Search results for: random threshold

Authors: Reena Murali, David Peter S.

Abstract:

The small interfering RNA (siRNA) alters the regulatory role of mRNA during gene expression by translational inhibition. Recent studies show that upregulation of mRNA because serious diseases like cancer. So designing effective siRNA with good knockdown effects plays an important role in gene silencing. Various siRNA design tools had been developed earlier. In this work, we are trying to analyze the existing good scoring second generation siRNA predicting tools and to optimize the efficiency of siRNA prediction by designing a computational model using Artificial Neural Network and whole stacking energy (%G), which may help in gene silencing and drug design in cancer therapy. Our model is trained and tested against a large data set of siRNA sequences. Validation of our results is done by finding correlation coefficient of experimental versus observed inhibition efficacy of siRNA. We achieved a correlation coefficient of 0.727 in our previous computational model and we could improve the correlation coefficient up to 0.753 when the threshold of whole tacking energy is greater than or equal to -32.5 kcal/mol.

Keywords: Artificial Neural Network, Double Stranded RNA, RNA Interference, Short Interfering RNA.

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Authors: S. Balaji

Abstract:

An edge based local search algorithm, called ELS, is proposed for the maximum clique problem (MCP), a well-known combinatorial optimization problem. ELS is a two phased local search method effectively £nds the near optimal solutions for the MCP. A parameter ’support’ of vertices de£ned in the ELS greatly reduces the more number of random selections among vertices and also the number of iterations and running times. Computational results on BHOSLIB and DIMACS benchmark graphs indicate that ELS is capable of achieving state-of-the-art-performance for the maximum clique with reasonable average running times.

Keywords: Maximum clique, local search, heuristic, NP-complete.

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Authors: Surendra K Pandey

Abstract:

Optical emission based on excitonic scattering processes becomes important in dense exciton systems in which the average distance between excitons is of the order of a few Bohr radii but still below the exciton screening threshold. The phenomena due to interactions among excited states play significant role in the emission near band edge of the material. The theory of two-exciton collisions for GaAs/AlGaAs quantum well systems is a mild attempt to understand the physics associated with the optical spectra due to excitonic scattering processes in these novel systems. The four typical processes considered give different spectral shape, peak position and temperature dependence of the emission spectra. We have used the theory of scattering together with the second order perturbation theory to derive the radiative power spontaneously emitted at an energy ħω by these processes. The results arrived at are purely qualitative in nature. The intensity of emitted light in quantum well systems varies inversely to the square of temperature, whereas in case of bulk materials it simply decreases with the  temperature.

Keywords: Exciton-Exciton Collisions, Excitonic Scattering Processes, Interacting Excitonic States, Quantum Wells.

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Authors: Alexander Goncharenko, Andrey Denisov, Sergey Alyamkin, Evgeny Terentev

Abstract:

The neural network quantization is highly desired procedure to perform before running neural networks on mobile devices. Quantization without fine-tuning leads to accuracy drop of the model, whereas commonly used training with quantization is done on the full set of the labeled data and therefore is both time- and resource-consuming. Real life applications require simplification and acceleration of quantization procedure that will maintain accuracy of full-precision neural network, especially for modern mobile neural network architectures like Mobilenet-v1, MobileNet-v2 and MNAS. Here we present a method to significantly optimize training with quantization procedure by introducing the trained scale factors for discretization thresholds that are separate for each filter. Using the proposed technique, we quantize the modern mobile architectures of neural networks with the set of train data of only ∼ 10% of the total ImageNet 2012 sample. Such reduction of train dataset size and small number of trainable parameters allow to fine-tune the network for several hours while maintaining the high accuracy of quantized model (accuracy drop was less than 0.5%). Ready-for-use models and code are available in the GitHub repository.

Keywords: Distillation, machine learning, neural networks, quantization.

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Authors: Alireza Ansariyar, Mansoureh Jeihani

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Light Detection and Ranging (LiDAR) sensors is capable of recording traffic data including the number of passing vehicles and bicyclists, the speed of vehicles and bicyclists, and the number of conflicts among both road users. In order to collect real-time traffic data and investigate the safety of different road users, a LiDAR sensor was installed at Cold Spring Ln – Hillen Rd intersection in Baltimore city. The frequency and severity of collected real-time conflicts were analyzed and the results highlighted that 122 conflicts were recorded over a 10-month time interval from May 2022 to February 2023. By employing an image-processing algorithm, a safety Measure of Effectiveness (MOE) aims to identify critical zones for bicyclists upon entering each respective zone at the signalized intersection. Considering the trajectory of conflicts, the results of analysis demonstrated that conflicts in the northern approach (zone N) are more frequent and severe. Additionally, sunny weather is more likely to cause severe vehicle-bike conflicts.

Keywords: LiDAR sensor, Post Encroachment Time threshold, vehicle-bike conflicts, measure of effectiveness, weather condition.

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Authors: Rongli Gai, Zhiyuan Chang, Xiaohong Wang, Jingyu Liu

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In view of various situations in the interpolation process, most researchers use self-adaptation to adjust the interpolation process, which is also one of the current and future research hotspots in the field of CNC (Computerized Numerical Control) machining. In the interpolation process, according to the overview of the spline curve interpolation algorithm, the adaptive analysis is carried out from the factors affecting the interpolation process. The adaptive operation is reflected in various aspects, such as speed, parameters, errors, nodes, feed rates, random period, sensitive point, step size, curvature, adaptive segmentation, adaptive optimization, etc. This paper will analyze and summarize the research of adaptive imputation in the direction of the above factors affecting imputation.

Keywords: Adaptive algorithm, CNC machining, interpolation constraints, spline curve interpolation.

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Authors: Y. Bentoutou

Abstract:

On-board Error Detection and Correction (EDAC) devices aim to secure data transmitted between the central processing unit (CPU) of a satellite onboard computer and its local memory. This paper presents a comparison of the performance of four low complexity EDAC techniques for application in Random Access Memories (RAMs) on-board small satellites. The performance of a newly proposed EDAC architecture is measured and compared with three different EDAC strategies, using the same FPGA technology. A statistical analysis of single-event upset (SEU) and multiple-bit upset (MBU) activity in commercial memories onboard Alsat-1 is given for a period of 8 years

Keywords: Error Detection and Correction; On-board computer; small satellite missions

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Authors: Youssef Khmou, Said Safi, Miloud Frikel

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The Maximum entropy principle in spectral analysis was used as an estimator of Direction of Arrival (DoA) of electromagnetic or acoustic sources impinging on an array of sensors, indeed the maximum entropy operator is very efficient when the signals of the radiating sources are ergodic and complex zero mean random processes which is the case for cosmic sources. In this paper, we present basic review of the maximum entropy method (MEM) which consists of rank one operator but not a projector, and we elaborate a new operator which is full rank and sum of all possible projectors. Two dimensional Simulation results based on Monte Carlo trials prove the resolution power of the new operator where the MEM presents some erroneous fluctuations.

Keywords: Maximum entropy, Cosmic source, Localization, operator, projector, azimuth, elevation, DoA, circular array.

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Authors: Pushp Lata Rajpoot, Pushpa Vaishnav

Abstract:

Anxiety is a common psychological problem and also implicated as a contributor to many chronic diseases which decreased quality of life even with pharmacological treatment. At the present time several yogic practices- meditation, pranayama, and mantra, etcetera are playing important role in treating physiological and psychological problems. Hence, the present investigation is aimed to see the effect of Trataka on the level of anxiety among adolescents. For the present study, a sample of 30 adolescents belonging to the age range 20-30 years was selected from Devsanskriti Vishwa Vidyalaya Haridwar through random sampling. In this investigation, Sinha’s Comprehensive anxiety test has been used to measure the level of anxiety. Statistical analysis has been done by using t-test. Findings of this study reveal that Trataka significantly decreases the level of anxiety among adolescents.

Keywords: Adolescents, Anxiety, Trataka.

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Authors: R.Karami, B.Kamkari, K.Kashefi

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Applying corona wind as a novel technique can lead to a great level of heat and mass transfer augmentation by using very small amount of energy. Enhancement of forced flow evaporation rate by applying electric field (corona wind) has been experimentally evaluated in this study. Corona wind produced by a fine wire electrode which is charged with positive high DC voltage impinges to water surface and leads to evaporation enhancement by disturbing the saturated air layer over water surface. The study was focused on the effect of corona wind velocity, electrode spacing and air flow velocity on the level of evaporation enhancement. Two sets of experiments, i.e. with and without electric field, have been conducted. Data obtained from the first experiment were used as reference for evaluation of evaporation enhancement at the presence of electric field. Applied voltages ranged from corona threshold voltage to spark over voltage at 1 kV increments. The results showed that corona wind has great enhancement effect on water evaporation rate, but its effectiveness gradually diminishes by increasing air flow velocity. Maximum enhancements were 7.3 and 3.6 for air velocities of 0.125 and 1.75 m/s, respectively.

Keywords: Electrohydodynamics (EHD), corona wind, high electric field, Evaporation enhancement

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Authors: Jason Zalev, Reza Sedaghat

Abstract:

This paper describes a complex energy signal model that is isomorphic with digital human fingerprint images. By using signal models, the problem of fingerprint matching is transformed into the signal processing problem of finding a correlation between two complex signals that differ by phase-rotation and time-scaling. A technique for minutiae matching that is independent of image translation, rotation and linear-scaling, and is resistant to missing minutiae is proposed. The method was tested using random data points. The results show that for matching prints the scaling and rotation angles are closely estimated and a stronger match will have a higher correlation.

Keywords: Affine Invariant, Fingerprint Recognition, Matching, Minutiae.

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Authors: Amir Hadi Ziaie

Abstract:

In the present work, we revisit the collapse process of a spherically symmetric homogeneous scalar field (in FRW background) minimally coupled to gravity, when the phase-space deformations are taken into account. Such a deformation is mathematically introduced as a particular type of noncommutativity between the canonical momenta of the scale factor and of the scalar field. In the absence of such deformation, the collapse culminates in a spacetime singularity. However, when the phase-space is deformed, we find that the singularity is removed by a non-singular bounce, beyond which the collapsing cloud re-expands to infinity. More precisely, for negative values of the deformation parameter, we identify the appearance of a negative pressure, which decelerates the collapse to finally avoid the singularity formation. While in the un-deformed case, the horizon curve monotonically decreases to finally cover the singularity, in the deformed case the horizon has a minimum value that this value depends on deformation parameter and initial configuration of the collapse. Such a setting predicts a threshold mass for black hole formation in stellar collapse and manifests the role of non-commutative geometry in physics and especially in stellar collapse and supernova explosion.

Keywords: Gravitational collapse, non-commutative geometry, spacetime singularity, black hole physics.

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Authors: H. Urbancova, J. Urbanec

Abstract:

Today the intangible assets are the capital of knowledge and are the most important and the most valuable resource for organizations. All employees have knowledge independently of the kind of jobs they do. Knowledge is thus an asset, which influences business operations. The objective of this article is to identify knowledge continuity as an objective of business continuity management. The article has been prepared based on the analysis of secondary sources and the evaluation of primary sources of data by means of a quantitative survey conducted in the Czech Republic. The conclusion of the article is that organizations that apply business continuity management do not focus on the preservation of the knowledge of key employees. Organizations ensure knowledge continuity only intuitively, on a random basis, non-systematically and discontinuously. The non-ensuring of knowledge continuity represents a threat of loss of key knowledge for organizations and can also negatively affect business continuity.

Keywords: Business continuity, knowledge, organizations, survey.

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Authors: Leong Lee, Cyriac Kandoth, Jennifer L. Leopold, Ronald L. Frank

Abstract:

Protein 3D structure prediction has always been an important research area in bioinformatics. In particular, the prediction of secondary structure has been a well-studied research topic. Despite the recent breakthrough of combining multiple sequence alignment information and artificial intelligence algorithms to predict protein secondary structure, the Q3 accuracy of various computational prediction algorithms rarely has exceeded 75%. In a previous paper [1], this research team presented a rule-based method called RT-RICO (Relaxed Threshold Rule Induction from Coverings) to predict protein secondary structure. The average Q3 accuracy on the sample datasets using RT-RICO was 80.3%, an improvement over comparable computational methods. Although this demonstrated that RT-RICO might be a promising approach for predicting secondary structure, the algorithm-s computational complexity and program running time limited its use. Herein a parallelized implementation of a slightly modified RT-RICO approach is presented. This new version of the algorithm facilitated the testing of a much larger dataset of 396 protein domains [2]. Parallelized RTRICO achieved a Q3 score of 74.6%, which is higher than the consensus prediction accuracy of 72.9% that was achieved for the same test dataset by a combination of four secondary structure prediction methods [2].

Keywords: data mining, protein secondary structure prediction, parallelization.

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Authors: Sara Ansari, Mohammad Hadi Sadreddini

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The increasing importance of data stream arising in a wide range of advanced applications has led to the extensive study of mining frequent patterns. Mining data streams poses many new challenges amongst which are the one-scan nature, the unbounded memory requirement and the high arrival rate of data streams. In this paper, we propose a new approach for mining itemsets on data stream. Our approach SFIDS has been developed based on FIDS algorithm. The main attempts were to keep some advantages of the previous approach and resolve some of its drawbacks, and consequently to improve run time and memory consumption. Our approach has the following advantages: using a data structure similar to lattice for keeping frequent itemsets, separating regions from each other with deleting common nodes that results in a decrease in search space, memory consumption and run time; and Finally, considering CPU constraint, with increasing arrival rate of data that result in overloading system, SFIDS automatically detect this situation and discard some of unprocessing data. We guarantee that error of results is bounded to user pre-specified threshold, based on a probability technique. Final results show that SFIDS algorithm could attain about 50% run time improvement than FIDS approach.

Keywords: Data stream, frequent itemset, stream mining.

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Authors: Rong Liu, Kuiying Chen, Ju Chen, Jingrong Zhao, Ming Liang

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This article presents modeling studies of NiAl alloy under solid-particle erosion and liquid-drop erosion. In the solid-particle erosion simulation, attention is paid to the oxide scale thickness variation on the alloy in high-temperature erosion environments. The erosion damage is assumed to be deformation wear and cutting wear mechanisms, incorporating the influence of the oxide scale on the eroded surface; thus the instantaneous oxide thickness is the result of synergetic effect of erosion and oxidation. For liquid-drop erosion, special interest is in investigating the effects of drop velocity and drop size on the damage of the target surface. The models of impact stress wave, mean depth of penetration, and maximum depth of erosion rate (Max DER) are employed to develop various maps for NiAl alloy, including target thickness vs. drop size (diameter), rate of mean depth of penetration (MDRP) vs. drop impact velocity, and damage threshold velocity (DTV) vs. drop size.

Keywords: Liquid-drop erosion, NiAl alloy, oxide scale thickness, solid-particle erosion.

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Authors: Erika Yamaguchi, Sirawadee Arunyanrt, Shunichi Ohmori, Kazuho Yoshimoto

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In this paper, we suggest a simulation tool to make a decision of monthly production planning for maximizing a profit of Specialty store retailer of Private label Apparel (SPA) firms. Most of SPA firms are fabless and make outsourcing deals for productions with factories of their subcontractors. Every month, SPA firms make a booking for production lines and manpower in the factories. The booking is conducted a few months in advance based on a demand prediction and a monthly production planning at that time. However, the demand prediction is updated month by month, and the monthly production planning would change to meet the latest demand prediction. Then, SPA firms have to change the capacities initially booked within a certain range to suit to the monthly production planning. The booking system is called “capacity-booking”. These days, though it is an issue for SPA firms to make precise monthly production planning, many firms are still conducting the production planning by empirical rules. In addition, it is also a challenge for SPA firms to match their products and factories with considering their demand predictabilities and regulation abilities. In this paper, we suggest a model for considering these two issues. An objective is to maximize a total profit of certain periods, which is sales minus costs of production, inventory, and capacity-booking penalty. To make a better monthly production planning at SPA firms, these points should be considered: demand predictabilities by random trends, previous and next month’s production planning of the target month, and regulation abilities of the capacity-booking. To decide matching products and factories for outsourcing, it is important to consider seasonality, volume, and predictability of each product, production possibility, size, and regulation ability of each factory. SPA firms have to consider these constructions and decide orders with several factories per one product. We modeled these issues as a linear programming. To validate the model, an example of several computational experiments with a SPA firm is presented. We suppose four typical product groups: basic, seasonal (Spring / Summer), seasonal (Fall / Winter), and spot product. As a result of the experiments, a monthly production planning was provided. In the planning, demand predictabilities from random trend are reduced by producing products which are different product types. Moreover, priorities to produce are given to high-margin products. In conclusion, we developed a simulation tool to make a decision of monthly production planning which is useful when the production planning is set every month. We considered the features of capacity-booking, and matching of products and factories which have different features and conditions.

Keywords: Capacity-booking, SPA, monthly production planning, linear programming.

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Authors: Q. Zafar, R. Akram, Kh.S. Karimov, T.A. Khan, M. Farooq, M.M. Tahir

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In this study we present the effect of elevated temperatures from 300K to 400K on the electrical properties of copper Phthalocyanine (CuPc) based organic field effect transistors (OFET). Thin films of organic semiconductor CuPc (40nm) and semitransparent Al (20nm) were deposited in sequence, by vacuum evaporation on a glass substrate with previously deposited Ag source and drain electrodes with a gap of 40 μm. Under resistive mode of operation, where gate was suspended it was observed that drain current of this organic field effect transistor (OFET) show an increase with temperature. While in grounded gate condition metal (aluminum) – semiconductor (Copper Phthalocyanine) Schottky junction dominated the output characteristics and device showed switching effect from low to high conduction states like Zener diode at higher bias voltages. This threshold voltage for switching effect has been found to be inversely proportional to temperature and shows an abrupt decrease after knee temperature of 360K. Change in dynamic resistance (Rd = dV/dI) with respect to temperature was observed to be -1%/K.

Keywords: Copper Phthalocyanine, Metal-Semiconductor Schottky Junction, Organic Field Effect Transistor, Switching effect, Temperature Sensor

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Authors: Farid Jamali Sheini, Dilip S. Joag, Mahendra A. More

Abstract:

A simple approach is demonstrated for growing large scale, nearly vertically aligned ZnO nanowire arrays by thermal oxidation method. To reveal effect of temperature on growth and physical properties of the ZnO nanowires, gold coated zinc substrates were annealed at 300 °C and 400 °C for 4 hours duration in air. Xray diffraction patterns of annealed samples indicated a set of well defined diffraction peaks, indexed to the wurtzite hexagonal phase of ZnO. The scanning electron microscopy studies show formation of ZnO nanowires having length of several microns and average of diameter less than 500 nm. It is found that the areal density of wires is relatively higher, when the annealing is carried out at higher temperature i.e. at 400°C. From the field emission studies, the values of the turn-on and threshold field, required to draw emission current density of 10 μA/cm2 and 100 μA/cm2 are observed to be 1.2 V/μm and 1.7 V/μm for the samples annealed at 300 °C and 2.9 V/μm and 3.7 V/μm for that annealed at 400 °C, respectively. The field emission current stability, investigated over duration of more than 2 hours at the preset value of 1 μA, is found to be fairly good in both cases. The simplicity of the synthesis route coupled with the promising field emission properties offer unprecedented advantage for the use of ZnO field emitters for high current density applications.

Keywords: ZnO, Nanowires, Thermal oxidation, FieldEmission.

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Authors: Xiaolai Zhang, Haitao Zhang, Qiwen Sun, Weixin Qian, Weiyong Ying

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High temperature Fischer-Tropsch synthesis process use fixed fluidized bed as a reactor. In order to understand the flow behavior in the fluidized bed better, the research of how the radial velocity affects the entire flow field is necessary. Laser Doppler Velocimetry (LDV) was used to study the radial velocity distribution along the diameter direction of the cross-section of the particle in a fixed fluidized bed. The velocity in the cross-section is fluctuating within a small range. The direction of the speed is a random phenomenon. In addition to r/R is 1, the axial velocity are more than 6 times of the radial velocity, the radial velocity has little impact on the axial velocity in a fixed fluidized bed.

Keywords: LDV, fixed fluidized bed, velocity, Fischer-Tropsch synthesis.

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Authors: Z. Popova, L. S. Pereira, М. Ivanova, P. Alexandrova, K. Doneva, V. Alexandrov, M. Kercheva

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This study assesses the vulnerability of Bulgarian agriculture to drought using the WINISAREG model and seasonal standard precipitation index SPI(2) for the period 1951-2004. This model was previously validated for maize on soils of different water holding capacity (TAW) in various locations. Simulations are performed for Plovdiv, Stara Zagora and Sofia. Results relative to Plovdiv show that in soils of large TAW (180 mm m-1) net irrigation requirements (NIRs) range 0-40 mm in wet years and 350-380 mm in dry years. In soils of small TAW (116 mm m-1), NIRs reach 440 mm in the very dry year. NIRs in Sofia are about 80 mm smaller. Rainfed maize is associated with great yield variability (29%91%) were found for seasonal agricultural drought relating the SPI (2) for “July-Aug" with the simulated RYD of rainfed maize while in Stara Zagora and Sofia the relationships are less accurate (R2>71%). When rainfed maize is grown on soils of large TAW economical losses are produced when high peak season SPI (2) < -0.50 in Plovdiv/Stara Zagora and SPI (2) < -0.90 in Sofia. The corresponding NIR thresholds were identified.

Keywords: Drought vulnerability, ISAREG simulation model, South Bulgaria, SPI-index

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Authors: Yan Yu, Wang Yu, Chen Xintong, Liu Yi, Zhang Yanzhong, Wang Yanji, Chen Xingyu, Zhang Miaocheng, Tong Yi

Abstract:

Neuromorphic computing based on spiking neural networks (SNNs) has emerged as a promising avenue for building the next generation of intelligent computing systems. Owing to their high-density integration, low power, and outstanding nonlinearity, memristors have attracted emerging attention on achieving SNNs. However, fabricating a low-power and robust memristor-based spiking neuron without extra electrical components is still a challenge for brain-inspired systems. In this work, we demonstrate a TiO2-based threshold switching (TS) memristor to emulate a leaky integrate-and-fire (LIF) neuron without auxiliary circuits, used to realize single layer fully connected (FC) SNNs. Moreover, our TiO2-based resistive switching (RS) memristors realize spiking-time-dependent-plasticity (STDP), originating from the Ag diffusion-based filamentary mechanism. This work demonstrates that TiO2-based memristors may provide an efficient method to construct hardware neuromorphic computing systems.

Keywords: Leaky integrate-and-fire, memristor, spiking neural networks, spiking-time-dependent-plasticity.

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Authors: M. Sinecen, M. Makinacı

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The purpose of this paper is to assess the value of neural networks for classification of cancer and noncancer prostate cells. Gauss Markov Random Fields, Fourier entropy and wavelet average deviation features are calculated from 80 noncancer and 80 cancer prostate cell nuclei. For classification, artificial neural network techniques which are multilayer perceptron, radial basis function and learning vector quantization are used. Two methods are utilized for multilayer perceptron. First method has single hidden layer and between 3-15 nodes, second method has two hidden layer and each layer has between 3-15 nodes. Overall classification rate of 86.88% is achieved.

Keywords: Artificial neural networks, texture classification, cancer diagnosis.

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Authors: Alexander B. Bichkov, Alla A. Mityureva, Valery V. Smirnov

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In present work are considered the scheme of evaluation the transition probability in quantum system. It is based on path integral representation of transition probability amplitude and its evaluation by means of a saddle point method, applied to the part of integration variables. The whole integration process is reduced to initial value problem solutions of Hamilton equations with a random initial phase point. The scheme is related to the semiclassical initial value representation approaches using great number of trajectories. In contrast to them from total set of generated phase paths only one path for each initial coordinate value is selected in Monte Karlo process.

Keywords: Path integral, saddle point method, semiclassical approximation, transition probability

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Authors: Subrahmanyam Kodukula, Nurbiya Maimaiti

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There have been many variations of technologies that helped educators in teaching & learning. From the past research it is evident that Information Technology significantly increases student participation and interactivity in the classrooms. This research started with a aim to find whether adoption of Wi-Fi environment by Malaysian Higher Educational Institutions (HEI) can benefit students and staff equally. The study was carried out in HEI-s of Klang Valley, Malaysia and the data is gathered through paper based surveys. A sample size of 237 units were randomly selected from 5 higher educational institutions in the Klang Valley using the Stratified Random sampling method and from the analysis of the data, it was found that the implementation of wireless technologies in HEIs have created lot of opportunities and also challenges.

Keywords: Wired Technologies, Wireless Classroom, HEI, Dense User Environment.

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Authors: L. Petrovičová, Ž. Balážová, Z. Gálová, M. Wójcik-Jagła, M. Rapacz

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In the present study, RAPD-PCR was used to assess genetic diversity of the rye including landrances and new rye cultivars coming from Central Europe and the Union of Soviet Socialist Republics (SUN). Five arbitrary random primers were used to determine RAPD polymorphism in the set of 38 rye genotypes. These primers amplified altogether 43 different DNA fragments with an average number of 8.6 fragments per genotypes. The number of fragments ranged from 7 (RLZ 8, RLZ 9 and RLZ 10) to 12 (RLZ 6). DI and PIC values of all RAPD markers were higher than 0.8 that generally means high level of polymorphism detected between rye genotypes. The dendrogram based on hierarchical cluster analysis using UPGMA algorithm was prepared. The cultivars were grouped into two main clusters. In this experiment, RAPD proved to be a rapid, reliable and practicable method for revealing of polymorphism in the rye cultivars.

Keywords: Genetic diversity, polymorphism, RAPD markers, Secalecereale L.

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Authors: Hechmi Saidi, Noureddine Hamdi

Abstract:

The performance of hybrid amplify and forward - decode and forward (AF-DF) hybrid radio frequency/free space optical (RF/FSO) communication system, that adopts M-ary pulse position modulation (MPPM) techniques, is analyzed. Both exact and approximate symbol-error rates (SERs) are derived. The random variations of the received optical irradiance, produced by the atmospheric turbulence, is modeled by the gamma-gamma (GG) statistical distribution. A closed-form expression for the probability density function (PDF) is derived for the whole above system is obtained. Thanks to the use of hybrid AF-DF hybrid RF/FSO configuration and MPPM, the effects of atmospheric turbulence is mitigated; hence the capacity of combating atmospheric turbulence and the transmissitted signal quality are improved.

Keywords: FSO, RF, hybrid, AF, DF, SER, SNR, GG channel.

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Authors: Poo Bee Tin, Rahmah Ismail, Norasmah Othman

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Globalisation is a phenomenon that cannot be avoided. As globalisation allowed free flow of inputs including labour, it may affect job opportunities for the locals. Therefore, investigate the determinants of labour supply is essential in understanding the structure of labour market in the new era of globalization. The objective of this article is to examine labour supply by taking into account the globalisation effect. The study covers 3885 households in Peninsular Malaysia who are chosen using stratified random sampling. The labour supply model will be the basis for the analysis. The basic labour supply determinants are own wage and non-labour income. However, the extended labour supply model incorporates other variables like spouse wage，number of children and individuals characteristics like education level and age. Besides, the globalization indicator will also be incorporated as another independent variable.

Keywords: globalization, head of households, labour supply, wage

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Authors: Rujira Kongnuy, Puntani Pongsumpun, I-Ming Tang

Abstract:

Mathematical models can be used to describe the dynamics of the spread of infectious disease between susceptibles and infectious populations. Dengue fever is a re-emerging disease in the tropical and subtropical regions of the world. Its incidence has increased fourfold since 1970 and outbreaks are now reported quite frequently from many parts of the world. In dengue endemic regions, more cases of dengue infection in pregnancy and infancy are being found due to the increasing incidence. It has been reported that dengue infection was vertically transmitted to the infants. Primary dengue infection is associated with mild to high fever, headache, muscle pain and skin rash. Immune response includes IgM antibodies produced by the 5th day of symptoms and persist for 30-60 days. IgG antibodies appear on the 14th day and persist for life. Secondary infections often result in high fever and in many cases with hemorrhagic events and circulatory failure. In the present paper, a mathematical model is proposed to simulate the succession of dengue disease transmission in pregnancy and infancy. Stability analysis of the equilibrium points is carried out and a simulation is given for the different sets of parameter. Moreover, the bifurcation diagrams of our model are discussed. The controlling of this disease in infant cases is introduced in the term of the threshold condition.

Keywords: Dengue infection, equilibrium states, maternalantibodies, pregnancy and infancy.

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Authors: Daniel Morariu, Lucian N. Vintan, Volker Tresp

Abstract:

Text categorization is the problem of classifying text documents into a set of predefined classes. After a preprocessing step, the documents are typically represented as large sparse vectors. When training classifiers on large collections of documents, both the time and memory restrictions can be quite prohibitive. This justifies the application of feature selection methods to reduce the dimensionality of the document-representation vector. In this paper, three feature selection methods are evaluated: Random Selection, Information Gain (IG) and Support Vector Machine feature selection (called SVM_FS). We show that the best results were obtained with SVM_FS method for a relatively small dimension of the feature vector. Also we present a novel method to better correlate SVM kernel-s parameters (Polynomial or Gaussian kernel).

Keywords: Feature Selection, Learning with Kernels, SupportVector Machine, and Classification.

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