Search results for: stylistic features

Authors: Zine Souhila Rached, Mazari Benyounes Bouzid, Mohamed Amine, Allaoui Tayeb

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Wind energy has many advantages, it does not pollute and it is an inexhaustible source. However, its high cost is a major constraint, especially on the less windy sites. The purpose of wind energy systems is to maximize energy efficiency, and extract maximum power from the wind speed. In other words, having a power coefficient is maximum and therefore the maximum power point tracking. In this case, the MPPT control becomes important.To realize this control, strategy conventional proportional and integral (PI) controller is usually used. However, this strategy cannot achieve better performance. This paper proposes a robust control of a turbine which optimizes its production, that is improve the quality and energy efficiency, namely, a strategy of sliding mode control. The proposed sliding mode control strategy presents attractive features such as robustness to parametric uncertainties of the turbine; the proposed sliding mode control approach has been simulated on three-blade wind turbine. The simulation result under Matlab\Simulink has validated the performance of the proposed MPPT strategy.

Keywords: wind turbine, maximum power point tracking, sliding mode, energy conversion systems

Procedia PDF Downloads 607

Authors: Imtiaz Ahmed Khan, Sabu John, Sania Waqar, Lijing Wang, Mac Fergusson, Ilija Najdovski

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Evolution of textiles, from its orthodox to more interactive role has stirred the researchers to uncover its application in numerous arenas. The idea of using textile based materials for wearable energy harvesting and storage devices have gained immense popularity. This is mainly due to textile comfort and flexibility features. In this work, nano-carbonous materials were infused on cellulosic fibers using caustic soda treatment. This paper presents the complete procedure of yarn supercapacitors fabrication process through dip coating technique and its characterization method. The main objective is to study, the effect of varying caustic soda concentration on mass loading of activated carbon on yarns and the related capacitance output of the designed yarn supercapacitor. Polyvinyl alcohol and Phosphoric acid were used as electrolyte in a two-electrode cell assembly to measure device electrochemical performance. The results show a promising increase in capacitance value using this technique.

Keywords: yarn supercapacitors, activated carbon, dip coating, caustic soda, electrolyte, electrochemical characterization

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Authors: Chia-Hao Chang, Geng-Gui Wang, Jee-Cheng Wu

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The prediction of a landslide is a difficult task because it requires a detailed study of past activities using a complete range of investigative methods to determine the changing condition. In this research, first step, LiDAR 1-meter by 1-meter resolution of digital elevation model (DEM) was used to generate six environmental factors of landslide. Then, back-propagation neural networks (BPNN) was adopted to identify scarp, landslide areas and non-landslide areas. The BPNN uses 6 environmental factors in input layer and 1 output layer. Moreover, 6 landslide areas are used as training areas and 4 landslide areas as test areas in the BPNN. The hidden layer is set to be 1 and 2; the hidden layer neurons are set to be 4, 5, 6, 7 and 8; the learning rates are set to be 0.01, 0.1 and 0.5. When using 1 hidden layer with 7 neurons and the learning rate sets to be 0.5, the result of Network training root mean square error is 0.001388. Finally, evaluation of BPNN classification accuracy by the confusion matrix shows that the overall accuracy can reach 94.4%, and the Kappa value is 0.7464.

Keywords: digital elevation model, DEM, environmental factors, back-propagation neural network, BPNN, LiDAR

Procedia PDF Downloads 139

Authors: Jameela Ali Alkrimi, Abdul Rahim Ahmad, Azizah Suliman, Loay E. George

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Red blood cells (RBCs) are among the most commonly and intensively studied type of blood cells in cell biology. The lack of RBCs is a condition characterized by lower than normal hemoglobin level; this condition is referred to as 'anemia'. In this study, a software was developed to isolate RBCs by using a machine learning approach to classify anemic RBCs in microscopic images. Several features of RBCs were extracted using image processing algorithms, including principal component analysis (PCA). With the proposed method, RBCs were isolated in 34 second from an image containing 18 to 27 cells. We also proposed that PCA could be performed to increase the speed and efficiency of classification. Our classifier algorithm yielded accuracy rates of 100%, 99.99%, and 96.50% for K-nearest neighbor (K-NN) algorithm, support vector machine (SVM), and neural network ANN, respectively. Classification was evaluated in highly sensitivity, specificity, and kappa statistical parameters. In conclusion, the classification results were obtained for a short time period with more efficient when PCA was used.

Keywords: red blood cells, pre-processing image algorithms, classification algorithms, principal component analysis PCA, confusion matrix, kappa statistical parameters, ROC

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Authors: Asmau Mukhtar Ahmed, Olga Duran

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Hydrocarbon (HC) spills constitute a significant problem that causes great concern to the environment. With the latest technology (hyperspectral images) and state of the earth techniques (image processing tools), hydrocarbon spills can easily be detected at an early stage to mitigate the effects caused by such menace. In this study; a controlled laboratory experiment was used, and clay soil was mixed and homogenized with different hydrocarbon types (diesel, bio-diesel, and petrol). The different mixtures were scanned with HYSPEX hyperspectral camera under constant illumination to generate the hypersectral datasets used for this experiment. So far, the Short Wave Infrared Region (SWIR) has been exploited in detecting HC spills with excellent accuracy. However, the Near-Infrared Region (NIR) is somewhat unexplored with regards to HC contamination and how it affects the spectrum of soils. In this study, Deep Neural Network (DNN) was applied to the controlled datasets to detect and quantify the amount of HC spills in soils in the Near-Infrared Region. The initial results are extremely encouraging because it indicates that the DNN was able to identify features of HC in the Near-Infrared Region with a good level of accuracy.

Keywords: hydrocarbon, Deep Neural Network, short wave infrared region, near-infrared region, hyperspectral image

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Authors: Yani Shi, Jiaqi Yan

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A new product is important for companies to extend consumers and manifest competitiveness. New product often involves new features that consumers might not be familiar with while it may also have a competitive advantage to attract consumers compared to established products. However, although most online retailers employ recommendation agents (RA) to influence consumers’ product choice decision, recommended new products are not accepted and chosen as expected. We argue that it might also be caused by providing a new product recommendation in the wrong way at the wrong time. This study seeks to discuss how new product evaluations sourced from third parties could be employed in RAs as evidence of the superiority for the new product and how the new product recommendation could be provided to a consumer at the right time so that it can be accepted and finally chosen during the consumer’s decision-making process. A 2*2 controlled laboratory experiment was conducted to understand the selection of new product recommendation sources and recommendation timing. Human subjects were randomly assigned to one of the four treatments to minimize the effects of individual differences on the results. Participants were told to make purchase choices from our product categories. We find that a new product recommended right after a similar existing product and with the source of the expert review will be more likely to be accepted. Based on this study, both theoretical and practical contributions are provided regarding new product recommendation.

Keywords: new product recommendation, recommendation timing, recommendation source, recommendation agents

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Authors: Zidani Abla

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Lymphoma is a common type of cancer that affects the lymphatic system, including the lymph nodes, spleen and other associated organs. There are two main types of lymphoma: Hodgkin's lymphoma and non-Hodgkin's lymphoma. The epidemiological, clinical and biological features of lymphoma are poorly studied in Algeria. The main objective of our study is to investigate the epidemiological, clinical, paraclinical, etiological, evolutionary and biological characteristics of non-Hodgkin's lymphoma (NHL) in the hematology department of the University Hospital Center (HUC) of Batna. This is a study of 10 patients diagnosed at Batna University Hospital. 70% were male and 30% female (sex ratio M/F= 2.33). Median age was 51.7 years. Pain, especially abdominal pain, was the main reason for consultation. Stage IV predominated (40%), followed by stage III (20%). Abdominal adenopathies (34%) were the most abundant. Secondary hepatic localization was predominant. Large B-cell NHL predominated, accounting for 60% of cases, followed by small B-cell NHL (30%). Serology for hepatitis B and C, and human immunodeficiency virus (HIV) was negative. Biologically, a predominance of hyperleukocytosis, polynuclear neutrophilic leukocytosis, lymphopenia and hypoalbuminemia were present in the majority of cases. In summary, our results remain to be compared with other works for other periods and other regions in order to generalize lymphoma percentages for the entire Algerian population.

Keywords: non Hodgkin's lymphoma, epidemiology, clinic, biology

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Authors: Ali Akbar Zeinali

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Lack of appropriate equivalences for the terms or technical words is the result of ineffective translation guidelines adopted in the translation processes. The increasing number of foreign words and specific terms incorporated into the native language are due to the ongoing development of technology and science. Many problems appear in medical translation when the Persian translators try to employ non-Persian or imported words in medical texts, in which multiple equivalents may be created for one particular word based on the individual preferences of authors and translators in the target language due to lack of standardization. The study attempted to discuss the findings based on the compatibility of the international naming criteria, considering the translation procedures. About 67% of 339 equivalents under this study were grouped as incompatible words while about 33% of them were compatible terms. The similarities and differences were investigated and discussed according to the compatibility status of the equivalents with Sager’s criteria. Such equivalents have been classified into several groups through bi-dimensional descriptions that were different features of translation procedures related to the international naming criteria. In review of the frequency distribution of compatibilities, the equivalents were divided into two categories of compatibles and incompatibles, indicating the effectiveness of the applied translation procedures.

Keywords: linguistics, medical translation, naming, terminology

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Authors: Yousif Mohamed Y. Abdallah, Eltayeb Wagallah

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Image enhancement allows the observer to see details in images that may not be immediately observable in the original image. Image enhancement is the transformation or mapping of one image to another. The enhancement of certain features in images is accompanied by undesirable effects. To achieve maximum image quality after denoising, a new, low order, local adaptive Gaussian scale mixture model and median filter were presented, which accomplishes nonlinearities from scattering a new nonlinear approach for contrast enhancement of bones in bone scan images using both gamma correction and negative transform methods. The usual assumption of a distribution of gamma and Poisson statistics only lead to overestimation of the noise variance in regions of low intensity but to underestimation in regions of high intensity and therefore to non-optional results. The contrast enhancement results were obtained and evaluated using MatLab program in nuclear medicine images of the bones. The optimal number of bins, in particular the number of gray-levels, is chosen automatically using entropy and average distance between the histogram of the original gray-level distribution and the contrast enhancement function’s curve.

Keywords: bone scan, nuclear medicine, Matlab, image processing technique

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Authors: Kathrin Endres, Daniela Greven

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Although chatbots are a widely known and established communication instrument in B2C customer services, B2B industries still hesitate to implement chatbots due to the incertitude of customer acceptance. While many studies examine the chatbot acceptance of B2C consumers, few studies are focusing on the B2B sector, where the customer is represented by a buying center consisting of several stakeholders. This study investigates the challenges of chatbot acceptance in B2B industries compared to challenges of chatbot acceptance from current B2C literature by interviewing experts from German chatbot vendors. The results show many similarities between the customer requirements of B2B customers and B2C consumers. Still, due to several stakeholders involved in the buying center, the features of the chatbot users are more diverse but obfuscated at the same time. Using a factorial survey, this study further examines the customer acceptance of varying situations of B2B chatbot designs based on the chatbot variables transparency, fault tolerance, complexity of products, value of products, as well as transfer to live chat service employees. The findings show that all variables influence the propensity to use the chatbot. The results contribute to a better understanding of how firms in B2B industries can design chatbots to advance their customer service and enhance customer satisfaction.

Keywords: chatbots, technology acceptance, B2B customer service, customer satisfaction

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Authors: Sefik Can Karakaya, Ibrahim Demir

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In this study, the relationship between musical preferences and personality traits has been investigated in terms of Spotify audio analysis features. The aim of this paper is to build such a classifier capable of segmenting people into their Myers-Briggs Type Index (MBTI) personality type based on their Spotify playlists. Music takes an important place in the lives of people all over the world and online music streaming platforms make it easier to reach musical contents. In this context, the motivation to build such a classifier is allowing people to gain access to their MBTI personality type and perhaps for more reliably and more quickly. For this purpose, logistic regression and deep neural networks have been selected for classifier and their performances are compared. In conclusion, it has been found that musical preferences differ statistically between personality traits, and evaluated models are able to distinguish personality types based on given musical data structure with over %60 accuracy rate.

Keywords: myers-briggs type indicator, music psychology, Spotify, behavioural user profiling, deep neural networks, logistic regression

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Authors: Somayeh Azizi, Mohammad Hossein Ehsani, Amir Zareidoost

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In this research, a technique has been developed to reduce the corrosion rate of magnesium (Mg) metal by creating Zr-Nb thin film coatings. In this regard, thin-film coatings of niobium (Nb) zirconium (Zr) double alloy are applied on pure Mg specimens under different processes conditions, such as the change of the substrate temperature, substrate bias, and coating thickness using the magnetron sputtering method. Then, deposited coatings are analyzed in terms of surface features via field-emission scanning electron microscopy (FE-SEM), thin-layer X-ray diffraction (GI-XRD), energy-dispersive X-ray spectroscopy (EDS), atomic force microscopy (AFM), and corrosion tests. Also, nano-scratch tests were carried out to investigate the adhesion of the thin film. The results showed that the (Zr-Nb) thin films could control the degradation rate of Mg in the simulated body fluid (SBF). The nano-scratch studies depicted that the (Zr-Nb) thin films have a proper adhesion with the Mg substrate. Therefore, this technique could be used to enhance the corrosion resistance of bare Mg and could result in improving the performance of the biodegradable Mg implant for orthopedic applications.

Keywords: (Zr-Nb) thin film, magnetron sputtering, biodegradable Mg, degradation rate

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Authors: Samson Davoyan, Ashot Davoyan, Ani Khachatryan

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The indexes (Global Competitiveness Index, Economic Freedom Index, Human Development Index, etc.) developed by different international and non-government organizations in time and space express the quantitative and qualitative features of different fields of various reforms implemented in different countries. The main objective of our research is to develop new methodology that we will use to create integral index based on many indexes and that will include many areas of reforms. To achieve our aim we have used econometric methods (regression model for panel data method). The basis of our methodology is the development of the new integral index based on quantitative assessment of the change of two main parameters: the score of the countries by different indexes and the change of the ranks of countries for following two periods of time. As a result of the usage of methods for analyzes we have defined the indexes that are used to create the new integral index and the scales for each of them. Analyzing quantitatively and qualitatively analysis through the integral index for more than 100 countries for 2009-2014, we have defined comparative efficiency that helps to conclude in which directions countries have implemented reforms more effectively compared to others and in which direction reforms have implemented less efficiently.

Keywords: development, rank, reforms, comparative, index, economic, corruption, social, program

Procedia PDF Downloads 323

Authors: Syed Basit Ali Shah Bukhari, Yan Qiang, Saad Abdul Rauf, Syed Saqlaina Bukhari

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Text Classification is the methodology to classify any given text into the respective category from a given set of categories. It is highly important and vital to use proper set of pre-processing , feature selection and classification techniques to achieve this purpose. In this paper we have used different ensemble techniques along with variance in feature selection parameters to see the change in overall accuracy of the result and also on some other individual class based features which include precision value of each individual category of the text. After subjecting our data through pre-processing and feature selection techniques , different individual classifiers were tested first and after that classifiers were combined to form ensembles to increase their accuracy. Later we also studied the impact of decreasing the classification categories on over all accuracy of data. Text classification is highly used in sentiment analysis on social media sites such as twitter for realizing people’s opinions about any cause or it is also used to analyze customer’s reviews about certain products or services. Opinion mining is a vital task in data mining and text categorization is a back-bone to opinion mining.

Keywords: Natural Language Processing, Ensemble Classifier, Bagging Classifier, AdaBoost

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Authors: Niddal Imam, Vassilios G. Vassilakis

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After analysing messages posted by health-related spam campaigns in Twitter Arabic hashtags, we found that these campaigns use unique hijacked accounts (we call them adversarial hijacked accounts) as adversarial examples to fool deployed ML-based spam detectors. Existing ML-based models build a behaviour profile for each user to detect hijacked accounts. This approach is not applicable for detecting spam in Twitter hashtags since they are computationally expensive. Hence, we propose an adversary-aware ML-based detector, which includes a newly designed feature (avg posts) to improve the detection of spam tweets posted by the adversarial hijacked accounts at a tweet-level in trending hashtags. The proposed detector was designed considering three key points: robustness, adaptability, and interpretability. The new feature leverages the account’s temporal patterns (i.e., account age and number of posts). It is faster to compute compared to features discussed in the literature and improves the accuracy of detecting the identified hijacked accounts by 73%.

Keywords: Twitter spam detection, adversarial examples, evasion attack, adversarial concept drift, account hijacking, trending hashtag

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Authors: Basiliyos Tilahun Betru, Charles Awono Onana, Bernabe Batchakui

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Recommender systems have become increasingly popular in recent years, and are utilized in numerous areas. Nowadays many web services provide several information for users and recommender systems have been developed as critical element of these web applications to predict choice of preference and provide significant recommendations. With the help of the advantage of deep learning in modeling different types of data and due to the dynamic change of user preference, building a deep model can better understand users demand and further improve quality of recommendation. In this paper, deep neural network models for recommender system are evaluated. Most of deep neural network models in recommender system focus on the classical collaborative filtering user-item setting. Deep learning models demonstrated high level features of complex data can be learned instead of using metadata which can significantly improve accuracy of recommendation. Even though deep learning poses a great impact in various areas, applying the model to a recommender system have not been fully exploited and still a lot of improvements can be done both in collaborative and content-based approach while considering different contextual factors.

Keywords: big data, decision making, deep learning, recommender system

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Authors: Manoj Kumar Gupta, Jayashree Bijwe

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Additives in lubricating oils are the focus of research attention since the further reduction in friction and wear properties of oils would lead to the further saving of tribo-materials and energy apart from improving their efficiency. Remarkable tribo-performance enhancement is reported in the literature due to addition of particles of solid lubricants in lubricating oils; especially that of nano-sizes. In the present work graphite nano-particles (NPs) in various amounts (1, 2, 3 and 4 wt. %) were used to explore the possible anti-wear (AW) performance enhancement in Group III oil. Polyisobutylene succinimide (PIBSI- 1 wt. %) was used as a dispersant for dispersing these NPs and to enhance the stability of these nano-suspensions. It was observed that PIBSI inclusion enhanced the stability of oil almost by eight times. NPs in all amounts enhanced the AW performance of oil considerably. The optimum amount was three wt. %, which led to the highest enhancement under all loads. The extent of benefits, however, were dependent on load. At the lowest (392 N) and highest loads (784 N), the benefits were not profound. At moderate load (588 N), highest improvement (around 60 %) was recorded. The SEM and AFM studies were done on the worn ball surfaces to reveal the detailed features of films transferred and proved useful to correlate the wear performance of oils.

Keywords: dispersant, graphite, nano-lubricant, anti-wear additive

Procedia PDF Downloads 157

Authors: Synthia Atas Sari, Engkos Achmad Kuncoro, Haryadi Sarjono

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The purpose of this paper is to examine the impact of reward system which is determined by government over the work of Board of Commissioners in implementing good corporate governance in Indonesian state-owned enterprises. To do so, this study analyses the adequacy of the remuneration, the job attractiveness, and the board commitment and dedication with the remuneration system. Qualitative method used to examine the significant features and challenges to the government policy over the remuneration determination for the board of commissioners to their roles. Data are gathered through semi-structure in-depth interview to the 21 participants over 10 Indonesian stated-owned enterprises and written documents. Findings in this study indicate that government policies over the remuneration system is not effective to increase the performance of board of commissioners in implementing good corporate governance in Indonesian state-owned enterprises due to unattractiveness of the remuneration amount, demotivate active members, and conflict interest over members of the remuneration committee.

Keywords: reward system, board of commissioners, state-owned enterprises, good corporate governance

Procedia PDF Downloads 378

Authors: Wang Zhaoming, Shao Shegang, Chen Xiaorong, Qi Yanan, Tian Lei, Wang Jian

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This study conducts a comparative analysis of the spectral curves of asphalt pavements at various aging stages to improve road information extraction from high-resolution remote sensing imagery. By examining the distinguishing capabilities and spectral characteristics, the research aims to establish a pavement information extraction methodology based on China's high-resolution satellite images. The process begins by analyzing the spectral features of asphalt pavements to construct a spectral assessment model suitable for evaluating pavement health. This model is then tested at a national highway traffic testing site in China, validating its effectiveness in distinguishing different pavement aging levels. The study's findings demonstrate that the proposed model can accurately assess road health, offering a valuable tool for road maintenance planning and infrastructure management.

Keywords: spectral analysis, asphalt pavement aging, high-resolution remote sensing, pavement health assessment

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Authors: Mohamed S. Selim, Nesreen A. Fatthallah, Shimaa A. Higazy, Zhifeng Hao, Xiang Chen

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After the prohibition of tin-based fouling-prevention coatings in 2003, the researchers were directed toward eco-friendly coatings. Because of their nonstick, environmental, and economic benefits, foul-release nanocoatings have received a lot of attention. They use physical anti-adhesion terminology to deter any fouling attachment.Natural bioinspired surfaces have micro/nano-roughness and low surface free energy features, which may inspire the design of dynamic antifouling coatings. Graphene-based nanocomposite surfaces were designed to combat marine-fouling adhesion with ecological as well as eco-friendly effects rather than biocidal solutions. Polymer–graphenenanofiller hybrids are a novel class of composite materials in fouling-prevention applications. The controlled preparation of nanoscale orientation, arrangement, and direction along the composite building blocks would result in superior fouling prohibition. This work representsfoul-release nanocomposite top coats for marine coating applications with superhydrophobicity, surface inertness against fouling adherence, cost-effectiveness, and increased lifetime.

Keywords: foul-release nanocoatings, graphene-based nanocomposite, polymer, nanofillers

Procedia PDF Downloads 137

Authors: Andriy Magula

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With the help of a special algorithm being the principle of multilevel periodicity, the periodic change of properties at the nuclear level of chemical elements was discovered and the variant for the periodic system of isotopes was presented. The periodic change in the properties of isotopes, as well as the vertical symmetry of subgroups, was checked for consistency in accordance with the following ten types of experimental data: mass ratio of fission fragments; quadrupole moment values; magnetic moment; lifetime of radioactive isotopes; neutron scattering; thermal neutron radiative capture cross-sections (n, γ); α-particle yield cross-sections (n, α); isotope abundance on Earth, in the Solar system and other stellar systems; features of ore formation and stellar evolution. For all ten cases, the correspondences for the proposed periodic structure of the nucleus were obtained. The system was formed in the usual 2D table, similar to the periodic system of elements, and the mass series of isotopes was divided into 8 periods and 4 types of ‘nuclear’ orbitals: sn, dn, pn, fn. The origin of ‘magic’ numbers as a set of filled charge shells of the nucleus was explained. Due to the isotope system, the periodic structure is shown at a new level of the universe, and the prospects of its practical use are opened up.

Keywords: periodic system, isotope, period, subgroup, “nuclear” orbital, nuclear reaction

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Authors: Elena A. Outkina, Marina V. Meledina, Aliaksandr A. Khodin

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Nanostructured thin films of SnSₓ, Cu₂ZnSnS₄ (CZTS) semiconductors were fabricated by chemical processing to produce thin-film photoactive layers for photocells as a prospective lowest-cost and environment-friendly alternative to Si, Cu(In, Ga)Se₂, and other traditional solar cells materials. To produce SnSₓ layers, the modified successive ionic layer adsorption and reaction (SILAR) technique were investigated, including successive cyclic dipping into Na₂S solution and SnCl₂, NaCl, triethanolamine solution. To fabricate CZTS layers, the cyclic dipping into CuSO₄ with ZnSO₄, SnCl₂, and Na₂S solutions was used with intermediate rinsing in distilled water. The nano-template aluminum/alumina substrate was used to control deposition processes. Micromorphology and optical characteristics of the fabricated layers have been investigated. Analysis of 2D-like layers deposition features using nano-template substrate is presented, including the effect of nanotips in a template on surface charge redistribution and transport.

Keywords: kesterite, nanotemplate, SILAR, solar cell, tin sulphide

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Authors: Man-Ching Yu

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In Chinese music, pentatonic collection is central in constituting all the harmonic and melodic elements; most of the traditional Chinese musicians particularly emphasize the importance of the smoothness between pentatonic collections when one collection modulates to another collection, articulating the roles of the pentatonic common tones. On the contrary, in post-tonal Chinese music the central features of the pentatonic modulations tend to reflect a larger number of semitonal relationships with a lesser number of common tones. This paper offers an analytical account of the transformations between pentatonic collections that arise in post-tonal Chinese music by adopting the methodology of the Tonnetz, in particular, Dual Interval Space (DIS), to elaborate and reexamine pentatonicism by focusing on the transformations between pentatonic elements, especially semitonal motion and common tones. In the essay, various pentatonic passages will be analyzed by means of DIS for highlighting the transformation of the collections. It will be shown that the pentatonic collections that are in semitonal, third, and augmented fourth relationships exhibit the maximum number of semitonal shifts.

Keywords: tonnetz, pentatonicism, post-tonal Chinese music, dual interval space, transformation

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Authors: J. Andrade-Garda, A. Anguera, J. Ares-Casal, M. Hidalgo-Lorenzo, J.-A. Lara, D. Lizcano, S. Suárez-Garaboa

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In the current constantly changing economic context, collaborative networks allow partners to undertake projects that would not be possible if attempted by them individually. These projects usually involve the performance of a group of tasks (named roles) that have to be distributed among the partners. Thus, an allocation/matching problem arises that will be referred to as Role-Partner Allocation problem. In real life this situation is addressed by negotiation between partners in order to reach ad hoc agreements. Besides taking a long time and being hard work, both historical evidence and economic analysis show that such approach is not recommended. Instead, the allocation process should be automated by means of a centralized matching scheme. However, as a preliminary step to start the search for such a matching mechanism (or even the development of a new one), the problem and its core components must be specified. To this end, this paper establishes (i) the definition of the problem and its constraints, (ii) the key features of the involved elements (i.e., roles and partners); and (iii) how to create preference lists both for roles and partners. Only this way it will be possible to conduct subsequent methodological research on the solution method.     

Keywords: collaborative network, matching, partner, preference list, role

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Authors: Bing-Wen Chen, Alvin W. Y. Su, Yu-Lin Wang

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Electromyography (EMG) is one of the important indicators during exercise, as it is closely related to the level of muscle activations. This work quantifies the muscle conditions of the lower limbs in a constant workload exercise. Surface EMG signals of the vastus laterals (VL), vastus medialis (VM), rectus femoris (RF), gastrocnemius medianus (GM), gastrocnemius lateral (GL) and Soleus (SOL) were recorded from fourteen healthy males. The EMG signals were segmented in two phases: activation segment (AS) and relaxation segment (RS). Period entropy (PE), peak count (PC), zero crossing (ZC), wave length (WL), mean power frequency (MPF), median frequency (MDF) and root mean square (RMS) are calculated to provide the quantitative information of the measured EMG segments. The outcomes reveal that the PE, PC, ZC and RMS have significantly changed (p<.001); WL presents moderately changed (p<.01); MPF and MDF show no changed (p>.05) during exercise. The results also suggest that the RS is also preferred for performance evaluation, while the results of the extracted features in AS are usually affected directly by the amplitudes. It is further found that the VL exhibits the most significant changes within six muscles during pedaling exercise. The proposed work could be applied to quantify the stamina analysis and to predict the instant muscle status in athletes.

Keywords: electromyographic feature extraction, muscle status, pedaling exercise, relaxation segment

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Authors: Zi Zhu

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The last decade has witnessed increasing urban environmental uprisings in China, as thousands of citizens swarmed into streets to express their deep concerns about the environmental threat and public health through various collective actions. The prevalent western approaches to collective actions, which usually treat urban riots and social movements as distinct phenomenon, have plagued an adequate analysis of the urban environmental uprisings in China. The increasing urban environmental contention can neither be categorized into riots nor social movements, as they carry the features of both: at first sight, they are spontaneous, disorganized and disruptive with an absence of observable mobilization process; however, unlike riots in the west, these collective actions conveyed explicit demand in a mostly non-destructive way rather than a pure expression of frustration. This article proposes a different approach to urban environmental uprisings in China which concerns the diminishing boundaries between riots and social movements and points to the underlying structural causes to the unique forms of urban environmental contention. Taking the urban anti-PX protests as examples, this article analyzes the societal and political structural environment faced by the Chinese environmental protesters and its influence on the origin and development of their contention.

Keywords: urban environmental uprisings, China, anti-PX protests, opportunity structure

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Authors: Babak Bashari Rad

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A metamorphic computer virus uses different code transformation techniques to mutate its body in duplicated instances. Characteristics and function of new instances are mostly similar to their parents, but they cannot be easily detected by the majority of antivirus in market, as they depend on string signature-based detection techniques. The purpose of this research is to propose a Hidden Markov Model for classification of metamorphic viruses in executable files. In the proposed solution, portable executable files are inspected to extract the instructions opcodes needed for the examination of code. A Hidden Markov Model trained on portable executable files is employed to classify the metamorphic viruses of the same family. The proposed model is able to generate and recognize common statistical features of mutated code. The model has been evaluated by examining the model on a test data set. The performance of the model has been practically tested and evaluated based on False Positive Rate, Detection Rate and Overall Accuracy. The result showed an acceptable performance with high average of 99.7% Detection Rate.

Keywords: malware classification, computer virus classification, metamorphic virus, metamorphic malware, Hidden Markov Model

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Authors: Qiang Zhang, Chun Yuan

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Since the PU (Prediction Unit) decision process is the most time consuming part of the emerging HEVC (High Efficient Video Coding) standardin intra and inter frame coding, this paper proposes the fast PU decision algorithm and speed up the algorithm using CUDA (Compute Unified Device Architecture). In intra frame coding, the fast PU decision algorithm uses the texture features to skip intra-frame prediction or terminal the intra-frame prediction for smaller PU size. In inter frame coding of HEVC, the fast PU decision algorithm takes use of the similarity of its own two Nx2N size PU's motion vectors and the hierarchical structure of CU (Coding Unit) partition to skip some modes of PU partition, so as to reduce the motion estimation times. The accelerate algorithm using CUDA is based on the fast PU decision algorithm which uses the GPU to make the motion search and the gradient computation could be parallel computed. The proposed algorithm achieves up to 57% time saving compared to the HM 10.0 with little rate-distortion losses (0.043dB drop and 1.82% bitrate increase on average).

Keywords: HEVC, PU decision, inter prediction, intra prediction, CUDA, parallel

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Authors: Harim Mohsin, Afshan Channa, Beena Saad

Abstract:

The Kocher Debre Semelaigne Syndrome (KDSS) is known as cretinism muscular hypertrophy. It is an unusual presentation in intellectually deficit children, commonly associated with congenital or iatrogenic hypothyroidism. The creatinine phosphokinase (CPK) is usually elevated and it’s commonly found in males, consanguineous marriage and ages 18 months to 10 years. It might be misdiagnosed without the classical features of hypothyroidism at first presentation. We present a case of 15 year old intellectually deficit female with epilepsy managed on phenytoin. She had rigidity, myxedema, calf muscle hypertrophy and agitation. The patient was managed as Neuroleptic Malignant Syndrome due to raised CPK of 40,680 IU/L and mixed presentation. Nevertheless, no improvement was noticed and thyroid profile was done to exclude alternative resources. Thyroid stimulating hormone (TSH) was 74.5 IU, Free T3 1.22 ng/dl, and Free T4 0.43 ng/dl. Thyroxine was started along with change in antiepileptic leading to recovery. This case report highlights the inconsistent finding of KDSS. The female gender, non-consanguineous marriage, delayed onset with primarily neuromuscular symptoms, and raised CPK is a rare demonstration in KDSS. Additionally, thyroid profile is not routinely done, which can lead to misdiagnosis and mismanagement.

Keywords: cretinism, hypothyroidism, intellectual deficit, KDSS

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Authors: Yakin Hajlaoui, Richard Labib, Jean-François Plante, Michel Gamache

Abstract:

This study introduces the Multi-Layer Inverse Distance Weighting Model (ML-IDW), inspired by the mathematical formulation of both multi-layer neural networks (ML-NNs) and Inverse Distance Weighting model (IDW). ML-IDW leverages ML-NNs' processing capabilities, characterized by compositions of learnable non-linear functions applied to input features, and incorporates IDW's ability to learn anisotropic spatial dependencies, presenting a promising solution for nonlinear spatial interpolation and learning from complex spatial data. it employ gradient descent and backpropagation to train ML-IDW, comparing its performance against conventional spatial interpolation models such as Kriging and standard IDW on regression and classification tasks using simulated spatial datasets of varying complexity. the results highlight the efficacy of ML-IDW, particularly in handling complex spatial datasets, exhibiting lower mean square error in regression and higher F1 score in classification.

Keywords: deep learning, multi-layer neural networks, gradient descent, spatial interpolation, inverse distance weighting

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