Search results for: enterprise intelligence

Authors: Shizra Waris, Bilal Shoaib, Munib Ahmad

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Background; The using of clinical decision support systems (CDSSs) may recover unceasing disease organization, which requires regular visits to multiple health professionals, treatment monitoring, disease control, and patient behavior modification. The objective of this survey is to determine if these CDSSs improve the processes of unceasing care including diagnosis, treatment, and monitoring of diseases. Though artificial intelligence is not a new idea it has been widely documented as a new technology in computer science. Numerous areas such as education business, medical and developed have made use of artificial intelligence Methods: The survey covers articles extracted from relevant databases. It uses search terms related to information technology and viral hepatitis which are published between 2000 and 2016. Results: Overall, 80% of studies asserted the profit provided by information technology (IT); 75% of learning asserted the benefits concerned with medical domain;25% of studies do not clearly define the added benefits due IT. The CDSS current state requires many improvements to hold up the management of liver diseases such as HCV, liver fibrosis, and cirrhosis. Conclusion: We concluded that the planned model gives earlier and more correct calculation of hepatitis B and it works as promising tool for calculating of custom hepatitis B from the clinical laboratory data.

Keywords: detection, hapataties, observation, disesese

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Authors: Yinghui Shang, Ziqing Zhou, Rong Han, Hang Wang, Xiaodi Liu, Yong Yang

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The design of advanced amorphous solids, such as metallic glasses, with targeted properties through artificial intelligence signifies a paradigmatic shift in physical metallurgy and materials technology. Here, we developed a machine-learning architecture that facilitates the generation of metallic glasses with targeted multifunctional properties. Our architecture integrates the state-of-the-art unsupervised generative adversarial network model with supervised models, allowing the incorporation of general prior knowledge derived from thousands of data points across a vast range of alloy compositions, into the creation of data points for a specific type of composition, which overcame the common issue of data scarcity typically encountered in the design of a given type of metallic glasses. Using our generative model, we have successfully designed copper-based metallic glasses, which display exceptionally high hardness or a remarkably low modulus. Notably, our architecture can not only explore uncharted regions in the targeted compositional space but also permits self-improvement after experimentally validated data points are added to the initial dataset for subsequent cycles of data generation, hence paving the way for the customized design of amorphous solids without human intervention.

Keywords: metallic glass, artificial intelligence, mechanical property, automated generation

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Authors: Ahmad Haidar

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Modern economic systems are characterized by growing complexity, and addressing their challenges requires innovative approaches. This study examines the implications of artificial intelligence (AI) on unemployment in Europe from a macroeconomic perspective, employing data modeling techniques to understand the relationship between AI integration and labor market dynamics. To understand the AI-unemployment nexus comprehensively, this research considers factors such as sector-specific AI adoption, skill requirements, workforce demographics, and geographical disparities. The study utilizes a panel data model, incorporating data from European countries over the last two decades, to explore the potential short-term and long-term effects of AI implementation on unemployment rates. In addition to investigating the direct impact of AI on unemployment, the study also delves into the potential indirect effects and spillover consequences. It considers how AI-driven productivity improvements and cost reductions might influence economic growth and, in turn, labor market outcomes. Furthermore, it assesses the potential for AI-induced changes in industrial structures to affect job displacement and creation. The research also highlights the importance of policy responses in mitigating potential negative consequences of AI adoption on unemployment. It emphasizes the need for targeted interventions such as skill development programs, labor market regulations, and social safety nets to enable a smooth transition for workers affected by AI-related job displacement. Additionally, the study explores the potential role of AI in informing and transforming policy-making to ensure more effective and agile responses to labor market challenges. In conclusion, this study provides a comprehensive analysis of the macroeconomic implications of AI on unemployment in Europe, highlighting the importance of understanding the nuanced relationships between AI adoption, economic growth, and labor market outcomes. By shedding light on these relationships, the study contributes valuable insights for policymakers, educators, and researchers, enabling them to make informed decisions in navigating the complex landscape of AI-driven economic transformation.

Keywords: artificial intelligence, unemployment, macroeconomic analysis, european labor market

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Authors: Yuval Reinfeld

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This paper explores the European Union’s expanding influence as a global regulatory power, particularly in the realms of legal, security, and privacy challenges within the digital landscape. As digital regulation becomes increasingly vital, the EU has positioned itself as a leading exporter of privacy and cybersecurity standards through landmark frameworks like the General Data Protection Regulation (GDPR), the Artificial Intelligence Act (AIA), and the Digital Services Act (DSA). These regulations have set global benchmarks, extending their influence well beyond Europe’s borders by shaping legal frameworks in third countries and guiding the development of global digital governance. Central to this regulatory diffusion is the European Court of Justice (CJEU), whose rulings consistently reinforce and extend the reach of EU standards on an international scale. Through mechanisms such as trade agreements, adequacy decisions, and multilateral cooperation, the EU has constructed a regulatory ecosystem that other jurisdictions increasingly adopt. This paper investigates key CJEU cases to illustrate how the EU’s legal instruments in privacy, security, and AI contribute to its role as a global standard-setter. By examining the intersection of digital governance, international law, and normative power, this research provides a thorough analysis of the EU’s regulatory impact on global privacy, cybersecurity, and AI frameworks.

Keywords: digital privacy, cybersecurity, GDPR, European Union Law, artificial intelligence, global normative power

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Authors: Zerroug Abdelhamid, Danielle Chassoux

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Various models are given to simulate homogeneous or heterogeneous cancerous tumors and extract in each case the boundary. The fractal dimension is then estimated by least squares method and compared to some previous methods.

Keywords: simulation, cancerous tumor, Markov fields, fractal dimension, extraction, recovering

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Authors: Leon C. Prieto, Simone T. A. Phipps

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There is a need to promote social entrepreneurship in order to solve some of the complex problems facing various countries in Africa (poverty, unemployment, crime, HIV, etc.). For example, one possible consequence of the HIV/AIDS crisis in Zimbabwe and elsewhere is a deterioration in the educational opportunities for orphans and other vulnerable children. Given that high returns are associated with education, the loss of education for a large segment of the population would likely worsen the already dire economic consequences of the HIV/AIDS crisis. Using a systems approach, this paper argues that social entrepreneurship can be used as a vehicle to promote national human resource development, which will assist in the alleviation of societal ills on the national level as well as throughout Africa.

Keywords: human resource development, pan-african, social entrepreneurship, social enterprise

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Authors: Babak Salekmahdy

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Microfinance is often seen as a means of repairing credit markets and unleashing the potential contribution of impoverished people who rely on self-employment. Since the 1990s, the microfinance industry has expanded rapidly, opening the path for additional kinds of social entrepreneurship and social investment. However, current data indicate relatively few average consumer effects, opposing pushback against microfinance. This research reconsiders microfinance statements, stressing the variety of data on impacts and the essential (but limited) role of reimbursements. The report finishes by explaining a shift in thinking: from microfinance as a strictly defined enterprise finance to microfinance as a more widely defined home finance. Microfinance, under this perspective, provides advantages by providing liquidity for various requirements rather than just by increasing income.

Keywords: microfinance, small business, economic development, credit markets

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Authors: Ke Qiu, Minzi Mao, Jianjun Ren, Yu Zhao

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Although much has been done to investigate the influence of obstructive sleep apnea (OSA) on cognitive function, little attention has been paid to the role which gender differences play in this association. In the present study, we aim to explore the gender-specific association between OSA and cognitive impairment. Participants from UK biobank who have completed at least one of the five baseline cognitive tests (visuospatial memory, prospective memory, fluid intelligence, short numeric memory and reaction time) were included and were further categorized into three groups: (1) OSA, (2) self-reported snoring but without OSA, and (3) healthy controls (without OSA or snoring). Multivariable regression analysis was performed to examine the associations among snoring, OSA and performance of each of the five cognitive domains. A total of 267,889 participants (47% male, mean age: 57 years old) were included in our study. In the multivariable regression analysis, female participants in the OSA group had a higher risk of having poor prospective memory (OR: 1.24, 95% CI: 1.02～1.50, p = 0.03). Meanwhile, among female participants, OSA were inversely associated with the performances of fluid intelligence (β: -0.29, 95% CI: -0.46～-0.13, p < 0.001) and short-numeric memory (β: -0.14, 95% CI: -0.35～0.08, p = 0.02). In contrast, among male participants, no significant association was observed between OSA and impairment of the five cognitive domains. Overall, OSA was significantly associated with cognitive impairment in female participants rather than in male participants, indicating that more special attention and timely interventions should be given to female OSA patients to prevent further cognitive impairment.

Keywords: obstructive sleep apnea (OSA), cognitive impairment, gender-specific association, UK biobank

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Authors: Boumaraf Malak

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The development of intelligent transportation systems (ITS) and artificial intelligence (AI) are spurring us to pave the way for the widespread adoption of autonomous vehicles (AVs). This is open again opportunities for smart roads, smart traffic safety, and mobility comfort. A highly intelligent decision-making system is essential for autonomous driving around dense, dynamic objects. It must be able to handle complex road geometry and topology, as well as complex multiagent interactions, and closely follow higher-level commands such as routing information. Autonomous vehicles have become a very hot research topic in recent years due to their significant ability to reduce traffic accidents and personal injuries. Using new artificial intelligence-based technologies handles important functions in scene understanding, motion planning, decision making, vehicle control, social behavior, and communication for AV. This paper focuses only on deep reinforcement learning-based methods; it does not include traditional (flat) planar techniques, which have been the subject of extensive research in the past because reinforcement learning (RL) has become a powerful learning framework now capable of learning complex policies in high dimensional environments. The DRL algorithm used so far found solutions to the four main problems of autonomous driving; in our paper, we highlight the challenges and point to possible future research directions.

Keywords: deep reinforcement learning, autonomous driving, deep deterministic policy gradient, deep Q-learning

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Authors: Yuandi Hu, Gang Yang

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In order to study the business ethics of the international enterprise, the thirteenth-largest car manufacturer and second-largest truck manufacturer in the world, Daimler AG was taken as research object. At first, Daimler AG is briefly introduced and the bribery affairs of Daimler AG in China are simply reviewed. Subsequently, the causes of the bribery are discussed in depth and the manifestations of the value conflict are analyzed in detail. Based on the analyzed results, the reasons why they bribe are investigated. Furthermore, some proposals for improving business ethics of international enterprises are put forward based on the study of Daimler bribery case.

Keywords: business ethics, Daimler AG, bribe, cultural conflict

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Authors: Shih-Fang Lo, Tzu-Wei Guo, Chih-Hsuan Lee

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Tropical fisheries are vulnerable to the physical and biogeochemical oceanic changes associated with climate change. Warmer temperatures and extreme weather have beendamaging the abundance and growth patterns of aquatic organisms. In recent year, the shrinking of fish stock and labor shortage have increased the threat to global aquacultural production. Thus, building a climate-resilient and sustainable mechanism becomes an urgent, important task for global citizens. To tackle the problem, Taiwanese fishermen applies the artificial intelligence (AI) technology. In brief, the AI system (1) measures real-time water quality and chemical parameters infish ponds; (2) monitors fish stock through segmentation, detection, and classification; and (3) implements fishermen’sprevious experiences, perceptions, and real-life practices. Applying this system can stabilize the aquacultural production and potentially increase the labor force. Furthermore, this AI technology can build up a more resilient and sustainable system for the fishermen so that they can mitigate the influence of extreme weather while maintaining or even increasing their aquacultural production. In the future, when the AI system collected and analyzed more and more data, it can be applied to different regions of the world or even adapt to the future technological or societal changes, continuously providing the most relevant and useful information for fishermen in the world.

Keywords: aquaculture, artificial intelligence (AI), real-time system, sustainable fishery

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Authors: Aamir Ayub, Yasir Shehzad, Shakeel Ahmad

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This research paper aims to understand how the Pashtuns have been represented in relationship to terrorism in post-9/11 Bollywood and Lollywood movies. It focuses particularly on ‘Torbaaz’ from Bollywood and ‘Waar’ from Lollywood in order to define the nature of Pashtun characterization, the functioning of intelligence agencies, as well as the socio-political side of the represented narratives. In this research, the analytical approach developed is applied to contemplate how these films represent or fail to represent Pashtun identity, taking into consideration the cultural, historical and social dimensions. The study also aims to examine the effects of the media, particularly on the different ethnic groups’ perceptions of terrorism. In this case, it covers how the movie relates actual events in society – specifically, socio-political – to the messages in the film regarding the Pashtun people and their portrayal. Such elements may constitute the portrayal of intelligence agencies and their fight against terrorism, state-security dynamics, and the Pashtun society. In conclusion, this research paper focuses on the representation of Pashtuns in films after 9/11 and addresses the issue concerning the representation of ethnic groups in the method of the theme of terrorism. It provides ideas about the role of media in influencing the mind of the society and their attitude towards certain communities after geopolitics upheavals.

Keywords: pashtun representation, terrorism, 9/11 attacks, socio-political implications, ethnic representation in media

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Authors: Corinne Zurmuehle, Andreas Christoph Weber

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In 2019, the Swiss Federal Institute of Sport Magglingen (SFISM) conducted a mixed-methods study on the Swiss elite sports system, which yielded a large quantity of research data. In a quantitative online survey, 1151 elite sports athletes, 542 coaches, and 102 Performance Directors of national sports federations (NF) have submitted their perceptions of the national support measures of the Swiss elite sports system. These data provide an essential database for the further development of the Swiss elite sports system. The results were published in a report presenting the results divided into 40 Olympic summer and 14 winter sports (Olympic classification). The authors of this paper assume that, in practice, this division is too unspecific to assess where further measures would be needed. The aim of this paper is to find appropriate parameters for data visualization in order to identify disparities in sports promotion that allow an assessment of where further interventions by Swiss Olympic (NF umbrella organization) are required. Method: First, the variable 'salary earned from sport' was defined as a variable to measure the impact of elite sports promotion. This variable was chosen as a measure as it represents an important indicator for the professionalization of elite athletes and therefore reflects national level sports promotion measures applied by Swiss Olympic. Afterwards, the variable salary was tested with regard to the correlation between Olympic classification [a], calculating the Eta coefficient. To estimate the appropriate parameters for data visualization, the correlation between salary and four further parameters was analyzed by calculating the Eta coefficient: [a] sport; [b] prioritization (from 1 to 5) of the sports by Swiss Olympic; [c] gender; [d] employment level in sports. Results & Discussion: The analyses reveal a very small correlation between salary and Olympic classification (ɳ² = .011, p = .005). Gender demonstrates an even small correlation (ɳ² = .006, p = .014). The parameter prioritization was correlating with small effect (ɳ² = .017, p = .001) as did employment level (ɳ² = .028, p < .001). The highest correlation was identified by the parameter sport with a moderate effect (ɳ² = .075, p = .047). The analyses show that the disparities in sports promotion cannot be determined by a particular parameter but presumably explained by a combination of several parameters. We argue that the possibility of combining parameters for data visualization should be enabled when the analysis is provided to Swiss Olympic for further strategic decision-making. However, the inclusion of multiple parameters massively multiplies the number of graphs and is therefore not suitable for practical use. Therefore, we suggest to apply interactive dashboards for data visualization using Business Intelligence Software. Practical & Theoretical Contribution: This contribution provides the first attempt to use Business Intelligence Software for strategic decision-making in national level sports regarding the prioritization of national resources for sports and athletes. This allows to set specific parameters with a significant effect as filters. By using filters, parameters can be combined and compared against each other and set individually for each strategic decision.

Keywords: data visualization, business intelligence, Swiss elite sports system, strategic decision-making

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Authors: Rushiraj Heshi, Smriti Bhandari

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Master Data Management requires creation of Central repository, applying constraints on Repository and designing processes to manage data. Designing of Repository, constraints on repository and business processes is very tedious and time consuming task for large Enterprise. Hence Visual Repository, constraints and Process (Workflow) modeling is the most critical step in Master Data Management.In this paper, we realize a Visual Modeling tool for implementing Repositories, Constraints and Processes based on Eclipse Plugin using GMF/EMF which follows principles of Model Driven Engineering (MDE).

Keywords: EMF, GMF, GEF, repository, constraint, process

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Authors: Harvey Hyman, Thomas Hull

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As one of the largest cancer treatment centers in the United States, we continuously confront the challenge of how to leverage the best possible technological solutions, in order to provide the highest quality of service to our customers – the doctors, nurses and patients at Moffitt who are fighting every day for the prevention and cure of cancer. This paper reports on the transition from a vertical to a horizontal IT infrastructure. We discuss how the new frameworks and methods such as public, private and hybrid cloud, brokering cloud services are replacing the traditional vertical paradigm for computing. We also report on the impact of containers, micro services, and the shift to continuous integration/continuous delivery. These impacts and changes in delivery methodology for computing are driving how we accomplish our strategic IT goals across the enterprise.

Keywords: cloud computing, IT infrastructure, IT architecture, healthcare

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Authors: Abdul Basit Kiani, Maryam Kiani

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Online developers now have the tools necessary to create online apps that are not only reliable but also highly interactive, thanks to the introduction of JavaScript frameworks and APIs. The objective is to give a broad overview of the recent advances in the area. The fusion of machine learning (ML) and artificial intelligence (AI) has expanded the possibilities for web development. Modern websites now include chatbots, clever recommendation systems, and customization algorithms built in. In the rapidly evolving landscape of modern websites, it has become increasingly apparent that user engagement and personalization are key factors for success. To meet these demands, websites now incorporate a range of innovative technologies. One such technology is chatbots, which provide users with instant assistance and support, enhancing their overall browsing experience. These intelligent bots are capable of understanding natural language and can answer frequently asked questions, offer product recommendations, and even help with troubleshooting. Moreover, clever recommendation systems have emerged as a powerful tool on modern websites. By analyzing user behavior, preferences, and historical data, these systems can intelligently suggest relevant products, articles, or services tailored to each user's unique interests. This not only saves users valuable time but also increases the chances of conversions and customer satisfaction. Additionally, customization algorithms have revolutionized the way websites interact with users. By leveraging user preferences, browsing history, and demographic information, these algorithms can dynamically adjust the website's layout, content, and functionalities to suit individual user needs. This level of personalization enhances user engagement, boosts conversion rates, and ultimately leads to a more satisfying online experience. In summary, the integration of chatbots, clever recommendation systems, and customization algorithms into modern websites is transforming the way users interact with online platforms. These advanced technologies not only streamline user experiences but also contribute to increased customer satisfaction, improved conversions, and overall website success.

Keywords: Javascript, machine learning, artificial intelligence, web development

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Authors: Abdal-Hafeez Alhussein

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Artificial Intelligence (AI) has become a transformative force in the field of information technologies, reshaping how data is processed, analyzed, and utilized across various domains. This paper explores the multifaceted applications of AI within information technology, focusing on three key areas: automation, scalability, and data-driven decision-making. We delve into how AI-powered automation is optimizing operational efficiency in IT infrastructures, from automated network management to self-healing systems that reduce downtime and enhance performance. Scalability, another critical aspect, is addressed through AI’s role in cloud computing and distributed systems, enabling the seamless handling of increasing data loads and user demands. Additionally, the paper highlights the use of AI in cybersecurity, where real-time threat detection and adaptive response mechanisms significantly improve resilience against sophisticated cyberattacks. In the realm of data analytics, AI models—especially machine learning and natural language processing—are driving innovation by enabling more precise predictions, automated insights extraction, and enhanced user experiences. The paper concludes with a discussion on the ethical implications of AI in information technologies, underscoring the importance of transparency, fairness, and responsible AI use. It also offers insights into future trends, emphasizing the potential of AI to further revolutionize the IT landscape by integrating with emerging technologies like quantum computing and IoT.

Keywords: artificial intelligence, information technology, automation, scalability

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Authors: Heather Fulford, Melanie Liddell

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The recruitment and retention of high quality employees can prove to be challenging for social enterprises, particularly in some of the core business support functions such as marketing, communications, IT and finance. This holds true for social enterprises in urban contexts, where roles with more attractive remuneration in these business functions can often be found quite readily in the private sector. For social enterprises situated in rural locations, the challenges of staff recruitment and retention are even more acute. Such challenges can lead to a skills deficit in rural social enterprises, which can, at best, hinder their growth potential, and worse, jeopardise their chances of survival. This in turn, can have a negative impact on the sustainability and resilience of the surrounding rural community in which the social enterprise is located. The purpose of this paper is to report on aspects of a collaborative initiative established to stimulate innovation and business growth in remote rural businesses in Scotland. Launched in 2010, this initiative was designed to attract young students and graduates from the region to stay in the region upon completion of their studies, and to attract others from outside the region to re-locate there post-university. To facilitate this, SMEs in the region were offered wage subsidies to encourage them to recruit a student or graduate on a work placement for up to one year to participate in an innovation or business growth-oriented project. A number of the employers offering work placements were social enterprises. Through analysis of the placement project and role specifications devised by the participating social enterprises, an overview is provided of their business development needs and the skills they require to stimulate innovation and growth. Scrutiny of the reflective accounts compiled by the students and graduates at the close of their work placements highlights the benefits they derived from being able to put their academic knowledge and skills into action within a social enterprise. Examination of interviews conducted with a sample of placement employers reveals the contribution the students and graduates made during the business development projects with the social enterprises. The challenges of hosting such placements are also discussed. The paper concludes with indications of the lessons learned and an outline of the wider implications for other remote rural locations in which social enterprises play an important role in the local economy and life of the community.

Keywords: resilience, rural development, regeneration, regional development, recruitment, resource management, retention, remuneration

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Authors: Amarachukwu B. Isiaka, Vivian N. Anakwenze, Chinyere C. Ezemba, Chiamaka R. Ilodinso, Chikodili G. Anaukwu, Chukwuebuka M. Ezeokoli, Ugonna H. Uzoka

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Infectious diseases continue to pose significant threats to global public health, necessitating advanced and timely detection methods for effective outbreak management. This study explores the integration of artificial intelligence (AI) in the early detection and management of infectious disease outbreaks. Leveraging vast datasets from diverse sources, including electronic health records, social media, and environmental monitoring, AI-driven algorithms are employed to analyze patterns and anomalies indicative of potential outbreaks. Machine learning models, trained on historical data and continuously updated with real-time information, contribute to the identification of emerging threats. The implementation of AI extends beyond detection, encompassing predictive analytics for disease spread and severity assessment. Furthermore, the paper discusses the role of AI in predictive modeling, enabling public health officials to anticipate the spread of infectious diseases and allocate resources proactively. Machine learning algorithms can analyze historical data, climatic conditions, and human mobility patterns to predict potential hotspots and optimize intervention strategies. The study evaluates the current landscape of AI applications in infectious disease surveillance and proposes a comprehensive framework for their integration into existing public health infrastructures. The implementation of an AI-driven early detection system requires collaboration between public health agencies, healthcare providers, and technology experts. Ethical considerations, privacy protection, and data security are paramount in developing a framework that balances the benefits of AI with the protection of individual rights. The synergistic collaboration between AI technologies and traditional epidemiological methods is emphasized, highlighting the potential to enhance a nation's ability to detect, respond to, and manage infectious disease outbreaks in a proactive and data-driven manner. The findings of this research underscore the transformative impact of harnessing AI for early detection and management, offering a promising avenue for strengthening the resilience of public health systems in the face of evolving infectious disease challenges. This paper advocates for the integration of artificial intelligence into the existing public health infrastructure for early detection and management of infectious disease outbreaks. The proposed AI-driven system has the potential to revolutionize the way we approach infectious disease surveillance, providing a more proactive and effective response to safeguard public health.

Keywords: artificial intelligence, early detection, disease surveillance, infectious diseases, outbreak management

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Authors: Ian M. Grey, Bryan Roche, Anna Dillon, Justin Thomas, Sarah Cassidy, Dylan Colbert, Ian Stewart

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This paper presents findings from a study conducted in two schools in Abu Dhabi. The hypothesis is that teaching young children to derive various relations between stimuli leads to increases in full-scale IQ scores of typically developing children. In the experimental group, sixteen 6-7-year-old children were exposed over six weeks to an intensive training intervention designed specifically for their age group. This training intervention, presented on a tablet, aimed to improve their understanding of the relations Same, Opposite, Different, contextual control over the concept of Sameness and Difference, and purely arbitrary derived relational responding for Sameness and Difference. In the control group, sixteen 6-7-year-old children interacted with KIBO robotics over six weeks. KIBO purports to improve cognitive skills through engagement with STEAM activities. Increases in full-scale IQ were recorded for most children in the experimental group, while no increases in full-scale IQ were recorded for the control group. These findings support the hypothesis that relational skills underlie many aspects of general cognitive ability.

Keywords: early childhood, derived relational responding, intelligence, relational frame theory, relational skills

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Authors: R. Granger-Ellis, R. B. Speaker, Jr., P. J. Austin

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This research study examined more than 340 gifted and talented students enrolled in various gifted and talented programs in a large southeastern United States metropolitan area (creative arts, urban charters, suburban public schools) for socio-affective psychological development and whether a particular curriculum encouraged developmental growth. This study focused on students receiving distinctive gifted and talented curricula (creative arts, arts-integrated, and academic acceleration) and analyzed for (1) socio-affective development levels and (2) whether a particular curriculum encouraged developmental growth. Research questions guiding the study: (1) How do academically and artistically gifted 10th and 11th grade students perform on psychological scales of social and emotional intelligence? (2) Do adolescents receiving distinctive gifted and talented curriculum differ in their socio-affective developmental profiles? Students’ performances on psychometric scales were compared over time and by curriculum type. Over the first semester of the academic year, participants took pre- and post-tests assessing socio-affective intelligence (BarOn EQ-I: YV). Differences in growth on these psychological scales (individuals and programs) were examined. Program artifacts provided insight for curriculum correlation.

Keywords: gifted and talented curriculum, social and emotional development, moral development, socio-affective curriculum

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Authors: Zheni S. Stefanova, Kandethody M. Ramachandran

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With the increasing dependency on our computer devices, we face the necessity of adequate, efficient and effective mechanisms, for protecting our network. There are two main problems that Intrusion Detection Systems (IDS) attempt to solve. 1) To detect the attack, by analyzing the incoming traffic and inspect the network (intrusion detection). 2) To produce a prompt response when the attack occurs (intrusion prevention). It is critical creating an Intrusion detection model that will detect a breach in the system on time and also challenging making it provide an automatic and with an acceptable delay response at every single stage of the monitoring process. We cannot afford to adopt security measures with a high exploiting computational power, and we are not able to accept a mechanism that will react with a delay. In this paper, we will propose an intrusion response mechanism that is based on artificial intelligence, and more precisely, reinforcement learning techniques (RLT). The RLT will help us to create a decision agent, who will control the process of interacting with the undetermined environment. The goal is to find an optimal policy, which will represent the intrusion response, therefore, to solve the Reinforcement learning problem, using a Q-learning approach. Our agent will produce an optimal immediate response, in the process of evaluating the network traffic.This Q-learning approach will establish the balance between exploration and exploitation and provide a unique, self-learning and strategic artificial intelligence response mechanism for IDS.

Keywords: cyber security, intrusion prevention, optimal policy, Q-learning

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Authors: John Battersby, Rhys Ball

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After the 15 March 2019 Mosque attacks in Christchurch, the New Zealand security sector has had to address its training and preparedness levels for dealing with contemporary terrorist threats as well as potential future manifestations of terrorism. From time to time, members of the academic community from Australia and New Zealand have been asked to assist agencies in this endeavour. In the course of 2018, New Zealand security sector professionals working in the counter-terrorism area were interviewed about how they regarded academic contributions to understanding terrorism and counter-terrorism. Responses were mixed, ranging from anti-intellectualism, a belief that the inability to access classified material rendered academic work practically useless - to some genuine interest and desire for broad based academic studies on issues practitioners did not have the time to look at. Twelve months later, researchers have revisited those spoken to prior to the Brenton Tarrant 15 March shooting to establish if there has been a change in the way academic research is perceived, viewed and valued, and what key factors have contributed to this shift in thinking. This paper takes this data, combined with a consideration of the literature on higher education within professional police and intelligence forces, and on the general perception of academics by practitioners, to present a series of findings that will contribute to a more proactive and effective set of engagements, between two distinct but important security sectors, that reflect more closely with international practice.

Keywords: academic, counter terrorism, intelligence, practitioner, research, security

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Authors: Riza Yilmaz, Samet Kiyak, Sezin Nur Yilmaz, Yasemin Yilmaz

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Child delinquency has been increasing in our country as well as in many countries of the world. Child intelligence, abilities, family's social environment and life conditions are the factors which affect the child delinquency. The reports of 73 cases ages of 12-15 which were sent to the University of Bulent Ecevit, School of Medicine, Forensic Medicine Department between January 2011-September 2015, in order to evaluate medically, children pushed to crime by the judicial authorities are examined in terms of age, gender, educational background, place of residence, reasons for being sent, whether it’s a repeating crime or not, type of intelligence test, results revealed by forensic medicine and department of mental and neurological disorders. When children pushed to crime examined in terms of their crimes, the most common type of crime was identified as theft (n = 24). The crimes with 19 physical attacks and 12 sexual abuse were seen. Following that other 12 crimes were determined as damage to property, hemp crop, insult, incitement to crime, forgery of private documents, illegal excavation, threatening, involuntary manslaughter. The alleged crimes in 6 cases were more than one. The children pushed to crime are one of the major social problems of many countries. In this sense, it is not only the responsibility of government agencies to protect children pushed to crime, also, the civil society organizations should take place in this struggle.

Keywords: delinquent behaviour, forensic medicine, crime, punishment

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Authors: Basem Kamal Abasakhiroun Farag

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Fashion, like many other areas of design, has undergone numerous developments over the centuries. The aim of the article is to recognize and evaluate the importance of advanced technologies in fashion design and to examine how they are transforming the role of contemporary fashion designers by transforming the creative process. It also discusses how contemporary culture is involved in such developments and how it influences fashion design in terms of conceptualization and production. The methodology used is based on examining various examples of the use of technology in fashion design and drawing parallels between what was feasible then and what is feasible today. Comparison of case studies, examples of existing fashion designs and experiences with craft methods; We therefore observe patterns that help us predict the direction of future developments in this area. Discussing the technological elements in fashion design helps us understand the driving force behind the trend. The research presented in the article shows that there is a trend towards significantly increasing interest and progress in the field of fashion technology, leading to the emergence of hybrid artisanal methods. In summary, as fashion technologies advance, their role in clothing production is becoming increasingly important, extending far beyond the humble sewing machine.

Keywords: fashion, identity, such, textiles ambient intelligence, proximity sensors, shape memory materials, sound sensing garments, wearable technology bio textiles, fashion trends, nano textiles, new materials, smart textiles, techno textiles fashion design, functional aesthetics, 3D printing.

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Authors: Mariangela Viviani, Daniele Germano, Simone Colace, Agostino Forestiero, Giuseppe Papuzzo, Sara Laurita

Abstract:

Nowadays, people spend most of their time in closed environments, in offices, or at home. Therefore, secure and highly livable environmental conditions are needed to reduce the probability of aerial viruses spreading. Also, to lower the human impact on the planet, it is important to reduce energy consumption. Heating, Ventilation, and Air Conditioning (HVAC) systems account for the major part of energy consumption in buildings [1]. Devising systems to control and regulate the airflow is, therefore, essential for energy efficiency. Moreover, an optimal setting for thermal comfort and air quality is essential for people’s well-being, at home or in offices, and increases productivity. Thanks to the features of Artificial Intelligence (AI) tools and techniques, it is possible to design innovative systems with: (i) Improved monitoring and prediction accuracy; (ii) Enhanced decision-making and mitigation strategies; (iii) Real-time air quality information; (iv) Increased efficiency in data analysis and processing; (v) Advanced early warning systems for air pollution events; (vi) Automated and cost-effective m onitoring network; and (vii) A better understanding of air quality patterns and trends. We propose AIR SAFE, an IoT-based infrastructure designed to optimize air quality and thermal comfort in indoor environments leveraging AI tools. AIR SAFE employs a network of smart sensors collecting indoor and outdoor data to be analyzed in order to take any corrective measures to ensure the occupants’ wellness. The data are analyzed through AI algorithms able to predict the future levels of temperature, relative humidity, and CO₂ concentration [2]. Based on these predictions, AIR SAFE takes actions, such as opening/closing the window or the air conditioner, to guarantee a high level of thermal comfort and air quality in the environment. In this contribution, we present the results from the AI algorithm we have implemented on the first s et o f d ata c ollected i n a real environment. The results were compared with other models from the literature to validate our approach.

Keywords: air quality, internet of things, artificial intelligence, smart home

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Authors: Joshua Harrell, Gideon Osei Bonsu, Susan Garza, Clarence Conner, Da’Neisha Harris, Emma Bukoswki, Zohreh Safari

Abstract:

The Social Vulnerability Index (SVI) stands as a pivotal tool for gauging community resilience amidst diverse stressors, including pandemics like COVID-19. This paper synthesizes recent research and underscores the significance of SVI in elucidating the differential impacts of crises on communities. Drawing on studies by Fox et al. (2023) and Mah et al. (2023), we delve into the application of SVI alongside emerging data sources to uncover nuanced insights into community vulnerability. Specifically, we explore the utilization of SVI in conjunction with mobility data from platforms like SafeGraph to probe the intricate relationship between social vulnerability and mobility dynamics during the COVID-19 pandemic. By leveraging 16 community variables derived from the American Community Survey, including socioeconomic status and demographic characteristics, SVI offers actionable intelligence for guiding targeted interventions and resource allocation. Building upon recent advancements, this paper contributes to the discourse on harnessing AI techniques to mitigate health disparities and fortify public health resilience in the face of pandemics and other crises.

Keywords: social vulnerability index, mobility dynamics, data analytics, health equity, pandemic preparedness, targeted interventions, data integration

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Authors: Hemad Zareiforoush, Saeed Minaei, Ahmad Banakar, Mohammad Reza Alizadeh

Abstract:

The quality inspection of rice (Oryza sativa L.) during its various processing stages is very important. In this research, an artificial intelligence-based model coupled with computer vision techniques was developed as a decision support system for qualitative grading of rice grains. For conducting the experiments, first, 25 samples of rice grains with different levels of percentage of broken kernels (PBK) and degree of milling (DOM) were prepared and their qualitative grade was assessed by experienced experts. Then, the quality parameters of the same samples examined by experts were determined using a machine vision system. A grading model was developed based on fuzzy logic theory in MATLAB software for making a relationship between the qualitative characteristics of the product and its quality. Totally, 25 rules were used for qualitative grading based on AND operator and Mamdani inference system. The fuzzy inference system was consisted of two input linguistic variables namely, DOM and PBK, which were obtained by the machine vision system, and one output variable (quality of the product). The model output was finally defuzzified using Center of Maximum (COM) method. In order to evaluate the developed model, the output of the fuzzy system was compared with experts’ assessments. It was revealed that the developed model can estimate the qualitative grade of the product with an accuracy of 95.74%.

Keywords: machine vision, fuzzy logic, rice, quality

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Authors: Adiba Matin, Daniel Cabrera, Javiera Bellolio, Jasmine Stewart, Dana Gerberi (librarian), Nathan Cummins, Fernanda Bellolio

Abstract:

OBJECTIVES: Artificial intelligence (AI) tools are becoming more prevalent in healthcare settings, particularly for diagnostic and therapeutic recommendations, with an expected surge in the incoming years. The bedside use of this technology for clinicians opens the possibility of disagreements between the recommendations from AI algorithms and clinicians’ judgment. There is a paucity in the literature analyzing nature and possible outcomes of these potential conflicts, particularly related to ethical considerations. The goal of this scoping review is to identify, analyze and classify current themes and potential strategies addressing ethical conflicts originating from the conflict between AI and human recommendations. METHODS: A protocol was written prior to the initiation of the study. Relevant literature was searched by a medical librarian for the terms of artificial intelligence, healthcare and liability, ethics, or conflict. Search was run in 2021 in Ovid Cochrane Central Register of Controlled Trials, Embase, Medline, IEEE Xplore, Scopus, and Web of Science Core Collection. Articles describing the role of AI in healthcare that mentioned conflict between humans and AI were included in the primary search. Two investigators working independently and in duplicate screened titles and abstracts and reviewed full-text of potentially eligible studies. Data was abstracted into tables and reported by themes. We followed methodological guidelines for Preferred Reporting Items for Systematic reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR). RESULTS: Of 6846 titles and abstracts, 225 full texts were selected, and 48 articles included in this review. 23 articles were included as original research and review papers. 25 were included as editorials and commentaries with similar themes. There was a lack of consensus in the included articles on who would be held liable for mistakes incurred by following AI recommendations. It appears that there is a dichotomy of the perceived ethical consequences depending on if the negative outcome is a result of a human versus AI conflict or secondary to a deviation from standard of care. Themes identified included transparency versus opacity of recommendations, data bias, liability of outcomes, regulatory framework, and the overall scope of artificial intelligence in healthcare. A relevant issue identified was the concern by clinicians of the “black box” nature of these recommendations and the ability to judge appropriateness of AI guidance. CONCLUSION AI clinical tools are being rapidly developed and adopted, and the use of this technology will create conflicts between AI algorithms and healthcare workers with various outcomes. In turn, these conflicts may have legal, and ethical considerations. There is limited consensus about the focus of ethical and liability for outcomes originated from disagreements. This scoping review identified the importance of framing the problem in terms of conflict between standard of care or not, and informed by the themes of transparency/opacity, data bias, legal liability, absent regulatory frameworks and understanding of the technology. Finally, limited recommendations to mitigate ethical conflicts between AI and humans have been identified. Further work is necessary in this field.

Keywords: ethics, artificial intelligence, emergency medicine, review

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Authors: Tran Duong Nguyen, Marwan Shagar, Qinghao Zeng, Aras Maqsoodi, Pardis Pishdad, Eunhwa Yang

Abstract:

Construction supply chain management (CSCM) involves the collaboration of many disciplines and actors, which generates vast amounts of data. However, inefficient, fragmented, and non-standardized data storage often hinders this data exchange. The industry has adopted building information modeling (BIM) -a digital representation of a facility's physical and functional characteristics to improve collaboration, enhance transmission security, and provide a common data exchange platform. Still, the volume and complexity of data require tailored information categorization, aligning with stakeholders' preferences and demands. To address this, artificial intelligence (AI) can be integrated to handle this data’s magnitude and complexities. This research aims to develop an integrated and efficient approach for data exchange in CSCM by utilizing AI. The paper covers five main objectives: (1) Investigate existing framework and BIM adoption; (2) Identify challenges in data exchange; (3) Propose an integrated framework; (4) Enhance data transmission security; and (5) Develop data exchange in CSCM. The proposed framework demonstrates how integrating BIM and other technologies, such as cloud computing, blockchain, and AI applications, can significantly improve the efficiency and accuracy of data exchange in CSCM.

Keywords: construction supply chain management, BIM, data exchange, artificial intelligence

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