Search results for: collective animal behavior algorithm

Authors: Eleni Panagiotarakou

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This paper argues that humans have a collective moral responsibility to help wild animals during the Anthropocene era. Seen from the perspective of deontic logic, this moral responsibility did not exist in the Holocene era (ca. 11,700 BC-1945 AD) on account of humanity’s limited impact on the natural environment. By contrast in the Anthropocene, human activities are causing significant disturbances to planetary ecosystems and by inference to wildlife communities. Under these circumstances controversial and deeply regrettable interventional methods such as Managed Relocations (MR) and synthetic biology should be expanded and become policy measures despite their known and unknown risks. The main rationale for the above stems from the fact that traditional management strategies are simply insufficient in the Anthropocene. If the same anthropogenic activities continue unabated they risk triggering a sixth mass species extinction.

Keywords: anthropocene, humanitarian interventions, managed relocations, species extinctions, synthetic biology

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Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

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Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

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Authors: Sudeepti Dayal, Neelesh Gupta

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Image abbreviation is utilized for reducing the size of a file without demeaning the quality of the image to an objectionable level. The depletion in file size permits more images to be deposited in a given number of spaces. It also minimizes the time necessary for images to be transferred. Storage of medical images is a most researched area in the current scenario. To store a medical image, there are two parameters on which the image is divided, regions of interest and non-regions of interest. The best way to store an image is to compress it in such a way that no important information is lost. Compression can be done in two ways, namely lossy, and lossless compression. Under that, several compression algorithms are applied. In the paper, two algorithms are used which are, discrete cosine transform, applied to non-region of interest (lossy), and discrete wavelet transform, applied to regions of interest (lossless). The paper introduces SPIHT (set partitioning hierarchical tree) algorithm which is applied onto the wavelet transform to obtain good compression ratio from which an image can be stored efficiently.

Keywords: Compression ratio, DWT, SPIHT, DCT

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Authors: Milad Abolfazli, Milad Bazli

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In this critical review, the topic of how composites maintain their tensile strength when exposed to elevated temperatures will be studied. A comprehensive database of 761 tests have been analyzed and closely examined to study the various factors that affect the strength retention. Conclusions are drawn from the collective research efforts of numerous scholars who have investigated this subject. Through the analysis of these tests, the relationships between the tensile strength retention and various effective factors are investigated. This review is meant to be a practical resource for researchers and engineers. It provides valuable information that can guide the development of composites tailored for high-temperature applications. By offering a deeper understanding of how composites behave in extreme heat, the paper contributes to the advancement of materials science and engineering.

Keywords: tesnile tests, high temperatures, FRP composites, mechanical perfomance

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Authors: F. Jiménez, R. Jódar, M. Martín, G. Sánchez, G. Sciavicco

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Abstract—Attribute or feature selection is one of the basic strategies to improve the performances of data classification tasks, and, at the same time, to reduce the complexity of classifiers, and it is a particularly fundamental one when the number of attributes is relatively high. Its application to unsupervised classification is restricted to a limited number of experiments in the literature. Evolutionary computation has already proven itself to be a very effective choice to consistently reduce the number of attributes towards a better classification rate and a simpler semantic interpretation of the inferred classifiers. We present a feature selection wrapper model composed by a multi-objective evolutionary algorithm, the clustering method Expectation-Maximization (EM), and the classifier C4.5 for the unsupervised classification of data extracted from a psychological test named BASC-II (Behavior Assessment System for Children - II ed.) with two objectives: Maximizing the likelihood of the clustering model and maximizing the accuracy of the obtained classifier. We present a methodology to integrate feature selection for unsupervised classification, model evaluation, decision making (to choose the most satisfactory model according to a a posteriori process in a multi-objective context), and testing. We compare the performance of the classifier obtained by the multi-objective evolutionary algorithms ENORA and NSGA-II, and the best solution is then validated by the psychologists that collected the data.

Keywords: evolutionary computation, feature selection, classification, clustering

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Authors: Qusay Alghazali, Tibor Cinkler, Abdulhalim Fayad

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In modern optical networks, the flex grid spectrum usage is most widespread, where higher bit rate streams get larger spectrum slices while lower bit rate traffic streams get smaller spectrum slices. To our practice, under the ITU-T recommendation, G.694.1, spectrum slices of 50, 75, and 100 GHz are being used with central frequency at 193.1 THz. However, when these spectrum slices are not sufficient, multiple spectrum slices can use either one next to another or anywhere in the optical wavelength. In this paper, we propose the analysis of the wavelength assignment problem. We compare different algorithms for this spectrum assignment with and without protection. As a reference for comparisons, we concluded that the Integer Linear Programming (ILP) provides the global optimum for all cases. The most scalable algorithm is the greedy one, which yields results in subsequent ranges even for more significant network instances. The algorithms’ benchmark implemented using the LEMON C++ optimization library and simulation runs based on a minimum number of spectrum slices assigned to lightpaths and their execution time.

Keywords: spectrum assignment, integer linear programming, greedy algorithm, international telecommunication union, library for efficient modeling and optimization in networks

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Authors: Timothy O. Adekunle, Sigridur Bjarnadottir

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Several studies have investigated thermal behavior of buildings with limited studies focusing on high-rise buildings. Of the limited investigations that have considered thermal performance of high-rise buildings, only a few studies have considered thermal behavior of high-rise structural sustainable buildings. As a result, this study investigates the thermal behavior of a high-rise structural timber building. The study aims to understand the thermal environment of a high-rise structural timber block of apartments located in East London, UK by comparing the indoor environmental conditions at different floors (ground and upper floors) of the building. The environmental variables (temperature and relative humidity) were measured at 15-minute intervals for a few weeks in the summer of 2012 to generate data that was considered for calibration and validation of the simulated results. The study employed mainly dynamic thermal building simulation using DesignBuilder by EnergyPlus and supplemented with environmental monitoring as major techniques for data collection and analysis. The weather file (Test Reference Years- TRYs) for the 2000s from the weather generator carried out by the Prometheus Group was considered for the simulation since the study focuses on investigating thermal behavior of high-rise structural timber buildings in the summertime and not in extreme summertime. In this study, the simulated results (May-September of the 2000s) will be the focus of discussion, but the results will be briefly compared with the environmental monitoring results. The simulated results followed a similar trend with the findings obtained from the short period of the environmental monitoring at the building. The results revealed lower temperatures are often predicted (at least 1.1°C lower) at the ground floor than the predicted temperatures at the upper floors. The simulated results also showed that higher temperatures are predicted in spaces at southeast facing (at least 0.5°C higher) than spaces in other orientations across the floors considered. There is, however, a noticeable difference between the thermal environment of spaces when the results obtained from the environmental monitoring are compared with the simulated results. The field survey revealed higher temperatures were recorded in the living areas (at least 1.0°C higher) while higher temperatures are predicted in bedrooms (at least 0.9°C) than living areas for the simulation. In addition, the simulated results showed spaces on lower floors of high-rise structural timber buildings are predicted to provide more comfortable thermal environment than spaces on upper floors in summer, but this may not be the same in wintertime due to high upward movement of hot air to spaces on upper floors.

Keywords: building simulation, high-rise, structural timber buildings, sustainable, temperatures, thermal behavior

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Authors: Nour Charara, Hussein Charara, Omar Abou Khaled, Hani Abdallah, Elena Mugellini

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In this paper, we present a human behavior modeling approach in videos scenes. This approach is used to model the normal behaviors in the conference halls. We exploited the Probabilistic Latent Semantic Analysis technique (PLSA), using the 'Bag-of-Terms' paradigm, as a tool for exploring video data to learn the model by grouping similar activities. Our term vocabulary consists of 3D spatio-temporal patch groups assigned by the direction of motion. Our video representation ensures the spatial information, the object trajectory, and the motion. The main importance of this approach is that it can be adapted to detect abnormal behaviors in order to ensure and enhance human security.

Keywords: activity modeling, clustering, PLSA, video representation

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Authors: Muhammet Dursun Kaya, Hasan Asil

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In recent years, different methods have been proposed to optimize question processing in database. Although different methods have been proposed to optimize the query, but the problem which exists here is that most of these methods destroy the query execution plan after executing the query. This research attempts to solve the above problem by using a combination of methods of communicating with the database (the present questions in the programming code and using store procedures) and making query processing adaptive in database, and proposing a new approach for optimization of query processing by introducing the idea of dynamic store procedures. This research creates dynamic store procedures in the database according to the proposed algorithm. This method has been tested on applied software and results shows a significant improvement in reducing the query processing time and also reducing the workload of DBMS. Other advantages of this algorithm include: making the programming environment a single environment, eliminating the parametric limitations of the stored procedures in the database, making the stored procedures in the database dynamic, etc.

Keywords: relational database, agent, query processing, adaptable, communication with the database

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Authors: Santi Termprasertsakul, Jakkrich Jearviriyaboonya

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This paper studies the market power and pricing behavior of products in iron and steel industry by investigating the impact of vertical product differentiation (VPD) on exchange rate pass-through (ERPT). Vietnam has become one of the major trading partners of Thailand since 2017. The iron and steel export value to Vietnam is more than $300 million a year. Particularly, the average growth rate of importing iron and steel is approximately 30% per year. The VPD is applied to analyze the quality difference of iron and steel between Thailand and Vietnam. The 20 products in iron and steel industry are investigated. The monthly pricing behavior of Harmonized Commodity Description and Coding System 4-digit products is observed from 2010 to 2019. The Nonlinear Autoregressive Distributed Lag is also used to analyze the asymmetry of ERPT in this paper. The empirical results basically reveal an incomplete pass-through between Thai Baht and Vietnamese Dong. The ERPT also varies with the degree of VPD. The product with higher VPD, indicating higher unit values, has higher ERPT. This result suggests the higher market power of the Thai iron and steel industry. In addition, the asymmetry of ERPT exists.

Keywords: exchange rate pass-through, iron and steel industry, pricing behavior, vertical product differentiation

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Authors: Subhajit Das, Nirjhar Dhang

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Structural damage detection is a challenging work in the field of structural health monitoring (SHM). The damage detection methods mainly focused on the determination of the location and severity of the damage. Model updating is a well known method to locate and quantify the damage. In this method, an error function is defined in terms of difference between the signal measured from ‘experiment’ and signal obtained from undamaged finite element model. This error function is minimised with a proper algorithm, and the finite element model is updated accordingly to match the measured response. Thus, the damage location and severity can be identified from the updated model. In this paper, an error function is defined in terms of modal data viz. frequencies and modal assurance criteria (MAC). MAC is derived from Eigen vectors. This error function is minimized by teaching-learning-based optimization (TLBO) algorithm, and the finite element model is updated accordingly to locate and quantify the damage. Damage is introduced in the model by reduction of stiffness of the structural member. The ‘experimental’ data is simulated by the finite element modelling. The error due to experimental measurement is introduced in the synthetic ‘experimental’ data by adding random noise, which follows Gaussian distribution. The efficiency and robustness of this method are explained through three examples e.g., one truss, one beam and one frame problem. The result shows that TLBO algorithm is efficient to detect the damage location as well as the severity of damage using modal data.

Keywords: damage detection, finite element model updating, modal assurance criteria, structural health monitoring, teaching learning based optimization

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Authors: Husam El-Asfour, Fateh Adhnouss, Kenneth McIsaac, Abdul Mutalib Wahaishi, Raafat Aburukba, Idris El-Feghia

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Although intelligence is commonly referred to as the observable behavior in various fields and domains, it must also be shown how it develops by exhibiting multiple forms and without observing the inherent behavior. There have been several official and informal definitions of intelligence in various areas; however, no scientific agreement on a definition has been agreed upon. There must be a single definition, structure, and precise modeling for articulating how intelligence is perceived by people and machines in order to comprehend intelligence. Another key challenge is defining the different environment types based on the integral elements (agents) and their possible interactions. On the basis of conceptualization, this paper proposes a formal model for defining and developing intelligence. Forms of intelligence are derived from an ontological view, and thus intelligence is defined, described, and modeled based on the various types of environments.

Keywords: intelligence, forms, transformation, conceptualization, ontological view

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Authors: Georges Ghazi, Marc-Henry Devillers, Ruxandra M. Botez

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Aircraft flight trajectory optimization has been identified to be a promising solution for reducing both airline costs and the aviation net carbon footprint. Nowadays, this role has been mainly attributed to the flight management system. This system is an onboard multi-purpose computer responsible for providing the crew members with the optimized flight plan from a destination to the next. To accomplish this function, the flight management system uses a variety of look-up tables to compute the optimal speed and altitude for each flight regime instantly. Because the cruise is the longest segment of a typical flight, the proposed algorithm is focused on minimizing fuel consumption for this flight phase. In this paper, a complete methodology to estimate the aircraft performance and subsequently compute the optimal speed in cruise is presented. Results showed that the obtained performance database was accurate enough to predict the flight costs associated with the cruise phase.

Keywords: Cessna Citation X, cruise speed optimization, flight cost, cost index, and golden section search

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Authors: Luiz G. Véras, Felipe L. Medeiros, Lamartine F. Guimarães

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This work approaches the automatic planning of paths for Unmanned Aerial Vehicles (UAVs) through the application of the Rapidly Exploring Random Tree Star-Smart (RRT*-Smart) algorithm. RRT*-Smart is a sampling process of positions of a navigation environment through a tree-type graph. The algorithm consists of randomly expanding a tree from an initial position (root node) until one of its branches reaches the final position of the path to be planned. The algorithm ensures the planning of the shortest path, considering the number of iterations tending to infinity. When a new node is inserted into the tree, each neighbor node of the new node is connected to it, if and only if the extension of the path between the root node and that neighbor node, with this new connection, is less than the current extension of the path between those two nodes. RRT*-smart uses an intelligent sampling strategy to plan less extensive routes by spending a smaller number of iterations. This strategy is based on the creation of samples/nodes near to the convex vertices of the navigation environment obstacles. The planned paths are smoothed through the application of the method called quintic pythagorean hodograph curves. The smoothing process converts a route into a dynamically-viable one based on the kinematic constraints of the vehicle. This smoothing method models the hodograph components of a curve with polynomials that obey the Pythagorean Theorem. Its advantage is that the obtained structure allows computation of the curve length in an exact way, without the need for quadratural techniques for the resolution of integrals.

Keywords: path planning, path smoothing, Pythagorean hodograph curve, RRT*-Smart

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Authors: Angga S. Fajar, Y. Takahashi, J. Kiyono, S. Sawada

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The problem of medium span bridge bearing support in the extreme temperatures fluctuation region is deterioration in case the suppression of superstructure that sustains temperature expansion. The other hand, the behavior and the parameter of RC column accompanied with friction damping mechanism were determined successfully based on the experiment and numerical analysis. This study proposes the structural system of RC pier accompanied with multi sliding friction damping mechanism to substitute the conventional system of pier together with bearing support. In this system, the pier has monolith behavior to the superstructure with flexible small deformation to accommodate thermal expansion of the superstructure. The flexible small deformation behavior is realized by adding the gap mechanism in the multi sliding friction devices form. The important performances of this system are sufficient lateral flexibility in small deformation, sufficient elastic deformation capacity, sufficient lateral force resistance, and sufficient energy dissipation. Numerical analysis performed for this system with fiber element model. It shows that the structural system has good performance not only under small deformation due to thermal expansion of the superstructure but also under seismic load.

Keywords: RC Pier, thermal expansion, multi sliding friction device, flexible small deformation

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Authors: Komol Phaisarn, Anuphan Suttimarn, Vitchanan Keawtong, Kittisak Thongyoun, Chaiyos Jamsawang

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This article is intended to analyze insurance information which contains information on the customer decision when purchasing life insurance pay package. The data were analyzed in order to present new customers with Life Insurance Perfect Pay package to meet new customers’ needs as much as possible. The basic data of insurance pay package were collect to get data mining; thus, reducing the scattering of information. The data were then classified in order to get decision model or decision tree using Algorithm C4.5 (J-48). In the classification, WEKA tools are used to form the model and testing datasets are used to test the decision tree for the accurate decision. The validation of this model in classifying showed that the accurate prediction was 68.43% while 31.25% were errors. The same set of data were then tested with other models, i.e. Naive Bayes and Zero R. The results showed that J-48 method could predict more accurately. So, the researcher applied the decision tree in writing the program used to introduce the product to new customers to persuade customers’ decision making in purchasing the insurance package that meets the new customers’ needs as much as possible.

Keywords: decision tree, data mining, customers, life insurance pay package

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Authors: David F. Nettleton, Elodie Bugnicourt, Christian Wasiak, Alejandro Rosales

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In this presentation, preliminary results are given for the modeling and calibration of two different industrial winding MIMO (Multiple Input Multiple Output) processes using machine learning techniques. In contrast to previous approaches which have typically used ‘black-box’ linear statistical methods together with a definition of the mechanical behavior of the process, we use non-linear machine learning algorithms together with a ‘white-box’ rule induction technique to create a supervised model of the fitting error between the expected and real force measures. The final objective is to build a precise model of the winding process in order to control de-tension of the material being wound in the first case, and the friction of the material passing through the die, in the second case. Case 1, Tension Control of a Winding Process. A plastic web is unwound from a first reel, goes over a traction reel and is rewound on a third reel. The objectives are: (i) to train a model to predict the web tension and (ii) calibration to find the input values which result in a given tension. Case 2, Friction Force Control of a Micro-Pullwinding Process. A core+resin passes through a first die, then two winding units wind an outer layer around the core, and a final pass through a second die. The objectives are: (i) to train a model to predict the friction on die2; (ii) calibration to find the input values which result in a given friction on die2. Different machine learning approaches are tested to build models, Kernel Ridge Regression, Support Vector Regression (with a Radial Basis Function Kernel) and MPART (Rule Induction with continuous value as output). As a previous step, the MPART rule induction algorithm was used to build an explicative model of the error (the difference between expected and real friction on die2). The modeling of the error behavior using explicative rules is used to help improve the overall process model. Once the models are built, the inputs are calibrated by generating Gaussian random numbers for each input (taking into account its mean and standard deviation) and comparing the output to a target (desired) output until a closest fit is found. The results of empirical testing show that a high precision is obtained for the trained models and for the calibration process. The learning step is the slowest part of the process (max. 5 minutes for this data), but this can be done offline just once. The calibration step is much faster and in under one minute obtained a precision error of less than 1x10-3 for both outputs. To summarize, in the present work two processes have been modeled and calibrated. A fast processing time and high precision has been achieved, which can be further improved by using heuristics to guide the Gaussian calibration. Error behavior has been modeled to help improve the overall process understanding. This has relevance for the quick optimal set up of many different industrial processes which use a pull-winding type process to manufacture fibre reinforced plastic parts. Acknowledgements to the Openmind project which is funded by Horizon 2020 European Union funding for Research & Innovation, Grant Agreement number 680820

Keywords: data model, machine learning, industrial winding, calibration

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Authors: Vahid Anari, Mina Bakhshi

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Visual search and identification of immunohistochemically stained tissue of meningioma was performed manually in pathologic laboratories to detect and diagnose the cancers type of meningioma. This task is very tedious and time-consuming. Moreover, because of cell's complex nature, it still remains a challenging task to segment cells from its background and analyze them automatically. In this paper, we develop and test a computerized scheme that can automatically identify cells in microscopic images of meningioma and classify them into positive (proliferative) and negative (normal) cells. Dataset including 150 images are used to test the scheme. The scheme uses Fuzzy C-means algorithm as a color clustering method based on perceptually uniform hue, saturation, value (HSV) color space. Since the cells are distinguishable by the human eye, the accuracy and stability of the algorithm are quantitatively compared through application to a wide variety of real images.

Keywords: positive cell, color segmentation, HSV color space, immunohistochemistry, meningioma, thresholding, fuzzy c-means

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Authors: Ho Yeon Park, Kyoung-Jae Kim

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The purpose of this study is to investigate whether friendship in social media can be an important factor in recommender system through social scientific analysis of friendship in popular social media such as Facebook and Twitter. For this purpose, this study analyzes data on friendship in real social media using component analysis and clique analysis among sub-group analysis in social network analysis. In this study, we propose an algorithm to reflect the results of sub-group analysis on the recommender system. The key to this algorithm is to ensure that recommendations from users in friendships are more likely to be reflected in recommendations from users. As a result of this study, outcomes of various subgroup analyzes were derived, and it was confirmed that the results were different from the results of the existing recommender system. Therefore, it is considered that the results of the subgroup analysis affect the recommendation performance of the system. Future research will attempt to generalize the results of the research through further analysis of various social data.

Keywords: sub-group analysis, social media, social network analysis, recommender systems

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Authors: Ali̇ Bayram, Semih Dalkilic, Remzi Yigiter

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N-methyl-D-aspartate (NMDA) receptor is a subtype of glutamate receptor and plays a pivotal role in learning, memory, neuronal plasticity, neurotoxicity and synaptic mechanisms. Animal experiments were suggested that glutamate-induced excitotoxic injuriy and NMDA receptor blockage lead to amnesia and other neurodegenerative diseases including Alzheimer’s disease (AD), Huntington’s disease, amyotrophic lateral sclerosis. Aim of this study is to investigate association between NMDA receptor coding gene GRIN2B expression level and Alzheimer disease. The study was approved by the local ethics committees, and it was conducted according to the principles of the Declaration of Helsinki and guidelines for the Good Clinical Practice. Peripheral blood was collected 50 patients who diagnosed AD and 49 healthy control individuals. Total RNA was isolated with RNeasy midi kit (Qiagen) according to manufacturer’s instructions. After checked RNA quality and quantity with spectrophotometer, GRIN2B expression levels were detected by quantitative real time PCR (QRT-PCR). Statistical analyses were performed, variance between two groups were compared with Mann Whitney U test in GraphpadInstat algorithm with 95 % confidence interval and p < 0.05. After statistical analyses, we have determined that GRIN2B expression levels were down regulated in AD patients group with respect to control group. But expression level of this gene in each group was showed high variability. İn this study, we have determined that NMDA receptor coding gene GRIN2B expression level was down regulated in AD patients when compared with healthy control individuals. According to our results, we have speculated that GRIN2B expression level was associated with AD. But it is necessary to validate these results with bigger sample size.

Keywords: Alzheimer’s disease, N-methyl-d-aspartate receptor, NR2B, GRIN2B, mRNA expression, RT-PCR

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Authors: R. Rama Kishore, Sunesh Malik

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Development in the usage of internet for different purposes in recent times creates great threat for the copy right protection of the digital images. Digital watermarking is the best way to rescue from the said problem. This paper presents detailed review of the different watermarking techniques, latest trends in the field and categorized like spatial and transform domain, blind and non-blind methods, visible and non visible techniques etc. It also discusses the different optimization techniques used in the field of watermarking in order to improve the robustness and imperceptibility of the method. Different measures are discussed to evaluate the performance of the watermarking algorithm. At the end, this paper proposes a watermarking algorithm using (k.n) shares of halftone visual cryptography (HVC) instead of (2, 2) share cryptography. (k,n) shares visual cryptography improves the security of the watermark. As halftone is a method of reprographic, it helps in improving the visual quality of watermark image. The proposed method uses fractional transformation to improve the robustness of the copyright protection of the method.

Keywords: digital watermarking, fractional transform, halftone, visual cryptography

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Authors: Hadi Chahal, Irini Djeran-Maigre

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This paper focuses on the hydro-mechanical behavior of polychlorinated biphenyl (PCB) polluted sediments when stored in landfills and the interaction between PCBs and geosynthetic clay liners (GCL) with respect to hydraulic performance of the liner and the overall performance and stability of landfills. A European decree, adopted in the French regulation forbids the reintroducing of contaminated dredged sediments containing more than 0,64mg/kg Σ 7 PCBs to rivers. At these concentrations, sediments are considered hazardous and a remediation process must be adopted to prevent the release of PCBs into the environment. Dredging and landfilling polluted sediments is considered an eco-environmental remediation solution. French regulations authorize the storage of PCBs contaminated components with less than 50mg/kg in municipal solid waste facilities. Contaminant migration via leachate may be possible. The interactions between PCBs contaminated sediments and the GCL barrier present in the bottom of a landfill for security confinement are not known. Moreover, the hydro-mechanical behavior of stored sediments may affect the performance and the stability of the landfill. In this article, hydro-mechanical characterization of the polluted sediment is presented. This characterization led to predict the behavior of the sediment at the storage site. Chemical testing showed that the concentration of PCBs in sediment samples is between 1.7 and 2,0 mg/kg. Physical characterization showed that the sediment is organic silty sand soil (%Silt=65, %Sand=27, %OM=8) characterized by a high plasticity index (Ip=37%). Permeability tests using permeameter and filter press showed that sediment permeability is in the order of 10-9 m/s. Compressibility tests showed that the sediment is a very compressible soil with Cc=0,53 and Cα =0,0086. In addition, effects of PCB on the swelling behavior of bentonite were studied and the hydraulic performance of the GCL in interaction with PCBs was examined. Swelling tests showed that PCBs don’t affect the swelling behavior of bentonite. Permeability tests were conducted on a 1.0 m pilot scale experiment, simulating a storage facility. PCBs contaminated sediments were directly placed over a passive barrier containing GCL to study the influence of the direct contact of polluted sediment leachate with the GCL. An automatic water system has been designed to simulate precipitation. Effluent quantity and quality have been examined. The sediment settlements and the water level in the sediment have been monitored. The results showed that desiccation affected the behavior of the sediment in the pilot test and that laboratory tests alone are not sufficient to predict the behavior of the sediment in landfill facility. Furthermore, the concentration of PCB in the sediment leachate was very low ( < 0,013 µg/l) and that the permeability of the GCL was affected by other components present in the sediment leachate. Desiccation and cracks were the main parameters that affected the hydro-mechanical behavior of the sediment in the pilot test. In order to reduce these infects, the polluted sediment should be stored at a water content inferior to its shrinkage limit (w=39%). We also propose to conduct other pilot tests with the maximum concentration of PCBs allowed in municipal solid waste facility of 50 mg/kg.

Keywords: geosynthetic clay liners, landfill, polychlorinated biphenyl, polluted dredged materials

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Authors: Iva Dvornik, Sandro Božić, Žana Božić Brkić

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System-dynamic simulating modelling is one of the most appropriate and successful scientific methods of the complex, non-linear, natural, technical and organizational systems. In the recent practice its methodology proved to be efficient in solving the problems of control, behavior, sensitivity and flexibility of the system dynamics behavior having a high degree of complexity, all these by computing simulation i.e. “under laboratory conditions” what means without any danger for observed realities. This essay deals with the research of the gas turbine dynamic process as well as the operating pump units and transformation of gas energy into hydraulic energy has been simulated. In addition, system mathematical model has been also researched (gas turbine- centrifugal pumps – pipeline pressure system – storage vessel).

Keywords: system dynamics, modelling, centrifugal pump, turbine, gases, continuous and discrete simulation, heuristic optimisation

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Authors: Zakir Husain

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Main objective of a power transformer design optimization problem requires minimizing the total overall cost and/or mass of the winding and core material by satisfying all possible constraints obligatory by the standards and transformer user requirement. The constraints include appropriate limits on winding fill factor, temperature rise, efficiency, no-load current and voltage regulation. The design optimizations tasks are a constrained minimum cost and/or mass solution by optimally setting the parameters, geometry and require magnetic properties of the transformer. In this paper, present the above design problems have been formulated by using genetic algorithm (GA) and simulated annealing (SA) on the MATLAB platform. The importance of the presented approach is stems for two main features. First, proposed technique provides reliable and efficient solution for the problem of design optimization with several variables. Second, it guaranteed to obtained solution is global optimum. This paper includes a demonstration of the application of the genetic programming GP technique to transformer design.

Keywords: optimization, power transformer, genetic algorithm (GA), simulated annealing technique (SA)

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Authors: Nseabasi Nsikakabasi Etim

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In recent times, there has been a significant shortfall in between the production and supply of animal protein to meet the ever increasing population. To meet the increasing demand for animal protein, there is a need to focus attention on the production of livestock whose nutritional requirement does not put much strain on the limited sources of feed ingredients to which men subscribe. An example of such livestock is the rabbit. Rabbit is a pseudo-ruminant herbivore which utilizes much undigested and unabsorbed feed materials as sources of nutrient for maintenance and production. Thus, this study was conducted to investigate the effects of feeding Aspilia africana as forage on the growth rates of female pseudo-ruminant herbivores (rabbits) at different physiological states. Thirty (30) Dutch breed rabbit does of 5–6 months of age were used for the experiment which was conducted in a completely randomized design for four months. The rabbits were divided into three treatment groups, ten does per treatment group; which consisted of mixed forages (Centrosema pubescent (200g), Panicum maximum (200g) and Ipomea batatas leaves (100g) without Aspilia africana (T1; control), fresh Aspilia africana (500g/dose/day) (T2) and wilted Aspilia africana (500g/dose/day) (T3). Rabbits in all treatment groups received the same concentrate (300g/animal/day) throughout the period of the study and mixed forages from the commencement of the experiment till the does kindled. After parturition, fresh and wilted Aspilia africana were introduced in treatments 2 and three respectively, whereas the control group continued on mixed forages throughout the study. The result of the study revealed that the initial average body weight of the rabbit does was 1.74kg. At mating and gestation periods, the body weights of the does in T2 was significantly higher (P<0.05) than the rest. There were no significant differences (P<0.05) in the body weights of does at kindling between the various treatment groups. During the physiological states of lactation, weaning and re-mating, the control group (T1) had significantly lower body weight than those of the treated groups (T2 and T3). Furthermore, T2 had significantly higher body weight than T3. The study revealed that Aspilia africana; mainly the fresh leaves have greater growth stimulating effects when fed to pseudo-ruminants (rabbits), thereby enhancing body weights of does during lactation and weaning.

Keywords: Aspilia africana, herbivores, pseudo-ruminants, physiological states

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Authors: S. Lecheb, A. Nour, A. Chellil, H. Mechakra, N. Tchina, H. Kebir

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This study concerned the dynamic behavior of the wind turbine rotor. Before all, we have studied the loads applied to the rotor, which allows the knowledge their effect on the fatigue. We also studied the movement of the longitudinal cracked rotor in order to determine stress, strain and displacement. Moreover, to study the issues of cracks in the critical zone ABAQUS software is used, which based to the finite element to give the results. In the first we compared the first six modes shapes between cracking and uncracking of HAWT rotor. In the second part, we show the evolution of six first naturals frequencies with longitudinal crack propagation. Finally, we conclude that the residual change in the naturals frequencies can be used as in shaft crack diagnosis predictive maintenance.

Keywords: wind turbine rotor, natural frequencies, longitudinal crack growth, life time

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Authors: Mahmoud Mohamed Emam, Yasser Fathy Hendawy Al-Mahdy

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Higher education institutions (HEIs) in Arab countries have witnessed large scope transformations as a result of embracing globalised practices. The introduction of program academic accreditation in HEIs in the Arab context has been regarded as a change enterprise that has proponents and opponents. In essence, introducing new systems or practices trigger changes that may shatter employees at a given organization. Therefore, it is argued that the interaction between organizational, contextual, and individual-related variables are likely to determine how the organization succeeds in facing resistance to change. This study investigated a mediated-effects model of organizational support and citizenship behavior. The model proposes organizational support as an antecedent of citizenship behavior and commitment to change as a mediator in the organizational support–citizenship behavior relationship. Survey data were collected and analyzed from university faculty (n=221) using structural equation modeling. Findings showed that organizational support significantly contributes to increasedcitizenshipbehaviour and the commitment of university faculty to program accreditation as a change enterprise, which has a significant and direct impact on their citizenship behaviour. We conclude that university-level organizational support shapes faculty’s commitment to change both directly and indirectly. The findings have significant practical implications for HEIs in Arab countries when they introduce new practices that aim at improving institutional effectiveness.

Keywords: organizational support, accreditation, commitment, citizenship behaviour

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Authors: M. Boumaiza, S. Mohamadi, B. Moussai

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In this article the study of the seismic response of an earth dam with sealing walls has been made by introducing the effect of the change of position and depth of the sealing wall and the effect of non-linear behavior of soil of the foundation by taking into account the variation of the viscous damping and shear modulus in each layer of soil on the seismic response of the dam. As a case study, we take the Algerian dam Kef-Eddir which lies in the far west of the territory of the Wilaya of Tipaza (wadi Eddamous), classified according to the RPA 2003 as a high seismicity zone (zone III). With a height of 71m above the foundation and a width of 478m. The seismic event applied to the rock, is the earthquake of Chenoua (29 October, 1989), with a magnitude Mw=6 that hit the region.

Keywords: earth dam, earthquake, sealing walls, viscous damping

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Authors: Irene H. Maralit

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Warning labels have been found to inform smokers about the health hazards of smoking, encourage smokers to quit, and prevent nonsmokers from starting to smoke. Warning labels on tobacco products are an ideal way of communicating with smokers. Since the intervention is delivered at the time of smoking, nearly all smokers are exposed to warning labels and pack-a-day smokers could be exposed to the warnings more than 7,000 times per year. Given the reach and frequency of exposure, the proponents want to know the effect of warning labels on smoking behavior. Its aims to identify the profile of the smokers associated with its behavioral variables that best describe the users’ perception. The behavioral variables are AVOID, THINK RISK and FORGO. This research study aims to determine if there is significant relationship between the effect of warning labels on cigarette package on Consumer behavior when grouped according to profile variable. The researcher used quota sampling to gather representative data through purposive means to determine the accurate representation of data needed in the study. Furthermore, the data was gathered through the use of a self-constructed questionnaire. The statistical method used were Frequency count, Chi square, multi regression, weighted mean and ANOVA to determine the scale and percentage of the three variables. After the analysis of data, results shows that most of the respondents belongs to age range 22–28 years old with percentage of 25.3%, majority are male with a total number of 134 with percentage of 89.3% and single with total number of 79 and percentage of 52.7%, mostly are high school graduates with total number of 59 and percentage of 39.3, with regards to occupation, skilled workers have the highest frequency of 37 with 24.7%, Majority of the income of the respondents falls under the range of Php 5,001-Php10,000 with 50.7%. And also with regards to the number of sticks consumed per day falls under 6–10 got the highest frequency with 33.3%. The respondents THINK RISK factor got the highest composite mean which is 2.79 with verbal interpretation of agree. It is followed by FORGO with 2.78 composite mean and a verbal interpretation of agree and AVOID variable with composite mean of 2.77 with agree as its verbal interpretation. In terms of significant relationship on the effects of cigarette label to consumer behavior when grouped according to profile variable, sex and occupation found to be significant.

Keywords: consumer behavior, smokers, warning labels, think risk avoid forgo

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Authors: Ashkan Zargar, Ali Taheri Mirghaed, Zein Talal Barakat, Alireza Khosravi, Hamed Paknejad

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With the increase of bacterial resistance to the chemical antibiotics, replacing it with ecofriendly herbal materials and with no adverse effects in the host body is very important. Therefore, in this study, the effect of combined essential oil (Thymus vulgaris-Origanum magorana and Ziziphora clinopodioides) on the growth behavior of Yersinia ruckeri, Aeromonas hydrophila and Lactococcus garvieae was evaluated. The compositions of the herbal essential oils used in this study were determined by gas chromatography-mass spectrometry (GC-MS) while, the investigating of antimicrobial effects was conducted by the agar-disc diffusion method, determination of minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC), and bacterial growth curves determination relied on optical density (OD) at 630 nm. The main compounds were thymol (40.60 %) and limonene (15.98 %) for Thymus vulgaris while carvacrol (57.86 %) and thymol (13.54 %) were the major compounds in Origanum magorana. As regards Ziziphora clinopodiodes, α-pinene (22.6 %) and carvacrol (21.1 %) represented the major constituents. Concerning Yersinia ruckeri, disc-diffusion results showed that t.O.z (50 % Origanum majorana) combined essential oil was presented the best inhibition zone (30.66 mm) but it was exhibited no significant differences with other tested commercial antibiotics except oxytetracycline (P <0/05). The inhibitory activity and the bactericidal effect of the t.O.z, unveiled by the MIC= 0.2 μL /mL and MBC= 1.6 μL /mL values, were clearly the best between all combined oils. The growth behaviour of Yersinia ruckeri was affected by this combined essential oil and changes in temperature and pH conditions affected herbal oil performance. As regard Aeromonas hydrophila, its results were so similar to Yersinia ruckeri results and t.O.z (50 % Origanum majorana) was the best between all combined oils (inhibition zone= 26 mm, MIC= 0.4 μL /mL and MBC= 3.2 μL /mL, combined essential oil was affected bacterial growth behavior). Also for Lactococcus garvieae, t.O.z (50 % Origanum majorana) was the best between all combined oils having the best inhibition zone= 20.66 mm, MIC= 0.8 μL /mL and MBC= 1.6 μL /mL and best effect on inhibiting bacterial growth. Combined herbal essential oils have a good and noticeable effect on the growth behavior of pathogenic bacteria in the laboratory, and by continuing research in the host, they may be a suitable alternative to control, prevent and treat diseases caused by these bacteria.

Keywords: bacterial pathogen, herbal medicine, growth behavior, fish

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