Level Of Intelligence Research Articles

A Study of Library Mobile Robot Book Classification and Transportation by Integrating DA and RMM

As the complexity of modern library management tasks increases, it is difficult for traditional mobile robots to meet the task of moving and classifying books. In order to design a mobile robot that can autonomously classify and transport books, the study realizes the tasks of book classification and transportation in libraries by fusing the differential speed algorithm and the robot motion model algorithm. First, the robot operating system is utilized to scan the books, classify the books, and obtain the category information of the books. Then, the differential speed algorithm is used to control the motion of the robot to ensure that the robot can accurately transport the books to the designated location. At the same time, combined with the robot motion model algorithm, the motion trajectory of the robot is planned to ensure that the robot can avoid obstacles and stably complete the book transportation task. Finally, the deep reinforcement learning algorithm is used to train the decision-making model of the robot to improve the intelligence level of the robot. The results of simulation experiments show that the research method has the highest accuracy, with an average accuracy of 99.98%, and the robot is able to accurately categorize the books and quickly avoid obstacles with strong stability. The results of the application experiments show that the research method has the shortest moving distance, with an average moving distance of 132 m and an average completion time of 34 seconds, which are lower than the remaining three types of robots. The research robot showed high accuracy in the task of returning books in four time periods within 10 days in the library, with an average accuracy of 99.58%. The experimental results validate the superiority of the research methodology and show that the robots are capable of accurately recognizing and classifying books and can autonomously perform transportation tasks in libraries. The research results help to improve the automation level and management efficiency of libraries and have important application value.

Effect of morningness-eveningness chronotype on academic performance of undergraduate students

BackgroundCircadian rhythms are crucial for physiological functioning, and they vary among people. There are three distinct chronotypes: morning, evening, and neither type. Morning types experience their highest level of activation during the early part of the day and tend to be more conscientious and focused on achieving their goals. Evening types, on the other hand, reach their peak performance during the latter portion of the day and exhibit a higher level of intelligence. University class schedules may occasionally clash with evening-type individuals’ circadian preferences, potentially impacting their academic performance in comparison to their morning-type peers. Therefore, this study was done to investigate if morningness-eveningness can affect college students’ academic performance as sleeping late or sleep deprivation can lead to disease in the young population.Materials and methodsA sample of 100 collegiate students aged 18–30 years were recruited via convenience sampling from Jamia Hamdard, Delhi, and informed consent was obtained from the subjects. The morningness-eveningness questionnaire (MEQ) was used to assess students’ chronotype, and the mean of the last three academic years percentage was used to assess the academic performance of the students.ResultsThe total academic performance score was better in the morning category (75.53 ± 7.28) as compared to the evening (66.42 ± 10.08) and intermediate (73.72 ± 6.46) categories. There was a low degree of positive correlation between total academic performance and TMEQ (total morning-evening questionnaire) score (ρ = 0.204).ConclusionThe academic performance of morning chronotype students was better than that of evening chronotype students.

A Gaussian hybrid clustering-based method for compensating for the loss of semi-persistent scheduling data in the middle station of large power grid regulation and control

In order to ensure the effectiveness of power grid scheduling decisions, ensure the stability, safety, and intelligence level of scheduling operations, and solve the semi persistent problem of station scheduling data loss in power grid scheduling, a Gaussian mixture clustering based method for compensating station scheduling data loss in power grid scheduling is proposed. Based on the role of the intermediate station and its semi persistent scheduling data generation mechanism in the control system of the large power grid, a Gaussian mixture model is constructed to calculate the conditional expected value of missing data as compensation value, and the final compensation result of the semi persistent scheduling data of the intermediate station is obtained. The experimental results show that in various types and degrees of scheduling data missing scenarios, this method performs well, and its Pearson correlation coefficient for compensating data is generally higher than 0.94, fully verifying the effectiveness and accuracy of this method. This achievement not only provides a practical and feasible solution to the problem of data loss in power grid scheduling, but also provides strong technical support for improving the accuracy of power grid regulation and ensuring the safe and stable operation of the power grid.

How do emotional intelligence, resilience, and parental employment affect anxiety and depression levels among Sudanese adolescents?

BackgroundAdolescents are often sensitive to emotions and vulnerable to psychiatric issues, including suicidal tendencies. Emotional intelligence is believed to play a significant role in resilience, which acts as a protective factor for mental well-being. The study investigated the level of emotional intelligence, resilience, depression, and anxiety in Sudanese adolescents and the relationship between these factors.MethodsA cross-sectional study was conducted among four high schools and included a total of 392 adolescents who completed a self-administered questionnaire consisting of the 30-item trait; Emotional Intelligence Questionnaire- short form (TEQ-SF), Brief Resilience Scale-6 (BRS-6) and Patient Health Questionnaire-4 (PHQ-4).ResultsEmotional intelligence levels were moderate to high (M = 131.53 ± SD 22.16). While resilience levels were low to normal (M = 3.20 ± SD 0.51). Levels of anxiety were present in 89% of students while levels of depression were present in 78% of students and both were positively correlated (r = 0.540). Emotional intelligence and resilience were positively correlated (r = 0.272, p < 0.0005). Also, Resilience was positively correlated with levels of anxiety (r = 0.105) and the latter had a significant negative association with academic performance (r = − 0.102). Parents' status significantly contributed to predicting resilience levels (p-value = 0.011), with an unstandardized beta coefficient of 0.368. The mothers’ unemployment (r = − 0.100) and the father’s employment (r = 0.105) were significantly linked to lower anxiety levels.ConclusionThe high prevalence of anxiety and depression levels among Sudanese adolescents is influenced by parental separation and employment. Improving students' mental health is crucial as it is linked to enhancing their academic performance. This underscores the importance of addressing mental well-being alongside educational outcomes.

Flexible curved multi-functional sensors for air flow and vibration signal measurement of train bogie

Abstract The fluid-solid coupling effects with the aerodynamic noise and structural vibration signal have great part in measurement accuracy and fault diagnosis of the train bogie. It is important to collect aerodynamic noise and structural vibration data for safety evaluation and on-line structural health monitoring (SHM) of the bogie, which brings new challenges to the integration and installation of the sensors in the limited space and complicated operation condition of the train bogie. Flexible curved multifunctional sensors (FCMFS) integrated with the hot film and structural vibration sensor units is designed to the air flow and structural vibration measurement of the train bogie. Bogie vibration and wind tunnel test platforms with FCMFS are built to the air flow velocity and vibration data measurement on the bogie surface under different operation conditions. Piezoelectric Polyvinylidene fluoride(PVDF) sensors have significant output in the wind tunnel and vibration experiments, and the hot film sensor is only sensitive to the air flow signal. The effect of vibration signal to the hot film sensor could be ignored, which is applied to decouple the aerodynamic noise and structural vibration signals for improving the measurement accuracy of the train bogie. The dynamic performance of FCMFS is verified in Rail Transmit Base of East China Jiaotong University, which is applied to SHM and rapid fault diagnosis of train bogie. The air flow distribution and vibration data are collected to safety assessment and fault diagnosis of train bogie, which would promote the intelligent maintenance level of the train bogie.

A Digital Twin System for Adaptive Aligning of Large Cylindrical Components

Most large aerospace cylindrical components still adopt a manual aligning method with low automation, large manual intervention, and heavy dependence on operator workers, resulting in the low quality and efficiency of large component aligning, which seriously prolongs the manufacturing time of aerospace products. To cope with this issue, based on closed-loop adaptive control and digital twin (DT) technologies, an adaptive aligning system for large cylindrical components, i.e., the DT aligning system, is proposed in this study. For the DT aligning system, through the DT multi-dimensional modeling, i.e., geometric modeling, physical modeling, functional modeling, and data modeling, it can be divided into a physical space, a virtue space, and twin data. Note that the association, mapping, and interaction between physical space and virtual space of the aligning system can be realized via the twin data, thereby realizing real-time virtual display, monitoring, and control of the large component aligning. In addition, based on the measured pose data, aligning stress, and predicted aligning error, an adaptive force/position control method for large component aligning is proposed, and it can achieve real-time decision-making and precise execution of the aligning process. Finally, through application validation, the DT process system can realize the real-time status perception and process execution decision during the large component aligning. Finally, through experimental validation, it is found that the proposed system, i.e., the DT aligning system, can improve the quality and efficiency of the large aerospace cylindrical component aligning, as well as the automation and intelligent level of the aligning system.

The Correlation Between Teachers' Emotional Intelligence and Students' Learning Engagement in EFL Class

The study aims to determine teachers’ emotional intelligence levels based on student perspectives and is supported by principal assessments. Besides, the research aims to identify the extent of learners’ engagement and assess if there is any connection between the emotional intelligence of teachers and learning engagement in EFL classes for students. The research population included students and principals of Madrasah Aliyah 5 Garut. For this study, a sample that involved 60 students was selected using disproportional stratified random sampling techniques from class X (n=30) and class XI (n=30). To collect data, the writers used a questionnaire that evaluated variables such as teachers’ emotional intelligence and students’ learning engagement in EFL classes. The descriptive analysis method was used to analyze data, while Pearson Product Moment with SPSS 22.0 for Windows was used for bivariate analysis. The research findings suggest that the emotional intelligence level of teachers is medium from the student's point of view with an average score of 10.07, while from the principal's assessment, it is high scoring an average of 12.2. There was a moderate level of students’ learning engagement, with a mean score of 12.46. Hypothesis analysis also found that there is a positive and significant relationship between teacher emotional intelligence and student learning engagement with a fairly strong correlation level. It can be concluded that teachers need to increase their emotional intelligence so that student learning engagement in EFL classes increases.

Self-care, Emotional Intelligence, and Self-assessed Wisdom: A Structural Equation Model on the Quality of Life among Elementary Public School Teachers

This quantitative research study intended to identify the best-fit model based on self-care, emotional intelligence, and self-assessed wisdom for quality of life among primary public school teachers in Region XI. This research employed a structural equation model (SEM) with 400 respondents who comprised the sample through stratified random technique. The data of this study were gathered using an electronic survey. The employed questionnaires underwent modifications and reliability and validity tests. This descriptive and causal investigation employed statistical measures such as mean, standard deviation, Pearson product-moment correlation, and structural equation model (SEM). The study found that both the levels of self-care and emotional intelligence were high, and the levels of self-assessed wisdom and quality of life were very high. On top of that, a significant relationship was discovered between emotional intelligence and quality of life, self-assessed wisdom and quality of life, and self-care and quality of life. Each of its exogenous variables significantly predicted the quality of life. Only Model 5 met all the requirements to be the best-fit model for quality of life as determined by its exogenous variables out of the five (5) generated models. As a result, it is regarded as the most relevant model for identifying and addressing the various aspects of quality of life that improve the general well-being of elementary public school teachers.

The Relationship Between Addiction and Emotional Intelligence in the Ukrainian Socio-Cultural Context: Challenges of Emigration

Introduction: The development of emotional intelligence has a positive impact on the process of adaptation of migrants from Ukraine in the European Union and prevents the spread of addictions. Aim: The aim of this research was to study the relationship between migrants' emotional intelligence and the development of addictions in the context of adaptation to new conditions due to the hostilities. Methods: the study used such methods as testing and questionnaires, respectively, ASI and Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT). Results: It was found that individuals with a higher level of emotional intelligence have a lower tendency to form various types of addictions, while those with a lower level of EI are more vulnerable to these problems. This suggests that a high level of emotional intelligence contributes to better emotion management, the ability to adapt to stressful situations, and a better understanding of one's own needs and motivations. Conclusion: a high level of emotional intelligence makes it possible to prevent the formation of dependencies in the process of cross-cultural adaptation of migrants from Ukraine to new conditions against the background of a hostilities in Ukraine.

Exploring the quantity and quality of symptoms of attention deficit hyperactivity disorder and intelligence in children with cerebral palsy: a case-control study.

ADHD is more common in CP and may be seen in a predominantly inattentive presentation. It is related to a worse motor disability and intelligence level in CP. • Compared to the general population, ADHD is reported to be more common in children with CP. • ADHD may be seen in a predominantly inattentive presentation rather than the other presentations in children with CP. • ADHD is related to a worse motor disability and intelligence level in CP.

Exploring the interplay between emotional intelligence and academic performance of undergraduate university students

This study aimed to investigate the relationship between perceived emotional intelligence and students' academic achievement. To this end, a quantitative research approach with a correlational design was employed. Participants were randomly selected using a stratified sampling technique, resulting in a sample of 135 second- and third-year undergraduate psychology students. An emotional intelligence scale was administered to these participants, and the collected data were subsequently analyzed using both descriptive statistics (including mean, frequency, and percentage) and inferential statistics (including independent sample t-tests, one-way ANOVA, Pearson product-moment correlation, multiple regression analysis, and mediation analysis). The findings indicated that students' perceived emotional intelligence levels were above average. Group comparison analyses revealed that, among various socio-demographic variables, only sex showed a significant difference in emotional intelligence. Similarly, the analysis showed that only the parents’ level of education significantly impacted students’ academic performance. Furthermore, a statistically significant positive relationship was found between emotional intelligence and academic achievement. Additionally, linear multiple regression analysis revealed that emotional intelligence significantly contributed to academic performance. Mediation analysis further revealed that the relationship between emotional intelligence and academic achievement is partially mediated by sex. Finally, the study concludes with recommendations aimed at enhancing the academic achievement of students.

Design and implementation of tilting‐plate drainage equipment for paddy field

AbstractDrainage equipment is an important part of paddy field drainage and irrigation system, and its performance research and application are conducive to the promotion and implementation of high‐standard farmland construction. Aiming at the different water demand of rice in different growing stages, low water level control precision, large manual operation and extensive management of existing drainage machinery, a tilting‐plate drainage equipment using upper overflow mode was proposed in this paper. Based on the Geneva mechanism principle, a unique linkage mechanism is designed, which can realize water retaining operation and silt discharge operation through a single motor drive, and the relationship between the motor angle and the water retaining height is established. Through mechanical analysis and dynamic simulation of the mechanism, the motion characteristics of the water baffle, silt plate and the change of the motor torque are studied. A remote control system based on a 4G module is designed to control the drainage equipment easily through the user layer. The performance test of the prototype shows that the precision error of the optimized water retaining height control is less than 3.75%. The maximum height of water retaining operation is 180 mm, and the maximum load of silt discharge operation is 10 kg, which can meet the actual operation requirements. The design is beneficial to improve the precision and intelligence level of paddy field drainage management, and the research process can provide some help for the development and application of this kind of equipment.

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 &amp; 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.