Search results for: user comfort

Authors: Sevelina Ndlovu

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Financial Technology (fintech) advancement is recognised as one of the most transformational innovations in the financial industry. Fintech has given rise to internet-only digital banking, a novel financial technology advancement, and innovation that allows banking services through internet applications with no need for physical branches. This technology is becoming a new banking normal among consumers for its ubiquitous and real-time access advantages. There is evident switching and migration from traditional banking towards these fintech facilities, which could possibly pose a systemic risk if not properly understood and monitored. Fintech advancement has also brought about the emergence and escalation of financial technology consumption themes such as trust, security, perceived risk, and sustainability within the banking industry, themes scarcely covered in existing theoretic literature. To that end, the objective of this research is to investigate factors that determine fintech adoption and propose an integrated adoption model. This study aims to establish what the significant drivers of adoption are and develop a conceptual model that integrates technological, behavioral, and environmental constructs by extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). It proposes integrating constructs that influence financial consumption themes such as trust, perceived risk, security, financial incentives, micro-investing opportunities, and environmental consciousness to determine the impact of these factors on the adoption and intention to use digital banking apps. The main advantage of this conceptual model is the consolidation of a greater number of predictor variables that can provide a fuller explanation of the consumer's adoption of digital banking Apps. Moderating variables of age, gender, and income are incorporated. To the best of author’s knowledge, this study is the first that extends the UTAUT2 model with this combination of constructs to investigate user’s intention to adopt internet-only digital banking apps in the UK context. By investigating factors that are not included in the existing theories but are highly pertinent to the adoption of internet-only banking services, this research adds to existing knowledge and extends the generalisability of the UTAUT2 in a financial services adoption context. This is something that fills a gap in knowledge, as highlighted to needing further research on UTAUT2 after reviewing the theory in 2016 from its original version of 2003. To achieve the objectives of this study, this research assumes a quantitative research approach to empirically test the hypotheses derived from existing literature and pilot studies to give statistical support to generalise the research findings for further possible applications in theory and practice. This research is explanatory or casual in nature and uses cross-section primary data collected through a survey method. Convenient and purposive sampling using structured self-administered online questionnaires is used for data collection. The proposed model is tested using Structural Equation Modelling (SEM), and the analysis of primary data collected through an online survey is processed using Smart PLS software with a sample size of 386 digital bank users. The results are expected to establish if there are significant relationships between the dependent and independent variables and establish what the most influencing factors are.

Keywords: banking applications, digital banking, financial technology, technology adoption, UTAUT2

Procedia PDF Downloads 73

Authors: Anubhav Kumar Dwivedi, Arshad Khan, S. N. Tripathi, Manish Joshi, Gaurav Mishra, Dinesh Nath, Naveen Tiwari, B. K. Sapra

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In a nuclear reactor accident scenario, a large number of fission products may release to the piping system of the primary heat transport. The released fission products, mostly in the form of the aerosol, get deposited on the inner surface of the piping system mainly due to gravitational settling and thermophoretic deposition. The removal processes in the complex piping system are controlled to a large extent by the thermal-hydraulic conditions like temperature, pressure, and flow rates. These parameters generally vary with time and therefore must be carefully monitored to predict the aerosol behavior in the piping system. The removal process of aerosol depends on the size of particles that determines how many particles get deposit or travel across the bends and reach to the other end of the piping system. The released aerosol gets deposited onto the inner surface of the piping system by various mechanisms like gravitational settling, Brownian diffusion, thermophoretic deposition, and by other deposition mechanisms. To quantify the correct estimate of deposition, the identification and understanding of the aforementioned deposition mechanisms are of great importance. These mechanisms are significantly affected by different flow and thermodynamic conditions. Thermophoresis also plays a significant role in particle deposition. In the present study, a series of experiments were performed in the piping system of the National Aerosol Test Facility (NATF), BARC using metal aerosols (zinc) in dry environments to study the spatial distribution of particles mass and number concentration, and their depletion due to various removal mechanisms in the piping system. The experiments were performed at two different carrier gas flow rates. The commercial CFD software FLUENT is used to determine the distribution of temperature, velocity, pressure, and turbulence quantities in the piping system. In addition to the in-built models for turbulence, heat transfer and flow in the commercial CFD code (FLUENT), a new sub-model PBM (population balance model) is used to describe the coagulation process and to compute the number concentration along with the size distribution at different sections of the piping. In the sub-model coagulation kernels are incorporated through user-defined function (UDF). The experimental results are compared with the CFD modeled results. It is found that most of the Zn particles (more than 35 %) deposit near the inlet of the plenum chamber and a low deposition is obtained in piping sections. The MMAD decreases along the length of the test assembly, which shows that large particles get deposited or removed in the course of flow, and only fine particles travel to the end of the piping system. The effect of a bend is also observed, and it is found that the relative loss in mass concentration at bends is more in case of a high flow rate. The simulation results show that the thermophoresis and depositional effects are more dominating for the small and larger sizes as compared to the intermediate particles size. Both SEM and XRD analysis of the collected samples show the samples are highly agglomerated non-spherical and composed mainly of ZnO. The coupled model framed in this work could be used as an important tool for predicting size distribution and concentration of some other aerosol released during a reactor accident scenario.

Keywords: aerosol, CFD, deposition, coagulation

Procedia PDF Downloads 144

Authors: Md. Abu Sufian, Robiqul Islam, Imam Hossain Shajid, Mahesh Hanumanthu, Jarasree Varadarajan, Md. Sipon Miah, Mingbo Niu

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Coronary Heart Disease (CHD) remains a principal cause of global morbidity and mortality, characterized by atherosclerosis—the build-up of fatty deposits inside the arteries. The study introduces an innovative methodology that leverages cloud-based platforms like AWS Live Streaming and Artificial Intelligence (AI) to early detect and prevent CHD symptoms in web applications. By employing novel simulation processes and AI algorithms, this research aims to significantly mitigate the health and societal impacts of CHD. Methodology: This study introduces a novel simulation process alongside a multi-phased model development strategy. Initially, health-related data, including heart rate variability, blood pressure, lipid profiles, and ECG readings, were collected through user interactions with web-based applications as well as API Integration. The novel simulation process involved creating synthetic datasets that mimic early-stage CHD symptoms, allowing for the refinement and training of AI algorithms under controlled conditions without compromising patient privacy. AWS Live Streaming was utilized to capture real-time health data, which was then processed and analysed using advanced AI techniques. The novel aspect of our methodology lies in the simulation of CHD symptom progression, which provides a dynamic training environment for our AI models enhancing their predictive accuracy and robustness. Model Development: it developed a machine learning model trained on both real and simulated datasets. Incorporating a variety of algorithms including neural networks and ensemble learning model to identify early signs of CHD. The model's continuous learning mechanism allows it to evolve adapting to new data inputs and improving its predictive performance over time. Results and Findings: The deployment of our model yielded promising results. In the validation phase, it achieved an accuracy of 92% in predicting early CHD symptoms surpassing existing models. The precision and recall metrics stood at 89% and 91% respectively, indicating a high level of reliability in identifying at-risk individuals. These results underscore the effectiveness of combining live data streaming with AI in the early detection of CHD. Societal Implications: The implementation of cloud-based AI for CHD symptom detection represents a significant step forward in preventive healthcare. By facilitating early intervention, this approach has the potential to reduce the incidence of CHD-related complications, decrease healthcare costs, and improve patient outcomes. Moreover, the accessibility and scalability of cloud-based solutions democratize advanced health monitoring, making it available to a broader population. This study illustrates the transformative potential of integrating technology and healthcare, setting a new standard for the early detection and management of chronic diseases.

Keywords: coronary heart disease, cloud-based ai, machine learning, novel simulation techniques, early detection, preventive healthcare

Procedia PDF Downloads 67

Authors: Yogesh Kumar Singh, T. S. Murugesh Prabhu, Upasana Dutta, Girishchandra Yendargaye, Rahul Yadav, Rohini Gopinath Kale, Binay Kumar, Manoj Khare

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The Indian subcontinent is severely affected by floods that cause intense irreversible devastation to crops and livelihoods. With increased incidences of floods and their related catastrophes, an Early Warning System for Flood Prediction and an efficient Flood Management System for the river basins of India is a must. Accurately modeled hydrological conditions and a web-based early warning system may significantly reduce economic losses incurred due to floods and enable end users to issue advisories with better lead time. This study describes the design and development of an EWS-FP using advanced computational tools/methods, viz. High-Performance Computing (HPC), Remote Sensing, GIS technologies, and open-source tools for the Mahanadi River Basin of India. The flood prediction is based on a robust 2D hydrodynamic model, which solves shallow water equations using the finite volume method. Considering the complexity of the hydrological modeling and the size of the basins in India, it is always a tug of war between better forecast lead time and optimal resolution at which the simulations are to be run. High-performance computing technology provides a good computational means to overcome this issue for the construction of national-level or basin-level flash flood warning systems having a high resolution at local-level warning analysis with a better lead time. High-performance computers with capacities at the order of teraflops and petaflops prove useful while running simulations on such big areas at optimum resolutions. In this study, a free and open-source, HPC-based 2-D hydrodynamic model, with the capability to simulate rainfall run-off, river routing, and tidal forcing, is used. The model was tested for a part of the Mahanadi River Basin (Mahanadi Delta) with actual and predicted discharge, rainfall, and tide data. The simulation time was reduced from 8 hrs to 3 hrs by increasing CPU nodes from 45 to 135, which shows good scalability and performance enhancement. The simulated flood inundation spread and stage were compared with SAR data and CWC Observed Gauge data, respectively. The system shows good accuracy and better lead time suitable for flood forecasting in near-real-time. To disseminate warning to the end user, a network-enabled solution is developed using open-source software. The system has query-based flood damage assessment modules with outputs in the form of spatial maps and statistical databases. System effectively facilitates the management of post-disaster activities caused due to floods, like displaying spatial maps of the area affected, inundated roads, etc., and maintains a steady flow of information at all levels with different access rights depending upon the criticality of the information. It is designed to facilitate users in managing information related to flooding during critical flood seasons and analyzing the extent of the damage.

Keywords: flood, modeling, HPC, FOSS

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Authors: Thilina Ranaweera, Enes Makalic, John L. Hopper, Adrian Bickerstaffe

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Data are the primary asset of biomedical researchers, and the engine for both discovery and research translation. As the volume and complexity of research datasets increase, especially with new technologies such as large single nucleotide polymorphism (SNP) chips, so too does the requirement for software to manage, process and analyze the data. Researchers often need to execute complicated queries and conduct complex analyzes of large-scale datasets. Existing tools to analyze such data, and other types of high-dimensional data, unfortunately suffer from one or more major problems. They typically require a high level of computing expertise, are too simplistic (i.e., do not fit realistic models that allow for complex interactions), are limited by computing power, do not exploit the computing power of large-scale parallel architectures (e.g. supercomputers, GPU clusters etc.), or are limited in the types of analysis available, compounded by the fact that integrating new analysis methods is not straightforward. Solutions to these problems, such as those developed and implemented on parallel architectures, are currently available to only a relatively small portion of medical researchers with access and know-how. The past decade has seen a rapid expansion of data management systems for the medical domain. Much attention has been given to systems that manage phenotype datasets generated by medical studies. The introduction of heterogeneous genomic data for research subjects that reside in these systems has highlighted the need for substantial improvements in software architecture. To address this problem, we have developed SPARK, an enabling and translational system for medical research, leveraging existing high performance computing resources, and analysis techniques currently available or being developed. It builds these into The Ark, an open-source web-based system designed to manage medical data. SPARK provides a next-generation biomedical data management solution that is based upon a novel Micro-Service architecture and Big Data technologies. The system serves to demonstrate the applicability of Micro-Service architectures for the development of high performance computing applications. When applied to high-dimensional medical datasets such as genomic data, relational data management approaches with normalized data structures suffer from unfeasibly high execution times for basic operations such as insert (i.e. importing a GWAS dataset) and the queries that are typical of the genomics research domain. SPARK resolves these problems by incorporating non-relational NoSQL databases that have been driven by the emergence of Big Data. SPARK provides researchers across the world with user-friendly access to state-of-the-art data management and analysis tools while eliminating the need for high-level informatics and programming skills. The system will benefit health and medical research by eliminating the burden of large-scale data management, querying, cleaning, and analysis. SPARK represents a major advancement in genome research technologies, vastly reducing the burden of working with genomic datasets, and enabling cutting edge analysis approaches that have previously been out of reach for many medical researchers.

Keywords: biomedical research, genomics, information systems, software

Procedia PDF Downloads 270

Authors: Caspar Goeke, Holger Finger, Dorena Diekamp, Peter König

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Online-based research has recently gained increasing attention from various fields of research in the cognitive sciences. Technological advances in the form of online crowdsourcing (Amazon Mechanical Turk), open data repositories (Open Science Framework), and online analysis (Ipython notebook) offer rich possibilities to improve, validate, and speed up research. However, until today there is no cross-platform integration of these subsystems. Furthermore, implementation of online studies still suffers from the complex implementation (server infrastructure, database programming, security considerations etc.). Here we propose and test a new JavaScript framework that enables researchers to conduct any kind of behavioral research in the browser without the need to program a single line of code. In particular our framework offers the possibility to manipulate and combine the experimental stimuli via a graphical editor, directly in the browser. Moreover, we included an action-event system that can be used to handle user interactions, interactively change stimuli properties or store participants’ responses. Besides traditional recordings such as reaction time, mouse and keyboard presses, the tool offers webcam based eye and face-tracking. On top of these features our framework also takes care about the participant recruitment, via crowdsourcing platforms such as Amazon Mechanical Turk. Furthermore, the build in functionality of google translate will ensure automatic text translations of the experimental content. Thereby, thousands of participants from different cultures and nationalities can be recruited literally within hours. Finally, the recorded data can be visualized and cleaned online, and then exported into the desired formats (csv, xls, sav, mat) for statistical analysis. Alternatively, the data can also be analyzed online within our framework using the integrated Ipython notebook. The framework was designed such that studies can be used interchangeably between researchers. This will support not only the idea of open data repositories but also constitutes the possibility to share and reuse the experimental designs and analyses such that the validity of the paradigms will be improved. Particularly, sharing and integrating the experimental designs and analysis will lead to an increased consistency of experimental paradigms. To demonstrate the functionality of the framework we present the results of a pilot study in the field of spatial navigation that was conducted using the framework. Specifically, we recruited over 2000 subjects with various cultural backgrounds and consequently analyzed performance difference in dependence on the factors culture, gender and age. Overall, our results demonstrate a strong influence of cultural factors in spatial cognition. Such an influence has not yet been reported before and would not have been possible to show without the massive amount of data collected via our framework. In fact, these findings shed new lights on cultural differences in spatial navigation. As a consequence we conclude that our new framework constitutes a wide range of advantages for online research and a methodological innovation, by which new insights can be revealed on the basis of massive data collection.

Keywords: cultural differences, crowdsourcing, JavaScript framework, methodological innovation, online data collection, online study, spatial cognition

Procedia PDF Downloads 258

Authors: Pomozova Kseniia, Gorlachev Gennadiy, Chernyaev Aleksandr, Golanov Andrey

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In clinical practice, and especially in stereotactic radiosurgery planning, the significance of diffusion-weighted imaging (DWI) is growing. This makes the existence of software capable of quickly processing and reliably visualizing diffusion data, as well as equipped with tools for their analysis in terms of different tasks. We are developing the «MRDiffusionImaging» software on the standard C++ language. The subject part has been moved to separate class libraries and can be used on various platforms. The user interface is Windows WPF (Windows Presentation Foundation), which is a technology for managing Windows applications with access to all components of the .NET 5 or .NET Framework platform ecosystem. One of the important features is the use of a declarative markup language, XAML (eXtensible Application Markup Language), with which you can conveniently create, initialize and set properties of objects with hierarchical relationships. Graphics are generated using the DirectX environment. The MRDiffusionImaging software package has been implemented for processing diffusion magnetic resonance imaging (dMRI), which allows loading and viewing images sorted by series. An algorithm for "masking" dMRI series based on T2-weighted images was developed using a deformable surface model to exclude tissues that are not related to the area of interest from the analysis. An algorithm of distortion correction using deformable image registration based on autocorrelation of local structure has been developed. Maximum voxel dimension was 1,03 ± 0,12 mm. In an elementary brain's volume, the diffusion tensor is geometrically interpreted using an ellipsoid, which is an isosurface of the probability density of a molecule's diffusion. For the first time, non-parametric intensity distributions, neighborhood correlations, and inhomogeneities are combined in one segmentation of white matter (WM), grey matter (GM), and cerebrospinal fluid (CSF) algorithm. A tool for calculating the coefficient of average diffusion and fractional anisotropy has been created, on the basis of which it is possible to build quantitative maps for solving various clinical problems. Functionality has been created that allows clustering and segmenting images to individualize the clinical volume of radiation treatment and further assess the response (Median Dice Score = 0.963 ± 0,137). White matter tracts of the brain were visualized using two algorithms: deterministic (fiber assignment by continuous tracking) and probabilistic using the Hough transform. The proposed algorithms test candidate curves in the voxel, assigning to each one a score computed from the diffusion data, and then selects the curves with the highest scores as the potential anatomical connections. White matter fibers were visualized using a Hough transform tractography algorithm. In the context of functional radiosurgery, it is possible to reduce the irradiation volume of the internal capsule receiving 12 Gy from 0,402 cc to 0,254 cc. The «MRDiffusionImaging» will improve the efficiency and accuracy of diagnostics and stereotactic radiotherapy of intracranial pathology. We develop software with integrated, intuitive support for processing, analysis, and inclusion in the process of radiotherapy planning and evaluating its results.

Keywords: diffusion-weighted imaging, medical imaging, stereotactic radiosurgery, tractography

Procedia PDF Downloads 85

Authors: Athina Thanou, Eirini Eleni Tsiropoulou, Symeon Papavassiliou

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The problem of museum visitor experience and congestion management – in various forms - has come increasingly under the spotlight over the last few years, since overcrowding can significantly decrease the quality of visitors’ experience. Evidence suggests that on busy days the amount of time a visitor spends inside a crowded house museum can fall by up to 60% compared to a quiet mid-week day. In this paper we consider the aforementioned problem, by treating museums as evolving social systems that induce constraints. However, in a cultural heritage space, as opposed to the majority of social environments, the momentum of the experience is primarily controlled by the visitor himself. Visitors typically behave selfishly regarding the maximization of their own Quality of Experience (QoE) - commonly expressed through a utility function that takes several parameters into consideration, with crowd density and waiting/visiting time being among the key ones. In such a setting, congestion occurs when either the utility of one visitor decreases due to the behavior of other persons, or when costs of undertaking an activity rise due to the presence of other persons. We initially investigate how visitors’ behavioral risk attitudes, as captured and represented by prospect theory, affect their decisions in resource sharing settings, where visitors’ decisions and experiences are strongly interdependent. Different from the majority of existing studies and literature, we highlight that visitors are not risk neutral utility maximizers, but they demonstrate risk-aware behavior according to their personal risk characteristics. In our work, exhibits are organized into two groups: a) “safe exhibits” that correspond to less congested ones, where the visitors receive guaranteed satisfaction in accordance with the visiting time invested, and b) common pool of resources (CPR) exhibits, which are the most popular exhibits with possibly increased congestion and uncertain outcome in terms of visitor satisfaction. A key difference is that the visitor satisfaction due to CPR strongly depends not only on the invested time decision of a specific visitor, but also on that of the rest of the visitors. In the latter case, the over-investment in time, or equivalently the increased congestion potentially leads to “exhibit failure”, interpreted as the visitors gain no satisfaction from their observation of this exhibit due to high congestion. We present a framework where each visitor in a distributed manner determines his time investment in safe or CPR exhibits to optimize his QoE. Based on this framework, we analyze and evaluate how visitors, acting as prospect-theoretic decision-makers, respond and react to the various pricing policies imposed by the museum curators. Based on detailed evaluation results and experiments, we present interesting observations, regarding the impact of several parameters and characteristics such as visitor heterogeneity and use of alternative pricing policies, on scalability, user satisfaction, museum capacity, resource fragility, and operation point stability. Furthermore, we study and present the effectiveness of alternative pricing mechanisms, when used as implicit tools, to deal with the congestion management problem in the museums, and potentially decrease the exhibit failure probability (fragility), while considering the visitor risk preferences.

Keywords: museum resource and visitor management, congestion management, propsect theory, cyber physical social systems

Procedia PDF Downloads 185

Authors: Soheila Sadeghi

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Accurate cost prediction is essential for effective project management and decision-making in the construction industry. This study aims to develop a cost prediction model for construction projects using Machine Learning techniques and Particle Swarm Optimization (PSO). The research utilizes a comprehensive dataset containing project cost estimates, actual costs, resource details, and project performance metrics from a road reconstruction project. The methodology involves data preprocessing, feature selection, and the development of an Artificial Neural Network (ANN) model optimized using PSO. The study investigates the impact of various input features, including cost estimates, resource allocation, and project progress, on the accuracy of cost predictions. The performance of the optimized ANN model is evaluated using metrics such as Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and R-squared. The results demonstrate the effectiveness of the proposed approach in predicting project costs, outperforming traditional benchmark models. The feature selection process identifies the most influential variables contributing to cost variations, providing valuable insights for project managers. However, this study has several limitations. Firstly, the model's performance may be influenced by the quality and quantity of the dataset used. A larger and more diverse dataset covering different types of construction projects would enhance the model's generalizability. Secondly, the study focuses on a specific optimization technique (PSO) and a single Machine Learning algorithm (ANN). Exploring other optimization methods and comparing the performance of various ML algorithms could provide a more comprehensive understanding of the cost prediction problem. Future research should focus on several key areas. Firstly, expanding the dataset to include a wider range of construction projects, such as residential buildings, commercial complexes, and infrastructure projects, would improve the model's applicability. Secondly, investigating the integration of additional data sources, such as economic indicators, weather data, and supplier information, could enhance the predictive power of the model. Thirdly, exploring the potential of ensemble learning techniques, which combine multiple ML algorithms, may further improve cost prediction accuracy. Additionally, developing user-friendly interfaces and tools to facilitate the adoption of the proposed cost prediction model in real-world construction projects would be a valuable contribution to the industry. The findings of this study have significant implications for construction project management, enabling proactive cost estimation, resource allocation, budget planning, and risk assessment, ultimately leading to improved project performance and cost control. This research contributes to the advancement of cost prediction techniques in the construction industry and highlights the potential of Machine Learning and PSO in addressing this critical challenge. However, further research is needed to address the limitations and explore the identified future research directions to fully realize the potential of ML-based cost prediction models in the construction domain.

Keywords: cost prediction, construction projects, machine learning, artificial neural networks, particle swarm optimization, project management, feature selection, road reconstruction

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Authors: Monica Racha, Siva Chandrasekaran, Alex Stojcevski

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The engineering programs focus on promoting students' personal and professional development by ensuring that students acquire technical and professional competencies during four-year studies. The traditional engineering laboratory provides an opportunity for students to "practice by doing," and laboratory facilities aid them in obtaining insight and understanding of their discipline. Due to rapid technological advancements and the current COVID-19 outbreak, the traditional labs were transforming into virtual learning environments. Aim: To better understand the limitations of the physical laboratory, this research study aims to use a Machine Learning (ML) algorithm that interfaces with the Augmented Reality HoloLens and predicts the image behavior to classify and detect the electronic components. The automated electronic components error classification and detection automatically detect and classify the position of all components on a breadboard by using the ML algorithm. This research will assist first-year undergraduate engineering students in conducting laboratory practices without any supervision. With the help of HoloLens, and ML algorithm, students will reduce component placement error on a breadboard and increase the efficiency of simple laboratory practices virtually. Method: The images of breadboards, resistors, capacitors, transistors, and other electrical components will be collected using HoloLens 2 and stored in a database. The collected image dataset will then be used for training a machine learning model. The raw images will be cleaned, processed, and labeled to facilitate further analysis of components error classification and detection. For instance, when students conduct laboratory experiments, the HoloLens captures images of students placing different components on a breadboard. The images are forwarded to the server for detection in the background. A hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm will be used to train the dataset for object recognition and classification. The convolution layer extracts image features, which are then classified using Support Vector Machine (SVM). By adequately labeling the training data and classifying, the model will predict, categorize, and assess students in placing components correctly. As a result, the data acquired through HoloLens includes images of students assembling electronic components. It constantly checks to see if students appropriately position components in the breadboard and connect the components to function. When students misplace any components, the HoloLens predicts the error before the user places the components in the incorrect proportion and fosters students to correct their mistakes. This hybrid Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) algorithm automating electronic component error classification and detection approach eliminates component connection problems and minimizes the risk of component damage. Conclusion: These augmented reality smart glasses powered by machine learning provide a wide range of benefits to supervisors, professionals, and students. It helps customize the learning experience, which is particularly beneficial in large classes with limited time. It determines the accuracy with which machine learning algorithms can forecast whether students are making the correct decisions and completing their laboratory tasks.

Keywords: augmented reality, machine learning, object recognition, virtual laboratories

Procedia PDF Downloads 137

Authors: Purna Bahadur Lamichhane

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Education planning and management are foundational pillars for developing effective educational systems. However, in many educational contexts, especially in developing nations, technology-enabled management is still emerging. In such settings, manual-based systems, where instructions and guidelines are physically documented, remain central to educational planning and management. This paper examines the effectiveness, challenges, and potential of manual-based education planning systems in fostering structured, reliable, and adaptable management frameworks. The objective of this study is to explore how a manual-based approach can successfully guide administrators, educators, and policymakers in delivering high-quality education. By using structured, accessible instructions, this approach serves as a blueprint for educational governance, offering clear, actionable steps to achieve institutional goals. Through an analysis of case studies from various regions, the paper identifies key strategies for planning school schedules, managing resources, and monitoring academic and administrative performance without relying on automated systems. The findings underscore the significance of organized documentation, standard operating procedures, and comprehensive manuals that establish uniformity and maintain educational standards across institutions. With a manual-based approach, management can remain flexible, responsive, and user-friendly, especially in environments where internet access and digital literacy are limited. Moreover, it allows for localization, where instructions can be tailored to the unique cultural and socio-economic contexts of the community, thereby increasing relevancy and ownership among local stakeholders. This paper also highlights several challenges associated with manual-based education management. Manual systems often require significant time and human resources for maintenance and updating, potentially leading to inefficiencies and inconsistencies over time. Furthermore, manual records can be susceptible to loss, damage, and limited accessibility, which may affect decision-making and institutional memory. There is also the risk of siloed information, where crucial data resides with specific individuals rather than being accessible across the organization. However, with proper training and regular oversight, many of these limitations can be mitigated. The study further explores the potential for hybrid approaches, combining manual planning with selected digital tools for record-keeping, reporting, and analytics. This transitional strategy can enable schools and educational institutions to gradually embrace digital solutions without discarding the familiarity and reliability of manual instructions. In conclusion, this paper advocates for a balanced, context-sensitive approach to education planning and management. While digital systems hold the potential to streamline processes, manual-based systems offer resilience, inclusivity, and adaptability for institutions where technology adoption may be constrained. Ultimately, by reinforcing the importance of structured, detailed manuals and instructional guides, educational institutions can build robust management frameworks that facilitate both short-term successes and long-term growth in their educational mission. This research aims to provide a reference for policymakers, educators, and administrators seeking practical, low-cost, and adaptable solutions for sustainable educational planning and management.

Keywords: educatoin, planning, management, manual

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Authors: F. Tang, S. Boskovich, A. Raheja, Z. Aliyazicioglu, S. Bhandari, N. Tsuchiya

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Calpoly Autonomous Transportation Experience (CATE) is a driverless vehicle that we are developing to provide safe, accessible, and efficient transportation of passengers throughout the Cal Poly Pomona campus for events such as orientation tours. Unlike the other self-driving vehicles that are usually developed to operate with other vehicles and reside only on the road networks, CATE will operate exclusively on walk-paths of the campus (potentially narrow passages) with pedestrians traveling from multiple locations. Safety becomes paramount as CATE operates within the same environment as pedestrians. As driverless vehicles assume greater roles in today’s transportation, this project will contribute to autonomous driving with pedestrian traffic in a highly dynamic environment. The CATE project requires significant interdisciplinary work. Researchers from mechanical engineering, electrical engineering and computer science are working together to attack the problem from different perspectives (hardware, software and system). In this abstract, we describe the software aspects of the project, with a focus on the requirements and the major components. CATE shall provide a GUI interface for the average user to interact with the car and access its available functionalities, such as selecting a destination from any origin on campus. We have developed an interface that provides an aerial view of the campus map, the current car location, routes, and the goal location. Users can interact with CATE through audio or manual inputs. CATE shall plan routes from the origin to the selected destination for the vehicle to travel. We will use an existing aerial map for the campus and convert it to a spatial graph configuration where the vertices represent the landmarks and edges represent paths that the car should follow with some designated behaviors (such as stay on the right side of the lane or follow an edge). Graph search algorithms such as A* will be implemented as the default path planning algorithm. D* Lite will be explored to efficiently recompute the path when there are any changes to the map. CATE shall avoid any static obstacles and walking pedestrians within some safe distance. Unlike traveling along traditional roadways, CATE’s route directly coexists with pedestrians. To ensure the safety of the pedestrians, we will use sensor fusion techniques that combine data from both lidar and stereo vision for obstacle avoidance while also allowing CATE to operate along its intended route. We will also build prediction models for pedestrian traffic patterns. CATE shall improve its location and work under a GPS-denied situation. CATE relies on its GPS to give its current location, which has a precision of a few meters. We have implemented an Unscented Kalman Filter (UKF) that allows the fusion of data from multiple sensors (such as GPS, IMU, odometry) in order to increase the confidence of localization. We also noticed that GPS signals can easily get degraded or blocked on campus due to high-rise buildings or trees. UKF can also help here to generate a better state estimate. In summary, CATE will provide on-campus transportation experience that coexists with dynamic pedestrian traffic. In future work, we will extend it to multi-vehicle scenarios.

Keywords: driverless vehicle, path planning, sensor fusion, state estimate

Procedia PDF Downloads 147

Authors: Nuno Biga

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The integration of serious games with gamification in management education and training has gained significant importance in recent years as innovative strategies are sought to improve target audience engagement and learning outcomes. This research builds on the author's previous work in this field and presents a case study that evaluates the ex-post impact of a sample of applications of the BIGAMES management simulator in the training of top managers from various hospital institutions. The methodology includes evaluating the reaction of participants after each edition of BIGAMES Accident & Emergency (A&E) carried out over the last 3 years, as well as monitoring the career path of a significant sample of participants and their feedback more than a year after their experience with this simulator. Control groups will be set up, according to the type of role their members held when they took part in the BIGAMES A&E simulator: Administrators, Clinical Directors and Nursing Directors. Former participants are invited to answer a questionnaire structured for this purpose, where they are asked, among other questions, about the importance and impact that the BIGAMES A&E simulator has had on their professional activity. The research methodology also includes an exhaustive literature review, focusing on empirical studies in the field of education and training in management and business that investigate the effectiveness of gamification and serious games in improving learning, team collaboration, critical thinking, problem-solving skills and overall performance, with a focus on training contexts in the health sector. The results of the research carried out show that gamification and serious games that simulate real scenarios, such as Business Interactive Games - BIGAMES©, can significantly increase the motivation and commitment of participants, stimulating the development of transversal skills, the mobilization of group synergies and the acquisition and retention of knowledge through interactive user-centred scenarios. Individuals who participate in game-based learning series show a higher level of commitment to learning because they find these teaching methods more enjoyable and interactive. This research study aims to demonstrate that, as executive education and training programs develop to meet the current needs of managers, gamification and serious games stand out as effective means of bridging the gap between traditional teaching methods and modern educational and training requirements. To this end, this research evaluates the medium/long-term effects of gamified learning on the professional performance of participants in the BIGAMES simulator applied to healthcare. Based on the conclusions of the evaluation of the effectiveness of training using gamification and taking into account the results of the opinion poll of former A&E participants, this research study proposes an integrated approach for the transversal application of the A&E Serious Game in various educational contexts, covering top management (traditionally the target audience of BIGAMES A&E), middle and operational management in healthcare institutions (functional area heads and professionals with career development potential), as well as higher education in medicine and nursing courses. The integrated solution called “BIGAMES A&E plus”, developed as part of this research, includes the digitalization of key processes and the incorporation of AI.

Keywords: artificial intelligence (AI), executive training, gamification, higher education, management simulators, serious games (SG), training effectiveness

Procedia PDF Downloads 15

Authors: Jose A. C. Venancio, Alexandrino J. M. Goncalves, Anabela Marto, Nuno C. S. Rodrigues, Rita M. T. Ascenso

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Nowadays, it is common to find people who are concerned about getting away from the everyday life routine, looking forward to outcome well-being and pleasant emotions. Trying to disconnect themselves from the usual places of work and residence, they pursue different places, such as tourist destinations, aiming to have unexpected experiences. In order to make this exploration process easier, cities and tourism agencies seek new opportunities and solutions, creating routes with diverse cultural landmarks, including natural landscapes and historic buildings. These offers frequently aspire to the preservation of the local patrimony. In nature and wildlife, birdwatching is an activity that has been increasing, both in cities and in the countryside. This activity seeks to find, observe and identify the diversity of birds that live permanently or temporarily in these places, and it is usually supported by birdwatching guides. Leiria (Portugal) is a well-known city, presenting several historical and natural landmarks, like the Lis river and the castle where King D. Dinis lived in the 13th century. Along the Lis River, a conservation process was carried out and a pedestrian route was created (Polis project). This is considered an excellent spot for birdwatching, especially for the gray heron (Ardea cinerea) and for the kingfisher (Alcedo atthis). There is also a route through the city, from the riverside to the castle, which encloses a characterized variety of species, such as the barn swallow (Hirundo rustica), known for passing through different seasons of the year. Birdwatching is sometimes a difficult task since it is not always possible to see all bird species that inhabit a given place. For this reason, a need to create a technological solution was found to ease this activity. This project aims to encourage people to learn about the various species of birds that live along the Lis River and to promote the preservation of nature in a conscious way. This work is being conducted in collaboration with Leiria Municipal Council and with the Environmental Interpretation Centre. It intends to show the majesty of the Lis River, a place visited daily by several people, such as children and families, who use it for didactic and recreational activities. We are developing a mobile multi-platform application (Guard@Lis) that allows bird species to be observed along a given route, using representative digital 3D models through the integration of augmented reality technologies. Guard@Lis displays a route with points of interest for birdwatching and a list of species for each point of interest, along with scientific information, images and sounds for every species. For some birds, to ensure their observation, the user can watch them in loco, in their real and natural environment, with their mobile device by means of augmented reality, giving the sensation of presence of these birds, even if they cannot be seen in that place at that moment. The augmented reality feature is being developed with Vuforia SDK, using a hybrid approach to recognition and tracking processes, combining marks and geolocation techniques. This application proposes routes and notifies users with alerts for the possibility of viewing models of augmented reality birds. The final Guard@Lis prototype will be tested by volunteers in-situ.

Keywords: augmented reality, birdwatching route, mobile application, nature tourism, watch birds using augmented reality

Procedia PDF Downloads 178

Authors: Wen-Chung Chiang, Lun-Jou Lo, Hsiu-Hsia Lin

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This study aimed to improve the evaluation of facial symmetry, which is crucial for planning and assessing outcomes in orthognathic surgery (OGS). Facial symmetry plays a key role in both aesthetic and functional aspects of OGS, making its accurate evaluation essential for optimal surgical results. To address the limitations of traditional methods, a different approach was developed, combining three-dimensional (3D) facial contour mapping with hyperdimensional (HD) computing to enhance precision and efficiency in symmetry assessments. The study was conducted at Chang Gung Memorial Hospital, where data were collected from 2018 to 2023 using 3D cone beam computed tomography (CBCT), a highly detailed imaging technique. A large and comprehensive dataset was compiled, consisting of 150 normal individuals and 2,800 patients, totaling 5,750 preoperative and postoperative facial images. These data were critical for training a machine learning model designed to analyze and quantify facial symmetry. The machine learning model was trained to process 3D contour data from the CBCT images, with HD computing employed to power the facial symmetry quantification system. This combination of technologies allowed for an objective and detailed analysis of facial features, surpassing the accuracy and reliability of traditional symmetry assessments, which often rely on subjective visual evaluations by clinicians. In addition to developing the system, the researchers conducted a retrospective review of 3D CBCT data from 300 patients who had undergone OGS. The patients’ facial images were analyzed both before and after surgery to assess the clinical utility of the proposed system. The results showed that the facial symmetry algorithm achieved an overall accuracy of 82.5%, indicating its robustness in real-world clinical applications. Postoperative analysis revealed a significant improvement in facial symmetry, with an average score increase of 51%. The mean symmetry score rose from 2.53 preoperatively to 3.89 postoperatively, demonstrating the system's effectiveness in quantifying improvements after OGS. These results underscore the system's potential for providing valuable feedback to surgeons and aiding in the refinement of surgical techniques. The study also led to the development of a web-based system that automates facial symmetry assessment. This system integrates HD computing and 3D contour mapping into a user-friendly platform that allows for rapid and accurate evaluations. Clinicians can easily access this system to perform detailed symmetry assessments, making it a practical tool for clinical settings. Additionally, the system facilitates better communication between clinicians and patients by providing objective, easy-to-understand symmetry scores, which can help patients visualize the expected outcomes of their surgery. In conclusion, this study introduced a valuable and highly effective approach to facial symmetry evaluation in OGS, combining 3D contour mapping, HD computing, and machine learning. The resulting system achieved high accuracy and offers a streamlined, automated solution for clinical use. The development of the web-based platform further enhances its practicality, making it a valuable tool for improving surgical outcomes and patient satisfaction in orthognathic surgery.

Keywords: facial symmetry, orthognathic surgery, facial contour mapping, hyperdimensional computing

Procedia PDF Downloads 28

Authors: Danlei Yang, Luofeng Huang

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The integration of digital twin technology with renewable energy systems offers an innovative approach to predicting and optimising performance throughout the entire lifecycle. A digital twin is a continuously updated virtual replica of a real-world entity, synchronised with data from its physical counterpart and environment. Many digital twin companies today claim to have mature digital twin products, but their focus is primarily on equipment visualisation. However, the core of a digital twin should be its model, which can mirror, shadow, and thread with the real-world entity, which is still underdeveloped. For a floating solar energy system, a digital twin model can be defined in three aspects: (a) the physical floating solar energy system along with environmental factors such as solar irradiance and wave dynamics, (b) a digital model powered by artificial intelligence (AI) algorithms, and (c) the integration of real system data with the AI-driven model and a user interface. The experimental setup for the floating solar energy system, is designed to replicate real-ocean conditions of floating solar installations within a controlled laboratory environment. The system consists of a water tank that simulates an aquatic surface, where a floating catamaran structure supports a solar panel. The solar simulator is set up in three positions: one directly above and two inclined at a 45° angle in front and behind the solar panel. This arrangement allows the simulation of different sun angles, such as sunrise, midday, and sunset. The solar simulator is positioned 400 mm away from the solar panel to maintain consistent solar irradiance on its surface. Stability for the floating structure is achieved through ropes attached to anchors at the bottom of the tank, which simulates the mooring systems used in real-world floating solar applications. The floating solar energy system's sensor setup includes various devices to monitor environmental and operational parameters. An irradiance sensor measures solar irradiance on the photovoltaic (PV) panel. Temperature sensors monitor ambient air and water temperatures, as well as the PV panel temperature. Wave gauges measure wave height, while load cells capture mooring force. Inclinometers and ultrasonic sensors record heave and pitch amplitudes of the floating system’s motions. An electric load measures the voltage and current output from the solar panel. All sensors collect data simultaneously. Artificial neural network (ANN) algorithms are central to developing the digital model, which processes historical and real-time data, identifies patterns, and predicts the system’s performance in real time. The data collected from various sensors are partly used to train the digital model, with the remaining data reserved for validation and testing. The digital twin model combines the experimental setup with the ANN model, enabling monitoring, analysis, and prediction of the floating solar energy system's operation. The digital model mirrors the functionality of the physical setup, running in sync with the experiment to provide real-time insights and predictions. It provides useful industrial benefits, such as informing maintenance plans as well as design and control strategies for optimal energy efficiency. In long term, this digital twin will help improve overall solar energy yield whilst minimising the operational costs and risks.

Keywords: digital twin, floating solar energy system, experiment setup, artificial intelligence

Procedia PDF Downloads 14

Authors: Ok-Kyun Im, Kyoung Hee Kim

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We live in an era that faces global challenges of climate changes and resource depletion. With the rapid urbanization and growing energy consumption in the built environment, building facades become ever more important in architectural practice and environmental stewardship. Furthermore, building facade undergoes complex dynamics of social, cultural, environmental and technological changes. Kinetic facades have drawn attention of architects, designers, and engineers in the field of adaptable, responsive and interactive architecture since 1980’s. Materials and building technologies have gradually evolved to address the technical implications of kinetic facades. The kinetic façade is becoming an independent system of the building, transforming the design methodology to sustainable building solutions. Accordingly, there is a need for a new design methodology to guide the design of a kinetic façade and evaluate its sustainable performance. The research objectives are two-fold: First, to establish a new design methodology for kinetic facades and second, to develop a micro-oculi façade system and assess its performance using the established design method. The design approach to the micro-oculi facade is comprised of 1) façade geometry optimization and 2) dynamic building energy simulation. The façade geometry optimization utilizes multi-objective optimization process, aiming to balance the quantitative and qualitative performances to address the sustainability of the built environment. The dynamic building energy simulation was carried out using EnergyPlus and Radiance simulation engines with scripted interfaces. The micro-oculi office was compared with an office tower with a glass façade in accordance with ASHRAE 90.1 2013 to understand its energy efficiency. The micro-oculi facade is constructed with an array of circular frames attached to a pair of micro-shades called a micro-oculus. The micro-oculi are encapsulated between two glass panes to protect kinetic mechanisms with longevity. The micro-oculus incorporates rotating gears that transmit the power to adjacent micro-oculi to minimize the number of mechanical parts. The micro-oculus rotates around its center axis with a step size of 15deg depending on the sun’s position while maximizing daylighting potentials and view-outs. A 2 ft by 2ft prototyping was undertaken to identify operational challenges and material implications of the micro-oculi facade. In this research, a systematic design methodology was proposed, that integrates multi-objectives of kinetic façade design criteria and whole building energy performance simulation within a holistic design process. This design methodology is expected to encourage multidisciplinary collaborations between designers and engineers to collaborate issues of the energy efficiency, daylighting performance and user experience during design phases. The preliminary energy simulation indicated that compared to a glass façade, the micro-oculi façade showed energy savings due to its improved thermal properties, daylighting attributes, and dynamic solar performance across the day and seasons. It is expected that the micro oculi façade provides a cost-effective, environmentally-friendly, sustainable, and aesthetically pleasing alternative to glass facades. Recommendations for future studies include lab testing to validate the simulated data of energy and optical properties of the micro-oculi façade. A 1:1 performance mock-up of the micro-oculi façade can suggest in-depth understanding of long-term operability and new development opportunities applicable for urban façade applications.

Keywords: energy efficiency, kinetic facades, sustainable architecture, urban facades

Procedia PDF Downloads 257

Authors: Melina Kopke, Jelle Van Dijk

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Persons with cognitive disabilities, such as Autism Spectrum Disorder (ASD) are often dependent on some form of professional support. Recent transformations in Dutch healthcare have spurred institutions to apply new, empowering methods and tools to enable their clients to cope (more) independently in daily life. Assistive Technologies (ATs) seem promising as empowering tools. While ATs can, functionally speaking, help people to perform certain activities without human assistance, we hold that, from a design-theoretical perspective, such technologies often fail to empower in a deeper sense. Most technologies serve either to prescribe or to monitor users’ actions, which in some sense objectifies them, rather than strengthening their agency. This paper proposes that theories of embodied interaction could help formulating a design vision in which interactive assistive devices augment, rather than replace, human agency and thereby add to a persons’ empowerment in daily life settings. It aims to close the gap between empowerment theory and the opportunities provided by assistive technologies, by showing how embodiment and empowerment theory can be applied in practice in the design of new, interactive assistive devices. Taking a Research-through-Design approach, we conducted a case study of designing to support independently living people with ASD with structuring daily activities. In three iterations we interlaced design action, active involvement and prototype evaluations with future end-users and healthcare professionals, and theoretical reflection. Our co-design sessions revealed the issue of handling daily activities being multidimensional. Not having the ability to self-manage one’s daily life has immense consequences on one’s self-image, and also has major effects on the relationship with professional caregivers. Over the course of the project relevant theoretical principles of both embodiment and empowerment theory together with user-insights, informed our design decisions. This resulted in a system of wireless light units that users can program as a reminder for tasks, but also to record and reflect on their actions. The iterative process helped to gradually refine and reframe our growing understanding of what it concretely means for a technology to empower a person in daily life. Drawing on the case study insights we propose a set of concrete design principles that together form what we call the embodied empowerment design framework. The framework includes four main principles: Enabling ‘reflection-in-action’; making information ‘publicly available’ in order to enable co-reflection and social coupling; enabling the implementation of shared reflections into an ‘endurable-external feedback loop’ embedded in the persons familiar ’lifeworld’; and nudging situated actions with self-created action-affordances. In essence, the framework aims for the self-development of a suitable routine, or ‘situated practice’, by building on a growing shared insight of what works for the person. The framework, we propose, may serve as a starting point for AT designers to create truly empowering interactive products. In a set of follow-up projects involving the participation of persons with ASD, Intellectual Disabilities, Dementia and Acquired Brain Injury, the framework will be applied, evaluated and further refined.

Keywords: assistive technology, design, embodiment, empowerment

Procedia PDF Downloads 280

Authors: Zachary Blanks, Solomon Sonya

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Poaching presents a serious threat to endangered animal species, environment conservations, and human life. Additionally, some poaching activity has even been linked to supplying funds to support terrorist networks elsewhere around the world. Consequently, agencies dedicated to protecting wildlife habitats have a near intractable task of adequately patrolling an entire area (spanning several thousand kilometers) given limited resources, funds, and personnel at their disposal. Thus, agencies need predictive tools that are both high-performing and easily implementable by the user to help in learning how the significant features (e.g. animal population densities, topography, behavior patterns of the criminals within the area, etc) interact with each other in hopes of abating poaching. This research develops a classification model using machine learning algorithms to aid in forecasting future attacks that is both easy to train and performs well when compared to other models. In this research, we demonstrate how data imputation methods (specifically predictive mean matching, gradient boosting, and random forest multiple imputation) can be applied to analyze data and create significant predictions across a varied data set. Specifically, we apply these methods to improve the accuracy of adopted prediction models (Logistic Regression, Support Vector Machine, etc). Finally, we assess the performance of the model and the accuracy of our data imputation methods by learning on a real-world data set constituting four years of imputed data and testing on one year of non-imputed data. This paper provides three main contributions. First, we extend work done by the Teamcore and CREATE (Center for Risk and Economic Analysis of Terrorism Events) research group at the University of Southern California (USC) working in conjunction with the Department of Homeland Security to apply game theory and machine learning algorithms to develop more efficient ways of reducing poaching. This research introduces ensemble methods (Random Forests and Stochastic Gradient Boosting) and applies it to real-world poaching data gathered from the Ugandan rain forest park rangers. Next, we consider the effect of data imputation on both the performance of various algorithms and the general accuracy of the method itself when applied to a dependent variable where a large number of observations are missing. Third, we provide an alternate approach to predict the probability of observing poaching both by season and by month. The results from this research are very promising. We conclude that by using Stochastic Gradient Boosting to predict observations for non-commercial poaching by season, we are able to produce statistically equivalent results while being orders of magnitude faster in computation time and complexity. Additionally, when predicting potential poaching incidents by individual month vice entire seasons, boosting techniques produce a mean area under the curve increase of approximately 3% relative to previous prediction schedules by entire seasons.

Keywords: ensemble methods, imputation, machine learning, random forests, statistical analysis, stochastic gradient boosting, wildlife protection

Procedia PDF Downloads 294

Authors: Marco Frieslaar, Bing Chu, Eric Rogers

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Stroke is a devastating illness that is the second biggest cause of death in the world (after heart disease). Where it does not kill, it leaves survivors with debilitating sensory and physical impairments that not only seriously harm their quality of life, but also cause a high incidence of severe depression. It is widely accepted that early intervention is essential for recovery, but current rehabilitation techniques largely favor hospital-based therapies which have restricted access, expensive and specialist equipment and tend to side-step the emotional challenges. In addition, there is insufficient funding available to provide the long-term assistance that is required. As a consequence, recovery rates are poor. The relatively unexplored solution is to develop therapies that can be harnessed in the home and are formulated from technologies that already exist in everyday life. This would empower individuals to take control of their own improvement and provide choice in terms of when and where they feel best able to undertake their own healing. This research seeks to identify how effective post-stroke, rehabilitation therapy can be applied to upper limb mobility, within the physical context of a home rather than a hospital. This is being achieved through the design and construction of an automation scheme, based on iterative learning control and the Riener muscle model, that has the ability to adapt to the user and react to their level of fatigue and provide tangible physical recovery. It utilizes a SMART Phone and laptop to construct an iterative learning control (ILC) system, that monitors upper arm movement in three dimensions, as a series of exercises are undertaken. The equipment generates functional electrical stimulation to assist in muscle activation and thus improve directional accuracy. In addition, it monitors speed, accuracy, areas of motion weakness and similar parameters to create a performance index that can be compared over time and extrapolated to establish an independent and objective assessment scheme, plus an approximate estimation of predicted final outcome. To further extend its assessment capabilities, nerve conduction velocity readings are taken by the software, between the shoulder and hand muscles. This is utilized to measure the speed of response of neuron signal transfer along the arm and over time, an online indication of regeneration levels can be obtained. This will prove whether or not sufficient training intensity is being achieved even before perceivable movement dexterity is observed. The device also provides the option to connect to other users, via the internet, so that the patient can avoid feelings of isolation and can undertake movement exercises together with others in a similar position. This should create benefits not only for the encouragement of rehabilitation participation, but also an emotional support network potential. It is intended that this approach will extend the availability of stroke recovery options, enable ease of access at a low cost, reduce susceptibility to depression and through these endeavors, enhance the overall recovery success rate.

Keywords: home-based therapy, iterative learning control, Riener muscle model, SMART phone, stroke rehabilitation

Procedia PDF Downloads 265

Authors: Oscar Bareiro, Johannes Sackmann, Thomas Gries

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Carbon fiber reinforced polymer composites (CFRP) are used in a wide variety of applications due to its advantageous properties and design versatility. The braiding process enables the manufacture of components with good toughness and fatigue strength. However, failure mechanisms of CFRPs are complex and still present challenges associated with their maintenance and repair. Within the broad scope of structural health monitoring (SHM), strain monitoring can be applied to composite materials to improve reliability, reduce maintenance costs and safely exhaust service life. Traditional SHM systems employ e.g. fiber optics, piezoelectrics as sensors, which are often expensive, time consuming and complicated to implement. A cost-efficient alternative can be the exploitation of the conductive properties of fiber-based sensors such as carbon, copper, or constantan - a copper-nickel alloy – that can be utilized as sensors within composite structures to achieve strain monitoring. This allows the structure to provide feedback via electrical signals to a user which are essential for evaluating the structural condition of the structure. This work presents a strategy for the in-process integration of resistance-based sensors (Elektrisola Feindraht AG, CuNi23Mn, Ø = 0.05 mm) into textile preforms during its manufacture via the braiding process (Herzog RF-64/120) to achieve strain monitoring of braided composites. For this, flat samples of instrumented composite laminates of carbon fibers (Toho Tenax HTS40 F13 24K, 1600 tex) and epoxy resin (Epikote RIMR 426) were manufactured via vacuum-assisted resin infusion. These flat samples were later cut out into test specimens and the integrated sensors were wired to the measurement equipment (National Instruments, VB-8012) for data acquisition during the execution of mechanical tests. Quasi-static tests were performed (tensile, 3-point bending tests) following standard protocols (DIN EN ISO 527-1 & 4, DIN EN ISO 14132); additionally, dynamic tensile tests were executed. These tests were executed to assess the sensor response under different loading conditions and to evaluate the influence of the sensor presence on the mechanical properties of the material. Several orientations of the sensor with regards to the applied loading and sensor placements inside the laminate were tested. Strain measurements from the integrated sensors were made by programming a data acquisition code (LabView) written for the measurement equipment. Strain measurements from the integrated sensors were then correlated to the strain/stress state for the tested samples. From the assessment of the sensor integration approach it can be concluded that it allows for a seamless sensor integration into the textile preform. No damage to the sensor or negative effect on its electrical properties was detected during inspection after integration. From the assessment of the mechanical tests of instrumented samples it can be concluded that the presence of the sensors does not alter significantly the mechanical properties of the material. It was found that there is a good correlation between resistance measurements from the integrated sensors and the applied strain. It can be concluded that the correlation is of sufficient accuracy to determinate the strain state of a composite laminate based solely on the resistance measurements from the integrated sensors.

Keywords: braiding process, in-process sensor integration, instrumented composite material, resistance-based sensor, strain monitoring

Procedia PDF Downloads 106

Authors: Ding Liangxiao

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The technological advancements in Micro-Electro-Mechanical Systems (MEMS) have significantly contributed to the development of new, flexible capacitive pressure sensors,which are pivotal in transforming wearable and medical device technologies. This study employs the sophisticated simulation tools available in COMSOL Multiphysics® to develop and analyze a MEMS-based sensor with a tri-layered design. This sensor comprises top and bottom electrodes made from gold (Au), noted for their excellent conductivity, a middle dielectric layer made from a composite of Silver Nanowires (AgNWs) embedded in Thermoplastic Polyurethane (TPU), and a flexible, durable substrate of Polydimethylsiloxane (PDMS). This research was directed towards understanding how changes in the physical characteristics of the AgNWs/TPU dielectric layer—specifically, its thickness and surface area—impact the sensor's operational efficacy. We assessed several key electrical properties: capacitance, electric potential, and membrane displacement under varied pressure conditions. These investigations are crucial for enhancing the sensor's sensitivity and ensuring its adaptability across diverse applications, including health monitoring systems and dynamic user interface technologies. To ensure the reliability of our simulations, we applied the Effective Medium Theory to calculate the dielectric constant of the AgNWs/TPU composite accurately. This approach is essential for predicting how the composite material will perform under different environmental and operational stresses, thus facilitating the optimization of the sensor design for enhanced performance and longevity. Moreover, we explored the potential benefits of innovative three-dimensional structures for the dielectric layer compared to traditional flat designs. Our hypothesis was that 3D configurations might improve the stress distribution and optimize the electrical field interactions within the sensor, thereby boosting its sensitivity and accuracy. Our simulation protocol includes comprehensive performance testing under simulated environmental conditions, such as temperature fluctuations and mechanical pressures, which mirror the actual operational conditions. These tests are crucial for assessing the sensor's robustness and its ability to function reliably over extended periods, ensuring high reliability and accuracy in complex real-world environments. In our current research, although a full dynamic simulation analysis of the three-dimensional structures has not yet been conducted, preliminary explorations through three-dimensional modeling have indicated the potential for mechanical and electrical performance improvements over traditional planar designs. These initial observations emphasize the potential advantages and importance of incorporating advanced three-dimensional modeling techniques in the development of Micro-Electro-Mechanical Systems (MEMS)sensors, offering new directions for the design and functional optimization of future sensors. Overall, this study not only highlights the powerful capabilities of COMSOL Multiphysics® for modeling sophisticated electronic devices but also underscores the potential of innovative MEMS technology in advancing the development of more effective, reliable, and adaptable sensor solutions for a broad spectrum of technological applications.

Keywords: MEMS, flexible sensors, COMSOL Multiphysics, AgNWs/TPU, PDMS, 3D modeling, sensor durability

Procedia PDF Downloads 47

Authors: M. Mueller, M. Kuehn, M. Voelker

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In small and medium-sized enterprises (SMEs), the challenge is to create a well-grounded and reliable basis for process analysis, optimization and planning due to a lack of data. SMEs have limited access to methods with which they can effectively and efficiently analyse processes and identify cause-and-effect relationships in order to generate the necessary database and derive optimization potential from it. The implementation of digitalization within the framework of Industry 4.0 thus becomes a particular necessity for SMEs. For these reasons, the abstract presents an analysis methodology that is subject to the objective of developing an SME-appropriate methodology for efficient, temporarily feasible data collection and evaluation in flexible production and logistics systems as a basis for process analysis and optimization. The overall methodology focuses on retrospective, event-based tracing and analysis of material flow objects. The technological basis consists of Bluetooth low energy (BLE)-based transmitters, so-called beacons, and smart mobile devices (SMD), e.g. smartphones as receivers, between which distance data can be measured and derived motion profiles. The distance is determined using the Received Signal Strength Indicator (RSSI), which is a measure of signal field strength between transmitter and receiver. The focus is the development of a software-based methodology for interpretation of relative movements of transmitters and receivers based on distance data. The main research is on selection and implementation of pattern recognition methods for automatic process recognition as well as methods for the visualization of relative distance data. Due to an existing categorization of the database regarding process types, classification methods (e.g. Support Vector Machine) from the field of supervised learning are used. The necessary data quality requires selection of suitable methods as well as filters for smoothing occurring signal variations of the RSSI, the integration of methods for determination of correction factors depending on possible signal interference sources (columns, pallets) as well as the configuration of the used technology. The parameter settings on which respective algorithms are based have a further significant influence on result quality of the classification methods, correction models and methods for visualizing the position profiles used. The accuracy of classification algorithms can be improved up to 30% by selected parameter variation; this has already been proven in studies. Similar potentials can be observed with parameter variation of methods and filters for signal smoothing. Thus, there is increased interest in obtaining detailed results on the influence of parameter and factor combinations on data quality in this area. The overall methodology is realized with a modular software architecture consisting of independently modules for data acquisition, data preparation and data storage. The demonstrator for initialization and data acquisition is available as mobile Java-based application. The data preparation, including methods for signal smoothing, are Python-based with the possibility to vary parameter settings and to store them in the database (SQLite). The evaluation is divided into two separate software modules with database connection: the achievement of an automated assignment of defined process classes to distance data using selected classification algorithms and the visualization as well as reporting in terms of a graphical user interface (GUI).

Keywords: event-based tracing, machine learning, process classification, parameter settings, RSSI, signal smoothing

Procedia PDF Downloads 134

Authors: Lily Ranjbar, Haori Yang

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Online education has been around for several decades, but the importance of online education became evident after the COVID-19 pandemic. Eventhough the online delivery approach works well for knowledge building through delivering content and oversight processes, it has limitations in developing hands-on laboratory skills, especially in the STEM field. During the pandemic, many education institutions faced numerous challenges in delivering lab-based courses, especially in the STEM field. Also, many students worldwide were unable to practice working with lab equipment due to social distancing or the significant cost of highly specialized equipment. The laboratory plays a crucial role in nuclear science and engineering education. It can engage students and improve their learning outcomes. In addition, online education and virtual labs have gained substantial popularity in engineering and science education. Therefore, developing virtual labs is vital for institutions to deliver high-class education to their students, including their online students. The School of Nuclear Science and Engineering (NSE) at Oregon State University, in partnership with SpectralLabs company, has developed an Advanced Virtual Radiation Detection and Measurement Lab (AVR-DML) to offer a fully online Master of Health Physics program. It was essential for us to use a system that could simulate nuclear modules that accurately replicate the underlying physics, the nature of radiation and radiation transport, and the mechanics of the instrumentations used in the real radiation detection lab. It was all accomplished using a Realistic, Adaptive, Interactive Learning System (RAILS). RAILS is a comprehensive software simulation-based learning system for use in training. It is comprised of a web-based learning management system that is located on a central server, as well as a 3D-simulation package that is downloaded locally to user machines. Users will find that the graphics, animations, and sounds in RAILS create a realistic, immersive environment to practice detecting different radiation sources. These features allow students to coexist, interact and engage with a real STEM lab in all its dimensions. It enables them to feel like they are in a real lab environment and to see the same system they would in a lab. Unique interactive interfaces were designed and developed by integrating all the tools and equipment needed to run each lab. These interfaces provide students full functionality for data collection, changing the experimental setup, and live data collection with real-time updates for each experiment. Students can manually do all experimental setups and parameter changes in this lab. Experimental results can then be tracked and analyzed in an oscilloscope, a multi-channel analyzer, or a single-channel analyzer (SCA). The advanced virtual radiation detection and measurement laboratory developed in this study enabled the NSE school to offer a fully online MHP program. This flexibility of course modality helped us to attract more non-traditional students, including international students. It is a valuable educational tool as students can walk around the virtual lab, make mistakes, and learn from them. They have an unlimited amount of time to repeat and engage in experiments. This lab will also help us speed up training in nuclear science and engineering.

Keywords: advanced radiation detection and measurement, virtual laboratory, realistic adaptive interactive learning system (rails), online education in stem fields, student engagement, stem online education, stem laboratory, online engineering education

Procedia PDF Downloads 91

Authors: Francesca Crivellari, Nicholas Mavrogiannis, Zachary Gagnon

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Portable diagnostic methods have become increasingly important for a number of different purposes: point-of-care screening in developing nations, environmental contamination studies, bio/chemical warfare agent detection, and end-user use for commercial health monitoring. The cheapest and most portable methods currently available are paper-based – lateral flow and dipstick methods are widely available in drug stores for use in pregnancy detection and blood glucose monitoring. These tests are successful because they are cheap to produce, easy to use, and require minimally invasive sampling. While adequate for their intended uses, in the realm of blood-borne pathogens and numerous cancers, these paper-based methods become unreliable, as they lack the nM/pM sensitivity currently achieved by clinical diagnostic methods. Clinical diagnostics, however, utilize techniques involving surface plasmon resonance (SPR) and enzyme-linked immunosorbent assays (ELISAs), which are expensive and unfeasible in terms of portability. To develop a better, competitive biosensor, we must reduce the cost of one, or increase the sensitivity of the other. Electric fields are commonly utilized in microfluidic devices to manipulate particles, biomolecules, and cells. Applications in this area, however, are primarily limited to interfaces formed between immiscible interfaces. Miscible, liquid-liquid interfaces are common in microfluidic devices, and are easily reproduced with simple geometries. Here, we demonstrate the use of electrical fields at liquid-liquid electrical interfaces, known as fluidic dielectrophoresis, (fDEP) for biodetection in a microfluidic device. In this work, we apply an AC electric field across concurrent laminar streams with differing conductivities and permittivities to polarize the interface and induce a discernible, near-immediate, frequency-dependent interfacial tilt. We design this aqueous electrical interface, which becomes the biosensing “substrate,” to be intelligent – it “moves” only when a target of interest is present. This motion requires neither labels nor expensive electrical equipment, so the biosensor is inexpensive and portable, yet still capable of sensitive detection. Nanoparticles, due to their high surface-area-to-volume ratio, are often incorporated to enhance detection capabilities of schemes like SPR and fluorimetric assays. Most studies currently investigate binding at an immobilized solid-liquid or solid-gas interface, where particles are adsorbed onto a planar surface, functionalized with a receptor to create a reactive substrate, and subsequently flushed with a fluid or gas with the relevant analyte. These typically involve many preparation and rinsing steps, and are susceptible to surface fouling. Our microfluidic device is continuously flowing and renewing the “substrate,” and is thus not subject to fouling. In this work, we demonstrate the ability to electrokinetically detect biomolecules binding to functionalized nanoparticles at liquid-liquid interfaces using fDEP. In biotin-streptavidin experiments, we report binding detection limits on the order of 1-10 pM, without amplifying signals or concentrating samples. We also demonstrate the ability to detect this interfacial motion, and thus the presence of binding, using impedance spectroscopy, allowing this scheme to become non-optical, in addition to being label-free.

Keywords: biodetection, dielectrophoresis, microfluidics, nanoparticles

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Authors: Linna Li

Abstract:

The capability to move around places is doubtless very important for individuals to maintain good health and social functions. People’s activities in space and time have long been a research topic in behavioral and socio-economic studies, particularly focusing on the highly dynamic urban environment. By analyzing groups of people who share similar activity patterns, many socio-economic and socio-demographic problems and their relationships with individual behavior preferences can be revealed. Los Angeles, known for its large population, ethnic diversity, cultural mixing, and entertainment industry, faces great transportation challenges such as traffic congestion, parking difficulties, and long commuting. Understanding people’s travel behavior and movement patterns in this metropolis sheds light on potential solutions to complex problems regarding urban mobility. This project visualizes people’s trajectories in Greater Los Angeles (L.A.) Area over a period of two months using Twitter data. A Python script was used to collect georeferenced tweets within the Greater L.A. Area including Ventura, San Bernardino, Riverside, Los Angeles, and Orange counties. Information associated with tweets includes text, time, location, and user ID. Information associated with users includes name, the number of followers, etc. Both aggregated and individual activity patterns are demonstrated using various geovisualization techniques. Locations of individual Twitter users were aggregated to create a surface of activity hot spots at different time instants using kernel density estimation, which shows the dynamic flow of people’s movement throughout the metropolis in a twenty-four-hour cycle. In the 3D geovisualization interface, the z-axis indicates time that covers 24 hours, and the x-y plane shows the geographic space of the city. Any two points on the z axis can be selected for displaying activity density surface within a particular time period. In addition, daily trajectories of Twitter users were created using space-time paths that show the continuous movement of individuals throughout the day. When a personal trajectory is overlaid on top of ancillary layers including land use and road networks in 3D visualization, the vivid representation of a realistic view of the urban environment boosts situational awareness of the map reader. A comparison of the same individual’s paths on different days shows some regular patterns on weekdays for some Twitter users, but for some other users, their daily trajectories are more irregular and sporadic. This research makes contributions in two major areas: geovisualization of spatial footprints to understand travel behavior using the big data approach and dynamic representation of activity space in the Greater Los Angeles Area. Unlike traditional travel surveys, social media (e.g., Twitter) provides an inexpensive way of data collection on spatio-temporal footprints. The visualization techniques used in this project are also valuable for analyzing other spatio-temporal data in the exploratory stage, thus leading to informed decisions about generating and testing hypotheses for further investigation. The next step of this research is to separate users into different groups based on gender/ethnic origin and compare their daily trajectory patterns.

Keywords: geovisualization, human mobility pattern, Los Angeles, social media

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Authors: Mohammadreza Kashizadeh, Mohammadamin Hashemi

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This paper explores a kinetic building facade designed for optimal energy capture and architectural expression. The system integrates photovoltaic panels with soft robotic actuators for precise solar tracking, resulting in enhanced electricity generation compared to static facades. Driven by the growing interest in dynamic building envelopes, the exploration of facade systems are necessitated. Increased energy generation and regulation of energy flow within buildings are potential benefits offered by integrating photovoltaic (PV) panels as kinetic elements. However, incorporating these technologies into mainstream architecture presents challenges due to the complexity of coordinating multiple systems. To address this, the design leverages soft robotic actuators, known for their compliance, resilience, and ease of integration. Additionally, the project investigates the potential for employing Large Language Models (LLMs) to streamline the design process. The research methodology involved design development, material selection, component fabrication, and system assembly. Grasshopper (GH) was employed within the digital design environment for parametric modeling and scripting logic, and an LLM was experimented with to generate Python code for the creation of a random surface with user-defined parameters. Various techniques, including casting, Three-dimensional 3D printing, and laser cutting, were utilized to fabricate physical components. A modular assembly approach was adopted to facilitate installation and maintenance. A case study focusing on the application of this facade system to an existing library building at Polytechnic University of Milan is presented. The system is divided into sub-frames to optimize solar exposure while maintaining a visually appealing aesthetic. Preliminary structural analyses were conducted using Karamba3D to assess deflection behavior and axial loads within the cable net structure. Additionally, Finite Element (FE) simulations were performed in Abaqus to evaluate the mechanical response of the soft robotic actuators under pneumatic pressure. To validate the design, a physical prototype was created using a mold adapted for a 3D printer's limitations. Casting Silicone Rubber Sil 15 was used for its flexibility and durability. The 3D-printed mold components were assembled, filled with the silicone mixture, and cured. After demolding, nodes and cables were 3D-printed and connected to form the structure, demonstrating the feasibility of the design. This work demonstrates the potential of soft robotics and Artificial Intelligence (AI) for advancements in sustainable building design and construction. The project successfully integrates these technologies to create a dynamic facade system that optimizes energy generation and architectural expression. While limitations exist, this approach paves the way for future advancements in energy-efficient facade design. Continued research efforts will focus on cost reduction, improved system performance, and broader applicability.

Keywords: artificial intelligence, energy efficiency, kinetic photovoltaics, pneumatic control, soft robotics, sustainable building

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Authors: Jun Byung Joon, Seo Seok Hyun, Lee Seo Young

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As carbon dioxide emissions increase due to rapid industrialization and reckless development, abnormal climates such as floods and droughts are occurring. In order to respond to such climate change, the use of existing fossil fuels is reduced, and the proportion of eco-friendly renewable energy is gradually increasing. Korea is an energy resource-poor country that depends on imports for 93% of its total energy. As the global energy supply chain instability experienced due to the Russia-Ukraine crisis increases, countries around the world are resetting energy policies to minimize energy dependence and strengthen security. Seawater thermal energy is a renewable energy that replaces the existing air heat energy. It uses the characteristic of having a higher specific heat than air to cool and heat main spaces of buildings to increase heat transfer efficiency and minimize power consumption to generate electricity using fossil fuels, and Carbon dioxide emissions can be minimized. In addition, the effect on the marine environment is very small by using only the temperature characteristics of seawater in a limited way. K-water carried out a demonstration project of supplying cooling and heating energy to spaces such as the central control room and presentation room in the management building by acquiring the heat source of seawater circulated through the power plant's waterway by using the characteristics of the tidal power plant. Compared to the East Sea and the South Sea, the main system was designed in consideration of the large tidal difference, small temperature difference, and low-temperature characteristics, and its performance was verified through operation during the demonstration period. In addition, facility improvements were made for major deficiencies to strengthen monitoring functions, provide user convenience, and improve facility soundness. To spread these achievements, the basic concept was to expand the seawater heating and cooling system with a scale of 200 USRT at the Tidal Culture Center. With the operational experience of the demonstration system, it will be possible to establish an optimal seawater heat cooling and heating system suitable for the characteristics of the west coast ocean. Through this, it is possible to reduce operating costs by KRW 33,31 million per year compared to air heat, and through industry-university-research joint research, it is possible to localize major equipment and materials and develop key element technologies to revitalize the seawater heat business and to advance into overseas markets. The government's efforts are needed to expand the seawater heating and cooling system. Seawater thermal energy utilizes only the thermal energy of infinite seawater. Seawater thermal energy has less impact on the environment than river water thermal energy, except for environmental pollution factors such as bottom dredging, excavation, and sand or stone extraction. Therefore, it is necessary to increase the sense of speed in project promotion by innovatively simplifying unnecessary licensing/permission procedures. In addition, support should be provided to secure business feasibility by dramatically exempting the usage fee of public waters to actively encourage development in the private sector.

Keywords: seawater thermal energy, marine energy, tidal power plant, energy consumption

Procedia PDF Downloads 103

Authors: Tessa Heeren

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The following proposal pertains to the complex process of successfully implementing health policies that are based on public health research. A systematic review was conducted by myself and faculty at the Cluj School of Public Health in Romania. The reviewed articles covered a wide range of topics, such as barriers and facilitators to multi-sector collaboration, differences in professional cultures, and systemic obstacles. The reviewed literature identified communication, collaboration, user-friendly dissemination, and documentation of processes in the execution of applied research as important themes for the promotion of evidence in the public health decision-making process. This proposal fits into the Academy Health National Health Policy conference because it identifies and examines differences between the worlds of research and politics. Implications and new insights for federal and/or state health policy: Recommendations made based on the findings of this research include using politically relevant levers to promote research (e.g. campaign donors, lobbies, established parties, etc.), modernizing dissemination practices, and reforms in which the involvement of external stakeholders is facilitated without relying on invitations from individual policy makers. Description of how evidence and/or data was or could be used: The reviewed articles illustrated shortcomings and areas for improvement in policy research processes and collaborative development. In general, the evidence base in the field of integrating research into policy lacks critical details of the actual process of developing evidence based policy. This shortcoming in logistical details creates a barrier for potential replication of collaborative efforts described in studies. Potential impact of the presentation for health policy: The reviewed articles focused on identifying barriers and facilitators that arise in cross sector collaboration, rather than the process and impact of integrating evidence into policy. In addition, the type of evidence used in policy was rarely specified, and widely varying interpretations of the definition of evidence complicated overall conclusions. Background: Using evidence to inform public health decision making processes has been proven effective; however, it is not clear how research is applied in practice. Aims: The objectives of the current study were to assess the extent to which evidence is used in public health decision-making process. Methods: To identify eligible studies, seven bibliographic databases, specifically, PubMed, Scopus, Cochrane Library, Science Direct, Web of Science, ClinicalKey, Health and Safety Science Abstract were screened (search dates: 1990 – September 2015); a general internet search was also conducted. Primary research and systematic reviews about the use of evidence in public health policy in Europe were included. The studies considered for inclusion were assessed by two reviewers, along with extracted data on objective, methods, population, and results. Data were synthetized as a narrative review. Results: Of 2564 articles initially identified, 2525 titles and abstracts were screened. Ultimately, 30 articles fit the research criteria by describing how or why evidence is used/not used in public health policy. The majority of included studies involved interviews and surveys (N=17). Study participants were policy makers, health care professionals, researchers, community members, service users, experts in public health.

Keywords: cross-sector, dissemination, health policy, policy implementation

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Authors: Mohd Zaimmudin Mohd Zain, Patsy Perry, Lee Quinn

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This study explores how fashion consumers are influenced by fashion bloggers towards pre-purchase decision for online fashion products in a non-Western context. Malaysians rank among the world’s most avid online shoppers, with apparel the third most popular purchase category. However, extant research on fashion blogging focuses on the developed Western market context. Numerous international fashion retailers have entered the Malaysian market from luxury to fast fashion segments of the market; however Malaysian fashion consumers must balance religious and social norms for modesty with their dress style and adoption of fashion trends. Consumers increasingly mix and match Islamic and Western elements of dress to create new styles enabling them to follow Western fashion trends whilst paying respect to social and religious norms. Social media have revolutionised the way that consumers can search for and find information about fashion products. For online fashion brands with no physical presence, social media provide a means of discovery for consumers. By allowing the creation and exchange of user-generated content (UGC) online, they provide a public forum that gives individual consumers their own voices, as well as access to product information that facilitates their purchase decisions. Social media empower consumers and brands have important roles in facilitating conversations among consumers and themselves, to help consumers connect with them and one another. Fashion blogs have become an important fashion information sources. By sharing their personal style and inspiring their followers with what they wear on popular social media platforms such as Instagram, fashion bloggers have become fashion opinion leaders. By creating UGC to spread useful information to their followers, they influence the pre-purchase decision. Hence, successful Western fashion bloggers such as Chiara Ferragni may earn millions of US dollars every year, and some have created their own fashion ranges and beauty products, become judges in fashion reality shows, won awards, and collaborated with high street and luxury brands. As fashion blogging has become more established worldwide, increasing numbers of fashion bloggers have emerged from non-Western backgrounds to promote Islamic fashion styles, such as Hassanah El-Yacoubi and Dian Pelangi. This study adopts a qualitative approach using netnographic content analysis of consumer comments on two famous Malaysian fashion bloggers’ Instagram accounts during January-March 2016 and qualitative interviews with 16 Malaysian Generation Y fashion consumers during September-October 2016. Netnography adapts ethnographic techniques to the study of online communities or computer-mediated communications. Template analysis of the data involved coding comments according to the theoretical framework, which was developed from the literature review. Initial data analysis shows the strong influence of Malaysian fashion bloggers on their followers in terms of lifestyle and morals as well as fashion style. Followers were guided towards the mix and match trend of dress with Western and Islamic elements, for example, showing how vivid colours or accessories could be worked into an outfit whilst still respecting social and religious norms. The blogger’s Instagram account is a form of online community where followers can communicate and gain guidance and support from other followers, as well as from the blogger.

Keywords: fashion bloggers, Malaysia, qualitative, social media

Procedia PDF Downloads 219