Search results for: Chien Chon Chen

Authors: Chau-Kuang Chen, Juanita Buford, Colette Davis, Raisha Allen, John Hughes, James Tyus, Dexter Samuels

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During the post-Civil War era, the city of Nashville, Tennessee, had the highest mortality rate in the country. The elevated death and disease among ex-slaves were attributable to the unavailability of healthcare. To address the paucity of healthcare services, the College, an institution with the mission of educating minority professionals and serving the under served population, was established in 1876. This study was designed to assess if the College has accomplished its mission of serving under served communities and contributed to the elimination of health disparities in the United States. The study objective was to quantify the impact of socioeconomic status and adverse health outcomes on primary care professionals serving disadvantaged communities, which, in turn, was significantly associated with a health professional shortage score partly designated by the U.S. Department of Health and Human Services. Various statistical methods were used to analyze the alumni data in years 1975 – 2013. K-means cluster analysis was utilized to identify individual medical and dental graduates into the cluster groups of the practice communities (Disadvantaged or Non-disadvantaged Communities). Discriminant analysis was implemented to verify the classification accuracy of cluster analysis. The independent t test was performed to detect the significant mean differences for clustering and criterion variables between Disadvantaged and Non-disadvantaged Communities, which confirms the “content” validity of cluster analysis model. Chi-square test was used to assess if the proportion of cluster groups (Disadvantaged vs Non-disadvantaged Communities) were consistent with that of practicing specialties (primary care vs. non-primary care). Finally, the partial least squares (PLS) path model was constructed to explore the “construct” validity of analytics model by providing the magnitude effects of socioeconomic status and adverse health outcome on primary care professionals serving disadvantaged community. The social ecological theory along with statistical models mentioned was used to establish the relationship between medical and dental graduates (primary care professionals serving disadvantaged communities) and their social environments (socioeconomic status, adverse health outcome, health professional shortage score). Based on social ecological framework, it was hypothesized that the impact of socioeconomic status and adverse health outcomes on primary care professionals serving disadvantaged communities could be quantified. Also, primary care professionals serving disadvantaged communities related to a health professional shortage score can be measured. Adverse health outcome (adult obesity rate, age-adjusted premature mortality rate, and percent of people diagnosed with diabetes) could be affected by the latent variable, namely socioeconomic status (unemployment rate, poverty rate, percent of children who were in free lunch programs, and percent of uninsured adults). The study results indicated that approximately 83% (3,192/3,864) of the College’s medical and dental graduates from 1975 to 2013 were practicing in disadvantaged communities. In addition, the PLS path modeling demonstrated that primary care professionals serving disadvantaged community was significantly associated with socioeconomic status and adverse health outcome (p < .001). In summary, the majority of medical and dental graduates from the College provide primary care services to disadvantaged communities with low socioeconomic status and high adverse health outcomes, which demonstrate that the College has fulfilled its mission.

Keywords: disadvantaged community, K-means cluster analysis, PLS path modeling, primary care

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Authors: Ngoc-Nguyen Nguyen, Hsiu-Ling Chen, Thanh-Truc Lai Huynh

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In foreign language learning, vocabulary acquisition has consistently posed challenges to learners, especially for those at lower levels. Conventional approaches often fail to promote vocabulary learning and ensure engaging experiences alike. The emergence of mobile learning, particularly the integration of chatbot systems, has offered alternative ways to facilitate this practice. Chatbots have proven effective in educational contexts by offering interactive learning experiences in a constructivist manner. These tools have caught attention in the field of mobile-assisted language learning (MALL) in recent years. This research is conducted in an English for Specific Purposes (ESP) course at the A2 level of the CEFR, designed for non-English majors. Participants are first-year Vietnamese students aged 18 to 20 at a university. This quasi-experimental study follows a pretest-posttest control group design over five weeks, with two classes randomly assigned as the experimental and control groups. The experimental group engages in chatbot-assisted spaced practice with SRL components, while the control group uses the same spaced practice without SRL. The two classes are taught by the same lecturer. Data are collected through pre- and post-tests, cognitive load surveys, and semi-structured interviews. The combination of self-regulated learning (SRL) and distributed practice, grounded in the spacing effect, forms the basis of the present study. SRL elements, which concern goal setting and strategy planning, are integrated into the system. The spaced practice method, similar to those used in widely recognized learning platforms like Duolingo and Anki flashcards, spreads out learning over multiple sessions. This study’s design features quizzes progressively increasing in difficulty. These quizzes are aimed at targeting both the Recognition-Recall and Comprehension-Use dimensions for a comprehensive acquisition of vocabulary. The mobile-based chatbot system is built using Golang, an open-source programming language developed by Google. It follows a structured flow that guides learners through a series of 4 quizzes in each week of teacher-led learning. The quizzes start with less cognitively demanding tasks, such as multiple-choice questions, before moving on to more complex exercises. The integration of SRL elements allows students to self-evaluate the difficulty level of vocabulary items, predict scores achieved, and choose appropriate strategy. This research is part one of a two-part project. The initial findings will determine the development of an upgraded chatbot system in part two, where adaptive features in response to the integration of SRL components will be introduced. The research objectives are to assess the effectiveness of the chatbot-assisted approach, based on the combination of spaced practice and SRL, in improving vocabulary acquisition and managing cognitive load, as well as to understand students' perceptions of this learning tool. The insights from this study will contribute to the growing body of research on mobile-assisted language learning and offer practical implications for integrating chatbot systems with spaced practice into educational settings to enhance vocabulary learning.

Keywords: mobile learning, mobile-assisted language learning, MALL, chatbots, vocabulary learning, spaced practice, spacing effect, self-regulated learning, SRL, self-regulation, EFL, cognitive load

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Authors: Hongyan Chen, Megan Jobson, Andrew J. Masters, Maria Gonzalez-Miquel, Simon Halstead, Mayri Diaz de Rienzo

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Ionic liquids offer excellent advantages over conventional solvents for industrial extraction of metals from aqueous solutions, where such extraction processes bring opportunities for recovery, reuse, and recycling of valuable resources and more sustainable production pathways. Recent research on the use of ionic liquids for extraction confirms their high selectivity and low volatility, but there is relatively little focus on how their properties can be best exploited in practice. This work addresses gaps in research on process modelling and simulation, to support development, design, and optimisation of these processes, focusing on the separation of the highly similar transition metals, cobalt, and nickel. The study exploits published experimental results, as well as new experimental results, relating to the separation of Co and Ni using trihexyl (tetradecyl) phosphonium chloride. This extraction agent is attractive because it is cheaper, more stable and less toxic than fluorinated hydrophobic ionic liquids. This process modelling work concerns selection and/or development of suitable models for the physical properties, distribution coefficients, for mass transfer phenomena, of the extractor unit and of the multi-stage extraction flowsheet. The distribution coefficient model for cobalt and HCl represents an anion exchange mechanism, supported by the literature and COSMO-RS calculations. Parameters of the distribution coefficient models are estimated by fitting the model to published experimental extraction equilibrium results. The mass transfer model applies Newman’s hard sphere model. Diffusion coefficients in the aqueous phase are obtained from the literature, while diffusion coefficients in the ionic liquid phase are fitted to dynamic experimental results. The mass transfer area is calculated from the surface to mean diameter of liquid droplets of the dispersed phase, estimated from the Weber number inside the extractor. New experiments measure the interfacial tension between the aqueous and ionic phases. The empirical models for predicting the density and viscosity of solutions under different metal loadings are also fitted to new experimental data. The extractor is modelled as a continuous stirred tank reactor with mass transfer between the two phases and perfect phase separation of the outlet flows. A multistage separation flowsheet simulation is set up to replicate a published experiment and compare model predictions with the experimental results. This simulation model is implemented in gPROMS software for dynamic process simulation. The results of single stage and multi-stage flowsheet simulations are shown to be in good agreement with the published experimental results. The estimated diffusion coefficient of cobalt in the ionic liquid phase is in reasonable agreement with published data for the diffusion coefficients of various metals in this ionic liquid. A sensitivity study with this simulation model demonstrates the usefulness of the models for process design. The simulation approach has potential to be extended to account for other metals, acids, and solvents for process development, design, and optimisation of extraction processes applying ionic liquids for metals separations, although a lack of experimental data is currently limiting the accuracy of models within the whole framework. Future work will focus on process development more generally and on extractive separation of rare earths using ionic liquids.

Keywords: distribution coefficient, mass transfer, COSMO-RS, flowsheet simulation, phosphonium

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Authors: Yi-Chu Liu, Hsiu-Huei Hung, Li-Hui Yang, Ming-Jyh Chen

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introduction: To promote environmental sustainability, the hospital formed a corporate volunteer team in 2016 to build the Green Experiential Education Center. Our green creation center utilizes attic space to achieve sustainability objectives such as energy efficiency and carbon reduction. Other than executing sustainable plans, the center emphasizes experiential education. We invite our community to actively participate in building a sustainable, economically viable environment. Since 2020, the China Medical University Hospital has provided medical care to the Tgbin community in Taichung City's Heping District. The tribe, primarily composed of Atayal people, the elderly comprise 18% of the total population, and these families' per capita income is relatively low compared to Taiwanese citizens elsewhere. Purpose / Methods: With the experiences at the Green Experiential Education Center, CMUH team identifies the following objectives: Create an aquaponic system to supply vulnerable local households with food. Create a solar renewable energy system to meet the electricity needs of vulnerable local households. Promote the purchase of green electricity certificates to reduce the hospital's carbon emissions and generate additional revenue for the local community. Materials and Methods: In March 2020, we visited the community and installed The aquaponic system in January 2021. CMUH spent 150,000NT (approximately 5000US dollars) in March 2021 to build a 100-square-meter aquaponic system. The production of vegetables and fish caught determines the number of vulnerable families that can be supported. The aquaponics system is a kind of Low energy consumption and environmentally friendly production method, and can simultaneously achieve energy saving, water saving, and fertilizer saving .In September 2023, CMUH will complete a solar renewable energy system. The system will cover an area of 308 square meters and costs approximately NT$240,000 (approximately US$8,000). The installation of electricity meters will enable statistical analysis of power generation. And complete the Taiwan National Renewable Energy Certificate application process. The green electricity certificate will be obtained based on the monthly power generation from the solar renewable energy system. Results: I Food availability and access are crucial considering the remote location and aging population. By creating a fish and vegetable symbiosis system, the vegetables and catches produced will enable economically disadvantaged families to lower food costs. In 2021 and 2022, the aquaponic system produced 52 kilograms of vegetables and 75 kilograms of catch. The production ensures the daily needs of 8 disadvantaged families. Conclusions: The hospital serves as a fortress for public health and the ideal setting for corporate social responsibility. China Medical University Hospital and the Green Experiential Education Center work to strengthen ties with rural communities and offer top-notch specialty medical care. We are committed to assisting people in escaping poverty and hunger as part of the 2030 Sustainable Development Goals.

Keywords: environmental education, sustainability, energy conservation, carbon emissions, rural area development

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Authors: Marco F. Benaglia, Mei H. Chen, Kune M. Tsai, Chia H. Hung

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As societal and family structures, consumer dietary habits, and awareness about food safety and quality continue to evolve in most developed countries, the demand for refrigerated and frozen foods has been growing, and the issues related to their preservation have gained increasing attention. A well-established cold chain logistics system is essential to avoid any temperature abuse; therefore, assessing potential disruptions in the operational processes of cold chain logistics centers becomes pivotal. This study preliminarily employs HACCP to find disruption factors in cold chain logistics centers that may cause temperature abuse. Then, concept mapping is applied: selected experts engage in brainstorming sessions to identify any further factors. The panel consists of ten experts, including four from logistics and home delivery, two from retail distribution, one from the food industry, two from low-temperature logistics centers, and one from the freight industry. Disruptions include equipment-related aspects, human factors, management aspects, and process-related considerations. The areas of observation encompass freezer rooms, refrigerated storage areas, loading docks, sorting areas, and vehicle parking zones. The experts also categorize the disruption factors based on perceived similarities and build a similarity matrix. Each factor is evaluated for its impact, frequency, and investment importance. Next, multiple scale analysis, cluster analysis, and other methods are used to analyze these factors. Simultaneously, key disruption factors are identified based on their impact and frequency, and, subsequently, the factors that companies prioritize and are willing to invest in are determined by assessing investors’ risk aversion behavior. Finally, Cumulative Prospect Theory (CPT) is applied to verify the risk patterns. 66 disruption factors are found and categorized into six clusters: (1) "Inappropriate Use and Maintenance of Hardware and Software Facilities", (2) "Inadequate Management and Operational Negligence", (3) "Product Characteristics Affecting Quality and Inappropriate Packaging", (4) "Poor Control of Operation Timing and Missing Distribution Processing", (5) "Inadequate Planning for Peak Periods and Poor Process Planning", and (6) "Insufficient Cold Chain Awareness and Inadequate Training of Personnel". This study also identifies five critical factors in the operational processes of cold chain logistics centers: "Lack of Personnel’s Awareness Regarding Cold Chain Quality", "Personnel Not Following Standard Operating Procedures", "Personnel’s Operational Negligence", "Management’s Inadequacy", and "Lack of Personnel’s Knowledge About Cold Chain". The findings show that cold chain operators prioritize prevention and improvement efforts in the "Inappropriate Use and Maintenance of Hardware and Software Facilities" cluster, particularly focusing on the factors of "Temperature Setting Errors" and "Management’s Inadequacy". However, through the application of CPT theory, this study reveals that companies are not usually willing to invest in the improvement of factors related to the "Inappropriate Use and Maintenance of Hardware and Software Facilities" cluster due to its low occurrence likelihood, but they acknowledge the severity of the consequences if it does occur. Hence, the main implication is that the key disruption factors in cold chain logistics centers’ processes are associated with personnel issues; therefore, comprehensive training, periodic audits, and the establishment of reasonable incentives and penalties for both new employees and managers may significantly reduce disruption issues.

Keywords: concept mapping, cold chain, HACCP, cumulative prospect theory

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Authors: Yu-Lin Chen, Kimberly Koester, Marissa Raymond-Flesh, Anika Thapar, Jay Thapar

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Purpose: Effectively communicating adolescent health topics to teens and their parents is crucial. This study emphasizes critically evaluating the optimal use of artificial intelligence tools (AI), which are increasingly prevalent in disseminating health information. By fostering a deeper understanding of AI voice preference in the context of health, the research aspires to have a ripple effect, enhancing the collective health literacy and decision-making capabilities of both teenagers and their parents. This study explores AI voices' potential within health learning modules for annual well-child visits. We aim to identify preferred voice characteristics and understand factors influencing perceived trustworthiness, ultimately aiming to improve health literacy and decision-making in both demographics. Methods: A cross-sectional study assessed preferences and trust perceptions of AI voices in learning modules among teens (11-18) and their parents/guardians in Northern California. The study involved the development of four distinct learning modules covering various adolescent health-related topics, including general communication, sexual and reproductive health communication, parental monitoring, and well-child check-ups. Participants were asked to evaluate eight AI voices across the modules, considering a set of six factors such as intelligibility, naturalness, prosody, social impression, trustworthiness, and overall appeal, using Likert scales ranging from 1 to 10 (the higher, the better). They were also asked to select their preferred choice of voice for each module. Descriptive statistics summarized participant demographics. Chi-square/t-tests explored differences in voice preferences between groups. Regression models identified factors impacting the perceived trustworthiness of the top-selected voice per module. Results: Data from 104 participants (teen=63; adult guardian = 41) were included in the analysis. The mean age is 14.9 for teens (54% male) and 41.9 for the parent/guardian (12% male). At the same time, similar voice quality ratings were observed across groups, and preferences varied by topic. For instance, in general communication, teens leaned towards young female voices, while parents preferred mature female tones. Interestingly, this trend reversed for parental monitoring, with teens favoring mature male voices and parents opting for mature female ones. Both groups, however, converged on mature female voices for sexual and reproductive health topics. Beyond preferences, the study delved into factors influencing perceived trustworthiness. Interestingly, social impression and sound appeal emerged as the most significant contributors across all modules, jointly explaining 71-75% of the variance in trustworthiness ratings. Conclusion: The study emphasizes the importance of catering AI voices to specific audiences and topics. Social impression and sound appeal emerged as critical factors influencing perceived trustworthiness across all modules. These findings highlight the need to tailor AI voices by age and the specific health information being delivered. Ensuring AI voices resonate with both teens and their parents can foster their engagement and trust, ultimately leading to improved health literacy and decision-making for both groups. Limitations and future research: This study lays the groundwork for understanding AI voice preferences for teenagers and their parents in healthcare settings. However, limitations exist. The sample represents a specific geographic location, and cultural variations might influence preferences. Additionally, the modules focused on topics related to well-child visits, and preferences might differ for more sensitive health topics. Future research should explore these limitations and investigate the long-term impact of AI voice on user engagement, health outcomes, and health behaviors.

Keywords: artificial intelligence, trustworthiness, voice, adolescent

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Authors: Yu-Hsiu Lin, Wen-Chun Lin, Yen-Chang Cheng, Chia-Ju Yeh, Yu-Chuan Chen, Tai-You Li

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Demand-Side Management (DSM) has the potential to reduce electricity costs and carbon emission, which are associated with electricity used in the modern society. A home Energy Management System (EMS) commonly used by residential consumers in a down-stream sector of a smart grid to monitor, control, and optimize energy efficiency to domestic appliances is a system of computer-aided functionalities as an energy audit for residential DSM. Implementing fault detection and classification to domestic appliances monitored, controlled, and optimized is one of the most important steps to realize preventive maintenance, such as residential air conditioning and heating preventative maintenance in residential/industrial DSM. In this study, a cloud intelligence-based green EMS that comes up with an Internet of Things (IoT) technology stack for residential DSM is developed. In the EMS, Arduino MEGA Ethernet communication-based smart sockets that module a Real Time Clock chip to keep track of current time as timestamps via Network Time Protocol are designed and implemented for readings of load phenomena reflecting on voltage and current signals sensed. Also, a Network-Attached Storage providing data access to a heterogeneous group of IoT clients via Hypertext Transfer Protocol (HTTP) methods is configured to data stores of parsed sensor readings. Lastly, a desktop computer with a WAMP software bundle (the Microsoft® Windows operating system, Apache HTTP Server, MySQL relational database management system, and PHP programming language) serves as a data science analytics engine for dynamic Web APP/REpresentational State Transfer-ful web service of the residential DSM having globally-Advanced Internet of Artificial Intelligence (AI)/Computational Intelligence. Where, an abstract computing machine, Java Virtual Machine, enables the desktop computer to run Java programs, and a mash-up of Java, R language, and Python is well-suited and -configured for AI in this study. Having the ability of sending real-time push notifications to IoT clients, the desktop computer implements Google-maintained Firebase Cloud Messaging to engage IoT clients across Android/iOS devices and provide mobile notification service to residential/industrial DSM. In this study, in order to realize edge intelligence that edge devices avoiding network latency and much-needed connectivity of Internet connections for Internet of Services can support secure access to data stores and provide immediate analytical and real-time actionable insights at the edge of the network, we upgrade the designed and implemented smart sockets to be embedded AI Arduino ones (called embedded AIduino). With the realization of edge analytics by the proposed embedded AIduino for data analytics, an Arduino Ethernet shield WizNet W5100 having a micro SD card connector is conducted and used. The SD library is included for reading parsed data from and writing parsed data to an SD card. And, an Artificial Neural Network library, ArduinoANN, for Arduino MEGA is imported and used for locally-embedded AI implementation. The embedded AIduino in this study can be developed for further applications in manufacturing industry energy management and sustainable energy management, wherein in sustainable energy management rotating machinery diagnostics works to identify energy loss from gross misalignment and unbalance of rotating machines in power plants as an example.

Keywords: demand-side management, edge intelligence, energy management system, fault detection and classification

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Authors: David J. Foran, Nhan Do, Samuel Ajjarapu, Wenjin Chen, Tahsin Kurc, Joel H. Saltz

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The large-scale data and computational requirements of investigators throughout the clinical and research communities demand an informatics infrastructure that supports both existing and new investigative and translational projects in a robust, secure environment. In some subspecialties of medicine and research, the capacity to generate data has outpaced the methods and technology used to aggregate, organize, access, and reliably retrieve this information. Leading health care centers now recognize the utility of establishing an enterprise-wide, clinical data warehouse. The primary benefits that can be realized through such efforts include cost savings, efficient tracking of outcomes, advanced clinical decision support, improved prognostic accuracy, and more reliable clinical trials matching. The overarching objective of the work presented here is the development and implementation of a flexible Intelligent Retrieval and Interrogation System (IRIS) that exploits the combined use of computational imaging, genomics, and data-mining capabilities to facilitate clinical assessments and translational research in oncology. The proposed System includes a multi-modal, Clinical & Research Data Warehouse (CRDW) that is tightly integrated with a suite of computational and machine-learning tools to provide insight into the underlying tumor characteristics that are not be apparent by human inspection alone. A key distinguishing feature of the System is a configurable Extract, Transform and Load (ETL) interface that enables it to adapt to different clinical and research data environments. This project is motivated by the growing emphasis on establishing Learning Health Systems in which cyclical hypothesis generation and evidence evaluation become integral to improving the quality of patient care. To facilitate iterative prototyping and optimization of the algorithms and workflows for the System, the team has already implemented a fully functional Warehouse that can reliably aggregate information originating from multiple data sources including EHR’s, Clinical Trial Management Systems, Tumor Registries, Biospecimen Repositories, Radiology PAC systems, Digital Pathology archives, Unstructured Clinical Documents, and Next Generation Sequencing services. The System enables physicians to systematically mine and review the molecular, genomic, image-based, and correlated clinical information about patient tumors individually or as part of large cohorts to identify patterns that may influence treatment decisions and outcomes. The CRDW core system has facilitated peer-reviewed publications and funded projects, including an NIH-sponsored collaboration to enhance the cancer registries in Georgia, Kentucky, New Jersey, and New York, with machine-learning based classifications and quantitative pathomics, feature sets. The CRDW has also resulted in a collaboration with the Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC) at the U.S. Department of Veterans Affairs to develop algorithms and workflows to automate the analysis of lung adenocarcinoma. Those studies showed that combining computational nuclear signatures with traditional WHO criteria through the use of deep convolutional neural networks (CNNs) led to improved discrimination among tumor growth patterns. The team has also leveraged the Warehouse to support studies to investigate the potential of utilizing a combination of genomic and computational imaging signatures to characterize prostate cancer. The results of those studies show that integrating image biomarkers with genomic pathway scores is more strongly correlated with disease recurrence than using standard clinical markers.

Keywords: clinical data warehouse, decision support, data-mining, intelligent databases, machine-learning.

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Authors: Zhi-Zhong Chen, Jun-Hao Lu, Jr., Shih-Ying Chueh

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Based on Unified Theory of Acceptance and Use of Technology (UTAUT), the study investigates people’s intention on using smart phones. The study additionally incorporates two new variables: 'self-efficacy' and 'attitude toward using'. Samples are collected by questionnaire survey, in which 240 are valid. After Correlation Analysis, Reliability Test, ANOVA, t-test and Multiple Regression Analysis, the study finds that social impact and self-efficacy have positive effect on use intentions, and the use intentions also have positive effect on use behavior.

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