoseDasgupta

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Tuberculosis caused by Mycobacteria tuberculosis is a dreadful disease and more so with the advent of extreme and total drug-resistant species. Mycobacterial pathogenesis is an ever-changing paradigm from phagosome maturation block to phagosomal escape into macrophage cytosol and finally acid tolerance and survival inside the lysosome. Mycobacteria are adept at subverting the host immune response by highjacking host cell signaling and secreting virulence factors. One such virulence factor is a ser/thr kinase; Protein kinase G (PknG), which is known to prevent phagosome maturation. The host substrates of PknG, allowing successful pathogenesis still remain an enigma. Hence we carried out a comparative phosphoproteomic screen and identified a number of substrates phosphorylated by PknG. We characterized some of these substrates in vivo and in vitro and observed that PknG mediated phosphorylation of these substrates leads to reduced TNFa production as well as decreased response to TNFa induced macrophage necroptosis, thus enabling mycobacterial survival and proliferation.

Keywords: mycobacteria, Protein kinase G, phosphoproteomics, necroptosis

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Authors: Elabed Saud

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This paper introduces the new ability of Artificial Neural Networks (ANNs) in estimating speed and controlling the separately excited DC motor. The neural control scheme consists of two parts. One is the neural estimator which is used to estimate the motor speed. The other is the neural controller which is used to generate a control signal for a converter. These two neutrals are training by Levenberg-Marquardt back-propagation algorithm. ANNs are the standard three layers feed-forward neural network with sigmoid activation functions in the input and hidden layers and purelin in the output layer. Simulation results are presented to demonstrate the effectiveness of this neural and advantage of the control system DC motor with ANNs in comparison with the conventional scheme without ANNs.

Keywords: Artificial Neural Network (ANNs), excited DC motor, convenional controller, speed Controller

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Authors: Jin-Woo Park, Sung-Soo Lee, Nong-Moon Hwang

Abstract:

The generation of charged nanoparticles in the gas phase during the Chemical Vapor Deposition (CVD) process has been frequently reported with their subsequent deposition into films and nanostructures in many systems such as carbon, silicon and zinc oxide. The microstructure evolution of films and nanostructures is closely related with the size distribution of charged nanoparticles. To confirm the generation of charged nanoparticles during GaN, the generation of GaN charged nanoparticles was examined in an atmospheric pressure CVD process using a Differential Mobility Analyser (DMA) combined with a Faraday Cup Electrometer (FCE). It was confirmed that GaN charged nanoparticles were generated under the condition where GaN nanostructures were synthesized on the bare and Au-coated Si substrates. In addition, the deposition behaviour depends strongly on the charge transfer rate of metal substrates. On the metal substrates of a lower CTR such as Mo, the deposition rate of GaN was much lower than on those of a higher CTR such as Fe. GaN nanowires tend to grow on the substrates of a lower CTR whereas GaN thin films tend to be deposited on the substrates of a higher CTR.

Keywords: chemical vapour deposition, charged cluster model, generation of charged nanoparticles, deposition behaviour, nanostructures, gan, charged transfer rate

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Authors: A. A. Behroozpoor, M. M. Mazarei

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In this paper, a fuzzy recurrent neural network is proposed for solving the classical quadratic control problem subject to linear equality and bound constraints. The convergence of the state variables of the proposed neural network to achieve solution optimality is guaranteed.

Keywords: REFERENCES [1] Xia, Y, A new neural network for solving linear and quadratic programming problems. IEEE Transactions on Neural Networks, 7(6), 1996, pp.1544–1548. [2] Xia, Y., & Wang, J, A recurrent neural network for solving nonlinear convex programs subject to linear constraints. IEEE Transactions on Neural Networks, 16(2), 2005, pp. 379–386. [3] Xia, Y., H, Leung, & J, Wang, A projection neural network and its application to constrained optimization problems. IEEE Transactions Circuits and Systems-I, 49(4), 2002, pp.447–458.B. [4] Q. Liu, Z. Guo, J. Wang, A one-layer recurrent neural network for constrained seudoconvex optimization and its application for dynamic portfolio optimization. Neural Networks, 26, 2012, pp. 99-109.

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Authors: Bharat Mishra, Sanjay Kumar Awasthi, Raj Kumar Rajak

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The devices, which convert the energy in the form of electricity from organic matters, are called microbial fuel cell (MFC). Recently, MFCs have been given a lot of attention due to their mild operating conditions, and various types of biodegradable substrates have been used in the form of fuel. Traditional MFCs were included in anode and cathode chambers, but there are single chamber MFCs. Microorganisms actively catabolize substrate, and bioelectricities are produced. In the field of power generation from non-conventional sources, apart from the benefits of this technique, it is still facing practical constraints such as low potential and power. In this study, most suitable, natural, low cost MFCs components are electrodes (anode and cathode), organic substrates, membranes and its design is selected on the basis of maximum potential (voltage) as an electrical parameter, which indicates a vital role of affecting factor in MFC for sustainable power production.

Keywords: substrates, electrodes, membranes, MFCs design, voltage

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Authors: Xin Zhao. Xuerong Zheng. Andrey L. Rogach

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Using single metal atoms has been considered an efficient way to develop new HER and OER catalysts. MXenes, a class of two-dimensional materials, have attracted tremendous interest as promising substrates for single-atom metal catalysts. However, there is still a lack of systematic investigations on the interaction mechanisms between various MXenes substrates and single atoms. Besides, due to the poor interaction between metal atoms and substrates resulting in low loading and stability, dual-atom MXenes-based catalysts have not been successfully synthesized. We summarized the electrocatalytic enhancement mechanism of three MXenes-based single-atom catalysts through experimental and theoretical results demonstrating the stronger hybridization between Co 3d and surface-terminated O 2p orbitals, optimizing the electronic structure of Co single atoms in the composite. This, in turn, lowers the OER and HER energy barriers and accelerates the catalytic kinetics in the case of the Co@V2CTx composite. The poor interaction between single atoms and substrates can be improved by a surface modification to synthesize dual-atom catalysts. The synergistic electronic structure enhances the stability and electrocatalytic activity of the catalyst. Our study provides guidelines for designing single-atom and dual-atom MXene-based electrocatalysts and sheds light on the origins of the catalytic activity of single-atoms on MXene substrates.

Keywords: dual-atom catalyst, single-atom catalyst, MXene substrates, water splitting

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Authors: Khaled S. Al Salhen

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Aldehyde oxidase (AO) and xanthine oxidoreductase (XOR) catalyze the oxidation of many different N-heterocyclic compounds as well as aliphatic and aromatic aldehydes to their corresponding lactam and carboxylic acids respectively. The present study examines the oxidation of dimethylamino-cinnamaldehyde (DMAC), vanillin and phenanthridine by AO and xanthine by XOR from Drosophila cytosol. Therefore, the results obtained in the present study showed the DMAC, vanillin and phenanthridine substrates used were found to be good substrates of Drosophila AO and xanthine is the preferred substrate for Drosophila XOR. Km values of AO substrates were observed with DMAC (50±5.4 µM), phenanthridine (80±9.1 µM) and vanillin (303±11.7 µM) respectively for Drosophila cytosol. The Km values for DMAC and phenanthridine were ~6 and ~4 fold lower than that for vanillin as a substrate. The Km for XOR with xanthine using NAD+ as an electron acceptor was 27±4.1 µM. Relatively low Vmax values were obtained with phenanthridine (1.78±0.38 nmol/min/mg protein) and DMAC (1.80±0.35 nmol/min/mg protein). The highest Vmax was obtained from Drosophila cytosol with vanillin (7.58±2.11 nmol/min/mg protein). It is concluded these results that AO and XOR in Drosophila were able to catalyse the biotransformation of numerous substrates of the well-characterised mammalian AO and XOR. The kinetic parameters have indicated that the activity of AO of Drosophila may be a significant factor the oxidation of aromatic aldehyde compounds.

Keywords: aldehyde oxidase, xanthine oxidoreductase, dimethylamino-cinnamaldehyde, vanillin, phenanthridine, Drosophila melanogaster

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Authors: Najmeh Mohsenifar, Narjes Mohsenifar, Abbas Kargar

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In this paper, has been presented a stable method for predicting the ECG signals through the RBF neural networks, by the PSO algorithm. In spite of quasi-periodic ECG signal from a healthy person, there are distortions in electro cardiographic data for a patient. Therefore, there is no precise mathematical model for prediction. Here, we have exploited neural networks that are capable of complicated nonlinear mapping. Although the architecture and spread of RBF networks are usually selected through trial and error, the PSO algorithm has been used for choosing the best neural network. In this way, 2 second of a recorded ECG signal is employed to predict duration of 20 second in advance. Our simulations show that PSO algorithm can find the RBF neural network with minimum MSE and the accuracy of the predicted ECG signal is 97 %.

Keywords: electrocardiogram, RBF artificial neural network, PSO algorithm, predict, accuracy

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Authors: M. Simchi, M. Amiri, E. Rezvani, I. Mirzaei, M. Berahman, A. Simchi, M. Fardmanesh

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Graphene is a single-atomic two-dimensional crystal of carbon atoms that has considerable properties due to its unique structure and physics with applications in different fields. Graphene has sensitive electrical properties due to its atomic-thin structure. Along with the substrate materials and their influence on the transport properties in graphene, design and fabrication of graphene-based devices for biomedical and biosensor applications are challenging. In this work, large-area high-quality graphene nanosheets were prepared by low pressure chemical vapor deposition using methane gas as carbon source on copper foil and transferred on the biocompatible substrates. Through deposition of titanium and gold contacts, current-voltage response of the transferred graphene on four biocompatible substrates, including PDMS, SU-8, Nitrocellulose, and Kapton (Fig. 2) were experimentally determined. The considerable effect of the substrate type on the electrical properties of graphene is shown. The sheet resistance of graphene is changed from 0.34 to 14.5 kΩ/sq, depending on the substrate.

Keywords: biocompatible substrates, electrical properties, graphene, sheet resistance

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Authors: Diego De Almeida Pereira, Diana Borchenko

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Artificial neural networks are used for many applications as they are able to learn complex nonlinear relationships between input and output data. As the number of neurons and layers in a neural network increases, it is possible to represent more complex behaviors. The present study proposes that artificial neural networks are a valuable tool for architecture and engineering professionals concerned with understanding how buildings influence human and social well-being based on theories of environmental psychology.

Keywords: environmental psychology, architecture, neural networks, human and social well-being

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Authors: Smrity Dwivedi

Abstract:

The present paper has revealed the effect of double substrates over a bandwidth performance for planar antennas. The used material has its own importance to get minimum return loss and improved directivity. The author has taken double substrates to enhance the efficiency in terms of gain of antenna. Metamaterial based antenna has its own specific structure which increased the performance of antenna. Improved return loss is -20 dB, and the voltage standing wave ratio (VSWR) is 1.2, which is better than single substrate having return loss of -15 dB and VSWR of 1.4. Complete results are obtained using commercial software CST microwave studio.

Keywords: CST microwave studio, metamaterial, return loss, VSWR

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Authors: Mohammad Reza Rahimi Khoygani, Reza Ghasemi

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In this paper, designing direct adaptive neural controller is applied for a class of a nonlinear pendulum dynamic system. The radial basis function (RBF) is used for the Neural network (NN). The adaptive neural controller is robust in presence of external and internal uncertainties. Both the effectiveness of the controller and robustness against disturbances are the merits of this paper. The promising performance of the proposed controllers investigates in simulation results.

Keywords: adaptive control, pendulum dynamical system, nonlinear control, adaptive neural controller, nonlinear dynamical, neural network, RBF, driven pendulum, position control

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Authors: H. Y. Jung, Jing Wang, J. H. Park, Hao Shen

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This brief addresses the passivity problem for neural networks with time-varying delays. The aim is focus on establishing the passivity condition of the considered neural networks.

Keywords: neural networks, passivity analysis, time-varying delays, linear matrix inequality

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Authors: Pallavi Thakur, Rakesh K. Garg

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Latent fingerprints are commonly encountered evidence at the scene of the crime. It is very important to decipher these fingerprints in order to explore their identity and a lot of research has been made on the visualization of latent fingermarks on various substrates by numerous researchers. During the past few years large number of powder formulations has been evolved for the development of latent fingermarks on different surfaces. This paper reports a new and simple fingerprint powder which is non-toxic and has been employed on different substrates successfully for the development and visualization of latent fingermarks upto the time period of twelve days in varying temperature conditions. In this study, a less expensive, simple and easily available catechu (kattha) powder has been used to decipher the latent fingermarks on different substrates namely glass, plastic, metal, aluminium foil, white paper, wall tile and wooden sheet. It is observed that it gives very clear results on all the mentioned substrates and can be successfully used for the development and visualization of twelve days old latent fingermarks in varying temperature conditions on wall tiles.

Keywords: fingermarks, catechu, visualization, aged fingermarks

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Authors: Hamid Rostami Jaz, Kamran Ameri Siahooei

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Up to now different methods have been used to forecast the index returns and the index rate. Artificial intelligence and artificial neural networks have been one of the methods of index returns forecasting. This study attempts to carry out a comparative study on the performance of different Radial Base Neural Network and Feed-Forward Perceptron Neural Network to forecast investment returns on the index. To achieve this goal, the return on investment in Tehran Stock Exchange index is evaluated and the performance of Radial Base Neural Network and Feed-Forward Perceptron Neural Network are compared. Neural networks performance test is applied based on the least square error in two approaches of in-sample and out-of-sample. The research results show the superiority of the radial base neural network in the in-sample approach and the superiority of perceptron neural network in the out-of-sample approach.

Keywords: exchange index, forecasting, perceptron neural network, Tehran stock exchange

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Authors: Meriem Hamla, Mohamed Benaicha, Sabrine Derbal

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In the present work, several materials such as M/glass (M = Pt, Mo) were investigated to test their suitability for studying the early nucleation stages and growth of copper-tin clusters. It was found that most of these materials stand as good substrates to be used in the study of the nucleation and growth of electrodeposited Cu-Sn alloys from aqueous solution containing CuCl2, SnCl2 as electroactive species and Na3C6H5O7 as complexing agent. Among these substrates, Pt shows instantaneous models followed by 3D diffusion-limited growth. On the other hand, the electrodeposited copper-tin thin films onto Mo substrate followed progressive nucleation. The deposition mechanism of the Cu-Sn films has been studied using stationary electrochemical techniques (cyclic voltammetery (CV) and chronoamperometry (CA). The structural, morphological and compositional of characterization have been studied using X-ray diffraction (XRD), scanning electron microscopy (SEM) and EDAX techniques respectively.

Keywords: electrodeposition, CuSn, nucleation, mechanism

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Authors: İkbal Eski

Abstract:

This investigation is researched on design of robust neural network predictors for analyzing vibration effects on moving parts of a drilling machine. Moreover, the research is divided two parts; first part is experimental investigation, second part is simulation analysis with neural networks. Therefore, a real time the drilling machine is used to vibrations during working conditions. The measured real vibration parameters are analyzed with proposed neural network. As results: Simulation approaches show that Radial Basis Neural Network has good performance to adapt real time parameters of the drilling machine.

Keywords: artificial neural network, vibration analyses, drilling machine, robust

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Authors: Sanaa Rashed Abdallah, Yasser F. Hassan

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The paper will introduce an approach where a rough sets, gene expression programming and rough neural networks are used cooperatively for learning and classification support. The Objective of gene expression programming rough neural networks (GEP-RNN) approach is to obtain new classified data with minimum error in training and testing process. Starting point of gene expression programming rough neural networks (GEP-RNN) approach is an information system and the output from this approach is a structure of rough neural networks which is including the weights and thresholds with minimum classification error.

Keywords: rough sets, gene expression programming, rough neural networks, classification

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Authors: Arbnor Pajaziti, Hasan Cana

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In this paper, the structural genetic algorithm is used to optimize the neural network to control the joint movements of robotic arm. The robotic arm has also been modeled in 3D and simulated in real-time in MATLAB. It is found that Neural Networks provide a simple and effective way to control the robot tasks. Computer simulation examples are given to illustrate the significance of this method. By combining Genetic Algorithm optimization method and Neural Networks for the given robotic arm with 5 D.O.F. the obtained the results shown that the base joint movements overshooting time without controller was about 0.5 seconds, while with Neural Network controller (optimized with Genetic Algorithm) was about 0.2 seconds, and the population size of 150 gave best results.

Keywords: robotic arm, neural network, genetic algorithm, optimization

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Authors: K. A. Laptinskiy, S. A. Burikov, A. M. Vervald, S. A. Dolenko, T. A. Dolenko

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The article presents the results of the application of artificial neural networks to separate the fluorescent contribution of nanodiamonds used as biomarkers, adsorbents and carriers of drugs in biomedicine, from a fluorescent background of own biological fluorophores. The principal possibility of solving this problem is shown. Use of neural network architecture let to detect fluorescence of nanodiamonds against the background autofluorescence of egg white with high accuracy - better than 3 ug/ml.

Keywords: artificial neural networks, fluorescence, data aggregation, biomarkers

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Authors: Kaoutar Abbahaddou, Mohammed Salah Chiadmi

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The need to find a relevant way to predict the next-day price of a stock index is a real concern for many financial stakeholders and researchers. We have known across years the proliferation of several methods. Nevertheless, among all these methods, the most controversial one is a machine learning algorithm that claims to be reliable, namely neural networks. Thus, the purpose of this article is to study the prediction power of neural networks in the particular case of Islamic finance as it is an under-looked area. In this article, we will first briefly present a review of the literature regarding neural networks and Islamic finance. Next, we present the architecture and principles of artificial neural networks most commonly used in finance. Then, we will show its empirical application on two Islamic stock indexes. The accuracy rate would be used to measure the performance of the algorithm in predicting the right price the next day. As a result, we can conclude that artificial neural networks are a reliable method to predict the next-day price for Islamic indices as it is claimed for conventional ones.

Keywords: Islamic finance, stock price prediction, artificial neural networks, machine learning

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Authors: H. I. Beloufa, M. Tarfaoui

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For many applications, adhesively bonded assemblies have gained an increasing interest in the industry due to several advantages over welding, riveting and bolting, such as reduction of stress concentrations, lightness, low cost and easy manufacturing. This work is largely concerned to show the effects of the loading rate of the adhesively bonded joints under different speed rates. The tensile tests were conducted at four different rates; static (5mm/min, 50mm/min) and dynamic tests (1m/s, and 10m/s). An attempt was made to determine the damage kinetic and a comparison between the use of aluminium and composite laminate substrates is introduced. Aluminum T6082 and glass/vinylester laminated composite Substrates were used to construct aluminum/aluminum and laminate/laminate specimens. The adhesive used in this study was Araldite 2015. The results showed the effects of the loading rate évolution on the double joint strength. The comparison of the results of static and dynamic tests showed a raise of the strength of the specimens while the load velocity is elevated. In the case of composite substrates double joint lap, the stiffness increased by more than 60% between static and dynamic tests. However, in the case of aluminum substrates, the rigidity improved about 28% from static to moderately high velocity loading. For both aluminum and composite double joint lap, the strength increased by approximately 25% when the tensile velocity is increased from 5 mm/min to 50 mm/min (static tests). Nevertheless, the tensile velocity is extended to 1m/s the strength increased by 13% and 25% respectively for composite and aluminum substrates.

Keywords: adhesive, double lap joints, static and dynamic behavior, tensile tests

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Authors: Shelby Simpson, William Stanley, Namir Naba, Xiaodi Wang

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Both t-SNE and UMAP are brand new state of art tools to predominantly preserve the local structure that is to group neighboring data points together, which indeed provides a very informative visualization of heterogeneity in our data. In this research, we develop a t-SNE and UMAP base neural network image classification algorithm to embed the original dataset to a corresponding low dimensional dataset as a preprocessing step, then use this embedded database as input to our specially designed neural network classifier for image classification. We use the fashion MNIST data set, which is a labeled data set of images of clothing objects in our experiments. t-SNE and UMAP are used for dimensionality reduction of the data set and thus produce low dimensional embeddings. Furthermore, we use the embeddings from t-SNE and UMAP to feed into two neural networks. The accuracy of the models from the two neural networks is then compared to a dense neural network that does not use embedding as an input to show which model can classify the images of clothing objects more accurately.

Keywords: t-SNE, UMAP, fashion MNIST, neural networks

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Authors: Mohammed Affanuddin H. Siddique, Jayesh S. Shukla, Chetan B. Meshram

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The neural networks are one of the power tools of machine learning. After the invention of perceptron in early 1980's, the neural networks and its application have grown rapidly. Neural networks are a technique originally developed for pattern investigation. The structure of a neural network consists of neurons connected through synapse. Here, we have investigated the different algorithms and cost function reduction techniques for optimization of vertical axis wind turbine (VAWT) rotor blades. The aerodynamic force coefficients corresponding to the airfoils are stored in a database along with the airfoil coordinates. A forward propagation neural network is created with the input as aerodynamic coefficients and output as the airfoil co-ordinates. In the proposed algorithm, the hidden layer is incorporated into cost function having linear and non-linear error terms. In this article, it is observed that the ANNs (Artificial Neural Network) can be used for the VAWT’s optimization.

Keywords: VAWT, ANN, optimization, inverse design

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