The Use of Children＇s Literature With University English as a Foreign Language Learners in Taiwan

This paper reports findings from a longitudinal qualitative study that investigated the use of children＇s literature for Taiwan Residents University English as a Foreign Language （EFL） students＇ reading. During the course of their sophomore year, 17 students participated and each student held two to seven individual reading sessions, to which they brought a self-selected children＇s picture storybook or children＇s novel they had finished reading on their own and orally read it to the researcher. Their oral reading and the discussion of each book with the researcher were audio recorded. To gain insight into the reading progress, these oral data were categorized and analyzed in terms of mispronunciation patterns, misunderstanding of vocabulary, misinterpretation of sentence or passage, and researcher＇s guidance. General findings of the 17 participants were presented in three categories： （1） vocabulary acquisition, （2） common comprehension problems, and （3） common pronunciation problems. Further analysis of two motivated students who read five to seven books revealed that （1） these two EFL learners gradually developed conscious awareness of their own pronunciation and comprehension errors and （2） they progressively acquired better competence to apply the pronunciation tips and reading comprehension techniques provided by the researcher during previous sessions. These findings and corresponding implications are discussed and further research suggestions are made.

Coupling Analysis of Multiple Machine Learning Models for Human Activity Recognition

Artificial intelligence(AI)technology has become integral in the realm of medicine and healthcare,particularly in human activity recognition(HAR)applications such as fitness and rehabilitation tracking.This study introduces a robust coupling analysis framework that integrates four AI-enabled models,combining both machine learning(ML)and deep learning(DL)approaches to evaluate their effectiveness in HAR.The analytical dataset comprises 561 features sourced from the UCI-HAR database,forming the foundation for training the models.Additionally,the MHEALTH database is employed to replicate the modeling process for comparative purposes,while inclusion of the WISDM database,renowned for its challenging features,supports the framework’s resilience and adaptability.The ML-based models employ the methodologies including adaptive neuro-fuzzy inference system(ANFIS),support vector machine(SVM),and random forest(RF),for data training.In contrast,a DL-based model utilizes one-dimensional convolution neural network(1dCNN)to automate feature extraction.Furthermore,the recursive feature elimination(RFE)algorithm,which drives an ML-based estimator to eliminate low-participation features,helps identify the optimal features for enhancing model performance.The best accuracies of the ANFIS,SVM,RF,and 1dCNN models with meticulous featuring process achieve around 90%,96%,91%,and 93%,respectively.Comparative analysis using the MHEALTH dataset showcases the 1dCNN model’s remarkable perfect accuracy(100%),while the RF,SVM,and ANFIS models equipped with selected features achieve accuracies of 99.8%,99.7%,and 96.5%,respectively.Finally,when applied to the WISDM dataset,the DL-based and ML-based models attain accuracies of 91.4%and 87.3%,respectively,aligning with prior research findings.In conclusion,the proposed framework yields HAR models with commendable performance metrics,exhibiting its suitability for integration into the healthcare services system through AI-driven applications.

Low-Brightness Object Recognition Based on Deep Learning

This research focuses on addressing the challenges associated with image detection in low-light environments,particularly by applying artificial intelligence techniques to machine vision and object recognition systems.The primary goal is to tackle issues related to recognizing objects with low brightness levels.In this study,the Intel RealSense Lidar Camera L515 is used to simultaneously capture color information and 16-bit depth information images.The detection scenarios are categorized into normal brightness and low brightness situations.When the system determines a normal brightness environment,normal brightness images are recognized using deep learning methods.In low-brightness situations,three methods are proposed for recognition.The first method is the SegmentationwithDepth image(SD)methodwhich involves segmenting the depth image,creating amask from the segmented depth image,mapping the obtained mask onto the true color(RGB)image to obtain a backgroundreduced RGB image,and recognizing the segmented image.The second method is theHDVmethod(hue,depth,value)which combines RGB images converted to HSV images(hue,saturation,value)with depth images D to form HDV images for recognition.The third method is the HSD(hue,saturation,depth)method which similarly combines RGB images converted to HSV images with depth images D to form HSD images for recognition.In experimental results,in normal brightness environments,the average recognition rate obtained using image recognition methods is 91%.For low-brightness environments,using the SD method with original images for training and segmented images for recognition achieves an average recognition rate of over 82%.TheHDVmethod achieves an average recognition rate of over 70%,while the HSD method achieves an average recognition rate of over 84%.The HSD method allows for a quick and convenient low-light object recognition system.This research outcome can be applied to nighttime surveillance systems or nighttime road safety systems.

Identifying Brand Consistency by Product Differentiation Using CNN

This paper presents a new method of using a convolutional neural network(CNN)in machine learning to identify brand consistency by product appearance variation.In Experiment 1,we collected fifty mouse devices from the past thirty-five years from a renowned company to build a dataset consisting of product pictures with pre-defined design features of their appearance and functions.Results show that it is a challenge to distinguish periods for the subtle evolution of themouse devices with such traditionalmethods as time series analysis and principal component analysis(PCA).In Experiment 2,we applied deep learning to predict the extent to which the product appearance variation ofmouse devices of various brands.The investigation collected 6,042 images ofmouse devices and divided theminto the Early Stage and the Late Stage.Results show the highest accuracy of 81.4%with the CNNmodel,and the evaluation score of brand style consistency is 0.36,implying that the brand consistency score converted by the CNN accuracy rate is not always perfect in the real world.The relationship between product appearance variation,brand style consistency,and evaluation score is beneficial for predicting new product styles and future product style roadmaps.In addition,the CNN heat maps highlight the critical areas of design features of different styles,providing alternative clues related to the blurred boundary.The study provides insights into practical problems for designers,manufacturers,and marketers in product design.It not only contributes to the scientific understanding of design development,but also provides industry professionals with practical tools and methods to improve the design process and maintain brand consistency.Designers can use these techniques to find features that influence brand style.Then,capture these features as innovative design elements and maintain core brand values.

Corporate Social Responsibility and Shipping Supply Chain Risks

This study aims to investigate whether Corporate Social Responsibility(CSR)activities reduce supply chain disruptions by examining the impact of the Suez Canal obstruction on the Ever Given container ship in March 2021.This study conclude that the more responsible companies have higher returns and are less affected by this event than the less responsible companies;the less responsible companies have lower returns.The companies with better CSR have a lower impact on their supply chains when faced with disruptions in the supply chain.

Developing a Risk Assessment Model for Typhoon-triggered Debris Flows

A methodology is developed for interactive risk assessment of physical infrastructure and spatially distributed response systems subjected to debris flows.The proposed framework is composed of three components,namely geotechnical engineering,geographical information systems and disaster management.With the integration of slope stability analysis,hazard scenario and susceptibility,geological conditions are considered as temporary static data,while meteorological conditions are treated as dynamic data with a focus on typhoons.In this research,the relevant parameters required for database building are defined,and the procedures for building the geological database and meteorological data sets are explained.Based on the concepts and data sets,Nantou and Hualien in Taiwan are used as the areas for case studies.

A Unified Framework of the Cloud Computing Service Model

After a comprehensive literature review and analysis, a unified cloud computing framework is proposed, which comprises MapReduce, a vertual machine, Hadoop distributed file system （HDFS）, Hbase, Hadoop, and virtualization. This study also compares Microsoft, Trend Micro, and the proposed unified cloud computing architecture to show that the proposed unified framework of the cloud computing service model is comprehensive and appropriate for the current complexities of businesses. The findings of this study can contribute to the knowledge for academics and practitioners to understand, assess, and analyze a cloud computing service application.

Analysis and Simulation of a High Performance Quasi Resonant PWM Inverter for Solar Power Generation

A quasi resonant pulse width modulation（PWM） inverter is used in a solar power system to convert the solar panel and battery charger＇s direct current（DC） output to alternating current（AC）.Although much has been published about DC to AC PWM inverters,none of the previous work has shown modeling and simulation results for DC to AC inverters.In this study,we suggest a new topology for a quasi resonant PWM inverter.Experimental results are also presented.

Processing Chinese hand-radicals activates the medial frontal gyrus A functional MRI investigation

Embodied semantics theory asserts that the meaning of action-related words is neurally represented through networks that overlap with or are identical to networks involved in sensory-motor processing. While some studies supporting this theory have focused on Chinese characters, less attention has been paid to their semantic radicals. Indeed, there is still disagreement about whether these radicals are processed independently. The present study investigated whether radicals are processed separately and, if so, whether this processing occurs in sensory-motor regions. Materials consisted of 72 high-frequency Chinese characters, with 18 in each of four categories： hand-action verbs with and without hand-radicals, and verbs not related to hand actions, with and without hand-radicals. Twenty-eight participants underwent functional MRI scans while reading the characters. Compared to characters without hand-radicals, reading characters with hand-radicals activated the right medial frontal gyrus. Verbs involving hand-action activated the left inferior parietal lobule, possibly reflecting integration of information in the radical with the semantic meaning of the verb. The findings may be consistent with embodied semantics theory and suggest that neural representation of radicals is indispensable in processing Chinese characters.

An Evolution, Present, and Future Changes of Cloud Computing Services

This study presents a clear evolution of computing and its key applications. Cloud computing services evolved from distributed, grid, and utility computing. Critical companies such as Salesforce,Amazon, Google, and Microsoft play important roles in cloud computing. Dramatic changes in the technology environment have created new challenges for current information technologies. This study discusses four significant challenges for cloud computing services,including the next-generation Internet, data synchronization, cloud security, and competitive advantages.And then it also discusses how managers can learn about the future of cloud computing services.

Design of Power Switching and Distribution Circuits for Solar Power Generation

Abstract--With the development of clean energy, switching and distribution issues in a photovoltaic system are getting much attention in recent years. This paper designs a DC to AC inverter and power switching and distribution system between a solar power system and the municipal system by using the Darlington amplifier structure with the photosensitive resistor and accompanying relays, and details the system circuits. The proposed system can achieve a stable output of IIOV AC, as well as self-generating driving voltage and switching between the municipal electrical system and the solar power system. The mathematic analysis and actually test results demonstrate that the proposed method is an easy, inexpensive, and low cost way to build a solar power switching and distribution system.

Non-Contact Physiological Measurement System for Wearing Masks During the Epidemic

Physiological signals indicate a person’s physical and mental state at any given time.Accordingly,many studies extract physiological signals from the human body with non-contact methods,and most of them require facial feature points.However,under COVID-19,wearing a mask has become a must in many places,so how non-contact physiological information measurements can still be performed correctly even when a mask covers the facial information has become a focus of research.In this study,RGB and thermal infrared cameras were used to execute non-contact physiological information measurement systems for heart rate,blood pressure,respiratory rate,and forehead temperature for peoplewearing masks due to the pandemic.Using the green(G)minus red(R)signal in the RGB image,the region of interest(ROI)is established in the forehead and nose bridge regions.The photoplethysmography(PPG)waveforms of the two regions are obtained after the acquired PPG signal is subjected to the optical flow method,baseline drift calibration,normalization,and bandpass filtering.The relevant parameters in Deep Neural Networks(DNN)for the regression model can correctly predict the heartbeat and blood pressure.In addition,the temperature change in the ROI of the mask after thermal image processing and filtering can be used to correctly determine the number of breaths.Meanwhile,the thermal image can be used to read the temperature average of the ROI of the forehead,and the forehead temperature can be obtained smoothly.The experimental results show that the above-mentioned physiological signals of a subject can be obtained in 6-s images with the error for both heart rate and blood pressure within 2%∼3%and the error of forehead temperature within±0.5°C.

A Wrapping Encryption Based on Double Randomness Mechanism

Currently,data security mainly relies on password(PW)or system channel key(SKCH)to encrypt data before they are sent,no matter whether in broadband networks,the 5th generation(5G)mobile communications,satellite communications,and so on.In these environments,a fixed password or channel key(e.g.,PW/SKCH)is often adopted to encrypt different data,resulting in security risks since thisPW/SKCH may be solved after hackers collect a huge amount of encrypted data.Actually,the most popularly used security mechanism Advanced Encryption Standard(AES)has its own problems,e.g.,several rounds have been solved.On the other hand,if data protected by the same PW/SKCH at different time points can derive different data encryption parameters,the system’s security level will be then greatly enhanced.Therefore,in this study,a security scheme,named Wrapping Encryption Based on Double Randomness Mechanism(WEBDR),is proposed by integrating a password key(or a system channel key)and an Initialization Vector(IV)to generate an Initial Encryption Key(IEK).Also,an Accumulated Shifting Substitution(ASS)function and a three-dimensional encryption method are adopted to produce a set of keys.Two randomness encryption mechanisms are developed.The first generates system sub-keys and calculates the length of the first pseudo-random numbers by employing IEK for providing subsequent encryption/decryption.The second produces a random encryption key and a sequence of internal feedback codes and computes the length of the second pseudo-random numbers for encrypting delivered messages.A wrapped mechanism is further utilized to pack a ciphertext file so that a wrapped ciphertext file,rather than the ciphertext,will be produced and then transmitted to its destination.The findings are as follows.Our theoretic analyses and simulations demonstrate that the security of the WEBDR in cloud communication has achieved its practical security.Also,AES requires 176 times exclusive OR(XOR)operations for both encryption and decryption,while the WEBDR consumes only 3 operations.That is why the WEBDR is 6.7∼7.09 times faster than the AES,thus more suitable for replacing the AES to protect data transmitted between a cloud system and its users.

Evaluation of the Anti-Inflammatory Activities of 5,8,11-<i>cis</i>-Eicosatrienoic Acid

The main eicosanoids inflammatory mediators, prostaglandins and leukotrienes, are both generated from arachidonic acid (AA;20:4 n-6). AA is a member of polyunsaturated fatty acids (PUFAs). Numerous studies have demonstrated that various contents of PUFAs can modulate the inflammatory responses. However, fewer studies have examined n-9PUFAs and their effects on the inflammatory responses. In the present study, the role of 5,8,11-cis-eicosatrienoic acid (ETrA;20:3 n-9, also called Mead acid) in the inflammatory responses has been investigated. The anti-inflammatory activities of ETrA were examined using an in vitro macrophage system and the inhibitory effect was confirmed by western blot analysis for iNOS and COX-2 expressions. The interactions between ETrA and COX-2 protein were simulated to produce a computer modeling protein-ligand complexes and the results suggest a possible mechanism for the effects of ETrA. In this study, we described a significant inhibition of the inflammatory activities initiated by ETrA. Since ETrA is a substance presented in the tissues of young animals, we therefore anticipate that ETrA can be utilized as a natural therapeutic supplement to inhibit inflammatory activities.

Experienced College Instructors＇ Personal Epistemology in Teaching Social Studies and Their Perception of Using Technology

Experience includes explicit and tacit knowledge. Explicit knowledge is from a person＇s ＂espoused theory＂ which is what a person believes and claims to follow. Tacit knowledge is from a person＇s ＂theory-in-use＂ which lies behind a person＇s action or behavior. The knowledge of teaching demonstrated in the classroom can be referred to as tacit knowledge or theory-in-use which is often the theory behind the practice of experienced teachers. Freema Elbaz （1983） points out that the ＂experience＂ is referred to as ＂practical knowledge＂, which ＂provides the basis for a conceptualization which sees the teacher as possessing valuable resources＂ （6） and allows teachers to explicitly indicate and tacitly demonstrate their experience in teaching. The purpose of the study is to investigate how experienced college instructors apply their good teaching qualities to teaching social studies. The participants are three experienced college instructors teaching social studies. A concept map and a final reflection are used to elicit experienced instructors＇ personal epistemology in teaching social studies and their perception of technology use in the classroom. Each participant was asked to generate nine good teaching qualities and draw their concept map based on the nine qualities. Their concept maps reflected their theory-in-use and showed the relationship among their teaching qualities by displaying them together in a graphic form and how each teaching quality is connected to another. Participants＇ technology use was also explored to get their perception of the role of technology and their actual use of it in teaching. Then they were asked to validate their concept map data and reflect on their classroom teaching and use of technology. The findings show the three instructors taught under different schema and decided what their means and ends should be and how technology can help facilitate teaching and learning. However, most of them seemed to treat the content （e.g., democracy education） as their ends and thus used pedagogy （e.g., technology） as the means to reach the ends. Their technology use also reflected their perception of technology in teaching and revealed their limited understanding of technology integration, which leads to potential problems.

Rebuilding the Ancestral Temple and Hosting Daluo Heaven and Earth Prayer and Enlightenment Ceremony

Kuo,Yeh-Tzu founded Taiwan’s Sung Shan Tsu Huei Temple in 1970.She organized more than 200 worshipers as a group named“Taiwan Tsu Huei Temple Queen Mother of the West Delegation to China to Worship at the Ancestral Temples”in 1990.At that time,the temple building of the Queen Mother Palace in Huishan of Gansu Province was in disrepair,and Temple Master Kuo,Yeh-Tzu made a vow to rebuild it.Rebuilding the ancestral temple began in 1992 and was completed in 1994.It was the first case of a Taiwan temple financing the rebuilding of a far-away Queen Mother Palace with its own donations.In addition,Sung Shan Tsu Huei Temple celebrated its 45th anniversary and hosted Yiwei Yuanheng Lizhen Daluo Tiandi Qingjiao(Momentous and Fortuitous Heaven and Earth Prayer Ceremony)in 2015.This is the most important and the grandest blessing ceremony of Taoism,a rare event for Taoism locally and abroad during this century.Those sacred rituals were replete with unprecedented grand wishes to propagate the belief in Queen Mother of the West.Stopping at nothing,Queen Mother’s love never ceases.

Remarks on the Complexity of Signed k-Domination on Graphs

This paper is motivated by the concept of the signed k-domination problem and dedicated to the complexity of the problem on graphs. For any fixed nonnegative integer k, we show that the signed k-domination problem is NP-complete for doubly chordal graphs. For strongly chordal graphs and distance-hereditary graphs, we show that the signed k-domination problem can be solved in polynomial time. We also show that the problem is linear-time solvable for trees, interval graphs, and chordal comparability graphs.

How much do social connections matter in fundraising outcomes?

This study examines the role of social connections and network centrality in attracting funders to crowdfunding campaigns.We classify social connections as either external(e.g.,Facebook)or internal(e.g.,investing in online platforms through resource exchange).Drawing from the 108,463 crowdfunding campaigns on the online platform Kickstarter from April 21,2009,to July 24,2019,we apply external linkages and online followers to estimate the effect of external social connections.We construct a digraph network for the internal social connections and use PageRank,HITS,and centrality to obtain the weights of the nodes.Next,we compare the performance change of several prediction algorithms by feeding social connection-related variables.This study has several findings.First,for external social connections,having more online followers improves the funding success rate of a campaign.Second,for internal social connections,only authority and degree in centrality positively affect the number of funders and the campaign’s financing progress among the weights of the nodes.Third,using social connection variables improves the prediction algorithms for funding outcomes.Fourth,external social connections exert greater funding outcomes than internal social connections.Fourth,entrepreneurs should extend their external social connections to their internal social connections,and network centrality expedites project financing.Fifth,the effect of social connections on fundraising outcomes varies among the campaign categories.Fundraisers who are online influencers should leverage their online social connections,notably for the project categories that matter.

Does an Auditor＇s Within-Industry Market Share Still Capture Auditor Industry Expertise in a Mandatory Audit Partner Rotation Regime？

Prior studies commonly use an auditor＇s market share in an industry as a proxy for auditor industry expertise and find that audit quality is positively related to an audit partner＇s within-industry market share in a voluntary audit partner rotation regime where the length of the client-partner relationship is not limited. Mandatory audit partner rotation, however, limits the length of the client-partner relationship and can artificially increase or decrease the market shares of incoming and departing partners, thus making the audit partner＇s within-industry market share an unreliable proxy for auditor industry expertise. Using a sample of banks in Taiwan, we find that audit quality is positively related to an audit partner＇s within-industry market share in the voluntary audit partner rotation regime. However, such a positive relation disappears in the mandatory audit partner rotation regime. Thus, we conclude that mandatory audit partner rotation decouples the link between an audit partner＇s within-industry market share and auditor industry expertise and caution researchers against using an audit partner＇s within-industry market share as a proxy for auditor industry expertise in a mandatory audit partner rotation regime.

A Study of Medical Insurance With Big Data in Taiwan

The structures of numerous industries, including the insurance industry, have been altered by the ongoing development of associated technologies. As the insurance industry undergoes this period of technology transformation, it is important to recognize the key role that big data play in the industry. Most critically, the industry could not function without the utilization of big data, which explains to a large extent why every insurance company maintains its own numeric database. Relatedly, Taiwan＇s Bureau of National Health Insurance recently established the Information Integration Application Service Center, to which qualified companies and institutions can submit applications for permission to analyze the bureau＇s collected disease data according to stipulated regulations. In effect, access to the center＇s data provides insurance companies with a further means of improving their operational effectiveness through the analysis of big data, with targets for potential improvements including the various strategies utilized to react to changes in the environment, such as those involved in marketing, administrative management, and product pricing and services. The foundation of the present study consisted of a literature review and survey, with the key objective being to determine and discuss the effects of big data analysis on the medical insurance industry, including the changes that the utilization of big data results in for the customers of medical insurance companies. With the issues discussed above in mind, the survey was designed to determine whether medical insurance consumers know about and understand the effects of big data. The survey data indicated the following key findings： （1） The two concepts exhibit clear differences in terms of population statistic wxiables; （2） The two concepts exhibit clear differences in terms of insurance purchasing variables; and （3） The two concepts exhibit clear differences in terms of the level of understanding regarding big data.