Level Of Intelligence Research Articles

THE EFFECT OF EMOTIONAL INTELLIGENCE ON EMPLOYEE GREEN BEHAVIOR: THE CASE OF THE REPUBLIC OF NORTH MACEDONIA

This study examines the relationship between emotional intelligence (EQ) and employee green behavior (EGB) in the workplace within the context of the Republic of North Macedonia. Given the increasing emphasis on sustainable business practices, understanding the factors influencing employees' environmentally friendly behaviors is essential. This research explores whether higher levels of emotional intelligence among employees contribute to more proactive engagement in green behaviors, such as energy conservation, waste management and recycling at work. The findings suggest that emotional intelligence significantly influences employee green behavior, with implications for organizational strategies aimed at fostering sustainability. Additionally, the study investigates gender differences in the relationship between EQ and EGB. Results indicate that female employees are more likely to engage in green behaviors compared to their male counterparts, although emotional intelligence remains the more substantial predictor of EGB regardless of gender. These insights highlight the importance of developing EQ across all employees while considering gender dynamics to enhance organizational sustainability efforts.

The Health Effects of Modern Gaming Devices and Phones n the Child's Mind and Intellectual Level

This study included 200 children between the ages of 2-12 years, who use mobile phones for multiple purposes. This study was conducted by distributing questionnaires to parents, and their answers were approved for their children. This study showed that the age group >=5 years had the highest percentage, reaching 60% of children. The study sample was mostly females (girls) at 54%, while the remaining section was males (boys) at 46%. This study showed that children in an urban environment use mobile phones more than children who live in a rural environment, where their percentage reached (66%). The study showed that the educational level of the parents of these children was good, as most of them held a university degree (38.0%, 36. %) for both the mother and the father, respectively. The study showed a significant relationship P = 0.0001 between parents’ occupation and Children used mobile phones and gaming devices, and the percentage was higher if both parents worked (42% and 78%) for the mother and father, respectively. The study showed that most children belong to a middle-income family environment. The study showed that the percentage of children who use modern phones and gaming devices reached (96%), while those who do not use mobile phones (4%). Most of the families had more than two phones at home (58%), and (78%) of the children used the phone throughout the day, and (48%) of them felt happy as a result of using the mobile phone, and (52%) of the study sample used the mobile phone for a period of time. (two years or more). The study showed that 56% of children suffer from lack of concentration and a low level of intelligence. According to the results of the study, 54% of them suffer from problems in their fingers as a result of frequent use of toys, and 70% of children suffer from dry eyes and a constant feeling of sleepiness as a result of radiation emitted from toys and devices. 93% of them suffer from anxiety and depression. The study also showed that (72%) of the children were addicted to the phone and the phone caused them headaches. It is clear from this study that children’s use of the phone causes them many problems, including psychological and physical ones. Therefore, parents must keep their children away from mobile phones as much as possible, and if they have to, allow them to use them. There must be a specific time for using phones, and the harmful effects of phones must be explained to them, whether in the short or long term.

Relationship between Emotional Intelligence and Leadership Abilities of Head Teachers of Girls Secondary Schools in District Rahim Yar Khan

The present study aims to measure emotional intelligence level leadership abilities and find out the relationship between emotional intelligence and leadership abilities of head teachers of girls' secondary school at district Rahim Yar Khan. This descriptive correlational nature study used a survey research design. Headteachers of girls’ secondary schools in the district of Rahim Yar Khan constituted the population of the study. The 96 head teachers contributed to data collection. An online questionnaire was used as a research tool for data collection. The collected data was analyzed by SPSS-22 and presented in tables and figures. The study found a significant positive correlation between the emotional intelligence and leadership abilities of secondary school head teachers at district Rahim Yar Khan. The researchers recommended that the head teachers be given proper training in emotional intelligence in order to provide them with sufficient know-how about emotional intelligence and teach them how to use it for handling and motivating the employees under their auspices. The researchers also recommended that the study be replicated in other districts and at different levels of education to pave the way for generalizing the results of studies already conducted in this regard.

Power data analysis and mining technology in smart grid

This study proposes a smart grid model named “GridOptiPredict”, which aims to achieve efficient analysis and processing of power system data through deep fusion of deep learning and graph neural network, so as to improve the intelligent level and overall efficiency of power grid operation. The model integrates three core functions of load forecasting, power grid state sensing and resource optimization into one, forming a closely connected and complementary framework. Through carefully designed experimental scheme, the practical value and effectiveness of “Grid OptiPredict” model are fully verified from three aspects: accuracy of load forecasting, sensitivity of power grid state sensing and efficiency of resource allocation strategy. Experimental results show that the model has significant advantages in prediction accuracy, model stability and robustness, resource optimization, security, information security, social and economic benefits and user experience.

Relationship of Cultural Intelligence and Global Citizenship Levels of Preservice Teachers in the Philippines

With the global developments, educational institutions play a vital role in fostering cultural intelligence and global citizenship among individuals, including preservice teachers. Despite the plethora of research on cultural intelligence and global citizenship in higher education abroad, little is known about these in the Philippines, let alone their correlation. Employing a quantitative descriptive correlational design, this study investigated the relationship between cultural intelligence and global citizenship levels of 316 Philippine preservice teachers using the Cultural Intelligence and Global Citizenship Scales. The results revealed that although their cognitive cultural intelligence necessitates further development, the preservice teachers are highly competent and confident in communicating and functioning effectively in multicultural contexts. However, they exhibit limited global citizenship attributes, particularly in social responsibility. Most ethnic groups they represented were also identified to have high cultural intelligence and average global citizenship levels. Additionally, while the preservice teachers’ specialization, year level, and overseas experience did not play significant roles in their cultural intelligence levels, the study found their year level to be the lone variable that affected their global citizenship levels substantially. Overall, a positive correlation between cultural intelligence and global citizenship was identified. Aside from implications for strengthening internationalization strategies, recommendations for further investigations were discussed.

Emotional Intelligence as Critical Competence in Nurses' Work Performance: A Cross-Sectional Study.

Emotional intelligence may help nurses to cope with demanding work environments where the need to improve the quality and safety of the care provided, as well as the care of the chronically ill, prevails. Although it is well known that emotional intelligence is positively related to work performance, the literature on nurses is limited. The aim of our study was to examine the impact of emotional intelligence on work performance in a sample of nurses in Greece. We conducted a cross-sectional study with 318 nurses. We collected data from a convenience sample of nurses during January 2024. Since we conducted an online survey through social media, our sample could not be representative of all nurses in Greece. For instance, older nurses may be underrepresented in our study due to limited access on social media. We measured emotional intelligence with the Trait Emotional Intelligence Questionnaire-Short Form (TEIQue-SF) and work performance with the Individual Work Performance Questionnaire (IWPQ). We created multivariable linear regression models adjusted for sex, age, educational level, and work experience. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines. We found that the four streams of emotional intelligence (i.e., well-being, self-control, emotionality, sociability) increased nurses' work performance. In particular, we found a positive relationship between well-being and task performance (adjusted beta = 0.210, 95% CI = 0.140 to 0.281, p-value < 0.001) and contextual performance (adjusted beta = 0.135, 95% CI = 0.050 to 0.221, p-value = 0.002). Similarly, there was a positive relationship between self-control and task performance (adjusted beta = 0.136, 95% CI = 0.030 to 0.241, p-value = 0.012). Additionally, sociability increased task performance (adjusted beta = 0.223, 95% CI = 0.151 to 0.295, p-value < 0.001) and contextual performance (adjusted beta = 0.198, 95% CI = 0.111 to 0.286, p-value < 0.001). Moreover, emotionality (adjusted beta = -0.198, 95% CI = -0.319 to -0.076, p-value = 0.002) and sociability (adjusted beta = -0.133, 95% CI = -0.221 to -0.044, p-value = 0.003) reduced counterproductive work behavior. Our multivariable models identified a positive impact of emotional intelligence on nurses' work performance. Nurse managers and healthcare organizations should adopt appropriate interventions to improve nurses' emotional intelligence. Enhancing emotional intelligence among nurses can improve work performance and, thus, healthcare outcomes. Moreover, higher levels of emotional intelligence may empower nurses' compassion and resilience, fostering a supportive work environment. In this context, the well-being of both nurses and patients may improve.

THE VALUE ORIENTATIONS OF ADOLESCENTS WITH DIFFERENT LEVELS OF EMOTIONAL INTELLIGENCE

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

Readiness, knowledge, and perception towards artificial intelligence of medical students at faculty of medicine, Pelita Harapan University, Indonesia: a cross sectional study

IntroductionArtificial intelligence (AI) enables machines to perform many complicated human skills which require various levels of human intelligence. In the field of medicine, AI helps physicians in making diagnoses and treatments for patients with more efficiency, accuracy, and precision. In order to prepare medical students who are the future healthcare workforce, it is important to enhance their readiness, knowledge and perception toward AI. This study aims to assess Pelita Harapan University (PHU) medical students’ readiness, knowledge, and perception toward AI.MethodsA quantitative cross-sectional study was conducted to assess respondents’ readiness, knowledge and perception toward AI. An online questionnaire was distributed via Google Forms to all batch of medical students. Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS-MS) questionnaire was used to evaluate AI readiness, while an adapted questionnaires was used to evaluate knowledge and perception toward AI. Data were then analyzed using IBM Statistical Package for Social Sciences (SPSS) version 23.0.ResultsA total of 650 respondents were included in this study. Most respondents were in pre-clinical phase (88%) while the remaining were in clinical phase (12%). Overall, the total mean score for AI readiness was 73.34 of 100. Respondents had a mean score 24.52 ± 5.26 of 40, 27.78 ± 4.65 of 40, 10.57 ± 2.07 of 15, and 10.47 ± 2.00 of 15 in the cognitive, ability, vision, and ethics domain respectively. Generally, respondents had sufficient knowledge and positive perception toward AI. There were also significant correlation between readiness and knowledge with gender, having studied coding previously in high school, and having family or close friends working in AI field. Social media also had a good influence on enchancing readiness in the domain of ability and ethics, and perception towards AI.ConclusionMedical students of PHU mostly showed neutral to favorable response on readiness, knowledge, and perception towards AI. Incorporating AI into high school and medical curriculum is an important step to prepare medical students’ encounter and partnership with AI as the future workforce in medicine.

Effects of Physical Exercise Input on the Exercise Adherence of College Students: The Chain Mediating Role of Sports Emotional Intelligence and Exercise Self-Efficacy.

The aims of this study were to investigate the effects and mechanisms of physical exercise input, sports emotional intelligence, and sports self-efficacy on exercise adherence, and to examine the chain-mediating role of sports emotional intelligence→sports self-efficacy. The Physical Exercise Input Scale, Exercise Adherence Scale, Sports Emotional Intelligence Scale, and Sports Self-Efficacy Scale were used to investigate 1390 college students in three universities in the Henan Province. (1) Physical exercise input was a significant positive predictor of exercise adherence (β = 0.29, t = 5.78, p < 0.001); (2) sports emotional intelligence and sports self-efficacy mediated the relationship between physical exercise input and exercise adherence; (3) physical exercise input influenced exercise adherence through the separate mediating role of sports emotional intelligence (β = 0.10, t = 5.98, p < 0.001), the separate mediating role of sports self-efficacy (β = 0.13, t = 2.64, p < 0.01), and the chain mediating role of sports emotional intelligence→sports self-efficacy (β = 0.09, t = 2.80, p < 0.01). (1) Physical exercise input can positively predict the level of sports emotional intelligence and sports self-efficacy of college students; (2) Physical exercise input can not only directly influence college students' exercise adherence but can also indirectly influence it through sports emotional intelligence or sports self-efficacy levels alone, as well as through the chain mediation of the two.

Social Intelligence Management in the Context of Promoting Professional Self-Education: Gender Aspects

The importance of lifelong learning and professional self-education in the modern world is beyond doubt. The search for external and internal incentives to increase people's interest in self-development is an important task for management sciences. External motivation includes the efforts of managers and HR managers to encourage employees to learn. In childhood and adolescence, parents, teachers and professors perform this function. However, it is important to understand what internal factors affect the desire for new knowledge and experience. According to numerous studies, social intelligence plays this role, as it affects the formation of positive qualities of a person from a social point of view. The purpose of this research was to verify the correlation between the level of social intelligence of an individual and his/her desire for self-development and additional training. The survey was conducted on a sample of students from a private online school that provides soft skills, financial and digital literacy training. The research results have complemented the previous survey among senior university students to form a comprehensive picture. High statistical reliability (p≤0.001***) was found for a significant relationship between social intelligence and its components and the desire to study further. The research results indicate the importance of developing a person’s social intelligence in order to stimulate professional self-education. Increasing the level of employees’ competence has a positive influence on their loyalty to the corporate culture, enhancing the personnel security of enterprises.

Digital Human Intelligent Interaction System Based on Multimodal Pre-training Mode

ABSTRACT As the forefront of human–computer intelligent interaction, digital humans have increasingly diverse application scenarios, but also face many challenges. In order to enhance the intelligence level and interaction capability of digital human, the study first optimizes the traditional UniLM model, then introduces the mechanism of multi-head attention, and mitigates the exposure bias by using adversarial training and random replacement of decoder to design an improved UniLM model, and finally applies the improved model to the digital human management system. The results show that the precision rate of the improved UniLM model is improved by 7.68%, 6.4%, and 4.96%, the recall rate is improved by 11.94%, 9.69%, and 8.83%, and the F1-score is improved by 8.34%, 6.41%, and 7.68% compared with the other three models, which proves that it has a better precision rate, robustness, and generalization ability. The perplexity of the improved UniLM model is 135, 95, 76, 71, and 55 under five text lengths, which is significantly lower than the other models, proving that its text generation ability is better. The above results demonstrate the performance of the research-designed digital human system based on the Improved UniLM model, which provides a direction for the further development of digital human technology.

The Effect of Sports Club Employees' Levels of Emotional Intelligence and Compassionate Love on Mobbing Perceptions

This research aims to reveal the effect of sports club employees' emotional intelligence and compassionate love levels on mobbing perceptions. The research study group consists of 184 personnel working in sports clubs in Izmir. The survey method was used as the data collection technique of the research. A personal information form to gather personal information of the participants, "Negative Behaviours Scale" developed by Einarsen &amp;amp; Raknes (1997) and adapted into Turkish by Cemaloğlu (2007) to measure mobbing perception of the participants, "Emotional Intelligence Scale" developed by Schutte et al. (1998) and adapted into Turkish by Şahin (2006), and the " Compassionate Love Scale" developed by Sprecher &amp;amp; Fehr (2005) and adapted into Turkish by Akın &amp;amp; Eker (2012) were used as data collection tools. The data gathered were analysed by using a statistical program. While the emotional intelligence levels of sports club employees were found to be a significant predictor of their perceptions of mobbing, their levels of compassionate love were found to have no significant effect.

Development and evaluation of a self-adaptable planting unit for an autonomous planting process of field vegetables

Today, the number of solutions for automated processes in agriculture is growing rapidly. This is primarily driven by the lack of available and affordable labour, pricing pressures, and regulatory requirements. Vegetable production in particular has a lot of potential for automation, as many process steps, such as planting, are performed partly manually. Fully automated systems for the planting process are characterized by their big size, which is only suitable for large farms. At the same time, these planters typically have a low level of intelligence, which is essential for a fully autonomous planting process performed by autonomous vehicles or robots. The following work therefore deals with the development and construction of a prototype for vegetable planting via a robotic platform. This prototype is designed to meet the requirements of a conventional planter and carry out the planting process automatically using a robotic platform. To ensure a robust robotic planting process, an AI-based control system has been integrated that can detect and adjust the planting quality. For this reason, the planting unit was designed to allow dynamic changes in working depth and furrow width. By dynamically controlling these planting parameters, there is potential for a more sustainable planting process with lower energy requirements. A number of evaluations have been carried out to validate the described characteristics of the prototype planting unit.

Research on the Construction of Scenarios for Blockchain to Empower Government Governance

This paper delves into the potential and practical challenges of blockchain in government governance. As a cutting-edge development in information and communication technology, blockchain offers unique capabilities in data management and value transfer, presenting new solutions for government governance. The article begins with an overview of the definition, connotation, and current application status of blockchain technology, and then constructs typical scenarios where blockchain technology empowers government governance, including electronic credentials, government data circulation, data resource sharing, collaborative office work, and digital rural revitalization. By realizing these scenarios, the paper analyzes the main problems and challenges faced by blockchain technology in government governance, such as inadequate infrastructure, data leakage risks, and difficulties in inter-departmental collaboration. Finally, a series of suggestions and prospects are put forward to promote the effective application of blockchain technology in digital government affairs, enhancing the transparency, efficiency, and intelligent level of government governance

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.