Search results for: strategic spiritual intelligence

Authors: Chrysovalanto Sofia Karatosidi, Dimitra Iordanoglou

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The overlap of leadership skills with personality traits, beliefs, values, and the integration of cognitive abilities, analytical and critical thinking skills into leadership competencies raises the need to segregate further and investigate them. Hence, the domains of cognitive functions that contribute to leadership effectiveness should also be identified. Organizational cognitive neuroscience and neuroleadership can shed light on the study of these critical leadership skills. As the first part of our research, this pilot study aims to explore the relationships between higher-order cognitive functions (executive functions), trait emotional intelligence (EI), personality, and general cognitive ability in leadership. Twenty-six graduate and postgraduate students were assessed on neuropsychological tests that measure important aspects of executive functions (EF) and completed self-reported questionnaires about trait EI, personality, leadership styles, and leadership effectiveness. Specifically, we examined four core EF—fluency (phonemic and semantic), information updating and monitoring, working memory, and inhibition of prepotent responses. Leadership effectiveness was positively associated with phonemic fluency (PF), which involves mental flexibility, in turn, an increasingly important ability for future leaders in this rapidly changing world. Transformational leadership was positively associated with trait EI, extraversion, and openness to experience, a result that is following previous findings. The relationship between specific EF constructs and leadership effectiveness emphasizes the role of higher-order cognitive functions in the field of leadership as an individual difference. EF brings a new perspective into leadership literature by providing a direct, non-invasive, scientifically-valid connection between brain function and leadership behavior.

Keywords: cognitive neuroscience, emotional intelligence, executive functions, leadership

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Authors: Anil Anand

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Despite enormous strides made during the past decade, particularly with the adoption and expansion of community policing, there remains much that police leaders can do to improve police-public relations. The urgency is particularly evident in cities across the United States and Europe where an increasing number of police interactions over the past few years have ignited large, sometimes even national, protests against police policy and strategy, highlighting a gap between what police leaders feel they have archived in terms of public satisfaction, support, and legitimacy and the perception of bias among many marginalized communities. The decision on which one policing strategy is chosen over another, how many resources are allocated, and how strenuously the policy is applied resides primarily with the police and the units and subunits tasked with its enforcement. The scope and opportunity for police officers in impacting social attitudes and social policy are important elements that cannot be overstated. How do police leaders, for instance, decide when to apply one strategy—say community-based policing—over another, like intelligence-led policing? How do police leaders measure performance and success? Should these measures be based on quantitative preferences over qualitative, or should the preference be based on some other criteria? And how do police leaders define, allow, and control discretionary decision-making? Mending Broken Fences Policing provides police and security services leaders with a model based on social cohesion, that incorporates intelligence-led and community policing (IP-CP), supplemented by a quality/quantity/crime (QQC) framework to provide a four-step process for the articulable application of police intervention, performance measurement, and application of discretion.

Keywords: social cohesion, quantitative performance measurement, qualitative performance measurement, sustainable leadership

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Authors: Diana Coyl-Shepherd, Lisa Newland

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Research and theory suggest that parenting and parent-child attachment influence emotional development and well-being. Studies indicate that adolescents often describe differences in relationships with each parent and may form different types of attachment to mothers and fathers. During adolescence and young adulthood, romantic partners may also become attachment figures, influencing well being, and providing a relational context for emotion skill development. Mothers, however, tend to be remain the primary attachment figure; fathers and romantic partners are more likely to be secondary attachment figures. The following hypotheses were tested: 1) participants would rate mothers as more accepting and less rejecting than fathers, 2) participants would rate secure attachment to mothers higher and insecure attachment lower compared to father and romantic partner, 3) parental rejection and insecure attachment would be negatively related to life satisfaction and emotional intelligence, and 4) secure attachment and parental acceptance would be positively related life satisfaction and emotional intelligence. After IRB and informed consent, one hundred fifty adolescents and young adults (ages 11-28, M = 19.64; 71% female) completed an online survey. Measures included parental acceptance, rejection, attachment (i.e., secure, dismissing, and preoccupied), emotional intelligence (i.e., seeking and providing comfort, use, and understanding of self emotions, expressing warmth, understanding and responding to others’ emotional needs), and well-being (i.e., self-confidence and life satisfaction). As hypothesized, compared to fathers’, mothers’ acceptance was significantly higher t (190) = 3.98, p = .000 and rejection significantly lower t (190) = - 4.40, p = .000. Group differences in secure attachment were significant, f (2, 389) = 40.24, p = .000; post-hoc analyses revealed significant differences between mothers and fathers and between mothers and romantic partners; mothers had the highest mean score. Group differences in preoccupied attachment were significant, f (2, 388) = 13.37, p = .000; post-hoc analyses revealed significant differences between mothers and romantic partners, and between fathers and romantic partners; mothers have the lowest mean score. However, group differences in dismissing attachment were not significant, f (2, 389) = 1.21, p = .30; scores for mothers and romantic partners were similar; father means score was highest. For hypotheses 3 and 4 significant negative correlations were found between life satisfaction and dismissing parent, and romantic attachment, preoccupied father and romantic attachment, and mother and father rejection variables; secure attachment variables and parental acceptance were positively correlated with life satisfaction. Self-confidence was correlated only with mother acceptance. For emotional intelligence, seeking and providing comfort were negatively correlated with parent dismissing and mother rejection; secure mother and romantic attachment and mother acceptance were positively correlated with these variables. Use and understanding of self-emotions were negatively correlated with parent and partner dismissing attachment, and parent rejection; romantic secure attachment and parent acceptance were positively correlated. Expressing warmth was negatively correlated with dismissing attachment variables, romantic preoccupied attachment, and parent rejection; whereas attachment secure variables were positively associated. Understanding and responding to others’ emotional needs were correlated with parent dismissing and preoccupied attachment variables and mother rejection; only secure father attachment was positively correlated.

Keywords: adolescent emotional intelligence, life satisfaction, parent and romantic attachment, parental rejection and acceptance

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Authors: Fatima Faraz

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BACKGROUND: While Artificial intelligence (AI) provides new opportunities across a wide variety of industries, healthcare is no exception. AI can lead to advancements in how the healthcare system functions and improves the quality of patient care. Developing countries like Pakistan are lagging in the implementation of AI-based solutions in healthcare. This demands increased knowledge and AI literacy among health care professionals. OBJECTIVES: To assess the level of awareness among medical students and faculty about AI in preparation for teaching AI basics and data science applications in clinical practice in an integrated medical curriculum. METHODS: An online 15-question semi-structured questionnaire, previously tested and validated, was delivered among participants through convenience sampling. The questionnaire composed of 3 parts: participant’s background knowledge, AI awareness, and attitudes toward AI applications in medicine. RESULTS: A total of 182 students and 39 faculty members from Rawalpindi Medical University, Pakistan, participated in the study. Only 26% of students and 46.2% of faculty members responded that they were aware of AI topics in clinical medicine. The major source of AI knowledge was social media (35.7%) for students and professional talks and colleagues (43.6%) for faculty members. 23.5% of participants answered that they personally had a basic understanding of AI. Students and faculty (60.1%) were interested in AI in patient care and teaching domain. These findings parallel similar published AI survey results. CONCLUSION: This survey concludes interest among students and faculty in AI developments and technology applications in healthcare. Further studies are required in order to correctly fit AI in the integrated modular curriculum of medical education.

Keywords: medical education, data science, artificial intelligence, curriculum

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Authors: Weichen Chang

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To create a guided teaching method for AI generative drawing design, this paper develops a set of teaching models for AI generative drawing (DCAI), which combines learning modes such as problem-solving, thematic inquiry, phenomenon-based, task-oriented, and DFC . Through the information security AI picture book learning guided programs and content, the application of participatory action research (PAR) and interview methods to explore the dual knowledge of Context and ChatGPT (DCAI) for AI to guide the development of students' AI learning skills. In the interviews, the students highlighted five main learning outcomes (self-study, critical thinking, knowledge generation, cognitive development, and presentation of work) as well as the challenges of implementing the model. Through the use of DCAI, students will enhance their consensus awareness of generative mapping analysis and group cooperation, and they will have knowledge that can enhance AI capabilities in DCAI inquiry and future life. From this paper, it is found that the conclusions are (1) The good use of DCAI can assist students in exploring the value of their knowledge through the power of stories and finding the meaning of knowledge communication; (2) Analyze the transformation power of the integrity and coherence of the story through the context so as to achieve the tension of ‘starting and ending’; (3) Use ChatGPT to extract inspiration, arrange story compositions, and make prompts that can communicate with people and convey emotions. Therefore, new knowledge construction methods will be one of the effective methods for AI learning in the face of artificial intelligence, providing new thinking and new expressions for interdisciplinary design and design education practice.

Keywords: artificial intelligence, task-oriented, contextualization, design education

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Authors: Dominika Collett

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AI applications, such as machine learning, require access to a vast amount of data in the training phase, which can often be the subject of copyright protection. During later usage, the various content with which the application works can be recorded or made available on the basis of which it produces the resulting output. The EU has recently adopted new legislation to secure machine access to protected works under the DSM Directive; but, the issue of machine use of copyright works is not clearly addressed. However, such clarity is needed regarding the increasing importance of AI and its development. Therefore, this paper provides a basic background of the technology used in the development of applications in the field of computer creativity. The second part of the paper then will focus on a legal analysis of machine use of the authors' works from the perspective of existing European and Czech legislation. The main results of the paper discuss the potential collision of existing legislation in regards to machine use of works with special focus on exceptions and limitations. The legal regulation of machine use of copyright work will impact the development of AI technology.

Keywords: copyright, artificial intelligence, legal use, infringement, Czech law, EU law, text and data mining

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Authors: Raquel Camprubí

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Cultural tourism has been postulated as relevant motivation for tourist over the world during the last decades. In this context, museums are the main attraction for cultural tourists who are seeking to connect with the history and culture of the visited place. From the point of view of an urban destination, museums and other cultural resources are essential to have a strong tourist supply at the destination, in order to be capable of catching attention and interest of cultural tourists. In particular, museums’ challenge is to be prepared to offer the best experience to their visitors without to forget their mission-based mainly on protection of its collection and other social goals. Thus, museums individually want to be competitive and have good positioning to achieve their strategic goals. The life cycle of the destination and the level of maturity of its tourism product influence the need of tourism agents to cooperate and collaborate among them, in order to rejuvenate their product and become more competitive as a destination. Additionally, prior studies have considered an approach of different models of a public and private partnership, and collaborative and cooperative relations developed among the agents of a tourism destination. However, there are no studies that pay special attention to museums and the strategic alliances developed to obtain mutual benefits. Considering this background, the purpose of this study is to analyze in what extent museums of a given urban destination have established strategic links and relations among them, in order to improve their competitive position at both individual and destination level. In order to achieve the aim of this study, the city of Girona (Spain) and the museums located in this city are taken as a case study. Data collection was conducted using in-depth interviews, in order to collect all the qualitative data related to nature, strengthen and purpose of the relational ties established among the museums of the city or other relevant tourism agents of the city. To conduct data analysis, a Social Network Analysis (SNA) approach was taken using UCINET software. Position of the agents in the network and structure of the network was analyzed, and qualitative data from interviews were used to interpret SNA results. Finding reveals the existence of strong ties among some of the museums of the city, particularly to create and promote joint products. Nevertheless, there were detected outsiders who have an individual strategy, without collaboration and cooperation with other museums or agents of the city. Results also show that some relational ties have an institutional origin, while others are the result of a long process of cooperation with common projects. Conclusions put in evidence that collaboration and cooperation of museums had been positive to increase the attractiveness of the museum and the city as a cultural destination. Future research and managerial implications are also mentioned.

Keywords: cultural tourism, competitiveness, museums, Social Network analysis

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Authors: Ma. Eliza Jijeth V. dela Cruz

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Technology is a recent prodigy in people’s everyday life that has taken off. It infiltrated almost every aspect of one’s lives, changing how people work, how people learn and how people perceive things. Academic Institutions, just like other organizations, have deeply modified its strategies to integrate technology into the institutional vision and corporate strategy that has never been greater. Information and Communications Technology (ICT) continues to be recognized as a major factor in organizations realizing its aims and objectives. Consequently, ICT has an important role in the mobilization of an academic institution’s strategy to support the delivery of operational, strategic or transformational objectives. This ICT strategy should align the institution with the radical changes of the ICT world through the use of Enterprise Architecture (EA). Hence, EA’s objective is to optimize the islands of legacy processes to be integrated that is receptive to change and supportive of the delivery of the strategy. In this paper, the focus is to explore the motivational antecedents during the adoption of EA in a Higher Education Institution in the Philippines for its ICT strategic plan. The seven antecedents (viewpoint, stakeholders, human traits, vision, revolutionary innovation, techniques and change components) provide understanding into EA adoption and the antecedents that influences the process of EA adoption.

Keywords: Enterprise Architecture, Adoption, Antecedents, Higher Educational Institutions

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Authors: Felix Liedel

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The rise of artificial intelligence has numerous and far-reaching consequences. In addition to the obvious consequences for entire professions, the increasing interaction with chatbots also has a wide range of social consequences and implications. We are already increasingly used to interacting with digital chatbots, be it in virtual consulting situations, creative development processes or even in building personal or intimate virtual relationships. A media-theoretical classification of these phenomena has so far been difficult, partly because the interactive element in the exchange with artificial intelligence has undeniable similarities to human-to-human communication but is not identical to it. The proposed study, therefore, aims to reformulate the concept of symbolic interaction in the tradition of George Herbert Mead as symbolic interactivity in communication with chatbots. In particular, Mead's socio-psychological considerations will be brought into dialog with the specific conditions of digital media, the special dispositive situation of chatbots and the characteristics of artificial intelligence. One example that illustrates this particular communication situation with chatbots is so-called consensus fiction: In face-to-face communication, we use symbols on the assumption that they will be interpreted in the same or a similar way by the other person. When briefing a chatbot, it quickly becomes clear that this is by no means the case: only the bot's response shows whether the initial request corresponds to the sender's actual intention. This makes it clear that chatbots do not just respond to requests. Rather, they function equally as projection surfaces for their communication partners but also as distillations of generalized social attitudes. The personalities of the chatbot avatars result, on the one hand, from the way we behave towards them and, on the other, from the content we have learned in advance. Similarly, we interpret the response behavior of the chatbots and make it the subject of our own actions with them. In conversation with the virtual chatbot, we enter into a dialog with ourselves but also with the content that the chatbot has previously learned. In our exchanges with chatbots, we, therefore, interpret socially influenced signs and behave towards them in an individual way according to the conditions that the medium deems acceptable. This leads to the emergence of situationally determined digital identities that are in exchange with the real self but are not identical to it: In conversation with digital chatbots, we bring our own impulses, which are brought into permanent negotiation with a generalized social attitude by the chatbot. This also leads to numerous media-ethical follow-up questions. The proposed approach is a continuation of my dissertation on moral decision-making in so-called interactive films. In this dissertation, I attempted to develop a concept of symbolic interactivity based on Mead. Current developments in artificial intelligence are now opening up new areas of application.

Keywords: artificial intelligence, chatbot, media theory, symbolic interactivity

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Authors: Katalin Kovacs

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In most forms of violence the perpetrators intend to hide their identities. Terrorism is different. Terrorist groups often take responsibility for their attacks, and consequently they reveal their identities. This unique characteristic of terrorism has been largely overlooked, and scholars are still puzzled as to why terrorist groups claim responsibility for their attacks. Certainly, the claim of responsibility is worth analysing. It would help to have a clearer picture of what terrorist groups try to achieve and how, but also to develop an understanding of the strategic planning of terrorist attacks and the message the terrorists intend to deliver. The research aims to answer the question why terrorist groups choose to claim responsibility for some of their attacks and not for others. In order to do so the claim of responsibility is considered to be a tactical choice, based on the assumption that terrorists weigh the costs and benefits of claiming responsibility. The main argument is that terrorist groups do not claim responsibility in cases when there is no tactical advantage gained from claiming responsibility. The idea that the claim of responsibility has tactical value offers the opportunity to test these assertions using a large scale empirical analysis. The claim of responsibility as a tactical choice depends on other tactical choices, such as the choice of target, the internationality of the attack, the number of victims and whether the group occupies territory or operates as an underground group. The structure of the terrorist groups and the level of decision making also affects the claim of responsibility. Terrorists on the lower level are less disciplined than the leaders. This means that the terrorists on lower levels pay less attention to the strategic objectives and engage easier in indiscriminate violence, and consequently they would less like to claim responsibility. Therefore, the research argues that terrorists, who are on a highest level of decision making would claim responsibility for the attacks as those are who takes into account the strategic objectives. As most studies on terrorism fail to provide definitions; therefore the researches are fragmented and incomparable. Separate, isolated researches do not support comprehensive thinking. It is also very important to note that there are only a few researches using quantitative methods. The aim of the research is to develop a new and comprehensive overview of the claim of responsibility based on strong quantitative evidence. By using well-established definitions and operationalisation the current research focuses on a broad range of attributes that can have tactical values in order to determine circumstances when terrorists are more likely to claim responsibility.

Keywords: claim of responsibility, leadership, tactical choice, terrorist group

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Authors: Gebremeskel Hagos Gebremedhin, Feng Chong, Heyan Huang

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Software system is the combination of architecture and organized components to accomplish a specific function or set of functions. A good software architecture facilitates application system development, promotes achievement of functional requirements, and supports system reconfiguration. We describe three studies demonstrating the utility of our architecture in the subdomain of mobile office robots and identify software engineering principles embodied in the architecture. The main aim of this paper is to analyze prove architecture design and automatic re-architecting using machine learning. Intelligence software architecture and automatic re-architecting process is reorganizing in to more suitable one of the software organizational structure system using the user access dataset for creating relationship among the components of the system. The 3-step approach of data mining was used to analyze effective recovery, transformation and implantation with the use of clustering algorithm. Therefore, automatic re-architecting without changing the source code is possible to solve the software complexity problem and system software reuse.

Keywords: intelligence, software architecture, re-architecting, software reuse, High level design

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Authors: Herke Csongor

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The artificial intelligence (AI) has been used in the United States of America in the decisionmaking process of the criminal justice system for decades. In the field of law, including criminal law, AI can provide serious assistance in decision-making in many places. The paper reviews four main areas where AI still plays a role in the criminal justice system and where it is expected to play an increasingly important role. The first area is the predictive policing: a number of algorithms are used to prevent the commission of crimes (by predicting potential crime locations or perpetrators). This may include the so-called linking hot-spot analysis, crime linking and the predictive coding. The second area is the Big Data analysis: huge amounts of data sets are already opaque to human activity and therefore unprocessable. Law is one of the largest producers of digital documents (because not only decisions, but nowadays the entire document material is available digitally), and this volume can only and exclusively be handled with the help of computer programs, which the development of AI systems can have an increasing impact on. The third area is the criminal statistical data analysis. The collection of statistical data using traditional methods required enormous human resources. The AI is a huge step forward in that it can analyze the database itself, based on the requested aspects, a collection according to any aspect can be available in a few seconds, and the AI itself can analyze the database and indicate if it finds an important connection either from the point of view of crime prevention or crime detection. Finally, the use of AI during decision-making in both investigative and judicial fields is analyzed in detail. While some are skeptical about the future role of AI in decision-making, many believe that the question is not whether AI will participate in decision-making, but only when and to what extent it will transform the current decision-making system.

Keywords: artificial intelligence, international criminal cooperation, planning and organizing of the investigation, risk assessment

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Authors: Bakr Zade, Paolo Cervera

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With the aim of maximising return on innovation investments, organisations create central units to support successful implementation of innovation management initiatives. The support units–referred to in this research as innovation management offices (IMOs)–range from small teams of innovation management champions to fully resourced centres of excellence for innovation management. However, roles and responsibilities of IMOs vary in different organisations. This research investigates the different types of IMO in organisations, based on their different roles and responsibilities in supporting innovation management processes. The research uses grounded theory methodology to uncover an IMO taxonomy from emergent concepts during innovation management maturity assessment exercises in twelve organisations from the United Kingdom and the United Arab Emirates. The taxonomy distinguishes five types of IMO, based on their roles and responsibilities in supporting innovation management processes, from organisational strategic foresight to managing innovation management project portfolios. The IMO taxonomy addresses a gap in research into innovation management support in organisations and offers a practical framework that diverse organisations can appreciate and use in designing IMOs that are aligned with their innovation management visions and strategies.

Keywords: future foresight, future shaping, innovation management, innovation management office, portfolio management

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Authors: Samuel Ntsanwisi

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This study investigates the transformative role of Artificial Intelligence (AI) and Virtual Reality (VR) in the preservation of endangered African languages. The study is contextualized within the film and television industry, highlighting disparities in screen representation for certain languages in South Africa, underscoring the need for increased visibility and preservation efforts; with globalization and cultural shifts posing significant threats to linguistic diversity, this research explores approaches to language preservation. By leveraging AI technologies, such as speech recognition, translation, and adaptive learning applications, and integrating VR for immersive and interactive experiences, the study aims to create a framework for teaching and passing on endangered African languages. Through digital documentation, interactive language learning applications, storytelling, and community engagement, the research demonstrates how these technologies can empower communities to revitalize their linguistic heritage. This study employs a dual-method approach, combining a rigorous literature review to analyse existing research on the convergence of AI, VR, and language preservation with primary data collection through interviews and surveys with ten filmmakers. The literature review establishes a solid foundation for understanding the current landscape, while interviews with filmmakers provide crucial real-world insights, enriching the study's depth. This balanced methodology ensures a comprehensive exploration of the intersection between AI, VR, and language preservation, offering both theoretical insights and practical perspectives from industry professionals.

Keywords: language preservation, endangered languages, artificial intelligence, virtual reality, interactive learning

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Authors: Dante Jose R. Amisola, Glenford M. Prospero

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'Driving What’s Next' is a strong campaign of the new administration of De La Salle Lipa in promoting social innovation in quality education. The new leadership directs social innovation in quality education in the institutional directions and initiatives to address real-world challenges with real-world solutions. This research under study aims to qualify the commitment of the institution to extend the Lasallian quality human and Christian education to all, as expressed in the Institution’s new mission-vision statement. The Classic Grounded Theory methodology is employed in the process of generating concepts in reference to the documents, a series of meetings, focus group discussions and other related activities that account for the conceptualization and formulation of the new mission-vision along with the new education innovation framework. Notably, Driving What’s Next is the emergent theory that encapsulates the commitment of giving quality human and Christian education to all. It directs the new leadership in driving social innovation in quality education initiatives. Correspondingly, Driving What’s Next is continually resolved through four interrelated strategies also termed as the institution's four strategic directions, namely: (1) driving social innovation in quality education, (2) embracing our shared humanity and championing social inclusion and justice initiatives, (3) creating sustainable futures and (4) engaging diverse stakeholders in our shared mission. Significantly, the four strategic directions capture and integrate the 17 UN sustainable development goals, making the innovative curriculum locally and globally relevant. To conclude, the main concern of the new administration and how it is continually resolved, provide meaningful and fun learning experiences and promote a new way of learning in the light of the 21^st century skills among the members of the academic community including stakeholders and extended communities at large, which are defined as: learning together and by association (collaboration), learning through engagement (communication), learning by design (creativity) and learning with social impact (critical thinking).

Keywords: DLSL four strategic directions , DLSL Lipa mission-vision, driving what's next, social innovation in quality education

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Authors: Carolijn Van Noort

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The aim of this research is to understand how the emerging power bloc BRICS employs infrastructure development narratives to construct a new world order. BRICS is an international body consisting of five emerging countries that collaborate on economic and political issues: Brazil, Russia, India, China, and South Africa. This study explores the projection of infrastructure development narratives through an analysis of BRICS’ attention to infrastructure investment and financing, its support of the New Partnership on African Development and the establishment of the New Development Bank in Shanghai. The theory of Strategic Narratives is used to explore BRICS’ commitment to infrastructure development and to distinguish three layers: system narratives (BRICS as a global actor to propose development reform), identity narratives (BRICS as a collective identity joining efforts to act upon development aspirations) and issue narratives (BRICS committed to a range of issues of which infrastructure development is prominent). The methodology that is employed is a narrative analysis of BRICS’ official documents, media statements, and website imagery. A comparison of these narratives illuminates tensions at the three layers and among the five member states. Identifying tensions among development infrastructure narratives provides an indication of how policymaking for infrastructure development could be improved. Subsequently, it advances BRICS’ ability to act as a global actor to construct a new world order.

Keywords: BRICS, emerging powers, infrastructure development, strategic narratives

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Authors: Maja Sajdak

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The aim of this paper is to present the concept of an agile enterprise model and to initiate discussion on the research assumptions of the model presented. The implementation of the research project "The agility of enterprises in the process of adapting to the environment and its changes" began in August 2014 and is planned to last three years. The article has the form of a work-in-progress paper which aims to verify and initiate a debate over the proposed research model. In the literature there are very few publications relating to research into agility; it can be concluded that the most controversial issue in this regard is the method of measuring agility. In previous studies the operationalization of agility was often fragmentary, focusing only on selected areas of agility, for example manufacturing, or analysing only selected sectors. As a result the measures created to date can only be treated as contributory to the development of precise measurement tools. This research project aims to fill a cognitive gap in the literature with regard to the conceptualization and operationalization of an agile company. Thus, the original contribution of the author of this project is the construction of a theoretical model that integrates manufacturing agility (consisting mainly in adaptation to the environment) and strategic agility (based on proactive measures). The author of this research project is primarily interested in the attributes of an agile enterprise which indicate that the company is able to rapidly adapt to changing circumstances and behave pro-actively.

Keywords: agile company, acuity, entrepreneurship, flexibility, research model, strategic leadership

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Authors: Angelis P. Barlampas

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The purpose of this study is to inform the reader, about the various applications of artificial intelligence (AI), in cardiac imaging. AI grows fast and its role is crucial in medical specialties, which use large amounts of digital data, that are very difficult or even impossible to be managed by human beings and especially doctors.Artificial intelligence (AI) refers to the ability of computers to mimic human cognitive function, performing tasks such as learning, problem-solving, and autonomous decision making based on digital data. Whereas AI describes the concept of using computers to mimic human cognitive tasks, machine learning (ML) describes the category of algorithms that enable most current applications described as AI. Some of the current applications of AI in cardiac imaging are the follows: Ultrasound: Automated segmentation of cardiac chambers across five common views and consequently quantify chamber volumes/mass, ascertain ejection fraction and determine longitudinal strain through speckle tracking. Determine the severity of mitral regurgitation (accuracy > 99% for every degree of severity). Identify myocardial infarction. Distinguish between Athlete’s heart and hypertrophic cardiomyopathy, as well as restrictive cardiomyopathy and constrictive pericarditis. Predict all-cause mortality. CT Reduce radiation doses. Calculate the calcium score. Diagnose coronary artery disease (CAD). Predict all-cause 5-year mortality. Predict major cardiovascular events in patients with suspected CAD. MRI Segment of cardiac structures and infarct tissue. Calculate cardiac mass and function parameters. Distinguish between patients with myocardial infarction and control subjects. It could potentially reduce costs since it would preclude the need for gadolinium-enhanced CMR. Predict 4-year survival in patients with pulmonary hypertension. Nuclear Imaging Classify normal and abnormal myocardium in CAD. Detect locations with abnormal myocardium. Predict cardiac death. ML was comparable to or better than two experienced readers in predicting the need for revascularization. AI emerge as a helpful tool in cardiac imaging and for the doctors who can not manage the overall increasing demand, in examinations such as ultrasound, computed tomography, MRI, or nuclear imaging studies.

Keywords: artificial intelligence, cardiac imaging, ultrasound, MRI, CT, nuclear medicine

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Authors: Upvan Chobera

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India bears the highest burden of tuberculosis globally, facing a significant incidence rate. To combat this public health challenge, the Ministry of Health and Family Welfare in India has launched an ambitious national strategic plan with the aim of achieving END TB targets by 2025. Addressing tuberculosis requires a comprehensive, multi-sectoral approach that encompasses factors such as nutritional support, living and working conditions, and improved access to diagnostics and treatment services. This study delves into the burden of tuberculosis in India, examining the government's strategic plan to combat the disease. Additionally, it explores the role of Ni-Kshay Mitra (community support) in this fight, encompassing various entities such as cooperative societies, corporations, elected representatives, individuals, institutions, non-government organizations, and political parties or individual donors. These efforts aim to enhance the response against tuberculosis, complementing the government's initiatives and catering to district-specific requirements, all coordinated with the district administration. It is important to note that the support provided under the Ni-Kshay Mitra initiative is supplementary to the free services offered by the National TB Elimination Program (NTEP) available to all patients.

Keywords: end TB targets, Ni-kshay Mitra, NTEP, tuberculosis burden in India

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Authors: Li Yong

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Using the data of 135 financial institutions in China from 2018 to 2022, this paper deeply analyzes the underlying theoretical mechanism of artificial intelligence (AI) technology promoting financial development and examines the impact of AI technology on the digital transformation performance of financial enterprises. It is found that the level of AI technology has a significant positive impact on the development of finance. Compared with the impact on the expansion of financial scale, AI technology plays a greater role in improving the performance of financial institutions, reflecting the trend characteristics of the current AI technology to promote the evolution of financial structure. By investigating the intermediary transmission effects, we found that AI technology plays a positive role in promoting the performance of financial institutions by reducing operating costs and improving customer satisfaction, but its function in innovating financial products and mitigating financial risks is relatively limited. In addition, the promotion of AI technology in financial development has significant heterogeneity in terms of the type, scale, and attributes of financial institutions.

Keywords: artificial intelligence technology, financial development, China, heterogeneity

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Authors: Justin Woulfe

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Siloed logistics and supply chain management systems throughout the Department of Defense (DOD) has led to disparate approaches to modeling and simulation (M&S), a lack of understanding of how one system impacts the whole, and issues with “optimal” solutions that are good for one organization but have dramatic negative impacts on another. Many different systems have evolved to try to understand and account for uncertainty and try to reduce the consequences of the unknown. As the DoD undertakes expansive digital transformation initiatives, there is an opportunity to fuse and leverage traditionally disparate data into a centrally hosted source of truth. With a streamlined process incorporating machine learning (ML) and artificial intelligence (AI), advanced M&S will enable informed decisions guiding program success via optimized operational readiness and improved mission success. One of the current challenges is to leverage the terabytes of data generated by monitored systems to provide actionable information for all levels of users. The implementation of a cloud-based application analyzing data transactions, learning and predicting future states from current and past states in real-time, and communicating those anticipated states is an appropriate solution for the purposes of reduced latency and improved confidence in decisions. Decisions made from an ML and AI application combined with advanced optimization algorithms will improve the mission success and performance of systems, which will improve the overall cost and effectiveness of any program. The Systecon team constructs and employs model-based simulations, cutting across traditional silos of data, aggregating maintenance, and supply data, incorporating sensor information, and applying optimization and simulation methods to an as-maintained digital twin with the ability to aggregate results across a system’s lifecycle and across logical and operational groupings of systems. This coupling of data throughout the enterprise enables tactical, operational, and strategic decision support, detachable and deployable logistics services, and configuration-based automated distribution of digital technical and product data to enhance supply and logistics operations. As a complete solution, this approach significantly reduces program risk by allowing flexible configuration of data, data relationships, business process workflows, and early test and evaluation, especially budget trade-off analyses. A true capability to tie resources (dollars) to weapon system readiness in alignment with the real-world scenarios a warfighter may experience has been an objective yet to be realized to date. By developing and solidifying an organic capability to directly relate dollars to readiness and to inform the digital twin, the decision-maker is now empowered through valuable insight and traceability. This type of educated decision-making provides an advantage over the adversaries who struggle with maintaining system readiness at an affordable cost. The M&S capability developed allows program managers to independently evaluate system design and support decisions by quantifying their impact on operational availability and operations and support cost resulting in the ability to simultaneously optimize readiness and cost. This will allow the stakeholders to make data-driven decisions when trading cost and readiness throughout the life of the program. Finally, sponsors are available to validate product deliverables with efficiency and much higher accuracy than in previous years.

Keywords: artificial intelligence, digital transformation, machine learning, predictive analytics

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Authors: Han-Yu Cheng

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This study aims to explore the impact of AI-generated illustration works on the aesthetic symbol consumption of consumers in Taiwan. The advancement of artificial intelligence drawing has lowered the barriers to entry, enabling more individuals to easily enter the field of illustration. Using Japanese anime style as an example, with the development of Generative Artificial Intelligence (Generative AI), an increasing number of illustration works are being generated by machines, sparking discussions about aesthetics and art consumption. Through surveys and the analysis of consumer perspectives, this research investigates how this influences consumers' aesthetic experiences and the resulting changes in the traditional art market and among creators. The study reveals that among consumers in Taiwan, particularly those interested in Japanese anime style, there is a pronounced interest and curiosity surrounding the emergence of Generative AI. This curiosity is particularly notable among individuals interested in this style but lacking the technical skills required for creating such artworks. These works, rooted in elements of Japanese anime style, find ready acceptance among enthusiasts of this style due to their stylistic alignment. Consequently, they have garnered a substantial following. Furthermore, with the reduction in entry barriers, more individuals interested in this style but lacking traditional drawing skills have been able to participate in producing such works. Against the backdrop of ongoing debates about artistic value since the advent of artificial intelligence (AI), Generative AI-generated illustration works, while not entirely displacing traditional art, to a certain extent, fulfill the aesthetic demands of this consumer group, providing a similar or analogous aesthetic consumption experience. Additionally, this research underscores the advantages and limitations of Generative AI-generated illustration works within this consumption environment.

Keywords: generative AI, anime aesthetics, Japanese anime illustration, art consumption

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Authors: Srikanth Pandurangi, Basheer Mohammed, Nezar Al Sayegh

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In order to meet the increasing demand for housing care for Kuwaiti citizens, the government authorities in Kuwait are undertaking a series of projects in the form of new large cities, outside the current urban area. Al Mutlaa City located to the north-west of the Kuwait Metropolitan Area is one such project out of the 15 planned new cities. The city accommodates a wide variety of residential developments, employment opportunities, commercial, recreational, health care and institutional uses. This paper examines the application of comprehensive transportation demand modeling works undertaken in VISUM platform to understand the future intracity and intercity travel distribution patterns in Kuwait. The scope of models developed varied in levels of detail: strategic model update, sub-area models representing future demand of Al Mutlaa City, sub-area models built to estimate the demand in the residential neighborhoods of the city. This paper aims at offering model update framework that facilitates easy integration between sub-area models and strategic national models for unified traffic forecasts. This paper presents the transportation demand modeling results utilized in informing the planning of multi-modal transportation system for Al Mutlaa City. This paper also presents the household survey data collection efforts undertaken using GPS devices (first time in Kuwait) and notebook computer based digital survey forms for interviewing representative sample of citizens and residents. The survey results formed the basis of estimating trip generation rates and trip distribution coefficients used in the strategic base year model calibration and validation process.

Keywords: innovative methods in transportation data collection, integrated public transportation system, traffic forecasts, transportation modeling, travel behavior

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Authors: Mark Darius Juszczak

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While epistemology, as a sub-field of philosophy, is generally concerned with theoretical questions about the nature of knowledge, the explosion in digital media technologies has resulted in an exponential increase in the storage and transmission of human information. That increase has resulted in a particular non-linear dynamic – digital epistemological functions are radically altering how and what we know. Neither the rate of that change nor the consequences of it have been well studied or taken into account in developing state-level strategies for epistemological functions. At the current time, US Federal policy, like that of virtually all other countries, maintains, at the national state level, clearly defined boundaries between various epistemological agencies - agencies that, in one way or another, mediate the functional use of knowledge. These agencies can take the form of patent and trademark offices, national library and archive systems, departments of education, departments such as the FTC, university systems and regulations, military research systems such as DARPA, federal scientific research agencies, medical and pharmaceutical accreditation agencies, federal funding for scientific research and legislative committees and subcommittees that attempt to alter the laws that govern epistemological functions. All of these agencies are in the constant process of creating, analyzing, and regulating knowledge. Those processes are, at the most general level, epistemological functions – they act upon and define what knowledge is. At the same time, however, there are no high-level strategic epistemological directives or frameworks that define those functions. The only time in US history where a proxy state-level epistemological strategy existed was between 1961 and 1969 when the Kennedy Administration committed the United States to the Apollo program. While that program had a singular technical objective as its outcome, that objective was so technologically advanced for its day and so complex so that it required a massive redirection of state-level epistemological functions – in essence, a broad and diverse set of state-level agencies suddenly found themselves working together towards a common epistemological goal. This paper does not call for a repeat of the Apollo program. Rather, its purpose is to investigate the minimum structural requirements for a national state-level epistemological strategy in the United States. In addition, this paper also seeks to analyze how the epistemological work of the multitude of national agencies within the United States would be affected by such a high-level framework. This paper is an exploratory study of this type of framework. The primary hypothesis of the author is that such a function is possible but would require extensive re-framing and reclassification of traditional epistemological functions at the respective agency level. In much the same way that, for example, DHS (Department of Homeland Security) evolved to respond to a new type of security threat in the world for the United States, it is theorized that a lack of coordination and alignment in epistemological functions will equally result in a strategic threat to the United States.

Keywords: strategic security, epistemological functions, epistemological agencies, Apollo program

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Authors: Ethan James

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Over one billion people worldwide suffer from some level of vision loss or blindness as a result of progressive retinal diseases. Many patients, particularly in developing areas, are incorrectly diagnosed or undiagnosed whatsoever due to unconventional diagnostic tools and screening methods. Artificial intelligence (AI) based on deep learning (DL) convolutional neural networks (CNN) have recently gained a high interest in ophthalmology for its computer-imaging diagnosis, disease prognosis, and risk assessment. Optical coherence tomography (OCT) is a popular imaging technique used to capture high-resolution cross-sections of retinas. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography, and visual fields, achieving robust classification performance in the detection of various retinal diseases including macular degeneration, diabetic retinopathy, and retinitis pigmentosa. However, there is no complete diagnostic model to analyze these retinal images that provide a diagnostic accuracy above 90%. Thus, the purpose of this project was to develop an AI model that utilizes machine learning techniques to automatically diagnose specific retinal diseases from OCT scans. The algorithm consists of neural network architecture that was trained from a dataset of over 20,000 real-world OCT images to train the robust model to utilize residual neural networks with cyclic pooling. This DL model can ultimately aid ophthalmologists in diagnosing patients with these retinal diseases more quickly and more accurately, therefore facilitating earlier treatment, which results in improved post-treatment outcomes.

Keywords: artificial intelligence, deep learning, imaging, medical devices, ophthalmic devices, ophthalmology, retina

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Authors: Hoda Manafian, Stephen Holland

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Problem statement: Rural areas in Iran are one of the most popular tourism destinations. Abyaneh Village is one of them with a long history behind it (more than 1500 years) which is a national heritage site and also is nominated as a world heritage site in UNESCO tentative list from 2007. There is a considerable foundation of religious-cultural heritage and also agricultural history and activities. However, this heritage site suffers from mass tourism which is beyond its social and physical carrying capacity, since the annual number of tourists exceed 500,000. While there are four adjacent villages around Abyaneh which can benefit from advantages of tourism. Local managers also can at the same time prorate the tourists’ flux of Abyaneh on those other villages especially in high-season. The other villages have some cultural and natural tourism attractions as well. Goal: The main goal of this study is to identify a feasible development strategy according to the current strengths, weaknesses, opportunities and threats of rural tourism in this area (Abyaneh Village and four adjacent villages). This development strategy can lead to sustainable management of these destinations. Method: To this end, we used SWOT analysis as a well-established tool for conducting a situational analysis to define a sustainable development strategy. The procedures included following steps: 1) Extracting variables of SWOT chart based on interviewing tourism experts (n=13), local elites (n=17) and personal observations of researcher. 2) Ranking the extracted variables from 1-5 by 13 tourism experts in Isfahan Cultural Heritage, Handcrafts and Tourism Organization (ICHTO). 3) Assigning weights to the ranked variables using Expert Choice Software and the method of Analytical Hierarchical Process (AHP). 4) Defining the Total Weighted Score (TWS) for each part of SWOT chart. 5) Identifying the strategic position according to the TWS 6) Selecting the best development strategy based on the defined position using the Strategic Position and Action Evaluation (SPACE) matrix. 7) Assessing the Probability of Strategic Success (PSS) for the preferred strategy using relevant formulas. 8) Defining two feasible alternatives for sustainable development. Results and recommendations: Cultural heritage attractions were first-ranked variable in strength chart and also lack of sufficient amenities for one-day tourists (catering, restrooms, parking, and accommodation) was firs-ranked weakness. The strategic position was in ST (Strength-Threat) quadrant which is a maxi-mini position. According this position we would suggest ‘Competitive Strategy’ as a development strategy which means relying on strengths in order to neutralization threats. The result of Probability of Strategic Success assessment which was 0.6 shows that this strategy could be successful. The preferred approach for competitive strategy could be rebranding the market of tourism in this area. Rebranding the market can be achieved by two main alternatives which are based on the current strengths and threats: 1) Defining a ‘Heritage Corridor’ from first adjacent village to Abyaneh as a final destination. 2) Focus on ‘educational tourism’ versus mass tourism and also green tourism by developing agritourism in that corridor.

Keywords: Abyaneh village, rural tourism, SWOT analysis, sustainable strategies

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Authors: Hemalatha Rallapalli, K. Lal Kishore

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In comparison to systems that are typically designed to provide capabilities over a narrow frequency range through hardware elements, the next generation cognitive radios are intended to implement a broader range of capabilities through efficient spectrum exploitation. This offers the user the promise of greater flexibility, seamless roaming possible on different networks, countries, frequencies, etc. It requires true paradigm shift i.e., liberalization over a wide band of spectrum as well as a growth path to more and greater capability. This work contributes towards the design and implementation of an open spectrum access (OSA) feature to unlicensed users thus offering a frequency agile radio platform that is capable of performing spectrum sensing over a wideband. Thus, an ultra-wideband (UWB) radio, which has the intelligence of spectrum sensing only, unlike the cognitive radio with complete intelligence, is named as a Smart Software Radio (SSR). The spectrum sensing mechanism is implemented based on energy detection. Simulation results show the accuracy and validity of this method.

Keywords: cognitive radio, energy detection, software radio, spectrum sensing

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Authors: Mahasa Zahirnia

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The purpose of this paper is to explore new and effective ways of optimizing satellite images using artificial intelligence, and the process of implementing reinforcement learning to enhance the quality of data captured within the image. In our implementation of Bellman's Reinforcement Learning equations, associated state diagrams, and multi-stage image processing, we were able to enhance image quality, detect and define objects. Reinforcement learning is the differentiator in the area of artificial intelligence, and Q-Learning relies on trial and error to achieve its goals. The reward system that is embedded in Q-Learning allows the agent to self-evaluate its performance and decide on the best possible course of action based on the current and future environment. Results show that within a simulated environment, built on the images that are commercially available, the rate of detection was 40-90%. Reinforcement learning through Q-Learning algorithm is not just desired but required design criteria for image optimization and enhancements. The proposed methods presented are a cost effective method of resolving uncertainty of the data because reinforcement learning finds ideal policies to manage the process using a smaller sample of images.

Keywords: Q-learning, image optimization, reinforcement learning, Markov decision process

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Authors: N. S. Yen, T. H. Yang, W. H. Huang, Y. F. Fang, H. W. Cho

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Ultimatum game is an experimental paradigm to study human decision making. There are two players, a proposer and a responder, to split a fixed amount of money. According to the traditional economic theory on ultimatum game, proposer should propose the selfish offers to responder as much as possible to maximize proposer’s own outcomes. However, most evidences had showed that people chose more fair offers, hence two hypotheses – fairness favoring and strategic concern were proposed. In current study, we induced the motivation in participants to be either selfish or altruistic, and manipulated the task variables, the stake sizes (NT$100, 1000, 10000) and the share sizes (the 40%, 30%, 20%, 10% of the sum as selfish offers, and the 60%, 70%, 80%, 90% of the sum as altruistic offers), to examine the two hypotheses. The results showed that most proposers chose more fair offers with longer reaction times (RTs) no matter in choosing between the fair and selfish offers, or between the fair and altruistic offers. However, the proposers received explicit self-interest instruction chose more selfish offers accompanied with longer RTs in choosing between the fair and selfish offers. Therefore, the results supported the strategic concern hypothesis that previous proposers choosing the fair offers might be resulted from the fear of rejection by responders. Proposers would become more self-interest if the fear of being rejected is eliminated.

Keywords: ultimatum game, proposer, self-interest, fear of rejection

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Authors: Gehan Labib Abuelenain, Azza Fahmi, Salma Awad Mahmoud

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Toxoplasmosis is a global parasitic disease caused by the protozoan Toxoplasma gondii (T. gondii), with a high infection rate that affects one third of the human population and results in severe implications in pregnant women, neonates, and immunocompromised patients. Anti-parasitic treatments and schemes available against toxoplasmosis have barely evolved over the last two decades. The available T. gondii therapeutics cannot completely eradicate tissue cysts produced by the parasite and are not well-tolerated by immunocompromised patients. This work aims to highlight new trends in Toxoplasma gondii diagnosis by providing a comprehensive overview of the field, summarizing recent findings, and discussing the new technological advancements in toxoplasma diagnosis and treatment. Advancements in therapeutics utilizing trends in molecular biophysics, such as biosensors, epigenetics, and artificial intelligence (AI), might provide solutions for disease management and prevention. These insights will provide tools to identify research gaps and proffer planning options for disease control.

Keywords: toxoplamosis, diagnosis, therapeutics, biosensors, AI

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