Level Of Intelligence Research Articles

The effect of cognitive intervention program on intelligence scores in preschool.

Dynamic assessment is an approach that aims to improve student performance through interventions. One of the important application areas of dynamic assessment is the assessment of intelligence. Within the scope of the study, after a Cognitive intervention program (CIP) was developed, its effects on intelligence scores were examined with a quasi-experimental research method. The CIP was prepared by adopting a dynamic assessment approach to improve the performance of students with expert support. This improvement would be provided by the clues and feedback given during the intervention within the scope of the CIP. The sample of the study included 173 students in the 5-6 age group (83 experimental group, 90 control group). The CIP developed by the researcher consisted of 54 worksheets and was applied to the experimental group for 9weeks. The implementation of the worksheets was supervised by classroom teachers. The digital application of the Anadolu-Sak Intelligence Scale (d-ASIS) and Raven's colored progressive matrices (RCPM) were applied to both the experimental and control groups as pretests and posttests. The increases in the intelligence scores of the experimental and control groups were analyzed by MANOVA. The analysis showed that the intelligence levels of the experimental group increased significantly more than the control group in terms of both d-ASIS and RCPM total gain scores (posttest to pretest). This result indicated that the CIP, which was developed by adopting a dynamic assessment approach, supported cognitive development.

Upaya Pencegahan Stunting Melalui Kegiatan Gasting (Garahan Bebas Stunting) di Desa Garahan, Kecamatan Silo

Stunting is a chronic nutritional problem that occurs worldwide caused by suboptimal nutritional intake from pregnancy to 24 months or 2 years of age. Stunting in childhood is associated with delays in motor development and lower intelligence levels. Symptoms shown by stunting sufferers include a child's height that is shorter than children of the same age, the child looks smaller for his age, low body weight, and delayed bone growth (Laska et al., 2024). Stunting has short-term and long-term impacts. In the short term, stunting causes depression of immune function, metabolic changes, decreased motor development, low cognitive values, and low academic values (Widjayatri et al., 2020). Meanwhile, the long-term impacts of children with stunting include, when they grow up, they will be at risk of obesity, coronary heart disease, hypertension, osteoporosis, decreased work performance, and productivity (Dekasari & Gunawan, 2024)

Aversion to external feedback suffices to ensure agent alignment

Ensuring artificial intelligence behaves in such a way that is aligned with human values is commonly referred to as the alignment challenge. Prior work has shown that rational agents, behaving in such a way that maximizes a utility function, will inevitably behave in such a way that is not aligned with human values, especially as their level of intelligence goes up. Prior work has also shown that there is no “one true utility function”; solutions must include a more holistic approach to alignment. This paper describes apprehensive agents: agents that are architected in such a way that their effective utility function is an aggregation of a partial utility function (built by designers, to be maximized) and an expectation of negative feedback on given states (reasoned about, to be minimized). Agents are also capable of performing a temporal reasoning process that approximates designers’ intentions in function of environment evolution (a necessary feature for severe mis-alignment to occur). We show that an apprehensive agent, behaving rationally, leverages this internal approximation of designers’ intentions to predict negative feedback, and, as a consequence, behaves in such a way that maximizes alignment, without actually receiving any external feedback. We evaluate this strategy on simulated environments that expose mis-alignment opportunities: we show that apprehensive agents are indeed better aligned than their base counterparts and, in contrast with extant techniques, chances of alignment actually improve as agent intelligence grows.

Level of emotional intelligence in medical-surgical nursing specialist nurses

Level of emotional intelligence in medical-surgical nursing specialist nurses

Deep learning in driverless: Research results, issues, challenges

As computing and communication technologies continue to evolve, leading technology companies are investing significant resources in self-driving car techniques. With the use of deep learning in driverless driving, driverless cars are enabling them to sense, make decisions and control in complex environments, leading to truly autonomous driving. Deep learning is used for environment perception, which enables driverless cars to understand their surroundings by recognizing information such as road surfaces, pedestrians and vehicles, and also used for decision formulation, by predicting the behavior of other vehicles, pedestrians and identifying the environment around the road conditions to control the driverless car to make the optimal driving decision. But in order to achieve fully autonomous driving, there are still some technical aspects. Therefore, we deep learning in the existing driverless technology is reviewed, mainly discussing the application in the direction of driverless implementation and safety inspection. Through reading a large amount of literature, we discuss the problems that hinder the development of the driverless field, and at the same time, how to solve the problems in the future to promote the breakthrough of the driverless field. The application of deep learning in driverless not only enhances the intelligence level of driverless cars, but also prepares for the commercial application of driverless cars in the future. It enables driverless cars to better identify and adapt to complex traffic environments. Finally, realizing truly autonomous driving.

Method of Motion Planning for Digital Twin Navigation and Cutting of Shearer.

To further enhance the intelligence level of coal mining faces and achieve the autonomous derivation, learning, and optimization of shearer navigation cutting, this paper proposes the methods of shearer digital twin navigation cutting motion planning based on the concept of shearer autonomous navigation cutting technology and intelligent coal mining face digital twins. This study includes the digital twin theory and the construction method of the shearer digital twin navigation cutting motion planning system based on this theory. Based on the digital twin theory, a shearer digital twin navigation cutting motion planning system was constructed. This system supports the service functions of the shearer cutting digital twin, dynamic navigation map digital twin, reinforcement learning environment construction, and motion planning through the physical perception layer, comprehensive data layer, and digital-model fusion analysis layer. Finally, by comparing the effects of the DQN-NAF and DDPG deep reinforcement learning algorithms in the shearer motion planning task within the constructed digital twin environment, the results show that the DQN-NAF algorithm demonstrates better performance and stability in solving the shearer digital twin motion planning task.

Dual-stage semantic segmentation of endoscopic surgical instruments.

Endoscopic instrument segmentation is essential for ensuring the safety of robotic-assisted spinal endoscopic surgeries. However, due to the narrow operative region, intricate surrounding tissues, and limited visibility, achieving instrument segmentation within the endoscopic view remainschallenging. This work aims to devise a method to segment surgical instruments in endoscopic video. By designing an endoscopic image classification model, features of frames before and after the video are extracted to achieve continuous and precise segmentation of instruments in endoscopicvideos. Deep learning techniques serve as the algorithmic core for constructing the convolutional neural network proposed in this study. The method comprises dual stages: image classification and instrument segmentation. MobileViT is employed for image classification, enabling the extraction of key features of different instruments and generating classification results. DeepLabv3+ is utilized for instrument segmentation. By training on distinct instruments separately, corresponding model parameters are obtained. Lastly, a flag caching mechanism along with a blur detection module is designed to effectively utilize the image features in consecutive frames. By incorporating specific parameters into the segmentation model, better segmentation of surgical instruments can be achieved in endoscopicvideos. The classification and segmentation models are evaluated on an endoscopic image dataset. In the dataset used for instrument segmentation, the training set consists of 7456 images, the validation set consists of 829 images, and the test set consists of 921 images. In the dataset used for image classification, the training set consists of 2400 images and the validation set consists of 600 images. The image classification model achieves an accuracy of 70% on the validation set. For the segmentation model, experiments are conducted on two common surgical instruments, and the mean Intersection over Union (mIoU) exceeds 98%. Furthermore, the proposed video segmentation method is tested using videos collected during surgeries, validating the effectiveness of the flag caching mechanism and blur detection module. Experimental results on the dataset demonstrate that the dual-stage video processing method excels in performing instrument segmentation tasks under endoscopic conditions. This advancement is significant for enhancing the intelligence level of robotic-assisted spinal endoscopic surgeries.

Parametric modeling and engineering application of the high-speed continuous beam bridge based on Building Information Modeling.

To improve the informatization and intelligence level of high-speed railway (HSR) bridge construction, a parametric modeling method for continuous beam bridges based on Building Information Modeling (BIM) is proposed in this study. By this method, the parametric families of continuous beam components and key construction machinery are established, and the rapid modeling of overall continuous beam bridge and the simulation of critical construction process are realized as well. Taking the Caoxian-Shangqiu bridge of Xiong'an-Shangqiu HSR as a case study, the parametric modeling method is applied to conduct the engineering application on the prestressed duct layout and rebar clash detection. The results indicate that the modeling efficiencies of HSR continuous beam bridge and construction machinery are significantly increased by the established parametric modeling method. Based on the BIM model of continuous beam bridge, the improvement in the precision of prestressed duct layout and the elimination of rebar clash points can be achieved. The research achievement can guide the visualization of construction disclosure, enhance construction efficiency, and provide reference and technical support for the construction management and control of HSR continuous beam bridges.

Informative genetic markers of predisposition to the formation of high-level intelligence

This study is devoted to investigating the association of intelligence with a person’s genotype. Comparative psychological and molecular genetic testing was carried out on a representative sample of Belarusian population representatives numbering 746 people. Intelligence quotient (IQ) was determined using D. Wechsler’s test. It was established that the distribution of general intelligence values among the Belarusian population representatives was shifted towards higher results. The persons were divided into low and high IQ groups using the quartile method. The association of intelligence level with genotype was determined using a panel of genetic markers consisting of 18 gene polymorphic variants of neurogenesis, synaptic plasticity and neurotransmitter systems. Molecular genetic testing was carried out using real-time PCR. To automate a search for informative gene combinations associated with cognitive abilities, we used a special computer program that we had previously developed, taking into account the gene interaction effects. 15 most informative combinations of polymorphic gene variants associated with a high intelligence level were identified. These gene variants can be used to assess a genetic predisposition to the formation of high-level intelligence, which will allow assessing the abilities of candidates for extreme activities.

Physical education learning outcomes of senior high school students based on gender and intellectual intelligence review

Abstract. This study examined the disparities in Physical Education (PE) learning Outcomes among high school pupils by considering their gender and intellectual aptitude. The study involved participants from a high school in Yogyakarta, selected using the Simple Random Sampling method. The data were then analysed using descriptive statistics and a two-way factorial inferential analysis of variance (ANOVA). The study divided the results into two groups based on their intellectual intelligence: low and high. In the low intellectual category, the average learning outcome for male students was 83.60. Meanwhile, the average for female students was 84.10. In the high intellectual intelligence category, the average results for male students were 85.30, while the average achievement for female students was 83.70. The ANOVA test for the intellectual intelligence variable yielded a p-value (Sig.) of 0.303, which is greater than the significance level of 0.05. This figure suggests that there is no statistically significant difference in students’ learning outcomes based on their level of intelligence, whether low or high. Similarly, the p-value of 0.383 for the gender variable was also greater than the significance level of 0.05, indicating no statistically significant variations in learning results. Additionally, the interaction effect between IQ and gender yielded a significance value of 0.100, which is greater than 0.05. It indicates no significant interaction between these two variables on the learning performance of high school students. The finding means that cognitive intelligence does not have a substantial impact on the performance of physical education (PE) among high school students. Moreover, there is no notable disparity in PE performance between male and female students, suggesting gender equality in both the opportunities and abilities in PE. Finally, the study found no notable correlation between gender and IQ levels in performance on intelligence tasks. These results diminish the importance of considering other criteria, such as physical aptitude, in evaluating PE performance and emphasize the need for comprehensive and inclusive physical education programs. Keywords: Learning Achievement, PE, Students, Adolescents, Gender, Intellectual Intelligence

Mechanical Neural Networks with Explicit and Robust Neurons.

Mechanical computing provides an information processing method to realize sensing-analyzing-actuation integrated mechanical intelligence and, when combined with neural networks, can be more efficient for data-rich cognitive tasks. The requirement of solving implicit and usually nonlinear equilibrium equations of motion in training mechanical neural networks makes computation challenging and costly. Here, an explicit mechanical neuron is developed of which the response can be directly determined without the need of solving equilibrium equations. A training method is proposed to ensure the robustness of the neuron, i.e., insensitivity to defects and perturbations. The explicitness and robustness of the neurons facilitate the assembly of various network structures. Two exemplified networks, a robust mechanical convolutional neural network and a mechanical recurrent neural network with long short-term memory capabilities for associative learning, are experimentally demonstrated. The introduction of the explicit and robust mechanical neuron streamlines the design of mechanical neural networks fulfilling robotic matter with a level of intelligence.

Measurement Invariance Testing Works.

Psychometricians have argued that measurement invariance (MI) testing is needed to know if the same psychological constructs are measured in different groups. Data from five experiments allowed that position to be tested. In the first, participants answered questionnaires on belief in free will and either the meaning of life or the meaning of a nonsense concept called "gavagai." Since the meaning of life and the meaning of gavagai conceptually differ, MI should have been violated when groups were treated like their measurements were identical. MI was severely violated, indicating the questionnaires were interpreted differently. In the second and third experiments, participants were randomized to watch treatment videos explaining figural matrices rules or task-irrelevant control videos. Participants then took intelligence and figural matrices tests. The intervention worked and the experimental group had an additional influence on figural matrix performance in the form of knowing matrix rules, so their performance on the matrices tests violated MI and was anomalously high for their intelligence levels. In both experiments, MI was severely violated. In the fourth and fifth experiments, individuals were exposed to growth mindset interventions that a twin study revealed changed the amount of genetic variance in the target mindset measure without affecting other variables. When comparing treatment and control groups, MI was attainable before but not after treatment. Moreover, the control group showed longitudinal invariance, but the same was untrue for the treatment group. MI testing is likely able to show if the same things are measured in different groups.

Risks and Opportunities Brought by Artificial Intelligence Empowering the Financial Industry

Intelligent risk management and trade optimization play pivotal roles in the realm of investment banking. Leveraging AI's capabilities, institutions harness vast datasets and machine learning algorithms to detect potential risks and fine-tune trading strategies. This results in more accurate decision-making processes and better risk mitigation measures. Furthermore, AI enables personalized customer service and precise market forecasting, elevating client satisfaction levels and enabling the anticipation of market trends with greater precision. Tailored services foster stronger client relationships, driving long-term partnerships and loyalty. In addition to enhancing operational efficiency and client satisfaction, AI drives innovation and development within investment banking. This includes the introduction of intelligent advisory services and the implementation of digital perception systems, revolutionizing traditional banking practices. However, the integration of AI also presents challenges such as data privacy concerns, regulatory compliance issues, the need for talent cultivation, and organizational transformation. Looking ahead, the future of investment banking is expected to witness increased AI adoption, leading to higher levels of intelligence, more sophisticated learning techniques, and collaborative innovation across the industry. This trajectory promises to reshape the landscape of investment banking, creating new opportunities for growth and advancement.

Hippocampal Cognitive Function Based on Deep Learning

Abstract This research focuses on the study of agent behavior decision-making based on hippocampal cognitive functions, aiming to enhance the decision-making capabilities of agents in complex task environments by deeply exploring the crucial role of the hippocampus in learning, memory, and cognitive processes. By drawing inspiration from the biological structure and functional characteristics of the hippocampus, researchers are dedicated to designing and developing more intelligent and adaptive decision-making models to enhance agents' behavioral performance, problem-solving abilities, and adaptability to new situations. To achieve this goal, the research integrates advanced artificial intelligence technologies such as reinforcement learning and deep learning to simulate the complex functions of the hippocampus in memory encoding, storage, retrieval, and cognitive reasoning. This research not only contributes to advancing intelligent systems towards higher levels of intelligence and personalization but also plays a significant role in improving the interaction between intelligent agents and humans, providing intelligent services that better meet user needs. We found that the neural network trained in multi-task learning benefits from a loss term that promotes relevant and irrelevant representations. Therefore, the complementary coding we found in CA3 can provide extensive computational advantages for solving complex tasks. Furthermore, the study emphasizes the importance of further elucidating the functional mechanisms of the hippocampus, with the expectation of providing a more solid theoretical foundation for the optimization and refinement of agent decision-making models in the future.

Inhibition or promotion? --A study on the impact of artificial intelligence on consumption upgrading based on the perspective of industrial structure and market upgrading

Artificial intelligence (AI) is essential in driving industrial transformation, upgrading value chains, and promoting global cooperation, and is also an important engine for promoting economic growth and technological innovation. Studying the relationship between AI and consumption upgrading helps drive the deepening development of the AI industry, while stimulating consumption vitality, improving consumption levels, and achieving consumption upgrading. This article utilizes data spanning from 2013 to 2022 across 30 provinces to empirically test the impact of artificial intelligence on household consumption upgrading through a bidirectional fixed effects model. The examination of its influence mechanism and spatial spillover effects is deepened through the utilization of the Bootstrap-based mediation effect model, threshold effect model, and spatial Durbin model. Research has found that the improvement of artificial intelligence level can significantly promote the upgrading of household consumption. The development of AI level will promote the upgrading of consumption and economic growth within the province, while also producing a "demonstration effect" on neighboring areas. After robustness testing, the conclusion is still valid. In-depth analysis indicates that industrial structure upgrading and market upgrading serve as two significant intermediary pathways, with the presence of a single threshold effect. This article provides decision-making references for China to better promote consumption expenditure upgrading in the artificial intelligence industry from the perspective of supply and demand integration.

Peculiarities of the influence of the level of motivation for cognitive activities on the development of future teachers’ intellectual abilities

Introduction. The article considers the problem of developing Education students’ intellectual abilities at different levels of their cognitive motivation. The purpose of the study is to reveal how low and high levels of motivation for cognitive activities influence the development of the psychological system of intellectual abilities in different periods of Education students’ learning and professional activities. Materials and Methods. The study follows the system-genetic variant of the activity-based approach, which enables the authors to explain the influence of personality abilities (motivation) on the development of the abilities of the subject of the activity (intellectual abilities). The main methods employed include a survey, testing, analysis of regulatory documents and scholarly literature on the research problems. The sample consisted in 156 undergraduate students majoring in Education. Results. The authors have revealed a nonlinear nature of the influence of cognitive motivation on the development of intellectual abilities of Education students. It was found that an increase in cognitive motivation does not lead to an increase in absolute indicators of intellectual abilities and the level of general intelligence; its influence is manifested in a change in the structural organization of intellectual abilities in different periods of learning and professional activities. A high level of cognitive motivation ensures successful restructuring of intellectual abilities at the beginning of teacher training, testing new methods of intellectual activities in the middle of the degree course, optimization of the structure of intelligence at the end of studying. Thus, the influence of cognitive motivation on intellectual development is mediated by the acquisition of experience in educational and professional activities, requiring testing different methods of arranging students’ intellectual activities. Conclusions. Managing the process of developing motivation and intellectual abilities in a future teacher should take into account the complex nature of relationships between these psychological phenomena. The effect of pedagogical influence on motivation and, as a consequence, intellectual development is of a delayed nature, mediated by the acquisition of experience in solving educational and professional problems by future teachers at different stages of pursuing the degree programme.

Design and realization of a water quality monitoring system based on the Internet of Things

ABSTRACT Water quality monitoring is a key means of ensuring the safety of water resources, protecting the ecological environment and maintaining public health. This paper describes a water quality monitoring system based on the Internet of Things (IoT), which supports real-time monitoring, remote control and intelligent early warnings. It is concluded that the system can effectively collect, process and display data on water quality. To further improve the level of intelligence of the water quality monitoring system, the ability of machine learning algorithm to analyze and predict the water quality data has also been separately tested. In this paper, the hardware design, software design, test results and algorithm evaluation of the system are described. The actual performance and reliability of the system in practical application under various field conditions have not yet been verified. This new system is compared with other water quality monitoring systems.

An examination of Emotional Intelligence disparities between first-year B.Sc. Students choosing Physical Education and BCA students: a Comparative Analysis

This research paper explores the dimensions of Emotional Intelligence (EI) among first-year college students in two distinct academic disciplines: Physical Education within the Bachelor of Science (B.Sc.) program and Computer Applications (BCA). Recognizing the potential impact of educational choices on emotional competencies, the study investigates whether students in these fields exhibit nuanced differences in EI. The focus arises from the divergent curricular emphases, with Physical Education emphasizing physical wellness, teamwork, and leadership, while BCA delves into the complexities of computer science. Emotional Intelligence is contextualized as a crucial factor in academic, personal, and professional realms, encompassing self-awareness, self-regulation, empathy, motivation, and social skills. The objectives involve both analysis and comparison of EI between the two groups of students. The research employed a survey method with random sampling of 40 male students from Holkar Science College, Indore, aged 18-21. Emotional Intelligence was measured using the Emotional Intelligence Scale (EIS) developed by Anukul Hyde, Sanjyot Pethe, and Upindar Dhar (2005). Descriptive statistics and the Independent T-test were applied for analysis. Results: reveal no significant differences in the ten factors of Emotional Intelligence between B.Sc. students in Physical Education and those pursuing BCA. The study concludes that, based on the statistical analysis, the chosen academic path does not lead to a significant variance in Emotional Intelligence levels among these first-year students. This finding contributes to our understanding of how different academic trajectories may not necessarily shape distinct emotional competencies during this crucial phase of higher education.

Study on the Psychological Factor Within the Middle School Footbal Team

The psychological factor is one of the least exploited aspects of football. In sports, developing performance capacity is the primary objective of training. Maintaining motivation and achieving performance are the foundations of the educational-instructional process and have great value as outcomes both individually and for the team. Research hypotheses. (H1). It is assumed that the members of the national football team have average/above-average emotional intelligence (test score => 100), significantly different from the rest of the students in grades VII-VIII, because teamwork and the psychological training included in the preparation develop the mental qualities and properties of the students who play football. (H2). It is assumed that there is a significant correlation between emotional intelligence and the performance of students in the experimental group. Research methods. In the present research, the initial testing was carried out among students in grades VII-VIII and the national football team (February 2024 – May 2024). This research was conducted at “Sf. Ilie” General School in Toplița. The research subjects were 68 students, 35 females and 33 males. Quantitative and qualitative methods were used, and the following methods were chosen: observation, emotional intelligence test (Roco, 2001), performance test (ball sense, heading the ball, complex test, shooting at two goals, bilateral game), and statistical analysis (t-test). Results. The level of emotional intelligence of the students is 86 and 81 in the national football team, below average, which raises a red flag. At the level of the national football team, a strong self-awareness among the students is noted. This indicator has the highest score, 500, far ahead of empathy (-180) and very far ahead of social skills (-360). After applying to the middle school team's training program, we see a significant increase in the level of emotional intelligence. The correlation statistical test and the t-test show a correlation of r=0.86 and a significance of p=0.00 between the initial and final test results. The Pearson correlation shows a strong link between emotional intelligence and the bilateral game (r=0.76), but does not show a significant relationship between emotional intelligence and the other performance indicators. To confirm if the value of r is statistically significant, i.e., if the two variables are correlated, the t-value was calculated. Conclusions. This research confirms that the psychological factor cannot be absent from the preparation of school representative teams. It was confirmed that a high level of emotional intelligence improves the performance of football players, especially in team play, increasing group cohesion. Keywords: football, psychological factor, performance, athletes, school team

Flexing the emotional muscle: Investigating how team reflexibility shapes performance via emotional intelligence

In team settings, the development of reflexibility and emotional intelligence significantly influences performance outcomes. Identifying these relationships is crucial for optimizing team dynamics and achieving superior results. Present research investigates the relationship between team reflexivity and team performance through the mediating role of Emotional Intelligence. Team reflexibility affects team performance positively and developing emotional intelligence skills improves team performance. SPSS and AMOS software was used to analyze the data and the hypothesis was tested using structural equation modeling (SEM) and data obtained from 317 IT company employees in Coimbatore, India. The outcomes of the study provide both practical and theoretical insights for IT company employees to increase team performance through flexible and emotional intelligence skills. Employees with a high level of Intelligence Quotient (IQ) are insufficient for today's business. To be successful, workers must have a certain level of Emotional Intelligence (EI). Employees with a high EI tend to have better relationships and connections, higher performance due to collaboration, and a more positive and peaceful workplace. This research gives managers and leaders an in-depth understanding of highly reflective teams, which will allow them to be more inventive while also altering their emotional abilities to perform better and retain the organization in the long run.