Search results for: graph representation of circuit networks

Authors: Dulal Bhaumik, Fei Jie, Runa Bhaumik, Bikas Sinha

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Human brain is an amazingly complex network. Aberrant activities in this network can lead to various neurological disorders such as multiple sclerosis, Parkinson’s disease, Alzheimer’s disease and autism. fMRI has emerged as an important tool to delineate the neural networks affected by such diseases, particularly autism. In this paper, we propose mixed-effects models together with an appropriate procedure for controlling false discoveries to detect disrupted connectivities in whole brain studies. Results are illustrated with a large data set known as Autism Brain Imaging Data Exchange or ABIDE which includes 361 subjects from 8 medical centers. We believe that our findings have addressed adequately the small sample inference problem, and thus are more reliable for therapeutic target for intervention. In addition, our result can be used for early detection of subjects who are at high risk of developing neurological disorders.

Keywords: ABIDE, autism spectrum disorder, fMRI, mixed-effects model

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Authors: Heather Wyman-Pain, Yuankai Bian, Furong Li

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The decreasing use of fossil fuel power stations has a negative effect on the stability of the electricity systems in many countries. Nuclear power stations have traditionally provided minimal ancillary services to support the system but this must change in the future as they replace fossil fuel generators. This paper explains the development of the four most popular reactor types still in regular operation across the world which have formed the basis for most reactor development since their commercialisation in the 1950s. The use of nuclear power in four countries with varying levels of capacity provided by nuclear generators is investigated, using the primary frequency response provided by generators as a measure for the electricity networks stability, to assess the need for nuclear generators to provide additional support as their share of the generation capacity increases.

Keywords: frequency control, nuclear power generation, power system stability, system inertia

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Authors: Tamara Vagg, Cathy Shortt, Claire Hickey, Joseph A. Eustace, Barry J. Plant, Sabin Tabirca

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Introduction: Paper-based patient passports have been used advantageously for older patients, patients with diabetes, and patients with learning difficulties. However, these passports can experience issues with data security, patients forgetting to bring the passport, patients being over encumbered, and uncertainty with who is responsible for entering and managing data in this passport. These issues could be resolved by transferring the paper-based system to a convenient platform such as a smartphone application (app). Background: Life expectancy for some Cystic Fibrosis (CF) patients are rising and as such new complications and procedures are predicted. Subsequently, there is a need for education and management interventions that can benefit CF adults. This research proposes a CF patient passport to record basic medical information through a smartphone app which will allow CF adults access to their basic medical information. Aim: To provide CF patients with their basic medical information via mobile multimedia so that they can receive care when traveling abroad or between CF centres. Moreover, by recording their basic medical information, CF patients may become more aware of their own condition and more active in their health care. Methods: This app is designed by a CF multidisciplinary team to be a lightweight reflection of a hospital patient file. The passport app is created using PhoneGap so that it can be deployed for both Android and iOS devices. Data entered into the app is encrypted and stored locally only. The app is password protected and includes the ability to set reminders and a graph to visualise weight and lung function over time. The app is introduced to seven participants as part of a stress test. The participants are asked to test the performance and usability of the app and report any issues identified. Results: Feedback and suggestions received via this testing include the ability to reorder the list of clinical appointments via date, an open format of recording dates (in the event specifics are unknown), and a drop down menu for data which is difficult to enter (such as bugs found in mucus). The app is found to be usable and accessible and is now being prepared for a pilot study with adult CF patients. Conclusions: It is anticipated that such an app will be beneficial to CF adult patients when travelling abroad and between CF centres.

Keywords: Cystic Fibrosis, digital patient passport, mHealth, self management

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Authors: S. J. Matlan, M. Mukhlisin, M. R. Taha

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Characterization of the engineering behavior of unsaturated soil is dependent on the soil-water characteristic curve (SWCC), a graphical representation of the relationship between water content or degree of saturation and soil suction. A reasonable description of the SWCC is thus important for the accurate prediction of unsaturated soil parameters. The measurement procedures for determining the SWCC, however, are difficult, expensive, and time-consuming. During the past few decades, researchers have laid a major focus on developing empirical equations for predicting the SWCC, with a large number of empirical models suggested. One of the most crucial questions is how precisely existing equations can represent the SWCC. As different models have different ranges of capability, it is essential to evaluate the precision of the SWCC models used for each particular soil type for better SWCC estimation. It is expected that better estimation of SWCC would be achieved via a thorough statistical analysis of its distribution within a particular soil class. With this in view, a statistical analysis was conducted in order to evaluate the reliability of the SWCC prediction models against laboratory measurement. Optimization techniques were used to obtain the best-fit of the model parameters in four forms of SWCC equation, using laboratory data for relatively coarse-textured (i.e., sandy) soil. The four most prominent SWCCs were evaluated and computed for each sample. The result shows that the Brooks and Corey model is the most consistent in describing the SWCC for sand soil type. The Brooks and Corey model prediction also exhibit compatibility with samples ranging from low to high soil water content in which subjected to the samples that evaluated in this study.

Keywords: soil-water characteristic curve (SWCC), statistical analysis, unsaturated soil, geotechnical engineering

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Authors: Mohsen Sanayei, Jerzy Szpunar

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The present study investigates the effects of deformation strain and annealing temperature on the formation of twin boundaries, deformation and recrystallization texture evolution and grain boundary networks and connectivity. The resulting microstructures were characterized using Electron Backscatter Diffraction (EBSD) and X-Ray Diffraction (XRD) both immediately following small amount of deformation and after short time annealing at high temperature to correlate the micro and macro texture evolution of these alloys. Furthermore, this study showed that the process of grain boundary engineering, consisting cycles of deformation and annealing, is found to substantially reduce the mass and size of random boundaries and increase the proportion of low Coincidence Site Lattice (CSL) grain boundaries.

Keywords: coincidence site lattice, grain boundary engineering, electron backscatter diffraction, texture, x-ray diffraction

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Authors: Azzouzi Fadoua

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This study focuses on the diagnosis of the dynamics of natural resources in a semi-arid mountainous weakened by natural vulnerability and anthropogenic action. This is evident in the forms of hydraulic erosion and degradation of agricultural land. The rate of this damaged land is 53%, with a strong presence of concentrated erosion; this shows that balanced and semi-balanced environments are less apparent to the Watershed, representing 47%. The results revealed the crucial role of the slopes and the density of the hydraulic networks to facilitate the transport of fine elements, at the level of the slopes with low vegetation intensity, to the lake of the dam. Something that endangers the siltation of the latter. After the study of natural and anthropogenic elements, it turned out that natural vulnerability is an integral part of the current dynamic, especially when it coincides with the overexploitation of natural resources, in this case, the exploitation of steep slopes for the cultivation of cereals and overgrazing. This causes the soil to pile up and increase the rate of runoff.

Keywords: watershed, erosion, natural vulnerability, anthropogenic

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Authors: Mokhtar Arazpour

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Objectives: Recently a new kind of stance control knee joint has been developed called the 'sensor lock.' This study aimed to develop and evaluate 'sensor lock', which could potentially solve the problems of walking parameters and gait symmetry in subjects with quadriceps weakness. Methods: Nine subjects with quadriceps weakness were enrolled in this study. A custom-made knee ankle foot orthosis (KAFO) with the same set of components was constructed for each participant. Testing began after orthotic gait training was completed with each of the KAFOs and subjects demonstrated that they could safely walk with crutches. Subjects rested 30 minutes between each trial. The 10 meters walking test is used to assess walking speed in meters/second (m/s). The total time taken to ambulate 6 meters (m) is recorded to the nearest hundredth of a second. 6 m is then divided by the total time (in seconds) taken to ambulate and recorded in m/s. The 6 Minutes Walking Test was used to assess walking endurance in this study. Participants walked around the perimeter of a set circuit for a total of six minutes. To evaluate Physiological cost index (PCI), the subjects were asked to walk using each type of KAFOs along a pre-determined 40 m rectangular walkway at their comfortable self-selected speed. A stopwatch was used to calculate the speed of walking by measuring the time between starting and stopping time and the distance walked. Results: The use of a KAFO fitted with the “sensor lock” knee joint resulted in improvements to walking speed, distance walked and physiological cost index when compared with the knee joint in lock mode. Conclusions: This study demonstrated that the use of a KAFO with the “sensor lock” knee joint could provide significant benefits for subjects with a quadriceps weakness when compared to a KAFO with the knee joint in lock mode.

Keywords: stance control knee joint, knee ankle foot orthosis, quadriceps weakness, walking, energy consumption

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Authors: Molla Tadesse Lakew

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Researchers tried to find out the socio-economic and supply-side constraint factors to adoption. However, they overlooked the role of social networks and relationships among the community. Therefore, the aims of this review were to review the roles and negative effects of social capital. Based on its contents, relevancy, and time duration, only 121 (journals, books, and paper reports) were selected. It concludes that social capital has an indispensable role in facilitating cooperation and connection between members of the farmers' community, informal and experiential knowledge sharing, and access to research-based knowledge and contributes to reducing the transaction cost of adoption. On the contrary, inside the black box of social capital, the negative effects include the exclusion of outsider’s knowledge and experiences, excessive claims on group members, and restrictions on individual freedom.

Keywords: social capital, local institutions, adoption, Ethiopia

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Authors: Iftekhar Hassan, Sayedil Morsalin, Easir A Khan

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Accidents involving fire occur frequently in recent times and their causes showing a great deal of variety which require intervention methods and risk assessment strategies are unique in each case. On September 4, 2020, a fire and explosion occurred in a confined space caused by a methane gas leak from an underground pipeline in Baitus Salat Jame mosque during Night (Esha) prayer in Narayanganj District, Bangladesh that killed 34 people. In this research, this incident is simulated using Fire Dynamics Simulator (FDS) software to analyze and understand the nature of the accident and associated consequences. FDS is an advanced computational fluid dynamics (CFD) system of fire-driven fluid flow which solves numerically a large eddy simulation form of the Navier–Stokes’s equations for simulation of the fire and smoke spread and prediction of thermal radiation, toxic substances concentrations and other relevant parameters of fire. This study focuses on understanding the nature of the fire and consequence evaluation due to thermal radiation caused by vapor cloud explosion. An evacuation modeling was constructed to visualize the effect of evacuation time and fractional effective dose (FED) for different types of agents. The results were presented by 3D animation, sliced pictures and graphical representation to understand fire hazards caused by thermal radiation or smoke due to vapor cloud explosion. This study will help to design and develop appropriate respond strategy for preventing similar accidents.

Keywords: consequence modeling, fire and explosion, fire dynamics simulation (FDS), thermal radiation

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Authors: Robert Ogbanje Okwori

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The paper explained the causes low students’ access to university education in Nigeria and how it can be remedied. It is discovered that low students’ access to university education in Nigeria is evident despite these number of universities in the country. In 2006/2007 academic session, 806,089 sat for Joint Unified Matriculation Board Examination (JAMB) into Nigerian universities and only 123,626 (15.3%) were admitted while 2011/2012 academic session, a total of 1,493,604 candidates sat for Joint Unified Matriculation Board Examination (JAMB) into Nigerian universities and only 65,073 (43.57%) were admitted. This necessitates for the research. Therefore, the study posed the following research questions. What are causes of low students’ access to university education in Nigeria? What are the challenges of students’ access to university education in Nigeria? How can students’ access to university education in Nigeria be improved? Sample survey research design was adopted for the study. A structured questionnaire was used to gather data for the study. Six hundred and eighty (680) respondents which comprised of 100 level university students; JAMB Officers and University administrators (Vice Chancellors, Registrars and Admission Officers) were used for the study. Stratified random sampling was applied for adequate representation of respondents from universities in the six geopolitical zones of Nigeria. Mean was used to answer research questions while Kuder-Richardson formula 20 was used to check the internal consistency of the instrument. The correlation coefficient of the instrument was 0.87. The major findings include the carrying capacity of each university contributes to low students’ access to university education and academic staff were inadequate. From the analysis of the study, it is concluded that the rate of access to university education is low, therefore, every university should establish distance learning programme to reduce university admission crisis. The training infrastructure in the universities should be improved upon by the owners to increase the carrying capacity of each university.

Keywords: access, causes, low, university

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Authors: Soumaya Sallem, Marc Olivas

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This contribution presents a method for detecting, locating, and characterizing soft faults in a complex wired network. The proposed method is based on multi-carrier reflectometry MCTDR (Multi-Carrier Time Domain Reflectometry) combined with a multi-objective genetic algorithm. In order to ensure complete network coverage and eliminate diagnosis ambiguities, the MCTDR test signal is injected at several points on the network, and the data is merged between different reflectometers (sensors) distributed on the network. An adapted multi-objective genetic algorithm is used to merge data in order to obtain more accurate faults location and characterization. The proposed method performances are evaluated from numerical and experimental results.

Keywords: wired network, reflectometry, network distributed diagnosis, multi-objective genetic algorithm

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Authors: Asmaa Tahiri, Mouktafi Amine

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A substantial number of traditional medical research has been put forward to managing and treating mental disorders. At the present time, to our best knowledge, it is believed that fundamental understanding of the underlying causes of the majority psychological disorders needs to be explored further to inform early diagnosis, managing symptoms and treatment. The emerging field of evolutionary psychology is a promising prospect to address the origin of mental disorders, potentially leading to more effective treatments. Schizophrenia as a topical mental disorder has been linked to the evolutionary adaptation of the human brain represented in the brain connectivity and asymmetry directly linked to humans higher brain cognition in contrast to other primates being our direct living representation of the structure and connectivity of our earliest common African ancestors. As proposed in the evolutionary psychology scientific literature the pathophysiology of schizophrenia is expressed and directly linked to altered connectivity between the Hippocampal Formation (HF) and Dorsolateral Prefrontal Cortex (DLPFC). This research paper presents the results of the use of tractography analysis using multiple open access Diffusion Weighted Imaging (DWI) datasets of healthy subjects, schizophrenia-affected subjects and primates to illustrate the relevance of the aforementioned brain regions connectivity and the underlying evolutionary changes in the human brain. Deterministic fiber tracking and streamline analysis were used to generate connectivity matrices from the DWI datasets overlaid to compute distances and highlight disconnectivity patterns in conjunction with other fiber tracking metrics; Fractional Anisotropy (FA), Mean Diffusivity (MD) and Radial Diffusivity (RD).

Keywords: tractography, evolutionary psychology, schizophrenia, brain connectivity

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Authors: Avneet Kaur

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India is a nation of villages. Traditionally, women had enjoyed a high social status in India. Our mythology, folklore and history are full of women who were epitomes of virtue, wisdom, power, and position. The important concern about their entry into the politics is of great importance all over the world. Women have performed excellently in social, economic and political sphere. However, the fact remains that despite constituting half of the population their representation among elected post continue to remain negligible in Panchayati Raj Institutions. Women in India suffered from many social economic handicaps such as illiteracy, economically dependent, social customs, traditions and rituals that are the main causes of their inactive participation in local governance. There is still widespread patriarchal outlook in the villages and the lack of experience on the part of women leadership are some of the major issues of debate. The implementation of the 73rd Amendment Act of the Indian Constitution in 1992 reserved 1/3 rd of the seats for women empowerment. It was a major step to encourage them to take part in the village politics. This kind of revolution was the beginning of women leadership in villages. The paper intends to study the role and importance of women leadership in Panchayati Raj Institutions in India. The paper is divided into four sections. First section deals with the introduction by taking into account the available research on this particular subject. Second section talks about the role played by women leadership in these institutions after the passing of 73rd Amendment Act. Third section deals with some of the critical insights of the study by discussing the problems faced by the active women’s leadership at the grassroots. Finally, the paper concludes with policy suggestions.

Keywords: women, leadership, grassroots, Panchayati Raj

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Authors: Jindong Gu, Matthias Schubert, Volker Tresp

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In many outlier detection tasks, only training data belonging to one class, i.e., the positive class, is available. The task is then to predict a new data point as belonging either to the positive class or to the negative class, in which case the data point is considered an outlier. For this task, we propose a novel corrupted Generative Adversarial Network (CorGAN). In the adversarial process of training CorGAN, the Generator generates outlier samples for the negative class, and the Discriminator is trained to distinguish the positive training data from the generated negative data. The proposed framework is evaluated using an image dataset and a real-world network intrusion dataset. Our outlier-detection method achieves state-of-the-art performance on both tasks.

Keywords: one-class classification, outlier detection, generative adversary networks, semi-supervised learning

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Authors: Reza Rezaeipour Honarmandzad

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In this paper we propose an ANN for detection and localization of fatigue damage in aircraft structures. We used network of piezoelectric transducers for Lamb-wave measurements in order to calculate damage indices. Data gathered by the sensors was given to neural network classifier. A set of neural network electors of different architecture cooperates to achieve consensus concerning the state of each monitored path. Sensed signal variations in the ROI, detected by the networks at each path, were used to assess the state of the structure as well as to localize detected damage and to filter out ambient changes. The classifier has been extensively tested on large data sets acquired in the tests of specimens with artificially introduced notches as well as the results of numerous fatigue experiments. Effect of the classifier structure and test data used for training on the results was evaluated.

Keywords: ANN, fatigue damage, aircraft structures, piezoelectric transducers, lamb-wave measurements

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Authors: Filiz Ersoz, Taner Ersoz, Erkin Guler

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This paper addresses the issues and technique for textile industry using data mining techniques. Data mining has been applied to the stitching of garments products that were obtained from a textile company. Data mining techniques were applied to the data obtained from the CHAID algorithm, CART algorithm, Regression Analysis and, Artificial Neural Networks. Classification technique based analyses were used while data mining and decision model about the production per person and variables affecting about production were found by this method. In the study, the results show that as the daily working time increases, the production per person also decreases. In addition, the relationship between total daily working and production per person shows a negative result and the production per person show the highest and negative relationship.

Keywords: data mining, textile production, decision trees, classification

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Authors: Jack L. Harris, Marya L. Doerfel, Hyunsook Youn, Minkyung Kim, Kautuki Sunil Jariwala

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Organizational resilience refers to the ability to sustain business or general work functioning despite wide-scale interruptions. We focus on organization and businesses as a pillar of their communities and how they attempt to sustain work when a natural disaster impacts their surrounding regions and economies. While it may be more common to think of resilience as a trait possessed by an organization, an emerging area of research recognizes that for organizations and businesses, resilience is a set of processes that are constituted through communication, social networks, and organizing. Indeed, five processes, robustness, rapidity, resourcefulness, redundancy, and external availability through social media have been identified as critical to organizational resilience. These organizing mechanisms involve multi-level coordination, where individuals intersect with groups, organizations, and communities. Because the nature of such interactions are often networks of people and organizations coordinating material resources, information, and support, they necessarily require some way to coordinate despite being displaced. Little is known, however, if physical and digital spaces can substitute one for the other. We thus are guided by the question, is digital space sufficient when disaster creates a scarcity of physical space? This study presents a cross-case comparison based on field research from four different regions of the United States that were impacted by Hurricanes Katrina (2005), Sandy (2012), Maria (2017), and Harvey (2017). These four cases are used to extend the science of resilience by examining multi-level processes enacted by individuals, communities, and organizations that together, contribute to the resilience of disaster-struck organizations, businesses, and their communities. Using field research about organizations and businesses impacted by the four hurricanes, we code data from interviews, participant observations, field notes, and document analysis drawn from New Orleans (post-Katrina), coastal New Jersey (post-Sandy), Houston Texas (post-Harvey), and the lower keys of Florida (post-Maria). This paper identifies an additional organizing mechanism, networked gathering spaces, where citizens and organizations, alike, coordinate and facilitate information sharing, material resource distribution, and social support. Findings show that digital space, alone, is not a sufficient substitute to effectively sustain organizational resilience during a disaster. Because the data are qualitative, we expand on this finding with specific ways in which organizations and the people who lead them worked around the problem of scarce space. We propose that gatherings after disaster are a sixth mechanism that contributes to organizational resilience.

Keywords: communication, coordination, disaster management, information and communication technologies, interorganizational relationships, resilience, work

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Authors: N. Tahouni, M. Gholami, M. H. Panjeshahi

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Gas flaring is one of the most GHG emitting sources in the oil and gas industries. It is also a major way for wasting such an energy that could be better utilized and even generates revenue. Minimize flaring is an effective approach for reducing GHG emissions and also conserving energy in flaring systems. Integrating waste and flared gases into the fuel gas networks (FGN) of refineries is an efficient tool. A fuel gas network collects fuel gases from various source streams and mixes them in an optimal manner, and supplies them to different fuel sinks such as furnaces, boilers, turbines, etc. In this article we use fuel gas network model proposed by Hasan et al. as a base model and modify some of its features and add constraints on emission pollution by gas flaring to reduce GHG emissions as possible. Results for a refinery case study showed that integration of flare gas stream with waste and natural gas streams to construct an optimal FGN can significantly reduce total annualized cost and flaring emissions.

Keywords: flaring, fuel gas network, GHG emissions, stream

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Authors: C. Pislaru, A. Shebani

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This paper uses the radial basis function neural network (RBFNN) for system identification of nonlinear systems. Five nonlinear systems are used to examine the activity of RBFNN in system modeling of nonlinear systems; the five nonlinear systems are dual tank system, single tank system, DC motor system, and two academic models. The feed forward method is considered in this work for modelling the non-linear dynamic models, where the K-Means clustering algorithm used in this paper to select the centers of radial basis function network, because it is reliable, offers fast convergence and can handle large data sets. The least mean square method is used to adjust the weights to the output layer, and Euclidean distance method used to measure the width of the Gaussian function.

Keywords: system identification, nonlinear systems, neural networks, radial basis function, K-means clustering algorithm

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Authors: Slim Chokri

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Load forecasting is one of the central functions in power systems operations. Electricity cannot be stored, which means that for electric utility, the estimate of the future demand is necessary in managing the production and purchasing in an economically reasonable way. A majority of the recently reported approaches are based on neural network. The attraction of the methods lies in the assumption that neural networks are able to learn properties of the load. However, the development of the methods is not finished, and the lack of comparative results on different model variations is a problem. This paper presents a new approach in order to predict the Tunisia daily peak load. The proposed method employs a computational intelligence scheme based on the Fuzzy neural network (FNN) and support vector regression (SVR). Experimental results obtained indicate that our proposed FNN-SVR technique gives significantly good prediction accuracy compared to some classical techniques.

Keywords: neural network, load forecasting, fuzzy inference, machine learning, fuzzy modeling and rule extraction, support vector regression

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Authors: O. Gsib, U. Peirera, C. Egles, S. A. Bencherif

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In skin regenerative medicine, one of the critical issues is to produce a three-dimensional scaffold with optimized porosity for dermal fibroblast infiltration and neovascularization, which exhibits high mechanical properties and displays sufficient wound healing characteristics. In this study, we report on the synthesis and characterization of macroporous sequential interpenetrating polymer networks (IPNs) combining skin wound healing properties of fibrin with the excellent physical properties of polyethylene glycol (PEG). Fibrin fibers serve as a provisional biologically active network to promote cell adhesion and proliferation while PEG provides the mechanical stability to maintain the entire 3D construct. After having modified both PEG and Serum Albumin (used for promoting enzymatic degradability) by adding methacrylate residues (PEGDM and SAM, respectively), Fibrin/PEGDM-SAM sequential IPNs were synthesized as follows: Macroporous sponges were first produced from PEGDM-SAM hydrogels by a freeze-drying technique and then rehydrated by adding the fibrin precursors. Environmental Scanning Electron Microscopy (ESEM) and Confocal Laser Scanning Microscopy (CLSM) were used to characterize their microstructure. Human dermal fibroblasts were cultivated during one week in the constructs and different cell culture parameters (viability, morphology, proliferation) were evaluated. Subcutaneous implantations of the scaffolds were conducted on five-week old male nude mice to investigate their biocompatibility in vivo. We successfully synthesized interconnected and macroporous Fibrin/PEGDM-SAM sequential IPNs. The viability of primary dermal fibroblasts was well maintained (above 90%) after 2 days of culture. Cells were able to adhere, spread and proliferate in the scaffolds suggesting the suitable porosity and intrinsic biologic properties of the constructs. The fibrin network adopted a spider web shape that covered partially the pores allowing easier cell infiltration into the macroporous structure. To further characterize the in vitro cell behavior, cell proliferation (EdU incorporation, MTS assay) is being studied. Preliminary histological analysis of animal studies indicated the persistence of hydrogels even after one-month post implantation and confirmed the absence of inflammation response, good biocompatibility and biointegration of our scaffolds within the surrounding tissues. These results suggest that our Fibrin/PEGDM-SAM IPNs could be considered as potential candidates for dermis regenerative medicine. Histological analysis will be completed to further assess scaffold remodeling including de novo extracellular matrix protein synthesis and early stage angiogenesis analysis. Compression measurements will be conducted to investigate the mechanical properties.

Keywords: fibrin, hydrogels for dermal reconstruction, polyethylene glycol, semi-interpenetrating polymer network

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Authors: Megat Zuhairy, Megat Tajuddin, Hadijah Iberahim, Noraini Ismail

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This paper aims to discuss the impact of external networking on innovation and organizational performance in the construction industry. In Malaysia, the construction industry is known to be one of the industries that contribute significantly towards her economic growth. The construction industry is described as a fragmented and complex product system as construction projects implementation requires involvement of varying combination of large and small organizations across the supply chain spectrum. The innovation and performance of Malaysian construction industry are reported to be at underachieving and efforts for its improvement have inspired this study initiative. External networking among industry players is capable in bringing them to work together as a team, reducing the adversarial relationships among them for innovation effort and greater performance. The instrument in measuring innovation and organizational performance specific to the construction industry was developed by adapting measures introduced by several scholars in these fields. Contractors and consulting companies were the sampling frames of this study representing the construction industry in Malaysia. The population lists were developed from the lists provided by CIDB, BEM, BOA and BQSM. The samples were selected based on a stratified sampling method to gauge representation of the different groups in the population. Regression analysis was performed in this quantitative study to assess relationships amongst variables. The results revealed that principally, external networking is significant in influencing both innovation and organizational performance. Nevertheless, external networking with different industry players has a different impact on innovation and organizational performance. The study revealed that external networking with project players is significant on project performance but not on innovation. On the other hand, external networking with government agencies, academic institutions and professional bodies is significant in influencing innovation but not on organizational performance.

Keywords: innovation, external networking, organizational performance, construction industry

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Authors: Mohammad Shams Esfand Abadi, Mohammad Ranjbar, Reza Ebrahimpour

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This work presents the distributed processing solution problem in a diffusion network based on the adapt then combine (ATC) and combine then adapt (CTA)selective partial update normalized least mean squares (SPU-NLMS) algorithms. Also, we extend this approach to dynamic selection affine projection algorithm (DS-APA) and ATC-DS-APA and CTA-DS-APA are established. The purpose of ATC-SPU-NLMS and CTA-SPU-NLMS algorithm is to reduce the computational complexity by updating the selected blocks of weight coefficients at every iteration. In CTA-DS-APA and ATC-DS-APA, the number of the input vectors is selected dynamically. Diffusion cooperation strategies have been shown to provide good performance based on these algorithms. The good performance of introduced algorithm is illustrated with various experimental results.

Keywords: selective partial update, affine projection, dynamic selection, diffusion, adaptive distributed networks

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Authors: Hela Ghali, Oumayma Ben Amor, Salwa Khefacha, Mohamed Ben Rejeb, Sirine Frigui, Meriam Tourki Dhidah, Lamine Dhidah, Houyem Said Laatiri

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Background: Blood exposure accidents are the most common problem in hospitals that threaten healthcare professionals with a high risk of infectious complications which weighs heavily on health systems worldwide. Paramedics are the highest risk group due to the nature of their daily activities. We aimed to determine knowledge and attitudes about the management of blood-exposure accidents among nurses and technicians in two pediatric departments. Materials/Methods: This is a cross-sectional descriptive study conducted on March 2017, carried out with the care staff of the pediatric ward of the Farhat Hached Teaching Hospital of Sousse and pediatric surgery of the Fattouma Bourguiba University Hospital in Monastir, using a pre- tested and self-administered questionnaire. Data entry and analysis were performed using Excel software. Results: The response rate was 85.1%. A female predominance (82.5%) was reported among respondents with a sex ratio of 0.21. 80% of the participants were under 35 years old. Seniority of less than 10 years was found in 77.5% of respondents. Only 22.5% knew the definition of a blood- exposure accident. 100% and 95% of participants reported the relative risk, respectively, to hepatitis and AIDS viruses. However, only 15% recognized the severity factors of a blood-exposure accident. Hygiene compliance was the most important dimension for almost the entire population for the prevention. On the other hand, only 12.5% knew the meaning of 'standard precautions' and ¼ considered them necessary for at-risk patients only. 40% reported being exposed at least once, among them, 87.5% used betadine, and 77.5% said that anti-infectious chemoprophylaxis is necessary regardless of the patient's serological status. However, 52.5% did not know the official reporting circuit of management of blood-exposure accident in their institutions. Conclusion: For better management of risks in hospitals and an improvement of the safety of the care, a reinforcement of the sensibilization of the caregivers with regard to the risks of blood exposure accident is necessary, while developing their knowledge to act in security.

Keywords: attitudes, blood-exposure accident, knowledge, pediatric department

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Authors: William Anderson, Kyle Kobold, Oleg Yakimenko

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Green microgrids using mostly renewable energy (RE) for generation, are complex systems with inherent nonlinear dynamics. Among a variety of different optimization tools there are only a few ones that adequately consider this complexity. This paper evaluates applicability of two somewhat similar optimization tools tailored for standalone RE microgrids and also assesses a machine learning tool for performance prediction that can enhance the reliability of any chosen optimization tool. It shows that one of these microgrid optimization tools has certain advantages over another and presents a detailed routine of preparing input data to simulate RE microgrid behavior. The paper also shows how neural-network-based predictive modeling can be used to validate and forecast solar power generation based on weather time series data, which improves the overall quality of standalone RE microgrid analysis.

Keywords: microgrid, renewable energy, complex systems, optimization, predictive modeling, neural networks

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Authors: Kamel Laidi, Khelifa Benmansour, Ouahid Bouchhida

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Neural networks-based approach for 2-cells serial converter has been developed and implemented. The approach is based on a behavioural description of the different operating modes of the converter. Each operating mode represents a well-defined configuration, and for which is matched an operating zone satisfying given invariance conditions, depending on the capacitors' voltages and the load current of the converter. For each mode, a control vector whose components are the control signals to be applied to the converter switches has been associated. Therefore, the problem is reduced to a classification task of the different operating modes of the converter. The artificial neural nets-based approach, which constitutes a powerful tool for this kind of task, has been adopted and implemented. The application to a 2-cells chopper has allowed ensuring efficient and robust control of the load current and a high capacitors voltages balancing.

Keywords: neural nets, control, multicellular converters, 2-cells chopper

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Authors: Mohammad Hadi Khorashadi Zadeh

Abstract:

Many new applications and internet services have been emerged since the innovation of mobile networks and devices. However, these applications have problems of security, management, and performance in business environments. Cloud systems provide information transfer, management facilities, and security for virtual environments. Therefore, an innovative internet service and a business model are proposed in the present study for creating a secure and consolidated environment for managing the mobile information of organizations based on cloud virtual phones (CVP) infrastructures. Using this method, users can run Android and web applications in the cloud which enhance performance by connecting to other CVP users and increases privacy. It is possible to combine the CVP with distributed protocols and central control which mimics the behavior of human societies. This mix helps in dealing with sensitive data in mobile devices and facilitates data management with less application overhead.

Keywords: BYOD, mobile cloud computing, mobile security, information management

Procedia PDF Downloads 317

Authors: Emad A. Mohammed

Abstract:

Utilizing the capabilities of Data Mining and Artificial Intelligence in the prediction of the minimum miscibility pressure (MMP) required for multi-contact miscible (MCM) displacement of reservoir petroleum by hydrocarbon gas flooding using Fuzzy Logic models and Artificial Neural Network models will help a lot in giving accurate results. The factors affecting the (MMP) as it is proved from the literature and from the dataset are as follows: XC2-6: Intermediate composition in the oil-containing C2-6, CO2 and H2S, in mole %, XC1: Amount of methane in the oil (%),T: Temperature (°C), MwC7+: Molecular weight of C7+ (g/mol), YC2+: Mole percent of C2+ composition in injected gas (%), MwC2+: Molecular weight of C2+ in injected gas. Fuzzy Logic and Neural Networks have been used widely in prediction and classification, with relatively high accuracy, in different fields of study. It is well known that the Fuzzy Inference system can handle uncertainty within the inputs such as in our case. The results of this work showed that our proposed models perform better with higher performance indices than other emprical correlations.

Keywords: MMP, gas flooding, artificial intelligence, correlation

Procedia PDF Downloads 144

Authors: Wenbo Wang, Yi-Fang Brook Wu

Abstract:

The spread of misinformation and disinformation has become a major concern, particularly with the rise of social media as a primary source of information for many people. As a means to address this phenomenon, automated fact-checking has emerged as a safeguard against the spread of misinformation and disinformation. Existing fact-checking approaches aim to determine whether a news claim is true or false, and they have achieved decent veracity prediction accuracy. However, the state-of-the-art methods rely on manually verified external information to assist the checking model in making judgments, which requires significant human resources. This study introduces a framework, SAC, which focuses on 1) augmenting the representation of a claim by incorporating additional context using general-purpose, comprehensive, and authoritative data; 2) developing a search function to automatically select relevant, new, and credible references; 3) focusing on the important parts of the representations of a claim and its reference that are most relevant to the fact-checking task. The experimental results demonstrate that 1) Augmenting the representations of claims and references through the use of a knowledge base, combined with the multi-head attention technique, contributes to improved performance of fact-checking. 2) SAC with auto-selected references outperforms existing fact-checking approaches with manual selected references. Future directions of this study include I) exploring knowledge graphs in Wikidata to dynamically augment the representations of claims and references without introducing too much noise, II) exploring semantic relations in claims and references to further enhance fact-checking.

Keywords: fact checking, claim verification, deep learning, natural language processing

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Authors: Saulutė Juzelėnienė, Skirmantė Šarkauskienė

Abstract:

In this paper, it is aimed to analyse the multimodal representations of the concepts of LOVE and PASSION in Lithuanian graphic novel “Gertrūda”, by Gerda Jord. The research is based on the earlier findings by Forceville (2005), Eerden (2009) as well as insights made by Shihara and Matsunaka (2009) and Kövecses (2000). The domains of target and source of LOVE and PASSION metaphors in comics are expressed by verbal and non-verbal cues. The analysis of non-verbal cues adopts the concepts of rune and indexes. A pictorial rune is a graphic representation of an object that does not exist in reality in comics, such as lines, dashes, text "balloons", and pictorial index – a graphically represented object of reality, a real symptom expressing a certain emotion, such as a wide smile, furrowed eyebrows, etc. Indexes are often hyperbolized in comics. The research revealed that most frequent source domains are CLOSINESS/UNITY, NATURAL/ PHYSICAL FORCE, VALUABLE OBJECT, PRESSURE. The target is the emotion of LOVE/PASSION which belongs to a more abstract domain of psychological experience. In this kind of metaphor, the picture can be interpreted as representing the emotion of happiness. Data are taken from Lithuanian comic books and Internet sites, where comics have been presented. The data and the analysis we are providing in this article aims to reveal that there are pictorial metaphors that manifest conceptual metaphors that are also expressed verbally and that methodological framework constructed for the analysis in the papers by Forceville at all is applicable to other emotions and culture specific pictorial manifestations.

Keywords: multimodal metaphor, conceptual metaphor, comics, graphic novel, concept of love/passion

Procedia PDF Downloads 67