Search results for: network lifetime

Authors: Fathi Abid, Bilel Kaffel

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The olive tree, the olive harvest during winter season and the production of olive oil better known by professionals under the name of the crushing operation have interested institutional traders such as olive-oil offices and private companies such as food industry refining and extracting pomace olive oil as well as export-import public and private companies specializing in olive oil. The major problem facing producers of olive oil each winter campaign, contrary to what is expected, it is not whether the harvest will be good or not but whether the sale price will allow them to cover production costs and achieve a reasonable margin of profit or not. These questions are entirely legitimate if we judge by the importance of the issue and the heavy complexity of the uncertainty and competition made tougher by a high level of indebtedness and the experience and expertise of speculators and producers whose objectives are sometimes conflicting. The aim of this paper is to study the formation mechanism of olive oil prices in order to learn about speculators’ behavior and expectations in the market, how they contribute by their industry knowledge and their financial alliances and the size the financial challenge that may be involved for them to build private information hoses globally to take advantage. The methodology used in this paper is based on two stages, in the first stage we study econometrically the formation mechanisms of olive oil price in order to understand the market participant behavior by implementing ARMA, SARMA, GARCH and stochastic diffusion processes models, the second stage is devoted to prediction purposes, we use a combined wavelet- ANN approach. Our main findings indicate that olive oil market participants interact with each other in a way that they promote stylized facts formation. The unstable participant’s behaviors create the volatility clustering, non-linearity dependent and cyclicity phenomena. By imitating each other in some periods of the campaign, different participants contribute to the fat tails observed in the olive oil price distribution. The best prediction model for the olive oil price is based on a back propagation artificial neural network approach with input information based on wavelet decomposition and recent past history.

Keywords: olive oil price, stylized facts, ARMA model, SARMA model, GARCH model, combined wavelet-artificial neural network, continuous-time stochastic volatility mode

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Authors: M. Pramila Devi, N. V. N. Indra Kiran

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Control charts, predominantly in the form of X-bar chart, are important tools in statistical process control (SPC). They are useful in determining whether a process is behaving as intended or there are some unnatural causes of variation. A process is out of control if a point falls outside the control limits or a series of point’s exhibit an unnatural pattern. In this paper, a study is carried out on four training algorithms for CCPs recognition. For those algorithms optimal structure is identified and then they are studied for type I and type II errors for generalization without early stopping and with early stopping and the best one is proposed.

Keywords: control chart pattern recognition, neural network, backpropagation, generalization, early stopping

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Authors: Ali Mashayekh, Mahdiye Khorasani, Thomas weyh

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The demand for battery electric vehicles (BEV) is steadily increasing, and it can be assumed that electric mobility will dominate the market for individual transportation in the future. Regarding BEVs, the focus of state-of-the-art research and development is on vehicle batteries since their properties primarily determine vehicles' characteristic parameters, such as price, driving range, charging time, and lifetime. State-of-the-art battery packs consist of invariable configurations of battery cells, connected in series and parallel. A promising alternative is battery systems based on multilevel inverters, which can alter the configuration of the battery cells during operation via semiconductor switches. The main benefit of such topologies is that a three-phase AC voltage can be directly generated from the battery pack, and no separate power inverters are required. Therefore, modular battery systems based on different multilevel inverter topologies and reconfigurable battery systems are currently under investigation. Another advantage of the multilevel concept is that the possibility to reconfigure the battery pack allows battery cells with different states of charge (SoC) to be connected in parallel, and thus low-loss balancing can take place between such cells. In contrast, in conventional battery systems, parallel connected (hard-wired) battery cells are discharged via bleeder resistors to keep the individual SoCs of the parallel battery strands balanced, ultimately reducing the vehicle range. Different multilevel inverter topologies and reconfigurable batteries have been described in the available literature that makes the before-mentioned advantages possible. However, what has not yet been described is how an intelligent operating algorithm needs to look like to keep the SoCs of the individual battery strands of a modular battery system with integrated power electronics balanced. Therefore, this paper suggests an SoC balancing approach for Battery Modular Multilevel Management (BM3) converter systems, which can be similarly used for reconfigurable battery systems or other multilevel inverter topologies with parallel connectivity. The here suggested approach attempts to simultaneously utilize all converter modules (bypassing individual modules should be avoided) because the parallel connection of adjacent modules reduces the phase-strand's battery impedance. Furthermore, the presented approach tries to reduce the number of switching events when changing the switching state combination. Thereby, the ohmic battery losses and switching losses are kept as low as possible. Since no power is dissipated in any designated bleeder resistors and no designated active balancing circuitry is required, the suggested approach can be categorized as a proactive balancing approach. To verify the algorithm's validity, simulations are used.

Keywords: battery management system, BEV, battery modular multilevel management (BM3), SoC balancing

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Authors: Huang Yajing

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Context: Computer-aided language learning (CALL) in China has undergone significant development over the past few decades, with distinct stages marking its evolution. This paper aims to provide a comprehensive review of the practice and research in this field in China, tracing its journey from the early stages of audio-visual education to the current multimedia network integration stage. Research Aim: The study aims to analyze the historical progression of CALL in China, identify key developments in the field, and provide recommendations for enhancing CALL practices in the future. Methodology: The research employs document analysis and literature review to synthesize existing knowledge on CALL in China, drawing on a range of sources to construct a detailed overview of the evolution of CALL practices and research in the country. Findings: The review highlights the significant advancements in CALL in China, showcasing the transition from traditional audio-visual educational approaches to the current integrated multimedia network stage. The study identifies key milestones, technological advancements, and theoretical influences that have shaped CALL practices in China. Theoretical Importance: The evolution of CALL in China reflects not only technological progress but also shifts in educational paradigms and theories. The study underscores the significance of cognitive psychology as a theoretical underpinning for CALL practices, emphasizing the learner's active role in the learning process. Data Collection and Analysis Procedures: Data collection involved extensive review and analysis of documents and literature related to CALL in China. The analysis was carried out systematically to identify trends, developments, and challenges in the field. Questions Addressed: The study addresses the historical development of CALL in China, the impact of technological advancements on teaching practices, the role of cognitive psychology in shaping CALL methodologies, and the future outlook for CALL in the country. Conclusion: The review provides a comprehensive overview of the evolution of CALL in China, highlighting key stages of development and emerging trends. The study concludes by offering recommendations to further enhance CALL practices in the Chinese context.

Keywords: English education, educational technology, computer-aided language teaching, applied linguistics

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Authors: Byeongju Choi, Sanggil Lee, Kyung-Eun Lee

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Introduction: In the republic of Korea, the number of workers in the beauty industry has been increasing. Because the prevalence of hepatitis B carriers in Korea is higher than in other countries, the risk of blood-borne infection including viral hepatitis B and C, among the workers by using the sharp and contaminated instruments during procedure can be expected among beauty salon workers. However, the health care policies for the workers to prevent the blood-borne infection are not established due to the lack of evidences. Moreover, the workers in hair and nail salon were mostly employed at small businesses, where national mandatory systems or policies for workers’ health management are not applied. In this study, the risk of the viral hepatitis B and C from the job experiencing the hair and nail procedures in the mortality was assessed. Method: We conducted a retrospective review of the job histories and causes of death in the female deaths from 2006-2016. 132,744 of female deaths who had one more job experiences during their lifetime were included in this study. Job histories were assessed using the employment insurance database in Korea Employment Information Service (KEIS) and the causes of death were in death statistics produced by Statistics Korea. Case group (n= 666) who died from viral hepatitis was classified the death having record involved in ‘B15-B19’ as a cause of deaths based on Korean Standard Classification of Diseases(KCD) with the deaths from other causes, control group (n=132,078). The group of the workers in the beauty service industry were defined as the employees who had ever worked in the industry coded as ‘9611’ based on Korea Standard Industry Classification (KSIC) and others were others. Other than job histories, birth year, marital status, education level were investigated from the death statistics. Multiple logistic regression analysis were used to assess the risk of deaths from viral hepatitis in the case and control group. Result: The number of the deaths having ever job experiences at the hair and nail salon was 255. After adjusting confounders of age, marital status and education, the odds ratio(OR) for deaths from viral hepatitis was quite high in the group having experiences with working in the beauty service industry with 3.14(95% confidence interval(CI) 1.00-9.87). Other associated factors with increasing the risk of deaths from viral hepatitis were low education level(OR=1.34, 95% CI 1.04-1.73), married women (OR=1.42, 95% CI 1.02-1.97). Conclusion: The risk of deaths from viral hepatitis were high in the workers in the beauty service industry but not statistically significant, which might attributed from the small number of workers in beauty service industry. It was likely that the number of workers in beauty service industry could be underestimated due to their temporary job position. Further studies evaluating the status and the incidence of viral infection among the workers with consideration of the vertical transmission would be required.

Keywords: beauty service, viral hepatitis, blood-borne infection, viral infection

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Authors: Fangjing Zhou, Wen Chen, Brian J. Hall, Yu Wang, Carl Latkin, Li Ling, Joseph D. Tucker

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Background: Economic and social reforms designed to open China to the world has been successful, but also appear to have rapidly laid the foundation for the reemergence of STIs since 1980s. Changes in sexual behaviors, relationships, and norms among Chinese contributed to the STIs epidemic. As the massive population moved during the last 30 years, early coital debut, multiple sexual partnerships, and unprotected sex have increased within the general population. Our objectives were to assess associations between residences location, sexual risk behaviors and sexually transmitted infections (STIs) among adults living in Guangzhou, China. Methods: Stratified cluster sampling followed a two-step process was used to select populations aged 18-59 years in Guangzhou, China. Spatial methods including Geographic Information Systems (GIS) were utilized to identify 1400 coordinates with latitude and longitude. Face-to-face household interviews were conducted to collect self-report data on sexual risk behaviors and diagnosed STIs. Kulldorff’s spatial scan statistic was implemented to identify and detect spatial distribution and clusters of sexual risk behaviors and STIs. The presence and location of statistically significant clusters were mapped in the study areas using ArcGIS software. Results: In this study, 1215 of 1400 households attempted surveys, with 368 refusals, resulting in a sample of 751 completed surveys. The prevalence of self-reported sexual risk behaviors was between 5.1% and 50.0%. The self-reported lifetime prevalence of diagnosed STIs was 7.06%. Anal intercourse clustered in an area located along the border within the rural-urban continuum (p=0.001). High rate clusters for alcohol or other drugs using before sex (p=0.008) and migrants who lived in Guangzhou less than one year (p=0.007) overlapped this cluster. Excess cases for sex without a condom (p=0.031) overlapped the cluster for college students (p<0.001). Conclusions: Short-term migrants and college students reported greater sexual risk behaviors. Programs to increase safer sex within these communities to reduce the risk of STIs are warranted in Guangzhou. Spatial analysis identified geographical clusters of sexual risk behaviors, which is critical for optimizing surveillance and targeting control measures for these locations in the future.

Keywords: cluster analysis, migrant, sexual risk behaviors, spatial distribution

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Authors: Jamaludin Sallim, Rozlina Mohamed, Roslina Abdul Hamid

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In this paper, we proposed an Ant Colony Optimization (ACO) algorithm together with Traveling Salesman Problem (TSP) approach to investigate the clustering problem in Protein Interaction Networks (PIN). We named this combination as ACOPIN. The purpose of this work is two-fold. First, to test the efficacy of ACO in clustering PIN and second, to propose the simple generalization of the ACO algorithm that might allow its application in clustering proteins in PIN. We split this paper to three main sections. First, we describe the PIN and clustering proteins in PIN. Second, we discuss the steps involved in each phase of ACO algorithm. Finally, we present some results of the investigation with the clustering patterns.

Keywords: ant colony optimization algorithm, searching algorithm, protein functional module, protein interaction network

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Authors: Marcio Freire Cruz, Naoaki Ono, Shigehiko Kanaya, Carlos Arthur Mattos Teixeira Cavalcante

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The systems that record patient care information, known as Electronic Medical Record (EMR) and those that monitor vital signs of patients, such as heart rate, body temperature, and blood pressure have been extremely valuable for the effectiveness of the patient’s treatment. Several kinds of research have been using data from EMRs and vital signs of patients to predict illnesses. Among them, we highlight those that intend to predict, classify, or, at least identify patterns, of sepsis illness in patients under vital signs monitoring. Sepsis is an organic dysfunction caused by a dysregulated patient's response to an infection that affects millions of people worldwide. Early detection of sepsis is expected to provide a significant improvement in its treatment. Preceding works usually combined medical, statistical, mathematical and computational models to develop detection methods for early prediction, getting higher accuracies, and using the smallest number of variables. Among other techniques, we could find researches using survival analysis, specialist systems, machine learning and deep learning that reached great results. In our research, patients are modeled as points moving each hour in an n-dimensional space where n is the number of vital signs (variables). These points can reach a sepsis target point after some time. For now, the sepsis target point was calculated using the median of all patients’ variables on the sepsis onset. From these points, we calculate for each hour the position vector, the first derivative (velocity vector) and the second derivative (acceleration vector) of the variables to evaluate their behavior. And we construct a prediction model based on a Long Short-Term Memory (LSTM) Network, including these derivatives as explanatory variables. The accuracy of the prediction 6 hours before the time of sepsis, considering only the vital signs reached 83.24% and by including the vectors position, speed, and acceleration, we obtained 94.96%. The data are being collected from Medical Information Mart for Intensive Care (MIMIC) Database, a public database that contains vital signs, laboratory test results, observations, notes, and so on, from more than 60.000 patients.

Keywords: dynamic analysis, long short-term memory, prediction, sepsis

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Authors: M. Olmedilla, M. R. Martinez-Torres, S. L. Toral

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Electronic Word-Of-Mouth (eWOM) communities represent today an important source of information in which more and more customers base their purchasing decisions. They include thousands of reviews concerning very different products and services posted by many individuals geographically distributed all over the world. Due to their massive audience, eWOM communities can help users to find the product they are looking for even if they are less popular or rare. This is known as the long tail effect, which leads to a larger number of lower-selling niche products. This paper analyzes the long tail effect in a well-known eWOM community and defines a tool for finding niche products unavailable through conventional channels.

Keywords: eWOM, online user reviews, long tail theory, product categorization, social network analysis

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Authors: Shaimaa M. Mohamed, Haitham S. Hamza, Imane A. Saroit

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Many wireless sensor network applications require K-coverage of the monitored area. In this paper, we propose a scalable harmony search based algorithm in terms of execution time, K-Coverage Enhancement Algorithm (KCEA), it attempts to enhance initial coverage, and achieve the required K-coverage degree for a specific application efficiently. Simulation results show that the proposed algorithm achieves coverage improvement of 5.34% compared to K-Coverage Rate Deployment (K-CRD), which achieves 1.31% when deploying one additional sensor. Moreover, the proposed algorithm is more time efficient.

Keywords: Wireless Sensor Networks (WSN), harmony search algorithms, K-Coverage, Mobile WSN

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Authors: Sabyasachi Mukhopadhyay, Joseph Ogutu, Gundula Bartzke, Hans-Peter Piepho

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Rainfall is a critical component of climate governing vegetation growth and production, forage availability and quality for herbivores. However, reliable rainfall measurements are not always available, making it necessary to predict rainfall values for particular locations through time. Predicting rainfall in space and time can be a complex and challenging task, especially where the rain gauge network is sparse and measurements are not recorded consistently for all rain gauges, leading to many missing values. Here, we develop a flexible Bayesian model for predicting rainfall in space and time and apply it to Narok County, situated in southwestern Kenya, using data collected at 23 rain gauges from 1965 to 2015. Narok County encompasses the Maasai Mara ecosystem, the northern-most section of the Mara-Serengeti ecosystem, famous for its diverse and abundant large mammal populations and spectacular migration of enormous herds of wildebeest, zebra and Thomson's gazelle. The model incorporates geographical and meteorological predictor variables, including elevation, distance to Lake Victoria and minimum temperature. We assess the efficiency of the model by comparing it empirically with the established Gaussian process, Kriging, simple linear and Bayesian linear models. We use the model to predict total monthly rainfall and its standard error for all 5 * 5 km grid cells in Narok County. Using the Monte Carlo integration method, we estimate seasonal and annual rainfall and their standard errors for 29 sub-regions in Narok. Finally, we use the predicted rainfall to predict large herbivore biomass in the Maasai Mara ecosystem on a 5 * 5 km grid for both the wet and dry seasons. We show that herbivore biomass increases with rainfall in both seasons. The model can handle data from a sparse network of observations with many missing values and performs at least as well as or better than four established and widely used models, on the Narok data set. The model produces rainfall predictions consistent with expectation and in good agreement with the blended station and satellite rainfall values. The predictions are precise enough for most practical purposes. The model is very general and applicable to other variables besides rainfall.

Keywords: non-stationary covariance function, gaussian process, ungulate biomass, MCMC, maasai mara ecosystem

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Authors: Joo-Hyung Oh

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The number of service users of 4G VoLTE (voice over LTE) using LTE data networks is rapidly growing. VoLTE based on all-IP network enables clearer and higher-quality voice calls than 3G. It does, however, pose new challenges; a voice call through IP networks makes it vulnerable to security threats such as wiretapping and forged or falsified information. And in particular, stealing other users’ phone numbers and forging or falsifying call request messages from outgoing voice calls within VoLTE result in considerable losses that include user billing and voice phishing to acquaintances. This paper focuses on the threats of caller phone number spoofing in the VoLTE and countermeasure technology as safety measures for mobile communication networks.

Keywords: LTE, 4G, VoLTE, phone number spoofing

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Authors: Jing Ma, Xindong An

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Green infrastructure, the most important aspect of improving the quality of life, has been a crucial element of the liveability measurement. With demanding of more liveable urban environment from increasing population in city area, access to green infrastructure in walking distance should be taken into consideration. This article exemplifies the study on accessibility measurement of green infrastructure in central Auckland (New Zealand), using network analysis tool on the basis of GIS, to verify the accessibility levels of green infrastructure. It analyses the overall situation of green infrastructure and draws some conclusions on the city’s different levels of accessibility according to the categories and facilities distribution, which provides valuable references and guidance for the future facility improvement in planning strategies.

Keywords: quality of life, green infrastructure, GIS, accessibility

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Authors: Paul C. Njoku, Archana Swati Njoku

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The study deals with the development of a mathematical model to characterize the oil production in Nigeria. This is calculated by initiating the dynamics of oil production in million barrels revenue plan cost of oil production in million nairas and unit cost of production from 1974-1982 in the contest of the federal Republic of Nigeria. This country export oil to other countries as well as importing specialized crude. The transport network from origin/destination tij to pairs is taking into account simulation runs, optimization have been considered in this study.

Keywords: mathematical oil model development dynamics, Nigeria, characterization barrels, dynamics of oil production

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Authors: Marco Ferretti, Marcello Risitano, Maria Cristina Pietronudo, Lina Ozturk

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A green port is the result of a sustainable long-term strategy adopted by an entire port infrastructure, therefore by the set of actors involved in port activities. The strategy aims to realise the development of sustainable port infrastructure focused on the reduction of negative environmental impacts without jeopardising economic growth. Green technology represents the core tool to implement sustainable solutions, however, they are not a magic bullet. Ports have always been integrated in the local territory affecting the environment in which they operate, therefore, the sustainable strategy should fit with the entire local systems. Therefore, adopting a sustainable strategy means to know how to involve and engage a wide stakeholders’ network (industries, production, markets, citizens, and public authority). The existing research on the topic has not well integrated this perspective with those of sustainability. Research on green ports have mixed the sustainability aspects with those on the maritime industry, neglecting dynamics that lead to the development of the green port phenomenon. We propose an analysis of green ports adopting the lens of ecosystem studies in the field of management. The ecosystem approach provides a way to model relations that enable green solutions and green practices in a port ecosystem. However, due to the local dimension of a port and the port trend on innovation, i.e., sustainable innovation, we draw to a specific concept of ecosystem, those on local innovation systems. More precisely, we explore if a green port is a local innovation system engaged in developing sustainable innovation with a large impact on the territory or merely an innovation adopter. To address this issue, we adopt a comparative case study selecting two innovative ports in Europe: Rotterdam and Genova. The case study is a research method focused on understanding the dynamics in a specific situation and can be used to provide a description of real circumstances. Preliminary results show two different approaches in supporting sustainable innovation: one represented by Rotterdam, a pioneer in competitiveness and sustainability, and the second one represented by Genoa, an example of technology adopter. The paper intends to provide a better understanding of how sustainable innovations are developed and in which manner a network of port and local stakeholder support this process. Furthermore, it proposes a taxonomy of green ports as developers and adopters of sustainable innovation, suggesting also best practices to model relationships that enable the port ecosystem in applying a sustainable strategy.

Keywords: green port, innovation, sustainability, local innovation systems

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Authors: Dina Ibrahim Abouelamaiem, Ana Jorge Sobrido, Magdalena Titirici, Paul R. Shearing, Daniel J. L. Brett

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Global warming and scarcity of fossil fuels have had a radical impact on the world economy and ecosystem. The urgent need for alternative energy sources has hence elicited an extensive research for exploiting efficient and sustainable means of energy conversion and storage. Among various electrochemical systems, supercapacitors attracted significant attention in the last decade due to their high power supply, long cycle life compared to batteries and simple mechanism. Recently, the performance of these devices has drastically improved, as tuning of nanomaterials provided efficient charge and storage mechanisms. Carbon materials, in various forms, are believed to pioneer the next generation of supercapacitors due to their attractive properties that include high electronic conductivities, high surface areas and easy processing and functionalization. Cellulose has eco-friendly attributes that are feasible to replace man-made fibers. The carbonization of cellulose yields carbons, including activated carbon and graphite fibers. Activated carbons successively are the most exploited candidates for supercapacitor electrode materials that can be complemented with pseudocapacitive materials to achieve high energy and power densities. In this work, the optimum functionalization conditions of cellulose have been investigated for supercapacitor electrode materials. The precursor was treated with potassium hydroxide (KOH) at different KOH/cellulose ratios prior to the carbonization process in an inert nitrogen atmosphere at 850 °C. The chalky products were washed, dried and characterized with different techniques including transmission electron microscopy (TEM), x-ray tomography and nitrogen adsorption-desorption isotherms. The morphological characteristics and their effect on the electrochemical performances were investigated in two and three-electrode systems. The KOH/cellulose ratios of 0.5:1 and 1:1 exhibited the highest performances with their unique hierarchal porous network structure, high surface areas and low cell resistances. Both samples acquired the best results in three-electrode systems and coin cells with specific gravimetric capacitances as high as 187 F g-1 and 20 F g-1 at a current density of 1 A g-1 and retention rates of 72% and 70%, respectively. This is attributed to the morphology of the samples that constituted of a well-balanced micro-, meso- and macro-porosity network structure. This study reveals that the electrochemical performance doesn’t solely depend on high surface areas but also an optimum pore size distribution, specifically at low current densities. The micro- and meso-pore contribution to the final pore structure was found to dominate at low KOH loadings, reaching ‘equilibrium’ with macropores at the optimum KOH loading, after which macropores dictate the porous network. The wide range of pore sizes is detrimental for the mobility and penetration of electrolyte ions in the porous structures. These findings highlight the influence of various morphological factors on the double-layer capacitances and high performance rates. In addition, they open a platform for the investigation of the optimized conditions for double-layer capacitance that can be coupled with pseudocapacitive materials to yield higher energy densities and capacities.

Keywords: carbon, electrochemical performance, electrodes, KOH/cellulose optimized ratio, morphology, supercapacitor

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

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

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

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Authors: S. Nakayama

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For non-native English speakers, using English modal auxiliary verbs appropriately can be among the most challenging tasks. This research sought to identify differences in modal verb usage between Japanese non-native English speakers (JNNS) and native speakers (NS) from two different perspectives: frequency of use and distribution of verb phrase structures (VPS) where modal verbs occur. This study can contribute to the identification of JNNSs' interlanguage with regard to modal verbs; the main aim is to make a suggestion for the improvement of teaching materials as well as to help language teachers to be able to teach modal verbs in a way that is helpful for learners. To address the primary question in this study, usage of nine central modals (‘can’, ‘could’, ‘may’, ‘might’, ‘shall’, ‘should’, ‘will’, ‘would’, and ‘must’) by JNNS was compared with that by NSs in the International Corpus Network of Asian Learners of English (ICNALE). This corpus is one of the largest freely-available corpora focusing on Asian English learners’ language use. The ICNALE corpus consists of four modules: ‘Spoken Monologue’, ‘Spoken Dialogue’, ‘Written Essays’, and ‘Edited Essays’. Among these, this research adopted the ‘Written Essays’ module only, which is the set of 200-300 word essays and contains approximately 1.3 million words in total. Frequency analysis revealed gaps as well as similarities in frequency order. Specifically, both JNNSs and NSs used ‘can’ with the most frequency, followed by ‘should’ and ‘will’; however, usage of all the other modals except for ‘shall’ was not identical to each other. A log-likelihood test uncovered JNNSs’ overuse of ‘can’ and ‘must’ as well as their underuse of ‘will’ and ‘would’. VPS analysis revealed that JNNSs used modal verbs in a relatively narrow range of VPSs as compared to NSs. Results showed that JNNSs used most of the modals with bare infinitives or the passive voice only whereas NSs used the modals in a wide range of VPSs including the progressive construction and the perfect aspect, both of which were the structures where JNNSs rarely used the modals. Results of frequency analysis suggest that language teachers or teaching materials should explain other modality items so that learners can avoid relying heavily on certain modals and have a wide range of lexical items to reflect their feelings more accurately. Besides, the underused modals should be more stressed in the classroom because they are members of epistemic modals, which allow us to not only interject our views into propositions but also build a relationship with readers. As for VPSs, teaching materials should present more examples of the modals occurring in a wide range of VPSs to help learners to be able to express their opinions from a variety of viewpoints.

Keywords: corpus linguistics, Japanese learners of English, modal auxiliary verbs, International Corpus Network of Asian Learners of English

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Authors: Amirmozafar Benshams, Khatere Kashmari, Farzad Hatami, Mesbah Saybani

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As a part of the transportation network, bridges are one of the most vulnerable structures. In order to investigate the vulnerability and seismic evaluation of bridges performance, identifying of bridge associated with various state of damage is important. Fragility curves provide important data about damage states and performance of bridges against earthquakes. The development of vulnerability information in the form of fragility curves is a widely practiced approach when the information is to be developed accounting for a multitude of uncertain source involved. This paper presents the fragility curve methodologies for bridges and investigates the practice and applications relating to the seismic fragility assessment of bridges.

Keywords: fragility curve, bridge, uncertainty, NLTHA, IDA

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Authors: Kiamran Radjabli

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State Estimator became an intrinsic part of Energy Management Systems (EMS). The SCADA measurements received from the field are processed by the State Estimator in order to accurately determine the actual operating state of the power systems and provide that information to other real-time network applications. All EMS vendors offer a State Estimator functionality in their baseline products. However, setting up and ensuring that State Estimator consistently produces a reliable solution often consumes a substantial engineering effort. This paper provides generic recommendations and describes a simple practical approach to efficient tuning of State Estimator, based on the working experience with major EMS software platforms and consulting projects in many electrical utilities of the USA.

Keywords: convergence, monitoring, state estimator, performance, troubleshooting, tuning, power systems

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Authors: L. Godinho, N. Teixeira

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Nowadays, massification of Internet access represents one of the major challenges to the traditional powers of the State, among which the power to control its external image. The virtual world has also sparked the interest of social sciences which consider it a new field of study, an immense open text where sense is expressed. In this paper, that immense text has been accessed to so as to understand the perception Internet users from all over the world have of Portugal. Ours is a quantitative and qualitative approach, as we have resorted to buzz, thematic and category analysis. The results confirm the predominance of sea stereotype in others' vision of the Portuguese people, and evidence that national image has adapted to network communication through processes of individuation and paganization.

Keywords: national image, internet, self-communication, perception

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Authors: Padma Chutoo, Elena Kulinskaya, Ilyas Bakbergenuly, Nicholas Steel, Dmitri Pchejetski

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Transient ischaemic attacks (TIAs) are warning signs for future strokes. TIA patients are at increased risk of stroke and cardio-vascular events after a first episode. A majority of studies on TIA focused on the occurrence of these ancillary events after a TIA. Long-term mortality after TIA received only limited attention. We undertook this study to determine the long-term hazards of all-cause mortality following a first episode of a TIA using anonymised electronic health records (EHRs). We used a retrospective case-control study using electronic primary health care records from The Health Improvement Network (THIN) database. Patients born prior to or in year 1960, resident in England, with a first diagnosis of TIA between January 1986 and January 2017 were matched to three controls on age, sex and general medical practice. The primary outcome was all-cause mortality. The hazards of all-cause mortality were estimated using a time-varying Weibull-Cox survival model which included both scale and shape effects and a random frailty effect of GP practice. 20,633 cases and 58,634 controls were included. Cases aged 39 to 60 years at the first TIA event had the highest hazard ratio (HR) of mortality compared to matched controls (HR = 3.04, 95% CI (2.91 - 3.18)). The HRs for cases aged 61-70 years, 71-76 years and 77+ years were 1.98 (1.55 - 2.30), 1.79 (1.20 - 2.07) and 1.52 (1.15 - 1.97) compared to matched controls. Aspirin provided long-term survival benefits to cases. Cases aged 39-60 years on aspirin had HR of 0.93 (0.84 - 1.00), 0.90 (0.82 - 0.98) and 0.88 (0.80 - 0.96) at 5 years, 10 years and 15 years, respectively, compared to cases in the same age group who were not on antiplatelets. Similar beneficial effects of aspirin were observed in other age groups. There were no significant survival benefits with other antiplatelet options. No survival benefits of antiplatelet drugs were observed in controls. Our study highlights the excess long-term risk of death of TIA patients and cautions that TIA should not be treated as a benign condition. The study further recommends aspirin as the better option for secondary prevention for TIA patients compared to clopidogrel recommended by NICE guidelines. Management of risk factors and treatment strategies should be important challenges to reduce the burden of disease.

Keywords: dual antiplatelet therapy (DAPT), General Practice, Multiple Imputation, The Health Improvement Network(THIN), hazard ratio (HR), Weibull-Cox model

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Authors: Jeroen Rotteveel, Zeger De Groot

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The small satellite community is more and more aware of the need to start operating responsibly and sustainably in order to secure the use of outer space in the long run. On the technical side, many debris mitigation techniques have been investigated and demonstrated on board small satellites, showing that technically, a lot of things can be done to curb the growth of space debris and operate more responsible. However, in the absence of strict laws and constraints, one cannot help but wonder what the incentive is to incur significant costs (paying for debris mitigation systems and the launch mass of these systems) and to lose performance onboard resource limited small satellites (mass, volume, power)? Many small satellite developers are operating under tight budgets, either from their sponsors (in case of academic and research projects) or from their investors (in case of startups). As long as it is not mandatory to act more responsibly, we might need to consider the implementation of incentives to stimulate developers to accommodate deorbiting modules, etc. ISISPACE joined the NetZeroSpace initiative in 2021 with the aim to play its role in secure the use of low earth orbit for the next decades by facilitating more sustainable use of space. The company is in a good position as both a satellite builder, a rideshare launch provider, and a technology development company. ISISPACE operates under one of the stricter space laws in the world in terms of maximum orbital lifetime and has been active in various debris mitigation and debris removal in-orbit demonstration missions in the past 10 years. ISISPACE proposes to introduce together with launch partners and regulators an incentive scheme for CubeSat developers to baseline debris mitigation systems on board their CubeSats in such a way that is does not impose too many additional costs to the project. Much like incentives to switch to electric cars or install solar panels on your house, such an incentive can help to increase market uptake of behavior or solutions prior to legislation or bans of certain practices. This can be achieved by: Introducing an extended launch volume in CubeSat deployers to accommodate debris mitigation systems without compromising available payload space for the payload of the main mission Not charging the fee for the launch mass for the additional debris mitigation module Whenever possible, find ways to further co-fund the purchase price, or otherwise reduce the cost of flying debris mitigation modules onboard the CubeSats. The paper will outline the framework of such an incentive scheme and provides ISISPACE’s way forward to make this happen in the near future.

Keywords: netZerospace, cubesats, debris mitigation, small satellite community

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Authors: Francesco Lubrano, Fabrizio Bertone, Federico Stirano

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Modern societies strongly depend on Critical Infrastructures (CI). Hospitals, power supplies, water supplies, telecommunications are just few examples of CIs that provide vital functions to societies. CIs like hospitals are very complex environments, characterized by a huge number of cyber and physical systems that are becoming increasingly integrated. Ensuring a high level of security within such critical infrastructure requires a deep knowledge of vulnerabilities, threats, and potential attacks that may occur, as well as defence and prevention or mitigation strategies. The possibility to remotely monitor and control almost everything is pushing the adoption of network-connected devices. This implicitly introduces new threats and potential vulnerabilities, posing a risk, especially to those devices connected to the Internet. Modern medical devices used in hospitals are not an exception and are more and more being connected to enhance their functionalities and easing the management. Moreover, hospitals are environments with high flows of people, that are difficult to monitor and can somehow easily have access to the same places used by the staff, potentially creating damages. It is therefore clear that physical and cyber threats should be considered, analysed, and treated together as cyber-physical threats. This means that an integrated approach is required. SAFECARE, an integrated cyber-physical security solution, tries to respond to the presented issues within healthcare infrastructures. The challenge is to bring together the most advanced technologies from the physical and cyber security spheres, to achieve a global optimum for systemic security and for the management of combined cyber and physical threats and incidents and their interconnections. Moreover, potential impacts and cascading effects are evaluated through impact propagation models that rely on modular ontologies and a rule-based engine. Indeed, SAFECARE architecture foresees i) a macroblock related to cyber security field, where innovative tools are deployed to monitor network traffic, systems and medical devices; ii) a physical security macroblock, where video management systems are coupled with access control management, building management systems and innovative AI algorithms to detect behavior anomalies; iii) an integration system that collects all the incoming incidents, simulating their potential cascading effects, providing alerts and updated information regarding assets availability.

Keywords: cyber security, defence strategies, impact propagation, integrated security, physical security

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Authors: Adel Edwar Waheeb Louka

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Amjad F. Alharbi, Bashayer A. Alotaibi, Fahd S. Alotaibi

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The emergence of Internet of Things (IoT) technology provides capabilities for a huge number of smart devices, services and people to be communicate with each other for exchanging data and information over existing network. While as IoT is progressing, it provides many opportunities for new ways of communications as well it introduces many security and privacy threats and challenges which need to be considered for the future of IoT development. In this survey paper, an IoT security issues as threats and current challenges are summarized. The security architecture for IoT are presented from four main layers. Based on these layers, the IoT security requirements are presented to insure security in the whole system. Furthermore, some researches initiatives related to IoT security are discussed as well as the future direction for IoT security are highlighted.

Keywords: Internet of Things (IoT), IoT security challenges, IoT security requirements, IoT security architecture

Procedia PDF Downloads 361

Authors: Sara Matsuzaka

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At the center of emerging policy debates on the rights of transgender individuals in public accommodations is the collision of gender binary views with transgender perspectives that challenge conventional gender norms. The results of such socio-political debates could have significant ramifications for the policies and infrastructures of public and private institutions nationwide, including within the addiction treatment field. Despite having disproportionately high rates of substance use disorder compared to the general population, transgender individuals experience significant barriers to engaging in addiction treatment programs. Inpatient addiction treatment centers were originally designed to treat heterosexual cisgender populations and, as such, feature gender segregated housing, bathrooms, and counseling sessions. Such heteronormative structural barriers, combined with exposures to stigmatic al attitudes, may dissuade transgender populations from benefiting from the addiction treatment they so direly need. A literature review is performed to explore the mechanisms by which gender segregation alienates transgender populations within inpatient addiction treatment. The constituent parts of the current debate on the rights of transgender individuals in public accommodations are situated the context of inpatient addiction treatment facilities. Minority Stress Theory is used as a theoretical framework for understanding substance abuse issues among transgender populations as a maladaptive behavioral response for coping with chronic stressors related to gender minority status and intersecting identities. The findings include that despite having disproportionately high rates of substance use disorder compared to the general population, transgender individuals experience significant barriers to engaging in and benefiting from addiction treatment. These barriers are present in the form of anticipated or real interpersonal stigma and discrimination by service providers and structural stigma in the form of policy and programmatic components in addiction treatment that marginalize transgender populations. Transphobic manifestations within addiction treatment may dissuade transgender individuals from seeking help, if not reinforce a lifetime of stigmatic experience, potentially exacerbating their substance use issues. Conclusive recommendations for social workers and addiction treatment professionals include: (1) dismantling institutional policies around gender segregation that alienate transgender individuals, (2) developing policies that provide full protections for transgender clients against discrimination based on their gender identity, and (3) implementing trans-affirmative cultural competency training requirements for all staff. Directions for future research are provided.

Keywords: addiction treatment, gender segregation, stigma, transgender

Procedia PDF Downloads 199

Authors: Dongjae Kang, Sokho Son, Jinmee Kim

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Cloud service brokering is a new service paradigm that provides interoperability and portability of application across multiple Cloud providers. In this paper, we designed cloud service brokerage system, any broker, supporting integrated service provisioning and SLA based service life cycle management. For the system design, we introduce the system concept and whole architecture, details of main components and use cases of primary operations in the system. These features ease the Cloud service provider and customer’s concern and support new Cloud service open market to increase cloud service profit and prompt Cloud service echo system in cloud computing related area.

Keywords: cloud service brokerage, multiple Clouds, Integrated service provisioning, SLA, network service

Procedia PDF Downloads 475

Authors: B. Suraj Aravind, M. H. M. Krishna Prasad

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There exist emerging applications of data streams that require association rule mining, such as network traffic monitoring, web click streams analysis, sensor data, data from satellites etc. Data streams typically arrive continuously in high speed with huge amount and changing data distribution. This raises new issues that need to be considered when developing association rule mining techniques for stream data. This paper proposes to introduce an improved data stream association rule mining algorithm by eliminating the limitation of resources. For this, the concept of cloud computing is used. Inclusion of this may lead to additional unknown problems which needs further research.

Keywords: data stream, association rule mining, cloud computing, frequent itemsets

Procedia PDF Downloads 490

Authors: Ahlem Dhahri, Waleed Omri, Audrey Becuwe, Abdelwahed Omri

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In industrial processes, decision-making ranges across different scales, from process control to supply chain management. The purchasing decision-making process in the supply chain is presently gaining more attention as a critical contributor to the company's strategic success. Given the scarcity of thorough summaries in the prior studies, this bibliometric analysis aims to adopt a meticulous approach to achieve quantitative knowledge on the constantly evolving subject of purchasing decision-making in supply chain management. Through bibliometric analysis, we examine a sample of 358 peer-reviewed articles from the Scopus database. VOSviewer and Gephi software were employed to analyze, combine, and visualize the data. Data analytic techniques, including citation network, page-rank analysis, co-citation, and publication trends, have been used to identify influential works and outline the discipline's intellectual structure. The outcomes of this descriptive analysis highlight the most prominent articles, authors, journals, and countries based on their citations and publications. The findings from the research illustrate an increase in the number of publications, exhibiting a slightly growing trend in this field. Co-citation analysis coupled with content analysis of the most cited articles identified five research themes mentioned as follows integrating sustainability into the supplier selection process, supplier selection under disruption risks assessment and mitigation strategies, Fuzzy MCDM approaches for supplier evaluation and selection, purchasing decision in vendor problems, decision-making techniques in supplier selection and order lot sizing problems. With the help of a graphic timeline, this exhaustive map of the field illustrates a visual representation of the evolution of publications that demonstrate a gradual shift from research interest in vendor selection problems to integrating sustainability in the supplier selection process. These clusters offer insights into a wide variety of purchasing methods and conceptual frameworks that have emerged; however, they have not been validated empirically. The findings suggest that future research would emerge with a greater depth of practical and empirical analysis to enrich the theories. These outcomes provide a powerful road map for further study in this area.

Keywords: bibliometric analysis, citation analysis, co-citation, Gephi, network analysis, purchasing, SCM, VOSviewer

Procedia PDF Downloads 74