Search results for: comprehensive metrics

Authors: Samrawit Tamrat Gebretsadik

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Recurrent wheezing is a common respiratory symptom among children, often indicative of underlying airway inflammation and hyperreactivity. Understanding the prevalence and associated factors of recurrent wheezing in specific age groups is crucial for targeted interventions and improved respiratory health outcomes. This study aimed to investigate the prevalence and associated factors of recurrent wheezing among 6-year-old children attending Adama Comprehensive Specialized Hospital Medical College in Ethiopia. A cross-sectional study design was employed, involving structured interviews with parents/guardians, medical records review, and clinical examination of children. Data on demographic characteristics, environmental exposures, family history of respiratory diseases, and socioeconomic status were collected. Logistic regression analysis was used to identify factors associated with recurrent wheezing. The study included X 6-year-old children, with a prevalence of recurrent wheezing found to be Y%. Environmental exposures, including tobacco smoke exposure (OR = Z, 95% CI: X-Y), indoor air pollution (OR = Z, 95% CI: X-Y), and presence of pets at home (OR = Z, 95% CI: X-Y), were identified as significant risk factors for recurrent wheezing. Additionally, a family history of asthma or allergies (OR = Z, 95% CI: X-Y) and low socioeconomic status (OR = Z, 95% CI: X-Y) were associated with an increased likelihood of recurrent wheezing. The impact of recurrent wheezing on the quality of life of affected children and their families was also assessed. Children with recurrent wheezing experienced a higher frequency of respiratory symptoms, increased healthcare utilization, and decreased physical activity compared to their non-wheezing counterparts. In conclusion, recurrent wheezing among 6-year-old children attending Adama Comprehensive Specialized Hospital Medical College is associated with various environmental, genetic, and socioeconomic factors. These findings underscore the importance of targeted interventions aimed at reducing exposure to known triggers and improving respiratory health outcomes in this population. Future research should focus on longitudinal studies to further elucidate the causal relationships between risk factors and recurrent wheezing and evaluate the effectiveness of preventive strategies.

Keywords: wheezing, inflammation, respiratory, crucial

Procedia PDF Downloads 15

Authors: Zijiao Chai, Wangming Li

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Under the background of increasing aging population, the situation of city endowment resources shortage gradually revealed. And many villages in the metropolitan area with the good natural ecological environment and leisure tourism base, have become one of the main destinations of urban old people for the off-site pension. This paper is based on a survey of more than ten villages which are characterized by leisure and endowment in Hangzhou metropolitan area, China. The satisfaction degree of the two main groups in the villages, dwellers, and aged visitors, is researched using the method of fuzzy comprehensive evaluation. The statistics are obtained from 535 questionnaires and qualitative interview. According to the satisfaction scores, it could be determined whether the dwellers and aged visitors have reached the equilibrium state. The equilibrium state is the development target of the villages, and it`s defined by environmentally friendly, proper for employment and pension, facilities sharing and harmonious life for each other. Furthermore, this paper comes up with some planning countermeasures in order to avoid "imbalance between dwellers and aged visitors" and obtain sustainable development while maintaining the economic benefit.

Keywords: aged visitors, balance between dwellers and aged visitors, dwellers, fuzzy comprehensive evaluation, Hangzhou metropolitan area, leisure and endowment characteristic villages

Procedia PDF Downloads 246

Authors: Julian Thrash, Douglas Folk, Michael Ciracy, Audrey C. Tseng, Kristen M. Stromsodt, Amber Younggren, Christopher Maciolek

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The cardiopulmonary system is comprised of the heart, lungs, and many dynamic feedback mechanisms that control its function based on a multitude of variables. The next generation of cardiopulmonary medical devices will involve adaptive control and smart pacing techniques. However, testing these smart devices on living systems may be unethical and exceedingly expensive. As a solution, a comprehensive computational model of the cardiopulmonary system was implemented in Simulink. The model contains over 240 state variables and over 100 equations previously described in a series of published articles. Simulink was chosen because of its ease of introducing machine learning elements. Initial results indicate that physiologically correct waveforms of pressures and volumes were obtained in the simulation. With the development of a comprehensive computational model, we hope to pioneer the future of predictive medicine by applying our research towards the initial stages of smart devices. After validation, we will introduce and train reinforcement learning agents using the cardiopulmonary model to assist in adaptive control system design. With our cardiopulmonary model, we will accelerate the design and testing of smart and adaptive medical devices to better serve those with cardiovascular disease.

Keywords: adaptive control, cardiopulmonary, computational model, machine learning, predictive medicine

Procedia PDF Downloads 142

Authors: Rit M., Girard R., Villot J., Thorel M.

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In a scenario of extensive residential housing renovation, stakeholders need models that support decision-making through a deep understanding of the existing building stock and accurate energy demand simulations. To address this need, we have modified an optimization model using open data that enables the study of renovation strategies at both territorial and national scales. This approach provides (1) a definition of a strategy to simplify decision trees from theoretical combinations, (2) input to decision makers on real-world renovation constraints, (3) more reliable identification of energy-saving measures (changes in technology or behaviour), and (4) discrepancies between currently planned and actually achieved strategies. The main contribution of the studies described in this document is the geographic scale: all residential buildings in the areas of interest were modeled and simulated using national data (geometries and attributes). These buildings were then renovated, when necessary, in accordance with the environmental objectives, taking into account the constraints applicable to each territory (number of renovations per year) or at the national level (renovation of thermal deficiencies (Energy Performance Certificates F&G)). This differs from traditional approaches that focus only on a few buildings or archetypes. This model can also be used to analyze the evolution of a building stock as a whole, as it can take into account both the construction of new buildings and their demolition or sale. Using specific case studies of French territories, this paper highlights a significant discrepancy between the strategies currently advocated by decision-makers and those proposed by our optimization model. This discrepancy is particularly evident in critical metrics such as the relationship between the number of renovations per year and achievable climate targets or the financial support currently available to households and the remaining costs. In addition, users are free to seek optimizations for their building stock across a range of different metrics (e.g., financial, energy, environmental, or life cycle analysis). These results are a clear call to re-evaluate existing renovation strategies and take a more nuanced and customized approach. As the climate crisis moves inexorably forward, harnessing the potential of advanced technologies and data-driven methodologies is imperative.

Keywords: residential housing renovation, MILP, energy demand simulations, data-driven methodology

Procedia PDF Downloads 43

Authors: Prashansa Singh, Manjot Kaur, Rohit Bajaj

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The proliferation of wireless sensor networks and Internet of Things (IoT) devices in the quickly changing digital landscape has highlighted the urgent need for strong security solutions that can handle these systems’ limited resources. A key solution to this problem is the emergence of ultralightweight security protocols, which provide strong security features while respecting the strict computational, energy, and memory constraints imposed on these kinds of devices. This in-depth analysis explores the field of ultralightweight security protocols, offering a thorough examination of their evolution, salient features, and the particular security issues they resolve. We carefully examine and contrast different protocols, pointing out their advantages and disadvantages as well as the compromises between resource limitations and security resilience. We also study these protocols’ application domains, including the Internet of Things, RFID systems, and wireless sensor networks, to name a few. In addition, the review highlights recent developments and advancements in the field, pointing out new trends and possible avenues for future research. This paper aims to be a useful resource for researchers, practitioners, and developers, guiding the design and implementation of safe, effective, and scalable systems in the Internet of Things era by providing a comprehensive overview of ultralightweight security protocols.

Keywords: wireless sensor network, machine-to-machine, MQTT broker, server, ultralightweight, TCP/IP

Procedia PDF Downloads 23

Authors: William Luke Hamel

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Existing conceptions of urban flooding caused by tidal fluctuations and sea-level rise have been inadequately conceptualized by metrics of resilience and methods of flow modeling. While a great deal of research has been devoted to the effects of urbanization on pluvial flooding, the kind of tidal flooding experienced by locations like Hampton Roads, Virginia, has not been adequately conceptualized as being a result of human factors such as urbanization and gray infrastructure. Resilience from sea level rise and its associated flooding has been pioneered in the region with the 2015 Norfolk Resilience Plan from 100 Resilient Cities as well as the 2016 Norfolk Vision 2100 plan, which envisions different patterns of land use for the city. Urban resilience still conceptualizes the city as having the ability to maintain an equilibrium in the face of disruptions. This economic and social equilibrium relies on the Elizabeth River, narrowly conceptualized. Intentionally or accidentally, the river was made to be a piece of infrastructure. Its development was meant to serve the docks, shipyards, naval yards, and port infrastructure that gives the region so much of its economic life. Inasmuch as it functions to permit the movement of cargo; the raising and lowering of ships to be repaired, commissioned, or decommissioned; or the provisioning of military vessels, the river as infrastructure is functioning properly. The idea that the infrastructure is malfunctioning when high tides and sea-level rise create flooding is predicated on the idea that the infrastructure is truly a human creation and can be controlled. The natural flooding cycles of an urban river, combined with the action of climate change and sea-level rise, are only abnormal so much as they encroach on the development that first encroached on the river. The urban political ecology of water provides the ability to view the river as an infrastructural extension of urban networks while also calling for its emancipation from stationarity and human control. Understanding the river and city as a hydrosocial territory or as a socio-natural system liberates both actors from the duality of the natural and the social while repositioning river flooding as a normal part of coexistence on a floodplain. This paper argues for the adoption of an urban political ecology lens in the analysis and governance of urban rivers like the Elizabeth River as a departure from the equilibrium-seeking and stability metrics of urban resilience.

Keywords: urban flooding, political ecology, Elizabeth river, Hampton roads

Procedia PDF Downloads 135

Authors: Shubham Gogri, Lucia Florescu

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Medical diagnosis and radiotherapy treatment planning using Computed Tomography (CT) rely on the quantitative accuracy and quality of the CT images. At the same time, requirements for CT imaging include reducing the radiation dose exposure to patients and minimizing scanning time. A solution to this is the sparse-view CT technique, based on a reduced number of projection views. This, however, introduces a new problem— the incomplete projection data results in lower quality of the reconstructed images. To tackle this issue, deep learning methods have been applied to enhance the quality of the sparse-view CT images. A first approach involved employing Mir-Net, a dedicated deep neural network designed for image enhancement. This showed promise, utilizing an intricate architecture comprising encoder and decoder networks, along with the incorporation of the Charbonnier Loss. However, this approach was computationally demanding. Subsequently, a specialized Generative Adversarial Network (GAN) architecture, rooted in the Pix2Pix framework, was implemented. This GAN framework involves a U-Net-based Generator and a Discriminator based on Convolutional Neural Networks. To bolster the GAN's performance, both Charbonnier and Wasserstein loss functions were introduced, collectively focusing on capturing minute details while ensuring training stability. The integration of the perceptual loss, calculated based on feature vectors extracted from the VGG16 network pretrained on the ImageNet dataset, further enhanced the network's ability to synthesize relevant images. A series of comprehensive experiments with clinical CT data were conducted, exploring various GAN loss functions, including Wasserstein, Charbonnier, and perceptual loss. The outcomes demonstrated significant image quality improvements, confirmed through pertinent metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) between the corrected images and the ground truth. Furthermore, learning curves and qualitative comparisons added evidence of the enhanced image quality and the network's increased stability, while preserving pixel value intensity. The experiments underscored the potential of deep learning frameworks in enhancing the visual interpretation of CT scans, achieving outcomes with SSIM values close to one and PSNR values reaching up to 76.

Keywords: generative adversarial networks, sparse view computed tomography, CT image correction, Mir-Net

Procedia PDF Downloads 116

Authors: Emin Gundogar, Burak Erkayman, Nusret Sazak

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Providing effective management performance through the whole supply chain is critical issue and hard to applicate. The proper evaluation of integrated data may conclude with accurate information. Analysing the supply chain data through OLAP (On-Line Analytical Processing) technologies may provide multi-angle view of the work and consolidation. In this study, association rules and classification techniques are applied to measure the supply chain performance metrics of an automotive manufacturer in Turkey. Main criteria and important rules are determined. The comparison of the results of the algorithms is presented.

Keywords: supply chain performance, performance measurement, data mining, automotive

Procedia PDF Downloads 480

Authors: Oluwaseun Adeleke, Samuel O. Ikani, Fidelis Edet, Anthony Nwala, Mopelola Raji, Simeon Christian Chukwu

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Introduction: Digital innovations have been useful in supporting a client’s contraceptive user journey from awareness to method initiation. The concept of contraceptive self-care is being promoted globally as a means for achieving universal access to quality contraceptive care; however, information about this approach is limited. An important determinant of the scale of awareness is the message construct, choice of information channel, and an understanding of the socio-epidemiological dynamics within the target audience. Significant gains have been made recently in expanding the awareness base of DMPA-SC -a relatively new entrant into the family planning method mix. The cornerstone of this success is a multichannel promotion campaign themed Discover your Power (DYP). The DYP campaign combines content marketing across select social media platforms, chatbots, Cyber-IPC, Interactive Voice Response (IVR), and radio campaigns. Methodology: During implementation, the project monitored predefined metrics of awareness and intent, such as the number of persons reached with the messages, the number of impressions, and meaningful engagement (link-clicks). Metrics/indicators are extracted through native insight/analytics tools across the various platforms. The project also enlists community mobilizers (CMs) who go door-to-door and engage WRA to advertise DISC’s online presence and support them to engage with IVR, digital companion (chatbot), Facebook page, and DiscoverYourPower website. Results: The result showed that the digital platforms recorded 242 million impressions and reached 82 million users with key DMPA-SC self-injection messaging in the three countries. As many as 3.4 million persons engaged (liked, clicked, shared, or reposted) digital posts -an indication of intention. Conclusion: Digital solutions and innovations are gradually becoming the archetype for the advancement of the self-care agenda. Digital innovations can also be used to increase awareness and normalize contraceptive self-care behavior amongst women of reproductive age if they are made an integral part of reproductive health programming.

Keywords: digital transformation, health systems, DMPA-SC, family planning, self-care

Procedia PDF Downloads 50

Authors: Alireza Zabihi, Saeed Peyghami, Hossein Mokhtari

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With the increasing deployment of renewable power plants, such as solar and wind, it is crucial to measure the harmonic situation of the grid. This paper proposes a global voltage harmonic index to measure the harmonic situation of the power grid with a focus on power transformers. The power electronics systems used to connect these plants to the network can introduce harmonics, leading to increased losses, reduced efficiency, false operation of protective relays, and equipment damage due to harmonic intensifications. The proposed index considers the losses caused by harmonics in power transformers which are of great importance and value to the network, providing a comprehensive measure of the harmonic situation of the grid. The effectiveness of the proposed index is evaluated on a real-world distribution network, and the results demonstrate its ability to identify the harmonic situation of the network, particularly in relation to power transformers. The proposed index provides a comprehensive measure of the harmonic situation of the grid, taking into account the losses caused by harmonics in power transformers. The proposed index has the potential to support power companies in optimizing their power systems and to guide researchers in developing effective mitigation strategies for harmonics in the power grid.

Keywords: global voltage harmonic index, harmonics, power grid, power quality, power transformers, renewable energy

Procedia PDF Downloads 87

Authors: Liuhui Zhu, Peng Zeng

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The rural revitalization strategy means to take the country and the city on the same level, and achieve urban-rural integration and comprehensive development of rural areas. Beijing-Tianjin-Hebei rural areas have always been the weak links in the region, with prominently uneven development between urban and rural areas. The rural areas need to join the overall regional synergy. Based on the analysis of the characteristics and problems of rural development in the region from the perspective of production-living-ecology space, the paper proposes the township as the basic unit for rural revitalization according to the overall requirements of the rural revitalization strategy. The basic unit helps to realize resource arrangement, functional organization, and collaborative governance organized by the production-living-ecology spatial network. The paper summarizes the planning strategies for the basic unit. Through spatial cognition and spatial reconstruction, the three space is networked through the base, nodes, and connections to improve the comprehensive value of rural areas and achieve the multiple goals of rural revitalization.

Keywords: rural revitalization, Beijing-Tianjin-Hebei region, township level, production-living-ecology spatial network

Procedia PDF Downloads 169

Authors: Mohammed Aladalah

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The emergence of digital citizens is no longer mere speculation; therefore, governments’ use of Web 2.0 tools (hereafter Gov 2.0) should be a part of all current and future e-government plans. The potential of Gov 2.0 to facilitate greater communication, participation, and collaboration with citizens has been highlighted and discussed extensively in recent literature. However, the current levels of citizens’ participation in Gov 2.0 have not lived up to the hype. Therefore, governments need to rethink the way in which they implement Gov 2.0, and take advantage of the digitally-engaged population. We propose a two-dimensional framework to tackle this issue: first, on the supply side, governments tend to use Gov 2.0 mainly for the dissemination of information and for self-promotion without the desire to encourage any interaction with citizens; this is due to many reasons, including the lack of time and the possibility of loss of control. The second dimension of the framework is the demand side; citizens are unwilling to participate in Gov 2.0 activities because they do not perceive its value or trust the government. We attempt to consider the elements of both supply and demand in order to provide a comprehensive solution whereby the potential of Gov 2.0 can be fully utilized. Our framework is based on the theoretical foundation of service science and value co-creation theory. This paper makes two significant contributions: (a) it provides an initial framework intended to increase citizens’ participation in Gov 2.0; and (b) it enhances the understanding of the government’s Gov 2.0 applications, particularly in terms of factors that ensure their attractiveness for citizens. This work is the first step in a comprehensive research undertaking, the purpose of which is to study public’s engagement with the Gov 2.0 concept. It contributes to providing a better understanding of e-government and its future.

Keywords: e-government, Gov 2.0, citizens participation, digital citizen

Procedia PDF Downloads 307

Authors: Monika Jurkeviciute, Amia Enam, Johanna Torres Bonilla, Henrik Eriksson

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Introduction: Many evaluations of eHealth interventions conclude that the evidence for improved clinical outcomes is limited, especially when the intervention is short, such as one year. Often, evaluation design does not address the feasibility of achieving clinical outcomes. Evaluations are designed to reflect upon clinical goals of intervention without utilizing the opportunity to illuminate effects on organizations and cost. A comprehensive design of evaluation can better support decision-making regarding the effectiveness and potential transferability of eHealth. Hence, the purpose of this paper is to present a feasible and comprehensive design of evaluation for eHealth intervention, including the design process in different contexts. Methodology: The situation of limited feasibility of clinical outcomes was foreseen in the European Union funded project called “DECI” (“Digital Environment for Cognitive Inclusion”) that is run under the “Horizon 2020” program with an aim to define and test a digital environment platform within corresponding care models that help elderly people live independently. A complex intervention of eHealth implementation into elaborate care models in four different countries was planned for one year. To design the evaluation, a participative approach was undertaken using Pettigrew’s lens of change and transformations, including context, process, and content. Through a series of workshops, observations, interviews, and document analysis, as well as a review of scientific literature, a comprehensive design of evaluation was created. Findings: The findings indicate that in order to get evidence on clinical outcomes, eHealth interventions should last longer than one year. The content of the comprehensive evaluation design includes a collection of qualitative and quantitative methods for data gathering which illuminates non-medical aspects. Furthermore, it contains communication arrangements to discuss the results and continuously improve the evaluation design, as well as procedures for monitoring and improving the data collection during the intervention. The process of the comprehensive evaluation design consists of four stages: (1) analysis of a current state in different contexts, including measurement systems, expectations and profiles of stakeholders, organizational ambitions to change due to eHealth integration, and the organizational capacity to collect data for evaluation; (2) workshop with project partners to discuss the as-is situation in relation to the project goals; (3) development of general and customized sets of relevant performance measures, questionnaires and interview questions; (4) setting up procedures and monitoring systems for the interventions. Lastly, strategies are presented on how challenges can be handled during the design process of evaluation in four different countries. The evaluation design needs to consider contextual factors such as project limitations, and differences between pilot sites in terms of eHealth solutions, patient groups, care models, national and organizational cultures and settings. This implies a need for the flexible approach to evaluation design to enable judgment over the effectiveness and potential for adoption and transferability of eHealth. In summary, this paper provides learning opportunities for future evaluation designs of eHealth interventions in different national and organizational settings.

Keywords: ehealth, elderly, evaluation, intervention, multi-cultural

Procedia PDF Downloads 289

Authors: Handie Pramana Putra, Ani Dijah Rahajoe

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The proliferation of smart devices and advancements in mobile communication technologies have permeated various facets of life with the widespread influence of e-commerce. Detecting abnormal transactions holds paramount significance in this realm due to the potential for substantial financial losses. Moreover, the fusion of data flow and control flow assumes a critical role in the exploration of process modeling and data analysis, contributing significantly to the accuracy and security of business processes. This paper introduces an alternative approach to identify abnormal transactions through a model that integrates both data and control flows. Referred to as the Extended Data Petri net (DPNE), our model encapsulates the entire process, encompassing user login to the e-commerce platform and concluding with the payment stage, including the mobile transaction process. We scrutinize the model's structure, formulate an algorithm for detecting anomalies in pertinent data, and elucidate the rationale and efficacy of the comprehensive system model. A case study validates the responsive performance of each system component, demonstrating the system's adeptness in evaluating every activity within mobile transactions. Ultimately, the results of anomaly detection are derived through a thorough and comprehensive analysis.

Keywords: database, data analysis, DPNE, extended data flow, e-commerce

Procedia PDF Downloads 22

Authors: Meichen He, Xuan Yang

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Background: In the field of psychotherapy, the role of the family of origin is of utmost importance. Over the past few decades, both individual-oriented and family-oriented approaches to child therapy have shown moderate success in reducing children's psychological and behavioral issues. Objective: However, in exploring how the family of origin influences individuals, it has been noted that there is a lack of comprehensive measurement indicators and an absence of an exact model to assess the impact of the family of origin on individual development. Therefore, this study aims to develop a model based on a literature review regarding the influence of the family of origin on children. Specifically, it will examine the effects of factors such as education level, economic status, maternal age, family integration, family violence, marital conflict, parental substance abuse, and alcohol consumption on children's self-confidence and life satisfaction. Through this research, we aim to further investigate the impact of the family of origin on children and provide directions for future research in positive psychology and psychotherapy. Methods: This study will employ a literature review methodology to gather and analyze relevant research articles on the influence of the family of origin on children. Subsequently, we will conduct quantitative analyses to establish a comprehensive model explaining how family of origin factors affect children's psychological and behavioral outcomes. Findings: the research has revealed that family of origin factors, including education level, economic status, maternal age, family integration, family violence, marital conflict, parental drug and alcohol consumption, have an impact on children's self-confidence and life satisfaction. These factors can affect children's psychological well-being and happiness through various pathways. Implications: The results of this study will contribute to a better understanding of the influence of the family of origin on children and provide valuable directions for future research in positive psychology and psychotherapy. This research will enhance awareness of children's psychological well-being and lay the foundation for improving psychotherapeutic methods.

Keywords: family of origion, positive psychology, developmental psychology, family education, social psychology, educational psychology

Procedia PDF Downloads 93

Authors: P. Nafisi Poor, P. Javid

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Over the past few years, the idea of adaptive buildings and specifically, adaptive building energy management systems (ABEMS) has become popular. Well-performed management in terms of energy is to create a balance between energy consumption and user comfort; therefore, in new energy management models, efficient energy consumption is not the sole factor and the user's comfortability is also considered in the calculations. One of the main ways of measuring this factor is by analyzing user feedback on the conditions to understand whether they are satisfied with conditions or not. This paper provides a comprehensive review of recent approaches towards energy management systems based on users' feedbacks and subsequently performs a comparison between them premised upon their efficiency and accuracy to understand which approaches were more accurate and which ones resulted in a more efficient way of minimizing energy consumption while maintaining users' comfortability. It was concluded that the highest accuracy rate among the presented works was 95% accuracy in determining satisfaction and up to 51.08% energy savings can be achieved without disturbing user’s comfort. Considering the growing interest in designing and developing adaptive buildings, these studies can support diverse inquiries about this subject and can be used as a resource to support studies and researches towards efficient energy consumption while maintaining the comfortability of users.

Keywords: adaptive buildings, energy efficiency, intelligent buildings, user comfortability

Procedia PDF Downloads 104

Authors: Rahma Amri, Narjès Bellamine Ben Saoud

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Sustainable development or sustainability is one of the most urgent issues in actual debate in almost domains. Particularly the significant way the software pervades our live should make it in the center of sustainability concerns. The social aspects of sustainability haven’t been well studied in the context of software systems and still immature research field that needs more interest among researchers’ community. This paper presents a Goal-Question-Metric based approach to assess social sustainability of software systems. The approach is based on a generic social sustainability model taken from Social sciences.

Keywords: software assessment approach, social sustainability, goal-question-metric paradigm, software project metrics

Procedia PDF Downloads 365

Authors: S. Mani, M. Kafil, E. Asadi

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Condition Monitoring (CM) of electrical equipment has gained remarkable importance during the recent years; due to huge production losses, substantial imposed costs and increases in vulnerability, risk and uncertainty levels. Power cables feed numerous electrical equipment such as transformers, motors, and electric furnaces; thus their condition assessment is of a very great importance. This paper investigates electrical, structural and environmental failure sources, all of which influence cables' performances and limit their uptimes; and provides a comprehensive framework entailing practical CM guidelines for maintenance of cables in industries. The multilevel CM framework presented in this study covers performance indicative features of power cables; with a focus on both online and offline diagnosis and test scenarios, and covers short-term and long-term threats to the operation and longevity of power cables. The study, after concisely overviewing the concept of CM, thoroughly investigates five major areas of power quality, Insulation Quality features of partial discharges, tan delta and voltage withstand capabilities, together with sheath faults, shield currents and environmental features of temperature and humidity; and elaborates interconnections and mutual impacts between those areas; using mathematical formulation and practical guidelines. Detection, location, and severity identification methods for every threat or fault source are also elaborated. Finally, the comprehensive, practical guidelines presented in the study are presented for the specific case of Electric Arc Furnace (EAF) feeder MV power cables in Mobarakeh Steel Company (MSC), the largest steel company in MENA region, in Iran. Specific technical and industrial characteristics and limitations of a harsh industrial environment like MSC EAF feeder cable tunnels are imposed on the presented framework; making the suggested package more practical and tangible.

Keywords: condition monitoring, diagnostics, insulation, maintenance, partial discharge, power cables, power quality

Procedia PDF Downloads 197

Authors: I. Baddari, A. Riahla, M. Mezghich

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Today in the literature, we discover a lot of routing algorithms which some have been the subject of normalization. Two great classes Routing algorithms are defined, the first is the class reactive algorithms and the second that of algorithms proactive. The aim of this work is to make a comparative study between some routing algorithms. Two comparisons are considered. The first will focus on the protocols of the same class and second class on algorithms of different classes (one reactive and the other proactive). Since they are not based on analytical models, the exact evaluation of some aspects of these protocols is challenging. Simulations have to be done in order to study their performances. Our simulation is performed in NS2 (Network Simulator 2). It identified a classification of the different routing algorithms studied in a metrics such as loss of message, the time transmission, mobility, etc.

Keywords: ad-hoc network routing protocol, simulation, NS2, delay, packet loss, wideband, mobility

Procedia PDF Downloads 355

Authors: Wesam Rababa, Jamal Al-Qawasmi

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The global climate is negatively impacted by CO2 emissions, which are mostly produced by buildings. Several green building rating systems (GBRS) have been proposed to impose low-carbon criteria in order to address this problem. The Green Globes certification is one such system that evaluates a building's sustainability level by assessing different categories of environmental impact and emerging concepts aimed at reducing environmental harm. Therefore, assessment tools at the national level are crucial in the developing world, where specific local conditions require a more precise evaluation. This study analyzed eight sustainable building assessment systems from different regions of the world, comparing a comprehensive list of CO2-related indicators with a various assessment system for conducting coverage analysis. The results show that GBRS includes both direct and indirect indicators in this regard. It reveals deep variation between examined practices, and a lack of consensus not only on the type and the optimal number of indicators used in a system, but also on the depth and breadth of coverage of various sustainable building SB attributes. Generally, the results show that most of the examined systems reflect a low comprehensive coverage, the highest of which is found in materials category. On the other hand, the most of the examined systems reveal a very low representative coverage.

Keywords: Assessment tools, CO2-related indicators, Comparative study, Green Building Rating Systems

Procedia PDF Downloads 20

Authors: Bolin Hua

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In city governance, various data are involved, including city component data, demographic data, housing data and all kinds of business data. These data reflects different aspects of people, events and activities. Data generated from various systems are different in form and data source are different because they may come from different sectors. In order to reflect one or several facets of an event or rule, data from multiple sources need fusion together. Data from different sources using different ways of collection raised several issues which need to be resolved. Problem of data fusion include data update and synchronization, data exchange and sharing, file parsing and entry, duplicate data and its comparison, resource catalogue construction. Governments adopt statistical analysis, time series analysis, extrapolation, monitoring analysis, value mining, scenario prediction in order to achieve pattern discovery, law verification, root cause analysis and public opinion monitoring. The result of Multi-source data fusion is to form a uniform central database, which includes people data, location data, object data, and institution data, business data and space data. We need to use meta data to be referred to and read when application needs to access, manipulate and display the data. A uniform meta data management ensures effectiveness and consistency of data in the process of data exchange, data modeling, data cleansing, data loading, data storing, data analysis, data search and data delivery.

Keywords: multi-source data fusion, urban comprehensive management, information fusion, government data

Procedia PDF Downloads 348

Authors: Tsega Asresa, Getahun Tigistu, Melaku Bayih

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Computer vision is a subfield of artificial intelligence that allows computers and systems to extract meaning from digital images and video. It is used in a wide range of fields of study, including self-driving cars, video surveillance, medical diagnosis, manufacturing, law, agriculture, quality control, health care, facial recognition, and military applications. Aromatic medicinal plants are botanical raw materials used in cosmetics, medicines, health foods, essential oils, decoration, cleaning, and other natural health products for therapeutic and Aromatic culinary purposes. These plants and their products not only serve as a valuable source of income for farmers and entrepreneurs but also going to export for valuable foreign currency exchange. In Ethiopia, there is a lack of technologies for the classification and identification of Aromatic medicinal plant parts and disease type cured by aromatic medicinal plants. Farmers, industry personnel, academicians, and pharmacists find it difficult to identify plant parts and disease types cured by plants before ingredient extraction in the laboratory. Manual plant identification is a time-consuming, labor-intensive, and lengthy process. To alleviate these challenges, few studies have been conducted in the area to address these issues. One way to overcome these problems is to develop a deep learning model for efficient identification of Aromatic medicinal plant parts with their corresponding disease type. The objective of the proposed study is to identify the aromatic medicinal plant parts and their disease type classification using computer vision technology. Therefore, this research initiated a model for the classification of aromatic medicinal plant parts and their disease type by exploring computer vision technology. Morphological characteristics are still the most important tools for the identification of plants. Leaves are the most widely used parts of plants besides roots, flowers, fruits, and latex. For this study, the researcher used RGB leaf images with a size of 128x128 x3. In this study, the researchers trained five cutting-edge models: convolutional neural network, Inception V3, Residual Neural Network, Mobile Network, and Visual Geometry Group. Those models were chosen after a comprehensive review of the best-performing models. The 80/20 percentage split is used to evaluate the model, and classification metrics are used to compare models. The pre-trained Inception V3 model outperforms well, with training and validation accuracy of 99.8% and 98.7%, respectively.

Keywords: aromatic medicinal plant, computer vision, convolutional neural network, deep learning, plant classification, residual neural network

Procedia PDF Downloads 127

Authors: Suleiman Aliyu

Abstract:

Integrating IoT applications with Fog systems presents challenges in optimization due to diverse environments and conflicting objectives. This study explores achieving Pareto optimal deployments for Fog-based IoT systems to address growing QoS demands. We introduce Pareto optimality to balance competing performance metrics. Using the FogTorch optimization framework, we propose a hybrid approach (Backtracking search with branch and bound) for scalable IoT deployments. Our research highlights the advantages of Pareto optimality over single-objective methods and emphasizes the role of FogTorch in this context. Initial results show improvements in IoT deployment cost in Fog systems, promoting resource-efficient strategies.

Keywords: pareto optimality, fog application deployment, resource allocation, internet of things

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Authors: Boris Melnikov, Ye Zhang, Dmitrii Chaikovskii

Abstract:

This study explores the pseudo-geometric version of the extensively researched Traveling Salesman Problem (TSP), proposing a novel generalization of existing algorithms which are traditionally confined to the geometric version. By adapting the "onion husk" method and introducing auxiliary algorithms, this research fills a notable gap in the existing literature. Through computational experiments using randomly generated data, several metrics were analyzed to validate the proposed approach's efficacy. Preliminary results align with expected outcomes, indicating a promising advancement in TSP solutions.

Keywords: optimization problems, traveling salesman problem, heuristic algorithms, “onion husk” algorithm, pseudo-geometric version

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Authors: Jennifer Leach, Umashanger Thayasivam

Abstract:

The use of technology has benefited society in more ways than one ever thought possible. Unfortunately, though, as society’s knowledge of technology has advanced, so has its knowledge of ways to use technology to manipulate people. This has led to a simultaneous advancement in the world of fraud. Machine learning techniques can offer a possible solution to help decrease this advancement. This research explores how the use of various machine learning techniques can aid in detecting fraudulent activity across two different types of fraudulent data, and the accuracy, precision, recall, and F1 were recorded for each method. Each machine learning model was also tested across five different training and testing splits in order to discover which testing split and technique would lead to the most optimal results.

Keywords: data science, fraud detection, machine learning, supervised learning

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Authors: Ross Goroshin, Joan Bruna, Jonathan Tompson, David Eigen, Yann LeCun

Abstract:

Current state-of-the-art classification and detection algorithms rely on supervised training. In this work we study unsupervised feature learning in the context of temporally coherent video data. We focus on feature learning from unlabeled video data, using the assumption that adjacent video frames contain semantically similar information. This assumption is exploited to train a convolutional pooling auto-encoder regularized by slowness and sparsity. We establish a connection between slow feature learning to metric learning and show that the trained encoder can be used to define a more temporally and semantically coherent metric.

Keywords: machine learning, pattern clustering, pooling, classification

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Authors: Kemal Polat

Abstract:

In this study, it has been estimated whether there is skin by using RBG values obtained from the camera and k-nearest neighbor (k-NN) classifier. The dataset used in this study has an unbalanced distribution and a linearly non-separable structure. This problem can also be called a big data problem. The Skin dataset was taken from UCI machine learning repository. As the classifier, we have used the k-NN method to handle this big data problem. For k value of k-NN classifier, we have used as 1. To train and test the k-NN classifier, 50-50% training-testing partition has been used. As the performance metrics, TP rate, FP Rate, Precision, recall, f-measure and AUC values have been used to evaluate the performance of k-NN classifier. These obtained results are as follows: 0.999, 0.001, 0.999, 0.999, 0.999, and 1,00. As can be seen from the obtained results, this proposed method could be used to predict whether the image is skin or not.

Keywords: k-NN classifier, skin or non-skin classification, RGB values, classification

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Authors: Miftah Hassen Jemal, Solomon Negash

Abstract:

This study focuses on undertaking a qualitative meta-analysis of qualitative studies conducted on ICT4D implementations. The interpretive approach of synthesis of the interpretation of qualitative studies is adopted to guide the whole process of the study. The traditional criteria of trustworthiness of qualitative studies in terms of transferability, consistency, and credibility are used as quality metrics of the output of the interpretive synthesis process. The findings of the study are anticipated to be of value for policymakers in providing guidance to decisions related to ICT4D implementations. The study is also anticipated to have contributions to research by extracting valuable insights from extant literature and identifying potential areas that warrant further investigation.

Keywords: ICT4D implementations, interpretive synthesis, qualitative meta-analysis, qualitative studies

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Authors: Kostas Metaxiotis, Kostas Liagkouras

Abstract:

This study presents an Expert System specially designed to be used with Multiobjective Evolutionary Algorithms (MOEAs) for the solution of the portfolio selection problem. The validation of the proposed hybrid System is done by using data sets from Hang Seng 31 in Hong Kong, DAX 100 in Germany and FTSE 100 in UK. The performance of the proposed system is assessed in comparison with the Non-dominated Sorting Genetic Algorithm II (NSGAII). The evaluation of the performance is based on different performance metrics that evaluate both the proximity of the solutions to the Pareto front and their dispersion on it. The results show that the proposed hybrid system is efficient for the solution of this kind of problems.

Keywords: expert systems, multi-objective optimization, evolutionary algorithms, portfolio selection

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Authors: Supriya H. S., Chandrakala B. M.

Abstract:

Deep learning based models have been recently successful adoption for network traffic prediction. However, training a deep learning model for various prediction tasks is considered one of the critical tasks due to various reasons. This research work develops Multi-Layer Hybrid Network (MLHN) for network traffic prediction and analysis; MLHN comprises the three distinctive networks for handling the different inputs for custom feature extraction. Furthermore, an optimized and efficient parameter-tuning algorithm is introduced to enhance parameter learning. MLHN is evaluated considering the “Big Data Challenge” dataset considering the Mean Absolute Error, Root Mean Square Error and R^2as metrics; furthermore, MLHN efficiency is proved through comparison with a state-of-art approach.

Keywords: MLHN, network traffic prediction

Procedia PDF Downloads 53