Development Of Intelligence Research Articles

Dynamic Integrated Scheduling of Production Equipment and Automated Guided Vehicles in a Flexible Job Shop Based on Deep Reinforcement Learning

The high-quality development of the manufacturing industry necessitates accelerating its transformation towards high-end, intelligent, and green development. Considering logistics resource constraints, the impact of dynamic disturbance events on production, and the need for energy-efficient production, the integrated scheduling of production equipment and automated guided vehicles (AGVs) in a flexible job shop environment is investigated in this study. Firstly, a static model for the integrated scheduling of production equipment and AGVs (ISPEA) is developed based on mixed-integer programming, which aims to optimize the maximum completion time and total production energy consumption (EC). In recent years, reinforcement learning, including deep reinforcement learning (DRL), has demonstrated significant advantages in handling workshop scheduling issues with sequential decision-making characteristics, which can fully utilize the vast quantity of historical data accumulated in the workshop and adjust production plans in a timely manner based on changes in production conditions and demand. Accordingly, a DRL-based approach is introduced to address the common production disturbances in emergency order insertions. Combined with the characteristics of the ISPEA problem and an event-driven strategy for handling dynamic events, four types of agents, namely workpiece selection, machine selection, AGV selection, and target selection agents, are set up, which refine workshop production status characteristics as observation inputs and generate rules for selecting workpieces, machines, AGVs, and targets. These agents are trained offline using the QMIX multi-agent reinforcement learning framework, and the trained agents are utilized to solve the dynamic ISPEA problem. Finally, the effectiveness of the proposed model and method is validated through a comparison of the solution performance with other typical optimization algorithms for various cases.

Examining teachers’ behavioural intention of using generative artificial intelligence tools for teaching and learning based on the extended technology acceptance model

The rapid development of generative artificial intelligence (GenAI) tools has given rise to a growing discussion of the potential challenges and benefits that the use of these technologies may present in the field of education. This study examines the acceptance of the use of GenAI tools for teaching and learning among primary and secondary school teachers in Hong Kong. It uses an extension of the technology acceptance model (TAM) with a modified framework that incorporates two key factors: self-efficacy and subjective norm. Data were collected from a sample of 367 primary and secondary school teachers in Hong Kong using questionnaires containing items for six constructs: self-efficacy, perceived usefulness, perceived ease of use, attitude towards using, subjective norm, and behavioural intention. The results show that fostering teachers’ self-efficacy, perceived usefulness, and attitude is essential for successfully increasing their behavioural intention to use GenAI tools. Subjective norm was also found to influence teachers’ behavioural intention. To enhance teachers’ effective use of GenAI for teaching, teacher development programmes should focus on equipping teachers with comprehensive conceptual knowledge and skills and an understanding of the application of these tools to teaching and learning. Policy support to create a conducive environment for the use of GenAI in teaching and learning would also be beneficial. The study has theoretical implications in its extension of the TAM model as well as implications for enhancing teachers’ AI literacy and developing pedagogies for the meaningful use of GenAI tools for teaching and learning in K–12 settings.

DEVELOPMENTAL STAGE OF GENERAL EMOTIONAL OF GENERAL EMOTIONAL INTELLIGENCE IN MODERN ADOLESCENTS

This article reveals the significance of emotional intelligence for a human being, in particular, for an adolescent; characteristic features of emotional intelligence development in adolescents of 14-17 years old are given. Also the article contains the results of the research of pupils of 9-11th grades. The comparative analysis of the above-mentioned research with the results of the study of emotional intelligence in schools in the 2023/2024 academic year gives a clear picture of the development of emotional intelligence in modern adolescents.

Digital transformation in Russia: Turning from a service model to ensuring technological sovereignty

The paper outlines core aspects of the digital transformation process in Russia since the early 2000s, as well as recent legislative initiatives and practices at the federal level. It considers the digitalization of public services, efforts towards ‘sovereignization’ of the Russian segment of the Internet, and the current focus on cybersecurity and the development of artificial intelligence. The paper highlights the tendency to strengthen the factor of protection of state interests and national security alongside control over online activities of citizens in comparison with the initial understanding of digital transformation as a human-oriented process aimed at increasing the accessibility and convenience of public services. It can be assumed that this change in the goals and methods of digital transformation is one of the manifestations of a broader political, social and cultural process of separation, primarily from the West, that Russian society is currently undergoing, amidst a growing official narrative of threats from both external and internal forces that require greater independence and increased vigilance, including in the digital domain.

Enhancing high-resolution reconstruction of flow fields using physics-informed diffusion model with probability flow sampling

The application of artificial intelligence (AI) technology in fluid dynamics is becoming increasingly prevalent, particularly in accelerating the solution of partial differential equations and predicting complex flow fields. Researchers have extensively explored deep learning algorithms for flow field super-resolution reconstruction. However, purely data-driven deep learning models in this domain face numerous challenges. These include susceptibility to variations in data distribution during model training and a lack of physical and mathematical interpretability in the predictions. These issues significantly impact the effectiveness of the models in practical applications, especially when input data exhibit irregular distributions and noise. In recent years, the rapid development of generative artificial intelligence and physics-informed deep learning algorithms has created significant opportunities for complex physical simulations. This paper proposes a novel approach that combines diffusion models with physical constraint information. By integrating physical equation constraints into the training process of diffusion models, this method achieves high-fidelity flow field reconstruction from low-resolution inputs. Thus, it not only leverages the advantages of diffusion models but also enhances the interpretability of the models. Experimental results demonstrate that, compared to traditional methods, our approach excels in generating high-resolution flow fields with enhanced detail and physical consistency. This advancement provides new insights into developing more accurate and generalized flow field reconstruction models.

Determinants of AI performance and their effect on national GDP: an empirical study

Abstract. Artificial intelligence is one of the most widely discussed topics in recent years, remarkably following the pandemic. During the pandemic, numerous industries and organizations have focused on the development of artificial intelligence due to the shift to remote learning and working. This shift also caused expanded investments and research on AI driven by the growing demand of for AI-powered tools across various sectors such as education, healthcare, and business. This paper analyses the determinants of AI performance and examines the relationship between a countrys AI performance and its economic growth, measured by Gross Domestic Product (GDP). This paper employs Pearson Pairwise Correlation Multiple Linear Regression, and Simple Linear Regression models with a sample of 62 different countries to uncover the most influential factors impacting a country's AI performance and its subsequent influence on GDP. During the process of constructing the multiple linear regression model, this paper also evaluates the collinearity diagnostics such as VIF and tolerance level to prevent multicollinearity. This study finds that a countrys innovation skill, featuring its research and development capabilities, is the primary driver influencing its success in AI development. Subsequently, this paper concludes that advanced AI performance can significantly contribute to a countrys economic growth, further emphasizing the importance of progressive AI development.

Decoding Scholarcy website: A Study on its Research Summarization Efficiency

The development of artificial intelligence (AI) has revolutionized various facets of education, particularly the research domain. With the rapid growth of scholarly literature, the need for an efficient tool for summarization has become increasingly paramount. This study proposes a quantitative investigation into the efficiency of Scholarcy (www.scholarcy.com), an AI-powered summarizer used to aid researchers in summarizing academic literature. Thirty-five postgraduate students were involved in a survey research design. After five weeks of using Scholarcy, participants were asked to complete a survey questionnaire via their email addresses to give feedback on this tool. The results indicated a positive reception when postgraduates found this AI summarizer convenient and useful for reading academic papers. On the other hand, the participants identified several drawbacks when using it including concerns about summary quality, the absence of a mobile application, and high cost, along with suggestions for further application. With the highlights of both benefits and limitations, the study hopes to contribute valuable insights into the development of AI-powered summarization tools in the future.

Artificial Intelligence: Intensifying or Mitigating Unemployment?

The rapid development of Artificial Intelligence (AI) is simultaneously fostering a proliferation of novel job opportunities while rendering some traditional roles obsolete and specific skills outdated. Previous research has failed to consider the short-, medium-, and long-term variations in AI’s impact on unemployment, which may lead to an incomplete understanding of the AI-employment relationship. This paper examines daily data from January 4, 2013, to August 12, 2024, utilising advanced wavelet-based Quantile on Quantile Regression (QQR) methodology to assess AI’s impact on the Unemployment Index (UI) across quantiles and time scales, with a sample size of 2820 drawn from a larger dataset totalling 4241 observations. The conclusions reveal that AI generally positively impacts UI in the short term, especially with AI at 0.6-0.7 quantiles, as automation replaces workers faster than new job roles emerge and skills transform. However, in the medium term, positive and negative effects balance as new jobs and skills emerge through continuous industrial restructuring. In the long run, AI predominantly mitigates UI by further enhancing economic development, fostering skill upgrading, and facilitating market adjustments, but this result does not hold during AI at 0.7 quantiles and UI at the highest quantiles, such as Coronavirus Disease 2019 (COVID-19). Under new technological revolution and industrial transformation, we formulate China-specific suggestions to avert potential AI-induced unemployment crisis from short-term, medium-term, long-term, and sector-specific perspectives.

Development and cultural adaptation of the Saudi pediatric speech intelligibility test.

To report the development and administration of the Saudi pediatric speech intelligibility (SPSI) test. This study was carried out at King Abdullah Ear Specialist Center, King Saud University, Riyadh, Saudi Arabia, between 2021 and 2023. A pilot study was carried out in 5 regions of Saudi Arabia including 100 normal hearing children aged 7-11 years old, with 20 children from each region. The final test materials were voice-recorded by a native Saudi radio announcer, and a software-based application for administering the SPSI was developed. During the pilot study, minor adjustments were carried out to the test for more clarity. The developed SPSI software is an android-based application that includes features like automatic result calculation, data saving, and individual user accounts for examiners to review results. The SPSI test comprises 4-word cards and 2 sentence cards, each with 5 pictures, and operates in 3 modes: quiet, background noise, and competing sentences. The SPSI test has been developed in an android-based software using the Saudi local dialects. It can facilitate comprehensive and accurate assessment of speech intelligibility, particularly in children with hearing impairments.

The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning

The Evolution of Artificial Intelligence in Medical Imaging: From Computer Science to Machine and Deep Learning

IDL-LTSOJ: Research and implementation of an intelligent online judge system utilizing DNN for defect localization

The evolution of artificial intelligence has thrust the Online Judge (OJ) systems into the forefront of research, particularly within programming education, with a focus on enhancing performance and efficiency. Addressing the shortcomings of the current OJ systems in coarse defect localization granularity and heavy task scheduling architecture, this paper introduces an innovative Integrated Intelligent Defect Localization and Lightweight Task Scheduling Online Judge (IDL-LTSOJ) system. Firstly, to achieve token-level fine-grained defect localization, a Deep Fine-Grained Defect Localization (Deep-FGDL) deep neural network model is developed. By integrating Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU), this model extracts fine-grained information from the abstract syntax tree (AST) of code, enabling more accurate defect localization. Subsequently, we propose a lightweight task scheduling architecture to tackle issues, such as limited concurrency in task evaluation and high equipment costs. This architecture integrates a Kafka messaging system with an optimized task distribution strategy to enable concurrent execution of evaluation tasks, substantially enhancing system evaluation efficiency. The experimental results demonstrate that the Deep-FGDL model improves the accuracy by 35.9% in the Top-20 rank compared to traditional machine learning benchmark methods for fine-grained defect localization tasks. Moreover, the lightweight task scheduling strategy notably reduces response time by nearly 6000ms when handling 120 task volumes, which represents a significant improvement in evaluation efficiency over centralized evaluation methods.

Generating the assembly instructions of helicopter subassemblies using the hierarchical pruning strategy and large language model

Assembly instructions are process documents in detail describing the operation steps, materials, tools, fixtures, and assembly sequences in assembly procedures. Due to assembly instructions including numerous contents, and the content being easy for workers to understand, process designers need to spend lots of time thinking and authoring assembly instructions to ensure that workers can complete the assembly task according to the assembly instructions. Focusing on the difficulties of the variety of assembly instructions and the process factors implicit in the standard languages of assembly instructions, a method of assembly instruction generation for helicopter subassemblies is proposed. First, a data representation model of multi-source heterogeneous knowledge and information based on knowledge graphs is designed and established. Then, a hierarchical pruning VF3 algorithm is presented to reuse assembly instructions according to hybrid similarity. Finally, a process factor revision model based on RoBERTa-BiLSTM-CRF is proposed to generate revised assembly instructions. Helicopter subassemblies, which contain 11,240 assembly procedures, are used to evaluate the performance of the method for generating assembly instructions. The proposed method greatly reduces the time cost of assembly instruction authoring and promotes the intelligent development of assembly process design.

Unveiling the Dynamic Mechanisms of Generative AI in English Language Learning: A Hybrid Study Based on fsQCA and System Dynamics

The burgeoning development of generative artificial intelligence (GenAI) has unleashed transformative potential in reshaping English language education. However, the complex interplay of learner, technology, pedagogy, and contextual factors that shape the effectiveness of GenAI-assisted language learning remains underexplored. This study employed a novel mixed-methods approach, integrating qualitative comparative analysis (QCA) and system dynamics (SD) modeling, to unravel the multi-dimensional, dynamic mechanisms underlying the impact of GenAI on English learning outcomes in higher education. Leveraging a sample of 33 English classes at the Harbin Institute of Technology, the QCA results revealed four distinct configurational paths to high and low learning effectiveness, highlighting the necessary and sufficient conditions for optimal GenAI integration. The SD simulation further captured the emergent, nonlinear feedback processes among learner attributes, human–computer interaction, pedagogical practices, and ethical considerations, shedding light on the temporal evolution of the GenAI-empowered language-learning ecosystem. The findings contribute to the theoretical advancement of intelligent language education by constructing an integrative framework encompassing learner, technology, pedagogy, and context dimensions. Practical implications are generated to guide the responsible design, implementation, and optimization of GenAI in English language education, paving the way for learner-centric, adaptive learning experiences in the intelligence era.

A multimodal educational robots driven via dynamic attention

IntroductionWith the development of artificial intelligence and robotics technology, the application of educational robots in teaching is becoming increasingly popular. However, effectively evaluating and optimizing multimodal educational robots remains a challenge.MethodsThis study introduces Res-ALBEF, a multimodal educational robot framework driven by dynamic attention. Res-ALBEF enhances the ALBEF (Align Before Fuse) method by incorporating residual connections to align visual and textual data more effectively before fusion. In addition, the model integrates a VGG19-based convolutional network for image feature extraction and utilizes a dynamic attention mechanism to dynamically focus on relevant parts of multimodal inputs. Our model was trained using a diverse dataset consisting of 50,000 multimodal educational instances, covering a variety of subjects and instructional content.Results and discussionThe evaluation on an independent validation set of 10,000 samples demonstrated significant performance improvements: the model achieved an overall accuracy of 97.38% in educational content recognition. These results highlight the model's ability to improve alignment and fusion of multimodal information, making it a robust solution for multimodal educational robots.

Artificial Intelligence: Communication, Technology, and Society (a Systematic Literature Review)

The development of artificial intelligence (AI) technology has transformed the way various social sectors operate, including healthcare, communications, and decision-making. The integration of AI into these domains not only opens up new opportunities for efficiency and innovation, but also presents significant challenges. Among these challenges, the emergence of algorithmic bias, inadequate data protection, technical limitations, and complex ethical dilemmas are of major concern. These issues affect the reliability and fairness of the use of AI technologies, potentially harming individuals and society as a whole. This study aims to investigate the barriers associated with the implementation of AI in society and provide theoretical and methodological solutions to address these challenges. This study uses a systematic literature review methodology to review 40 journals, with the aim of identifying key issues and suggesting solutions for the ethical and successful integration of AI. The findings show that the potential of AI is often hampered by biased algorithms, inadequate privacy laws, and a lack of transparency in decision-making processes. The study recommends the use of qualitative and quantitative methodologies, including experimental research and case studies, to investigate practical applications of AI. In addition, a multidisciplinary approach is suggested to address the technical and social dimensions of AI applications.

Trends in the industry of clinical trials

Background. The clinical trial (CT) industry is subject to changes, some of which are rapidly developing and their directions can be predicted, while others develop gradually, forming stable tendencies, which requires analysis to confirm and predict them. According to the generally accepted time gradation, there are an operational (up to 1 month), short-term (up to 1 year), medium-term (up to 5 years), long-term (up to 20 years), and long-term (over 20 years) prognoses. A short-term forecast is common in CT industry.Objective: to identify trends in the development of CT industry until the end of 2024.Material and methods. We searched publications in Russian and English segments of the Internet in open access sources in ClinicalTrials.gov, PubMed/MEDLINE, Google Scholar, Academia, ResearchGate, CyberLeninka, eLibrary databases by key query “trends in clinical trials” for the period from January to March 2023. The query “history of clinical trials” was also used in Russian part of the Internet through Google search. The analysis included publications on the history of CT development over 1936–2023. The total number of sources analyzed was 59 (9 Russian and 50 English).Results. The tendencies in CT development can be divided into two groups. The first one is related to CT organization and conducting, while another is associated with development of innovative drugs. In the first group, the trends have persisted since 2022 and are expressed in the ongoing digitalization of operational activities, a shift from centralized research to decentralization, while the protocol design has changed towards patient-centricity. In the second group, the number of expected drugs has decreased and a shift towards biological drugs, gene and cell therapy has become more pronounced.Conclusion. Trends in CI are characterized by a number of innovations, primarily related to digitalization, the development of telemedicine technologies, mathematical modeling, artificial intelligence, virtual CI, decentralization and patient-centricity.

THE INNOVATIVE IMPACT OF GENERATIVE ARTIFICIAL INTELLIGENCE ON DIGITAL BUSINESS TRANSFORMATION

In the context of intensifying competition and evolving market dynamics, the deployment of cutting-edge technologies has become not merely a discretionary choice, but an indispensable imperative for any enterprise aspiring to achieve successful growth. Generative artificial intelligence, with its substantial potential for automation, personalisation and optimisation of business processes, is emerging as a highly promising avenue of digital transformation. This study is dedicated to investigating approaches and delineating strategies for aligning generative artificial intelligence with the requirements of digital business transformation. The research examines the development of artificial intelligence, with a focus on symbolic artificial intelligence, machine learning, deep learning and generative artificial intelligence. In addition, it considers the impact of these developments on business processes. The article identifies the potential benefits and challenges associated with the adaptation of generative artificial intelligence to the needs of modern business, in the areas of marketing, sales and data analysis. The utilisation of diverse methodologies and techniques, including prompts, fine-tuning, and the incorporation of interactive guidance systems, can enhance the efficacy and precision of generative AI in a business setting, thereby facilitating optimal outcomes in a multitude of tasks. The authors put forth the proposition of employing generative artificial intelligence technology in conjunction with Retrieval-Augmented Generation, with the objective of enhancing the quality and relevance of responses to user queries. Additionally, they advocate for the utilisation of agents or orchestration tools to provide guidance to models. The successful implementation of generative artificial intelligence hinges on three key factors: the clear definition of objectives, the selection of suitable tools and technologies, and the assurance of managerial and staff support. The implementation of generative artificial intelligence will contribute to increased efficiency through the automation of routine tasks, enhanced competitiveness through personalisation and innovation, optimised cost structures that increase profitability, and expanded opportunities for research and development.

Exploring the Landscape of Explainable Artificial Intelligence (XAI): A Systematic Review of Techniques and Applications

Artificial intelligence (AI) encompasses the development of systems that perform tasks typically requiring human intelligence, such as reasoning and learning. Despite its widespread use, AI often raises trust issues due to the opacity of its decision-making processes. This challenge has led to the development of explainable artificial intelligence (XAI), which aims to enhance user understanding and trust by providing clear explanations of AI decisions and processes. This paper reviews existing XAI research, focusing on its application in the healthcare sector, particularly in medical and medicinal contexts. Our analysis is organized around key properties of XAI—understandability, comprehensibility, transparency, interpretability, and explainability—providing a comprehensive overview of XAI techniques and their practical implications.

GPS Tracking and Indoor Navigation System

This project is an idea of developing an application based on a navigation system formed by Graph Data Structure. An indoor positioning system (IPS) can be thought of as a global positioning system (GPS) for indoor venues. While GPS is used outdoors, IPS can detect real-time locations to accurately determine the coordinates of people or assets inside a building. These coordinates are typically represented visually by a blue dot on a digital indoor map to provide additional context to users and features such as wayfinding. The project aims to have certain objectives such as a tracking system, navigation system that is inside a small area of a building. This idea aims to track the location of any particular device within a building and show a navigation to that device in an efficient manner. Not only tracking of devices but also solving a problem of finding a particular room in the building through data of different paths that is stored. This project comes under the intelligent system design and development domain and falls under the sector of analysing and designing hardware, software and operating systems for computers. The main objective of this system design is to navigate through the area and find places to reach with ease and as fast as possible, it is helpful for people who are new at that place and even finding a user in that particular vicinity by live tracking technique.

Mastering Data Pipelines for Al: A Beginner's Guide to Building Efficient Workflows

This comprehensive article explores the critical role of data pipelines in Artificial Intelligence (AI) development, emphasizing their importance in ensuring high-quality, relevant data for machine learning models. It covers key stages of the data pipeline process, including data ingestion, cleaning and preprocessing, transformation and feature engineering, and storage and loading. The article discusses various tools, techniques, and best practices for each stage, addressing challenges in handling diverse data sources, scalability issues, and the need for efficient data management. It highlights the significance of robust data pipelines in enhancing AI model performance and reliability, while also considering the dynamic nature of the AI field and the necessity for continuous learning and adaptation.