National Yunlin University of Science and Technology

IMPORTANCE: The Global Burden of Diseases, Injuries, and Risk Factors Study 2019 (GBD 2019) provided systematic estimates of incidence, morbidity, and mortality to inform local and international efforts toward reducing cancer burden. OBJECTIVE: To estimate cancer burden and trends globally for 204 countries and territories and by Sociodemographic Index (SDI) quintiles from 2010 to 2019. EVIDENCE REVIEW: The GBD 2019 estimation methods were used to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-adjusted life years (DALYs) in 2019 and over the past decade. Estimates are also provided by quintiles of the SDI, a composite measure of educational attainment, income per capita, and total fertility rate for those younger than 25 years. Estimates include 95% uncertainty intervals (UIs). FINDINGS: In 2019, there were an estimated 23.6 million (95% UI, 22.2-24.9 million) new cancer cases (17.2 million when excluding nonmelanoma skin cancer) and 10.0 million (95% UI, 9.36-10.6 million) cancer deaths globally, with an estimated 250 million (235-264 million) DALYs due to cancer. Since 2010, these represented a 26.3% (95% UI, 20.3%-32.3%) increase in new cases, a 20.9% (95% UI, 14.2%-27.6%) increase in deaths, and a 16.0% (95% UI, 9.3%-22.8%) increase in DALYs. Among 22 groups of diseases and injuries in the GBD 2019 study, cancer was second only to cardiovascular diseases for the number of deaths, years of life lost, and DALYs globally in 2019. Cancer burden differed across SDI quintiles. The proportion of years lived with disability that contributed to DALYs increased with SDI, ranging from 1.4% (1.1%-1.8%) in the low SDI quintile to 5.7% (4.2%-7.1%) in the high SDI quintile. While the high SDI quintile had the highest number of new cases in 2019, the middle SDI quintile had the highest number of cancer deaths and DALYs. From 2010 to 2019, the largest percentage increase in the numbers of cases and deaths occurred in the low and low-middle SDI quintiles. CONCLUSIONS AND RELEVANCE: The results of this systematic analysis suggest that the global burden of cancer is substantial and growing, with burden differing by SDI. These results provide comprehensive and comparable estimates that can potentially inform efforts toward equitable cancer control around the world.

Purpose The purpose of this paper is to investigate the factors that affect the adoption of cloud computing by firms belonging to the high-tech industry. The eight factors examined in this study are relative advantage, complexity, compatibility, top management support, firm size, technology readiness, competitive pressure, and trading partner pressure. Design/methodology/approach A questionnaire-based survey was used to collect data from 111 firms belonging to the high-tech industry in Taiwan. Relevant hypotheses were derived and tested by logistic regression analysis. Findings The findings revealed that relative advantage, top management support, firm size, competitive pressure, and trading partner pressure characteristics have a significant effect on the adoption of cloud computing. Research limitations/implications The research was conducted in the high-tech industry, which may limit the generalisability of the findings. Practical implications The findings offer cloud computing service providers with a better understanding of what affects cloud computing adoption characteristics, with relevant insight on current promotions. Originality/value The research contributes to the application of new technology cloud computing adoption in the high-tech industry through the use of a wide range of variables. The findings also help firms consider their information technologies investments when implementing cloud computing.

With the liberalization and internalization of financial markets, in terms of the entrance of the World Trade Organization, banks in Taiwan face pressures in service quality and administrative efficiency. Predicting customers’ intention to adopt Internet banking is an important issue. Attempts to understand how an individual's belief, embracing attitude, subjective norm and perceived behavioral control, can influence intention. Two versions of the model of the theory of planned behavior (TPB) – pure and decomposed – are examined and compared to the theory of reasoned action (TRA). Data are collected from approximately 425 respondents and structural equation modeling is used to analyze the responses. Results generally support TRA and TPB and provide a good fit to the data.

Photovoltaic systems normally use a maximum power point tracking (MPPT) technique to continuously deliver the highest possible power to the load when variations in the insulation and temperature occur. It overcomes the problem of mismatch between the solar arrays and the given load. A simple method of tracking the maximum power points (MPPs) and forcing the system to operate close to these points is presented. The principle of energy conservation is used to derive the large- and small-signal model and transfer function. By using the proposed model, the drawbacks of the state-space-averaging method can be overcome. The TI320C25 digital signal processor (DSP) was used to implement the proposed MPPT controller, which controls the DC/DC converter in the photovoltaic system. Simulations and experimental results show excellent performance.

This paper proposes a new mix design method for self-compacting concrete (SCC). First, the amount of aggregates required is determined, and the paste of binders is then filled into the voids of aggregates to ensure that the concrete thus obtained has flowability, self-compacting ability and other desired SCC properties. The amount of aggregates, binders and mixing water, as well as type and dosage of superplasticizer (SP) to be used are the major factors influencing the properties of SCC. Slump flow, V-funnel, L-flow, U-box and compressive strength tests were carried out to examine the performance of SCC, and the results indicate that the proposed method could produce successfully SCC of high quality. Compared to the method developed by the Japanese Ready-Mixed Concrete Association (JRMCA), this method is simpler, easier for implementation and less time-consuming, requires a smaller amount of binders and saves cost.

Abstract This paper aims to investigate how a gamified learning approach influences science learning, achievement and motivation, through a context‐aware mobile learning environment, and explains the effects on motivation and student learning. A series of gamified learning activities, based on MGLS (Mobile Gamification Learning System), was developed and implemented in an elementary school science curriculum to improve student motivation and to help students engage more actively in their learning activities. The responses to our questionnaire indicate that students valued the outdoor learning activities made possible by the use of a smartphone and its functions. Pre‐ and post‐test results demonstrated that incorporating mobile and gamification technologies into a botanical learning process could achieve a better learning performance and a higher degree of motivation than either non‐gamified mobile learning or traditional instruction. Further, they revealed a positive relationship between learning achievement and motivation. The correlation coefficient for ARCS dimensions and post‐test shows that the ARCS‐A (attention) is greater than ARCS‐R, ARCS‐C and ARCS‐S. This means that the attention (ARCS‐A) of this system is an important dimension in this research. The results could provide parents, teachers and educational organizations with the necessary data to make more relevant educational decision.

This study examines a behavioral model of wetlands tourism using variables of destination image, attitude, motivation, satisfaction and future behavior for tourists at Cigu, Sihcao and Haomeiliao in southwestern Taiwan. Empirical results indicate that destination image directly affects satisfaction and indirectly affects future behavior. Tourist attitude directly affects satisfaction and indirectly affects future behavior, while tourist motivation directly affects satisfaction and indirectly affects future behavior. Tourist satisfaction had a significant influence on future behavior, and satisfaction proved a significant mediating variable within this behavioral model.

Purpose The paper aims to examine how trust interacts with factors affecting interorganizational knowledge sharing in green supply chains, where cooperation and competition coexist. Design/methodology/approach A new research model is developed which comprises nine constructs and 13 research hypotheses, with trust as a mediating construct. The nine constructs are measured by well‐supported measures in the literature. The hypotheses are tested on data collected from 288 major green manufacturing firms in Taiwan, using structural equation modeling. Findings The paper finds that trust is the pivot of the factors influencing interorganizational knowledge sharing. The more a factor contributes to trust positively (such as participation and communication) or negatively (such as opportunistic behavior), the more the factor contributes to knowledge sharing correspondingly. The factors with no significant influence on trust (such as shared values and learning capacity) have no or less influence on knowledge sharing. Research limitations/implications The empirical study is conducted on green supply chains, with data collected from Taiwan's green manufacturing firms. With the research model developed, cross‐industrial studies on various forms of supply chains can be conducted to investigate whether differences between supply chains exist about the role that trust plays in interorganizational knowledge sharing. Practical implications The findings of the paper provide useful insights into how supply chain members should reinforce their collaborative behaviors and activities that would enhance the trust‐based relationships, in order to achieve the competitive advantage of knowledge sharing for the supply chain as a whole. Originality/value The new research model developed allows the relationships between trust and other influencing factors on interorganizational knowledge sharing to be explored. The model reflects the coexistence of the cooperation and competition relationships between supply chain members, which is not dealt with in previous studies.

The nature of stock market movement has always been ambiguous for investors because of various influential factors. This study aims to significantly reduce the risk of trend prediction with machine learning and deep learning algorithms. Four stock market groups, namely diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange, are chosen for experimental evaluations. This study compares nine machine learning models (Decision Tree, Random Forest, Adaptive Boosting (Adaboost), eXtreme Gradient Boosting (XGBoost), Support Vector Classifier (SVC), Naïve Bayes, K-Nearest Neighbors (KNN), Logistic Regression and Artificial Neural Network (ANN)) and two powerful deep learning methods (Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators from ten years of historical data are our input values, and two ways are supposed for employing them. Firstly, calculating the indicators by stock trading values as continuous data, and secondly converting indicators to binary data before using. Each prediction model is evaluated by three metrics based on the input ways. The evaluation results indicate that for the continuous data, RNN and LSTM outperform other prediction models with a considerable difference. Also, results show that in the binary data evaluation, those deep learning methods are the best; however, the difference becomes less because of the noticeable improvement of models' performance in the second way.

This study examines a behavioral model using latent variables of place attachment, recreation involvement, conservation commitment and environmentally responsible behavior among tourists visiting wetlands. In total, 928 usable questionnaires were collected. Confirmatory factor analysis and structural equation modeling were applied to the data by using LISREL 8.70 for Windows. Analytical results, which further elucidate the behavioral models of nature-based tourism, suggest that place attachment, recreation involvement and conservation commitment critically impact environmentally responsible behavior. In this behavioral model, conservation commitment simultaneously and partially mediates the relationships between place attachment and environmentally responsible behavior and between recreation involvement and environmentally responsible behavior. A series of management implications are drawn, including the need to use this information via a visitor interpretation strategy, greater use of partnerships with local communities and businesses to spread the importance of wetlands and of environmentally friendly behavior, and the need to work with other wetlands to share the type of visitor motivations best suited to encourage environmentally friendly behavior.

Detection and Classification of a brain tumor is an important step to better understanding its mechanism. Magnetic Reasoning Imaging (MRI) is an experimental medical imaging technique that helps the radiologist find the tumor region. However, it is a time taking process and requires expertise to test the MRI images, manually. Nowadays, the advancement of Computer-assisted Diagnosis (CAD), machine learning, and deep learning in specific allow the radiologist to more reliably identify brain tumors. The traditional machine learning methods used to tackle this problem require a handcrafted feature for classification purposes. Whereas deep learning methods can be designed in a way to not require any handcrafted feature extraction while achieving accurate classification results. This paper proposes two deep learning models to identify both binary (normal and abnormal) and multiclass (meningioma, glioma, and pituitary) brain tumors. We use two publicly available datasets that include 3064 and 152 MRI images, respectively. To build our models, we first apply a 23-layers convolution neural network (CNN) to the first dataset since there is a large number of MRI images for the training purpose. However, when dealing with limited volumes of data, which is the case in the second dataset, our proposed "23-layers CNN" architecture faces overfitting problem. To address this issue, we use transfer learning and combine VGG16 architecture along with the reflection of our proposed "23 layers CNN" architecture. Finally, we compare our proposed models with those reported in the literature. Our experimental results indicate that our models achieve up to 97.8% and 100% classification accuracy for our employed datasets, respectively, exceeding all other state-of-the-art models. Our proposed models, employed datasets, and all the source codes are publicly available at: (https://github.com/saikat15010/Brain-Tumor-Detection).

This paper presents a fuzzy-optimization approach for solving the generation scheduling problem with consideration of wind and solar energy systems. Wind and solar energy are being considered in the power system to schedule unit power output to minimize the total thermal unit fuel cost. When performing the generation scheduling problem in conventional methods, the hourly load, available water, wind speed, solar radiation must be forecasted to prevent errors. However, actually there are always errors in these forecasted values. A characteristic feature of the proposed fuzzy-optimization approach is that the forecast hourly load, available water, wind speed and solar radiation errors can be taken into account using fuzzy sets. Fuzzy set notations in the hourly load, available water, wind speed, solar radiation, spinning reserve and total fuel cost are developed to obtain the optimal generation schedule under an uncertain environment. To demonstrate the effectiveness of the proposed method, the generation scheduling problem is performed in a simplified generation system. The results show that a proper generating schedule for each unit can be reached using the proposed method.

Forecasting of fast fluctuated and high-frequency financial data is always a challenging problem in the field of economics and modelling. In this study, a novel hybrid model with the strength of fractional order derivative is presented with their dynamical features of deep learning, long-short term memory (LSTM) networks, to predict the abrupt stochastic variation of the financial market. Stock market prices are dynamic, highly sensitive, nonlinear and chaotic. There are different techniques for forecast prices in the time-variant domain and due to variability and uncertain behavior in stock prices, traditional methods, such as data mining, statistical approaches, and non-deep neural networks models are not suited for prediction and generalized forecasting stock prices. While autoregressive fractional integrated moving average (ARFIMA) model provides a flexible tool for classes of long-memory models. The advancement of machine learning-based deep non-linear modelling confirms that the hybrid model efficiently extracts profound features and model non-linear functions. LSTM networks are a special kind of recurrent neural network (RNN) that map sequences of input observations to output observations with capabilities of long-term dependencies. A novel ARFIMA-LSTM hybrid recurrent network is presented in which ARFIMA model-based filters having the linear tendencies better than ARIMA model in the data and passes the residual to the LSTM model that captures nonlinearity in the residual values with the help of exogenous dependent variables. The model not only minimizes the volatility problem but also overcome the over fitting problem of neural networks. The model is evaluated using PSX company data of the stock market based on RMSE, MSE and MAPE along with a comparison of ARIMA, LSTM model and generalized regression radial basis neural network (GRNN) ensemble method independently. The forecasting performance indicates the effectiveness of the proposed AFRIMA-LSTM hybrid model to improve around 80% accuracy on RMSE as compared to traditional forecasting counterparts.

The inevitable rise and development of artificial intelligence (AI) was not a sudden occurrence. The greater the effect that AI has on humans, the more pressing the need is for us to understand it. This paper addresses research on the use of AI to evaluate new design methods and tools that can be leveraged to advance AI research, education, policy, and practice to improve the human condition. AI has the potential to educate, train, and improve the performance of humans, making them better at their tasks and activities. The use of AI can enhance human welfare in numerous respects, such as through improving the productivity of food, health, water, education, and energy services. However, the misuse of AI due to algorithm bias and a lack of governance could inhibit human rights and result in employment, gender, and racial inequality. We envision that AI can evolve into human-centered AI (HAI), which refers to approaching AI from a human perspective by considering human conditions and contexts. Most current discussions on AI technology focus on how AI can enable human performance. However, we explore AI can also inhibit the human condition and advocate for an in-depth dialog between technology- and humanity-based researchers to improve understanding of HAI from various perspectives.

. Here we sequenced the genomes of 442 humans from archaeological sites across Europe and Greenland (to a median depth of about 1×) to understand the global influence of this expansion. We find the Viking period involved gene flow into Scandinavia from the south and east. We observe genetic structure within Scandinavia, with diversity hotspots in the south and restricted gene flow within Scandinavia. We find evidence for a major influx of Danish ancestry into England; a Swedish influx into the Baltic; and Norwegian influx into Ireland, Iceland and Greenland. Additionally, we see substantial ancestry from elsewhere in Europe entering Scandinavia during the Viking Age. Our ancient DNA analysis also revealed that a Viking expedition included close family members. By comparing with modern populations, we find that pigmentation-associated loci have undergone strong population differentiation during the past millennium, and trace positively selected loci-including the lactase-persistence allele of LCT and alleles of ANKA that are associated with the immune response-in detail. We conclude that the Viking diaspora was characterized by substantial transregional engagement: distinct populations influenced the genomic makeup of different regions of Europe, and Scandinavia experienced increased contact with the rest of the continent.

A procedure for designing AC power line EMI filters is presented. This procedure is based on the analysis of conducted EMI problems and the use of a noise separator. Design examples are given, and results are experimentally verified.

BACKGROUND: The aim was to evaluate the diagnostic value of procalcitonin, C-reactive protein (CRP) and white blood cell count (WBC) in uncomplicated or complicated appendicitis by means of a systematic review and meta-analysis. METHODS: The Embase, MEDLINE and Cochrane databases were searched, along with reference lists of relevant articles, without language restriction, to September 2012. Original studies were selected that reported the performance of procalcitonin alone or in combination with CRP or WBC in diagnosing appendicitis. Test performance characteristics were summarized using hierarchical summary receiver operating characteristic (ROC) curves and bivariable random-effects models. RESULTS: Seven qualifying studies (1011 suspected cases, 636 confirmed) from seven countries were identified. Bivariable pooled sensitivity and specificity were 33 (95 per cent confidence interval (c.i.) 21 to 47) and 89 (78 to 95) per cent respectively for procalcitonin, 57 (39 to 73) and 87 (58 to 97) per cent for CRP, and 62 (47 to 74) and 75 (55 to 89) per cent for WBC. ROC curve analysis showed that CRP had the highest accuracy (area under ROC curve 0·75, 95 per cent c.i. 0·71 to 0·78), followed by WBC (0·72, 0·68 to 0·76) and procalcitonin (0·65, 0·61 to 0·69). Procalcitonin was found to be more accurate in diagnosing complicated appendicitis, with a pooled sensitivity of 62 (33 to 84) per cent and specificity of 94 (90 to 96) per cent. CONCLUSION: Procalcitonin has little value in diagnosing acute appendicitis, with lower diagnostic accuracy than CRP and WBC. However, procalcitonin has greater diagnostic value in identifying complicated appendicitis. Given the imperfect accuracy of these three variables, new markers for improving medical decision-making in patients with suspected appendicitis are highly desirable.

BACKGROUND: Timely and comprehensive analyses of causes of death stratified by age, sex, and location are essential for shaping effective health policies aimed at reducing global mortality. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2023 provides cause-specific mortality estimates measured in counts, rates, and years of life lost (YLLs). GBD 2023 aimed to enhance our understanding of the relationship between age and cause of death by quantifying the probability of dying before age 70 years (70q0) and the mean age at death by cause and sex. This study enables comparisons of the impact of causes of death over time, offering a deeper understanding of how these causes affect global populations. METHODS: GBD 2023 produced estimates for 292 causes of death disaggregated by age-sex-location-year in 204 countries and territories and 660 subnational locations for each year from 1990 until 2023. We used a modelling tool developed for GBD, the Cause of Death Ensemble model (CODEm), to estimate cause-specific death rates for most causes. We computed YLLs as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. Probability of death was calculated as the chance of dying from a given cause in a specific age period, for a specific population. Mean age at death was calculated by first assigning the midpoint age of each age group for every death, followed by computing the mean of all midpoint ages across all deaths attributed to a given cause. We used GBD death estimates to calculate the observed mean age at death and to model the expected mean age across causes, sexes, years, and locations. The expected mean age reflects the expected mean age at death for individuals within a population, based on global mortality rates and the population's age structure. Comparatively, the observed mean age represents the actual mean age at death, influenced by all factors unique to a location-specific population, including its age structure. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 250-draw distribution for each metric. Findings are reported as counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2023 include a correction for the misclassification of deaths due to COVID-19, updates to the method used to estimate COVID-19, and updates to the CODEm modelling framework. This analysis used 55 761 data sources, including vital registration and verbal autopsy data as well as data from surveys, censuses, surveillance systems, and cancer registries, among others. For GBD 2023, there were 312 new country-years of vital registration cause-of-death data, 3 country-years of surveillance data, 51 country-years of verbal autopsy data, and 144 country-years of other data types that were added to those used in previous GBD rounds. FINDINGS: The initial years of the COVID-19 pandemic caused shifts in long-standing rankings of the leading causes of global deaths: it ranked as the number one age-standardised cause of death at Level 3 of the GBD cause classification hierarchy in 2021. By 2023, COVID-19 dropped to the 20th place among the leading global causes, returning the rankings of the leading two causes to those typical across the time series (ie, ischaemic heart disease and stroke). While ischaemic heart disease and stroke persist as leading causes of death, there has been progress in reducing their age-standardised mortality rates globally. Four other leading causes have also shown large declines in global age-standardised mortality rates across the study period: diarrhoeal diseases, tuberculosis, stomach cancer, and measles. Other causes of death showed disparate patterns between sexes, notably for deaths from conflict and terrorism in some locations. A large reduction in age-standardised rates of YLLs occurred for neonatal disorders. Despite this, neonatal disorders remained the leading cause of global YLLs over the period studied, except in 2021, when COVID-19 was temporarily the leading cause. Compared to 1990, there has been a considerable reduction in total YLLs in many vaccine-preventable diseases, most notably diphtheria, pertussis, tetanus, and measles. In addition, this study quantified the mean age at death for all-cause mortality and cause-specific mortality and found noticeable variation by sex and location. The global all-cause mean age at death increased from 46·8 years (95% UI 46·6-47·0) in 1990 to 63·4 years (63·1-63·7) in 2023. For males, mean age increased from 45·4 years (45·1-45·7) to 61·2 years (60·7-61·6), and for females it increased from 48·5 years (48·1-48·8) to 65·9 years (65·5-66·3), from 1990 to 2023. The highest all-cause mean age at death in 2023 was found in the high-income super-region, where the mean age for females reached 80·9 years (80·9-81·0) and for males 74·8 years (74·8-74·9). By comparison, the lowest all-cause mean age at death occurred in sub-Saharan Africa, where it was 38·0 years (37·5-38·4) for females and 35·6 years (35·2-35·9) for males in 2023. Lastly, our study found that all-cause 70q0 decreased across each GBD super-region and region from 2000 to 2023, although with large variability between them. For females, we found that 70q0 notably increased from drug use disorders and conflict and terrorism. Leading causes that increased 70q0 for males also included drug use disorders, as well as diabetes. In sub-Saharan Africa, there was an increase in 70q0 for many non-communicable diseases (NCDs). Additionally, the mean age at death from NCDs was lower than the expected mean age at death for this super-region. By comparison, there was an increase in 70q0 for drug use disorders in the high-income super-region, which also had an observed mean age at death lower than the expected value. INTERPRETATION: We examined global mortality patterns over the past three decades, highlighting-with enhanced estimation methods-the impacts of major events such as the COVID-19 pandemic, in addition to broader trends such as increasing NCDs in low-income regions that reflect ongoing shifts in the global epidemiological transition. This study also delves into premature mortality patterns, exploring the interplay between age and causes of death and deepening our understanding of where targeted resources could be applied to further reduce preventable sources of mortality. We provide essential insights into global and regional health disparities, identifying locations in need of targeted interventions to address both communicable and non-communicable diseases. There is an ever-present need for strengthened health-care systems that are resilient to future pandemics and the shifting burden of disease, particularly among ageing populations in regions with high mortality rates. Robust estimates of causes of death are increasingly essential to inform health priorities and guide efforts toward achieving global health equity. The need for global collaboration to reduce preventable mortality is more important than ever, as shifting burdens of disease are affecting all nations, albeit at different paces and scales. FUNDING: Gates Foundation.

A model of interactive information technology (IT) usage that integrates network externalities with traditional usage motivations is proposed and is validated by a survey of instant messaging (IM) usage by university students in Taiwan. Network benefit, found to be a significant usage motivation, arises from direct and indirect sources, conceptualized as referent network size and perceived complementarity, respectively. Network benefit has a direct effect on user intention to use interactive IT and an indirect effect mediated by perceived enjoyment, and in turn it is affected by perceived complementarity. IT vendors can enhance product value by investing in value-added complementary products and services. Implications for IT usage theories and managerial practice are discussed.

Discontinuities caused by abrupt changes in cross-sectional dimensions or by concentrated loads result in discontinuity regions due to disturbance in the flow of internal forces. A simplified method, based on the softened strut-and-tie model, for determining the shear strength of discontinuity regions failing in diagonal compressions is proposed in this paper. Strength predictions of the resulting expressions correlate well with the 449 test results of deep beams, corbels, squat walls, and beam-column joints available from the literature. The proposed method incorporates the shear resisting mechanisms as postulated by the softened strut-and-tie model, and it is a function of the concrete strength, horizontal shear reinforcement, vertical shear reinforcement, and geometrical configuration of the discontinuity regions. A numerical example is included for illustration.