Intelligence Perspective Research Articles

Transformation and Contradiction: Reconstructing Art in the Perspective of Artificial Intelligence

With the continuous advancement of the Internet, big data and artificial intelligence technology, it has brought far-reaching impacts and potential opportunities to traditional art, and at the same time continues to shape its content and form. From generating art to analyzing and preserving artworks to innovating the art market, AI is reconfiguring the ecosystem of art in an unprecedented way, which expands the boundaries of art creation, and promotes the integration of art and technology. However, the reconfiguration of art also faces many challenges and opportunities. The balance between technology and humanities is crucial to prevent art from losing its humanistic connotation due to over-reliance on technology. At the same time, we also need to face the limitations of AI algorithms and the originality of art works, to reveal the internal mechanism and external manifestation of art reconstruction, and to further explore the path of art development under the symbiosis of human and AI.

Construction of Smart Learning Spaces in Private Universities' Accounting Programs under the Dual Perspective of Digital Intelligence and Industry-education Integration

This study explores the construction model of smart learning spaces in private universities' accounting programs within the dual context of digital intelligence and industry-education integration. Utilizing literature review and theoretical analysis, the research systematically examines the current applications of digital intelligence in education and the impact of industry-education integration on educational reform. Initially, the concept of smart learning spaces is defined, and their application value in accounting programs is analyzed. Subsequently, the study investigates how digital intelligence enhances the smart, personalized, and interactive aspects of learning spaces, and how industry-education integration effectively aligns educational content with industrial needs. Finally, based on constructivist and ecosystem theories, a theoretical model of smart learning spaces is constructed, along with implementation strategies. The findings indicate that the construction of smart learning spaces effectively improves the teaching quality and students' professional abilities in accounting programs, providing new insights and methods for the reform and development of private universities' accounting programs.

The Current Situation, Characteristics and Prospects of the Research on Tennis Theme Literature in China from the Perspective of Artificial Intelligence

With the rapid development of artificial intelligence technology, the function of artificial intelligence to help the high-quality development of sports is increasingly visible, and the continuous, comprehensive and deep integration of artificial intelligence and sports will be an inevitable trend. This paper summarized and analyzed the characteristics of the tennis research theme literature published in the core journals of China from 2012 to 2022 by using the methods of literature and logical induction. The results show that, in the past 10 years, there are problems such as insufficient use of artificial intelligence in technical and tactical statistical analysis, insufficient use in tennis professionalization research, lack of artificial intelligence perspectives in tennis tournament research, and shortage of artificial intelligence use in tennis psychology research. In the future, scientific research on tennis in China should rely on scientific and technological means such as 5G technology, big data technology, computer vision and intelligent wearable devices. etc, cross integrate multidisciplinary knowledge, accurately locate the physical fitness weaknesses of Chinese tennis players, focus on building an intelligent measurement system for tennis players, and scientifically develop a big data platform for tennis skills and tactics; And guided by practical problems, we should focus on exploring intelligent wearable devices suitable for tennis sports programs, and integrating computer vision technology with intelligent wearable devices to achieve complementary advantages and enhance the accuracy and credibility of data collection. And use natural science methods to conduct quantitative analysis to provide scientific solutions for the development of tennis.

Graphic, Quantitation, Visualization, Standardization, Digitization, and Intelligence of Electrolyte and Electrolyte‐Electrode Interface

AbstractElectrolytes have recently regained significant attention in rechargeable batteries due to the discovery that the electrolyte microstructures play a determinant role in battery performance. By adjusting the compositions of electrolytes to cater to various functionalities, such as high‐voltage, fast‐charging, wide‐temperature operation, and non‐flammable features, a diverse range of batteries can be developed to adapt to different environmental working conditions. Nevertheless, elucidating the electrolyte microstructures and understanding the associated electrode interfacial behaviors remain challenging. These challenges arise from the interdisciplinary nature of the research, encompassing subjects such as solution chemistry, interface chemistry, electrochemistry, and organic chemistry. This topic holds particular significance because solution chemistry and solution‐solid interface chemistry are ubiquitous in daily lives, yet their behaviors remain unclear due to their inherent complexity, dynamic nature, and rapid variability. In this context, electrolyte and electrolyte‐electrode interface research are used as an illustrative example and summarize their progress from six key perspectives of graphic, quantitation, visualization, standardization, digitization, and intelligence. It is aimed to provide a multi‐faceted understanding of electrolyte microstructures and their behaviors on the electrode interface. This comprehensive approach enables the effective design of electrolytes and enhances the accuracy of predicting battery performance, servicing the development of solution and solution‐solid interface.

Optimization Path of University Management Personnel Assessment Mechanism from the Perspective of Artificial Intelligence

Optimization Path of University Management Personnel Assessment Mechanism from the Perspective of Artificial Intelligence

Symbolic Knowledge Extraction and Injection with Sub-symbolic Predictors: A Systematic Literature Review

In this article, we focus on the opacity issue of sub-symbolic machine learning predictors by promoting two complementary activities— symbolic knowledge extraction (SKE) and symbolic knowledge injection (SKI)—from and into sub-symbolic predictors. We consider as symbolic any language being intelligible and interpretable for both humans and computers. Accordingly, we propose general meta-models for both SKE and SKI, along with two taxonomies for the classification of SKE and SKI methods. By adopting an explainable artificial intelligence (XAI) perspective, we highlight how such methods can be exploited to mitigate the aforementioned opacity issue. Our taxonomies are attained by surveying and classifying existing methods from the literature, following a systematic approach, and by generalising the results of previous surveys targeting specific sub-topics of either SKE or SKI alone. More precisely, we analyse 132 methods for SKE and 117 methods for SKI, and we categorise them according to their purpose, operation, expected input/output data and predictor types. For each method, we also indicate the presence/lack of runnable software implementations. Our work may be of interest for data scientists aiming at selecting the most adequate SKE/SKI method for their needs, and may also work as suggestions for researchers interested in filling the gaps of the current state-of-the-art as well as for developers willing to implement SKE/SKI-based technologies.

The MMI-HCPS method: Integrating perception-decision-control capabilities of multiple mine inspectors

Due to the complex mining environment, the development of equipment intelligence has stagnated, with challenges in the application of key technologies. Additionally, underground inspectors exhibit a lower level of intelligence in monitoring, decision-making, and control aspects. This paper proposes an MMI-HCPS (Multiple Mine Inspectors- Human-Cyber-Physical System) method that integrates the perception, decision, and control capabilities of multiple mine inspectors. Firstly, it reviews the current status of equipment intelligentization in fully mechanized mining faces to highlight the necessity of equipment intelligentization. Secondly, the paper establishes the theoretical framework MMI-HCPS and further refines the overall structure of the entire document based on MMI-HCPS, composed of high-performance multi-site and multi-device communication network (MCN), intelligent inference system (IIS), inspection-human-system (IHS), and center-human-system (CHS). MCN forms the foundation for the overall architecture to operate smoothly, while IIS, IHS, and CHS constitute the core components driving the operation of MMI-HCPS. Both IIS and CHS have their own monitoring and control subsystems, and their decision subsystems are in a loop with IIS, achieving closed-loop inference. Finally, a utilizing the laboratory environment, separate tests were conducted on the network operation status of MCN, monitoring and control capabilities of IHS and CHS, experimental validation of decision-making abilities of multiple inspectors, and ultimately, an experiment was undertaken to assess the overall inspection process. The results indicate that promoting the intelligence of inspectors in the fully mechanized mining face from an intelligent perspective is feasible with this method. The method achieves a shared perception of the fully mechanized mining face among different inspectors, complementing each other’s strengths and weaknesses, enhancing information processing efficiency, and improving individual work efficiency. Furthermore, the method enables an interactive loop between human, computers, and the physical mining face, providing support for further intelligent development of mining equipment.

Artificial intelligence in respiratory care: Current scenario and future perspective.

This narrative review aims to explore the current state and future perspective of artificial intelligence (AI) in respiratory care. The objective is to provide insights into the potential impact of AI in this field. A comprehensive analysis of relevant literature and research studies was conducted to examine the applications of AI in respiratory care and identify areas of advancement. The analysis included studies on remote monitoring, early detection, smart ventilation systems, and collaborative decision-making. The obtained results highlight the transformative potential of AI in respiratory care. AI algorithms have shown promising capabilities in enabling tailored treatment plans based on patient-specific data. Remote monitoring using AI-powered devices allows for real-time feedback to health-care providers, enhancing patient care. AI algorithms have also demonstrated the ability to detect respiratory conditions at an early stage, leading to timely interventions and improved outcomes. Moreover, AI can optimize mechanical ventilation through continuous monitoring, enhancing patient comfort and reducing complications. Collaborative AI systems have the potential to augment the expertise of health-care professionals, leading to more accurate diagnoses and effective treatment strategies. By improving diagnosis, AI has the potential to revolutionize respiratory care, treatment planning, and patient monitoring. While challenges and ethical considerations remain, the transformative impact of AI in this domain cannot be overstated. By leveraging the advancements and insights from this narrative review, health-care professionals and researchers can continue to harness the power of AI to improve patient outcomes and enhance respiratory care practices. Based on the findings, future research should focus on refining AI algorithms to enhance their accuracy, reliability, and interpretability. In addition, attention should be given to addressing ethical considerations, ensuring data privacy, and establishing regulatory frameworks to govern the responsible implementation of AI in respiratory care.

The Role of Emotional Intelligence in Leadership Effectiveness

The aim of this study is to emphasize the role that emotional intelligence plays in leadership. The ability to focus on one's own emotional state as well as the emotional signals that others are displaying in the dynamic context of an interaction is known as emotional intelligence. Keeping an eye on other people's and your own emotions during a heated discussion helps you handle that specific circumstance well. Mutual understanding is the best kind of relationship that people can have, and it happens most quickly when one is still conscious of the dynamics of emotional intelligence. The benefits that emotionally intelligent leaders provide to their followers and organisations will be examined in this study.Emotional skills can be used to manipulate other human beings in different situations. Emotional intelligence and leadership are related to each, the extent to which leadership is impacted by emotional intelligence, with the help of extensive theoretical and empirical review of literature. This paper will help to understand the different perspectives of emotional intelligence along with related components and to study the relationship between emotional intelligence and leadership. Keywords: emotional intelligence, leadership, self-awareness, self-management, social, awareness, relationship management

From Campus to Career: A Conceptual Paper on Emotional Intelligence and Graduates' Organizational Commitment

This study aims to explore the subtle connections that occur between university graduates' emotional intelligence and organizational commitment as they move from Campus to Career, intending to reduce the problem of high turnover rates prevalent among Chinese university graduates. The rising turnover rate among college graduates as a population poses a great challenge to society, companies, government departments, and educational institutions. While existing organizational behavior studies have mainly examined the impact on organizational commitment from the perspectives of economic and social factors, this study uniquely explores the impact on organizational commitment from the domain of psychological factors, i.e., from the perspective of emotional intelligence. In China, the emotional intelligence of fresh graduates is a key factor affecting employment stability. Therefore, this study highlights the key role of emotional intelligence in influencing graduates' organizational commitment. By shedding light on this critical relationship, this study aims to provide insights that can be used to reduce graduate turnover, increase organizational commitment, and contribute to a more comprehensive understanding of the characteristics that influence graduate organizational commitment in societies, firms, government departments, and educational institutions.

The Analysis and Reexamination of Functionalism from the Perspective of Artificial Intelligence

The Analysis and Reexamination of Functionalism from the Perspective of Artificial Intelligence

Investigation and analysis of maker education curriculum from the perspective of artificial intelligence

As an education model that focuses on the cultivation of students’ creativity and practical ability, the integration of maker education and the development of artificial intelligence is of great significance. In order to analyze and study the application effect of artificial intelligence in maker education, based on this, this study adopts the method of questionnaire and case analysis to investigate and analyze students from three dimensions: learning experience, learning attitude and learning emotion, learning effect and transfer. The survey results show that, on the whole, the integration of artificial intelligence technology into maker education can significantly improve students’ learning feelings and attitudes, and enhance the enthusiasm of learning emotions. The research also finds that it has a positive impact on students’ learning effect and transfer ability. Learning experience, learning attitude and learning emotion, learning effect and learning transfer are related to gender, teaching time, teaching subjects or majors. Therefore, the article will suggest creating a maker cultural atmosphere, creating a maker resource space, and improving the maker education system. This provides a valuable reference for further promoting the development of maker education courses in the field of artificial intelligence.

Stretchable Magneto-Mechanical Configurations with High Magnetic Sensitivity Based on "Gel-Type" Soft Rubber for Intelligent Applications.

"Gel-type" soft and stretchable magneto-mechanical composites made of silicone rubber and iron particles are in focus because of their high magnetic sensitivity, and intelligence perspective. The "intelligence" mentioned here is related to the "smartness" of these magneto-rheological elastomers (MREs) to tune the "mechanical stiffness" and "output voltage" in energy-harvesting applications by switching magnetic fields. Hence, this work develops "gel-type" soft composites based on rubber reinforced with iron particles in a hybrid with piezoelectric fillers such as barium titanate. A further aspect of the work relies on studying the mechanical stability of intelligence and the stretchability of the composites. For example, the stretchability was 105% (control), and higher for 158% (60 per 100 parts of rubber (phr) of barium titanate, BaTiO3), 149% (60 phr of electrolyte iron particles, EIP), and 148% (60 phr of BaTiO3 + EIP hybrid). Then, the magneto-mechanical aspect will be investigated to explore the magnetic sensitivity of these "gel-type" soft composites with a change in mechanical stiffness under a magnetic field. For example, the anisotropic effect was 14.3% (60 phr of EIP), and 4.4% (60 phr of hybrid). Finally, energy harvesting was performed. For example, the isotropic samples exhibit ~20 mV (60 phr of BaTiO3), ~5.4 mV (60 phr of EIP), and ~3.7 mV (60 phr of hybrid). However, the anisotropic samples exhibit ~5.6 mV (60 phr of EIP), and ~8.8 mV (60 phr of hybrid). In the end, the composites prepared have three configurations, namely one with electro-mechanical aspects, another with magnetic sensitivity, and a third with both features. Overall, the experimental outcomes will make fabricated composites useful for different intelligent and stretchable applications.

Study on Deconstruction and Governance of Intellectual Property Rights Caused by Machine Writing Ethics Anomie in The Era of Artificial Intelligence

When machine writing enters the field of knowledge production, it causes a series of intelligent ethical risks, and the existing intellectual property system is deconstructed. The innovation of science and technology will break the balance mechanism of the existing intellectual property, and machine writing will become a new force of knowledge production. The intelligent ethical risk brought by machine writing is undoubtedly a "revolution" for the existing intellectual property system. From the ethical perspective of artificial intelligence, this paper from the ethical relations, ethics, ethical behavior and ethics profiling machine writing anomie of intellectual property rights subject status, object attribute, the legislative idea, the impact on the level of the judicial practice, and then from the legal environment, algorithm design, governance mechanism, platform regulation puts forward four human coexistence pattern of knowledge production, The reinvention of intellectual property rights.

Threat Analysis of Nipah Virus as a New Pandemic from the Perspective of Medical Intelligence

According to World Health Organization (WHO), Nipah virus is a type of infectious disease from 10 types of infectious diseases that can pose a risk to the public health. Besides, it has the potential to become a new pandemic. The State Intelligence Agency has the authority, mandate, and responsibility to actively engage in early detection of a disease that could potentially become an epidemic and cause a threat to national security and interests. The formulation of this research problem is " Threat Analysis of Nipah Virus as a New Pandemic from the Perspective of a Medical Intelligence." The authors used a qualitative descriptive method approach through interviews and literature studies related to Nipah virus research. The results of the research indicated that the Nipah virus has the potential to become a new pandemic that can threaten the Indonesian people health and even the world. Thus, early detection of the Nipah virus spreading in Indonesia is necessary, by collaborating across ministries in implementing preventive measures using a one health concept approach.

Exploring Pedagogical Ideologies and Strategies for College English Writing Instruction from a Generative Artificial Intelligence Perspective

This study, drawing on the commonalities between generative artificial intelligence and foreign language writing instruction, outlines the core ideology of digital humanities-based college English writing instruction, including auxiliary use of generative artificial intelligence tools, primary focus on humanistic education, and the re-production of knowledge, aiming to foster students’ critical thinking, collaborative skills, and creativity. Building on this foundation, the study delves into generative artificial intelligence tools applicable to different stages of process-genre writing and their strategic applications. The use of generative artificial intelligence tools is beneficial for students to present, discuss, and share writing content, encouraging them to enhance their writing, collaboration, critical thinking, and creative abilities through deep interaction with model essays and creative discourses.

Management Information Systems and Correlation Between E-Business and Information Security from a Business Intelligence Perspective

The research focus was motivated by the emergence of electronic business which increased during and after the COVID-19 pandemic. The research is a literature review and its purpose is to build awareness about the importance of information security and to analyze the correlation between information security and electronic business in an environment where electronic business is emerging and the gap between electronic business and information security is enlarging. The research is a review of peer-reviewed articles retrieved from case studies, empirical research, case analysis, literature reviews, comparative studies, systematic reviews, and conceptual analysis dating from the years of 2018 to 2023. This literature review defines four business intelligence concepts: management information systems, value driven business, electronic business, and information security. This literature review also reveals the effects of all four business intelligence concepts on organizations’ decision-making and financial objectives. Findings of this literature review revealed that electronic businesses need a stronger risk management approach regarding information security. The conclusion shows that the current technological approach and information security tools such as encryption key management, mantraps, and network intrusion detection systems do not ensure trust and/or eliminate digital security risks.

Spatial-temporal evolution analysis of the impact of smart transportation policies on urban carbon emissions

The impact of smart transportation policies on carbon emission control has become a new issue in urban development. Existing literature has primarily focused on smart transportation technology and new energy vehicles, while ignoring the impact of smart transportation policies. By using a quasi-natural experiment from China, this paper analyzes the impact of smart transportation policies on urban carbon emissions. The impact mechanism is analyzed from the perspectives of vehicle intelligence, road intelligence, and cloud; the empirical analysis of spatial difference is based on the panel data of 285 cities in China. The results show that: (1) smart transportation policies and vehicle-road-cloud collaboration have a positive inhibitory effect on urban carbon emissions. (2) The inhibitory effect of smart transportation policies on urban carbon emissions is influenced by the level of urban economic development but not by transportation infrastructure. (3) The spatial spillover effect of smart transportation policies on urban carbon emissions is influenced by transportation technology. Cities with higher transportation technology capabilities show inhibitory effects, while cities with low transportation technology capabilities show promoting effects. This study provides a reference for the sustainable development of urban smart transportation systems and environmental management.

A Fault Diagnosis Method for 5G Cellular Networks Based on Knowledge and Data Fusion.

As 5G networks become more complex and heterogeneous, the difficulty of network operation and maintenance forces mobile operators to find new strategies to stay competitive. However, most existing network fault diagnosis methods rely on manual testing and time stacking, which suffer from long optimization cycles and high resource consumption. Therefore, we herein propose a knowledge- and data-fusion-based fault diagnosis algorithm for 5G cellular networks from the perspective of big data and artificial intelligence. The algorithm uses a generative adversarial network (GAN) to expand the data set collected from real network scenarios to balance the number of samples under different network fault categories. In the process of fault diagnosis, a naive Bayesian model (NBM) combined with domain expert knowledge is firstly used to pre-diagnose the expanded data set and generate a topological association graph between the data with solid engineering significance and interpretability. Then, as the pre-diagnostic prior knowledge, the topological association graph is fed into the graph convolutional neural network (GCN) model simultaneously with the training data set for model training. We use a data set collected by Minimization of Drive Tests under real network scenarios in Lu'an City, Anhui Province, in August 2019. The simulation results demonstrate that the algorithm outperforms other traditional models in fault detection and diagnosis tasks, achieving an accuracy of 90.56% and a macro F1 score of 88.41%.

Smart carbon-based sensors for the detection of non-coding RNAs associated with exposure to micro(nano)plastics: an artificial intelligence perspective.

Micro(nano)plastics (MNPs) are pervasive environmental pollutants that individuals eventually consume. Despite this, little is known about MNP's impact on public health. In this article, we assess the evidence for potentially harmful consequences of MNPs in the human body, concentrating on molecular toxicity and exposure routes. Since MNPs are present in various consumer products, foodstuffs, and the air we breathe, exposure can occur through ingestion, inhalation, and skin contact. MNPs exposure can cause mitochondrial oxidative stress, inflammatory lesions, and epigenetic modifications, releasing specific non-coding RNAs in circulation, which can be detected to diagnose non-communicable diseases. This article examines the most fascinating smart carbon-based nanobiosensors for detecting circulating non-coding RNAs (lncRNAs and microRNAs). Carbon-based smart nanomaterials offer many advantages over traditional methods, such as ease of use, sensitivity, specificity, and efficiency, for capturing non-coding RNAs. In particular, the synthetic methods, conjugation chemistries, doping, and in silico approach for the characterization of synthesized carbon nanodots and their adaptability to identify and measure non-coding RNAs associated with MNPs exposure is discussed. Furthermore, the article provides insights into the use of artificial intelligence tools for designing smart carbon nanomaterials.