Search results for: bi-directional long and short-term memory networks

Authors: Khalid Kaddoura, Tarek Zayed

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Sewer networks are constructed to protect the communities and the environment from any contact with the sewer mediums. Pipelines, being laterals or sewer mains, and manholes form the huge underground infrastructure in every urban city. Due to the sewer networks importance, the infrastructure asset management field has extensive advancement in condition assessment and rehabilitation decision models. However, most of the focus was devoted to pipelines giving little attention toward manholes condition assessment. In fact, recent studies started to emerge in this area to preserve manholes from any malfunction. Therefore, the main objective of this study is to propose a condition assessment model for sewer manholes. The model divides the manhole into several components and determines the relative importance weight of each component using the Analytic Network Process (ANP) decision-making method. Later, the condition of the manhole is computed by aggregating the condition of each component with its corresponding weight. Accordingly, the proposed assessment model will enable decision-makers to have a final index suggesting the overall condition of the manhole and a backward analysis to check the condition of each component. Consequently, better decisions are made pertinent to maintenance, rehabilitation, and replacement actions.

Keywords: Analytic Network Process (ANP), condition assessment, decision-making, manholes

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Authors: Gaye Ibrahima, Mendy Gervais, Seck Diaraf, Ouya Samuel

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In this paper, we consider the influence maximization in social networks. Here we give importance to initial diffuser called the seeds. The goal is to find efficiently a subset of k elements in the social network that will begin and maximize the information diffusion process. A new approach which treats the social network before to determine the seeds, is proposed. This treatment eliminates the information feedback toward a considered element as seed by extracting an acyclic spanning social network. At first, we propose two algorithm versions called SCG − algoritm (v1 and v2) (Spanning Connected Graphalgorithm). This algorithm takes as input data a connected social network directed or no. And finally, a generalization of the SCG − algoritm is proposed. It is called SG − algoritm (Spanning Graph-algorithm) and takes as input data any graph. These two algorithms are effective and have each one a polynomial complexity. To show the pertinence of our approach, two seeds set are determined and those given by our approach give a better results. The performances of this approach are very perceptible through the simulation carried out by the R software and the igraph package.

Keywords: acyclic spanning graph, centrality measures, information feedback, influence maximization, social network

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Authors: Bai-Chen Xie, Biao Li

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With the deepening of international exchanges and investment, the international comparison of power grid firms has become the focus of regulatory authorities. Ignoring the differences in the economic environment, resource endowment, technology, and other aspects of different countries or regions may lead to efficiency bias. Based on the Meta-frontier model, this paper divides China and Japan into two groups by using the data of China and Japan from 2006 to 2020. While preserving the differences between the two countries, it analyzes and compares the efficiency of the transmission and distribution industries of the two countries. Combined with the four-component stochastic frontier model, the efficiency is divided into transient and persistent efficiency. We found that there are obvious differences between the transmission and distribution sectors in China and Japan. On the one hand, the inefficiency of the two countries is mostly caused by long-term and structural problems. The key to improve the efficiency of the two countries is to focus more on solving long-term and structural problems. On the other hand, the long-term and structural problems that cause the inefficiency of the two countries are not the same. Quality factors have different effects on the efficiency of the two countries, and this different effect is captured by the common frontier model but is offset in the overall model. Based on these findings, this paper proposes some targeted policy recommendations.

Keywords: transmission and distribution industries, transient efficiency, persistent efficiency, meta-frontier, international comparison

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Authors: Jongho Park, Ui-Cheol Shin, Jinwoong Choi, Sungnam Hong, Sun-Kyu Park

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While the numerous structures built the industrialization are aging, the effort for the maintenance is concentrated in many countries. However, the traffic jam, environmental damage, and enormous maintenance cost, and etc become a problem. So, in order to solve this, the modular bridge has been studied. This bridge is the structure which utilizes and assembles the standard precast member. Through this, the substitution of the existing bridge and advantage of the easy maintenance will be achieved. However, the reliability in the long-term behavior is insufficient due to the junction part between modular precast members. Therefore, in this research, the cyclic load loading experiment was performed on the junction and deflection was analyzed by long-term service in modular slab connection. The deflection of modular slab with junction was mostly generated when initial and final test.

Keywords: modular bridge, deflection, cyclic loading, junction

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Authors: Rita U. Onolemhemhen, Saheed L. Bello, Akin P. Iwayemi

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The effectiveness of energy demand policy depends on identifying the key drivers of energy demand both in the short-run and the long-run. This paper examines the influence of regional differences on the link between energy demand and other explanatory variables for Nigeria, China and USA using the Vector Error Correction Model (VECM) approach. This study employed annual time series data on energy consumption (ED), real gross domestic product (GDP) per capita (RGDP), real energy prices (P) and urbanization (N) for a thirty-six-year sample period. The utilized time-series data are sourced from World Bank’s World Development Indicators (WDI, 2016) and US Energy Information Administration (EIA). Results from the study, shows that all the independent variables (income, urbanization, and price) substantially affect the long-run energy consumption in Nigeria, USA and China, whereas, income has no significant effect on short-run energy demand in USA and Nigeria. In addition, the long-run effect of urbanization is relatively stronger in China. Urbanization is a key factor in energy demand, it therefore recommended that more attention should be given to the development of rural communities to reduce the inflow of migrants into urban communities which causes the increase in energy demand and energy excesses should be penalized while energy management should be incentivized.

Keywords: economic growth, energy demand, income, real GDP, urbanization, VECM

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Authors: Kherafa Abdennasser

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The application of Information and Communication Technologies (ICT) to the training area led to the creation of this new reality called E-learning. That last one is described like the marriage of multi- media (sound, image and text) and of the internet (diffusion on line, interactivity). Distance learning became an important totality for training and that last pass in particular by the setup of a distance learning platform. In our memory, we will use an open source platform named Dokeos for the management of a distance training of GPS called e-GPS. The learner is followed in all his training. In this system, trainers and learners communicate individually or in group, the administrator setup and make sure of this system maintenance.

Keywords: ICT, E-learning, learning plate-forme, Dokeos, GPS

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Authors: Maoxin Ye

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This study examines the relationship between social network sites (SNS) usage and social capital. Because SNS usage can expand the users’ networks, and people who are connected in this networks may become resources to SNS users and lead them to advantage in some situation, it is important to estimate the relationship between SNS usage and ‘who’ is connected or what resources the SNS users can get. Additionally, ‘who’ can be divided in two aspects – people who possess high position and people who are different, hence, it is important to estimate the relationship between SNS usage and high position people and different people. This study adapts Lin’s definition of social capital and the measurement of position generator which tells us who was connected, and can be divided into the same two aspects as well. A national data of America (N = 2,255) collected by Pew Research Center is utilized to do a general regression analysis about SNS usage and social capital. The results indicate that SNS usage is negatively associated with each factor of social capital, and it suggests that, in fact, comparing with non-users, although SNS users can get more connections, the variety and resources of these connections are fewer. For this reason, we could lose benefits through SNS usage.

Keywords: social network sites, social capital, position generator, general regression

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Authors: Mohammad Rostami, Mohammad Reza Forghani, Elahe Neshat, Fatemeh Yaghoobi

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An Ad Hoc network consists of wireless mobile equipment which connects to each other without any infrastructure, using connection equipment. The best way to form a hierarchical structure is clustering. Various methods of clustering can form more stable clusters according to nodes' mobility. In this research we propose an algorithm, which allocates some weight to nodes based on factors, i.e. link stability and power reduction rate. According to the allocated weight in the previous phase, the cellular learning automaton picks out in the second phase nodes which are candidates for being cluster head. In the third phase, learning automaton selects cluster head nodes, member nodes and forms the cluster. Thus, this automaton does the learning from the setting and can form optimized clusters in terms of power consumption and link stability. To simulate the proposed algorithm we have used omnet++4.2.2. Simulation results indicate that newly formed clusters have a longer lifetime than previous algorithms and decrease strongly network overload by reducing update rate.

Keywords: mobile Ad Hoc networks, clustering, learning automaton, cellular automaton, battery power

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Authors: Khalid Mohammed Idrees

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Endodontic treatment aims to reverse the disease process and thereby eliminate the associated signs of symptoms. When the treatment itself appears to initiate the onset of pain and /or swelling (endodontic flare-up), the result can be distressing to both the patient and the operator. Patient might even consider postoperative symptoms as a bench mark against which the clinician’s skills are measured. Obviously the treatment with the lowest prevalence of postoperative pain is usually the treatment of choice as long as effectiveness and cost are not compromised. Knowledge of the cause and mechanism behind intra appointment flare-up is of utmost importance for the clinician to properly prevent or manage this undesirable condition. This review lecture will discuss the causative factors of flare-up with special attention to the microorganism role, various modalities of preventive measures would be discussed. Those measures are based on scientific evidence combined with the long clinical experience of the lecturer.

Keywords: endodontic flare-up, causative factors, inflammatory mediators, preventive measures

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Authors: Denis Duagi, Ben Carter, Maria Farrelly, Stephen Lisk, June S. L. Brown

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Background: Adolescence represents a distinctive phase of development, and variables linked to this developmental period could affect the efficiency of prevention and treatment for depression and anxiety, as well as the long-term prognosis. The objectives of this study were to investigate the long-term effectiveness of psychosocial interventions for adolescents on depression and anxiety symptoms and to assess the influence of different intervention parameters on the long-term effects. Methods: Searches were carried out on the 11ᵗʰ of August 2022 using five databases (Cochrane Library, Embase, Medline, PsychInfo, Web of Science), as well as trial registers. Randomized controlled trials of psychosocial interventions targeting specifically adolescents were included if they assessed outcomes at 1-year post-intervention or more. The Cochrane risk of bias-2 quality assessment tool was used. The primary outcome was depression, and studies were pooled using a standardised mean difference, with an associated 95% confidence interval, p-value, and I². The study protocol was pre-registered (CRD42022348668). Findings: A total of 57 reports (n= 46,678 participants) were included in the review. Psychosocial interventions led to small reductions in depressive symptoms, with a standardised mean difference (SMD) at 1-year of -0.08 (95%CI -0.20, -0.03, p=0.002, I²=72%), 18-months SMD=-0.12, 95% CI -0.22, -0.01, p=0.03, I²=63%) and 2-years SMD=-0.12 (95% CI -0.20, -0.03, p=0.01, I²=68%). Sub-group analyses indicated that targeted interventions produced stronger effects, particularly when delivered by trained mental health professionals (K=18, SMD=-0.24, 95% CI -0.38, -0.10, p=0.001, I²=60%). No effects were detected for anxiety at any assessment. Conclusion: Psychosocial interventions specifically targeting adolescents were shown to have small but positive effects on depression symptoms but not anxiety symptoms, which were sustained for up to 2 years. These findings highlight the potential population-level preventive effects if such psychosocial interventions become widely implemented in accessible settings such as schools.

Keywords: psychosocial, adolescent, interventions, depression, anxiety, meta-analysis, randomized controlled trial

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Authors: Farzaneh Tahmoorian, Bijan Samali, John Yeaman

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Road networks are expanding all over the world during the past few decades to meet the increasing freight volumes created by the population growth and industrial development. At the same time, the rate of generation of solid wastes in the society is increasing with the population growth, technological development, and changes in the lifestyle of people. Thus, the management of solid wastes has become an acute problem. Accordingly, there is a need for greater efficiency in the construction and maintenance of road networks, in reducing the overall cost, especially the utilization of natural materials such as aggregates. An efficient means to reduce construction and maintenance costs of road networks is to replace natural (virgin) materials by secondary, recycled materials. Recycling will also help to reduce pressure on landfills and demand for extraction of natural virgin materials thus ensuring sustainability. Application of solid wastes in asphalt layer reduces not only environmental issues associated with waste disposal but also the demand for virgin materials which will subsequently result in sustainability. Therefore, this research aims to investigate the feasibility of the application of some of the waste materials such as glass, construction and demolition wastes, etc. as alternative materials in pavement construction, particularly flexible pavements. To this end, various combination of different waste materials in certain percentages is considered in designing the asphalt mixture. One of the goals of this research is to determine the optimum percentage of all these materials in the mixture. This is done through a series of tests to evaluate the volumetric properties and resilient modulus of the mixture. The information and data collected from these tests are used to select the adequate samples for further assessment through advanced tests such as triaxial dynamic test and fatigue test, in order to investigate the asphalt mixture resistance to permanent deformation and also cracking. This paper presents the results of these investigations on the application of waste materials in asphalt mixture for production of a sustainable asphalt mix.

Keywords: asphalt, glass, pavement, recycled aggregate, sustainability

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Authors: Mohammad Zaeimzade

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The development of communication technologies is clearly and rapidly moving towards reducing the distance between the virtual and real worlds. Of course, living in a two-spatial or two-globalized world or any other interpretation that means mixing real and virtual life is still relevant and debatable. In the present age of communication, where social networks have transformed the message equation and turned the audience out of passivity and turned into a user. Platforms have penetrated widely in various aspects of human life, from culture and education and economy. Among the messengers, Instagram, which is one of the most extensive image-based interactive networks, plays a significant role in the new economic life. It doesn't need much explanation that the era of thinking of every messenger as a non-insulating conductor that is just a neutral load has passed. Every messenger has its own economic, political and of course security background, Instagram is no exception to this rule and of course it leaves its effects in bio-economics as well. Iran, as the 19th largest economy in the world, has not been unaffected by new platforms, including Instagram, and their consequences in the economy. Generally, in the policy-making space, there are two simple and inflexible pessimistic or optimistic views on this issue, and each of the holders of these views usually have their own one-dimensional policy recommendations regarding how to deal with Instagram. Prescriptions that are usually very different and sometimes contradictory. In this article, we show that this confusion of policymakers is the result of not accurately describing the reality of its effect, and the reason for this inaccurate description is the existence of a conflict of interests in the eyes of describers and researchers. In this article, we first take a look at the main indicators of the Iranian economy, estimate the role of the digital economy in Iran's economic growth, then study the conflicting descriptions of the Instagram-based digital economy, the statistics that show the tolerance of economic users of Instagram in Iran. 300 thousand to 9 million have been estimated. Finally, we take a look at the government's actions in this matter, especially in the context of street riots in October and November 2022. And we suggest an intermediate idea.

Keywords: digital economy, instagram, conflict of interest, social networks

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Authors: Laura Bickel

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This paper aims to investigate to what extent Merleau-Ponty’s philosophy of body memory serves as a viable resource for the enactive approach to cognitive science and its first-person experience-based research on ‘recurrent depressive disorder’ coded F33 in ICD-10. In pursuit of this goal, the analysis begins by revisiting the neuroreductive paradigm. This paradigm serves biological psychiatry to explain the condition of vital contact in terms of underlying neurophysiological mechanisms. It is demonstrated that the neuroreductive model cannot sufficiently account for the depressed person’s episodical withdrawal in causal terms. The analysis of the irregular loss of vital resonance requires integrating the body as the subject of experience and its phenomenological time. Then, it is shown that the enactive approach to depression as disordered sense-making is a promising alternative. The enactive model of perception implies that living beings do not register pre-existing meaning ‘out there’ but unfold ‘sense’ in their action-oriented response to the world. For the enactive approach, Husserl’s passive synthesis of inner time consciousness is fundamental for what becomes perceptually present for action. It seems intuitive to bring together the enactive approach to depression with the long-standing view in phenomenological psychopathology that explains the loss of vital contact by appealing to the disruption of the temporal structure of consciousness. However, this paper argues that the disruption of the temporal structure is not justified conceptually. Instead, one may integrate Merleau-Ponty’s concept of the past as the unconscious into the enactive approach to depression. From this perspective, the living being’s experiential and biological past inserts itself in the form of habit and bodily skills and ensures action-oriented responses to the environment. Finally, it is concluded that the depressed person’s withdrawal indicates the impairment of this application process. The person suffering from F33 cannot actualize sedimented meaning to respond to the valences and tasks of a given situation.

Keywords: depression, enactivism, neuroreductionsim, phenomenology, temporality

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Authors: Megha Gupta, Nupur Prakash

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Identification of plant diseases has been performed using machine learning and deep learning models on the datasets containing images of healthy and diseased plant leaves. The current study carries out an evaluation of some of the deep learning models based on convolutional neural network (CNN) architectures for identification of plant diseases. For this purpose, the publicly available New Plant Diseases Dataset, an augmented version of PlantVillage dataset, available on Kaggle platform, containing 87,900 images has been used. The dataset contained images of 26 diseases of 14 different plants and images of 12 healthy plants. The CNN models selected for the study presented in this paper are AlexNet, ZFNet, VGGNet (four models), GoogLeNet, and ResNet (three models). The selected models are trained using PyTorch, an open-source machine learning library, on Google Colaboratory. A comparative study has been carried out to analyze the high degree of accuracy achieved using these models. The highest test accuracy and F1-score of 99.59% and 0.996, respectively, were achieved by using GoogLeNet with Mini-batch momentum based gradient descent learning algorithm.

Keywords: comparative analysis, convolutional neural networks, deep learning, plant disease identification

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Authors: Luke McDonagh

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Over the past three decades, free and open source software (FOSS) programmers have developed new, innovative and legally binding licences that have in turn enabled the creation of innumerable pieces of everyday software, including Linux, Mozilla Firefox and Open Office. That FOSS has been highly successful in competing with 'closed source software' (e.g. Microsoft Office) is now undeniable, but in noting this success, it is important to examine in detail why this system of FOSS has been so successful. One key reason is the existence of networks or communities of programmers, who are bound together by a key shared social norm of 'reciprocity'. At the same time, these FOSS networks are not unitary – they are highly diverse and there are large divergences of opinion between members regarding which licences are generally preferable: some members favour the flexible ‘free’ or 'no copyleft' licences, such as BSD and MIT, while other members favour the ‘strong open’ or 'strong copyleft' licences such as GPL. This paper argues that without both the existence of the shared norm of reciprocity and the diversity of licences, it is unlikely that the innovative legal framework provided by FOSS would have succeeded to the extent that it has.

Keywords: open source, copyright, licensing, copyleft

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Authors: Zheng Zhihao

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Recently, Knowledge Graph Framework Network (KGCN) has developed powerful capabilities in knowledge representation and reasoning tasks. However, traditional KGCN often uses a fixed weight mechanism when aggregating information, failing to make full use of rich structural information, resulting in a certain expression ability of node representation, and easily causing over-smoothing problems. In order to solve these challenges, the paper proposes an new graph neural network model called KGCN-STAR (Knowledge Graph Convolutional Network with Structural Adaptive Receptive Fields). This model dynamically adjusts the perception of each node by introducing a structural adaptive receptive field. wild range, and a subgraph aggregator is designed to capture local structural information more effectively. Experimental results show that KGCN-STAR shows significant performance improvement on multiple knowledge graph data sets, especially showing considerable capabilities in the task of representation learning of complex structures.

Keywords: knowledge graph, graph neural networks, structural adaptive receptive fields, information aggregation

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Authors: Mustafa Al Sukar, Azzam Sleit, Abdullatif Abu-Dalhoum, Bassam Al-Kasasbeh

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Use and abuse of drugs by teens is very common and can have dangerous consequences. The drugs contribute to physical and sexual aggression such as assault or rape. Some teenagers regularly use drugs to compensate for depression, anxiety or a lack of positive social skills. Teen resort to smoking should not be minimized because it can be "gateway drugs" for other drugs (marijuana, cocaine, hallucinogens, inhalants, and heroin). The combination of teenagers' curiosity, risk taking behavior, and social pressure make it very difficult to say no. This leads most teenagers to the questions: "Will it hurt to try once?" Nowadays, technological advances are changing our lives very rapidly and adding a lot of technologies that help us to track the risk of drug abuse such as smart phones, Wireless Sensor Networks (WSNs), Internet of Things (IoT), etc. This technique may help us to early discovery of drug abuse in order to prevent an aggravation of the influence of drugs on the abuser. In this paper, we have developed a Decision Support System (DSS) for detecting the drug abuse using Artificial Neural Network (ANN); we used a Multilayer Perceptron (MLP) feed-forward neural network in developing the system. The input layer includes 50 variables while the output layer contains one neuron which indicates whether the person is a drug addict. An iterative process is used to determine the number of hidden layers and the number of neurons in each one. We used multiple experiment models that have been completed with Log-Sigmoid transfer function. Particularly, 10-fold cross validation schemes are used to access the generalization of the proposed system. The experiment results have obtained 98.42% classification accuracy for correct diagnosis in our system. The data had been taken from 184 cases in Jordan according to a set of questions compiled from Specialists, and data have been obtained through the families of drug abusers.

Keywords: drug addiction, artificial neural networks, multilayer perceptron (MLP), decision support system

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Authors: Patricia Portugal Marques de Carvalho Lourenco

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A three-phase methodological multi-lingual path was designed, constructed and carried out using the 2020 Chinese Internet Drama Series We Are All Alone as a case study. Phase one, the backbone of the research, comprised of secondary data analysis, providing the structure on which the next two phases would be built on. Phase one incorporated a Google Scholar and a Baidu Index analysis, Star Network Influence Index and Mydramalist.com top two drama reviews, along with an article written about the drama and scrutiny of Chinese related blogs and websites. Phase two was field research elaborated across Latin Europe, and phase three was social media focused, having into account that perceptions are going to be memory conditioned based on past ideas recall. Overall, research has shown the poor cultural expression of Chinese entertainment in Latin Europe and demonstrated the inexistence of Chinese content in French, Italian, Portuguese and Spanish Business to Consumer retailers; a reflection of their low significance in Latin European markets and the short-life cycle of entertainment products in general, bubble-gum, disposable goods without a mid to long-term effect in consumers lives. The process of conducting comprehensive international research was complex and time-consuming, with data not always available in Mandarin, the researcher’s linguistic deficiency, limited Chinese Cultural Knowledge and cultural equivalence. Despite steps being taken to minimize the international proposed research, theoretical limitations concurrent to Latin Europe and China still occurred. Data accuracy was disputable; sampling, data collection/analysis methods are heterogeneous; ascertaining data requirements and the method of analysis to achieve a construct equivalence was challenging and morose to operationalize. Secondary data was also not often readily available in Mandarin; yet, in spite of the array of limitations, research was done, and results were produced.

Keywords: research methodologies, international research, primary data, secondary data, research limitations, online dramas, china, latin europe

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Authors: Shahin A. Abdel-Naby, James P. Colgan, Michael S. Pindzola

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A time-dependent close-coupling method is developed to calculate a total, double and triple autoionization rates for hollow atomic ions of four-electron systems. This work was motivated by recent observations of the four-electron Auger process in near K-edge photoionization of C+ ions. The time-dependent close-coupled equations are solved using lattice techniques to obtain a discrete representation of radial wave functions and all operators on a four-dimensional grid with uniform spacing. Initial excited states are obtained by relaxation of the Schrodinger equation in imaginary time using a Schmidt orthogonalization method involving interior subshells. The radial wave function grids are partitioned over the cores on a massively parallel computer, which is essential due to the large memory requirements needed to store the coupled-wave functions and the long run times needed to reach the convergence of the ionization process. Total, double, and triple autoionization rates are obtained by the propagation of the time-dependent close-coupled equations in real-time using integration over bound and continuum single-particle states. These states are generated by matrix diagonalization of one-electron Hamiltonians. The total autoionization rates for each L excited state is found to be slightly above the single autoionization rate for the excited configuration using configuration-average distorted-wave theory. As expected, we find the double and triple autoionization rates to be much smaller than the total autoionization rates. Future work can be extended to study electron-impact triple ionization of atoms or ions. The work was supported in part by grants from the American University of Sharjah and the US Department of Energy. Computational work was carried out at the National Energy Research Scientific Computing Center (NERSC) in Berkeley, California, USA.

Keywords: hollow atoms, autoionization, auger rates, time-dependent close-coupling method

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Authors: Shokoufeh Abrisham, Seyed Mahmoud Hossieni, Elham Pishbin

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In this paper, a hybrid method based on the fault tree analysis (FTA) and Bayesian networks (BNs) are employed to evaluate the team performance quality of advanced cardiac life support (ACLS) procedures in emergency department. According to American Heart Association (AHA) guidelines, a category relying on staff action leading to clinical incidents and also some discussions with emergency medicine experts, a fault tree model for ACLS procedure is obtained based on the human performance. The obtained FTA model is converted into BNs, and some different scenarios are defined to demonstrate the efficiency and flexibility of the presented model of BNs. Also, a sensitivity analysis is conducted to indicate the effects of team leader presence and uncertainty knowledge of experts on the quality of ACLS. The proposed model based on BNs shows that how the results of risk analysis can be closed to reality comparing to the obtained results based on only FTA in medical procedures.

Keywords: advanced cardiac life support, fault tree analysis, Bayesian belief networks, numan performance, healthcare systems

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Authors: Maja Ruzic-Baf, Vedrana Keteles, Andrea Debeljuh

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Younger and younger children are now using a smartphone, a device which has become ‘a must have’ and the life of children would be almost ‘unthinkable’ without one. Devices are becoming lighter and lighter but offering an array of options and applications as well as the unavoidable access to the Internet, without which it would be almost unusable. Numerous features such as taking of photographs, listening to music, information search on the Internet, access to social networks, usage of some of the chatting and messaging services, are only some of the numerous features offered by ‘smart’ devices. They have replaced the alarm clock, home phone, camera, tablet and other devices. Their use and possession have become a part of the everyday image of young people. Apart from the positive aspects, the use of smartphones has also some downsides. For instance, free time was usually spent in nature, playing, doing sports or other activities enabling children an adequate psychophysiological growth and development. The greater usage of smartphones during classes to check statuses on social networks, message your friends, play online games, are just some of the possible negative aspects of their application. Considering that the age of the population using smartphones is decreasing and that smartphones are no longer ‘foreign’ to children of pre-school age (smartphones are used at home or in coffee shops or shopping centers while waiting for their parents, playing video games often inappropriate to their age), particular attention must be paid to a very sensitive group, the teenagers who almost never separate from their ‘pets’. This paper is divided into two sections, theoretical and empirical ones. The theoretical section gives an overview of the pros and cons of the usage of smartphones, while the empirical section presents the results of a research conducted in three elementary schools regarding the usage of smartphones and, specifically, their usage during classes, during breaks and to search information on the Internet, check status updates and 'likes’ on the Facebook social network.

Keywords: education, smartphone, social networks, teenagers

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Authors: Haiyan Wu, Ying Liu, Shaoyun Shi

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Authorship attribution is to extract features to identify authors of anonymous documents. Many previous works on authorship attribution focus on statistical style features (e.g., sentence/word length), content features (e.g., frequent words, n-grams). Modeling these features by regression or some transparent machine learning methods gives a portrait of the authors' writing style. But these methods do not capture the syntactic (e.g., dependency relationship) or semantic (e.g., topics) information. In recent years, some researchers model syntactic trees or latent semantic information by neural networks. However, few works take them together. Besides, predictions by neural networks are difficult to explain, which is vital in authorship attribution tasks. In this paper, we not only utilize the statistical style and content features but also take advantage of both syntactic and semantic features. Different from an end-to-end neural model, feature selection and prediction are two steps in our method. An attentive n-gram network is utilized to select useful features, and logistic regression is applied to give prediction and understandable representation of writing style. Experiments show that our extracted features can improve the state-of-the-art methods on three benchmark datasets.

Keywords: authorship attribution, attention mechanism, syntactic feature, feature extraction

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Authors: Ching Louis Liu, Edmund Kazmierczak, Tim Miller

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Safety analysis for multi-agent systems is complicated by the, potentially nonlinear, interactions between agents. This paper proposes a method for analyzing the safety of multi-agent systems by explicitly focusing on interactions and the accident data of systems that are similar in structure and function to the system being analyzed. The method creates a Bayesian network using the accident data from similar systems. A feature of our method is that the events in accident data are labeled with HAZOP guide words. Our method uses an Ontology to abstract away from the details of a multi-agent implementation. Using the ontology, our methods then constructs an “Interaction Map,” a graphical representation of the patterns of interactions between agents and other artifacts. Interaction maps combined with statistical data from accidents and the HAZOP classifications of events can be converted into a Bayesian Network. Bayesian networks allow designers to explore “what it” scenarios and make design trade-offs that maintain safety. We show how to use the Bayesian networks, and the interaction maps to improve multi-agent system designs.

Keywords: multi-agent system, safety analysis, safety model, integration map

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Authors: Wissal Ben Arfi, Lubica Hikkerova, Jean-Michel Sahut

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This study examines interfirm networks in the digital transformation era, and in particular, how tacit knowledge codification affects innovation success within digital platforms. Hence, one of the most important features of digital transformation and innovation process outcomes is the emergence of digital platforms, as an interfirm network, at the heart of open innovation. This research aims to illuminate how digital platforms influence inter-organizational innovation through virtual team interactions and knowledge sharing practices within an interfirm network. Consequently, it contributes to the respective strategic management literature on new product development (NPD), open innovation, industrial management, and its emerging interfirm networks’ management. The empirical findings show, on the one hand, that knowledge conversion may be enhanced, especially by the socialization which seems to be the most important phase as it has played a crucial role to hold the virtual team members together. On the other hand, in the process of socialization, the tacit knowledge codification is crucial because it provides the structure needed for the interfirm network actors to interact and act to reach common goals which favor the emergence of open innovation. Finally, our results offer several conditions necessary, but not always sufficient, for interfirm managers involved in NPD and innovation concerning strategies to increasingly shape interconnected and borderless markets and business collaborations. In the digital transformation era, the need for adaptive and innovative business models as well as new and flexible network forms is becoming more significant than ever. Supported by technological advancements and digital platforms, companies could benefit from increased market opportunities and creating new markets for their innovations through alliances and collaborative strategies, as a mode of reducing or eliminating uncertainty environments or entry barriers. Consequently, an efficient and well-structured interfirm network is essential to create network capabilities, to ensure tacit knowledge sharing, to enhance organizational learning and to foster open innovation success within digital platforms.

Keywords: interfirm networks, digital platform, virtual teams, open innovation, knowledge sharing

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Authors: Sergo Esadze

Abstract:

Seismic resistance norms require calculation of cantilevers on vertical components of the base seismic acceleration. Long span cantilevers, as a rule, must be calculated as a separate construction element. According to the architectural-planning solution, functional purposes and environmental condition of a designing buildings/structures, long span cantilever construction may be of very different types: both by main bearing element (beam, truss, slab), and by material (reinforced concrete, steel). A choice from these is always linked with bearing construction system of the building. Research of vertical seismic vibration of these constructions requires individual approach for each (which is not specified in the norms) in correlation with model of seismic load. The latest may be given both as deterministic load and as a random process. Loading model as a random process is more adequate to this problem. In presented paper, two types of long span (from 6m – up to 12m) reinforcement concrete cantilever beams have been considered: a) bearing elements of cantilevers, i.e., elements in which they fixed, have cross-sections with large sizes and cantilevers are made with haunch; b) cantilever beam with load-bearing rod element. Calculation models are suggested, separately for a) and b) types. They are presented as systems with finite quantity degree (concentrated masses) of freedom. Conditions for fixing ends are corresponding with its types. Vertical acceleration and vertical component of the angular acceleration affect masses. Model is based on assumption translator-rotational motion of the building in the vertical plane, caused by vertical seismic acceleration. Seismic accelerations are considered as random processes and presented by multiplication of the deterministic envelope function on stationary random process. Problem is solved within the framework of the correlation theory of random process. Solved numerical examples are given. The method is effective for solving the specific problems.

Keywords: cantilever, random process, seismic load, vertical acceleration

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Authors: Reinhard Neck, Guido Schäfer

Abstract:

In this paper, we explore the macroeconomic effects of the European Single Market on Austria by simulating the McKibbin-Sachs Global Model. Global interdependence and the impact of long-run effects on short-run adjustments are taken into account. We study the sensitivity of the results with respect to different assumptions concerning monetary and fiscal policies for the countries and regions of the world economy. The consequences of different assumptions about budgetary policies in Austria are also investigated. The simulation results are contrasted with ex-post evaluations of the actual impact of Austria’s membership in the Single Market. As a result, it can be concluded that the Austrian participation in the European Single Market entails considerable long-run gains for the Austrian economy with nearly no adverse side-effects on any macroeconomic target variable.

Keywords: macroeconomics, European Union, simulation, sensitivity analysis

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Authors: Asim Cengiz, Dede Basturk, Hakan Ozalp

Abstract:

Practicing recurring resistance workout such as may cause changes in human muscle. These changes may be because combination if several factors determining physical fitness. Thus, it is important to identify these changes. Several studies were reviewed to investigate these changes. As a result, the changes included positive modifications in amplified citrate synthase (CS) maximal activity, increased capacity for pyruvate oxidation, improvement on molecular signaling on human performance, amplified resting muscle glycogen and whole GLUT4 protein content, better health outcomes such as enhancement in cardiorespiratory fitness. Sprint training also have numerous long long-term changes inhuman body such as better enzyme action, changes in muscle fiber and oxidative ability. This is important because SV is the critical factor influencing maximal cardiac output and therefore oxygen delivery and maximal aerobic power.

Keywords: sprint, training, performance, exercise

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Authors: Boriana Karamanova, Svetlana Veleva, Luybomir Soserov, Ana Arenillas, Francesco Lufrano, Antonia Stoyanova

Abstract:

Supercapacitors have received a lot of research attention and are promising energy storage devices due to their high power and long cycle life. In order to developed an advanced device with significant capacity for storing charge and cheap carbon materials, efforts must focus not only on improving synthesis by controlling the morphology and pore size but also on improving electrode-electrolyte compatibility of the resulting systems. The present study examines the relationship between the surface chemistry of two activated carbon xerogels, the electrolyte type, and the electrochemical properties of supercapacitors. Activated carbon xerogels were prepared by varying the initial pH of the resorcinol-formaldehyde aqueous solution. The materials produced are physicochemical characterized by DTA/TGA, porous characterization, and SEM analysis. The carbon xerogel based electrodes were prepared by spreading over glass plate a slurry containing the carbon gel, graphite, and poly vinylidene difluoride (PVDF) binder. The layer formed was dried consecutively at different temperatures and then detached by water. After, the layer was dried again to improve its mechanical stability. The developed electrode materials and the Aquivion® E87-05S membrane (Solvay Specialty Polymers), socked in Na2SO4 as a polymer electrolyte, were used to assembly the solid-state supercapacitor. Symmetric supercapacitor cells composed by same electrodes and 1 M KOH electrolytes are also assembled and tested for comparison. The supercapacitor performances are verified by different electrochemical methods - cyclic voltammetry, galvanostatic charge/discharge measurements, electrochemical impedance spectroscopy, and long-term durability tests in neutral and alkaline electrolytes. Specific capacitances, energy, and power density, energy efficiencies, and durability were compared into studied supercapacitors. Ex-situ physicochemical analyses on the synthesized materials have also been performed, which provide information about chemical and structural changes in the electrode morphology during charge / discharge durability tests. They are discussed on the basis of electrode-electrolyte interaction. The obtained correlations could be of significance in order to design sustainable solid-state supercapacitors with high power and energy density. Acknowledgement: This research is funded by the Ministry of Education and Science of Bulgaria under the National Program "European Scientific Networks" (Agreement D01-286 / 07.10.2020, D01-78/30.03.2021). Authors gratefully acknowledge.

Keywords: carbon xerogel, electrochemical tests, neutral and alkaline electrolytes, supercapacitors

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Authors: Azam Abkhiz, Abolghasem Nasir

Abstract:

To achieve sustainable competitive advantage in the market, it is necessary to provide and improve customer satisfaction and Loyalty. To reach this objective, companies need to identify and analyze their customers. Thus, it is critical to measure the level of customer satisfaction and Loyalty very carefully. This study attempts to build a conceptual model to provide clear insights of customer loyalty. Using Bayesian networks (BNs), a model is proposed to evaluate customer loyalty and its consequences, such as repurchase and positive word-of-mouth. BN is a probabilistic approach that predicts the behavior of a system based on observed stochastic events. The most relevant determinants of customer loyalty are identified by the literature review. Perceived value, service quality, trust, corporate image, satisfaction, and switching costs are the most important variables that explain customer loyalty. The data are collected by use of a questionnaire-based survey from 1430 customers of a home appliances manufacturer in Iran. Four scenarios and sensitivity analyses are performed to run and analyze the impact of different determinants on customer loyalty. The proposed model allows businesses to not only set their targets but proactively manage their customer behaviors as well.

Keywords: customer satisfaction, customer loyalty, Bayesian networks, home appliances industry

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Authors: Vethathirri Ramanujam Srinivasan, S. M. Shiva Nagendra

Abstract:

Air quality management in urban areas is a serious concern in both developed and developing countries. In this regard, more number of air quality monitoring stations are planned to mitigate air pollution in urban areas. In India, Central Pollution Control Board has set up 574 air quality monitoring stations across the country and proposed to set up another 500 stations in the next few years. The number of monitoring stations for each city has been decided based on population data. The setting up of ambient air quality monitoring stations and their operation and maintenance are highly expensive. Therefore, there is a need to optimize monitoring networks for air quality management. The present paper discusses the various methods such as Indian Standards (IS) method, US EPA method and European Union (EU) method to arrive at the minimum number of air quality monitoring stations. In addition, optimization of rain-gauge method and Inverse Distance Weighted (IDW) method using Geographical Information System (GIS) are also explored in the present work for the design of air quality network in Chennai city. In summary, additionally 18 stations are required for Chennai city, and the potential monitoring locations with their corresponding land use patterns are ranked and identified from the 1km x 1km sized grids.

Keywords: air quality monitoring network, inverse distance weighted method, population based method, spatial variation

Procedia PDF Downloads 189