Intelligent Components Research Articles

THE EVOLUTION OF CONSCIOUSNESS FROM THE POINT OF VIEW OF MODERN THEORY OF INFORMATION AND TELECOMMUNICATIONS

The question of the nature of the evolution of consciousness is considered. It is shown that the answer to this question can be given by considering the evolution of intelligence as one of the components of the complex structure of personality. It is proved that the evolution of intelligence is an objective process that continues and even accelerates at the present time. The basis for the proof is the theory of the formation of the transpersonal level of information processing, according to which interpersonal communication is interpreted as the exchange of signals between neurons located in different brains. As a result, a common neural network arises, and different transpersonal information objects are formed in it. The existence of a transpersonal level of information processing leads to the fact that the intellect of a person has a dual nature. It simultaneously has both an individual and a collective component. The fact, in particular, makes it possible to establish the nature of the collective unconscious. The collective component of intelligence is being transformed due to changes in the communication environment, including due to the rapid development of telecommunication and information technologies.

Personal life environment within the socio-political context of modern Russia: status according to the attribute of certainty

The subject of the article relates to the sociological discourse on the category of uncertainty at the personal level of analysis. The uncertainty of the environment inherently contains risks as a potential possibility of damage from an adverse outcome of events, phenomena, processes. The strategy of the analysis presented in the article takes as the initial and independent position of the study the fact that there is such a context in the environment, such conditions that can serve as sources of risks of various levels. In the research proposed in the article, the author proceeds from the fact that a fundamentally risky socio-political context at the country level (SMO, increasing sanctions pressure) by a particular person may not be perceived as causing uncertainty in the space of his personal life environment. The empirical basis of the analysis is data from a representative survey of the Russian population conducted on a quota sample in June 2023. A methodological approach is applied, the peculiarity of which is the formation within the sample the target groups according to the criterion corresponding to the main goal of the analysis – self-determination of the living environment status. The data was rearranged and analyzed under an intelligence strategy aimed at identifying similarities and differences between those who positively and clearly see their life prospects and those who are in a situation of uncertainty about the future and the present. The analysis of those resources and features that have the greatest weight in differentiating the selfdetermination of the living environment status within the risky external environment made it possible to assess their internal relationship and carry out an empirical (real), as opposed to a priori (theoretical) grouping. The empirical grouping revealed three blocks of resource determinants of differences between target groups in determining the adaptive status of the living environment, which correlate with the components of subjective intelligence: emotional as the ability to recognize, understand and use emotions, with social as skills and experience of communication with the institutional environment, rationality as the presence of skills of self-organization and self-control.

Artificial intelligence: a new cutting-edge tool in spine surgery.

The purpose of this narrative review was to comprehensively elaborate the various components of artificial intelligence (AI), their applications in spine surgery, practical concerns, and future directions. Over the years, spine surgery has been continuously transformed in various aspects, including diagnostic strategies, surgical approaches, procedures, and instrumentation, to provide better-quality patient care. Surgeons have also augmented their surgical expertise with rapidly growing technological advancements. AI is an advancing field that has the potential to revolutionize many aspects of spine surgery. We performed a comprehensive narrative review of the various aspects of AI and machine learning in spine surgery. To elaborate on the current role of AI in spine surgery, a review of the literature was performed using PubMed and Google Scholar databases for articles published in English in the last 20 years. The initial search using the keywords "artificial intelligence" AND "spine," "machine learning" AND "spine," and "deep learning" AND "spine" extracted a total of 78, 60, and 37 articles and 11,500, 4,610, and 2,270 articles on PubMed and Google Scholar. After the initial screening and exclusion of unrelated articles, duplicates, and non-English articles, 405 articles were identified. After the second stage of screening, 93 articles were included in the review. Studies have shown that AI can be used to analyze patient data and provide personalized treatment recommendations in spine care. It also provides valuable insights for planning surgeries and assisting with precise surgical maneuvers and decisionmaking during the procedures. As more data become available and with further advancements, AI is likely to improve patient outcomes.

Critical thinking and artificial intelligence in tandem: A nursing perspective

The human race is forced to engage in a very rapid adaptation process whenever it is confronted with technological change in any sphere of life. The unabated progress of artificial intelligence (AI) has also impacted the field of critical thinking. It is fascinating that critical thinking, an essential component of intellectual intelligence in nursing, seems to be disrupted by an artificial brain-alike machine that can automatically analyze and synthesize a series of contexts. This has been an improvisation of ideas of intellectual intelligence for a very long time. Perspectives on both sides of the coin bring up interesting questions about the role that AI will play in the future, such as whether it will disrupt the critical thinking skills of nurses or whether it may be engaged as a tool to increase the critical thinking skills capability, especially in the fields of nursing care.

Design and additive manufacturing of bionic hybrid structure inspired by cuttlebone to achieve superior mechanical properties and shape memory function

Lightweight porous materials with high load-bearing, damage tolerance and energy absorption (EA) as well as intelligence of shape recovery after material deformation are beneficial and critical for many applications, e.g. aerospace, automobiles, electronics, etc. Cuttlebone produced in the cuttlefish has evolved vertical walls with the optimal corrugation gradient, enabling stress homogenization, significant load bearing, and damage tolerance to protect the organism from high external pressures in the deep sea. This work illustrated that the complex hybrid wave shape in cuttlebone walls, becoming more tortuous from bottom to top, creates a lightweight, load-bearing structure with progressive failure. By mimicking the cuttlebone, a novel bionic hybrid structure (BHS) was proposed, and as a comparison, a regular corrugated structure and a straight wall structure were designed. Three types of designed structures have been successfully manufactured by laser powder bed fusion (LPBF) with NiTi powder. The LPBF-processed BHS exhibited a total porosity of 0.042% and a good dimensional accuracy with a peak deviation of 17.4 μm. Microstructural analysis indicated that the LPBF-processed BHS had a strong (001) crystallographic orientation and an average size of 9.85 μm. Mechanical analysis revealed the LPBF-processed BHS could withstand over 25 000 times its weight without significant deformation and had the highest specific EA value (5.32 J·g−1) due to the absence of stress concentration and progressive wall failure during compression. Cyclic compression testing showed that LPBF-processed BHS possessed superior viscoelastic and elasticity energy dissipation capacity. Importantly, the uniform reversible phase transition from martensite to austenite in the walls enables the structure to largely recover its pre-deformation shape when heated (over 99% recovery rate). These design strategies can serve as valuable references for the development of intelligent components that possess high mechanical efficiency and shape memory capabilities.

Correlation Between Emotional Intelligence and Readiness for Self-Directed Learning in Medical Undergraduate Students.

The success of self-directed learning depends mainly on the readiness of students to adapt it to their learning domain. Medical students must meet certain criteria to become self-directed learners, which are also significant components of emotional intelligence (EI). Clarification is required on whether the students are ready for self-directed learning according to their level of EIas soon as they enter the medical institute. The survey was conducted on first-year MBBS students, between 18 and 21 years of age. Demographic data of the participants was collected. EI was assessed by using the Schutte Self-Report Emotional Intelligence Test (SSEIT). Fisher's 40-item self-directed learning readiness (SDLR) scale was used to assess the readiness for self-directed learning. Pearson's correlation and regression analysis was carried out to assess the relationship between the two. Approximately 71% of students had average EI, whereas only 5% had high EI. However, 63% of students were found to have low SDLR, while just 37% of participants had high SDLR. EI and SDLR both were found to be higher in males. Pearson's correlation "r"between the two parameters shows a strong positive correlation with statistical significance. Certain training modules need to be incorporated into the medical education program to improve the EI of medical undergraduate students. Such a module might help in improving the readiness for self-directed learning and prepare the medical undergraduates as active lifelong learners, which is the prime goal for an Indian Medical Graduate according to the new Competency-Based Medical Education (CBME) curriculum.

EMOTIONAL INTELLIGENCE AS A PREDICTOR OF PSYCHOLOGICAL AND SOCIAL WELL-BEING OF STUDENTS

The article studies how emotional intelligence influences the psychological and social well-being of students. The components of emotional intelligence have been found to be positively associated with the psychological and social well-being of students. It has been revealed that such components of emotional intelligence as understanding emotions, managing one’s own and others’ emotions have the greatest impact on psychological and social well-being.

Cyber risk assessment of cyber-enabled autonomous cargo vessel

The increasing interest in autonomous ships within the maritime industry is driven by the pursuit of revenue optimization, operational efficiency, safety improvement and going greener. However, the industry’s increasing reliance on emerging technologies for the development of autonomous ships extends the attack surface, leaving the underlying ship systems vulnerable to potential exploitation by malicious actors. In response to these emerging challenges, this research extends an existing cyber risk assessment approach called FMECA-ATT&CK based on failure modes, effects and criticality analysis (FMECA), and the MITRE ATT&CK framework. As a part of our work, we have expanded the FMECA-ATT&CK approach to assessing cyber risks related to systems with artificial intelligence components in cyber-enabled autonomous ships (e.g. autonomous engine monitoring and control). This new capability was developed using the information and semantics encoded in the MITRE ATLAS framework. FMECA-ATT&CK has been adopted due to its comprehensive and adaptable nature and its promising venue for supporting continuous cyber risk assessment. It helps evaluate the cyber risks associated with the complex and state-of-the-art operational technologies on board autonomous ships. The cyber risk assessment approach assists cybersecurity experts in aligning mitigation strategies for the cyber defence of autonomous ships. It also contributes towards advancing overall cybersecurity in the maritime industry and ensures the safe and secure sailing of autonomous ships. Our key findings after applying the proposed approach against a model of an autonomous cargo ship is the identification of the Navigation Situation Awareness System (NSAS) of the ship as being at the highest risk followed by the Autonomous Engine Monitoring and Control (AEMC) system. Additionally, we identified 3 high, 48 medium, and 5776 low risks across 29 components.

The association of empathy and the work-family conflict in doctors of dental medicine.

Work-family conflict is a growing problem worldwide, because of changing work-family demographic trends and the spread of technology. Empathy, as the ability to understand and share the feelings of other people, is the essential component of emotional intelligence that plays a crucial role in healthcare settings. This study aimed to assess the level of emotional empathy and investigate its relationship with work-family role conflicts among dental medicine doctors. Materials and methods: In this study participated 589 doctors of dental medicine from Croatia, Serbia, Bosnia and Herzegovina. The questionnaire included general and demographic data, the Emotional Empathy Scale questionnaire and the Work and Family Role Conflict Assessment Scale questionnaire. Results: The research results indicate no significant association between empathy and conflict between work and family roles (R = 0.032, p = 0.435). Women have achieved significantly higher scores in Emotional Empathy Scale (p ≤ 0.001), while there was no difference in Work and Family Role Conflict Assessment Scale according to gender (p = 0.194). A difference in emotional empathy was observed depending on where the respondents were employed (p = 0.045) and depending on the specialisation of the dental medicine (p = 0.021). Conclusion: Female doctors of dental medicine demonstrated higher emotional empathy while the work-family role conflict is experienced equally by both genders.

Current state of implementation of in silico tools in the biopharmaceutical industry-Proceedings of the 5th modeling workshop.

The fifth modeling workshop (5MW) was held in June 2023 at Favrholm, Denmark and sponsored by Recovery of Biological Products Conference Series. The goal of the workshop was to assemble modeling practitioners to review and discuss the current state, progress since the last fourth mini modeling workshop (4MMW), gaps and opportunities for development, deployment and maintenance of models in bioprocess applications. Areas of focus were four categories: biophysics and molecular modeling, mechanistic modeling, computational fluid dynamics (CFD) and plant modeling. Highlights of the workshop included significant advancements in biophysical/molecular modeling to novel protein constructs, mechanistic models for filtration and initial forays into modeling of multiphase systems using CFD for a bioreactor and mapped strategically to cell line selection/facility fit. A significant impediment to more fully quantitative and calibrated models for biophysics is the lack of large, anonymized datasets. A potential solution would be the use of specific descriptors in a database that would allow for detailed analyzes without sharing proprietary information. Another gap identified was the lack of a consistent framework for use of models that are included or support a regulatory filing beyond the high-level guidance in ICH Q8-Q11. One perspective is that modeling can be viewed as a component or precursor of machine learning (ML) and artificial intelligence (AI). Another outcome was alignment on a key definition for "mechanistic modeling." Feedback from participants was that there was progression in all of the fields of modeling within scope of the conference. Some areas (e.g., biophysics and molecular modeling) have opportunities for significant research investment to realize full impact. However, the need for ongoing research and development for all model types does not preclude the application to support process development, manufacturing and use in regulatory filings. Analogous to ML and AI, given the current state of the four modeling types, a prospective investment in educating inter-disciplinary subject matter experts (e.g., data science, chromatography) is essential to advancing the modeling community.

Designing Multifunctional Multiferroic Composites for Advanced Electronic Applications

This paper presents a novel approach for the fabrication of magnetoelectric composites aimed at enhancing cross-coupling between electrical and magnetic phases for potential applications in intelligent sensors and electronic components. Unlike previous methodologies known for their complexity and expense, our method offers a simple and cost-effective assembly process conducted at room temperature, preserving the original properties of the components and avoiding undesired phases. The composites, composed of PZT fibers, cobalt (CoFe2O4), and a polymeric resin, demonstrate the uniform distribution of PZT-5A fibers within the cobalt matrix, as demonstrated by scanning electron microscopy. Detailed morphological analyses reveal the interface characteristics crucial for determining overall performance. Dielectric measurements indicate stable behaviors, particularly when PZT-5A fibers are properly poled, showcasing potential applications in sensors or medical devices. Furthermore, H-dependence studies illustrate strong magnetoelectric interactions, suggesting promising avenues for enhancing coupling efficiency. Overall, this study lays the basic work for future optimization of composite composition and exploration of its long-term stability, offering valuable insights into the potential applications of magnetoelectric composites in various technological domains.

The role of mentoring in developing leaders’ emotional intelligence: exploring mentoring types, emotional intelligence, organizational factors, and gender

Emotional awareness, emotional regulation, empathy, and resilience are key components of emotional intelligence. Twenty-first-century leaders require such competencies, and prior research establishes a positive impact of emotional intelligence on leadership and well-being. The mechanisms through which leaders develop these competencies remain unclear. Mentoring, a developmental tool linked with well-being, has not been extensively studied for its role in emotional intelligence development. The current study investigates this relationship within the context of vocational education and training in South Africa. The mentoring framework includes individual, peer group, and key performance area mentoring. In previous research on this mentoring framework, leaders perceived emotional well-being as the most important outcome of mentoring and development, constituting another vital factor. Data were collected from a treatment group of leaders who have participated in the mentoring framework and a control group of leaders and lecturers (N = 139). The present study used exploratory and confirmatory factor analysis to validate the Schutte Self-Report Emotional Intelligence Test within this context. In the next step, we employed descriptive analysis to answer which mentoring type was best perceived to support emotional intelligence. Using the Mann–Whitney U test, we tested for significant differences in the identified factors between treatment and control group. Mediated and moderated mediation analyses explored variables such as gender, occupational role, organization, and work sector. Results indicate a six-factor structure of emotional intelligence, with significant differences observed between groups in the factor empathy difficulty. Peer group mentoring emerged as an effective method for emotional intelligence development among leaders. The perceived importance of emotional intelligence for one’s job position, the organization, and the work sector mediated five of the six factors. The moderated mediation analyses showed an indirect effect of gender, where being male was associated with more trustworthy visionary and empathy. The findings underscore the significance of peer mentoring practices and organizational factors in nurturing emotional intelligence, highlighting its value for personal and organizational well-being. Overall, the study sheds light on developing emotional intelligence at all organizational levels to support individual and collective well-being.

Emotional intelligence and specificity of brain cortical activity in coronary heart disease

Testing the components of emotional intelligence (EI) in patients with coronary heart disease (CHD) showed that they are more likely to react to emotionally significant situations than to express their emotions. The most pronounced influence on the frequency-spatial organization of brain activity, mainly in the θ1-, α2- and β1-ranges, is induced by such EI components as the expression of positive emotions, the use of emotions in decision-making and empathy. An increase in self-assessment of positive expression is associated with an increase in the power of the β1-rhythm in the parieto-occipital cortex with the dominance of the left hemisphere and an increase in the θ1 not only in these areas, but also in the posterofrontal cortical sites. The increase in empathy is accompanied by a decrease in the power of the β1-rhythm, mainly in the temporal cortex of the left hemisphere. Gender differences were found in the association of self-assessment of negative expression and the power of θ- and β-oscillations with greater involvement of the left frontal areas in women. The identified features of the relationship between EI and EEG parameters may be a consequence of the reorganization of cortical activity in patients with coronary heart disease, which develops as a result of chronic cerebral ischemia.

Comparing the Components of Psychological Well-Being and Emotional Intelligence in Players with and Without Computer Game Addiction in Secondary School Students of Alborz City: A Descriptive Study

Comparing the Components of Psychological Well-Being and Emotional Intelligence in Players with and Without Computer Game Addiction in Secondary School Students of Alborz City: A Descriptive Study

The effectiveness of a moral intelligence training program in reducing secondary school students’ academic entitlement

The research aimed to investigate the effectiveness of a training program based on moral intelligence components in reducing academic entitlement among secondary school students. The study sample consisted of a single experimental group of (32) students. The researcher administered the Academic Entitlement Scale (pre-test) to the participants before the moral intelligence-based training program, then after the program (post-test), and again a month later (as a follow-up assessment). Through statistical data processing using the t-test, the study results revealed significant differences in the mean scores of the experimental group on the Academic Entitlement Scale and its components between the pre-test and post-test in favor of the post-test. However, there were no statistically significant differences in the mean scores of the experimental group on the Academic Entitlement Scale and its components between the post-test and follow-up test (a month later). The researcher provided a comprehensive discussion of the study variables: academic entitlement and its components, and the moral intelligence-based training program in the current study. Additionally, recommendations and proposed future research were presented

Desktop Assistant Based on NLP

Natural Language Processing (NLP) has emerged as a critical component of artificial intelligence, enabling machines to comprehend and interact with human language. This research paper explores the current state of the art in NLP, highlighting recent innovations, trends, and ongoing challenges. It delves into various applications of NLP, discusses the datasets and models that drive advancements, and examines the evaluation metrics used to assess NLP systems. Key innovations such as transformers, pre-trained language models, and transfer learning have revolutionized the field, leading to significant improvements in performance across a variety of tasks. Additionally, the paper addresses the growing emphasis on ethical AI and bias mitigation, as well as the integration of NLP with other AI technologies to create multimodal systems. Applications of NLP in text classification, sentiment analysis, machine translation, conversational agents, and information retrieval are thoroughly examined. The discussion extends to the critical role of benchmark datasets and pre-trained models in driving progress. Furthermore, the paper evaluates the effectiveness of various metrics used to measure the performance of NLP systems. Finally, the future prospects and potential research directions are considered, highlighting the ongoing efforts to push the boundaries of what NLP can achieve in an increasingly interconnected and data-driven world. Keywords Natural language processing . Natural language understanding . Natural language generation

Neuromodulatory effects of parietal high-definition transcranial direct-current stimulation on network-level activity serving fluid intelligence.

Fluid intelligence (Gf) involves rational thinking skills and requires the integration of information from different cortical regions to resolve novel complex problems. The effects of non-invasive brain stimulation on Gf have been studied in attempts to improve Gf, but such studies are rare and the few existing have reached conflicting conclusions. The parieto-frontal integration theory of intelligence (P-FIT) postulates that the parietal and frontal lobes play a critical role in Gf. To investigate the suggested role of parietal cortices, we applied high-definition transcranial direct current stimulation (HD-tDCS) to the left and right parietal cortices of 39 healthy adults (age 19-33years) for 20min in three separate sessions (left active, right active and sham). After completing the stimulation session, the participants completed a logical reasoning task based on Raven's Progressive Matrices during magnetoencephalography. Significant neural responses at the sensor level across all stimulation conditions were imaged using a beamformer. Whole-brain, spectrally constrained functional connectivity was then computed to examine the network-level activity. Behaviourally, we found that participants were significantly more accurate following left compared to right parietal stimulation. Regarding neural findings, we found significant HD-tDCS montage-related effects in brain networks thought to be critical for P-FIT, including parieto-occipital, fronto-occipital, fronto-parietal and occipito-cerebellar connectivity during task performance. In conclusion, our findings showed that left parietal stimulation improved abstract reasoning abilities relative to right parietal stimulation and support both P-FIT and the neural efficiency hypothesis. KEY POINTS: Abstract reasoning is a critical component of fluid intelligence and is known to be served by multispectral oscillatory activity in the fronto-parietal cortices. Recent studies have aimed to improve abstract reasoning abilities and fluid intelligence overall through behavioural training, but the results have been mixed. High-definition transcranial direct-current stimulation (HD-tDCS) applied to the parietal cortices modulated task performance and neural oscillations during abstract reasoning. Left parietal stimulation resulted in increased accuracy and decreased functional connectivity between occipital regions and frontal, parietal, and cerebellar regions. Future studies should investigate whether HD-tDCS alters abstract reasoning abilities in those who exhibit declines in performance, such as healthy ageing populations.

The Relationship between Communication Skills and Emotional Intelligence among Students Attending Medical School: A Cross-Sectional Study

Background: Intelligence quotient (IQ) cannot predict how a person will react to the ups and downs of life, while emotional intelligence is a thoughtful ability that determines how we can use our other skills, including IQ, in the best way. The impact of emotional intelligence components on people›s success in healthcare organizations is remarkable. This study discussed the relationship between communication skills and emotional intelligence among medical students. Methods: This cross-sectional study was conducted at Hamadan University of Medical Sciences in 2021 among 114 medical students studying basic science. The questionnaires used to collect data were demographic information questionnaires, Goleman’s emotional intelligence, and Queen Dam’s communication skills. After collecting data, data analysis was performed using SPSS software. For this analysis, a significance level of less than 0.05 was used. Results: 114 questionnaires were completed. The average level of emotional intelligence of all students was 99.82±6.18. Also, in the study of communication skills, the average communication skill of all students was 109.1±8.01 (both moderate to high). There was no significant relationship between marital, gender, average age, and type of acceptance with emotional intelligence and communication skills. Students’ highest emotional intelligence skill was motivation, and the lowest was social skills. A significant correlation was found Between communication skills and educational status with emotional intelligence. Conclusion: This study showed meaningful connections between communication skills and emotional intelligence, which can be set in the training program, to increase emotional intelligence, which can increase effective communication between medical students and promote community health.

Towards a high-energy efficiency world: Assessing the impact of artificial intelligence on urban energy efficiency

Global warming has increased the frequency of natural disasters, making enhancing energy efficiency an inevitable choice to address the worsening global climate risks. Whether industrial robots, as a crucial component of artificial intelligence, can contribute to improving energy efficiency remains a subject of debate. This study employs the SBM and super-efficiency SBM models to assess the energy efficiency of 262 Chinese cities from 2010 to 2020. We investigate the impact and mechanisms of industrial robots at the city level on energy efficiency. The research findings indicate that the application of industrial robots promotes enhanced energy efficiency, which remains robust after subjecting it to a battery of tests, including SYS-GMM and instrumental variable analyses. However, a significant nonlinear relationship exists between industrial robot utilization and energy efficiency, with diminishing marginal effects. Mechanism analysis reveals that the fundamental mechanisms facilitating this improvement are the digital economy, industrial structural adjustments, and innovations in green technology. Heterogeneity analysis suggests that industrial robots have a more pronounced impact on cities with higher energy efficiency, greater manufacturing levels in resource-dependent areas, and lower population densities. Furthermore, the application of industrial robots can influence the energy efficiency of neighboring regions through spatial spillover effects.

Recent Trends in Machine and Deep Learning for Verbal and Non-verbal Emotion Detection

Abstract: Emotion recognition, both verbal and non-verbal, is a crucial component of artificial intelligence, psychology, and human-computer interaction. Emotion recognition is an integral component that significantly contributes to the improvement of communication and interaction. The research endeavors to conduct a thorough analysis and synthesis of the most recent developments in Deep Learning (DL) and Machine Learning (ML) techniques. Specifically, the study concentrates on the recognition of both verbal and non-verbal emotions. In contrast to previous research concentrated on verbal or non-verbal emotion detection separately, the study attempts to reconcile the gap between the two by demonstrating how ML and DL can be utilized effectively to detect emotions. The study also examines new methods, including multimodal data and integration of contextual information. Additionally, the research has examined the ethical implications and difficulties associated with emotion detection technologies. Findings have also revealed the wide-reaching implications for various sectors, including healthcare, education, customer service, and entertainment, where comprehending human emotions plays a crucial role in enhancing user experience and outcomes. In conclusion, the study provides invaluable knowledge to practitioners and researchers, which may facilitate the development of more advanced and accurate systems.