Intelligent Applications Research Articles

Colloidal Quantum Dots‐Based Photoelectrochemical‐Type Optoelectronic Synapse

AbstractCurrent artificial neuromorphic systems are mainly constructed using solid‐state optoelectronic synaptic devices, limiting their potential artificial intelligence applications in liquid medium. Here, colloidal CuInGaSe/ZnSe quantum dots (QDs) with environment‐benign feature and broad visible light absorption are prepared to fabricate a photoelectrochemical (PEC)‐type optoelectronic synaptic device operating in aqueous electrolyte, enabling unique biological synaptic behaviors including paired‐pulse facilitation (PPF), short‐term plasticity (STP), long‐term plasticity (LTP) and learning‐forgetting‐relearning process when simulated by optical pulses with various wavelength, pulse number, frequency, power density, and applied voltage. These results demonstrate the feasibility of building PEC‐type optoelectronic synapses using colloidal QDs, paving the way to realize available optoelectronic synaptic devices for prospective underwater artificial intelligence technology.

Supplier relationship management for sustainability: A comprehensive review of business intelligence applications

Supplier relationship management for sustainability: A comprehensive review of business intelligence applications

Application and Prospect of Artificial Intelligence in Personalized Learning

With the rapid development of "big data, intelligence, cloud, things and mobile" technology, artificial intelligence has been widely used in the field of education, providing technical support for personalized learning. The traditional education model cannot meet the personalized needs of students. Education reform calls for the introduction of artificial intelligence technology to achieve the optimal allocation of educational resources and improve teaching effectiveness. The knowledge economy era has put forward higher requirements for talents' innovation ability and lifelong learning ability. Personalized learning is an important way to cultivate these abilities. This paper first takes the main application of artificial intelligence in personalized learning as the starting point, and then analyzes the advantages and challenges of artificial intelligence in personalized learning. It is concluded that artificial intelligence plays a positive role in innovating teaching methods, achieving educational equality, and giving new impetus to learning, but at the same time it also faces challenges in various aspects.

Application of Business Intelligence in the Brazilian Educational System

In this article, we will discuss an application of Business Intelligence (BI) in analyzing data from the Brazilian educational system, specifically working with INEP (The Instituto Brasileiro de Geografia e Estatística — IBGE). This paper then discusses BI implementation's positive contributions and critical challenges in this domain. From the construction of an integrated BI system, our study aimed to offer a helpful tool that was easy to handle and touched on visuals such as dashboards, tables, and graphs to help us understand and manage the educational scenery throughout Brazil. The methodology used has been applied, with case studies and bibliographical research, based on data analysis techniques such as ETL (Extract, Transform, Load) and Data Warehousing. This is all leveraged to interactive dashboards, which should enable data-driven decisions and forge the way for advanced analytics like data mining.

AI and Thinkable-Assisted learning media for physical education: a descriptive study on collaborative lecturer education

This research aims to determine the role of Artificial Intelligence (AI) and Thunkable interactive learning media application design in cerebrating practices of lecturer education in the context of physical education (PE) methods integrated training. Questions arise on how lecturers can reinforce PE lessons with technologies such as AI and Thunkable and what challenges they face in doing so. This study utilized descriptive research design and data was collected by means of semi-structured interviews with PE lecturers who were knowledgeable about and had utilized AI or Thunkable in their teaching practice. Analysis of quantitative data was also descriptive but focused on transcribing the qualitative data based on, among others, lecturers' technological competence, benefits and challenges of the implementation, student outcome, and qualitative, lecturers' responses. Results found out that despite 70% of lecturers’ awareness of AI in PE lessons, only 30% of them used the available AI tools for the lessons. Out of the few lecturers who attempted to use the Thunkable application, only 25% were able to generate apps which is the focus of the application. Lecturers suggested that students would benefit a lot from AI especially through active participation rewarded by instant feedback without possibilities of concerns being addressed properly due to insufficient training and technical infrastructures. The implications of this study will focus on educational linguistics considering practical uses of AI tools in the language of education in terms of physical education. The use of AI systems in PE lessons can enhance communication and interaction in a multilingual setting that promotes both language and movement skills. This research aims to determine the role of Artificial Intelligence (AI) and Thunkable interactive learning media application design in cerebrating practices of lecturer education in the context of physical education (PE) methods integrated training. Questions arise on how lecturers can reinforce PE lessons with technologies such as AI and Thunkable and what challenges they face in doing so. This study utilized descriptive research design and data was collected by means of semi-structured interviews with PE lecturers who were knowledgeable about and had utilized AI or Thunkable in their teaching practice. Analysis of quantitative data was also descriptive but focused on transcribing the qualitative data based on, among others, lecturers' technological competence, benefits and challenges of the implementation, student outcome, and qualitative, lecturers' responses. Results found out that despite 70% of lecturers’ awareness of AI in PE lessons, only 30% of them used the available AI tools for the lessons. Out of the few lecturers who attempted to use the Thunkable application, only 25% were able to generate apps which is the focus of the application. Lecturers suggested that students would benefit a lot from AI especially through active participation rewarded by instant feedback without possibilities of concerns being addressed properly due to insufficient training and technical infrastructures. The implications of this study will focus on educational linguistics considering practical uses of AI tools in the language of education in terms of physical education. The use of AI systems in PE lessons can enhance communication and interaction in a multilingual setting that promotes both language and movement skills.

Assessment of relationship between emotional intelligence and conflict resolution among nurse managers at tertiary Health Institutions of North Eastern States of Nigeria

Conflict in health care settings may be effectively managed if nurse managers possess conflict management skills such as reconciliation, listening, and effective communication to bring individuals closer and work together to find fair solutions and meet balanced needs. The study aimed at identifying the relationship between emotional intelligence and conflict resolution among nurse managers at tertiary Health Facilities, in the Northern States of Nigeria. The objectives of the study are to assess the level of emotional intelligence among nurse managers, also to identify the most common types of conflict, determine the causes of conflict, determine the conflict resolution strategies applied by the nurse managers and to identify relationship between emotional intelligence and conflict resolution among nurse managers in tertiary health institutions of North Eastern States. A mixed methods research design (quantitative and qualitative methods) was used. A total of 188 nurse managers and three Head Nursing Services departments formed the sample size. The nurse managers were given structured questionnaires and the Head of the Nursing Services department was interviewed (key informant interview). Three instruments were employed for data collection; Interview Guide, Structured Questionnaire and Participants Demographics Sheet. A simple random sampling technique was used for the study. The obtained data were coded and computed in an SPSS Version 26 and analyzed using percentages, mean and standard deviation for descriptive statistics while the Pearson Correlation Coefficient was used for the inferential statistics. The result of the study revealed that most of the nurse managers have a fair level of emotional intelligence when dealing with conflict among nurse managers. It also revealed that the most common type of conflict among nurses was related to environmental factors. The working environment was the highest cause of conflict among nurses. Social skill strategy was mostly used in conflict resolution. The study revealed that there was no significant relationship between years of experience and conflict resolution r=0.173. Based on the findings the researcher concluded that most of the respondents highly make use of social awareness in the application of emotional intelligence in conflict resolution, most of the causes of conflict among nurses were related to a working environment, and social skills were mostly used in conflict resolution among nurses. Based on the findings of the study, the researcher recommended that other sources of conflict management should be identified for nurse managers to manage conflict, nurse managers should use the skills they have to solve problems and conflict management to be part of a school of nursing curriculum.

Analysis of the Application of Artificial Intelligence in Mahjong

Over the past few years, AI-based models for Mahjong have received a substantial amount of contribution due to the advancements in machine learning and game theory. In this review, these state-of-the-art AI models are considered, including Tjong, Kanachan, Suphx, and Zhejiang University’s developed model. The deployed approaches are disparately represented by reinforcement learning, Monte Carlo tree search (MCTS), and deep neural networks, all of which aim at solving the issues that come with the game’s structure, such as incomplete and overburdening information. The Tjong model complements the power of the transformer architecture with hierarchical decision-making and in turn, brings strategic depth to the gameplay. Kanachan employs Q-learning and MCTS to optimize decision-making, while Suphx combines these methods with the novel ideas of Double Q-learning and Thompson Sampling to achieve higher performance than seen before. The integration of reinforcement learning and a new evaluation model imbues the model with the unique properties of both learning machines and human expertise, namely depth and intelligence. As a result of all these exploits, obstacles still exist, the most notable among them are the management of completely unknown information, handling long-distance games, and the strategic balance between offense and defense. In the future, determinants of probabilistic reasoning, the improvement of deep learning systems, and the prowess of reward shaping will result in AI improvement in Mahjong.

Discussion of AIGC Technology in a Photography Course at a Higher Vocational College

This study explores the teaching strategies of photography courses at a higher vocational college in China, focusing on the application of Artificial Intelligence Generated Content (AIGC) technology, specifically Stable Diffusion. Utilising the AI-TPACK theory as a theoretical framework, data were collected from teachers and students through 1) observation, 2) semi-structured interviews, and 3) focus group discussions. The researcher analysed the data using a phenomenological approach to capture the following core themes: 1) practical photography skills, 2) innovation and efficiency in post-processing, and 3) exploration of personalised styles and creative thinking. The results of this study contribute to the ongoing development of photography courses in higher vocational colleges amid technological innovation, demonstrate the positive effects of AIGC technology on enhancing the quality of photography teaching and creativity, and provide both theoretical support and practical guidance for curricular reform and teaching strategies.

The Investigation of Artificial Intelligence in Cultural Relics Protection

It is well-known that the importance of cultural heritage in history, culture, art and other aspects is self-evident. However, due to various force majeure damage, the situation of cultural heritage protection is becoming more and more severe. This article will review and look forward to the application of Artificial Intelligence (AI) in cultural relics protection. The method section of this paper demonstrates various AI technologies such as Generative Adversarial Networks (GAN), deep learning, and reinforcement learning, and applies them to cultural relic digitization, damage detection, virtual restoration, and cultural heritage management and display. The next section outlines the specific tasks for which these AI techniques are applicable, demonstrating the potential of these innovations to improve accuracy and efficiency in work. In the subsequent discussion section, the current limitations and challenges of AI in this field are analyzed, such as data dependency and “black box” characteristics. At the same time, the future direction of AI is discussed, such as interpretable AI (XAI) and adaptive learning. Finally, this paper concludes that although AI has challenges, it will play an increasingly important role in the field of cultural heritage protection in the future.

Application and Innovation of Artificial Intelligence in Forensic Medicine

As the fourth industrial revolution, artificial intelligence is reshaping many industries. It has become a new cornerstone of digital conversion, and it is no exception in the field of forensic medicine.This paper mainly discusses how artificial intelligence can solve the problems related to forensic medicine.How to use artificial intelligence technology to develop into an auxiliary tool in the field of forensic medicine.This paper summarizes the exploratory data analysis, statistical modeling and machine learning in artificial intelligence, extracts insights and knowledge from the data, and applies them to various fields of forensic medicine.Artificial intelligence will improve the accuracy and efficiency of forensic work: it can automate some tasks and improve the quality of evidence. The comprehensive analysis results show that the artificial intelligence proposed in this paper will be an important auxiliary in the three directions of forensic medicine.

Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives.

The applications of artificial intelligence (AI) in pharmaceutical sectors have advanced drug discovery and development methods. AI has been applied in virtual drug design, molecule synthesis, advanced research, various screening methods, and decision-making processes. In the fourth industrial revolution, when medical discoveries are happening swiftly, AI technology is essential to reduce the costs, effort, and time in the pharmaceutical industry. Further, it will aid "genome-based medicine" and "drug discovery." AI may prepare proactive databases according to diseases, disorders, and appropriate usage of drugs which will facilitate the required data for the process of drug development. The application of AI has improved clinical trials on patient selection in a population, stratification, and sample assessment such as biomarkers, effectiveness measures, dosage selection, and trial length. Various studies suggest AI could be perform better compared to conventional techniques in drug discovery. The present review focused on the positive impact of AI in drug discovery and development processes in the pharmaceutical industry and beneficial usage in health sectors as well.

The Application and Challenges of Artificial Intelligence in G Protein-Coupled Receptor Drug Virtual Screening

G protein-coupled receptors play important roles in various diseases and are key targets for drug discovery. Virtual screening has traditionally been used to identify potential drug candidates from large compound libraries but faces challenges in computational resources and time constraints. The integration of artificial intelligence technologies has improved screening precision and reduced costs. This review discusses current developments, challenges, and future directions in leveraging artificial intelligence to advance GPCR drug screening.

PE-MCAT: Leveraging Image Sensor Fusion and Adaptive Thresholds for Semi-Supervised 3D Object Detection.

Existing 3D object detection frameworks in sensor-based applications heavily rely on large-scale annotated data to achieve optimal performance. However, obtaining such annotations from sensor data-like LiDAR or image sensors-is both time-consuming and costly. Semi-supervised learning offers an efficient solution to this challenge and holds significant potential for sensor-driven artificial intelligence (AI) applications. While it reduces the need for labeled data, semi-supervised learning still depends on a small amount of labeled samples for training. In the initial stages, relying on such limited samples can adversely affect the effective training of student-teacher networks. In this paper, we propose PE-MCAT, a semi-supervised 3D object detection method that generates high-precision pseudo-labels. First, to address the challenges of insufficient local feature capture and poor robustness in point cloud data, we introduce a point enrichment module. This module incorporates information from image sensors and combines multiple feature fusion methods of local and self-features to directly enhance the quality of point clouds and pseudo-labels, compensating for the limitations posed by using only a few labeled samples. Second, we explore the relationship between the teacher network and the pseudo-labels it generates. We propose a multi-class adaptive threshold strategy to initially filter and create a high-quality pseudo-label set. Furthermore, a joint variable threshold strategy is introduced to refine this set further, enhancing the selection of superior pseudo-labels.Extensive experiments demonstrate that PE-MCAT consistently outperforms recent state-of-the-art methods across different datasets. Specifically, on the KITTI dataset and using only 2% of labeled samples, our method improved the mean Average Precision (mAP) by 0.7% for cars, 3.7% for pedestrians, and 3.0% for cyclists.

A Survey of Artificial Intelligence Applications in Nuclear Power Plants

Nuclear power plants (NPPs) rely on critical, complex systems that require continuous monitoring to ensure safe operation under both normal and abnormal conditions. Despite the potential of artificial intelligence (AI) to enhance predictive capabilities in these systems, limited research has been conducted on the application of AI algorithms within NPPs. This presents a knowledge gap in the integration of AI for improving safety, reliability, and decision making in NPP. In this study, we explore the use of AI methods, including machine learning and real-time data analytics, applied to NPP components to address the nonlinearity and dynamic behavior inherent in reactor operations. Through the implementation of AI and Internet of Things (IoT) devices, we propose a system that enables early warning and real-time data transmission to regulatory authorities and decision-makers, ensuring better coordination during incidents. Lessons from past nuclear accidents, such as Chernobyl, emphasize the importance of timely information dissemination to mitigate risks. However, this integration also presents challenges, including cybersecurity risks and the need for updated regulations to address AI use in safety-critical environments. The results of this study highlight the urgent need for further research on the application of AI in NPPs, with a particular focus on addressing these challenges to ensure safe implementation.

Further Application Of Automatic Control Based On Servo Motor In Smart Grid

The paper summarize the advantages and applications of servo motor. As a kind of equipment that can replace manual long-distance inspection, the intelligent inspection device for overhead transmission lines can significantly improve inspection efficiency and reduce labor intensity. The servo motor with high power density and low torque ripple can guarantee the high precision and stable operation of the intelligent inspection device in line inspection, which provides a certain practical intelligent application significance for transmission line inspection.

Application of artificial intelligence to study the causality in public health: a systematic review

Abstract Introduction In epidemiology, establishing causation requires meeting rigorous criteria beyond mere association. Traditional methodologies often struggle to provide definitive causal conclusions due to confounding variables. However, leveraging artificial Intelligence (AI) and machine Learning (ML) offers a promising solution by modeling complex interactions among relevant factors. This systematic review explores the AI techniques utilized to uncover causal relationships in public health applications. Methods A literature search was conducted in Pubmed, Web of Science, and Scopus, employing the following search strategy: (Causalit*[Title/abstract] OR Causation*[Title/abstract] OR Causal[Title/abstract]) AND (‘Machine Learning’[Title/abstract] OR “Deep Learning” [Title/abstract] OR ‘Artificial Intelligence’ [Title/abstract] OR Algorithm* [Title/abstract]). The search aimed to identify studies encompassing causal inference techniques utilizing ML and AI methodologies in public health. Results From a total of 28,230 articles published up to August 2023, after removing duplicates (11,158) and articles that did not meet the inclusion criteria (16,168 post title/abstract screening, and 856 post full-text screening), the systematic review included 48 articles. Bayesian additive regression trees, Bayesian network models, causal forests analysis, and graphical causal models emerged as the prevailing causal methodologies. AI-driven causal methodologies have been investigated across various domains within public health, encompassing disease prediction, evaluation of treatment efficacy, identification of risk factors, analysis of health behaviors, precision Public Health initiatives, and environmental health assessment. Discussion While our systematic review affirms the potential of AI-driven causal approaches across several public health domains, further research is warranted before these emerging techniques can be effectively integrated into real settings. Key messages • In epidemiology, establishing causation requires meeting rigorous criteria beyond mere association and traditional methodologies often struggle to provide definitive causal conclusions. • This systematic review explores the AI techniques utilized to uncover causal relationships in public health applications.

AI-Driven Threat Intelligence: Transforming Cybersecurity for Proactive Risk Management in Critical Sectors

Notably, the current progresses in the field of information technology have highlighted the need to have adequate security measures that will help overcome the complicated dangers originating from the cyber space. In this journal article, the author discusses what AI means for the practice area of cybersecurity, what it means, and its application of threat intelligence. Stress is made on the pro-active identification and mitigation of threats, the processes and adjustment of the cyber defense, various issues and moral concerns connected with the use of AI in this area. The Future evolution of AI in cybersecurity is discussed, along with its prospects, Including AI-integrated SOCs and automated response capabilities. As will be highlighted by understanding and application of these advancements, vital areas can strengthen their resilience against cyber threats.

Artificial intelligence – NPHIs’ role in utilizing its opportunities and addressing the risks

Abstract Issue & Problem National Public Health Institutes (NPHIs) in many countries are at the forefront of action to mitigate the impact of current and future threats to health as well as improving population health and wellbeing. Through research, collaboration, and support, NPHIs can approach these new realities together. One of the many emerging realities that NPHIs will need to engage in is how NPHIs best can utilise the powers of artificial intelligence and deal with the ethical and legal issues that come with it. Methods In preparation of IANPHI’s (International Association of National Public Health Institutes) Europe Regional network meeting in April 2024 a survey was conducted among the 42 members. 16 countries replied to questions, around e.g., 1) current applications of (generative) artificial intelligence (AI) in the work of institutes, 2) which experiences were made and 3) which measures NPHIs take to best utilize the possibilities and overcome challenges of AI technologies. Within four weeks 16 replies were shared. Lessons & Conclusions Some institutes have not yet adopted AI, while others report multiple applications but the primary areas of application vary considerably among Europe. Applications include summarizing and reformatting text; translation; transcribing and documenting meetings; search information in documents or databases; coding; creating visuals; literature review; surveillance of infectious diseases; disease forecasting; analysis of transmission dynamics; vaccine efficacy assessment and development; image recognition; diagnosis and prediction of diseases from medical datasets; text mining; symptom extraction; social listening; prediction of nitrate concentration in groundwater; drug analysis and measuring psychological resilience in social crisis. Key messages • AI poses opportunities and risks for public health and work of national public health institutes and capacity building initiatives are essential. • IANPHI decided to develop a joint framework for the use of AI in its member organisations with a focus on research and responsible use.

The assessment of society’s acceptance of artificial intelligence use in healthcare

Abstract Background We observe the surge of interest and application of artificial intelligence (AI) in various areas. AI use in healthcare and medicine is followed by society with interest; however, acceptance varies depending on the area of its application. This study aimed to understand the differences in acceptance depending on the type of solution and explain the factors influencing the attitude toward the use of AI in healthcare. Methods The data from the online survey was collected from a representative sample of 1109 adult Internet users in Poland. The respondents were asked about their acceptance of AI use for several purposes in healthcare. Based on the responses to these items, an ad-hoc score of AI acceptance in healthcare (AIAHC) was developed. The determinants of AIAHC were assessed with uni- (ULRM) and multivariable linear regression (MLRM) models. Results The highest acceptance of AI use was found for monitoring medication intake at home: mean (standard deviation, SD)-3.81 (1.39), the lowest for AI making decisions about the mode of therapy -2.78 (1.35). ULRMs showed that significant predictors of AIAHC were age, gender, chronic disease, vocational status, income, the use of the Internet (IU), and e-health literacy (eHL). In MLRM, only gender, vocational status, income, and eHL maintained a significant relationship with AIAHC. Males showed significantly higher AIAHC than females (B, 95%CI: 2.56, 1.42 - 3.71), and students had higher AIAHC than employees (B, 95%CI: 3.01, 0.14 - 5.89). Persons who refused to reveal their income demonstrated lower acceptance of AI use than those who revealed their income (B, 95%CI: -4.64, -6.54 - -2.74). Finally, greater eHL favored higher AIAHC (B, 95%CI: 0.46, 0.34 - 0.57). Conclusions Evidently, there is a hierarchy of potential areas of application in terms of society’s acceptance. Improving eHL should exert a positive effect on the acceptance of innovative technologies, including AI, in healthcare. Key messages • Citizens show moderate enthusiasm toward the broader use of AI in healthcare. • E-health literacy exerts a positive effect on the introduction of AI-based solutions in healthcare.

How advanced is your digital public health system? A qualitative analysis of suitable indicators

Abstract Background Revealing the full potential of digital public health (DiPH) systems necessitates a wide-ranging tool to assess their maturity. Essential domains that need to be considered include the literacy and interest in DiPH tool application by society and the workforce and the legal and ICT maturity of DiPH systems. No review has investigated indicators on these national DiPH system maturity (DiPHSM) domains yet. Our narrative review and qualitative analysis aimed to map the landscape of indicators related to DiPHSM measurement and rank them based on their importance for such assessments. Methods As original indicators were not published in scientific databases but as grey literature, we used DuckDuckGo to apply a pre-defined search strategy for 11 countries from all continents classified as having reached level 4 of 5 in the Global Digital Health Monitor. Of the 1484 identified references, 137 were included which named 15806 indicators. Consensus on importance was defined as at least 3 or 4 authors rating an indicator as important. Results We recognized 180 indicators on different constructs with importance for DiPHSM analysis, including the availability and use of Smartphones, computers, the Internet or the DiPH intervention, the existence of skilled workforce, infrastructure (investment), and interoperability between interventions, the secondary use of health data, a DiPH strategy and controlling agency, or the application of big data and artificial intelligence for health data collection, analysis, and sharing. Conclusions Our study holds the potential to develop more comprehensive tools for DiPHSM assessments. Further examination is required to analyze the suitability and applicability of all identified indicators for diverse healthcare settings. By working towards a uniform evaluation of DiPHSM, we foster informed decision-making among healthcare planners and practitioners, improve resource distribution, and continue to drive innovation in healthcare delivery. Key messages • Maturity assessment tools need to consider the complexity of DiPH systems. Thereby, DiPH system evaluations need to be accompanied by analyses of the legal, ICT, and literacy perspective maturities. • New methods are needed to systematically assess and use multidisciplinary grey literature for research.