Intelligent Components Research Articles

Generative Adversarial Networks in Fashion Retailing and Customer Purchase Intention: An Extension of Theory of Consumption Value

The emergence of generative adversarial networks (GANs) in marketing, a component of artificial intelligence (AI) technology, has the potential to influence customer behaviour in a variety of ways. In various contexts, the theory of consumption value (TCV) has elucidated customer behaviour. Few studies have applied TCV to GAN in marketing, particularly in fashion retailing. To close the gap, this study extends TCV to GAN-fashion retailing. A mixed research approach began with a 12-participant explorative focus group discussion to identify factors and develop the questionnaire. Second, we conducted a stratified random sampling-based cross-sectional survey and distributed the questionnaire to 258 diverse respondents. Analytical methods included structural equation modelling. It is discovered that decision-intelligent infrastructure (representing functional value) was a major driver of purchase intention in the GAN-fashion retailing context. This study has also found contradictory results and also discussed the reasons behind them. Overall, this has contributed in both the theoretical area, by extending the TCV framework in GAN-fashion retailing, and the managerial area, by making some suggestions. Also, it has opened up many new avenues for future study in the GAN-fashion retailing context.

EMOTIONAL INTELLIGENCE IN NURSING

Nurses are often exposed to work under pressure, the challenges of making extremely quick and responsible decisions, and dealing with other people's pain, suffering and losses. Of all healthcare professionals, they spend the most time with the patient, creating a special relationship based on trust. Previously, it was an intuitive process where nurses were only sometimes successful. Today, in evidence-based nursing, we are aware of how important emotional intelligence is in all aspects of nursing and nursing education. Physical fatigue is less of a problem for nurses than emotional exhaustion and compassion fatigue. Therefore, effective emotional regulation is necessary to improve business satisfaction. Managing emotions is achieved by developing emotional intelligence, which has become a more ubiquitous concept in nursing in recent years. The most desirable employees in healthcare (and elsewhere) are those with developed academic intellectual abilities, but also emotional intelligence. With the help of the components of emotional intelligence, the internal mechanism of the specific nurse-patient relationship could be explained, which has proven to help both the patient in achieving favorable health care outcomes and the nurses themselves in acting and understanding the very essence of nursing. Emotional intelligence as a mental skill helps an individual in various personal, social, professional, communication and financial domains such as: time management, decision making, service delivery, responsibility, empathy, presentation skills, stress tolerance, trust and communication management. This overview paper will try to present its importance in nursing.

A Comprehensive Review of Deep Learning: Architectures, Recent Advances, and Applications

Deep learning (DL) has become a core component of modern artificial intelligence (AI), driving significant advancements across diverse fields by facilitating the analysis of complex systems, from protein folding in biology to molecular discovery in chemistry and particle interactions in physics. However, the field of deep learning is constantly evolving, with recent innovations in both architectures and applications. Therefore, this paper provides a comprehensive review of recent DL advances, covering the evolution and applications of foundational models like convolutional neural networks (CNNs) and Recurrent Neural Networks (RNNs), as well as recent architectures such as transformers, generative adversarial networks (GANs), capsule networks, and graph neural networks (GNNs). Additionally, the paper discusses novel training techniques, including self-supervised learning, federated learning, and deep reinforcement learning, which further enhance the capabilities of deep learning models. By synthesizing recent developments and identifying current challenges, this paper provides insights into the state of the art and future directions of DL research, offering valuable guidance for both researchers and industry experts.

Extending Fraud Detection in Students Exams Using AI

The objective of the research is to enhance the functionality of the FraudDetector software agent within the Distributed eLearning Center (DeLC), a platform providing extensive support for e-learning activities. DeLC assists students and teachers in organizing learning materials, addressing knowledge gaps, conducting exams, and fostering personalized elearning environments. The project scope encompasses various extensions, including an agent-oriented environment that enriches functionalities with reactive and proactive intelligent components, referred to as agents or assistants. This paper focuses on the latest evolution of the FraudDetector software agent, transitioning from its base functionality for fraud detection to leveraging artificial intelligence (AI) capabilities. The goal is to integrate AI, specifically the knowledgebase provided by ChatGPT, to enhance FraudDetector's effectiveness. This integration is the primary contribution of the research, aimed at improving fraud detection precision. Experimentation reveals promising results, suggesting that involving ChatGPT enriches FraudDetector's functionality and enhances the agent's precision. Moving forward, the agent's architecture should remain open for collaboration with external AI providers, with efforts to decouple components responsible for integration. The real-world implementation of these findings is pending, warranting further validation through production environment testing.

Емпатія як складова професійної компетентності диспетчера служби екстреної (швидкої) медичної допомоги

The article highlights the problem of empathy of emergency medical service dispatchers, since it is the dispatcher who is the first person with whom a person comes into contact in a crisis situation. The sample of researchers consisted of 63 people, mostly from the eastern regions of Ukraine, who were divided into groups based on gender, place of residence, and length of service. The results of an empirical study of the features of empathy as a component of the professional competence of the E(S)MH dispatcher are presented. The distribution of E(S)MH service personnel depending on the levels of emotional intelligence components is determined; compared to the previous year, depending on different types of activities, the presence of problems with work or other regular daily activities due to physical health, emotional problems that also interfere with normal social life with family, friends, etc. The empirical study made it possible to state that about half of the study subjects are characterized by an average integrative level of emotional intelligence, who are able to manage their emotions at a high level, low levels of self-motivation and empathy; a high level of emotional intelligence is characteristic of only one tenth of the study subjects. There are statistically significant differences in emotional intelligence and its components among EMS workers depending on their gender, place of residence and length of service: men living in rural areas have twice as high levels of emotional intelligence, emotional awareness, emotion management, empathy and recognition of other people's emotions as men living in cities and women, regardless of their place of residence; male medical professionals with 11 to 20 years of work experience show significantly higher levels of empathy compared to women with this work experience; women are characterized by a gradual decrease in the level of empathy with the acquisition of experience during their professional career; regardless of the length of service, men's empathy scores are slightly higher than women's, which, in turn, indicates that men are easier to adapt to difficult working conditions; the highest rates of emotional intelligence, as well as empathy, were found in men with work experience from 21 to 30 years. Prospects for further research in the context of creating a program of education and training for the development of empathic skills in E(S)Md dispatchers are outlined. It is especially important to pay attention to modeling crisis situations that reflect real challenges in their work and improve communication with potential ambulance patients

A Network Analysis of Emotional Intelligence in Chinese Preschool Teachers

Emotional intelligence significantly influences various aspects of teachers’ professional and personal lives, notably affecting preschoolers’ social skills and emotional development during formative years. This study utilizes a network analysis approach to explore the complex relationships among four components of emotional intelligence: emotional labor, emotional regulation, professional well-being, and professional identity. Participants included 2069 frontline Chinese teachers (34 males, 2035 females; M = 28.64, SD = 5.98; M years of teaching = 6.88, SD = 5.29) with no leadership roles, categorized into three stages of their careers based on years of teaching experience: novice (0–3 years; n = 612), advanced beginners (4–6 years; n = 537), and competent (7–40 years; n = 920). Findings revealed that joy of teaching, role value, and professional value were identified as the most critical elements within the emotional state network of early childhood education teachers. The strongest connections in teachers’ emotional networks were found between school connectedness and joy of teaching (r = 0.474), surface acting behavior and natural acting behavior (r = 0.419), and professional value and professional behavior (r = 0.372). Furthermore, teachers across different career stages exhibited similar characteristics and intrinsic connections among emotional state components. These findings deepen our understanding of the emotional state networks of ECE teachers, highlighting shared features and interconnected mechanisms, and suggest that enhancing teachers’ emotional intelligence through targeted professional development can improve both teacher well-being and preschoolers’ social–emotional outcomes. Policies that foster strong school connectedness and reduce emotional labor are key to promoting sustained joy in teaching, particularly for novice and advanced beginner teachers.

Hybrid Intelligence approach to study post-processing impact on the mechanical performance of notched additively manufactured AlSi10Mg

This study introduces a Hybrid Intelligence approach to investigate the Process-Structure-Property-Performance (PSSP) relationship in additively manufactured (AM) materials, specifically focusing on V-notched laser powder bed fused (L-PBF) AlSi10Mg specimens. The Humen Intelligence (HI) component managed the design, manufacturing processes, post-processing, structural characterization, mechanical testing, and data collection. In parallel, Artificial Intelligence (AI), utilizing advanced machine learning (ML) algorithms, performed tasks related to prediction, sensitivity analysis, and parametric analysis. AI identified patterns and developed predictive models that provided deeper insights into how process parameters affect material properties and performance. This integration of HI and AI enabled a more thorough exploration of these relationships; data collected from our previous research were complemented with new experiments conducted to assess the effects of various heat treatments (HTs) and surface post-treatments (SPTs) on the fatigue behavior of the specimens. The techniques applied included stress relief (SR), T6 thermal treatments, sand blasting (SB), shot peening (SP), severe vibratory peening (SVP), laser shock peening (LSP), tumble finishing (TF), abrasive flow machining (AFM), chemical polishing (CP), electrochemical polishing (ECP), and chemical milling (CM), along with their combinations. A total of 54 different post-processing techniques were examined in this study. The experimental data, covering surface texture, microstructure, porosity, hardness, and residual stress, were used to develop an ML model that analyzed the fatigue behavior of the specimens. This approach represents a significant advancement toward integrated mechanistic and data-driven materials engineering, offering valuable insights for optimizing fatigue performance in practical applications.

Model-Driven Development Towards Distributed Intelligent Systems

A Distributed Intelligent System (DIS) encompasses a set of intelligent subsystems and components that collaborate to perform tasks and solve problems. Given the advancements of paradigms such as the Internet of Things, along with the advancements of technologies such as Machine Learning and Digital Twins, DISs are on the rise. These systems are increasingly integrating components that perform intelligent functions, and these intelligent functions are increasingly heterogeneous and varied. Moreover, there is no standardized framework to help researchers and practitioners adequately address DISs. As a result, the complexity, interoperability issues, and development time and costs of these systems are growing. However, Model-Driven Development (MDD) can help to address these challenges by providing a Domain-Specific Language (DSL) for developing DISs. In this work, a DSL for the design, validation, generation, and deployment of DISs is proposed. Firstly, the proposed DSL captures in a metamodel the key and high-level abstract concepts of the distinct DISs documented in the literature. Then, it allows modeling of DISs conforming to this metamodel. Subsequently, the DSL enables formal validation of the modeled systems. Lastly, it allows the generation and deployment of all DISs into production. Therefore, the work undertaken in this communication provides a methodological, formal, and standardized approach to defining and developing DISs from a high level of abstraction. This work allows users to address DISs by facilitating agility, minimizing manual tasks, and reducing the number of defects introduced in their development. To illustrate the applicability of the proposed DSL, a real case study of an agricultural digital twin is presented.

A Practical Guide for Improving and Evaluating the Components of Emotional Intelligence in Healthcare Students

A Practical Guide for Improving and Evaluating the Components of Emotional Intelligence in Healthcare Students

The impact of emotional and moral intelligence on nurses’ organizational commitment

BackgroundEmotion control, adherence to ethics, and organizational commitment are among the widely addressed topics in response to turbulent and complex organizational environments. The aim of this study was to investigate emotional and moral intelligence and their effect on organizational commitment among nurses working in public hospitals.MethodsThis descriptive-analytical cross-sectional study was conducted on 385 nurses. The data collection tools were standard questionnaires on emotional intelligence, moral intelligence, and organizational commitment. The data were analyzed using t-test, ANOVA, Pearson correlation coefficient, and multiple linear regression with SPSS23 software.ResultsThe mean scores indicated moderate levels of emotional and moral intelligence and a low levels of organizational commitment among nurses. There was a significant positive correlation between emotional intelligence and moral intelligence with organizational commitment. Based on the results of multiple linear regression, components of emotional intelligence and moral intelligence, including responsibility, compassion, self-awareness, forgiveness, integrity, relationship management, self-management, and social awareness were identified as predictors of organizational commitment.ConclusionEmotional intelligence and moral intelligence were estimated to be at moderate levels, and organizational commitment among the nurses was at a low level. Improving emotional and moral intelligence may enhance nurses’ organizational commitment. Therefore, it is recommended that hospital managers provide training courses and workshops to improve nurses’ emotional and moral intelligence, which can, in turn, promote their organizational commitment.

Development of Whole System Digital Twins for Advanced Reactors: Leveraging Graph Neural Networks and SAM Simulations

In this work, we introduce a novel method to develop whole system digital twins (DTs) for advanced nuclear reactors. This method treats a complex reactor system as a heterogeneous graph: with the system components as different types of graph nodes and their physical interconnections as edges. Based on the heterogeneous graph, a graph neural network combining graph convolution and temporal node attention is developed as the DT, facilitating a comprehensive understanding of the system’s dynamic behavior. By utilizing the System Analysis Module (SAM) code for simulating various operational transients, we develop a graph-based database that trains the DT. This DT is characterized by two primary functions: It can infer the entire system’s status using sparse node information, and it can predict the progress of transients based on current and historical system information. Our approach is validated through case studies on the Experimental Breeder Reactor II (EBR-II) system and a generic Fluoride-salt-cooled High-temperature Reactor (gFHR), demonstrating the DT’s accuracy in forecasting operational transients. The DT’s rapid computation capabilities enhance its potential for supporting advanced reactor operations, offering benefits in intelligent simulation, autonomous control, and anomaly detection, paving the way for improved safety analysis and intelligent component health management for advanced reactor systems and reducing their operations and maintenance cost.

The always changing data problem of using AI in manufacturing : Using synthetic data from the digital twin to feed AI models

Production is becoming increasingly flexible, which also requires the flexibility of the support system for the production. And the key here is the speed of decisions, in which the support of modern artificial intelligence systems can be crucial. Flexible production is based on a well-planned control of production, which increasingly uses some artificial intelligence component. Artificial intelligence can already be useful in the early stages of planning the production line, and of course it can also control the daily operation of the production line after the installation of the production place or line. The biggest problem is supplying the neural network that controls the artificial intelligence with training data. The production lines typically change every 2-4 months, new products appear, the layout changes, and the main process data also changes due to the development of the processes. This results in the training data becoming outdated or obsolete very quickly and thus cannot be used to train models anymore. High-quality learning data can be produced by digital twin models of production lines. Such synthetic data has several advantages over data collected from production. In this article, we investigate how useful this synthetic data is during the life cycle of the production line.

Deep learning to capture leaf shape in plant images: Validation by geometric morphometrics.

Plant leaves play a pivotal role in automated species identification using deep learning (DL). However, achieving reproducible capture of leaf variation remains challenging due to the inherent "black box" problem of DL models. To evaluate the effectiveness of DL in capturing leaf shape, we used geometric morphometrics (GM), an emerging component of eXplainable Artificial Intelligence (XAI) toolkits. We photographed Ranunculus auricomus leaves directly in situ and after herbarization. From these corresponding leaf images, we automatically extracted DL features using a neural network and digitized leaf shapes using GM. The association between the extracted DL features and GM shapes was then evaluated using dimension reduction and covariation models. DL features facilitated the clustering of leaf images by source populations in both in situ and herbarized leaf image datasets, and certain DL features were significantly associated with biological leaf shape variation as inferred by GM. DL features also enabled leaf classification into morpho-phylogenomic groups within the intricate R. auricomus species complex. We demonstrated that simple in situ leaf imaging and DL reproducibly captured leaf shape variation at the population level, while combining this approach with GM provided key insights into the shape information extracted from images by computer vision, a necessary prerequisite for reliable automated plant phenotyping.

GABAergic inhibition in human hMT+ predicts visuo-spatial intelligence mediated through the frontal cortex.

The prevailing opinion emphasizes fronto-parietal network (FPN) is key in mediating general fluid intelligence (gF). Meanwhile, recent studies show that human MT complex (hMT+), located at the occipito-temporal border and involved in 3D perception processing, also plays a key role in gF. However, the underlying mechanism is not clear, yet. To investigate this issue, our study targets visuo-spatial intelligence, which is considered to have high loading on gF. We use ultra-high field magnetic resonance spectroscopy (MRS) to measure GABA/Glu concentrations in hMT+ combining resting-state fMRI functional connectivity (FC), behavioral examinations including hMT+ perception suppression test and gF subtest in visuo-spatial component. Our findings show that both GABA in hMT+ and frontal-hMT+ functional connectivity significantly correlate with the performance of visuo-spatial intelligence. Further, serial mediation model demonstrates that the effect of hMT+ GABA on visuo-spatial gF is fully mediated by the hMT+ frontal FC. Together our findings highlight the importance in integrating sensory and frontal cortices in mediating the visuo-spatial component of general fluid intelligence.

GABAergic inhibition in human hMT+ predicts visuo-spatial intelligence mediated through the frontal cortex

The prevailing opinion emphasizes fronto-parietal network (FPN) is key in mediating general fluid intelligence (gF). Meanwhile, recent studies show that human MT complex (hMT+), located at the occipito-temporal border and involved in 3D perception processing, also plays a key role in gF. However, the underlying mechanism is not clear, yet. To investigate this issue, our study targets visuo-spatial intelligence, which is considered to have high loading on gF. We use ultra-high field magnetic resonance spectroscopy (MRS) to measure GABA/Glu concentrations in hMT+ combining resting-state fMRI functional connectivity (FC), behavioral examinations including hMT+ perception suppression test and gF subtest in visuo-spatial component. Our findings show that both GABA in hMT+ and frontal-hMT+ functional connectivity significantly correlate with the performance of visuo-spatial intelligence. Further, serial mediation model demonstrates that the effect of hMT+ GABA on visuo-spatial gF is fully mediated by the hMT+ frontal FC. Together our findings highlight the importance in integrating sensory and frontal cortices in mediating the visuo-spatial component of general fluid intelligence.

Digital Metrics of Agro-Industrial Complex: Intelligent Monitoring of Grain Production

Abstract The article examines the basic conditions for the digitalization of grain production monitoring, including data collection organization, IT infrastructure selection, as well as changes in organizational culture and personnel training. The digitalization of the monitoring process involves the use of modern technologies such as neural networks and computer vision to enhance the accuracy and promptness of crop condition monitoring. Special attention is given to the implementation of digital solutions and overcoming barriers that hinder the digital transformation of grain fields. The principles of monitoring organization presented in the article lay the foundation for efficient and sustainable production, ensuring the quality and safety of grain products, contributing to the strengthening of the country’s food security, enhancing the resilience of the agro-industrial complex, and can become a conceptual basis for the development of the intelligent component of grain production.

Correlation between students’ emotional intelligence and emotional dependency

The aim of the article is to determine the specifics of the relationship between the components of emotional intelligence (EI) and the emotional dependency (ED) reactions in student age. The MSCEIT (The Mayer–Salovey–Caruzo Emotional Intelligence Test) and R. Hirshfield’s Interpersonal Dependence Test were used to achieve the aim and objectives of the study. The article analyses the dynamics and correlation of indicators for educational courses at the bachelor’s level of higher education institutions (HEIs). The prevalence of the medium indicators of the levels of EI and manifestations of ED was found during the studied period. A significant increase in EI indicators was determined between the second and third years of study. A gradual decrease in the ED reaction before the completion of studies in HEIs was recorded. Significant correlations of the ED parameters with the conscious management of emotions, the ability to understand and identify experiences were calculated. Most often, the connections between the EI components were established to manifested with the level of self-confidence. It can be stated that high EI indicators determine a decrease in EI. The research prospects may be the development and verification of the effectiveness of programmes for EI development at student age.

Emotion Recognition on Turkish Mobile Operator Turkcell’s Call Center Calls

A fundamental component of human intelligence is the capacity for feeling. In addition to being founded on logic and reason, human conduct is also greatly influenced by the emotions that people experience. For the purpose of this study, we classified one thousand real-life call center client voice data in the Turkish language based on the way they expressed their emotions using text emotion detection. We made use of Ekman's emotional labeling and techniques from the field of artificial intelligence, such as deep learning and other similar methods.

The game as a methodological means of training students of humanities at the university

The article is written in the context of an integrative approach to psychology that combines methods for studying personality and group from different areas of human knowledge, contributing to the creation of a comprehensive psychosocial methodological apparatus aimed at studying the process of providing social and psychological support and assistance in an individual and group format. The article reveals the essence of a psychological game as a resource that contributes to the formation and development of professionally significant personal qualities and skills of university students majoring in the humanities (psychology students). In the course of the study, we proved the possibility of developing empathy in students with the help of psychological game practices in the educational environment of the university. Conceptualization of the definitions of the phenomena of the emotional sphere of personality functioning in bachelors of the 1st and 2nd years of study was carried out. The effectiveness of the development of game materials was assessed: bingo sheets and emotionally rich dialogues, as a stimulus material for recognizing emotions. A methodological justification for the effective use of gamification technologies in the educational process at the university is given. As a result of the empirical study, the training focus of the developed game «Empati-Bingo» was determined, prospects for improving the stimulus material, the possibilities of diagnosing the level of empathy development in students using game tools were determined. It was revealed that the psychological game «Empati-Bingo» requires a high level of empathy development in its leaders. The accuracy of recognition and determination of emotion can reflect the level of development of the cognitive component of empathy as a component of emotional intelligence. To improve the internal validity of the psychological game, the range of emotions that were proposed to be defined was expanded for the declared constructs of emotion and emotional intelligence: dialogues containing negative emotions were added, some emotionally neutral situations arising in interpersonal interaction were removed, which made the stimulus material more understandable for the game participants.

THE VALUE ORIENTATIONS OF ADOLESCENTS WITH DIFFERENT LEVELS OF EMOTIONAL INTELLIGENCE

This article presents general characteristics of the concepts of emotional intelligence and value orientations from the perspective of several authors. There are presented some theoretical approaches with reference to the components of emotional intelligence and the types of value orientations. Moreover, in this paper some sequences of the research carried out on the subject of emotional intelligence and value orientations in adolescents are described. They directly relate to the hierarchies of terminal and instrumental value orientations of adolescents with different levels of emotional intelligence development: high level and low level. At a comparative level, priority values and values rejected by teenagers are presented, which denote different levels of emotional intelligence. The obtained results demonstrate that the value hierarchies in the experimental subjects are not identical. We emphasize that the research sample consists of 341 teenagers from the Republic of Moldova.