Search results for: open-source intelligence

Authors: B. Lefranc, C. Aufauvre-Poupon, C. Martin-Krumm, M. Trousselard

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Isolated and confined environments (ICE) present several challenges which may adversely affect human’s psychology and physiology. Submariners in Sub-Surface Ballistic Nuclear (SSBN) mission exposed to these environmental constraints must be able to perform complex tasks as part of their normal duties, as well as during crisis periods when emergency actions are required or imminent. The operational and environmental constraints they face contribute to challenge human adaptability. The impact of such a constrained environment has yet to be explored. Establishing a knowledge framework is a determining factor, particularly in view of the next long space travels. Ensuring that the crews are maintained in optimal operational conditions is a real challenge because the success of the mission depends on them. This study focused on the evaluation of the impact of stress on mental health and sensory degradation of submariners during a mission on SSBN using cardiac biosignal (heart rate variability, HRV) clustering. This is a pragmatic exploratory study of a prospective cohort included 19 submariner volunteers. HRV was recorded at baseline to classify by clustering the submariners according to their stress level based on parasympathetic (Pa) activity. Impacts of high Pa (HPa) versus low Pa (LPa) level at baseline were assessed on emotional state and sensory perception (interoception and exteroception) as a cardiac biosignal during the patrol and at a recovery time one month after. Whatever the time, no significant difference was found in mental health between groups. There are significant differences in the interoceptive, exteroceptive and physiological functioning during the patrol and at recovery time. To sum up, compared to the LPa group, the HPa maintains a higher level in psychosensory functioning during the patrol and at recovery but exhibits a decrease in Pa level. The HPa group has less adaptable HRV characteristics, less unpredictability and flexibility of cardiac biosignals while the LPa group increases them during the patrol and at recovery time. This dissociation between psychosensory and physiological adaptation suggests two treatment modalities for ICE environments. To our best knowledge, our results are the first to highlight the impact of physiological differences in the HRV profile on the adaptability of submariners. Further studies are needed to evaluate the negative emotional and cognitive effects of ICEs based on the cardiac profile. Artificial intelligence offers a promising future for maintaining high level of operational conditions. These future perspectives will not only allow submariners to be better prepared, but also to design feasible countermeasures that will help support analog environments that bring us closer to a trip to Mars.

Keywords: adaptation, exteroception, HRV, ICE, interoception, SSBN

Procedia PDF Downloads 182

Authors: Adam Gushgari

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The SARS-CoV-2 pandemic has catalyzed the rapid adoption of wastewater-based epidemiology (WBE) methodologies both domestically and internationally. To support the rapid scale-up of pandemic-response wastewater surveillance systems, multiple federal agencies (i.e. US CDC), non-government organizations (i.e. Water Environment Federation), and private charities (i.e. Bill and Melinda Gates Foundation) have funded over $220 million USD supporting development and expanding equitable access of surveillance methods. Funds were primarily distributed directly to municipalities under the CARES Act (90.6%), followed by academic projects (7.6%), and initiatives developed by private companies (1.8%). In addition to federal funding for wastewater monitoring primarily conducted at wastewater treatment plants, state/local governments and private companies have leveraged wastewater sampling to obtain health and lifestyle data on student, prison inmate, and employee populations. We explore the viable paths for expansion of the WBE m1ethodology across a variety of analytical methods; the development of WBE-specific samplers and real-time wastewater sensors; and their application to various governments and private sector industries. Considerable investment in, and public acceptance of WBE suggests the methodology will be applied to other future notifiable diseases and health risks. Early research suggests that WBE methods can be applied to a host of additional “biological insults” including communicable diseases and pathogens, such as influenza, Cryptosporidium, Giardia, mycotoxin exposure, hepatitis, dengue, West Nile, Zika, and yellow fever. Interest in chemical insults is also likely, providing community health and lifestyle data on narcotics consumption, use of pharmaceutical and personal care products (PPCP), PFAS and hazardous chemical exposure, and microplastic exposure. Successful application of WBE to monitor analytes correlated with carcinogen exposure, community stress prevalence, and dietary indicators has also been shown. Additionally, technology developments of in situ wastewater sensors, WBE-specific wastewater samplers, and integration of artificial intelligence will drastically change the landscape of WBE through the development of “smart sewer” networks. The rapid expansion of the WBE field is creating significant business opportunities for professionals across the scientific, engineering, and technology industries ultimately focused on community health improvement.

Keywords: wastewater surveillance, wastewater-based epidemiology, smart cities, public health, pandemic management, substance abuse

Procedia PDF Downloads 108

Authors: Elaheh Talebi, Fariba Yavari, Lucy Philip, Lesley Town

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The mining industry generates vast amounts of data, necessitating robust data management systems and advanced analytics tools to achieve better decision-making processes in the development of mining production and maintaining safety. This paper highlights the advantages of Power BI, a powerful intelligence tool, over traditional Excel-based approaches for effectively managing and harnessing mining data. Power BI enables professionals to connect and integrate multiple data sources, ensuring real-time access to up-to-date information. Its interactive visualizations and dashboards offer an intuitive interface for exploring and analyzing geotechnical data. Advanced analytics is a collection of data analysis techniques to improve decision-making. Leveraging some of the most complex techniques in data science, advanced analytics is used to do everything from detecting data errors and ensuring data accuracy to directing the development of future project phases. However, while Power BI is a robust tool, specific visualizations required by geotechnical engineers may have limitations. This paper studies the capability to use Python or R programming within the Power BI dashboard to enable advanced analytics, additional functionalities, and customized visualizations. This dashboard provides comprehensive tools for analyzing and visualizing key geotechnical data metrics, including spatial representation on maps, field and lab test results, and subsurface rock and soil characteristics. Advanced visualizations like borehole logs and Stereonet were implemented using Python programming within the Power BI dashboard, enhancing the understanding and communication of geotechnical information. Moreover, the dashboard's flexibility allows for the incorporation of additional data and visualizations based on the project scope and available data, such as pit design, rock fall analyses, rock mass characterization, and drone data. This further enhances the dashboard's usefulness in future projects, including operation, development, closure, and rehabilitation phases. Additionally, this helps in minimizing the necessity of utilizing multiple software programs in projects. This geotechnical dashboard in Power BI serves as a user-friendly solution for analyzing, visualizing, and communicating both new and historical geotechnical data, aiding in informed decision-making and efficient project management throughout various project stages. Its ability to generate dynamic reports and share them with clients in a collaborative manner further enhances decision-making processes and facilitates effective communication within geotechnical projects in the mining industry.

Keywords: geotechnical data analysis, power BI, visualization, decision-making, mining industry

Procedia PDF Downloads 92

Authors: Soowon Park, Min-Ji Kim, Jun-Young Lee

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Motivation: The stigma of mental illness has been studied in many disciplines, including social psychology, counseling psychology, sociology, psychiatry, public health care, and related areas, because individuals labeled as ‘mentally ill’ are often deprived of their rights and their life opportunities. To understand the factors that deepen the stigma of mental illness, it is important to understand the influencing factors of the stigma. Problem statement: Depression is a common disorder in adults, but the incidence of help-seeking is low. Researchers have believed that this poor help-seeking behavior is related to the stigma of mental illness, which results from low mental health literacy. However, it is uncertain that increasing mental health literacy decreases mental health stigmatization. Furthermore, even though decreasing stigmatization is important, the stigma of mental illness is still a stable and long-lasting phenomenon. Thus, factors other than knowledge about mental disorders have the power to maintain the stigma. Investigating the influencing factors that facilitate the stigma of psychiatric disease could help lower the social stigmatization. Approach: Face-to-face interviews were conducted with a multi-clustering sample. A total of 700 Korean participants (38% male), ranging in age from 18 to 78 (M(SD)age= 48.5(15.7)) answered demographical questions, Korean version of Link’s Perceived Devaluation and Discrimination (PDD) scale for the assessment of social stigmatization against depression, and the Korean version of the WHO-Composite International Diagnostic Interview for the assessment of mental disorders. Multiple-regression was conducted to find the predicting factors of social stigmatization against depression. Ages, sex, years of education, income, living location, and experience of mental illness were used as the predictors. Results: Predictors accounted for 14% of the variance in the stigma of depressive disorders (F(6, 693) = 20.27, p < .001). Among those, only age, years of education, and experience of mental illness significantly predicted social stigmatization against depression. The standardized regression coefficient of age had a negative association with stigmatization (β = -.20, p < .001), but years of education (β = .20, p < .001) and experience of mental illness (β = .08, p < .05) positively predicted depression stigmatization. Conclusions: The present study clearly demonstrates the association between personal factors and depressive disorder stigmatization. Younger age, more education, and self-stigma appeared to increase the stigmatization. Young, highly educated, and mentally ill people tend to reject patients with depressive disorder as friends, teachers, or babysitters; they also tend to think that those patients have lower intelligence and abilities. These results suggest the possibility that people from a high social class, or highly educated people, who have the power to make decisions, help maintain the social stigma against mental illness patients. To increase the awareness that people from high social classes have more stigmatization against depressive disorders will help decrease the biased attitudes against mentally ill patients.

Keywords: depressive disorder stigmatization, age, education, self-stigma

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Authors: Marcos Silva Tavares, Jamile Raquel Regazzo, Edson José de Souza Sardinha, Murilo Mesquita Baesso

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Common beans are a widely cultivated and consumed legume globally, serving as a staple food for humans, especially in developing countries, due to their nutritional characteristics. Nitrogen (N) is the most limiting nutrient for productivity, and foliar analysis is crucial to ensure balanced nitrogen fertilization. Excessive N applications can cause, either isolated or cumulatively, soil and water contamination, plant toxicity, and increase their susceptibility to diseases and pests. However, the quantification of N using conventional methods is time-consuming and costly, demanding new technologies to optimize the adequate supply of N to plants. Thus, it becomes necessary to establish constant monitoring of the foliar content of this macronutrient in plants, mainly at the V4 stage, aiming at precision management of nitrogen fertilization. In this work, the objective was to evaluate the performance of a deep learning model, Resnet-50, in the classification of foliar nitrogen in common beans using RGB images. The BRS Estilo cultivar was sown in a greenhouse in a completely randomized design with four nitrogen doses (T1 = 0 kg N ha-1, T2 = 25 kg N ha-1, T3 = 75 kg N ha-1, and T4 = 100 kg N ha-1) and 12 replications. Pots with 5L capacity were used with a substrate composed of 43% soil (Neossolo Quartzarênico), 28.5% crushed sugarcane bagasse, and 28.5% cured bovine manure. The water supply of the plants was done with 5mm of water per day. The application of urea (45% N) and the acquisition of images occurred 14 and 32 days after sowing, respectively. A code developed in Matlab© R2022b was used to cut the original images into smaller blocks, originating an image bank composed of 4 folders representing the four classes and labeled as T1, T2, T3, and T4, each containing 500 images of 224x224 pixels obtained from plants cultivated under different N doses. The Matlab© R2022b software was used for the implementation and performance analysis of the model. The evaluation of the efficiency was done by a set of metrics, including accuracy (AC), F1-score (F1), specificity (SP), area under the curve (AUC), and precision (P). The ResNet-50 showed high performance in the classification of foliar N levels in common beans, with AC values of 85.6%. The F1 for classes T1, T2, T3, and T4 was 76, 72, 74, and 77%, respectively. This study revealed that the use of RGB images combined with deep learning can be a promising alternative to slow laboratory analyses, capable of optimizing the estimation of foliar N. This can allow rapid intervention by the producer to achieve higher productivity and less fertilizer waste. Future approaches are encouraged to develop mobile devices capable of handling images using deep learning for the classification of the nutritional status of plants in situ.

Keywords: convolutional neural network, residual network 50, nutritional status, artificial intelligence

Procedia PDF Downloads 19

Authors: Beatrice Arthur

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The rapid development of artificial intelligence (AI) has transformed various industries, enabling faster and more accurate decision-making processes. However, with these advancements come increased risks, including data privacy issues, systemic biases, and challenges related to transparency and accountability. As AI technologies become more integrated into business processes, there is a growing need for comprehensive auditing and assurance frameworks to manage these risks and ensure ethical use. This paper provides a literature review on AI auditing and assurance programs, highlighting the importance of adapting traditional audit methodologies to the complexities of AI-driven systems. Objective: The objective of this review is to explore current AI audit practices and their role in mitigating risks, ensuring accountability, and fostering trust in AI systems. The study aims to provide a structured framework for developing audit programs tailored to AI technologies while also investigating how AI impacts governance, risk management, and regulatory compliance in various sectors. Methodology: This research synthesizes findings from academic publications and industry reports from 2014 to 2024, focusing on the intersection of AI technologies and IT assurance practices. The study employs a qualitative review of existing audit methodologies and frameworks, particularly the COBIT 2019 framework, to understand how audit processes can be aligned with AI governance and compliance standards. The review also considers real-time auditing as an emerging necessity for influencing AI system design during early development stages. Outcomes: Preliminary findings indicate that while AI auditing is still in its infancy, it is rapidly gaining traction as both a risk management strategy and a potential driver of business innovation. Auditors are increasingly being called upon to develop controls that address the ethical and operational risks posed by AI systems. The study highlights the need for continuous monitoring and adaptable audit techniques to handle the dynamic nature of AI technologies. Future Directions: Future research will explore the development of AI-specific audit tools and real-time auditing capabilities that can keep pace with evolving technologies. There is also a need for cross-industry collaboration to establish universal standards for AI auditing, particularly in high-risk sectors like healthcare and finance. Further work will involve engaging with industry practitioners and policymakers to refine the proposed governance and audit frameworks. Funding/Support Acknowledgements: This research is supported by the Information Systems Assurance Management Program at Concordia University of Edmonton.

Keywords: AI auditing, assurance, risk management, governance, COBIT 2019, transparency, accountability, machine learning, compliance

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Authors: Waleed Omri

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With the growth of the small business sector in emerging markets, developing a better understanding of what drives 'day-to-day' entrepreneurial activities has become an important issue for academicians and practitioners. Innovation, as an entrepreneurial behavior, revolves around individuals who creatively engage in new organizational efforts. In a similar vein, the innovation behaviors and processes at the organizational member level are central to any corporate entrepreneurship strategy. Despite the broadly acknowledged importance of entrepreneurship and innovation at the individual level in the establishment of successful ventures, the literature lacks evidence on how entrepreneurs can effectively harness their skills and knowledge in the workplace. The existing literature illustrates that religion can impact the day-to-day work behavior of entrepreneurs, managers, and employees. Religious beliefs and practices could affect daily entrepreneurial activities by fostering mental abilities and traits such as creativity, intelligence, and self-efficacy. In the present study, we define religious capital as a set of personal and intangible resources, skills, and competencies that emanate from an individual’s religious values, beliefs, practices, and experiences and may be used to increase the quality of economic activities. Religious beliefs and practices give individuals a religious satisfaction, which can lead them to perform better in the workplace. In addition, religious ethics and practices have been linked to various positive employee outcomes in terms of organizational change, job satisfaction, and entrepreneurial intensity. As investigations of their consequences beyond direct task performance are still scarce, we explore if religious capital plays a role in entrepreneurs’ innovative behavior. In sum, this study explores the determinants of individual entrepreneurial behavior by investigating the relationship between religious capital and entrepreneurs’ innovative behavior in the context of small businesses. To further explain and clarify the religious capital-innovative behavior link, the present study proposes a model to examine the mediating role of entrepreneurial creativity. We use both Islamic work ethics (IWE) and Islamic religious practices (IRP) to measure Islamic religious capital. We use structural equation modeling with a robust maximum likelihood estimation to analyze data gathered from 289 Tunisian small businesses and to explore the relationships among the above-described variables. In line with the theory of planned behavior, only religious work ethics are found to increase the innovative behavior of small businesses’ owner-managers. Our findings also clearly demonstrate that the connection between religious capital-related variables and innovative behavior is better understood if the influence of entrepreneurial creativity, as a mediating variable of the aforementioned relationship, is taken into account. By incorporating both religious capital and entrepreneurial creativity into the innovative behavior analysis, this study provides several important practical implications for promoting innovation process in small businesses.

Keywords: entrepreneurial behavior, small business, religion, creativity

Procedia PDF Downloads 243

Authors: Éva Pintér, Péter Bagó, Nikolett Deutsch, Miklós Hetényi

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The process of digital transformation has a profound influence on all sectors of the worldwide economy and the business environment. The influence of blockchain technology can be observed in the digital economy and e-government, rendering it an essential element of a nation's growth strategy. The banking industry is experiencing significant expansion and development of financial technology firms. Utilizing developing technologies such as artificial intelligence (AI), machine learning (ML), and big data (BD), these entrants are offering more streamlined financial solutions, promptly addressing client demands, and presenting a challenge to incumbent institutions. The advantages of digital transformation are evident in the corporate realm, and firms that resist its adoption put their survival at risk. The advent of digital technologies has revolutionized the business environment, streamlining processes and creating opportunities for enhanced communication and collaboration. Thanks to the aid of digital technologies, businesses can now swiftly and effortlessly retrieve vast quantities of information, all the while accelerating the process of creating new and improved products and services. Big data analytics is generally recognized as a transformative force in business, considered the fourth paradigm of science, and seen as the next frontier for innovation, competition, and productivity. Big data, an emerging technology that is shaping the future of the banking sector, offers numerous advantages to banks. It enables them to effectively track consumer behavior and make informed decisions, thereby enhancing their operational efficiency. Banks may embrace big data technologies to promptly and efficiently identify fraud, as well as gain insights into client preferences, which can then be leveraged to create better-tailored products and services. Moreover, the utilization of big data technology empowers banks to develop more intelligent and streamlined models for accurately recognizing and focusing on the suitable clientele with pertinent offers. There is a scarcity of research on big data analytics in the banking industry, with the majority of existing studies only examining the advantages and prospects associated with big data. Although big data technologies are crucial, there is a dearth of empirical evidence about the role of big data analytics (BDA) capabilities in bank performance. This research addresses a gap in the existing literature by introducing a model that combines the resource-based view (RBV), the technical organization environment framework (TOE), and dynamic capability theory (DC). This study investigates the influence of Big Data Analytics (BDA) utilization on the performance of market and risk management. This is supported by a comparative examination of Hungarian mobile banking services.

Keywords: big data, digital transformation, dynamic capabilities, mobile banking

Procedia PDF Downloads 64

Authors: Pardis Moslemzadeh Tehrani

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Due to the emergence of blockchain technology and the benefits of cryptocurrencies, intelligent or smart contracts are gaining traction. Artificial intelligence (AI) is transforming our lives, and it is being embraced by a wide range of sectors. Smart contracts, which are at the heart of blockchains, incorporate AI characteristics. Such contracts are referred to as "smart" contracts because of the underlying technology that allows contracting parties to agree on terms expressed in computer code that defines machine-readable instructions for computers to follow under specific situations. The transmission happens automatically if the conditions are met. Initially utilised for financial transactions, blockchain applications have since expanded to include the financial, insurance, and medical sectors, as well as supply networks. Raw material acquisition by suppliers, design, and fabrication by manufacturers, delivery of final products to consumers, and even post-sales logistics assistance are all part of supply chains. Many issues are linked with managing supply chains from the planning and coordination stages, which can be implemented in a smart contract in a blockchain due to their complexity. Manufacturing delays and limited third-party amounts of product components have raised concerns about the integrity and accountability of supply chains for food and pharmaceutical items. Other concerns include regulatory compliance in multiple jurisdictions and transportation circumstances (for instance, many products must be kept in temperature-controlled environments to ensure their effectiveness). Products are handled by several providers before reaching customers in modern economic systems. Information is sent between suppliers, shippers, distributors, and retailers at every stage of the production and distribution process. Information travels more effectively when individuals are eliminated from the equation. The usage of blockchain technology could be a viable solution to these coordination issues. In blockchains, smart contracts allow for the rapid transmission of production data, logistical data, inventory levels, and sales data. This research investigates the legal and technical advantages and disadvantages of AI-blockchain technology in the supply chain business. It aims to uncover the applicable legal problems and barriers to the use of AI-blockchain technology to supply chains, particularly in the food industry. It also discusses the essential legal and technological issues and impediments to supply chain implementation for stakeholders, as well as methods for overcoming them before releasing the technology to clients. Because there has been little research done on this topic, it is difficult for industrial stakeholders to grasp how blockchain technology could be used in their respective operations. As a result, the focus of this research will be on building advanced and complex contractual terms in supply chain smart contracts on blockchains to cover all unforeseen supply chain challenges.

Keywords: blockchain, supply chain, IoT, smart contract

Procedia PDF Downloads 126

Authors: Sanjeev Kumar Appicharla

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This paper presents the results of the modelling and analysis of the European Railway Traffic Management (ERTMS) safety-critical incident to raise awareness of biases in the systems engineering process on the Cambrian Railway in the UK using the RAIB 17/2019 as a primary input. The RAIB, the UK independent accident investigator, published the Report- RAIB 17/2019 giving the details of their investigation of the focal event in the form of immediate cause, causal factors, and underlying factors and recommendations to prevent a repeat of the safety-critical incident on the Cambrian Line. The Systems for Investigation of Railway Interfaces (SIRI) is the methodology used to model and analyze the safety-critical incident. The SIRI methodology uses the Swiss Cheese Model to model the incident and identify latent failure conditions (potentially less than adequate conditions) by means of the management oversight and risk tree technique. The benefits of the systems for investigation of railway interfaces methodology (SIRI) are threefold: first is that it incorporates the “Heuristics and Biases” approach advanced by 2002 Nobel laureate in Economic Sciences, Prof Daniel Kahneman, in the management oversight and risk tree technique to identify systematic errors. Civil engineering and programme management railway professionals are aware of the role “optimism bias” plays in programme cost overruns and are aware of bow tie (fault and event tree) model-based safety risk modelling techniques. However, the role of systematic errors due to “Heuristics and Biases” is not appreciated as yet. This overcomes the problems of omission of human and organizational factors from accident analysis. Second, the scope of the investigation includes all levels of the socio-technical system, including government, regulatory, railway safety bodies, duty holders, signaling firms and transport planners, and front-line staff such that lessons are learned at the decision making and implementation level as well. Third, the author’s past accident case studies are supplemented with research pieces of evidence drawn from the practitioner's and academic researchers’ publications as well. This is to discuss the role of system thinking to improve the decision-making and risk management processes and practices in the IEC 15288 systems engineering standard and in the industrial context such as the GB railways and artificial intelligence (AI) contexts as well.

Keywords: accident analysis, AI algorithm internal audit, bounded rationality, Byzantine failures, heuristics and biases approach

Procedia PDF Downloads 188

Authors: Jayashree And Gajjam

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Intellectual humility (IH) is defined as a virtuous mean between intellectual arrogance and intellectual self-diffidence by the ‘Doxastic Account of IH’ studied, researched and developed by western scholars not earlier than 2015 at the University of Edinburgh. Ancient Indian philosophical texts or the Upanisads written in the Sanskrit language during the later Vedic period (circa 600-300 BCE) have long addressed the virtue of being humble in several stories and narratives. The current research paper questions and revisits these character traits in an Indian context following an experimental method. Based on the subjective reports of more than 400 Indian teenagers and adults, it argues that while a few traits of IH (such as trustworthiness, respectfulness, intelligence, politeness, etc.) are panhuman and pancultural, a few are not. Some attributes of IH (such as proper pride, open-mindedness, awareness of own strength, etc.) may be taken for arrogance by the Indian population, while other qualities of Intellectual Diffidence such as agreeableness, surrendering can be regarded as the characteristic of IH. The paper then gives the reasoning for this discrepancy that can be traced back to the ancient Indian (Upaniṣadic) teachings that are still prevalent in many Indian families and still anchor their views on IH. The name Upanisad itself means ‘sitting down near’ (to the Guru to gain the Supreme knowledge of the Self and the Universe and setting to rest ignorance) which is equivalent to the three traits among the BIG SEVEN characterized as IH by the western scholars viz. ‘being a good listener’, ‘curious to learn’, and ‘respect to other’s opinion’. The story of Satyakama Jabala (Chandogya Upanisad 4.4-8) who seeks the truth for several years even from the bull, the fire, the swan and waterfowl, suggests nothing but the ‘need for cognition’ or ‘desire for knowledge’. Nachiketa (Katha Upanisad), a boy with a pure mind and heart, follows his father’s words and offers himself to Yama (the God of Death) where after waiting for Yama for three days and nights, he seeks the knowledge of the mysteries of life and death. Although the main aim of these Upaniṣadic stories is to give the knowledge of life and death, the Supreme reality which can be identical with traits such as ‘curious to learn’, one cannot deny that they have a lot more to offer than mere information about true knowledge e.g., ‘politeness’, ‘good listener’, ‘awareness of own limitations’, etc. The possible future scope of this research includes (1) finding other socio-cultural factors that affect the ideas on IH such as age, gender, caste, type of education, highest qualification, place of residence and source of income, etc. which may be predominant in current Indian society despite our great teachings of the Upaniṣads, and (2) to devise different measures to impart IH in Indian children, teenagers, and younger adults for the harmonious future. The current experimental research can be considered as the first step towards these goals.

Keywords: ethics and virtue epistemology, Indian philosophy, intellectual humility, upaniṣadic texts in ancient India

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Authors: Iris Xiaohong Quan

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The global digital health market reached 175 billion U.S. dollars in 2019, and is expected to grow at about 25% CAGR to over 650 billion USD by 2025. Different terms such as digital health, e-health, mHealth, telehealth have been used in the field, which can sometimes cause confusion. The term digital health was originally introduced to refer specifically to the use of interactive media, tools, platforms, applications, and solutions that are connected to the Internet to address health concerns of providers as well as consumers. While mHealth emphasizes the use of mobile phones in healthcare, telehealth means using technology to remotely deliver clinical health services to patients. According to FDA, “the broad scope of digital health includes categories such as mobile health (mHealth), health information technology (IT), wearable devices, telehealth and telemedicine, and personalized medicine.” Some researchers believe that digital health is nothing else but the cultural transformation healthcare has been going through in the 21st century because of digital health technologies that provide data to both patients and medical professionals. As digital health is burgeoning, but research in the area is still inadequate, our paper aims to clear the definition confusion and provide an overall picture of digital health companies. We further investigate how business models are designed and differentiated in the emerging digital health sector. Both quantitative and qualitative methods are adopted in the research. For the quantitative analysis, our research data came from two databases Crunchbase and CBInsights, which are well-recognized information sources for researchers, entrepreneurs, managers, and investors. We searched a few keywords in the Crunchbase database based on companies’ self-description: digital health, e-health, and telehealth. A search of “digital health” returned 941 unique results, “e-health” returned 167 companies, while “telehealth” 427. We also searched the CBInsights database for similar information. After merging and removing duplicate ones and cleaning up the database, we came up with a list of 1464 companies as digital health companies. A qualitative method will be used to complement the quantitative analysis. We will do an in-depth case analysis of three successful unicorn digital health companies to understand how business models evolve and discuss the challenges faced in this sector. Our research returned some interesting findings. For instance, we found that 86% of the digital health startups were founded in the recent decade since 2010. 75% of the digital health companies have less than 50 employees, and almost 50% with less than 10 employees. This shows that digital health companies are relatively young and small in scale. On the business model analysis, while traditional healthcare businesses emphasize the so-called “3P”—patient, physicians, and payer, digital health companies extend to “5p” by adding patents, which is the result of technology requirements (such as the development of artificial intelligence models), and platform, which is an effective value creation approach to bring the stakeholders together. Our case analysis will detail the 5p framework and contribute to the extant knowledge on business models in the healthcare industry.

Keywords: digital health, business models, entrepreneurship opportunities, healthcare

Procedia PDF Downloads 183

Authors: Ryan A. Black, Stacey A. McCaffrey

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Over the past few decades, great strides have been made towards improving the science in the measurement of psychological constructs. Item Response Theory (IRT) has been the foundation upon which statistical models have been derived to increase both precision and accuracy in psychological measurement. These models are now being used widely to develop and refine tests intended to measure an individual's level of academic achievement, aptitude, and intelligence. Recently, the field of clinical psychology has adopted IRT models to measure psychopathological phenomena such as depression, anxiety, and addiction. Because advances in IRT measurement models are being made so rapidly across various fields, it has become quite challenging for psychologists and other behavioral scientists to keep abreast of the most recent developments, much less learn how to employ and decide which models are the most appropriate to use in their line of work. In the same vein, IRT measurement models vary greatly in complexity in several interrelated ways including but not limited to the number of item-specific parameters estimated in a given model, the function which links the expected response and the predictor, response option formats, as well as dimensionality. As a result, inferior methods (a.k.a. Classical Test Theory methods) continue to be employed in efforts to measure psychological constructs, despite evidence showing that IRT methods yield more precise and accurate measurement. To increase the use of IRT methods, this study endeavors to provide a comprehensive overview of binary IRT models; that is, measurement models employed on test data consisting of binary response options (e.g., correct/incorrect, true/false, agree/disagree). Specifically, this study will cover the most basic binary IRT model, known as the 1-parameter logistic (1-PL) model dating back to over 50 years ago, up until the most recent complex, 4-parameter logistic (4-PL) model. Binary IRT models will be defined mathematically and the interpretation of each parameter will be provided. Next, all four binary IRT models will be employed on two sets of data: 1. Simulated data of N=500,000 subjects who responded to four dichotomous items and 2. A pilot analysis of real-world data collected from a sample of approximately 770 subjects who responded to four self-report dichotomous items pertaining to emotional consequences to alcohol use. Real-world data were based on responses collected on items administered to subjects as part of a scale-development study (NIDA Grant No. R44 DA023322). IRT analyses conducted on both the simulated data and analyses of real-world pilot will provide a clear demonstration of how to construct, evaluate, and compare binary IRT measurement models. All analyses will be performed using the new IRT procedure in SAS 9.4. SAS code to generate simulated data and analyses will be available upon request to allow for replication of results.

Keywords: instrument development, item response theory, latent trait theory, psychometrics

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Authors: Soumava Boral, Sanjay K. Chaturvedi, Ian Howard, Kristoffer McKee, V. N. A. Naikan

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Due to strict regulations from government to enhance the possibilities of sustainability practices in industries, and noting the advances in sustainable manufacturing practices, it is necessary that the associated processes are also sustainable. Maintenance of large scale and complex machines is a pivotal task to maintain the uninterrupted flow of manufacturing processes. Appropriate maintenance practices can prolong the lifetime of machines, and prevent associated breakdowns, which subsequently reduces different cost heads. Selection of the best maintenance strategies for such machines are considered as a burdensome task, as they require the consideration of multiple technical criteria, complex mathematical calculations, previous fault data, maintenance records, etc. In the era of the fourth industrial revolution, organizations are rapidly changing their way of business, and they are giving their utmost importance to sensor technologies, artificial intelligence, data analytics, automations, etc. In this work, the effectiveness of several maintenance strategies (e.g., preventive, failure-based, reliability centered, condition based, total productive maintenance, etc.) related to a large scale and complex gearbox, operating in a steel processing plant is evaluated in terms of economic, social, environmental and technical criteria. As it is not possible to obtain/describe some criteria by exact numerical values, these criteria are evaluated linguistically by cross-functional experts. Fuzzy sets are potential soft-computing technique, which has been useful to deal with linguistic data and to provide inferences in many complex situations. To prioritize different maintenance practices based on the identified sustainable criteria, multi-criteria decision making (MCDM) approaches can be considered as potential tools. Multi-Attributive Ideal Real Comparative Analysis (MAIRCA) is a recent addition in the MCDM family and has proven its superiority over some well-known MCDM approaches, like TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ELECTRE (ELimination Et Choix Traduisant la REalité). It has a simple but robust mathematical approach, which is easy to comprehend. On the other side, due to some inherent drawbacks of Intuitionistic Fuzzy Sets (IFS) and Pythagorean Fuzzy Sets (PFS), recently, the use of Fermatean Fuzzy Sets (FFSs) has been proposed. In this work, we propose the novel concept of FF-MAIRCA. We obtain the weights of the criteria by experts’ evaluation and use them to prioritize the different maintenance practices according to their suitability by FF-MAIRCA approach. Finally, a sensitivity analysis is carried out to highlight the robustness of the approach.

Keywords: Fermatean fuzzy sets, Fermatean fuzzy MAIRCA, maintenance strategy selection, sustainable manufacturing, MCDM

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Authors: Hengameh Hosseini

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Design of large healthcare facilities – such as hospitals, multi-service line clinics, and nursing facilities - that can accommodate patients with wide-ranging disabilities is a challenging endeavor and one that is poorly understood among healthcare facility managers, administrators, and executives. An even less-understood extension of this problem is the implications of weakly or insufficiently accommodative design of facilities for healthcare workers in physically-intensive jobs who may also suffer from a range of disabilities and who are therefore at increased risk of workplace accident and injury. Combine this reality with the vast range of facility types, ages, and designs, and the problem of universal accommodation becomes even more daunting and complex. In this study, we focus on the implication of facility design for healthcare workers suffering with low vision who also have physically active jobs. The points of difficulty are myriad and could span health service infrastructure, the equipment used in health facilities, and transport to and from appointments and other services can all pose a barrier to health care if they are inaccessible, less accessible, or even simply less comfortable for people with various disabilities. We conduct a series of surveys and interviews with employees and administrators of 7 facilities of a range of sizes and ownership models in the Northeastern United States and combine that corpus with in-facility observations and data collection to identify five major points of failure common to all the facilities that we concluded could pose safety threats to employees with vision impairments, ranging from very minor to severe. We determine that lack of design empathy is a major commonality among facility management and ownership. We subsequently propose three methods for remedying this lack of empathy-informed design, to remedy the dangers posed to employees: the use of an existing open-sourced Augmented Reality application to simulate the low-vision experience for designers and managers; the use of a machine learning model we develop to automatically infer facility shortcomings from large datasets of recorded patient and employee reviews and feedback; and the use of a computer vision model fine tuned on images of each facility to infer and predict facility features, locations, and workflows, that could again pose meaningful dangers to visually impaired employees of each facility. After conducting a series of real-world comparative experiments with each of these approaches, we conclude that each of these are viable solutions under particular sets of conditions, and finally characterize the range of facility types, workforce composition profiles, and work conditions under which each of these methods would be most apt and successful.

Keywords: artificial intelligence, healthcare workers, facility design, disability, visually impaired, workplace safety

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Authors: Elaine Lima Da Fonseca, Eliomar Pereira Da Silva Filho

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The modeling of areas susceptible to soil loss by hydro erosive processes consists of a simplified instrument of reality with the purpose of predicting future behaviors from the observation and interaction of a set of geoenvironmental factors. The models of potential areas for soil loss will be obtained through binary logistic regression, artificial neural networks, and support vector machines. The choice of the municipality of Colorado do Oeste in the south of the western Amazon is due to soil degradation due to anthropogenic activities, such as agriculture, road construction, overgrazing, deforestation, and environmental and socioeconomic configurations. Initially, a soil erosion inventory map constructed through various field investigations will be designed, including the use of remotely piloted aircraft, orbital imagery, and the PLANAFLORO/RO database. 100 sampling units with the presence of erosion will be selected based on the assumptions indicated in the literature, and, to complement the dichotomous analysis, 100 units with no erosion will be randomly designated. The next step will be the selection of the predictive parameters that exert, jointly, directly, or indirectly, some influence on the mechanism of occurrence of soil erosion events. The chosen predictors are altitude, declivity, aspect or orientation of the slope, curvature of the slope, composite topographic index, flow power index, lineament density, normalized difference vegetation index, drainage density, lithology, soil type, erosivity, and ground surface temperature. After evaluating the relative contribution of each predictor variable, the erosion susceptibility model will be applied to the municipality of Colorado do Oeste - Rondônia through the SPSS Statistic 26 software. Evaluation of the model will occur through the determination of the values of the R² of Cox & Snell and the R² of Nagelkerke, Hosmer and Lemeshow Test, Log Likelihood Value, and Wald Test, in addition to analysis of the Confounding Matrix, ROC Curve and Accumulated Gain according to the model specification. The validation of the synthesis map resulting from both models of the potential risk of soil erosion will occur by means of Kappa indices, accuracy, and sensitivity, as well as by field verification of the classes of susceptibility to erosion using drone photogrammetry. Thus, it is expected to obtain the mapping of the following classes of susceptibility to erosion very low, low, moderate, very high, and high, which may constitute a screening tool to identify areas where more detailed investigations need to be carried out, applying more efficient social resources.

Keywords: modeling, susceptibility to erosion, artificial intelligence, Amazon

Procedia PDF Downloads 66

Authors: José Manuel Palma, Paula Pereira, Margarida Tomás

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Foreseen the future: Human factors integration in European Horizon Projects The development of new technology as artificial intelligence, smart sensing, robotics, cobotics or intelligent machinery must integrate human factors to address the need to optimize systems and processes, thereby contributing to the creation of a safe and accident-free work environment. Human Factors Integration (HFI) consistently pose a challenge for organizations when applied to daily operations. AGILEHAND and FORTIS projects are grounded in the development of cutting-edge technology - industry 4.0 and 5.0. AGILEHAND aims to create advanced technologies for autonomously sort, handle, and package soft and deformable products, whereas FORTIS focuses on developing a comprehensive Human-Robot Interaction (HRI) solution. Both projects employ different approaches to explore HFI. AGILEHAND is mainly empirical, involving a comparison between the current and future work conditions reality, coupled with an understanding of best practices and the enhancement of safety aspects, primarily through management. FORTIS applies HFI throughout the project, developing a human-centric approach that includes understanding human behavior, perceiving activities, and facilitating contextual human-robot information exchange. it intervention is holistic, merging technology with the physical and social contexts, based on a total safety culture model. In AGILEHAND we will identify safety emergent risks, challenges, their causes and how to overcome them by resorting to interviews, questionnaires, literature review and case studies. Findings and results will be presented in “Strategies for Workers’ Skills Development, Health and Safety, Communication and Engagement” Handbook. The FORTIS project will implement continuous monitoring and guidance of activities, with a critical focus on early detection and elimination (or mitigation) of risks associated with the new technology, as well as guidance to adhere correctly with European Union safety and privacy regulations, ensuring HFI, thereby contributing to an optimized safe work environment. To achieve this, we will embed safety by design, and apply questionnaires, perform site visits, provide risk assessments, and closely track progress while suggesting and recommending best practices. The outcomes of these measures will be compiled in the project deliverable titled “Human Safety and Privacy Measures”. These projects received funding from European Union’s Horizon 2020/Horizon Europe research and innovation program under grant agreement No101092043 (AGILEHAND) and No 101135707 (FORTIS).

Keywords: human factors integration, automation, digitalization, human robot interaction, industry 4.0 and 5.0

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Authors: Veena Easvaradoss, Ebenezer Joseph, Sumathi Chandrasekaran, Sweta Jain, Aparna Anna Mathai, Senta Christy

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Working memory refers to a cognitive processing space where information is received, managed, transformed, and briefly stored. It is an operational process of transforming information for the execution of cognitive tasks in different and new ways. Many class room activities require children to remember information and mentally manipulate it. While the impact of chess training on intelligence and academic performance has been unequivocally established, its impact on working memory needs to be studied. This study, funded by the Cognitive Science Research Initiative, Department of Science & Technology, Government of India, analyzed the effect of one-year chess training on the working memory of children. A pretest–posttest with control group design was used, with 52 children in the experimental group and 50 children in the control group. The sample was selected from children studying in school (grades 3 to 9), which included both the genders. The experimental group underwent weekly chess training for one year, while the control group was involved in extracurricular activities. Working memory was measured by two subtests of WISC-IV INDIA. The Digit Span Subtest involves recalling a list of numbers of increasing length presented orally in forward and in reverse order, and the Letter–Number Sequencing Subtest involves rearranging jumbled alphabets and numbers presented orally following a given rule. Both tasks require the child to receive and briefly store information, manipulate it, and present it in a changed format. The Children were trained using Winning Moves curriculum, audio- visual learning method, hands-on- chess training and recording the games using score sheets, analyze their mistakes, thereby increasing their Meta-Analytical abilities. They were also trained in Opening theory, Checkmating techniques, End-game theory and Tactical principles. Pre equivalence of means was established. Analysis revealed that the experimental group had significant gains in working memory compared to the control group. The present study clearly establishes a link between chess training and working memory. The transfer of chess training to the improvement of working memory could be attributed to the fact that while playing chess, children evaluate positions, visualize new positions in their mind, analyze the pros and cons of each move, and choose moves based on the information stored in their mind. If working-memory’s capacity could be expanded or made to function more efficiently, it could result in the improvement of executive functions as well as the scholastic performance of the child.

Keywords: chess training, cognitive development, executive functions, school children, working memory

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Authors: Rowan P. Martnishn

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During the execution of a National Endowment of the Arts grant, approximately 55 interviews were collected from professionals across various fields. These interviews were used to create deliverables – historical connections for creations that began as art and evolved entirely into computing technology. With dozens of hours’ worth of transcripts to be analyzed by qualitative coders, a quantitative methodology was created to sift through the documents. The initial step was to both clean and format all the data. First, a basic spelling and grammar check was applied, as well as a Python script for normalized formatting which used an open-source grammatical formatter to make the data as coherent as possible. 10 documents were randomly selected to manually review, where words often incorrectly translated during the transcription were recorded and replaced throughout all other documents. Then, to remove all banter and side comments, the transcripts were spliced into paragraphs (separated by change in speaker) and all paragraphs with less than 300 characters were removed. Secondly, a keyword extractor, a form of natural language processing where significant words in a document are selected, was run on each paragraph for all interviews. Every proper noun was put into a data structure corresponding to that respective interview. From there, a Bidirectional and Auto-Regressive Transformer (B.A.R.T.) summary model was then applied to each paragraph that included any of the proper nouns selected from the interview. At this stage the information to review had been sent from about 60 hours’ worth of data to 20. The data was further processed through light, manual observation – any summaries which proved to fit the criteria of the proposed deliverable were selected, as well their locations within the document. This narrowed that data down to about 5 hours’ worth of processing. The qualitative researchers were then able to find 8 more connections in addition to our previous 4, exceeding our minimum quota of 3 to satisfy the grant. Major findings of the study and subsequent curation of this methodology raised a conceptual finding crucial to working with qualitative data of this magnitude. In the use of artificial intelligence there is a general trade off in a model between breadth of knowledge and specificity. If the model has too much knowledge, the user risks leaving out important data (too general). If the tool is too specific, it has not seen enough data to be useful. Thus, this methodology proposes a solution to this tradeoff. The data is never altered outside of grammatical and spelling checks. Instead, the important information is marked, creating an indicator of where the significant data is without compromising the purity of it. Secondly, the data is chunked into smaller paragraphs, giving specificity, and then cross-referenced with the keywords (allowing generalization over the whole document). This way, no data is harmed, and qualitative experts can go over the raw data instead of using highly manipulated results. Given the success in deliverable creation as well as the circumvention of this tradeoff, this methodology should stand as a model for synthesizing qualitative data while maintaining its original form.

Keywords: B.A.R.T.model, keyword extractor, natural language processing, qualitative coding

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Authors: C. A. Barros, Ana P. Barroso

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Any industrial company needs to determine the amount of variation that exists within its measurement process and guarantee the reliability of their data, studying the performance of their measurement system, in terms of linearity, bias, repeatability and reproducibility and stability. This issue is critical for automotive industry suppliers, who are required to be certified by the 16949:2016 standard (replaces the ISO/TS 16949) of International Automotive Task Force, defining the requirements of a quality management system for companies in the automotive industry. Measurement System Analysis (MSA) is one of the mandatory tools. Frequently, the measurement system in companies is not connected to the equipment and do not incorporate the methods proposed by the Automotive Industry Action Group (AIAG). To address these constraints, an R&D project is in progress, whose objective is to develop a web and cloud-based MSA tool. This MSA tool incorporates Industry 4.0 concepts, such as, Internet of Things (IoT) protocols to assure the connection with the measuring equipment, cloud computing, artificial intelligence, statistical tools, and advanced mathematical algorithms. This paper presents the preliminary findings of the project. The web and cloud-based MSA tool is innovative because it implements all statistical tests proposed in the MSA-4 reference manual from AIAG as well as other emerging methods and techniques. As it is integrated with the measuring devices, it reduces the manual input of data and therefore the errors. The tool ensures traceability of all performed tests and can be used in quality laboratories and in the production lines. Besides, it monitors MSAs over time, allowing both the analysis of deviations from the variation of the measurements performed and the management of measurement equipment and calibrations. To develop the MSA tool a ten-step approach was implemented. Firstly, it was performed a benchmarking analysis of the current competitors and commercial solutions linked to MSA, concerning Industry 4.0 paradigm. Next, an analysis of the size of the target market for the MSA tool was done. Afterwards, data flow and traceability requirements were analysed in order to implement an IoT data network that interconnects with the equipment, preferably via wireless. The MSA web solution was designed under UI/UX principles and an API in python language was developed to perform the algorithms and the statistical analysis. Continuous validation of the tool by companies is being performed to assure real time management of the ‘big data’. The main results of this R&D project are: MSA Tool, web and cloud-based; Python API; New Algorithms to the market; and Style Guide of UI/UX of the tool. The MSA tool proposed adds value to the state of the art as it ensures an effective response to the new challenges of measurement systems, which are increasingly critical in production processes. Although the automotive industry has triggered the development of this innovative MSA tool, other industries would also benefit from it. Currently, companies from molds and plastics, chemical and food industry are already validating it.

Keywords: automotive Industry, industry 4.0, Internet of Things, IATF 16949:2016, measurement system analysis

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Authors: Julio Jesus Salazar, Julio Jesus De Lama

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the fundamental objective of hydrological studies applied to the engineering field is to determine the statistically consistent volumes or water flows that, in each case, allow us to size or design a series of elements or structures to effectively manage and develop a river basin. To determine these values, there are several ways of working within the framework of traditional hydrology: (1) Study each of the factors that influence the hydrological cycle, (2) Study the historical behavior of the hydrology of the area, (3) Study the historical behavior of hydrologically similar zones, and (4) Other studies (rain simulators or experimental basins). Of course, this range of studies in a certain basin is very varied and complex and presents the difficulty of collecting the data in real time. In this complex space, the study of variables can only be overcome by collecting and transmitting data to decision centers through the Internet of things and artificial intelligence. Thus, this research work implemented the learning project of the sub-basin of the Shullcas river in the Andean basin of the Mantaro river in Peru. The sensor firmware to collect and communicate hydrological parameter data was programmed and tested in similar basins of the European Union. The Machine Learning applications was programmed to choose the algorithms that direct the best solution to the determination of the rainfall-runoff relationship captured in the different polygons of the sub-basin. Tests were carried out in the mountains of Europe, and in the sub-basins of the Shullcas river (Huancayo) and the Yauli river (Jauja) with heights close to 5000 m.a.s.l., giving the following conclusions: to guarantee a correct communication, the distance between devices should not pass the 15 km. It is advisable to minimize the energy consumption of the devices and avoid collisions between packages, the distances oscillate between 5 and 10 km, in this way the transmission power can be reduced and a higher bitrate can be used. In case the communication elements of the devices of the network (internet of things) installed in the basin do not have good visibility between them, the distance should be reduced to the range of 1-3 km. The energy efficiency of the Atmel microcontrollers present in Arduino is not adequate to meet the requirements of system autonomy. To increase the autonomy of the system, it is recommended to use low consumption systems, such as the Ashton Raggatt McDougall or ARM Cortex L (Ultra Low Power) microcontrollers or even the Cortex M; and high-performance direct current (DC) to direct current (DC) converters. The Machine Learning System has initiated the learning of the Shullcas system to generate the best hydrology of the sub-basin. This will improve as machine learning and the data entered in the big data coincide every second. This will provide services to each of the applications of the complex system to return the best data of determined flows.

Keywords: hydrology, internet of things, machine learning, river basin

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Authors: Daniel O. Kleinsman

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The 2019 terrorist attack on two masjidain in Christchurch, New Zealand, was a shocking demonstration of the harm that can be caused by hate crime. As legal and governmental responses to the attack struggle to provide effective remedies to its victims, restorative justice has emerged as a tool that can assist, in terms of both meeting victims’ needs and discharging the obligations of the state under the International Covenant on Civil and Political Rights (ICCPR), arts 2(3), 26, 27. Restorative justice is a model that emphasizes the repair of harm caused or revealed by unjust behavior. It also prioritises the facilitation of dialogue, the restoration of equitable relationships, and the prevention of future harm. Returning to the case study, in the remarks of the sentencing judge, the terrorist’s actions were described as a hate crime of vicious malevolence that the Court was required to decisively reject, as anathema to the values of acceptance, tolerance and mutual respect upon which New Zealand’s inclusive society is based and which the country strives to maintain. This was one of the reasons for which the terrorist received a life sentence with no possibility of parole. However, in the report of the Royal Commission of Inquiry into the Attack, it was found that victims felt the attack occurred within the context of widespread racism, discrimination and Islamophobia, where hostile behaviors, including hate-based threats and attacks, were rarely recorded, analysed or acted on. It was also found that the Government had inappropriately concentrated intelligence resources on the risk of ‘Islamist’ terrorism and had failed to adequately respond to concerns raised about threats against the Muslim community. In this light, the remarks of the sentencing judge can be seen to reflect a criminal justice system that, in the absence of other remedies, denies systemic accountability and renders hate crime an isolated incident rather than an expression of more widespread discrimination and hate to be holistically addressed. One of the recommendations of the Royal Commission was to explore with victims the desirability and design of restorative justice processes. This presents an opportunity for victims to meet with state representatives and pursue effective remedies (ICCPR art 2(3)) not only for the harm caused by the terrorist but the harm revealed by a system that has exposed the minority Muslim community in New Zealand to hate in all forms, including but not limited to violent extremism. In this sense, restorative justice can also assist the state in discharging its wider obligations to protect all persons from discrimination (art 26) and allow ethnic and religious minorities to enjoy their own culture and profess and practice their own religion (art 27). It can also help give effect to the law and its purpose as a remedy to hate crime, as expressed in this case study by the sentencing judge.

Keywords: hate crime, restorative justice, minorities, victims' rights

Procedia PDF Downloads 111

Authors: Chung-Yuan Wang, Min Hsu, Bo-Ya Juan, Hsiv Ching Lin, Hsveh Min Lin, Hsiu-Fang Yeh

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The success of Taiwan's National Health Insurance (NHI) system attracts widespread praise from the international society. However, the availability of medical care in a emote area is limited. Without the convenient public transportation system and mature social welfare policy, these people are difficult to regain their health and prevent disability. Preterm children have more risk to get development delay. Preterm children in a remote area have the human right to get rehabilitation resources as those in the city area. Therefore, the aim of this study was to show the importance of development screening to preterm children in a remote area and a tract the government to notice the issue. In Pingtung, children who are suspected development delay would be suggested to take a skillful screening evaluation in our hospital. Those preterm children (within 1-year-old) visited our pediatric clinic would also be referred to take the development evaluation. After the physiatrist’s systemic evaluation, the subjects would be scheduled to take the development evaluation. Gross motor, fine motor, speech comprehension/expression and mental study were included. The evaluation was in-charged by a physical therapist, occupational therapy, speech therapist and pediatric psychologist. The tools were Peabody developmental scale, Bayley Scales of Infant and Toddler Development (Bayley-III) and Wechsler Preschool & Primary Scale of Intelligence-Revised (WPPSI-R). In 2013, 459 children received the service in our hospital. Among these children, fifty-seven were noted with preterm baby history (gestation within 37 weeks). Thirty-six of these preterm children, who had never receive development evaluation, were included in this study. Thirty-six subjects (twenty-six male and ten female) were included. Nineteen subjects were found development delay. Six subjects were found suspected development delay. In gross motor, six subjects were development delay and eight were suspected development delay. In fine motor, five subjects were development delay and three were suspected development delay. In speech, sixteen subjects were development delay and six were suspected development delay. In our study, through the provision of development evaluation service, 72.2% preterm baby were found their development delay or suspected delay. They need further early intervention rehabilitation service. We made their parents realize that when development delay was recognized at the early stage, they are often reversible. No only the patients but also their families were improved their health status. The number of the subjects was limited in our study. Further study might be needed. Compared with 770 physical therapist (PT) and 370 occupational therapy (OT) in Taipei, there are only 108 PT and 54 OT in Pingtung. Further, there are much fewer therapists working on the field of pediatric rehabilitation. Living healthy is a human's right, no matter where does he live. For those development delay children in remote area, particularly preterm children, early detection, and early intervention rehabilitation service could play an important role in decreasing their disability and improving their quality of life. Through this study, we suggest the government to add more national resources on the development evaluation to preterm children in a remote area.

Keywords: development, early intervention, preterm children, rehabilitation

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Authors: Selin Küçük

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Pre-industrial architecture is integration of natural and subsequent properties by intelligence and experience. Since various settlements relatively industrialized or non-industrialized at any time, ‘pre-industrial’ term does not refer to a definite time. Natural properties, which are existent conditions and materials in natural local environment, are climate, geomorphology and local materials. Subsequent properties, which are all anthropological comparatives, are culture of societies, requirements of people and construction techniques that people use. Yet, after industrialization, technology took technique’s place, cultural effects are manipulated, requirements are changed and local/natural properties are almost disappeared in architecture. Technology is universal, global and expands simply; conversely technique is time and experience dependent and should has a considerable cultural background. This research is about construction techniques according to natural properties of a region and classification of these techniques. Understanding local architecture is only possible by searching its background which is hard to reach. There are always changes in positive and negative in architectural techniques through the time. Archaeological layers of a region sometimes give more accurate information about transformation of architecture. However, natural properties of any region are the most helpful elements to perceive construction techniques. Many international sources from different cultures are interested in local architecture by mentioning natural properties separately. Unfortunately, there is no literature deals with this subject as far as systematically in the correct way. This research aims to improve a clear perspective of local architecture existence by categorizing archetypes according to natural properties. The ultimate goal of this research is generating a clear classification of local architecture independent from subsequent (anthropological) properties over the world such like a handbook. Since local architecture is the most sustainable architecture with refer to its economic, ecologic and sociological properties, there should be an excessive information about construction techniques to be learned from. Constructing the same buildings in all over the world is one of the main criticism of modern architectural system. While this critics going on, the same buildings without identity increase incrementally. In post-industrial term, technology widely took technique’s place, yet cultural effects are manipulated, requirements are changed and natural local properties are almost disappeared in architecture. These study does not offer architects to use local techniques, but it indicates the progress of pre-industrial architectural evolution which is healthier, cheaper and natural. Immigration from rural areas to developing/developed cities should be prohibited, thus culture and construction techniques can be preserved. Since big cities have psychological, sensational and sociological impact on people, rural settlers can be convinced to not to immigrate by providing new buildings designed according to natural properties and maintaining their settlements. Improving rural conditions would remove the economical and sociological gulf between cities and rural. What result desired to arrived in, is if there is no deformation (adaptation process of another traditional buildings because of immigration) or assimilation in a climatic region, there should be very similar solutions in the same climatic regions of the world even if there is no relationship (trade, communication etc.) among them.

Keywords: climate zones, geomorphology, local architecture, local materials

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Authors: Mballa Abanda Serge, Henda Gnakate Biba, Romaric Guemno Kuate, Akono Rufine Nicole, Petfiang Sidonie, Bella Sidonie

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The main hypotheses proposed around the definition of the syllable and of music, of the common origin of music and language, should lead the reader to reflect on the cross-cutting questions raised by the debate on the notion of universals in linguistics and musicology. These are objects of controversy, and there lies its interest: the debate raises questions that are at the heart of theories on language. It is an inventive, original and innovative research thesis. The main hypotheses proposed around the definition of the syllable and of music, of the common origin of music and language, should lead the reader to reflect on the cross-cutting questions raised by the debate on the notion of universals in linguistics and musicology. These are objects of controversy, and there lies its interest: the debate raises questions that are at the heart of theories on language. It is an inventive, original and innovative research thesis. A contribution to the theoretical, musicological, ethno musicological and linguistic conceptualization of languages, giving rise to the practice of interlocution between the social and cognitive sciences, the activities of artistic creation and the question of modeling in the human sciences: mathematics, computer science, translation automation and artificial intelligence. When you apply this theory to any text of a folksong of a world-tone language, you do not only piece together the exact melody, rhythm, and harmonies of that song as if you knew it in advance but also the exact speaking of this language. The author believes that the issue of the disappearance of tonal languages and their preservation has been structurally resolved, as well as one of the greatest cultural equations related to the composition and creation of tonal, polytonal and random music. The experimentation confirming the theorization, It designed a semi-digital, semi-analog application which translates the tonal languages of Africa (about 2,100 languages) into blues, jazz, world music, polyphonic music, tonal and anatonal music and deterministic and random music). To test this application, I use a music reading and writing software that allows me to collect the data extracted from my mother tongue, which is already modeled in the musical staves saved in the ethnographic (semiotic) dictionary for automatic translation ( volume 2 of the book). Translation is done (from writing to writing, from writing to speech and from writing to music). Mode of operation: you type a text on your computer, a structured song (chorus-verse), and you command the machine a melody of blues, jazz and world music or variety etc. The software runs, giving you the option to choose harmonies, and then you select your melody.

Keywords: music, entanglement, langauge, science

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Authors: Nasser Salah Eldin Mohammed Salih Shebka

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Current problematic issues in AI fields are mainly due to those of knowledge representation conceptual theories, which in turn reflected on the entire scope of cognitive sciences. Knowledge representation methods and tools are driven from theoretical concepts regarding human scientific perception of the conception, nature, and process of knowledge acquisition, knowledge engineering and knowledge generation. And although, these theoretical conceptions were themselves driven from the study of the human knowledge representation process and related theories; some essential factors were overlooked or underestimated, thus causing critical methodological deficiencies in the conceptual theories of human knowledge and knowledge representation conceptions. The evaluation criteria of human cumulative knowledge from the perspectives of nature and theoretical aspects of knowledge representation conceptions are affected greatly by the very materialistic nature of cognitive sciences. This nature caused what we define as methodological deficiencies in the nature of theoretical aspects of knowledge representation concepts in AI. These methodological deficiencies are not confined to applications of knowledge representation theories throughout AI fields, but also exceeds to cover the scientific nature of cognitive sciences. The methodological deficiencies we investigated in our work are: - The Segregation between cognitive abilities in knowledge driven models.- Insufficiency of the two-value logic used to represent knowledge particularly on machine language level in relation to the problematic issues of semantics and meaning theories. - Deficient consideration of the parameters of (existence) and (time) in the structure of knowledge. The latter requires that we present a more detailed introduction of the manner in which the meanings of Existence and Time are to be considered in the structure of knowledge. This doesn’t imply that it’s easy to apply in structures of knowledge representation systems, but outlining a deficiency caused by the absence of such essential parameters, can be considered as an attempt to redefine knowledge representation conceptual approaches, or if proven impossible; constructs a perspective on the possibility of simulating human cognition on machines. Furthermore, a redirection of the aforementioned expressions is required in order to formulate the exact meaning under discussion. This redirection of meaning alters the role of Existence and time factors to the Frame Work Environment of knowledge structure; and therefore; knowledge representation conceptual theories. Findings of our work indicate the necessity to differentiate between two comparative concepts when addressing the relation between existence and time parameters, and between that of the structure of human knowledge. The topics presented throughout the paper can also be viewed as an evaluation criterion to determine AI’s capability to achieve its ultimate objectives. Ultimately, we argue some of the implications of our findings that suggests that; although scientific progress may have not reached its peak, or that human scientific evolution has reached a point where it’s not possible to discover evolutionary facts about the human Brain and detailed descriptions of how it represents knowledge, but it simply implies that; unless these methodological deficiencies are properly addressed; the future of AI’s qualitative progress remains questionable.

Keywords: cognitive sciences, knowledge representation, ontological reasoning, temporal logic

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Authors: Yildiz Stella Dak, Jale Tezcan

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Ground motion models that relate a strong motion parameter of interest to a set of predictive seismological variables describing the earthquake source, the propagation path of the seismic wave, and the local site conditions constitute a critical component of seismic hazard analyses. When a sufficient number of strong motion records are available, ground motion relations are developed using statistical analysis of the recorded ground motion data. In regions lacking a sufficient number of recordings, a synthetic database is developed using stochastic, theoretical or hybrid approaches. Regardless of the manner the database was developed, ground motion relations are developed using regression analysis. Development of a ground motion relation is a challenging process which inevitably requires the modeler to make subjective decisions regarding the inclusion criteria of the recordings, the functional form of the model and the set of seismological variables to be included in the model. Because these decisions are critically important to the validity and the applicability of the model, there is a continuous interest on procedures that will facilitate the development of ground motion models. This paper proposes the use of the Least Absolute Shrinkage and Selection Operator (LASSO) in selecting the set predictive seismological variables to be used in developing a ground motion relation. The LASSO can be described as a penalized regression technique with a built-in capability of variable selection. Similar to the ridge regression, the LASSO is based on the idea of shrinking the regression coefficients to reduce the variance of the model. Unlike ridge regression, where the coefficients are shrunk but never set equal to zero, the LASSO sets some of the coefficients exactly to zero, effectively performing variable selection. Given a set of candidate input variables and the output variable of interest, LASSO allows ranking the input variables in terms of their relative importance, thereby facilitating the selection of the set of variables to be included in the model. Because the risk of overfitting increases as the ratio of the number of predictors to the number of recordings increases, selection of a compact set of variables is important in cases where a small number of recordings are available. In addition, identification of a small set of variables can improve the interpretability of the resulting model, especially when there is a large number of candidate predictors. A practical application of the proposed approach is presented, using more than 600 recordings from the National Geospatial-Intelligence Agency (NGA) database, where the effect of a set of seismological predictors on the 5% damped maximum direction spectral acceleration is investigated. The set of candidate predictors considered are Magnitude, Rrup, Vs30. Using LASSO, the relative importance of the candidate predictors has been ranked. Regression models with increasing levels of complexity were constructed using one, two, three, and four best predictors, and the models’ ability to explain the observed variance in the target variable have been compared. The bias-variance trade-off in the context of model selection is discussed.

Keywords: ground motion modeling, least absolute shrinkage and selection operator, penalized regression, variable selection

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Authors: Alua Masalimova, Dameli Sulubecova, Talgat Isaev, Raushan Magzumova

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The usual relation to nurses of heads of medical organizations in Kazakhstan is to use them only for per performing medical manipulations, but new economic conditions require the introduction of nursing innovations. There is an increasing need for managers of hospital departments and regions of ambulatory clinics to ensure comfortable conditions for doctors, nurses, aides, as well as monitoring marketing technology (the needs and satisfaction of staff work, the patient satisfaction of the department). It is going to the past the nursing activities as physician assistant performing his prescriptions passively. We are suggesting a model for the developing the head nurse as the manager on the example of Blood Service. We have studied in the scientific-production center of blood transfusion head nurses by the standard method of interviewing for involvement in coordinating the flow of information, promoting the competitiveness of the department. Results: the average age of the respondents 43,1 ± 9,8, female - 100%; manager in the Organization – 9,3 ± 10,3 years. Received positive responses to the knowledge of the nearest offices in providing similar medical service - 14,2%. The cost of similar medical services in other competitive organizations did not know 100%, did a study of employee satisfaction Division labour-85,7% answered negatively, the satisfaction donors work staff studied in 50.0% of cases involved in attracting paid Services Division showed a 28.5% of the respondent. Participation in management decisions medical organization: strategic planning - 14,2%, forming analysis report for the year – 14,2%, recruitment-30.0%, equipment-14.2%. Participation in the social and technical designing workplaces Division staff showed 85,0% of senior nurses. Participate in the cohesion of the staff of the Division method of the team used the 10.0% of respondents. Further, we have studied the behavioral competencies for senior sisters: customer focus – 20,0% of respondents have attended, the ability to work in a team – 40,0%. Personal qualities senior nurses were apparent: sociability – 80,0%, the ability to manage information – 40,0%, to make their own decisions - 14,2%, 28,5% creativity, the desire to improve their professionalism – 50,0%. Thus, the modern market conditions dictate this organization, which works for the rights of economic management; include the competence of the post of the senior nurse knowledge and skills of Marketing Management Department. Skills to analyses the information collected and use of management offers superior medical leadership organization. The medical organization in the recruitment of the senior nurse offices take into account personal qualities: flexibility, fluency of thinking, communication skills and ability to work in a team. As well as leadership qualities, ambition, high emotional and social intelligence, that will bring out the medical unit on competitiveness within the country and abroad.

Keywords: blood service, head nurse, manager, skills

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Authors: Heikki Ruohomaa, Vesa Salminen

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Regions are facing huge competition to attract companies, businesses, inhabitants, students, etc. This way to improve living and business environment, which is rapidly changing due to digitalization. On the other hand, from the industry's point of view, the availability of a skilled labor force and an innovative environment are crucial factors. In this context, qualified staff has been seen to utilize the opportunities of digitalization and respond to the needs of future skills. World Manufacturing Forum has stated in the year 2019- report that in next five years, 40% of workers have to change their core competencies. Through digital transformation, new technologies like cloud, mobile, big data, 5G- infrastructure, platform- technology, data- analysis, and social networks with increasing intelligence and automation, enterprises can capitalize on new opportunities and optimize existing operations to achieve significant business improvement. Digitalization will be an important part of the everyday life of citizens and present in the working day of the average citizen and employee in the future. For that reason, the education system and education programs on all levels of education from diaper age to doctorate have been directed to fulfill this ecosystem strategy. Goal: The Fourth Industrial Revolution will bring unprecedented change to societies, education organizations and business environments. This article aims to identify how education, education content, the way education has proceeded, and overall whole the education business is changing. Most important is how we should respond to this inevitable co- evolution. Methodology: The study aims to verify how the learning process is boosted by new digital content, new learning software and tools, and customer-oriented learning environments. The change of education programs and individual education modules can be supported by applied research projects. You can use them in making proof- of- the concept of new technology, new ways to teach and train, and through the experiences gathered change education content, way to educate and finally education business as a whole. Major findings: Applied research projects can prove the concept- phases on real environment field labs to test technology opportunities and new tools for training purposes. Customer-oriented applied research projects are also excellent for students to make assignments and use new knowledge and content and teachers to test new tools and create new ways to educate. New content and problem-based learning are used in future education modules. This article introduces some case study experiences on customer-oriented digital transformation projects and how gathered knowledge on new digital content and a new way to educate has influenced education. The case study is related to experiences of research projects, customer-oriented field labs/learning environments and education programs of Häme University of Applied Sciences.

Keywords: education process, digitalization content, digital tools for education, learning environments, transdisciplinary co-operation

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Authors: Haya Salah, Srinivas Sharan

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Healthcare facilities use appointment systems to schedule their appointments and to manage access to their medical services. With the growing demand for outpatient care, it is now imperative to manage physician's time effectively. However, high variation in consultation duration affects the clinical scheduler's ability to estimate the appointment duration and allocate provider time appropriately. Underestimating consultation times can lead to physician's burnout, misdiagnosis, and patient dissatisfaction. On the other hand, appointment durations that are longer than required lead to doctor idle time and fewer patient visits. Therefore, a good estimation of consultation duration has the potential to improve timely access to care, resource utilization, quality of care, and patient satisfaction. Although the literature on factors influencing consultation length abound, little work has done to predict it using based data-driven approaches. Therefore, this study aims to predict consultation duration using supervised machine learning algorithms (ML), which predicts an outcome variable (e.g., consultation) based on potential features that influence the outcome. In particular, ML algorithms learn from a historical dataset without explicitly being programmed and uncover the relationship between the features and outcome variable. A subset of the data used in this study has been obtained from the electronic medical records (EMR) of four different outpatient clinics located in central Pennsylvania, USA. Also, publicly available information on doctor's characteristics such as gender and experience has been extracted from online sources. This research develops three popular ML algorithms (deep learning, random forest, gradient boosting machine) to predict the treatment time required for a patient and conducts a comparative analysis of these algorithms with respect to predictive performance. The findings of this study indicate that ML algorithms have the potential to predict the provider service time with superior accuracy. While the current approach of experience-based appointment duration estimation adopted by the clinic resulted in a mean absolute percentage error of 25.8%, the Deep learning algorithm developed in this study yielded the best performance with a MAPE of 12.24%, followed by gradient boosting machine (13.26%) and random forests (14.71%). Besides, this research also identified the critical variables affecting consultation duration to be patient type (new vs. established), doctor's experience, zip code, appointment day, and doctor's specialty. Moreover, several practical insights are obtained based on the comparative analysis of the ML algorithms. The machine learning approach presented in this study can serve as a decision support tool and could be integrated into the appointment system for effectively managing patient scheduling.

Keywords: clinical decision support system, machine learning algorithms, patient scheduling, prediction models, provider service time

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