Search results for: football analytics

Authors: Alexander Musyoki, Collins Odour

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Over the past few years, the bakery industry in Kenya has experienced significant growth largely in part to the increased adoption of technology and automation in their processes; more specifically due to the adoption of bakery management systems to help in running bakeries. While they have been largely responsible for the improved productivity and efficiency in bakeries, most of them are now outdated and pose more challenges than benefits. The proposed online bakery management system mentioned in this paper aims to address this by allowing bakery owners to track inventory, budget, job progress, and data analytics on each job and in doing so, promote the Sustainable Development Goals 3 and 12, which aim to ensure healthy lives and promote sustainable economic growth as the proposed benefits of these features include scalability, easy accessibility, reduced acquisition costs, better reliability, and improved functionality that will allow bakeries to become more competitive, reduce waste and track inventory more efficiently. To better understand the challenges, a comprehensive study has been performed to assess these traditional systems and try to understand if an online bakery management system can prove to be advantageous to bakery owners. The study conducted gathered feedback from bakery owners and employees in Nairobi County, Kenya using an online survey with a response rate of about 86% from the target population. The responses cited complex and hard to use bakery management systems (59.7%), lack of portability from one device to the other (58.1%) and high acquisition costs (51.6%) as the top challenges of traditional bakery management systems. On the other hand, some of the top benefits that most of the respondents would realize from the online bakery management system was better reliability (58.1%) and reduced acquisition costs (58.1%). Overall, the findings suggest that an online bakery management system has a lot of advantages over traditional systems and is likely to be well-received in the market. In conclusion, the proposed online bakery management system has the potential to improve the efficiency and competitiveness of small-sized bakeries in Nairobi County. Further research is recommended to expand the sample size and diversity of respondents and to conduct more in-depth analyses of the data collected.

Keywords: ICT, technology and automation, bakery management systems, food innovation

Procedia PDF Downloads 78

Authors: Sonu Rana, Jyoti Verma, A. K. Gautam

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In most developing countries like India, the majority of the population heavily relies on agriculture for their livelihood. The yield of agriculture is heavily dependent on uncertain weather conditions like a monsoon, soil fertility, availability of irrigation facilities and fertilizers as well as support from the government. The agricultural yield is quite less compared to the effort put in due to inefficient agricultural facilities and obsolete farming practices on the one hand and lack of knowledge on the other hand, and ultimately agricultural community does not prosper. It is therefore essential for the farmers to improve their harvest yield by the acquisition of related data such as soil condition, temperature, humidity, availability of irrigation facilities, availability of, manure, etc., and adopt smart farming techniques using modern agricultural equipment. Nowadays, using IOT technology in agriculture is the best solution to improve the yield with fewer efforts and economic costs. The primary focus of this work-related is IoT technology in the agriculture field. By using IoT all the parameters would be monitored by mounting sensors in an agriculture field held at different places, will collect real-time data, and could be transmitted by a transmitting device like an antenna. To improve the system, IoT will interact with other useful systems like Wireless Sensor Networks. IoT is exploring every aspect, so the radio frequency spectrum is getting crowded due to the increasing demand for wireless applications. Therefore, Federal Communications Commission is reallocating the spectrum for various wireless applications. An antenna is also an integral part of the newly designed IoT devices. The main aim is to propose a new antenna structure used for IoT agricultural applications and compatible with this new unlicensed frequency band. The main focus of this paper is to present work related to these technologies in the agriculture field. This also presented their challenges & benefits. It can help in understanding the job of data by using IoT and correspondence advancements in the horticulture division. This will help to motivate and educate the unskilled farmers to comprehend the best bits of knowledge given by the huge information investigation utilizing smart technology.

Keywords: smart agriculture, IoT, agriculture technology, data analytics, smart technology

Procedia PDF Downloads 116

Authors: Jovian Cheung, Thomas Kwok, Victor Wong

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Buildings are entwined with smart city developments. Building performance relies heavily on electrical and mechanical (E&M) systems and services accounting for about 40 percent of global energy use. By cohering the advancement of technology as well as energy and operation-efficient initiatives into the buildings, people are enabled to raise building performance and enhance the sustainability of the built environment in their daily lives. Digital transformation in the buildings is the profound development of the city to leverage the changes and opportunities of digital technologies To optimize the building performance, intelligent power quality and energy management system is developed for transforming data into actions. The system is formed by interfacing and integrating legacy metering and internet of things technologies in the building and applying big data techniques. It provides operation and energy profile and actionable insights of a building, which enables to optimize the building performance through raising people awareness on E&M services and energy consumption, predicting the operation of E&M systems, benchmarking the building performance, and prioritizing assets and energy management opportunities. The intelligent power quality and energy management system comprises four elements, namely the Integrated Building Performance Map, Building Performance Dashboard, Power Quality Analysis, and Energy Performance Analysis. It provides predictive operation sequence of E&M systems response to the built environment and building activities. The system collects the live operating conditions of E&M systems over time to identify abnormal system performance, predict failure trends and alert users before anticipating system failure. The actionable insights collected can also be used for system design enhancement in future. This paper will illustrate how intelligent power quality and energy management system provides operation and energy profile to optimize the building performance and actionable insights to revitalize an existing building into a smart building. The system is driving building performance optimization and supporting in developing Hong Kong into a suitable smart city to be admired.

Keywords: intelligent buildings, internet of things technologies, big data analytics, predictive operation and maintenance, building performance

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Authors: Anushtha Saxena

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This paper examines the process of data monetisation bye-commerce companies operating in India. Data monetisation is collecting, storing, and analysing consumers’ data to use further the data that is generated for profits, revenue, etc. Data monetisation enables e-commerce companies to get better businesses opportunities, innovative products and services, a competitive edge over others to the consumers, and generate millions of revenues. This paper analyses the issues and challenges that are faced due to the process of data monetisation. Some of the issues highlighted in the paper pertain to the right to privacy, protection of data of e-commerce consumers. At the same time, data monetisation cannot be prohibited, but it can be regulated and monitored by stringent laws and regulations. The right to privacy isa fundamental right guaranteed to the citizens of India through Article 21 of The Constitution of India. The Supreme Court of India recognized the Right to Privacy as a fundamental right in the landmark judgment of Justice K.S. Puttaswamy (Retd) and Another v. Union of India . This paper highlights the legal issue of how e-commerce businesses violate individuals’ right to privacy by using the data collected, stored by them for economic gains and monetisation and protection of data. The researcher has mainly focused on e-commerce companies like online shopping websitesto analyse the legal issue of data monetisation. In the Internet of Things and the digital age, people have shifted to online shopping as it is convenient, easy, flexible, comfortable, time-consuming, etc. But at the same time, the e-commerce companies store the data of their consumers and use it by selling to the third party or generating more data from the data stored with them. This violatesindividuals’ right to privacy because the consumers do not know anything while giving their data online. Many times, data is collected without the consent of individuals also. Data can be structured, unstructured, etc., that is used by analytics to monetise. The Indian legislation like The Information Technology Act, 2000, etc., does not effectively protect the e-consumers concerning their data and how it is used by e-commerce businesses to monetise and generate revenues from that data. The paper also examines the draft Data Protection Bill, 2021, pending in the Parliament of India, and how this Bill can make a huge impact on data monetisation. This paper also aims to study the European Union General Data Protection Regulation and how this legislation can be helpful in the Indian scenarioconcerning e-commerce businesses with respect to data monetisation.

Keywords: data monetization, e-commerce companies, regulatory framework, GDPR

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Authors: Oksana Koval, Stephen Nabareseh

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Talent attraction is identified as a top priority between 2015 – 2020 for Shared Service Centers (SSCs) based on an industry-wide studies. Due to market dynamics and the structure of labour force, shared service industries in Eastern and Central Europe strive for qualified graduates with appropriate and unique skills to occupy such job places. The inbuilt interest and course prescriptions undertaken by prospective job seekers determine whether SSCs will eventually admit such professionals. This paper assesses students’ overall perception of careers in the shared services industry and further diagnosis gender impact and influence on the job preferences among students. Questionnaires were distributed among students in the Czech Republic universities using an online mode. Respondents vary by study year, gender, age, course of study, and work preferences. A total of 1283 student responses has been analyzed using Stata data analytics software. It was discovered that over 70% of respondents who are aware of SSCs are quite ignorant of the job opportunities offered by the centers. While majority of respondents are interested in support positions (e.g. procurement specialist, planning specialist, human resource specialist, process improvement specialist and payroll specialist, etc.), around a third of respondents (32.8 percent) will decline a job offer from SSCs. The analysis also revealed that males are more likely than females to seek careers in international companies, hence, tend to be more favorable towards shared service jobs. Females, however, have stronger preferences towards marketing and PR jobs. The research results provide insights into the job aspirations of students interviewed. The findings provide a huge resource for recruitment agencies and shared service industries to renew and redirect their search for talents into SSCs. Based on the fact that great portion of respondents are planning to start their career within 6-12 months, the research provides important highlights for the talent attraction and recruitment strategies in the industry and provides a curriculum direction in academia.

Keywords: Czech Republic labour market, gender, talent attraction, shared service centers, students

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Authors: Samah Senbel

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Database Design is a fundamental part of the Computer Science and Information technology curricula in any school, as well as in the study of management, business administration, and data analytics. In this study, we compare the performance of two groups of students studying the same database design and implementation course at Sacred Heart University in the fall of 2018. Both courses used the same textbook and were taught by the same professor, one for seven graduate students and one for 26 undergraduate students (juniors). The undergraduate students were aged around 20 years old with little work experience, while the graduate students averaged 35 years old and all were employed in computer-related or management-related jobs. The textbook used was 'Database Systems, Design, Implementation, and Management' by Coronel and Morris, and the course was designed to follow the textbook roughly a chapter per week. The first 6 weeks covered the design aspect of a database, followed by a paper exam. The next 6 weeks covered the implementation aspect of the database using SQL followed by a lab exam. Since the undergraduate students are on a 16 week semester, we spend the last three weeks of the course covering NoSQL. This part of the course was not included in this study. After the course was over, we analyze the results of the two groups of students. An interesting discrepancy was observed: In the database design part of the course, the average grade of the graduate students was 92%, while that of the undergraduate students was 77% for the same exam. In the implementation part of the course, we observe the opposite: the average grade of the graduate students was 65% while that of the undergraduate students was 73%. The overall grades were quite similar: the graduate average was 78% and that of the undergraduates was 75%. Based on these results, we concluded that having both classes follow the same time schedule was not beneficial, and an adjustment is needed. The graduates could spend less time on design and the undergraduates would benefit from more design time. In the fall of 2019, 30 students registered for the undergraduate course and 15 students registered for the graduate course. To test our conclusion, the undergraduates spend about 67% of time (eight classes) on the design part of the course and 33% (four classes) on the implementation part, using the exact exams as the previous year. This resulted in an improvement in their average grades on the design part from 77% to 83% and also their implementation average grade from 73% to 79%. In conclusion, we recommend using two separate schedules for teaching the database design course. For undergraduate students, it is important to spend more time on the design part rather than the implementation part of the course. While for the older graduate students, we recommend spending more time on the implementation part, as it seems that is the part they struggle with, even though they have a higher understanding of the design component of databases.

Keywords: computer science education, database design, graduate and undergraduate students, pedagogy

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Authors: Shalaka Kadam, Dinesh Apte, Sagar Mainkar

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The life cycle of products is becoming shorter and shorter due to increased competition in market, shorter product development time and increased product diversity. Short life cycles are normal in retail industry, style business, entertainment media, and telecom and semiconductor industry. The subject of accurate forecasting for demand of short lifecycle products is of special enthusiasm for many researchers and organizations. Due to short life cycle of products the amount of historical data that is available for forecasting is very minimal or even absent when new or modified products are launched in market. The companies dealing with such products want to increase the accuracy in demand forecasting so that they can utilize the full potential of the market at the same time do not oversupply. This provides the challenge to develop a forecasting model that can forecast accurately while handling large variations in data and consider the complex relationships between various parameters of data. Many statistical models have been proposed in literature for forecasting time series data. Traditional time series forecasting models do not work well for short life cycles due to lack of historical data. Also artificial neural networks (ANN) models are very time consuming to perform forecasting. We have studied the existing models that are used for forecasting and their limitations. This work proposes an effective and powerful forecasting approach for short life cycle time series forecasting. We have proposed an approach which takes into consideration different scenarios related to data availability for short lifecycle products. We then suggest a methodology which combines statistical analysis with structured judgement. Also the defined approach can be applied across domains. We then describe the method of creating a profile from analogous products. This profile can then be used for forecasting products with historical data of analogous products. We have designed an application which combines data, analytics and domain knowledge using point-and-click technology. The forecasting results generated are compared using MAPE, MSE and RMSE error scores. Conclusion: Based on the results it is observed that no one approach is sufficient for short life-cycle forecasting and we need to combine two or more approaches for achieving the desired accuracy.

Keywords: forecast, short life cycle product, structured judgement, time series

Procedia PDF Downloads 358

Authors: Batuhan Kocaoglu

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Nowadays digital transformation is a hot topic both in service and production business. For the companies who want to stay alive in the following years, they should change how they do their business. Industry leaders started to improve their ERP (Enterprise Resource Planning) like backbone technologies to digital advances such as analytics, mobility, sensor-embedded smart devices, AI (Artificial Intelligence) and more. Selecting the appropriate technology for the related business problem also is a hot topic. Besides this, to operate in the modern environment and fulfill rapidly changing customer expectations, a digital transformation of the business is required and change the way the business runs, affect how they do their business. Even the digital transformation term is trendy the literature is limited and covers just the philosophy instead of a solid implementation plan. Current studies urge firms to start their digital transformation, but few tell us how to do. The huge investments scare companies with blur definitions and concepts. The aim of this paper to solidify the steps of the digital transformation and offer a roadmap for the companies and academicians. The proposed roadmap is developed based upon insights from the literature review, semi-structured interviews, and expert views to explore and identify crucial steps. We introduced our roadmap in the form of 8 main steps: Awareness; Planning; Operations; Implementation; Go-live; Optimization; Autonomation; Business Transformation; including a total of 11 sub-steps with examples. This study also emphasizes four dimensions of the digital transformation mainly: Readiness assessment; Building organizational infrastructure; Building technical infrastructure; Maturity assessment. Finally, roadmap corresponds the steps with three main terms used in digital transformation literacy as Digitization; Digitalization; and Digital Transformation. The resulted model shows that 'business process' and 'organizational issues' should be resolved before technology decisions and 'digitization'. Companies can start their journey with the solid steps, using the proposed roadmap to increase the success of their project implementation. Our roadmap is also adaptable for relevant Industry 4.0 and enterprise application projects. This roadmap will be useful for companies to persuade their top management for investments. Our results can be used as a baseline for further researches related to readiness assessment and maturity assessment studies.

Keywords: digital transformation, digital business, ERP, roadmap

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Authors: Chante Van Tonder, Bart Bossink, Chris Schachtebeck, Cecile Nieuwenhuizen

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Small and Medium-Sized Enterprises (SMEs) play a key role in national economies around the world, being contributors to economic and social well-being. Due to this, the success, growth and competitiveness of SMEs are critical. However, there are many factors that undermine this, such as resource constraints, poor information communication infrastructure (ICT), skills shortages and poor management. The Fourth Industrial Revolution offers new tools and opportunities such as digital transformation and business model innovation (BMI) to the SME sector to enhance its competitiveness. Adopting and leveraging digital technologies such as cloud, mobile technologies, big data and analytics can significantly improve business efficiencies, value proposition and customer experiences. Digital transformation can contribute to the growth and competitiveness of SMEs. However, SMEs are lagging behind in the participation of digital transformation. Extant research lacks conceptual and empirical research on how digital transformation drives BMI and the impact it has on the growth and competitiveness of SMEs. The purpose of the study is, therefore, to close this gap by developing and empirically validating a conceptual model to determine if SMEs are achieving BMI through digital transformation and how this is impacting the growth, competitiveness and overall business performance. An empirical study is being conducted on 300 SMEs, consisting of 150 South-African and 150 Dutch SMEs, to achieve this purpose. Structural equation modeling is used, since it is a multivariate statistical analysis technique that is used to analyse structural relationships and is a suitable research method to test the hypotheses in the model. Empirical research is needed to gather more insight into how and if SMEs are digitally transformed and how BMI can be driven through digital transformation. The findings of this study can be used by SME business owners, managers and employees at all levels. The findings will indicate if digital transformation can indeed impact the growth, competitiveness and overall performance of an SME, reiterating the importance and potential benefits of adopting digital technologies. In addition, the findings will also exhibit how BMI can be achieved in light of digital transformation. This study contributes to the body of knowledge in a highly relevant and important topic in management studies by analysing the impact of digital transformation on BMI on a large number of SMEs that are distinctly different in economic and cultural factors

Keywords: business models, business model innovation, digital transformation, SMEs

Procedia PDF Downloads 240

Authors: C. J. Rossouw, T. I. van Niekerk

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The global manufacturing industry is utilizing the internet and cloud-based services to further explore the anatomy and optimize manufacturing processes in support of the movement into the Fourth Industrial Revolution (4IR). The 4IR from a third world and African perspective is hindered by the fact that many manufacturing systems that were developed in the third industrial revolution are not inherently equipped to utilize the internet and services of the 4IR, hindering the progression of third world manufacturing industries into the 4IR. This research focuses on the development of a non-invasive and cost-effective cyber-physical IoT system that will exploit a machine’s vibration to expose semantic characteristics in the manufacturing process and utilize these results through a real-time cloud-based machine condition monitoring system with the intention to optimize the system. A microcontroller-based IoT sensor was designed to acquire a machine’s mechanical vibration data, process it in real-time, and transmit it to a cloud-based platform via Wi-Fi and the internet. Time-frequency Fourier analysis was applied to the vibration data to form an image representation of the machine’s behaviour. This data was used to train a Convolutional Neural Network (CNN) to learn semantic characteristics in the machine’s behaviour and relate them to a state of operation. The same data was also used to train a Convolutional Autoencoder (CAE) to detect anomalies in the data. Real-time edge-based artificial intelligence was achieved by deploying the CNN and CAE on the sensor to analyse the vibration. A cloud platform was deployed to visualize the vibration data and the results of the CNN and CAE in real-time. The cyber-physical IoT system was deployed on a semi-automated metal granulation machine with a set of trained machine learning models. Using a single sensor, the system was able to accurately visualize three states of the machine’s operation in real-time. The system was also able to detect a variance in the material being granulated. The research demonstrates how non-IoT manufacturing systems can be equipped with edge-based artificial intelligence to establish a remote machine condition monitoring system.

Keywords: IoT, cyber-physical systems, artificial intelligence, manufacturing, vibration analytics, continuous machine condition monitoring

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Authors: Ali Al-Shehri, Abdulaziz Al-Qasim, Abdulkarim Sofi, Ali Yousef

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The oil and gas industry has played a major role in improving the prosperity of mankind and driving the world economy. According to the International Energy Agency (IEA) and Integrated Environmental Assessment (EIA) estimates, the world will continue to rely heavily on hydrocarbons for decades to come. This growing energy demand mandates taking sustainability measures to prolong the availability of reliable and affordable energy sources, and ensure lowering its environmental impact. Unlike any other industry, the oil and gas upstream operations are energy-intensive and scattered over large zonal areas. These challenging conditions require unique sustainability solutions. In recent years there has been a concerted effort by the oil and gas industry to develop and deploy innovative technologies to: maximize efficiency, reduce carbon footprint, reduce CO2 emissions, and optimize resources and material consumption. In the past, the main driver for research and development (R&D) in the exploration and production sector was primarily driven by maximizing profit through higher hydrocarbon recovery and new discoveries. Environmental-friendly and sustainable technologies are increasingly being deployed to balance sustainability and profitability. Analyzing technology and its sustainability impact is increasingly being used in corporate decision-making for improved portfolio management and allocating valuable resources toward technology R&D.This paper articulates and discusses a novel workflow to identify strategic sustainable technologies for improved portfolio management by addressing existing and future upstream challenges. It uses a systematic approach that relies on sustainability key performance indicators (KPI’s) including energy efficiency quotient, carbon footprint, and CO2 emissions. The paper provides examples of various technologies including CCS, reducing water cuts, automation, using renewables, energy efficiency, etc. The use of 4IR technologies such as Artificial Intelligence, Machine Learning, and Data Analytics are also discussed. Overlapping technologies, areas of collaboration and synergistic relationships are identified. The unique sustainability analyses provide improved decision-making on technology portfolio management.

Keywords: sustainability, oil& gas, technology portfolio, key performance indicator

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Authors: Ahmad Haidar

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This research paper investigates the concept of modern management, concentrating on the development of managerial practices and the adoption of innovative strategies in response to the fast-changing business landscape caused by Artificial Intelligence (AI). The study begins by examining the historical context of management theories, tracing the progression from classical to contemporary models, and identifying key drivers of change. Through a comprehensive review of existing literature and case studies, this paper provides valuable insights into the principles and practices of modern management, offering a roadmap for organizations aiming to navigate the complexities of the contemporary business world. The paper examines the growing role of digital technology in modern management, focusing on incorporating AI, machine learning, and data analytics to streamline operations and facilitate informed decision-making. Moreover, the research highlights the emergence of new principles, such as adaptability, flexibility, public participation, trust, transparency, and digital mindset, as crucial components of modern management. Also, the role of business leaders is investigated by studying contemporary leadership styles, such as transformational, situational, and servant leadership, emphasizing the significance of emotional intelligence, empathy, and collaboration in fostering a healthy organizational culture. Furthermore, the research delves into the crucial role of environmental sustainability, corporate social responsibility (CSR), and corporate digital responsibility (CDR). Organizations strive to balance economic growth with ethical considerations and long-term viability. The primary research question for this study is: "What are the key principles, practices, and innovations that define modern management, and how can organizations effectively implement these strategies to thrive in the rapidly changing business landscape?." The research contributes to a comprehensive understanding of modern management by examining its historical context, the impact of digital technologies, the importance of contemporary leadership styles, and the role of CSR and CDR in today's business landscape.

Keywords: modern management, digital technology, leadership styles, adaptability, innovation, corporate social responsibility, organizational success, corporate digital responsibility

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Authors: Raj Mukta Sundaram

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The location of Ayodhya is distinctly described along river Sarayu in the epic Ramayan. On the contrary, even elaborate descriptions of Lanka and its environs are still proving elusive to human ingenuity to find a direct correlation on the ground. His-torically, there were hardly any attempts to locate Lanka, but some speculations have been made very recently, of which Sri Lanka has gained widespread public ac-ceptance for obvious reasons, such as Sri and Lanka. This belief is almost secured by the impression of Ram Setu on the satellite images, which has led the government to initiate a scientific mission to determine its age. In fact, other viewpoints believe Lanka to be somewhere far-flung along the equator, and another has long proclaimed it to be in central regions of India, but both are diminished by contemporary belief. This study emanates from the fact that Sri Lanka has no correlation to epic, and more importantly, satellite images are deceptive. So the objectives are twofold - firstly, to interpret the text from a holistic approach by analyzing the ecosystem, settlements, geological as-pects, and most importantly, the timeline of key events. Secondly, it explains the pit-falls in the rationale behind contemporary belief. At the outset, it categorically rejects the notion of Ram Setu, which, in geological terms, is merely a part of the continental shelf developed millions of years ago. It also refutes the misconception created by the word “Sri,” which is, in fact, an official name adopted by the country in the seventies with no correlation whatsoever with the events of Ramayana. Likewise, the study ar-gues for the establishment of a prosperous kingdom on a remote island with adverse climatic conditions for any civilization at that time. Eventually, the study demonstrates that travel time for the distances covered by Lord Rama does not corroborate with the description in the epic. It all leads to one conclusion that Lanka cannot be in Sri Lanka. Rather, it needs to be somewhere in the central-eastern parts of India. That region jus-tifies the environs and timelines for the journeys undertaken by Lord Rama, besides the fact that the tribes of the region show strong allegiance to Ravana. The study strongly recommends looking into the central-east region of India for the golden abode of a demon king and rejuvenating tourism of a scenic and culturally rich region hitherto marred by disturbances.

Keywords: spatial analysis, Ramayan, heritage, tourism

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Authors: Helena Baffoe

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Context: In the United States, there have been significant advancements in programs aimed at improving the lives of individuals with mental health disorders and substance abuse problems. However, public attitudes and beliefs regarding these issues have not improved correspondingly. This study aims to explore the perceptions and beliefs surrounding mental health disorders and substance abuse in the context of data analytics in the field of social work. Research Aim: The aim of this research is to provide empirical evidence on the beliefs and perceptions regarding mental health disorders and substance abuse. Specifically, the study seeks to answer the question of whether being diagnosed with a mental disorder implies a diagnosis of substance abuse. Additionally, the research aims to analyze the specific roles that social workers can play in addressing individuals with mental disorders. Methodology: This research adopts a data-driven methodology, acquiring comprehensive data from the Substance Abuse and Mental Health Services Administration (SAMHSA). A noteworthy causal connection between mental disorders and substance abuse exists, a relationship that current literature tends to overlook critically. To address this gap, we applied logistic regression with an Instrumental Variable approach, effectively mitigating potential endogeneity issues in the analysis in order to ensure robust and unbiased results. This methodology allows for a rigorous examination of the relationship between mental disorders and substance abuse. Empirical Findings: The analysis of the data reveals that depressive, anxiety, and trauma/stressor mental disorders are the most common in the United States. However, the study does not find statistically significant evidence to support the notion that being diagnosed with these mental disorders necessarily implies a diagnosis of substance abuse. This suggests that there is a misconception among the public regarding the relationship between mental health disorders and substance abuse. Theoretical Importance: The research contributes to the existing body of literature by providing empirical evidence to challenge prevailing beliefs and perceptions regarding mental health disorders and substance abuse. By using a novel methodological approach and analyzing new US data, the study sheds light on the cultural and social factors that influence these attitudes.

Keywords: mental health disorder, substance abuse, empirical evidence, logistic regression with IV

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Authors: Princess Ncube

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In the context of South Africa's banking sector, the prevention of bank failures is of paramount importance to ensure financial stability and economic growth. This paper focuses on the role of regulatory frameworks in safeguarding the resilience of South African banks and mitigating the risks of failures. It aims to assess the effectiveness of existing regulatory measures and proposes strategies to enhance the resilience of financial institutions in the country. The paper begins by examining the specific regulatory frameworks in place in South Africa, including capital adequacy requirements, stress testing methodologies, risk management guidelines, and supervisory practices. It delves into the evolution of these measures in response to lessons learned from past financial crises and their relevance in the unique South African banking landscape. Drawing on empirical evidence and case studies specific to South Africa, this paper evaluates the effectiveness of regulatory frameworks in preventing bank failures within the country. It analyses the impact of these frameworks on crucial aspects such as early detection of distress signals, improvements in risk management practices, and advancements in corporate governance within South African financial institutions. Additionally, it explores the interplay between regulatory frameworks and the specific economic environment of South Africa, including the role of macroprudential policies in preventing systemic risks. Based on the assessment, this paper proposes recommendations to strengthen regulatory frameworks and enhance their effectiveness in bank failure prevention in South Africa. It explores avenues for refining existing regulations to align capital requirements with the risk profiles of South African banks, enhancing stress testing methodologies to capture specific vulnerabilities, and fostering better coordination among regulatory authorities within the country. Furthermore, it examines the potential benefits of adopting innovative approaches, such as leveraging technology and data analytics, to improve risk assessment and supervision in the South African banking sector.

Keywords: banks, resolution, liquidity, regulation

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Authors: C. Zimmermann, E. Lackner, M. Ebner

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Massive Open Online Courses (MOOCs) are increasingly popular learning hubs that are boasting considerable participant numbers, innovative technical features, and a multitude of instructional resources. Still, there is a high level of evidence showing that almost all MOOCs suffer from a declining frequency of participant activity and fairly low completion rates. In this paper, we would like to share the lessons learned in implementing several design patterns that have been suggested in order to foster participant activity. Our conclusions are based on experiences with the ‘Dr. Internet’ MOOC, which was created as an xMOOC to raise awareness for a more critical approach to online health information: participants had to diagnose medical case studies. There is a growing body of recommendations (based on Learning Analytics results from earlier xMOOCs) as to how the decline in participant activity can be alleviated. One promising focus in this regard is instructional design patterns, since they have a tremendous influence on the learner’s motivation, which in turn is a crucial trigger of learning processes. Since Medieval Age storytelling, micro-learning units and specific comprehensible, narrative structures were chosen to animate the audience to follow narration. Hence, MOOC participants are not likely to abandon a course or information channel when their curiosity is kept at a continuously high level. Critical aspects that warrant consideration in this regard include shorter course duration, a narrative structure with suspense peaks (according to the ‘storytelling’ approach), and a course schedule that is diversified and stimulating, yet easy to follow. All of these criteria have been observed within the design of the Dr. Internet MOOC: 1) the standard eight week course duration was shortened down to six weeks, 2) all six case studies had a special quiz format and a corresponding resolution video which was made available in the subsequent week, 3) two out of six case studies were split up in serial video sequences to be presented over the span of two weeks, and 4) the videos were generally scheduled in a less predictable sequence. However, the statistical results from the first run of the MOOC do not indicate any strong influences on the retention rate, so we conclude with some suggestions as to why this might be and what aspects need further consideration.

Keywords: case study, Dr. internet, experience, MOOCs, design patterns

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Authors: Ju Peng Poh, Jun Yu Charles Lee, Jonathan Chew Hoe Khoo

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The emergence of digital twin technology, a digital replica of physical world, has improved the real-time access to data from sensors about the performance of buildings. This digital transformation has opened up many opportunities to improve the management of the building by using the data collected to help monitor consumption patterns and energy leakages. One example is the integration of predictive models for anomaly detection. In this paper, we use the GAM (Generalised Additive Model) for the anomaly detection of Air Handling Units (AHU) power consumption pattern. There is ample research work on the use of GAM for the prediction of power consumption at the office building and nation-wide level. However, there is limited illustration of its anomaly detection capabilities, prescriptive analytics case study, and its integration with the latest development of digital twin technology. In this paper, we applied the general GAM modelling framework on the historical data of the AHU power consumption and cooling load of the building between Jan 2018 to Aug 2019 from an education campus in Singapore to train prediction models that, in turn, yield predicted values and ranges. The historical data are seamlessly extracted from the digital twin for modelling purposes. We enhanced the utility of the GAM model by using it to power a real-time anomaly detection system based on the forward predicted ranges. The magnitude of deviation from the upper and lower bounds of the uncertainty intervals is used to inform and identify anomalous data points, all based on historical data, without explicit intervention from domain experts. Notwithstanding, the domain expert fits in through an optional feedback loop through which iterative data cleansing is performed. After an anomalously high or low level of power consumption detected, a set of rule-based conditions are evaluated in real-time to help determine the next course of action for the facilities manager. The performance of GAM is then compared with other approaches to evaluate its effectiveness. Lastly, we discuss the successfully deployment of this approach for the detection of anomalous power consumption pattern and illustrated with real-world use cases.

Keywords: anomaly detection, digital twin, generalised additive model, GAM, power consumption, supervised learning

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Authors: John-Nelson Ekumah, Selorm Yao-Say Solomon Adade, Mingming Zhong, Yufan Sun, Qiufang Liang, Muhammad Safiullah Virk, Xorlali Nunekpeku, Nana Adwoa Nkuma Johnson, Bridget Ama Kwadzokpui, Xiaofeng Ren

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Enhancing starch gel strength and stability is crucial. However, traditional gel property assessment methods are destructive, time-consuming, and resource-intensive. Thus, understanding ethanol treatment effects on kudzu starch gel strength and developing a rapid, nondestructive gel strength assessment method is essential for optimizing the treatment process and ensuring product quality consistency. This study investigated the effects of different ethanol concentrations on the microstructure of kudzu starch gels using a comprehensive microstructural analysis. We also developed a nondestructive method for predicting gel strength and classifying treatment levels using near-infrared (NIR) spectroscopy, and advanced data analytics. Scanning electron microscopy revealed progressive network densification and pore collapse with increasing ethanol concentration, correlating with enhanced mechanical properties. NIR spectroscopy, combined with various variable selection methods (CARS, GA, and UVE) and modeling algorithms (PLS, SVM, and ELM), was employed to develop predictive models for gel strength. The UVE-SVM model demonstrated exceptional performance, with the highest R² values (Rc = 0.9786, Rp = 0.9688) and lowest error rates (RMSEC = 6.1340, RMSEP = 6.0283). Pattern recognition algorithms (PCA, LDA, and KNN) successfully classified gels based on ethanol treatment levels, achieving near-perfect accuracy. This integrated approach provided a multiscale perspective on ethanol-induced starch gel modification, from molecular interactions to macroscopic properties. Our findings demonstrate the potential of NIR spectroscopy, coupled with advanced data analysis, as a powerful tool for rapid, nondestructive quality assessment in starch gel production. This study contributes significantly to the understanding of starch modification processes and opens new avenues for research and industrial applications in food science, pharmaceuticals, and biomaterials.

Keywords: kudzu starch gel, near-infrared spectroscopy, gel strength prediction, support vector machine, pattern recognition algorithms, ethanol treatment

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Authors: Rachel M. Edelstein, John D. Van Horn, Karen M. Schmidt, Sydney N. Cushing

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As a consequence of sports-related concussions, female athletes have been documented as reporting more symptoms than their male counterparts, in addition to incurring longer periods of recovery. However, the role of sex and its potential influence on symptom reporting and recovery outcomes in concussion management has not been completely explored. The present aims to investigate the relationship between female concussion symptom severity and the presence of assessment bias. The Sport Concussion Assessment Tool 3 (SCAT3), collected by the NCAA and DoD CARE Consortium, was quantified at five different time points post-concussion. N= 1,258 NCAA athletes, n= 473 female (soccer, rugby, lacrosse, ice hockey) and n=785 male athletes (football, rugby, lacrosse, ice hockey). A polytomous Item Response Theory (IRT) Graded Response Model (GRM) was used to assess the relationship between sex and symptom reporting. Differential Item Functioning (DIF) and Differential Group Functioning (DGF) were used to examine potential group-level bias. Interactions for DIF were utilized to explore the impact of sex on symptom reporting among NCAA male and female athletes throughout and after their concussion recovery. DIF was significantly detected after B-H corrections displayed in limited items; however, one symptom, “Pressure in Head” (-0.29, p=0.04 vs -0.20, p =0.04), was statistically significant at both < 6 hours and 24-48 hours. Thus, implies that at < 6 hours, males were 29% less likely to indicate “Pressure in the Head” compared to female athletes and 20% less likely at 24-48 hours. Overall, the DGF suggested significant group differences, suggesting that male athletes might be at a higher risk for returning to play prematurely (logits = -0.38, p < 0.001). However, after analyzing the SCAT 3, a clinically relevant trend was discovered. Twelve out of the twenty-two symptoms suggest higher difficulty in female athletes within three or more of the five-time points. These symptoms include Balance Problems, Blurry Vision, Confusion, Dizziness, Don’t Feel Right, Feel in Fog, Feel Slow Down, Low Energy, Neck Pain, Sensitivity to Light, Sensitivity to Noise, Trouble Falling Asleep. Despite a lack of statistical significance, this tendency is contrary to current literature stating that males may be unclear on symptoms, but females may be more honest in reporting symptoms. Further research, which includes possible modifying socioecological factors, is needed to determine whether females may consistently experience more symptoms and require longer recovery times or if, parsimoniously, males tend to present their symptoms and readiness for play differently than females. Such research will help to improve the validity of current assumptions concerning male as compared to female head injuries and optimize individualized treatments for sports-related head injuries.

Keywords: female athlete, sports-related concussion, item response theory, concussion assessment

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Authors: Nicholas S. Norbert Jr., John E. Bischoff, Christopher J. Willy

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Knowledge Management (KM) is globally recognized as a crucial element in securing competitive advantage through building and maintaining organizational memory, codifying and protecting intellectual capital and business intelligence, and providing mechanisms for collaboration and innovation. KM frameworks and approaches have been developed and defined identifying critical success factors for conducting KM within numerous industries ranging from scientific to business, and for ranges of organization scales from small groups to large enterprises. However, engineering and technical teams operating within restricted environments are subject to unique barriers and KM challenges which cannot be directly treated using the approaches and tools prescribed for other industries. This research identifies barriers in conducting KM within Hardware Development Engineering (HDE) teams and statistically compares significance to barriers upholding the four KM pillars of organization, technology, leadership, and learning for HDE teams. HDE teams suffer from restrictions in knowledge sharing (KS) due to classification of information (national security risks), customer proprietary restrictions (non-disclosure agreement execution for designs), types of knowledge, complexity of knowledge to be shared, and knowledge seeker expertise. As KM evolved leveraging information technology (IT) and web-based tools and approaches from Web 1.0 to Enterprise 2.0, KM may also seek to leverage emergent tools and analytics including expert locators and hybrid recommender systems to enable KS across barriers of the technical teams. The research will test hypothesis statistically evaluating if KM barriers for HDE teams affect the general set of expected benefits of a KM System identified through previous research. If correlations may be identified, then generalizations of success factors and approaches may also be garnered for HDE teams. Expert elicitation will be conducted using a questionnaire hosted on the internet and delivered to a panel of experts including engineering managers, principal and lead engineers, senior systems engineers, and knowledge management experts. The feedback to the questionnaire will be processed using analysis of variance (ANOVA) to identify and rank statistically significant barriers of HDE teams within the four KM pillars. Subsequently, KM approaches will be recommended for upholding the KM pillars within restricted environments of HDE teams.

Keywords: engineering management, knowledge barriers, knowledge management, knowledge sharing

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

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

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

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Authors: Yu-Shun Elisa Pong

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Music aesthetics related to phenomenology is rarely discussed and still in the ascendant while multi-dimensional discourses of philosophy were emerged to be an important trend in the 20th century. In the present study, a basic theory of phenomenology from Edmund Husserl (1859-1938) is revealed and discussed followed by the introduction of intentionality concepts, eidetic reduction, horizon, world, and inter-subjectivity issues. Further, phenomenology of music and general art was brought to attention by the introduction of Roman Ingarden’s The Work of Music and the Problems of its Identity (1933) and Mikel Dufrenne’s The Phenomenology of Aesthetic Experience (1953). Finally, Debussy’s music will be analyzed and discussed from the perspective of phenomenology. Phenomenology is not so much a methodology or analytics rather than a common belief. That is, as much as possible to describe in detail the different human experience, relative to the object of purpose. Such idea has been practiced in various guises for centuries, only till the early 20th century Phenomenology was better refined through the works of Husserl, Heidegger, Sartre, Merleau-Ponty and others. Debussy was born in an age when the Western society began to accept the multi-cultural baptism. With his unusual sensitivity to the oriental culture, Debussy has presented considerable inspiration, absorption, and echo in his music works. In fact, his relationship with nature is far from echoing the idea of Chinese ancient literati and nature. Although he is not the first composer to associate music with human and nature, the unique quality and impact of his works enable him to become a significant figure in music aesthetics. Debussy’s music tried to develop a quality analogous of nature, and more importantly, based on vivid life experience and artistic transformation to achieve the realm of pure art. Such idea that life experience comes before artwork, either clear or vague, simple or complex, was later presented abstractly in his late works is still an interesting subject worth further discussion. Debussy’s music has existed for more than or close to a century. It has received musicology researcher’s attention as much as other important works in the history of Western music. Among the pluralistic discussion about Debussy’s art and ideas, phenomenological aesthetics has enlightened new ideas and view angles to relook his great works and even gave some previous arguments legitimacy. Overall, this article provides a new insight of Debussy’s music from phenomenological exploration and it is believed phenomenology would be an important pathway in the research of the music aesthetics.

Keywords: Debussy's music, music esthetics, oriental culture, phenomenology

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Authors: Sulemana Ibrahim

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Food security remains a paramount global challenge, with vulnerable regions grappling with issues of hunger and malnutrition. This study embarks on a comprehensive exploration of data-driven strategies aimed at ameliorating food security in such regions. Our research employs a multifaceted approach, integrating data analytics to predict crop yields, optimizing supply chains, and enhancing food distribution networks. The study unfolds as a multi-dimensional analysis, commencing with the development of robust machine learning models harnessing remote sensing data, historical crop yield records, and meteorological data to foresee crop yields. These predictive models, underpinned by convolutional and recurrent neural networks, furnish critical insights into anticipated harvests, empowering proactive measures to confront food insecurity. Subsequently, the research scrutinizes supply chain optimization to address food security challenges, capitalizing on linear programming and network optimization techniques. These strategies intend to mitigate loss and wastage while streamlining the distribution of agricultural produce from field to fork. In conjunction, the study investigates food distribution networks with a particular focus on network efficiency, accessibility, and equitable food resource allocation. Network analysis tools, complemented by data-driven simulation methodologies, unveil opportunities for augmenting the efficacy of these critical lifelines. This study also considers the ethical implications and privacy concerns associated with the extensive use of data in the realm of food security. The proposed methodology outlines guidelines for responsible data acquisition, storage, and usage. The ultimate aspiration of this research is to forge a nexus between data science and food security policy, bestowing actionable insights to mitigate the ordeal of food insecurity. The holistic approach converging data-driven crop yield forecasts, optimized supply chains, and improved distribution networks aspire to revitalize food security in the most vulnerable regions, elevating the quality of life for millions worldwide.

Keywords: data-driven strategies, crop yield prediction, supply chain optimization, food distribution networks

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Authors: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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Authors: Vaikunthan Rajaratnam

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Maintaining continuous professional development among clinicians has been a challenge. Hand surgery is a unique speciality with the coming together of orthopaedics, plastics and trauma surgeons. The requirements for a team-based approach to care with the inclusion of other experts such as occupational, physiotherapist and orthotic and prosthetist provide the impetus for the creation of communities of practice. This study analysed the community of practice in hand surgery that was created through a social networking website for professionals. The main objectives were to discover the usefulness of this community of practice created in the platform of the group function of LinkedIn. The second objective was to determine the usability of this platform for the purposes of continuing professional development among members of this community of practice. The methodology used was one of mixed methods which included a quantitative analysis on the usefulness of the social network website as a community of practice, using the analytics provided by the LinkedIn platform. Further qualitative analysis was performed on the various postings that were generated by the community of practice within the social network website. This was augmented by a respondent driven survey conducted online to assess the usefulness of the platform for continuous professional development. A total of 31 respondents were involved in this study. This study has shown that it is possible to create an engaging and interactive community of practice among hand surgeons using the group function of this professional social networking website LinkedIn. Over three years the group has grown significantly with members from multiple regions and has produced engaging and interactive conversations online. From the results of the respondents’ survey, it can be concluded that there was satisfaction of the functionality and that it was an excellent platform for discussions and collaboration in the community of practice with a 69 % of satisfaction. Case-based discussions were the most useful functions of the community of practice. This platform usability was graded as excellent using the validated usability tool. This study has shown that the social networking site LinkedIn’s group function can be easily used as a community of practice effectively and provides convenience to professionals and has made an impact on their practice and better care for patients. It has also shown that this platform was easy to use and has a high level of usability for the average healthcare professional. This platform provided the improved connectivity among professionals involved in hand surgery care which allowed for the community to grow and with proper support and contribution of relevant material by members allowed for a safe environment for the exchange of knowledge and sharing of experience that is the foundation of a community practice.

Keywords: community of practice, online community, hand surgery, lifelong learning, LinkedIn, social media, continuing professional development

Procedia PDF Downloads 316

Authors: Brenagh E. Schlingermann, Clare Lodge, Paula Rankin

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Background: Gaelic football, hurling and camogie are highly popular field games in Ireland. Research into the epidemiology of injury in Gaelic games revealed that approximately three quarters of the injuries in the games occur in the lower extremity. These injuries can have player, team and institutional impacts due to multiple factors including financial burden and time loss from competition. Research has shown it is possible to record injury data consistently with the GAA through a closed online recording system known as the GAA injury surveillance database. It has been established that determining the incidence of injury is the first step of injury prevention. The goals of this study were to create a dynamic GAA15 injury prevention programme which addressed five key components/goals; avoid positions associated with a high risk of injury, enhance flexibility, enhance strength, optimize plyometrics and address sports specific agilities. These key components are internationally recognized through the Prevent Injury, Enhance performance (PEP) programme which has proven reductions in ACL injuries by 74%. In national Gaelic games the programme is known as the GAA15 which has been devised from the principles of the PEP. No such injury prevention strategies have been published on this cohort in Gaelic games to date. This study will investigate the effects of the GAA15 on injury incidence and neuromuscular function in Gaelic games. Methods: A total of 154 players (mean age 20.32 ± 2.84) were recruited from the GAA teams within the Institute of Technology Carlow (ITC). Preseason and post season testing involved two objective screening tests; Y balance test and Three Hop Test. Practical workshops, with ongoing liaison, were provided to the coaches on the implementation of the GAA15. The programme was performed before every training session and game and the existing GAA injury surveillance database was accessed to monitor player’s injuries by the college sports rehabilitation athletic therapist. Retrospective analysis of the ITC clinic records were performed in conjunction with the database analysis as a means of tracking injuries that may have been missed. The effects of the programme were analysed by comparing the intervention groups Y balance and three hop test scores to an age/gender matched control group. Results: Year 1 results revealed significant increases in neuromuscular function as a result of the GAA15. Y Balance test scores for the intervention group increased in both the posterolateral (p=.005 and p=.001) and posteromedial reach directions (p= .001 and p=.001). A decrease in performance was determined for the three hop test (p=.039). Overall twenty-five injuries were reported during the season resulting in an injury rate of 3.00 injuries/1000hrs of participation; 1.25 injuries/1000hrs training and 4.25 injuries/1000hrs match play. Non-contact injuries accounted for 40% of the injuries sustained. Year 2 results are pending and expected April 2016. Conclusion: It is envisaged that implementation of the GAA15 will continue to reduce the risk of injury and improve neuromuscular function in collegiate Gaelic games athletes.

Keywords: GAA15, Gaelic games, injury prevention, neuromuscular training

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Authors: Dangut Maren David, Skaf Zakwan

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Adequate monitoring of vehicle component in other to obtain high uptime is the goal of predictive maintenance, the major challenge faced by businesses in industries is the significant cost associated with a delay in service delivery due to system downtime. Most of those businesses are interested in predicting those problems and proactively prevent them in advance before it occurs, which is the core advantage of Prognostic Health Management (PHM) application. The recent emergence of industry 4.0 or industrial internet of things (IIoT) has led to the need for monitoring systems activities and enhancing system-to-system or component-to- component interactions, this has resulted to a large generation of data known as big data. Analysis of big data represents an increasingly important, however, due to complexity inherently in the dataset such as imbalance classification problems, it becomes extremely difficult to build a model with accurate high precision. Data-driven predictive modeling for condition-based maintenance (CBM) has recently drowned research interest with growing attention to both academics and industries. The large data generated from industrial process inherently comes with a different degree of complexity which posed a challenge for analytics. Thus, imbalance classification problem exists perversely in industrial datasets which can affect the performance of learning algorithms yielding to poor classifier accuracy in model development. Misclassification of faults can result in unplanned breakdown leading economic loss. In this paper, an advanced approach for handling imbalance classification problem is proposed and then a prognostic model for predicting aircraft component replacement is developed to predict component replacement in advanced by exploring aircraft historical data, the approached is based on hybrid ensemble-based method which improves the prediction of the minority class during learning, we also investigate the impact of our approach on multiclass imbalance problem. We validate the feasibility and effectiveness in terms of the performance of our approach using real-world aircraft operation and maintenance datasets, which spans over 7 years. Our approach shows better performance compared to other similar approaches. We also validate our approach strength for handling multiclass imbalanced dataset, our results also show good performance compared to other based classifiers.

Keywords: prognostics, data-driven, imbalance classification, deep learning

Procedia PDF Downloads 174

Authors: Harry B. Santoso, Lia Sadita, Firlia Sandyta, Musa Alfatih, Nove Spalo, Nu'man Naufal, Nuryahya P. Utomo, Putu A. Paramatha, Rezka Aufar Leonandya, Tommy Anugrah, Aulia Chairunisa, M. Fadly Uzzaki, Riandy D. Banimahendra

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The numbers of Internet active users in Indonesia reach out over 88.1 million, where 48% of them are daily active users. Seeing these numbers, it is the best opportunity for IT companies to grow their business, especially e-Commerce. In fact, the growth of e-Commerce companies in Indonesia is proportional with internet daily active users. This phenomenon shows that competition happening among the e-Commerce companies is raising high. It triggers many e-Commerce companies to improve their services. The authors hypothesized that one of the best ways to improve the services is by improving their usability. So, the authors had done a study to evaluate and find out ways to improve usability of those e-Commerce websites. The authors chose four e-Commerce websites which each of them has different business focus and profiles. Each company is labeled as A, B, C, and D. Company A is a fashion-based e-Commerce services with two-million desktop visits Indonesia. Company B is an international online shopping mall for everyday appliances with 48,3-million desktop visits in Indonesia. Company C is a localized online shopping mall with 3,2-million desktop visits in Indonesia. Company D is an online shopping mall with one-million desktop visits in Indonesia. Writers used popular web traffic analytics platform to gain the numbers. There are some approaches to evaluate the usability of e-Commerce websites. In this study, the authors used usability testing method supported by the User Experience Questionnaire. This method involved the user in interacting directly with the services provided by the e-Commerce company. This study was conducted within two months including preparation, data collection, data analysis, and reporting. We used a pair of computers, a screen-capture video application named Smartboard, and User Experience Questionnaire. A team was built to conduct this study. They consisted of one supervisor, two assistants, four facilitators and four observers. For each e-Commerce, three users aged 17-25 years old were invited to do five task scenarios. Data collected in this study included demographic information of the users, usability testing results, and users’ responses to the questionnaire. Some findings were revealed from the usability testing and the questionnaire. Compared to the other three companies, Company D had the least score for the experiences. One of the most painful issues figured out by the authors from the evaluation was most users claimed feeling confused by user interfaces in these e-Commerce websites. We believe that this study will help e-Commerce companies to improve their services and business in the future.

Keywords: e-commerce, evaluation, usability testing, user experience

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Authors: Michael Mihalicz, Aziz Guergachi

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Background: The science of modelling choice preferences has evolved over centuries into an interdisciplinary field contributing to several branches of Microeconomics and Mathematical Psychology. Early theories in Decision Science rested on the logic of rationality, but as it and related fields matured, descriptive theories emerged capable of explaining systematic violations of rationality through cognitive mechanisms underlying the thought processes that guide human behaviour. Cognitive limitations are not, however, solely responsible for systematic deviations from rationality and many are now exploring the effect of visceral factors as the more dominant drivers. The current study builds on the existing literature by exploring sleep deprivation, thermal comfort, stress, hunger, fear, anger and sadness as moderators to three distinct elements that define individual risk preference under Cumulative Prospect Theory. Methodology: This study is designed to compare the risk preference of participants experiencing an elevated affective or visceral state to those in a neutral state using nonparametric elicitation methods across three domains. Two experiments will be conducted simultaneously using different methodologies. The first will determine visceral states and risk preferences randomly over a two-week period by prompting participants to complete an online survey remotely. In each round of questions, participants will be asked to self-assess their current state using Visual Analogue Scales before answering a series of lottery-style elicitation questions. The second experiment will be conducted in a laboratory setting using psychological primes to induce a desired state. In this experiment, emotional states will be recorded using emotion analytics and used a basis for comparison between the two methods. Significance: The expected results include a series of measurable and systematic effects on the subjective interpretations of gamble attributes and evidence supporting the proposition that a portion of the variability in human choice preferences unaccounted for by cognitive limitations can be explained by interacting visceral states. Significant results will promote awareness about the subconscious effect that emotions and other drive states have on the way people process and interpret information, and can guide more effective decision making by informing decision-makers of the sources and consequences of irrational behaviour.

Keywords: decision making, emotions, prospect theory, visceral factors

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Authors: Ramin Mehdiabadi, Neda Menbari, Mohammad Nazir Menbari

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As a pressing public health concern, breast cancer stands as the predominant oncological diagnosis and principal cause of cancer-related mortality among women globally, accounting for 11.7% of new cancer incidences and 6.9% of cancer-related deaths. The annual figures indicate that approximately 230,480 women are diagnosed with breast cancer in the United States alone, with 39,520 succumbing to the disease. While developed economies have reported a deceleration in both incidence and mortality rates across various forms of cancer, including breast cancer, emerging and low-income economies manifest a contrary escalation, largely attributable to lifestyle-mediated risk factors such as tobacco usage, physical inactivity, and high caloric intake. Breast cancer is distinctly characterized by molecular heterogeneity, manifesting in specific subtypes delineated by biomarkers—Estrogen Receptors (ER), Progesterone Receptors (PR), and Human Epidermal Growth Factor Receptor 2 (HER2). These subtypes, comprising Luminal A, Luminal B, HER2-enriched, triple-negative/basal-like, and normal-like, necessitate nuanced, subtype-specific therapeutic regimens, thereby challenging the applicability of generalized treatment protocols. Within this molecular complexity, the transcription factor Forkhead Box M1 (FoxM1) has garnered attention as a significant driver of cellular proliferation, tumorigenesis, metastatic progression, and treatment resistance in a spectrum of human malignancies, including breast cancer. Concurrently, microRNAs (miRs), specifically miR-216b-5p, have been identified as post-transcriptional gene expression regulators and potential tumor suppressors. The overarching objective of this academic investigation is to explicate the multifaceted interrelationship between FoxM1 and miR-216b-5p across the disparate molecular subtypes of breast cancer. Employing a methodologically rigorous, interdisciplinary research design that incorporates cutting-edge molecular biology techniques, sophisticated bioinformatics analytics, and exhaustive meta-analyses of extant clinical data, this scholarly endeavor aims to unveil novel biomarker-specific therapeutic pathways. By doing so, this research is positioned to make a seminal contribution to the advancement of personalized, efficacious, and minimally toxic treatment paradigms, thus profoundly impacting the global efforts to ameliorate the burden of breast cancer.

Keywords: breast cancer, fox m1, microRNAs, mir-216b-5p, gene expression

Procedia PDF Downloads 74