Intelligent Management Research Articles

LEGAL ANALYSIS OF EU ARTIFICIAL INTELLIGENCE ACT (2024): INSIGHTS FROM PERSONAL DATA GOVERNANCE AND HEALTH POLICY

Background: This study correlates the up-to-date ethical, functional and legal evaluations related to the management and governance of artificial intelligence (AI) under European Union (EU) law, particularly impacting the health data sector and medical standards as provided by the Artificial Intelligence Act within the Regulation adopted by the European Council in May 2024. The initial proposal for the management and governance of the AI sector was submitted in April 2021. Three years later, on 13 March 2024, the European Union Artificial Intelligence Act (EU AIA) was adopted by the European Parliament. Subsequently, on 21 May 2024, the Council adopted an innovative legislative framework that harmonises the standards and rules for AI regulation. This framework is set to take effect in May 2026, with the central objective of stimulating and motivating a fair, safe, legal single market that respects the principles of ethics and the fundamental rights of the human person. Methods: The current legal analysis focuses on the European Union’s new institutional governance involving a multistage approach to managing health data, ethical artificial intelligence, generative artificial intelligence and classification of types of AI by considering the degree of risk (e.g. artificial intelligence systems with limited risk and systems with high risk) and medical devices. It outlines the legal framework for AI regulation and governance in the EU by focusing on compliance with the previously adopted legislation in the Medical Devices Regulation (2017) and the In-Vitro Diagnostic Regulation (2017). The paper also examines the application of the newly adopted EU Artificial Intelligence Act in relation to national justice systems, previous EU regulations on medical devices and personal data protection regulation, and its correlation with the European Court of Human Rights jurisprudence. This opens up complex discussions related to judicial reform and access to justice. For this purpose, as a research objective, the legal analysis includes an innovative perspective following an integrative discussion on the latest legal reforms and regulations of the AI sector in Eastern Europe launched in 2024 with a special focus on the latest developments in the EU Candidate Countries namely Ukraine and the Republic of Moldova. Results and conclusions: The present research facilitates the exploration of the real benefits of managing innovative AI systems for medical data, research, and development, as well as within the medical technology industry.

Psychological Preconditions of Conflict Proneness of Personality and Mental Health: The Role of Self-Regulation, Emotional Intelligence and Mutual Understanding in The Context of Interpersonal Relations

This study aims at the investigation of psychological preconditions of conflict proneness in personality and mental health, with a specific focus on the roles of self-regulation, emotional intelligence, and mutual understanding. The research was oriented towards the application of qualitative methodology, particularly the analysis of transcripts of interview sessions. The findings showed that psychological preconditions of conflict proneness exist in professional, educational, and personal settings. Self-regulation, mutual understanding, and emotional intelligence were found to be important qualities which help to avoid conflicts. They contribute to increased motivation for conflict-less communication and interaction, cooperation, flexibility, improvement of active listening and problem solving, increased patience, formation of stress management skills, constructive feedback, reflective thinking, and building resilience. The special attention was paid towards the development of special strategies aimed at overcoming conflict proneness. The research findings demonstrated that the strategies include formation of communication skills, development of emotional intelligence and conflict management skills, improvement of active listening, building of stress management, development of team building activities, definition of responsibilities, creation of supporting environment, organization of immediate feedback, establishment of open communication, promotion of respect, creation of rituals, and encouragement of empathy. The outcomes can be used for the development of curriculum on the formation of conflict resolution skills.

Developing a BIM based digital twin system for structural health monitoring of civil infrastructure

Abstract The utilization of building information modeling (BIM) within digital technology facilitates the creation of three-dimensional representations for monitoring data in large-scale civil infrastructure. In response to the need for intelligent structural management, this study establishes a structural health monitoring (SHM) system and foundational framework based on digital twins. This framework integrates information from various sources and facilitates collaborative efforts for structural operation and maintenance. Additionally, the SHM system integrates actual monitoring measurements and early warning mechanisms to consolidate multi-source monitoring data with BIM. Through real-time analysis, the system provides insights into the operational status of bridges, capturing geometric, physical, and performance evolution characteristics. To construct the system, engineering challenges are initially digitized, with appropriate sensors deployed on real bridge structures to monitor dynamic (acceleration) and static (strain, displacement) physical information during bridge operation. Subsequently, through wireless communication and data storage technologies, the monitored physical data serves as input for mode identification and early warning algorithms, facilitating the acquisition of structural performance information. Finally, three-dimensional display technology enables real-time calculation and rendering of BIM models, fostering the exchange and interaction of monitoring and BIM information, thus enhancing the intelligence of SHM system.

The Role of Diversified Geo-Information Technologies in Urban Governance: A Literature Review

Global urbanization has made urban governance a crucial aspect of sustainable urban development. While geo-information technologies have emerged as indispensable tools for effective urban governance, a comprehensive analysis of their application in this context remains lacking. This study seeks to review and assess the pivotal role of geo-information technologies in the field of urban governance. A total of 219 related studies were used for bibliometric analysis and key content analysis. Planning Support Systems (PSSs), Participatory Geographic Information Systems (PGISs), Building Information Modeling (BIM), and City Information Modeling (CIM) are identified as the main information technologies progressively employed across diverse stages of urban planning and construction over recent decades. These advancements have propelled the digital and intelligent management of urban areas, yielding significant benefits such as enhanced visualization, informed decision-making, and increased opportunities for citizen participation. However, a noticeable disparity between supply and demand during the application process arises from a lack of transdisciplinary cooperation. This study sheds light on the existing literature and offers policy implications and recommendations for more effective utilization of geo-information technologies in future spatial governance.

Research on the Influence of Firm Digital Intelligence Transformation and Management Innovation on Performance and Sustainable Development: Empirical Evidence from China

From the perspective of firm capability, this study explores the impact of digital intelligence transformation and management innovation on the performance and sustainable development of Chinese firms. On the basis of constructing an evaluation index system of firm digital intelligence transformation and management innovation, a comprehensive evaluation model of firm digital intelligence transformation and management innovation is constructed via the analytic network process (ANP) and fuzzy comprehensive evaluation method. Taking Chinese film and television firms as an example, this study selects 160 data samples from 85 firms and constructs a structural equation model to empirically analyze the impact of digital intelligence transformation and management innovation on the performance of firms. The study results show that, in the context of digitalization and intelligence, firm digital intelligence capability and dynamic capability have a significant positive effect on firm performance. Firm digital intelligence transformation and management innovation directly impact performance and can play a role through firm digital intelligence capability and dynamic capability as intermediaries, and the chain intermediary effect of digital intelligence capability and dynamic capability is significant. The research results show that firm digital intelligence and dynamic capabilities provide a functional path for firm digital intelligence transformation and management innovation to improve performance. Moreover, the innovative application of digital intelligence technology has an important impact on the sustainable development of firms. Digital transformation and management innovation can help firms enhance their ability to manage environmental uncertainties, improve firm performance and foster the achievement of sustainable development goals.

Intelligent energy management and operation efficiency of electric vehicles based on artificial intelligence algorithms and thermal energy optimization

As the global concern for environmental protection and sustainable development intensifies, electric vehicles (EVs), as a representative of clean transportation, are gradually receiving attention. This study aims to explore an intelligent energy management system for electric vehicles based on artificial intelligence algorithms, and focuses on optimizing heat energy utilization to improve the overall operating efficiency of electric vehicles. This paper uses machine learning and deep learning technologies to build intelligent decision-making models, and realizes dynamic optimization of energy demand and heat recovery by analyzing real-time data of electric vehicles under different working conditions. At the same time, a set of thermal management system is designed, which combines the driving state and environmental conditions of the vehicle to optimize the thermal management strategy of the battery and the motor. The experimental results show that the intelligent energy management system based on this research shows good adaptability and stability under different climate conditions, and significantly reduces energy consumption. Therefore, the intelligent energy management system of electric vehicles based on artificial intelligence algorithm and thermal energy optimization effectively improves the operating efficiency of electric vehicles.

Energy storage battery state of health estimation based on singular value decomposition for noise reduction and improved LSTM neural network

Accurate estimation of the State of Health (SOH) of batteries is important for intelligent battery management in energy storage systems. To solve the problems of poor quality of data features as well as the difficulty of model parameter adjustment, this study proposes a method for estimating the SOH of lithium batteries based on denoising battery health features and an improved Long Short-Term Memory (LSTM) neural network. First, in this study, three health features related to SOH decrease were selected from the battery charge/discharge data, and the singular value decomposition technique was applied to the noise reduction of the features to improve their correlation with the SOH. Then, the whale optimization algorithm is improved using cubic chaotic mapping to enhance its global optimization-seeking capability. Then, the Improved Whale Optimization Algorithm (IWOA) is used to optimize the model parameters of LSTM, and the IWOA-LSTM model is applied to the battery SOH estimation. Finally, the model proposed in this research is validated against the Center for Advanced Life Cycle Engineering (CALCE) battery dataset. The experimental results show that the prediction error of battery SOH by the method proposed in this study is less than 0.96%, and the prediction error is reduced by 49.42% compared to its baseline model. The method presented in the article achieves accurate estimation of the SOH, providing a reference for practical engineering applications.

The Role of Artificial Intelligence Technology in Promoting Socio-Economic Development

This paper comprehensively examines the pivotal role of artificial intelligence (AI) technology in promoting socio-economic development. It commences by introducing the rapid advancements and current status of AI technology, which has permeated diverse industries through breakthroughs in big data, cloud computing, and deep learning. The paper highlights AI’s contributions to economic growth by enhancing production efficiency (e.g., 45% increase in efficiency through automation), optimizing resource allocation (25% raw material utilization improvement), and fostering innovative business models such as personalized customization and intelligent supply chain management. AI also fosters the emergence of new industries (intelligent medicine, education, finance) and creates substantial job opportunities directly for data scientists and algorithm engineers, and indirectly through driving related industries. Furthermore, AI facilitates industrial upgrading, transforming manufacturing into smart manufacturing, enhancing service quality in industries like retail, and modernizing agriculture with smart equipment. A case study on Chongqing Soft River Turing AI Technology Co., Ltd. showcases AI’s practical applications and corporate competitiveness enhancement. Overall, the paper underscores AI’s significance in economic growth, job creation, and industry transformation, while acknowledging challenges like data privacy and job displacement, emphasizing the need for strategic policy guidance and regulation.

Intelligent Energy Management Strategy and Sizing Methodology for Hybrid Systems in Isolated Regions

Intelligent Energy Management Strategy and Sizing Methodology for Hybrid Systems in Isolated Regions

Risks and Opportunities Brought by Artificial Intelligence Empowering the Financial Industry

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

Highly hygroscopic needle-punched carbon fiber felt with high evaporative cooling efficiency and fire resistance for safe operation of ultrahigh-rate lithium-ion batteries

The effective thermal management of lithium-ion batteries is the key to ensuring their fast charging-discharging, safe and efficient operation. Herein, inspired by transpiration-driven water transport in plants, we report a highly hygroscopic needle-punched carbon fiber felt (HS/CFF) with high evaporative cooling efficiency and fire resistance for the safe operation of lithium-ion batteries working at ultrahigh-rate conditions. The three-dimensional fiber skeleton structure constructed by needle punching in the carbon fiber felt enables effective water transport and storage in HS/CFF, without any water leakage. At an ultra-high discharge rate of 10 C, HS/CFF can reduce the maximum temperature of commercial lithium-ion batteries by 18 °C, and can keep the battery temperature below 60 °C. During 500 cycles of charge-discharge, HS/CFF maintains stable evaporative heat dissipation performance, which helps to improve the safety of lithium-ion batteries and extend their service life. Moreover, HS/CFF remains non-combustible even under exposure to a flame (600-700 °C) for 10 min, and the HS/CFF can be reused after the burning test, with the original excellent heat dissipation effect unchanged. This flexible, fire-resistant cooling material offers a promising avenue for low-energy intelligent thermal management of lithium-ion batteries and other heat-generating electronic devices.

Improving the Air Quality Monitoring Framework Using Artificial Intelligence for Environmentally Conscious Development

This study aims to significantly improve air quality monitoring through the innovative application of Artificial Intelligence (AI). Introducing the Artificial Intelligence Kualitas Udara (AIKU) model, this research offers a novel approach by integrating advanced machine learning algorithms with environmental sensors to predict air quality in real-time more accurately than traditional methods. The novelty of the AIKU model lies in its sophisticated data analytics framework, which processes high-frequency environmental data to assess air quality changes dynamically. The technique employs calibrating and deploying the AIKU model across various urban and suburban settings and analyzing its performance against conventional monitoring systems such as the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). The results demonstrate that AIKU significantly outperforms these traditional systems in both accuracy and speed of response, highlighting its effectiveness in real-time environmental monitoring. Furthermore, the AIKU model's scalability and adaptability are tested, showing promising potential for application in densely populated urban areas and less populated rural settings. This research contributes to environmental monitoring by demonstrating how AI can transform traditional methodologies into more effective, scalable, and intelligent ecological management systems. This research provides substantial evidence that the AIKU model can serve as a powerful tool for sustainable and smart development worldwide, enhancing the ability of governments and organizations to respond to environmental challenges promptly and effectively. Doi: 10.28991/HIJ-2024-05-03-017 Full Text: PDF

Design and Development of an Intelligent Laboratory Management System Based on STM Processors

Abstract In response to the escalating inventory of laboratory equipment, the inadequacy of traditional manual management practices has become increasingly apparent. Current management challenges are underscored by historical data, which reveals a borrowing rate of approximately 30%, a concerning return rate of only about 5%, and an equipment loss rate of 10%. To address these inefficiencies, this paper introduces an advanced, intelligent laboratory equipment management system leveraging STM32 and GM65 technologies, designed to significantly enhance the return rate and minimize equipment loss. The system is meticulously engineered, featuring three integral modules: the scanning module, the core board module, and the communication module. The scanning module employs GM65 technology to rapidly scan and decode QR codes, facilitating the swift transmission of critical data to the core board for intelligent processing and interaction with the laboratory equipment. At the heart of the system, the core board module serves as the central processing unit, adeptly managing data from the scanning module. It intelligently discerns various information types and dynamically updates the TFT-LCD screen with the current status of equipment, ensuring that all users have access to real-time and accurate information regarding the borrowing and returning of equipment. The communication module is pivotal in bolstering the system's connectivity capabilities. Designed with a robust structure, the system is characterized by its simplicity of operation and robust practicality. It not only significantly improves the efficiency of laboratory equipment management but also plays a transformative role in modernizing laboratory management practices. By streamlining the management process and ensuring the optimal utilization of resources, this system provides a solid foundation for the sustainable advancement of laboratory operations.

Scenario-Information Analysis Method for Intelligent Management of Adaptive Training of Groups of Specialists

Scenario-Information Analysis Method for Intelligent Management of Adaptive Training of Groups of Specialists

Research on the identification and detection technology of electricity theft in power distribution areas under a smart grid environment

Abstract With the extensive application of smart meters, power companies can access a wealth of customer electricity usage data, which has not yet been fully utilized. This paper proposes a substation area user identification and electricity theft detection method based on smart meter data, aiming to enhance the level of intelligent management of power grid enterprises and reduce operational costs. By using the grey relational analysis method, analyzing the measurement data collected by smart meters at different time points, it is possible to determine the substation area and phase to which the user belongs and to detect electricity theft behavior. Finally, through simulation, the effectiveness of the proposed method is verified.

Intelligent Energy Management across Smart Grids Deploying 6G IoT, AI, and Blockchain in Sustainable Smart Cities

In response to the growing need for enhanced energy management in smart grids in sustainable smart cities, this study addresses the critical need for grid stability and efficient integration of renewable energy sources, utilizing advanced technologies like 6G IoT, AI, and blockchain. By deploying a suite of machine learning models like decision trees, XGBoost, support vector machines, and optimally tuned artificial neural networks, grid load fluctuations are predicted, especially during peak demand periods, to prevent overloads and ensure consistent power delivery. Additionally, long short-term memory recurrent neural networks analyze weather data to forecast solar energy production accurately, enabling better energy consumption planning. For microgrid management within individual buildings or clusters, deep Q reinforcement learning dynamically manages and optimizes photovoltaic energy usage, enhancing overall efficiency. The integration of a sophisticated visualization dashboard provides real-time updates and facilitates strategic planning by making complex data accessible. Lastly, the use of blockchain technology in verifying energy consumption readings and transactions promotes transparency and trust, which is crucial for the broader adoption of renewable resources. The combined approach not only stabilizes grid operations but also fosters the reliability and sustainability of energy systems, supporting a more robust adoption of renewable energies.

Monitoring and zoning soybean maturity using UAV remote sensing

Timely and accurate determination of the maturity status during the soybean harvest period is crucial for devising strategic harvesting plans, significantly enhancing the intelligent management of soybean production and minimizing losses. To explore the maturation dynamics of soybeans and pinpoint the precise maturity stage for optimal harvesting, we collected Unmanned Aerial Vehicle (UAV) remote sensing data, along with physical and chemical parameters, during the Beginning maturity(R7)-Full maturity(R8) stages from 2021 to 2022. We analyzed the variations in these parameters throughout the maturation period. Employing UAV multispectral technology and correlation analysis algorithms, we established a relationship between the spectral and physicochemical parameters during the maturation period, which led to the formulation of a soybean maturity evaluation index. Furthermore, we incorporated the concept of precision management zoning. In line with actual field production requirements, this index, combined with real-time moisture content, established a standard for grading soybean maturity. Additionally, our study examined the impact of spatial scale on maturity assessments by conducting a theoretical analysis of regionalized variables and analyzing the spatial correlations and variability of soybean maturity across different field plots. We determined the optimal theoretical model for various sampling distances, thereby establishing feasible zoning ranges. Comparative analysis of zoning algorithms, including Hierarchical Clustering (HC), K-Means, and its optimized versions K-Means++ and Mini Batch K-Means, was conducted using the partition performance index as the criterion. This analysis identified the most suitable algorithm for assessing soybean field maturity, which was subsequently verified through field tests. Results indicated that soybean leaf chlorophyll content and photosynthesis decreased sharply initially, then stabilized at a low rate. Water content in both soybean plants and seeds decreased rapidly before slowing, with plant dehydration rates exceeding those of the seeds. Green Normalized Difference Vegetation Index(GNDVI) showed a positive correlation with various physiological indicators, and soybean maturity was classified into three levels using the unit gridding management method, considering actual moisture content. Spatial correlation analysis of maturity variations in small and large test fields revealed Moran indices ranging from 0.78 to 0.95 and 0.70–0.90, respectively. Optimal detection ranges were determined to be 2.00–30.00 m for small fields and 2.00–67.00 m for large fields. The zoning categories for small and large fields were three and four, respectively. Comparative studies showed that the Mini Batch K-Means algorithm, an optimization of K-Means, achieved Calinski-Harabasz Index(CH) scores and silhouette coefficients of 10521.97, 109508.27, and 0.57, 0.54, respectively, indicating comparable zoning effectiveness to K-Means and K-Means++ and superiority over the HC algorithm. The zoning speeds were 12.50 s and 191.68 s, respectively, underscoring its efficiency in maturity zoning. Furthermore, zoning results corresponded well with actual soybean maturity. Terrain had little impact on soybean maturity, which was more susceptible to slope aspect. This study provides a theoretical foundation and practical guidelines for applying UAV remote sensing technology in determining soybean maturity and optimal harvesting periods, thereby reducing potential harvest losses.

Integration of Team Performance, Emotional Intelligence, and Human Resource Management Practices: A Systematic Review

In this article, we will discuss how to combine team performance with emotional intelligence and use human resource management practices in an increasingly competitive globalization situation. Research data was collected through trusted journal databases such as Scopus and Google Scholar to ensure broad and relevant literature coverage. You can search for literature using Publish or Perish (POP) applications and enter keywords relevant to the research subject. In order to gain a more holistic understanding of the research subject, data collection involved combined methods using quantitative and qualitative analysis. In the current era of globalization, the level of productivity and sustainability of an organization can be improved through teams that work effectively, have good emotional intelligence skills, and implement optimal human resource management practices. The importance of this Research is highly visible in increasing understanding of the relationship between these factors and how they affect business today

Damage Detection and Localization Methodology Based on Strain Measurements and Finite Element Analysis: Structural Health Monitoring in the Context of Industry 4.0

This paper investigates the integration of Structural Health Monitoring (SHM) within the frame of Industry 4.0 (I4.0) technologies, highlighting the potential for intelligent infrastructure management through the utilization of big data analytics, machine learning (ML), and the Internet of Things (IoT). This study presents a success case focused on a novel SHM methodology for detecting and locating damages in metallic aircraft structures, employing dimensional reduction techniques such as Principal Component Analysis (PCA). By analyzing strain data collected from a network of sensors and comparing it to a baseline pristine condition, the methodology aims to identify subtle changes in local strain distribution indicative of damage. Through extensive Finite Element Analysis (FEA) simulations and a PCA contribution analysis, the research explores the influence of various factors on damage detection, including sensor placement, noise levels, and damage size and type. The findings demonstrate the effectiveness of the proposed methodology in detecting cracks and holes as small as 2 mm in length, showcasing the potential for early damage identification and targeted interventions in diverse sectors such as aerospace, civil engineering, and manufacturing. Ultimately, this paper underscores the synergistic relationship between SHM and I4.0, paving the way for a future of intelligent, resilient, and sustainable infrastructure.

Research and Application of Intelligent Pedestrian Traffic Light System

This study aims to develop an intelligent pedestrian traffic light system that utilizes artificial intelligence and visual learning technology to optimize the intelligent management of traffic lights, in order to improve traffic efficiency, reduce traffic congestion, reduce the incidence of traffic accidents, and provide people with a safer and more convenient travel environment. The research team uses high-definition cameras to capture pedestrians and vehicles in real-time, and uses computer vision technology for pedestrian and vehicle detection and feature extraction. Through age classification and feature extraction, a population transit time model was constructed, and the model was optimized to improve prediction accuracy. The model was applied to traffic management for decision-making. In addition, the research also involves traffic flow analysis and real-time adjustment mechanisms, as well as the construction and application of pedestrian and vehicle models. Discover traffic patterns through data mining techniques and develop corresponding traffic management strategies. The construction and optimization adjustments of the predictive model support the implementation of the decision control model, which is based on data-driven and algorithmic optimization. The effectiveness of the model has been verified through simulation experiments and on-site testing. Finally, machine learning was used to recognize pedestrians, vehicles, and movement directions, and deep learning was applied to construct a prediction model, achieving intelligent control of pedestrian traffic lights.