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While increasing productivity and economic growth, the application of artificial intelligence (AI) may ultimately require millions of people around the world to change careers or improve their skills. These disruptive effects contribute to the general public anxiety toward AI development. Despite the rising levels of AI anxiety (AIA) in recent decades, no AI anxiety scale (AIAS) has been developed. Given the limited utility of existing self-report instruments in measuring AIA, the aim of this paper is to develop a standardized tool to measure this phenomenon. Specifically, this paper introduces and defines the construct of AIA, develops a generic AIAS, and discusses the theoretical and practical applications of the instrument. The procedures used to conceptualize the survey, create the measurement items, collect data, and validate the multi-item scale are described. By analyzing data obtained from a sample of 301 respondents, the reliability, criterion-related validity, content validity, discriminant validity, convergent validity, and nomological validity of the constructs and relationships are fully examined. Overall, this empirically validated instrument advances scholarly knowledge regarding AIA and its associated behaviors.

This study explores the trends of chatbots in education studies by conducting a literature review to analyze relevant papers published in the Social Science Citation Index (SSCI) journals by searching the Web of Science (WoS) database. From the analysis results, it was found that the United States, Taiwan and Hong Kong are the top three contributing countries or regions. In addition, most studies adopted quantitative methods in their research design, such as ANOVA (Analysis of variance), descriptive statistics, t test, and correlation analysis. ANCOVA (Analysis of covariance) was the most frequently adopted approach for comparing the performances or perceptions of different groups of students. From the analysis results, the greatest proportion of studies adopted guided learning, followed by no learning activities. It was determined that the studies related to chatbots in education are still in an early stage since there are few empirical studies investigating the use of effective learning designs or learning strategies with chatbots. This implies much room for conducting relevant research to drive innovative teaching in terms of improving the learning process and learning outcomes. Finally, we highlight the research gaps and suggest several directions for future research based on the findings in the present study.

Purpose: This paper aims to present a comprehensive review of the present state and trends of smart education research. It addresses the need to have a systematic review of smart education to depict its research landscape in view of the growing volume of related publications. Design/methodology/approach: A bibliometric analysis of publications on smart education published in 2011 to 2020 was conducted, covering their patterns and trends in terms of collaboration, key publications, major topics and trends. A total of 1,317 publications with 29,317 cited references were collected from the Web of Science and Scopus for the bibliometric analysis. Findings: Research on smart education has been widely published in various sources. The most frequently cited references are all theoretical or discussion articles. Researchers in the USA, China, South Korea, India and Russia have been most active in research collaborations. However, international collaborations have remained infrequent except for those involving the USA. The research on smart education broadly covered smart technologies as well as teaching and learning. The emerging topics have addressed areas such as the Internet of Things, big data, flipped learning and gamification. Originality/value: This study depicts the intellectual landscape of smart education research, and illustrated the evolution and emerging trends in the field. The results highlight its latest developments and research needs, and suggest future work related to research collaborations on a larger scale and more studies on smart pedagogies.

This paper analyses military-grade AI initiatives by the United States (US) and China in the cyber domain. It sheds light on these nations’ strategic priorities and implications for the ever-changing landscape of AI-driven cybersecurity. Since 1949, with evolving dynamics among China, the US, and Taiwan, marked by the unresolved civil war and the inception of the Taiwan Relations Act, China has emerged as a potential rival and the US as a possible ally. Understanding the disparity in their AI interests is now vital for Taiwan. Do so to avoid a lag in the swiftly advancing realm of AI-driven cybersecurity. Recognising this urgency, Taiwan should contemplate strategic adjustments to bridge the gap between China and the US in this domain. Our analysis reveals that the US and China prioritise addressing supply chain vulnerabilities but differ in their secondary priorities for various reasons. For the US, the secondary focus lies in enhancing defence capabilities, whereas China emphasises augmenting intelligence, surveillance, and reconnaissance (ISR). These commonalities and differences in direction are discussed in the context of the two nations’ respective national strategies and current situations. Thus, this paper contributes to Taiwan’s proactive consideration of information to help shape its cybersecurity strategy and ensure Taiwan remains competitive in an increasingly AI-centric world. [ABSTRACT FROM AUTHOR]

Nitrogen dioxide (NO 2 ) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO 2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO 2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO 2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R 2 values of 0.92 and 0.93, respectively. The study revealed that mean NO 2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO 2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO 2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Recent progress in wireless and sensor technologies has lead to a new development of learning environments, called context-aware ubiquitous learning environment, which is able to sense the situation of learners and provide adaptive supports. Many researchers have been investigating the development of such new learning environments; nevertheless, the criteria of establishing a context-aware ubiquitous learning environment have not yet been clearly defined, not to mention the strategies of conducting effective learning activities. To resolve these problems, this paper presents the basic criteria, strategies, and research issues of context-aware ubiquitous learning, and identifies the necessary check items as well for the development of such learning environment. Illustrative examples of conducting context-aware ubiquitous learning activities and the requirements of setting up such learning environment are also presented at the end of this paper. (Contains 3 figures and 2 tables.)

Promoting the development of students' scientific inquiry capabilities is a major learning objective in science education. As a result, teachers require effective assessment approaches to evaluate students' scientific inquiry-related performance. Teachers must also be able to offer appropriate supplementary instructions, as needed, to students. Scientific inquiry capabilities should be assessed by evaluating students' scientific inquiry portfolios in actual hands-on experiments. Although virtual laboratory systems can reduce the cost of conducting scientific inquiry experiments, the manual portfolio assessment approach is still difficult and time-consuming for teachers. Therefore, in this paper, in order to provide students with personalized learning guidance concerning not only the conceptual knowledge, but also the high-order, integrative abilities of scientific inquiry, an Online Portfolio Assessment and Diagnosis Scheme, called OPASS, was proposed to assist teachers in automatically assessing and diagnosing students' abilities as they relate to scientific inquiry performance. Personalized diagnostic reports were generated by employing the rule-based inference approach, which diagnosed learning problems and provided corresponding reasons and remedial suggestions based on teacher-defined assessment knowledge of the scientific inquiry experiment. For the evaluation, experimental results showed that the OPASS was helpful and beneficial for both students and teachers. (Contains 13 tables and 11 figures.)

PM 2.5 concentrations are higher during rush hours at background stations compared to the average concentration across these stations. Few studies have investigated PM 2.5 concentration and its spatial distribution during rush hours using machine learning models. This study employs a geospatial-artificial intelligence (Geo-AI) prediction model to estimate the spatial and temporal variations of PM 2.5 concentrations during morning and dusk rush hours in Taiwan. Mean hourly PM 2.5 measurements were collected from 2006 to 2020, and aggregated into morning (7 a.m.-9 a.m.) and dusk (4 p.m.-6 p.m.) rush-hour mean concentrations. The Geo-AI prediction model was generated by integrating kriging interpolation, land-use regression, machine learning, and a stacking ensemble approach. A forward stepwise variable selection method based on the SHapley Additive exPlanations (SHAP) index was used to identify the most influential variables. The performance of the Geo-AI models for morning and dusk rush hours had accuracy scores of 0.95 and 0.93, respectively and these results were validated, indicating robust model performance. Spatially, PM 2.5 concentrations were higher in southwestern Taiwan for morning rush hours, and suburban areas for dusk rush hours. Key predictors included kriged PM 2.5 values, SO 2 concentrations, forest density, and the distance to incinerators for both morning and dusk rush hours. These PM 2.5 estimates for morning and dusk rush hours can support the development of alternative commuting routes with lower concentrations., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Background: Behavioural intention (BI) has been predicted using other variables by adopting the technology acceptance model (TAM). However, few studies have examined whether BI can predict learning performance. Objectives: The present study used an extended TAM to investigate whether students' BI is a predictor of their listening learning performance (LLP) through vocabulary learning performance (VLP) in the context of mobile vocabulary‐assisted listening learning by using two mobile learning tools. Methods: A total of 129 college students with a pre‐intermediate level of English were recruited as participants, and a 10‐week mobile vocabulary‐assisted, listening‐learning course was conducted in 2022. In each task of this course, the students had to learn target words from a listening passage on Quizlet and then engage in listening activities on Randall's ESL Cyber Listening Lab. Quantitative responses obtained through an online questionnaire were analysed through partial‐least‐squares structural equation modelling. Results: The analysis results indicated that BI significantly predicted LLP through VLP. Perceived ease of use (PEU) and perceived usefulness (PU) were significant antecedents of BI. However, PEU did not significantly predict PU because of the difficulty of navigating between the two technological tools used in this study. The extended model demonstrated its effectiveness in explaining listening learning performance, as evidenced by an explained variance (R2) of 69%. Conclusion: The extended model validates the influence of BI on learning performance and it can also draw teachers' focus toward the significance of enhancing students' BI to improve their listening learning performance. Pedagogical implications based on the results are provided in this paper. Lay Description: What is already known about this topic?: TAM was used to study learners' acceptance of mobile‐assisted language learning.TAM incorporates latent variables to explore mobile‐assisted language learning.Investigating factors influencing BI is a primary research focus in extended TAM literature.Mobile tools could improve listening learning and vocabulary retention. What this paper adds to that: Learning performance was considered as a dependent variable in an extended TAM.BI might predict students' learning performance in vocabulary and listening in an extended TAM.Teachers used two mobile tools to design mobile vocabulary‐assisted listening tasks.Pre‐learning the target words facilitate students' listening learning performance. Implications for practice and/or policy: We show the importance of BI on predicting listening learning performance.The impact of BI on other factors became another focus of TAM research.Results highlight pre‐learning target words' importance for better listening performance.Existing mobile tools improve listening performance, avoiding new system development. [ABSTRACT FROM AUTHOR]

This paper conducts a comparative analysis of data governance mechanisms concerning the secondary use of health data in Taiwan and the European Union (EU). Both regions have adopted distinctive approaches and regulations for utilizing health data beyond primary care, encompassing areas such as medical research and healthcare system enhancement. Through an examination of these models, this study seeks to elucidate the strategies, frameworks, and legal structures employed by Taiwan and the EU to strike a delicate balance between the imperative of data-driven healthcare innovation and the safeguarding of individual privacy rights. This paper examines and compares several key aspects of the secondary use of health data in Taiwan and the EU. These aspects include data governance frameworks, legal and regulatory frameworks, data access and sharing mechanisms, and privacy and security considerations. This comparative exploration offers invaluable insights into the evolving global landscape of health data governance. It provides a deeper understanding of the strategies implemented by these regions to harness the potential of health data while upholding the ethical and legal considerations surrounding its secondary use. The findings aim to inform best practices for responsible and effective health data utilization, particularly in the context of medical AI applications. [ABSTRACT FROM AUTHOR]

Artificial intelligence is the unification of philosophy, cognitive science, mathematics, neurophysiology, psychology, computer science, information theory, cybernetics, and uncertainty theory. Therefore, it is feasible and necessary to utilize STEAM (Science, Technology, Engineering, Liberal Arts, and Mathematics) education to learn artificial intelligence courses. Computational thinking skills are of vital importance to high school students. This paper integrates artificial intelligence education with STEAM model with the aim of enhancing students' computational thinking skills. First, we investigate the feasibility of this model and set teaching objectives about artificial intelligence curricula. Second, artificial intelligence curricula with STEAM model is proposed to carry out interdisciplinary artificial intelligence knowledge acquisition. Finally, the effects of this model on students' computational thinking skills, learning motivation, and self-efficacy are evaluated. One hundred thirty-six participants are recruited from a high school in Beijing. The results reveal that the integration of artificial intelligence education with STEAM is able to promote computational thinking skills, learning motivation, and self-efficacy of the students in the experimental group. The main implication of this study is that artificial intelligence education in light of STEAM model can be used as a teaching guide for the combination of artificial intelligence curricula with multi-disciplinary knowledge at the primary and secondary levels. [ABSTRACT FROM AUTHOR]

Scholars have identified that learning in an authentic environment with quality contextual and procedural supports can engage students in thorough observations and knowledge construction. Moreover, the target is that students are able to experience and make sense of all of the learning activities in the real-world environment with meaningful supports, such that their learning motivation can be promoted, knowledge can be sensibly constructed, and skills can be fully developed. To develop potential tutoring strategies and learning activity models using mobile, wireless, and sensing information and communication technologies (ICT) in a real-world learning environment, a four-year national e-learning research project entitled "Mindtool-Assisted In-field Learning (MAIL)" has been funded by the National Science Council of Taiwan since 2008 in an effort to lead the development and innovation of Learning Technology. The integrated project aimed to develop Mindtool-assisted knowledge construction models, assessment models, guidance models, and reflection strategies for cutting-edge context-aware ubiquitous learning. Moreover, a series of learning activities has been conducted to examine the effectiveness of the proposed learning strategies and models. Each year, more than 1,500 students have participated in the in-field learning activities with the designed approaches. Based on the results of a series of experiments, it was found that the students' learning performance as well as their in-field inquiry ability was significantly improved, showing the effectiveness of the Mindtool-assisted ubiquitous learning approach and the success of the MAIL project. In this paper, the background, objectives, theoretical foundations, systems, research issues, applications, and findings of the MAIL project are presented. Finally, the scaling-up plan for applying these research-proven learning models to all levels of educational settings in Taiwan is also addressed.

Purpose: As the application of artificial intelligence (AI) becomes more prevalent, many high-tech firms have employed AI applications to deal with emerging societal, technological and environmental challenges. Big data analytical capability (BDAC) has become increasingly important in the AI application processes. Drawing upon the resource-based view and the theory of planned behavior, this study aims to investigate how BDAC and collaboration affect new product performance (NPP). Practically, a harmonic working team is particularly important for creating management synergies, this empirical analysis demonstrates the importance of BDAC and collaboration for NPP. Design/methodology/approach: This paper focuses on the performance of firms that applied AI in their operations. This study collected data from firms in Greater China, including China and Taiwan, as Greater China is currently the leading manufacturer of semiconductor, electronic and electric products for AI applications in the manufacturing process. Confirmatory factor analysis and structural equation modeling is employed for statistical analysis. Findings: The analytical results indicate that BDAC positively relates to collaboration capability (CC) in AI applications but not to team collaboration (TC). CC positively correlates with TC, and both CC and TC positively correlate with NPP. Further, the mediating effect was examined using the Sobel t-test, which reveals that CC is a significant mediator in the influence of BDAC on NPP. Practical implications: The strategic implementation of BDAC and collaboration can allow an enterprise to improve its NPP when driven by the external environment to use AI, which further enhances NPP. These processes indicate that AI and BDAC are both crucial for the success of a company's collaboration and for effective management to improve NPP in the face of global competition. Originality/value: This study introduces the concept of BDAC to explain the relationship between CC and TC, as they pertain to NPP. This study presented a discussion of the theoretical and practical implications of the research findings and could provide a framework for managing BDAC. [ABSTRACT FROM AUTHOR]

Due to an arising COVID-19 positive confirmed case in Taiwan, the screening of body temperature, mask wearing and quarantined violation is enhanced. A mobile robot that conducts this task is demanded to reduce the human labor. However, conventional robots suffer from several limitations, perceptual aliasing (e.g., different places/objects can appear identical), occlusion (e.g., place/object appearance changes between visits), different viewpoints, the scale of objects, low mobility, less functionality, and some environmental limitations. As for the thermal imager, it displays the current heat spectrum colors, and needs manual monitoring. This paper proposes applying Simultaneous Localization and Mapping in an unknown environment and using deep learning for detection of temperature, mask wearing, and human face on the Raspberry Pi to overcome these problems. It also uses the A* algorithm to do path planning and obstacle avoidance via 3D Light Detection and Ranging to make the robot move more smoothly. Evaluating and implementing different Simultaneous Localization and Mapping algorithms and deep learning models, then selecting the most suitable method. Root Mean Square Error of three Simultaneous Localization and Mapping algorithms are compared. The predictions of deep learning models are evaluated via the metrics (model speed, accuracy, complexity, precision, recall, precision–recall curve, F1 score). In conclusion, Google Cartographer for building a map, Convolutional Neural Network for mask wearing detection, and only looking once for human face detection achieve the best result among all algorithms. [ABSTRACT FROM AUTHOR]

Purpose: Taiwan experiences frequent seismic activity. Major earthquakes in recent history have seriously damaged the school buildings. School buildings in Taiwan are intended to serve both as places of education and as temporary shelters in the aftermath of major earthquakes. Therefore, the seismic performance assessments of school buildings are critical issues that deserve investigation. Design/methodology/approach: This paper develops a methodology that uses principal component analysis to generalize the seismic factors from the basic seismic parameters of school buildings, uses data mining to cluster different school building sizes and uses grey theory to analyze the relationship between seismic factors and the seismic performance of school buildings. Additionally, this paper employs the Artificial Neural Network (ANN) to deduce the seismic assessment model for school buildings. Finally, it adopts support vector machine to validate the ANN's deductive results. Findings: An empirical study was conducted on 326 school buildings in the central area of Taichung City, Taiwan, to illustrate the effectiveness of the proposed approach. Results show that thickness of wall and width of middle-row column relate significantly with school-building seismic performance. Originality/value: This paper provides a model that structural engineers or architects may use to design school buildings that are adequately resistant to earthquakes as well as a reference for future academic research. [ABSTRACT FROM AUTHOR]

An intelligent genetic algorithm (IGA) is proposed to solve Japanese nonograms and is used as a method in a university course to learn evolutionary algorithms. The IGA combines the global exploration capabilities of a canonical genetic algorithm (CGA) with effective condensed encoding, improved fitness function, and modified crossover and mutation. In this paper, the condensed encoding ensures that the chromosome is a feasible solution in all rows for Japanese nonograms. In the reconstruction process of a Japanese nonogram, the numbers in the left column are used as encoding conditions, and the numbers in the top row with the improved fitness function are employed to evaluate the reconstruction result. From the computational experiments, the proposed IGA approach is applied to solve Japanese nonograms effectively, with better results than using a CGA. The students of the Department of Computer Science, National Pingtung University of Education, Taiwan, have gained practical experience of applying evolutionary algorithms to solve Japanese nonograms using both the proposed IGA and a CGA. The students learn that the IGA can find the right solution of the puzzle effectively, but the CGA cannot. (Contains 4 figures.)

This paper presents a new model for simulating procedural knowledge in the problem solving process with our ontological system, InfoMap. The method divides procedural knowledge into two parts: process control and action performer. By adopting InfoMap, we hope to help teachers construct curricula (declarative knowledge) and teaching strategies by capturing students' problem-solving processes (procedural knowledge) dynamically. Using the concept of declarative and procedural knowledge in intelligent tutoring systems, we can accumulate and duplicate the knowledge of the curriculum manager and student. (Contains 12 figures.)

Today, the various abilities that nurses require to meet patients' healthcare needs adequately are all affected by AI-enabled systems. This research used an experimental study design in which 60 subjects were randomly assigned to either an experimental (AI image e-book guidance) group or a control (text paper guidance) group after meeting the admission conditions and agreeing to participate in the study. It was proven that providing AI image e-book guidance before surgery significantly changed the behavior of patients and promoted relief of urinary catheter discomfort through self-efficacy to reduce urinary catheter pain after surgery (p < 0.001). It was found that providing AI image e-book guidance can shorten the time for health education and provide patients with repeated medical education and familiarity with health guidance, which can help to address the important clinical service demand issue and the shortage of nursing staff. [ABSTRACT FROM AUTHOR]

Wind energy has become a trend in Brazil, particularly in the northeastern region of the country. Despite its advantages, wind power generation has been hindered by the high volatility of exogenous factors, such as weather, temperature, and air humidity, making long-term forecasting a highly challenging task. Another issue is the need for reliable solutions, especially for large-scale wind farms, as this involves integrating specific optimization tools and restricted-access datasets collected locally at the power plants. Therefore, in this paper, the problem of forecasting the energy generated at the Praia Formosa wind farm, an eco-friendly park located in the state of Ceará, Brazil, which produces around 7% of the state's electricity, was addressed. To proceed with our data-driven analysis, publicly available data were collected from multiple Brazilian official sources, combining them into a unified database to perform exploratory data analysis and predictive modeling. Specifically, three machine-learning-based approaches were applied: Extreme Gradient Boosting, Random Forest, and Long Short-Term Memory Network, as well as feature-engineering strategies to enhance the precision of the machine intelligence models, including creating artificial features and tuning the hyperparameters. Our findings revealed that all implemented models successfully captured the energy-generation trends, patterns, and seasonality from the complex wind data. However, it was found that the LSTM-based model consistently outperformed the others, achieving a promising global MAPE of 4.55%, highlighting its accuracy in long-term wind energy forecasting. Temperature, relative humidity, and wind speed were identified as the key factors influencing electricity production, with peak generation typically occurring from August to November. [ABSTRACT FROM AUTHOR]

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Bankruptcy prediction is considered one of the most important research topics in the field of finance and accounting. The rapid increase of data science, artificial intelligence, and machine learning has led researchers to build an accurate bankruptcy prediction model. Recent studies show that ensemble methods perform better than traditional machine learning models for predicting corporate failure, especially with highly imbalanced datasets. However, the black box property of these techniques remains challenging to interpret the result and generate corporate classes without any explanation. To this end, we propose to build an accurate and interpretable classification model that generates a set of prediction rules for output. Tri-eXtreme Gradient Boosting (Tri-XGBoost), a semi-supervised technique, is recommended in this paper. The proposed method combines Borderline-Smote (BLSmote) based on Edited Nearest Neighbor (ENN) sampling techniques with three different XGBoost methods as weak classifiers (gbtree, gblinear, and dart). First, the resampling techniques are used to produce more representative synthetic data and balance the distribution of the datasets. To this end, BLSmote is applied to increase the proportion of instances in the minority class (bankrupt data). Then, ENN is used to eliminate the noisy samples from both classes. In addition, the most crucial features that increase predictive accuracy are chosen using XGBoost. Finally, in order to make the model more understandable for both applicants and experts, our result is presented as "IF–THEN" rules. Our proposed model is validated using the imbalanced Polish and Taiwan bankruptcy datasets. Our obtained results demonstrate that our suggested model performs better than the existing models based on the area under the ROC curve (AUC), F1-score, and G-mean performance measures. Our proposed model significantly improves classification accuracy, which is greater than 95% for Polish datasets and more than 93% for Taiwanese dataset in terms of AUC, G-mean and F1-score. [ABSTRACT FROM AUTHOR]

This paper proposes a new enterprise intelligentization framework, by making the transition from process transformation to artificial intelligence (AI) transformation. The novel transformation framework can be decomposed into the conceptual model of AI strategic planning, the procedural model, the operational model, and the analytics model. For leading-edge microchip production, a new AI transformation project regarding the reticle SMIF pod (RSP) transport system designed by a medium-sized semiconductor tool vendor in Taiwan is presented. The technical advantages, gained from the implementation of the presented AI transformation project, over the existing RSP systems are manifold. The throughput and yield rate significantly increase on a semiconductor-fabrication-plant basis. The clean room construction costs less by approximately 3 million dollars per FAB, mainly attributed to the redesigned automatic optical inspection flow. The proposed model-based framework proves to be a viable tool from the process transformation to the AI transformation in the semiconductor manufacturing. [ABSTRACT FROM AUTHOR]

Featured Application: This work will be materialized into the Bioimage Diagnostic system in Kaohsiung Branch of Chang Gung Hospital, Taiwan. Image recognition through the use of deep learning (DL) techniques has recently become a hot topic in many fields. Especially for bioimage informatics, DL-based image recognition has been successfully used in several applications, such as cancer and fracture detection. However, few previous studies have focused on detecting scaphoid fractures, and the related effectiveness is also not significant. Aimed at this issue, in this paper, we present a two-stage method for scaphoid fracture recognition by conducting an effectiveness analysis of numerous state-of-the-art artificial neural networks. In the first stage, the scaphoid bone is extracted from the radiograph using object detection techniques. Based on the object extracted, several convolutional neural networks (CNNs), with or without transfer learning, are utilized to recognize the segmented object. Finally, the analytical details on a real data set are given, in terms of various evaluation metrics, including sensitivity, specificity, precision, F1-score, area under the receiver operating curve (AUC), kappa, and accuracy. The experimental results reveal that the CNNs with transfer learning are more effective than those without transfer learning. Moreover, DenseNet201 and ResNet101 are found to be more promising than the other methods, on average. According to the experimental results, DenseNet201 and ResNet101 can be recommended as considerable solutions for scaphoid fracture detection within a bioimage diagnostic system. [ABSTRACT FROM AUTHOR]

This paper presents a teaching tool for schoolchildren to learn artificial intelligence (AI) technology through which a variety of banknotes can be recognized. This was done by first using a pretrained YOLOv3 object detection model. Secondly, transfer learning was conducted on the pretrained model using 11 collected banknotes, including US$, Euro, Japanese Yen, and NT$. The banknote detection model was experimentally validated to give an average precision (AP) of up to 99.09% if the threshold Intersection over Union (IoU) is not less than 0.8. Once a banknote was successfully recognized, the face value and the country name thereon were displayed, and schoolchildren can access suggested websites, i.e., Wikipedia, Google Maps, and the Bank of Taiwan, to learn more about the exchange rate between currencies and the history and location of the country that issued the banknote. Consequently, schoolchildren can have fun using this tool and acquire a more a global outlook. Moreover, they may be motivated to become AI professionals in the future. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper, because of the complexity of demand time series and the need to construct a more accurate hybrid model that can model all relationships in data, is to propose a parallel-series hybridization of seasonal neural networks and statistical models for demand time series forecasting. Design/methodology/approach: The main idea of proposed model is centered around combining parallel and series hybrid methodologies to use the benefit of unique advantages of both hybrid strategies as well as intelligent and classic seasonal time series models simultaneously for achieving results that are more accurate for the first time. In the proposed model, in contrast of traditional parallel and series hybrid strategies, it can be generally shown that the performance of the proposed model will not be worse than components. Findings: Empirical results of forecasting two well-known seasonal time series data sets, including the total production value of the Taiwan machinery industry and the sales volume of soft drinks, indicate that the proposed model can effectively improve the forecasting accuracy achieved by either of their components used in isolation. In addition, the proposed model can achieve more accurate results than parallel and series hybrid model with same components. Therefore, the proposed model can be used as an appropriate alternative model for seasonal time series forecasting, especially when higher forecasting accuracy is needed. Originality/value: To the best of the authors' knowledge, the proposed model, for first time and in contrast of traditional parallel and series hybrid strategies, is developed. [ABSTRACT FROM AUTHOR]

Research background: The global financial crisis from 2007 to 2012, the COVID-19 pandemic, and the current war in Ukraine have dramatically increased the risk of consumer bankruptcies worldwide. All three crises negatively impact the financial situation of households due to increased interest rates, inflation rates, volatile exchange rates, and other significant macroeconomic factors. Financial difficulties may arise when the private person is unable to maintain a habitual standard of living. This means that anyone can become financially vulnerable regardless of wealth or education level. Therefore, forecasting consumer bankruptcy risk has received increasing scientific and public attention. Purpose of the article: This study proposes artificial intelligence solutions to address the increased importance of the personal bankruptcy phenomenon and the growing need for reliable forecasting models. The objective of this paper is to develop six models for forecasting personal bankruptcies in Poland and Taiwan with the use of three soft-computing techniques. Methods: Six models were developed to forecast the risk of insolvency: three for Polish households and three for Taiwanese consumers, using fuzzy sets, genetic algorithms, and artificial neural networks. This research relied on four samples. Two were learning samples (one for each country), and two were testing samples, also one for each country separately. Both testing samples contain 500 bankrupt and 500 nonbankrupt households, while each learning sample consists of 100 insolvent and 100 solvent natural persons. Findings & value added: This study presents a solution for effective bankruptcy risk forecasting by implementing both highly effective and usable methods and proposes a new type of ratios that combine the evaluated consumers' financial and demographic characteristics. The usage of such ratios also improves the versatility of the presented models, as they are not denominated in monetary value or strictly in demographic units. This would be limited to use in only one country but can be widely used in other regions of the world. [ABSTRACT FROM AUTHOR]

Due to strong mobility, low costs, and easy parking, the scooter has become an important mode of transportation in Taiwan. The number of traffic accidents is also increasing because of drivers' bad driving habits. For road traffic injury prevention, the government has approved the road safety policy to promote awareness of road safety. Thus, we propose an AI-based driving assistance system (AI-DAS) for scooter riders to prevent traffic accidents. In the AI-DAS, Raspberry Pi controls the camera and sensors for the scooter. The AI-DAS detects pedestrians and monitors lanes not only to warn riders and pedestrians by sound and light but also to drastically reduce accidents and traffic jams. The AI-DAS is implemented using the python programming language and is tested on the Raspberry Pi hardware platform. [ABSTRACT FROM AUTHOR]

In recent years, the development and application of artificial intelligence have both been topics of concern. In the medical field, an important direction of medical technology development is the extraction and use of applicable information from existing medical records to provide more accurate and helpful diagnosis suggestions. Therefore, this paper proposes using the development of diseases with easily discernible symptoms to predict the development of other medically related but distinct diseases that lack similar data. The aim of this study is to improve the ease of assessing the development of diseases in which symptoms are difficult to detect, and to improve the utilization of medical data. First, a time series model was used to capture the continuous manifestations of diseases with symptoms that could be easily found at different time intervals. Then, through transfer learning and attention mechanism, the general features captured were applied to the predictive model of the development of diseases with insufficient data and symptoms that are difficult to detect. Finally, we conducted a comprehensive experimental study based on a dataset collected from the National Health Insurance Research Database in Taiwan. The results demonstrate that the effectiveness of our transfer learning approach outperforms state-of-the-art deep learning prediction models for disease course prediction. [ABSTRACT FROM AUTHOR]

Background: As the COVID-19 epidemic increases in severity, the burden of quarantine stations outside emergency departments (EDs) at hospitals is increasing daily. To address the high screening workload at quarantine stations, all staff members with medical licenses are required to work shifts in these stations. Therefore, it is necessary to simplify the workflow and decision-making process for physicians and surgeons from all subspecialties., Objective: The aim of this paper is to demonstrate how the National Cheng Kung University Hospital artificial intelligence (AI) trilogy of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm improves medical care and reduces quarantine processing times., Methods: This observational study on the emerging COVID-19 pandemic included 643 patients. An "AI trilogy" of diversion to a smart quarantine station, AI-assisted image interpretation, and a built-in clinical decision-making algorithm on a tablet computer was applied to shorten the quarantine survey process and reduce processing time during the COVID-19 pandemic., Results: The use of the AI trilogy facilitated the processing of suspected cases of COVID-19 with or without symptoms; also, travel, occupation, contact, and clustering histories were obtained with the tablet computer device. A separate AI-mode function that could quickly recognize pulmonary infiltrates on chest x-rays was merged into the smart clinical assisting system (SCAS), and this model was subsequently trained with COVID-19 pneumonia cases from the GitHub open source data set. The detection rates for posteroanterior and anteroposterior chest x-rays were 55/59 (93%) and 5/11 (45%), respectively. The SCAS algorithm was continuously adjusted based on updates to the Taiwan Centers for Disease Control public safety guidelines for faster clinical decision making. Our ex vivo study demonstrated the efficiency of disinfecting the tablet computer surface by wiping it twice with 75% alcohol sanitizer. To further analyze the impact of the AI application in the quarantine station, we subdivided the station group into groups with or without AI. Compared with the conventional ED (n=281), the survey time at the quarantine station (n=1520) was significantly shortened; the median survey time at the ED was 153 minutes (95% CI 108.5-205.0), vs 35 minutes at the quarantine station (95% CI 24-56; P<.001). Furthermore, the use of the AI application in the quarantine station reduced the survey time in the quarantine station; the median survey time without AI was 101 minutes (95% CI 40-153), vs 34 minutes (95% CI 24-53) with AI in the quarantine station (P<.001)., Conclusions: The AI trilogy improved our medical care workflow by shortening the quarantine survey process and reducing the processing time, which is especially important during an emerging infectious disease epidemic., (©Ping-Yen Liu, Yi-Shan Tsai, Po-Lin Chen, Huey-Pin Tsai, Ling-Wei Hsu, Chi-Shiang Wang, Nan-Yao Lee, Mu-Shiang Huang, Yun-Chiao Wu, Wen-Chien Ko, Yi-Ching Yang, Jung-Hsien Chiang, Meng-Ru Shen. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 14.10.2020.)

Knowledge graphs are useful sources for various AI applications, however the basic paradigm to support pilot training is still unclear. In the paper, It is proposed to generate the customized knowledge graph of flight trainings using machine learning method for the flight training program. In order to provide the successful key to the further understanding of the learning problems between the students and the instructors. In this research, we collected data from an aeronautical academic in Taiwan that students were trained for Recreation Pilot License Program. We performed a test on 24 students at the first of each training course, 16 data of collected been used on building the module, 8 of them used to exam the module. There are 12 courses in the training program, and 30 hours total time were suggested by academic. The score which we applied on test were based on LCG method which is the sum of Maneuver and SRM Grades. For the indicators of course component in Learner Centered Grading, namely (a) CCS1: Operation & Effect of Controls; (b) CCS2: Straight & Level; (c) CCS3: Climbing & Descending; (d) CCS4: Turning; (e) CCS5: Stalling; (f) CCS6: Revision; (g) CCS7: Circuits; (h) CCS8: Cross-Wind Training; (i) CCS9: Circuit Emergency; (j) CCS10: Solo Circuit; (k) CCS11: Forced Landing; and (l) CCS12: Precautionary & Searching Landing. Through the method of Knowledge Graph, we deduct and predict the number of hours that need to be added for each student's learning. Using the dynamic knowledge graph to display the key issues of the course learning continuously, and make follow-up decisions for the students, instructors and airliners. [ABSTRACT FROM AUTHOR]

In this paper, we study the forest harvesting problem (FHP). A forest is assumed to be divided into several identical square units, and each unit has its harvesting value based on its type. Harvesting a unit will affect the growth and values of its neighboring units. In this FHP, the best harvesting plan of a unit must be identified to maximize three various objectives simultaneously. The FHP is a multiobjective mathematical and an NP-hard problem. We apply three artificial intelligence algorithms, namely, immune algorithm, genetic algorithm, and particle swarm optimization, for maximizing the weighted objective to solve the FHP. We also solve the following two sets of test problems: (i) a set of randomly generated FHP problems and (ii) a practical problem in Taiwan. Numerical results show the performance of the three algorithms for the test problems. Finally, we compare and discuss the effects of various weights for the three objectives. [ABSTRACT FROM AUTHOR]

Automated clinical decision support systems (CDSS) are knowledge-based systems that provide patient-specific information and data to clinicians at the proper time for enhancing the clinical workflow of hospital organizations. Nowadays, it is adopted by most of the health care professionals for clinical decision-making that helps to reduce the adverse clinical care events occurring during the treatment. In this article, we present a recent literature review on the adoption of computer-based CDSSs in the area of health care based on qualitative and quantitative techniques, published between 2007 and 2022. For this purpose, we searched Google Scholar and identified different adoption factors by using textual analysis from the included publications. We then ranked the different factors based on the total number of occurrences and represented them as a conceptual framework. A total of 14 different adoption factors were found from 13 studies, among them the usefulness of the system is the most prominent factor that influences the adoption of CDSS to a great extent. This literature review and the framework could be helpful to researchers and healthcare professionals working in the field of technology adoption, providing an overall idea of factors and techniques in this field of research. We have also mentioned the limitations and future research gaps of different studies, which will help the researchers to take an initiation towards these types of research. We also conducted a case study on adoption of fully automatic digital blood pressure monitor and identified that "usefulness" and "ease of use" could influence the adoption of fully automatic digital blood pressure monitor system. [ABSTRACT FROM AUTHOR]

The techniques pertaining to soft computing are the base for commencement, model and organization of intelligent systems in order to offer more perfect, economical and realistic solution which has minimal complexity levels. The concept of intelligent systems has for long supported objectives on sustainability, improvisation of efficiency and symbolized various kinds of activities like creation of jobs, earning profits, providing services and improvement in capacities in ICT. The applications based on this system are widespread in the technological market because of massive development in grid, cloud, mobile and big data applications and its corresponding connectivity advantages. ICT triggers data scientists to satisfy technical-based demands of intelligent systems around data analytics and big data applications. The major features of big data like that of reservation of designs on information and knowledge have offered the public undertaking an opportunity to enhance production levels, improved efficiency and its effectiveness. The main objective of this paper is to enhance the accuracy of predictive modelling using an optimized predictive modelling based on rough set (RS) and Elman neural network (ElNN). These advanced predictive models are designed on the basis of RS approach in initial stages and in later processes enhanced with the support of Elman-NN. RS has an excellent feature selection capability, and Elman-NN is the best at nonlinear system modelling. By integrating them, the proposed method can limit the input dimension and optimize the structure for ElNN modelling. This can reduce the mathematical computation complexity with the progress of predictive models. The experimental results indicate that through RS feature selection and the structure of Elman-NN, the predictive model can be simplified significantly with enhanced model performance. The predictive accuracy of data sets, namely air quality in Northern Taiwan, hazardous air pollutants, historical hourly weather data and US pollutants through optimization, is above 99%, and this model proves that the results of optimized predictive error are far better than those obtained by other neural networks like PCA-RBF, PCA-NN, FFNN-BP with PCA, MLR, FFNN-BP, ELM, SOM, RBF and ART2. [ABSTRACT FROM AUTHOR]

With Taiwan now an "aged society", home safety for older individuals has become a very important issue. The purpose of establishing early warning systems in homes and/or communities is to generate and disseminate meaningful warning information to medical institutions or rescue units in a timely manner so that they may take timely and appropriate action. The main purpose of this paper is to introduce the current application of information and communication technology (ICT, especially the Internet of Things and artificial intelligence) in early warning systems for home and community care. Two approaches to developing these systems are introduced: instant detection and prevention monitoring. Instant detection facilitates fall detection and personnel tracking, while the focus of prevention monitoring is on preventing falls and physiological status monitoring. The challenges faced by in incorporating ICT into these early monitoring systems are discussed as well. [ABSTRACT FROM AUTHOR]

Objective. The Fugl-Meyer assessment for upper extremity (FMA-UE) is a measure for assessing upper extremity motor function in patients with stroke. However, the considerable administration time of the assessment decreases its feasibility. This study aimed to develop an accumulative assessment system of upper extremity motor function (AAS-UE) based on the FMA-UE to improve administrative efficiency while retaining sufficient psychometric properties. Methods. The study used secondary data from 3 previous studies having FMA-UE datasets, including 2 follow-up studies for subacute stroke individuals and 1 test-retest study for individuals with chronic stroke. The AAS-UE adopted deep learning algorithms to use patients' prior information (ie, the FMA-UE scores in previous assessments, time interval of adjacent assessments, and chronicity of stroke) to select a short and personalized item set for the following assessment items and reproduce their FMA-UE scores. Results. Our data included a total of 682 patients after stroke. The AAS-UE administered 10 different items for each patient. The AAS-UE demonstrated good concurrent validity (r =0.97-0.99 with the FMA-UE), high test-retest reliability (intra-class correlation coefficient=0.96), low random measurement error (percentage of minimal detectable change=15.6%), good group-level responsiveness (standardized response mean=0.65-1.07), and good individual-level responsiveness (30.5%-53.2% of patients showed significant improvement). These psychometric properties were comparable to those of the FMA-UE. Conclusion. The AAS-UE uses an innovative assessment method, which makes good use of patients' prior information to achieve administrative efficiency with good psychometric properties. Impact. This study demonstrates a new assessment method to improve administrative efficiency while retaining psychometric properties, especially individual-level responsiveness and random measurement error, by making good use of patients' basic information and medical records. [ABSTRACT FROM AUTHOR]

This paper proposes an Adaptive Fuzzy Neural Agent (AFNA) with a Patch Learning Mechanism and IEEE 1855 Fuzzy Markup Language (FML) for human and machine co-learning. There are three phases of patch learning mechanism embedded in AFNA, including (1) training an initial global model, (2) training a patch model for each identified patch, and (3) updating the global model using the training data that do not fall into any patch. The AFNA can be applied to construct the student and robot co-learning regression model, as well as the regression model for the dataset retrieved from the game of Go. First, students generate human learning data through interactions with handheld devices or robots based on the AFNA in Taiwan and Japan. Then, the AFNA utilizes the student learning data collected in the classroom and the Go game data provided by both Google DeepMind and Facebook AI Research open-source OpenGo to train the Fuzzy Machine-Learning Model. In addition, the trained Fuzzy Machine-Learning Model of AFNA is deployed to the robots to make students and machines co-learn together based on IEEE 1855 FML. The experiments show that the AFNA with Patch Learning Mechanism and Fuzzy Machine-Learning Model can improve the performance of regression model based on the datasets of student learning and Go game. In the future, we hope to apply the AFNA with robots to the other domain areas, embed it with the Artificial Intelligence of Things devices, and introduce it to more teaching fields in various countries. [ABSTRACT FROM AUTHOR]

In an era where artificial intelligence (AI) bridges crucial communication gaps, this study extends AI's utility to American and Taiwan Sign Language (ASL and TSL) communities through advanced models like the hierarchical vision transformer with shifted windows (Swin). This research evaluates Swin's adaptability across sign languages, aiming for a universal platform for the unvoiced. Utilizing deep learning and transformer technologies, it has developed prototypes for ASL-to-English translation, supported by an educational framework to facilitate learning and comprehension, with the intention to include more languages in the future. This study highlights the efficacy of the Swin model, along with other models such as the vision transformer with deformable attention (DAT), ResNet-50, and VGG-16, in ASL recognition. The Swin model's accuracy across various datasets underscore its potential. Additionally, this research explores the challenges of balancing accuracy with the need for real-time, portable language recognition capabilities and introduces the use of cutting-edge transformer models like Swin, DAT, and video Swin transformers for diverse datasets in sign language recognition. This study explores the integration of multimodality and large language models (LLMs) to promote global inclusivity. Future efforts will focus on enhancing these models and expanding their linguistic reach, with an emphasis on real-time translation applications and educational frameworks. These achievements not only advance the technology of sign language recognition but also provide more effective communication tools for the deaf and hard-of-hearing community. [ABSTRACT FROM AUTHOR]

Introduction: This study used artificial intelligence and strategic management to predict sports industry development in Taiwan. Through public government information, we aimed to determine the dynamics of the sports industry and analyze the current situation of domestic sports industries and sports industry sectors. We also explored national sports policies and development issues to construct a Taiwan Sports Industry Gross Domestic Product Forecasting Model that aligns with the requirements of government agencies and the sports industry. Methods: This study explored time series data for 12 sports industries and 40 sports industry sectors from 2009 to 2018. We used artificial neural networks to predict future gross domestic product (GDP). We also used the moving average method to calculate the trend direction and used strategic management and brand positioning analysis to analyze industry positioning and trend development. Results: Only one sports industry, Sports Venue or Facility Construction, had an optimistic forecast for growth, while the GDP of the remaining 11 sports industries was predicted to decline. The average GDP forecasting accuracy for the sports industry was 94% in 2017 and 97.2% in 2018. Among the 40 sports industry sectors, eight had growth forecasts, while the GDP of the other 32 sectors was predicted to decline. The average GDP forecasting accuracy for sports industry sectors was 99.5% in 2017 and 99.4% in 2018. In the strategic matrix analysis, all 12 sports industries showed a growth trend. All sports industries showed a consistent upward trend in the GDP time series, indicating overall continuous growth in the sports industry. Similarly, among the 40 sports industry sectors, the Star and Cash Cow sectors showed growth trends and trend lines in the strategic positioning matrix and GDP time series. In contrast, the Question Marks and Dogs sectors showed declining trends and trend lines. Conclusion: This study makes six specific recommendations: (1) Seek solutions for the sports industry through prediction and estimation, (2) use strategic management to chart the course for developing the sports industry, (3) explore and propose future trends in the sports industry, (4) explore and propose future trends in the various sports industry sectors, (5) define the scope of the sports industry and address significant data management and its implications, and (6) foster research and development in technology management and its academic applications. These recommendations aim to provide valuable insights and guidance for the advancement and strategic planning of the sports industry, considering future trends and technological advances. [ABSTRACT FROM AUTHOR]

Seismic early warning has been very important and has become feasible in Taiwan. Perhaps because of the lack of quick and reliable estimations of the induced structural response, however, the triggering criteria of almost all of the existing earthquake protection or early warning systems in the world are merely based on the collected or estimated data of the ground motion, without any information regarding the structural response. This paper presents a methodology of generating quick seismic response estimations of a prestressed concrete (PC) bridge using artificial neural networks (ANNs), which may be incorporated in a seismic early warning system for the bridge. In the methodology ANNs were applied to model the critical structural response of a PC bridge subjected to earthquake excitation of various magnitudes along various directions. The objective was to implement a well-trained network that is capable of providing a quick prediction for the critical response of the target bridge. The well-known multilayer perception (MLP) networks with back propagation algorithm were employed. A simple augmented form of MLP that can be quantitatively determined was proposed. These networks were trained and tested based on the analytical data obtained from the nonlinear dynamic finite fiber element analyses of the target PC bridge. The augmented MLPs were found to be much more efficient than the MLPs in modeling the critical bending moments of the piers and girder of the PC bridge. [ABSTRACT FROM AUTHOR]

Up until now montane cloud forest (MCF) in Taiwan has only been mapped for selected areas of vegetation plots. This paper presents the first comprehensive map of MCF distribution for the entire island. For its creation, a Random Forest model was trained with vegetation plots from the National Vegetation Database of Taiwan that were classified as “MCF” or “non-MCF”. This model predicted the distribution of MCF from a raster data set of parameters derived from a digital elevation model (DEM), Landsat channels and texture measures derived from them as well as ground fog frequency data derived from the Moderate Resolution Imaging Spectroradiometer. While the DEM parameters and Landsat data predicted much of the cloud forest’s location, local deviations in the altitudinal distribution of MCF linked to the monsoonal influence as well as the Massenerhebung effect (causing MCF in atypically low altitudes) were only captured once fog frequency data was included. Therefore, our study suggests that ground fog data are most useful for accurately mapping MCF. [ABSTRACT FROM AUTHOR]

Over the past century, conscientiousness has become seen as the preeminent trait for predicting performance. This consensus is due in part to these employees' ability to work with traditional 20th-century technology. Such pairings balance the systematic nature of conscientious employees with the technology's need for user input and direction to perform tasks--resulting in a complementary match. However, the 21st century has seen the incorporation of intelligent machines (e.g., artificial intelligence, robots, and algorithms) into employees' jobs. Unlike traditional technology, these new machines are equipped with the capability to make decisions autonomously. Thus, their nature overlaps with the orderliness subdimension of conscientious employees--resulting in a non-complementary mismatch. This calls into question whether the consensus about conscientious employees' effectiveness with 20th-century technology applies to 21st-century jobs. Integrating complementarity and role theory, we refine this consensus. Across three studies using distinct samples (an experience sampling study, a field experiment, and an online experiment from working adults in Malaysia, Taiwan, and the United States), each focused on a different type of intelligent machine, we show not only that using intelligent machines has benefits and consequences, but, importantly, that conscientious (i.e., orderly) employees are less likely to benefit from working with them. [ABSTRACT FROM AUTHOR]

The application of artificial intelligence (AI) to health has increased, including to COVID-19. This study aimed to provide a clear overview of COVID-19-related AI publication trends using longitudinal bibliometric analysis. A systematic literature search was conducted on the Web of Science for English language peer-reviewed articles related to AI application to COVID-19. A search strategy was developed to collect relevant articles and extracted bibliographic information (e.g., country, research area, sources, and author). VOSviewer (Leiden University) and Bibliometrix (R package) were used to visualize the co-occurrence networks of authors, sources, countries, institutions, global collaborations, citations, co-citations, and keywords. We included 729 research articles on the application of AI to COVID-19 published between 2020 and 2021. PLOS One (33/729, 4.52%), Chaos Solution Fractals (29/729, 3.97%), and Journal of Medical Internet Research (29/729, 3.97%) were the most common journals publishing these articles. The Republic of China (190/729, 26.06%), the USA (173/729, 23.73%), and India (92/729, 12.62%) were the most prolific countries of origin. The Huazhong University of Science and Technology, Wuhan University, and the Chinese Academy of Sciences were the most productive institutions. This is the first study to show a comprehensive picture of the global efforts to address COVID-19 using AI. The findings of this study also provide insights and research directions for academic researchers, policymakers, and healthcare practitioners who wish to collaborate in these domains in the future. [ABSTRACT FROM AUTHOR]

Frequent road inspections are key to maintaining road quality and avoiding casualties associated with poor road conditions. In Taiwan, open contractors conduct inspections of roads and ancillary facilities daily or weekly according to the requirements of the agency awarding the contracts. Unfortunately, the equipment used for inspections the inspection data lacks follow-up applications and numerical conversions, such as the Pavement Condition Index (PCI), to compile a large-scale database to facilitate the long-term conservation of roads. In this study, this paper developed back-end image recognition software using existing road inspection methods and existing equipment. This was aimed at enhancing inspection efficiency by enabling the automatic identification of road damage. Resulting observations can then be converted into PCI values in accordance with ASTM D6433-16 to be exported as a numeric value indicative of road quality. A vehicle-mounted traffic recorder and imaging device with Wi-Fi transmission capability are used as hardware, and the relationship between the captured images and the speed of the car is used to obtain an accurate indication of road conditions across the surface. The simple linear iterative clustering (SLIC) Superpixels algorithm is used to identify areas with pavement damage as patches, potholes, longitudinal cracking, and crocodile cracking. The results of the proposed fully-automated method conform strongly with those obtained using semi-automated pavement inspection software. Despite the restrictions imposed by the limited depth measurement of 2D images, our method achieved results close to those obtained using manual inspection. Future developments will include the application of artificial intelligence to enhance the effectiveness of this software. [ABSTRACT FROM AUTHOR]

(1) Background: AI (artificial intelligence) chatbots have been widely applied. ChatGPT could enhance individual learning capabilities and clinical reasoning skills and facilitate students' understanding of complex concepts in healthcare education. There is currently less emphasis on its application in nursing education. The application of ChatGPT in nursing education needs to be verified. (2) Methods: A descriptive study was used to analyze the scores of ChatGPT on the registered nurse license exam (RNLE) in 2022~2023, and to explore the response and explanations of ChatGPT. The process of data measurement encompassed input sourcing, encoding methods, and statistical analysis. (3) Results: ChatGPT promptly responded within seconds. The average score of four exams was around 51.6 to 63.75 by ChatGPT, and it passed the RNLE in 2022 1st and 2023 2nd. However, ChatGPT may generate misleading or inaccurate explanations, or it could lead to hallucination; confusion or misunderstanding about complicated scenarios; and languages bias. (4) Conclusions: ChatGPT may have the potential to assist with nursing education because of its advantages. It is recommended to integrate ChatGPT into different nursing courses, to assess its limitations and effectiveness through a variety of tools and methods. [ABSTRACT FROM AUTHOR]

In recent years, initiatives and the resulting application of precision education have been applied with increasing frequency in Taiwan; the accompanying discourse has focused on identifying potential applications for artificial intelligence and how to use learning analytics to improve teaching quality and learning outcomes. This study used the established dropout risk prediction model to improve student learning effectiveness. The model was based on the academic portfolios of past students and built with statistical learning and deep learning methods. This study used this model to predict the dropout risk of 2205 freshmen enrolled in the fall semester of 2018 (graduated in June 2022) in the field of sustainable education. A total of 176 students with a dropout risk of more than 20% were considered high-risk students. After tracking and the appropriate guidance, the dropout risk of 91 students fell from > 20% to < 20%. To discuss the results from the perspective of gender and financial disadvantages, the improvement rate of the dropout risk for male students was 10.2% better than that of female students at 2.9%. The improvement rate in dropout risk for students with disadvantageous financial situations was as high as 12.0%, surpassing the 5.9% rate among general students. Overall, the dropout rate in the second year of the 2018 freshman cohort was lower than that of the 2016 and 2017 freshman cohorts. A predictive model established by statistical learning and deep learning methods was used as a tool to promote precision education, accurately and efficiently identifying students who are having difficulty learning, as well as leading to a better understanding of AI (artificial intelligence) in smart learning for sustainable education. [ABSTRACT FROM AUTHOR]

Background: Aging leads to changes in the body system, such as sarcopenia. This can result in several health issues, particularly physical and mobility dysfunction. Asian people typically have little awareness of sarcopenia. Thus, this study incorporated nursing instruction into the mobile application design to allow users to easily learn about sarcopenia. Objective: This study evaluated a model for predicting high-risk populations for sarcopenia in home settings. We further developed a sarcopenia nursing guidance mobile application and assessed the effectiveness of this application in influencing sarcopenia-related knowledge and self-care awareness among participants. Methods: Using a one-group pretest–posttest design, data were collected from 120 participants at a teaching hospital in northern Taiwan. This study used an artificial intelligence algorithm to evaluate a model for predicting high-risk populations for sarcopenia. We developed and assessed the sarcopenia nursing guidance mobile application using a questionnaire based on the Mobile Application Rating Scale. Results: The application developed in this study enhanced participants' sarcopenia-related knowledge and awareness regarding self-care. After the three-month intervention, the knowledge and awareness was effectively increase, total score was from 4.15 ± 2.35 to 6.65 ± 0.85 and were significant for all questionnaire items (p values < 0.05). On average, 96.1% of the participants were satisfied with the mobile app. The artificial intelligence algorithm positively evaluated the home-use model for predicting high-risk sarcopenia groups. Conclusions: The mobile application of the sarcopenia nursing guidance for public use in home settings may help alleviate sarcopenia symptoms and reduce complications by enhancing individuals' self-care awareness and ability. Trial registration: NCT05363033, registered on 02/05/2022. [ABSTRACT FROM AUTHOR]

To improve students' learning performance through review learning activities, we developed a personalized intervention tutoring approach that leverages learning analysis based on artificial intelligence. The proposed intervention first uses text-processing artificial intelligence technologies, namely bidirectional encoder representations from transformers and generative pretrained transformer-2, to automatically generate Python programming remedial materials; subsequently, learning performance prediction models constructed using various machine learning methods are used to determine students' learning type, enabling the automatic generation of personalized remedial materials. The participants in this study were 78 students from a university in northern Taiwan enrolled in an 8-week Python course. Students in the experimental (n = 36) and control (n = 42) groups engaged in the same programming learning activities during the first 5 weeks of the course, and they completed a pretest of programming knowledge in Week 6. For the review activity in Week 7, the 36 students in the experimental group received personalized intervention, whereas those in the control group received traditional class tutoring. We examined the effect of the self-regulated learning and personalized intervention on the learning performance of students. Compared with the traditional class tutoring, the personalized intervention review activity not only helped students obtain higher learning performance but also prompted greater improvements in the following learning strategies: rehearsal, critical thinking, metacognitive self-regulation, effort regulation, and peer learning. We also observed that students' rehearsal and help-seeking learning strategies indirectly affected learning performance through students' note-taking in the provided e-book. [ABSTRACT FROM AUTHOR]

Aquaculture has long been a critical economic sector in Taiwan. Since a key factor in aquaculture production efficiency is water quality, an effective means of monitoring the dissolved oxygen content (DOC) of aquaculture water is essential. This study developed an internet of things system for monitoring DOC by collecting essential data related to water quality. Artificial intelligence technology was used to construct a water quality prediction model for use in a complete system for managing water quality. Since aquaculture water quality depends on a continuous interaction among multiple factors, and the current state is correlated with the previous state, a model with time series is required. Therefore, this study used recurrent neural networks (RNNs) with sequential characteristics. Commonly used RNNs such as long short-term memory model and gated recurrent unit (GRU) model have a memory function that appropriately retains previous results for use in processing current results. To construct a suitable RNN model, this study used Taguchi method to optimize hyperparameters (including hidden layer neuron count, iteration count, batch size, learning rate, and dropout ratio). Additionally, optimization performance was also compared between 5-layer and 7-layer network architectures. The experimental results revealed that the 7-layer GRU was more suitable for the application considered in this study. The values obtained in tests of prediction performance were mean absolute percentage error of 3.7134%, root mean square error of 0.0638, and R-value of 0.9984. Therefore, thewater qualitymanagement system developed in this study can quickly provide practitioners with highly accurate data, which is essential for a timely response to water quality issues. This study was performed in collaboration with the Taiwan Industrial Technology Research Institute and a local fishery company. Practical application of the system by the fishery company confirmed that the monitoring system is effective in improving the survival rate of farmed fish by providing data needed to maintain DOC higher than the standard value. [ABSTRACT FROM AUTHOR]

The clinical symptoms of metabolic disorders are rarely apparent during the neonatal period, and if they are not treated earlier, irreversible damages, such as mental retardation or even death, may occur. Therefore, the practice of newborn screening is essential to prevent permanent disabilities in newborns. In the paper, we design, implement a newborn screening system using Support Vector Machine (SVM) classifications. By evaluating metabolic substances data collected from tandem mass spectrometry (MS/MS), we can interpret and determine whether a newborn has a metabolic disorder. In addition, National Taiwan University Hospital Information System (NTUHIS) has been developed and implemented to integrate heterogeneous platforms, protocols, databases as well as applications. To expedite adapting the diversities, we deploy Service-Oriented Architecture (SOA) concepts to the newborn screening system based on web services. The system can be embedded seamlessly into NTUHIS.

In this paper, we present a new method to predict the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX) based on fuzzy-trend logical relationship groups (FTLRGs). The proposed method divides fuzzy logical relationships into FTLRGs based on the trend of adjacent fuzzy sets appearing in the antecedents of fuzzy logical relationships. First, we apply an automatic clustering algorithm to cluster the historical data into intervals of different lengths. Then, we define fuzzy sets based on these intervals of different lengths. Then, the historical data are fuzzified into fuzzy sets to derive fuzzy logical relationships. Then, we divide the fuzzy logical relationships into FTLRGs for forecasting the TAIEX. Moreover, we also apply the proposed method to forecast the enrollments and the inventory demand, respectively. The experimental results show that the proposed method gets higher average forecasting accuracy rates than the existing methods.