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The steady rise in carbon emissions has significantly exacerbated the global climate crisis, posing a severe threat to ecosystems due to the greenhouse gas effect. As one of the most pressing challenges of our time, the need for an immediate transition to renewable energy is imperative to meet the carbon reduction targets set by the Paris Agreement. Buildings, as major contributors to global energy consumption, play a pivotal role in climate change. This study diverges from previous research by employing multi-task deep learning techniques to develop a predictive model for electricity load in commercial buildings, incorporating auxiliary tasks such as temperature and cloud coverage. Using real data from a commercial building in Taiwan, this study explores the effects of varying batch sizes (100, 125, 150, and 200) on the model's performance. The findings reveal that the multi-task deep learning model consistently surpasses single-task models in predicting electricity load, demonstrating superior accuracy and stability. These insights are crucial for companies aiming to enhance energy efficiency and formulate effective renewable energy procurement strategies, contributing to broader sustainability efforts and aligning with global climate action goals. [ABSTRACT FROM AUTHOR]

This study seeks to identify the impact of sustainable development goals (SDGs) in predicting corporate financial performance (CFP) in the information communications technology (ICT) industry. Data over the period of 2016–2020 that are relevant to financial reporting and corporate social responsibility (CSR) reporting have been extracted for 208 firms in the ICT industry. Important variables have been identified to help predict the financial performance in the following years upon the publication of CSR reports. Drawing on resource‐based view and stakeholder theory, the purpose of this study is to find the quintessential variables that influence the prediction accuracy of financial performance. To better forecast earnings per share (EPS), machine learning feature selection methods have been implemented. The findings suggest that certain variables such as return on total assets, SDGs adoption and whether the firm has established KPI for SDGs achievements can help enhance EPS prediction. With the various predictive models, the artificial neural network model is the most effective in predicting CFPs. Most importantly, the adoption of SDGs can be utilized to sharpen the forecast on financial performance as it enables firms to bolster stakeholder engagement and evaluate environmental, social, and corporate governance efforts. [ABSTRACT FROM AUTHOR]

The European Union intends to adopt a more aggressive unilateral policy-carbon tariffs to promote the reduction of global carbon emissions. In our research, a global supply chain model is developed to determine the effects of carbon tariffs along with domestic carbon policies. Domestic carbon policies in which the retailer country has a (1) carbon tax or (2) cap-and-trade, while the manufacturing country has (1) a carbon tax or (2) no carbon policy are considered. The proposed production - inventory model for a three-stage supply chain comprises the supply of raw materials, a single export manufacturer's productionand delivery, and a single import retailer's orders and sales for deteriorating items. The exchange rate for transactions among supply chain members is also taken into consideration. Mathematical programming analysis is used to determine the manufacturer's optimal material supply, production, and delivery strategies, as well as the retailer's optimal pricing and replenishment strategies. The main goal is to maximize joint total profit in both settings, and this is achieved through numerical and sensitivity analysis. This study found that carbon tariffs have a positive impact on the emission reduction effect of cross-border supply chain systems. Additionally, it revealed that within a multinational three-stage supply chain, considering the deterioration of raw materials and finished products, the increase in the carbon tariff rates and exchange rates enhance the emission reduction effect of the supply chain and have a positive impact on the joint total profits of the supply chain. Finally, unlike previous studies, this study also explored that the adjustment of carbon tariff relief rate have a complex impact on the profits and emissions reduction of the supply chain members. These results can help enterprises understand the impact of carbon tariffs on supply chain inventory and pricing decisions. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has taken business uncertainty to an unprecedented level and put business agility and leadership under harsh tests, confronting a diverse and complicated stakeholder reality. Through original interview data and case studies from the hospitality industry in Taiwan collected and compiled since the beginning of the COVID-19 pandemic in 2020, the present research explores how strategic agility can be built, fostered, and enhanced through responsible leadership and CSR initiatives during a time of grand challenges in the hospitality industry. We also discuss and highlight the role played by one stakeholder, namely, the local government, in contributing to strategic agility through reinforcing and possibly enabling responsible leadership during hardships. This paper sheds light both empirically and theoretically on the roles played by business leaders in contributing to strategic agility, which, in turn, helps to build a more socially responsible organization. [ABSTRACT FROM AUTHOR]

The development of physicians' core competency is related to the quality of medical practice. Accordingly, knowing how to cultivate and evaluate core competency is an important issue for medical education and management. This study used the comprehensive core competency framework proposed by the Accreditation Council for Graduate Medical Education (ACGME) and employed fuzzy AHP (FAHP) and DEMANTEL methods to analyze the weight and priority, as well as the cause-and-effect cluster. The FAHP analysis yielded the following rankings of factors' importance: (1) patient care (C1; 27.83%), (2) medical knowledge (C2; 20.77%), (3) professionalism (C5; 17.93%), (4) interpersonal and communication skills (C4; 17.41%), (5) practice-based learning and improvement (C3; 15.52%), and (6) systems-based practice (C6; 8.233%). In terms of the DEMANTEL, the effect cluster included patient care (C1), professionalism (C5) and systems-based practice (C6), and the cause cluster included medical knowledge (C2), practice-based learning and improvement (C3), and interpersonal and communication skills (C4). The findings showed that patient care (C1) results from attitudes, patience, and the other five ACGME Core Competencies. Furthermore, emergency physicians' development also requires humanities and ethics training and practice to ensure practice-based learning (C3). This study demonstrates the importance of various factors in developing emergency physicians' core competencies, and the findings may provide a reference for future attempts at such competency development. [ABSTRACT FROM AUTHOR]

With the rapid development of medicine and technology, machine learning (ML) techniques are extensively applied to medical informatics and the suboptimal health field to identify critical predictor variables and risk factors. Metabolic syndrome (MetS) and chronic kidney disease (CKD) are important risk factors for many comorbidities and complications. Existing studies that utilize different statistical or ML algorithms to perform CKD data analysis mostly analyze the early-stage subjects directly, but few studies have discussed the predictive models and important risk factors for the stage-III CKD high-risk health screening population. The middle stages 3a and 3b of CKD indicate moderate renal failure. This study aims to construct an effective hybrid important risk factor evaluation scheme for subjects with MetS and CKD stages III based on ML predictive models. The six well-known ML techniques, namely random forest (RF), logistic regression (LGR), multivariate adaptive regression splines (MARS), extreme gradient boosting (XGBoost), gradient boosting with categorical features support (CatBoost), and a light gradient boosting machine (LightGBM), were used in the proposed scheme. The data were sourced from the Taiwan health examination indicators and the questionnaire responses of 71,108 members between 2005 and 2017. In total, 375 stage 3a CKD and 50 CKD stage 3b CKD patients were enrolled, and 33 different variables were used to evaluate potential risk factors. Based on the results, the top five important variables, namely BUN, SBP, Right Intraocular Pressure (R-IOP), RBCs, and T-Cho/HDL-C (C/H), were identified as significant variables for evaluating the subjects with MetS and CKD stage 3a or 3b. [ABSTRACT FROM AUTHOR]

Background: Knee osteoarthritis (OA) affects the quality of life (QOL) and balance control of elderly people; our study explored the balance factors that affected the QOL in patients with knee OA. Objectives: To determine the balance factors that affected the QOL of patients with knee OA who attended general clinics. Method: A total of 30 healthy controls and 60 patients with mild-to-moderate bilateral knee OA, all aged 55–75 years, were enrolled in our cross-sectional study. All participants were interviewed; the Medical Outcomes Study 36-Item Short-Form Health Survey was used to assess their QOL in eight dimensions, and the Balance Master System was used to evaluate their balance control according to six parameters. Descriptive statistics were used to reduce the data; an independent t -test determined differences between the two groups, and a multiple regression analysis was undertaken to establish associations between variables from the balance control test and SH36 physical and mental health components. The level of statistical significance was set at 5%. Results: In the OA group, significant negative correlations were observed between sway velocity and the physical health component (p = 0.003) and between sway velocity and the mental health component (p = 0.006). Thus, sway velocity had a major impact on the QOL of patients with knee OA. Conclusions: The sway velocity at the centre of gravity in balance control was a crucial factor for determining the QOL of patients with bilateral knee OA. Clinical implications: Sway velocity is a key factor affecting the QOL and may provide a basis to formulate preventive actions and design treatment goals for patients with knee OA. [ABSTRACT FROM AUTHOR]

Purpose: As global aging progresses, the health management of chronic diseases has become an important issue of concern to governments. Influenced by the aging of its population and improvements in the medical system and healthcare in general, Taiwan's population of patients with chronic kidney disease (CKD) has tended to grow year by year, including the incidence of high-risk cases that pose major health hazards to the elderly and middle-aged populations. Methods: This study analyzed the annual health screening data for 65,394 people from 2010 to 2015 sourced from the MJ Group – a major health screening center in Taiwan – including data for 18 risk indicators. We used five prediction model analysis methods, namely, logistic regression (LR) analysis, C5.0 decision tree (C5.0) analysis, stochastic gradient boosting (SGB) analysis, multivariate adaptive regression splines (MARS), and eXtreme gradient boosting (XGboost), with estimated glomerular filtration rate (e-GFR) data to determine G3a, G3b & G4 stage CKD risk factors. Results: The LR analysis (AUC=0.848), SGB analysis (AUC=0.855), and XGboost (AUC=0.858) generated similar classification performance levels and all outperformed the C5.0 and MARS methods. The study results showed that in terms of CKD risk factors, blood urea nitrogen (BUN) and uric acid (UA) were identified as the first and second most important indicators in the models of all five analysis methods, and they were also clinically recognized as the major risk factors. The results for systolic blood pressure (SBP), SGPT, SGOT, and LDL were similar to those of a related study. Interestingly, however, socioeconomic status-related education was found to be the third important indicator in all three of the better performing analysis methods, indicating that it is more important than the other risk indicators of this study, which had different levels of importance according to the different methods. Conclusion: The five prediction model methods can provide high and similar classification performance in this study. Based on the results of this study, it is recommended that education as the socioeconomic status should be an important factor for CKD, as high educational level showed a negative and highly significant correlation with CKD. The findings of this study should also be of value for further discussions and follow-up research. [ABSTRACT FROM AUTHOR]

This paper investigated a multistage sustainable production–inventory model for deteriorating items (i.e., raw materials and finished goods) with price-dependent demand and collaborative carbon reduction technology investment under carbon tax regulation. The model was developed by first defining the total profit of the supply chain members under carbon tax regulation and, second, considering a manufacturer (leader)–retailer (follower) Stackelberg game. The optimal equilibrium solutions that maximize the manufacturer's and retailer's total profits were determined through the method analysis. An algorithm complemented the model to determine the optimal equilibrium solutions, which were then treated with sensitivity analyses for the major parameters. Based on the numerical analysis, (a) carbon tax policies help reduce carbon emissions for both the manufacturer and retailer; (b) most carbon emissions from supply chain operations negatively impact the total profits of both members; (c) the retailer may increase the optimal equilibrium selling price to respond to an increase in carbon emissions from supply chain operations or carbon tax; and (d) autonomous consumption positively affects both members' optimal equilibrium policies and total profits, whereas induced consumption does the opposite. These findings are very managerial and instructive for companies seeking profits and fulfilling environmental responsibility and governments. [ABSTRACT FROM AUTHOR]

The study utilized co-word analysis to explore papers in the field of Internet of Things to examine the scientific development in the area. The research data were retrieved from the WOS database from the period between 2000 and 2014, which consists of 758 papers. By using co-word analysis, this study found 7 clusters that represent the intellectual structure of IoT, including 'IoT and Security', 'Middleware', 'RFID', 'Internet', 'Cloud computing', 'Wireless sensor networks' and '6LoWPAN'. To understand these intellectual structures, this study used a co-occurrence matrix based on Pearson's correlation coefficient to create a clustering of the words using the hierarchical clustering technique. To visualize these intellectual structures, this study carried out a multidimensional scaling analysis, to which a PROXCAL algorithm was applied. [ABSTRACT FROM AUTHOR]

E-commerce (EC) is sweep across the globe and has become a most important commercial activity. Accordingly, EC also causes the academia's research interests. A lot of research achievements have been gained in recent years. This paper takes these achievements as research object and collects 8488 research papers published in academic journals during 2000-2013 included in Web of Science database. Using text mining techniques, 68 terms are identified as the main keywords of EC field. Then the scientific structure of the EC is mapped through multidimensional scaling, based upon the co-occurrence of the main terms in the academic journals. The results show that the EC domain is composed of three main fields, such as technology, management and customer. Furthermore, knowledge graph based on the EC research network is visualized and it shows that the whole EC research papers covering seven important subnets, which are: internet, consumer behaviour, customer satisfaction, online shopping, reputation, Taiwan and knowledge management. [ABSTRACT FROM AUTHOR]

Background: to date, no data on the effect of adjuvant whole breast radiotherapy (WBRT) on oncologic outcomes, such as all-cause death, locoregional recurrence (LRR), and distant metastasis (DM), are available in women with left-side breast invasive ductal carcinoma (IDC) and heart failure with reduced ejection fraction (HFrEF). Patients and Methods: we included 294 women with left-breast IDC at clinical stages IA–IIIC and HFrEF receiving breast-conserving surgery (BCS) followed by adjuvant WBRT or non-adjuvant WBRT. We categorized them into two groups based on their adjuvant WBRT status and compared their overall survival (OS), LRR, and DM outcomes. We calculated the propensity score and applied inverse probability of treatment weighting (IPTW) to create a pseudo-study cohort. Furthermore, we performed a multivariate analysis of the propensity score–weighted population to obtain hazard ratios (HRs). Results: in the IPTW-adjusted model, adjuvant WBRT (adjusted HR [aHR]: 0.60; 95% confidence interval [CI]: 0.44–0.94) was a significant independent prognostic factor for all-cause death (p = 0.0424), and the aHR (95% CI) of LRR and DM for adjuvant WBRT was 0.33 (0.24–0.71; p = 0.0017) and 0.37 (0.22–0.63; p = 0.0004), respectively, compared with the non-adjuvant WBRT group. Conclusion: Adjuvant WBRT was associated with a decrease in all-cause death, LRR, and DM in women with left IDC and HFrEF compared with non-adjuvant WBRT. [ABSTRACT FROM AUTHOR]

Knee osteoarthritis (OA) affects the quality of life (QOL) of elderly people; this study examines the demographic characteristics and QOL of patients with knee OA and identifies demographic characteristics that affect the QOL of these patients. In this cross-sectional study, 30 healthy controls and 60 patients with mild-to-moderate bilateral knee OA aged between 55 and 75 years were enrolled. All participants completed a questionnaire containing questions on 10 demographic characteristics and the Medical Outcome Study 36-Item Short-Form Health Survey (SF-36), and their QOL scores in the eight dimensions of the SF-36 were evaluated. In the OA group, significant correlations were observed between monthly disposable income and physical and mental health components. Monthly disposable income was found to considerably affect the QOL of patients with bilateral knee OA (i.e., it is a crucial factor affecting these patients). The findings of this study may provide a reference for formulating preventive strategies for healthy individuals and for future confirmatory research. [ABSTRACT FROM AUTHOR]

The study investigates the information content of SGX-DT Nikkei 225 futures prices during the non-cash-trading (NCT) period using an artificial neural network model. The cash market closing index, the futures prices from a period in the same trading day and on the following trading day are utilized to determine the appropriate input nodes of a back propagation neural network model in forecasting the opening cash price index. Sensitivity analysis is first employed to address and solve the issue of finding the appropriate network topology. Extensive studies are then performed on the robustness of the constructed network by using different training and testing sample sizes. The effectiveness of the method is demonstrated on data from a 6-month historical record (1998–99). Analytic results demonstrate that the proposed neural network model outperforms a neural network model with the previous day's closing index as the input node and the random walk model forecasts. It, therefore, indicates that there is valuable information involved in futures prices during the NCT period that can be used to forecast the opening cash market price index. [ABSTRACT FROM AUTHOR]

Background: Low-dose aspirin (100 mg) is widely used in preventing cardiovascular disease in chronic kidney disease (CKD) because its benefits outweighs the harm, however, its effect on clinical outcomes in patients with predialysis advanced CKD is still unclear. This study aimed to assess the effect of aspirin use on clinical outcomes in such group. Methods: Patients were selected from a nationwide diabetes database from January 2009 to June 2017, and divided into two groups, a case group with aspirin use (n = 3021) and a control group without aspirin use (n = 9063), by propensity score matching with a 1:3 ratio. The Cox regression model was used to estimate the hazard ratio (HR). Moreover, machine learning method feature selection was used to assess the importance of parameters in the clinical outcomes. Results: In a mean follow-up of 1.54 years, aspirin use was associated with higher risk for entering dialysis (HR, 1.15 [95%CI, 1.10–1.21]) and death before entering dialysis (1.46 [1.25–1.71]), which were also supported by feature selection. The renal effect of aspirin use was consistent across patient subgroups. Nonusers and aspirin users did not show a significant difference, except for gastrointestinal bleeding (1.05 [0.96–1.15]), intracranial hemorrhage events (1.23 [0.98–1.55]), or ischemic stroke (1.15 [0.98–1.55]). Conclusions: Patients with predialysis advanced CKD and anemia who received aspirin exhibited higher risk of entering dialysis and death before entering dialysis by 15% and 46%, respectively. [ABSTRACT FROM AUTHOR]

Developing an effective sports performance analysis process is an attractive issue in sports team management. This study proposed an improved sports outcome prediction process by integrating adaptive weighted features and machine learning algorithms for basketball game score prediction. The feature engineering method is used to construct designed features based on game-lag information and adaptive weighting of variables in the proposed prediction process. These designed features are then applied to the five machine learning methods, including classification and regression trees (CART), random forest (RF), stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and extreme learning machine (ELM) for constructing effective prediction models. The empirical results from National Basketball Association (NBA) data revealed that the proposed sports outcome prediction process could generate a promising prediction result compared to the competing models without adaptive weighting features. Our results also showed that the machine learning models with four game-lags information and adaptive weighting of power could generate better prediction performance. [ABSTRACT FROM AUTHOR]

Most patients face expensive healthcare management after coronary artery bypass grafting (CABG) surgery, which brings a substantial financial burden to the government. The National Health Insurance Research Database (NHIRD) is a complete database containing over 99% of individuals' medical information in Taiwan. Our research used the latest data that selected patients who accepted their first CABG surgery between January 2014 and December 2017 (n = 12,945) to predict which factors will affect medical expenses, and built the prediction model using different machine learning algorithms. After analysis, our result showed that the surgical expenditure (X4) and 1-year medical expenditure before the CABG operation (X14), and the number of hemodialysis (X15), were the key factors affecting the 1-year medical expenses of CABG patients after discharge. Furthermore, the XGBoost and SVR methods are both the best predictive models. Thus, our research suggests enhancing the healthcare management for patients with kidney-related diseases to avoid costly complications. We provide helpful information for medical management, which may decrease health insurance burdens in the future. [ABSTRACT FROM AUTHOR]

Secondary hormone therapy, abiraterone and enzalutamide, has improved outcomes for metastatic castration-resistant prostate cancer (mCRPC) and prolonged patients' lives significantly. Various studies have compared the cancer-related outcomes, adverse effects, and drug-induced comorbidities in patients with mCRPC who are treated with abiraterone or enzalutamide. However, few studies have explored associations between survival and comorbidities or comprehensive analyzed newly developed comorbidities during and after secondary hormone therapy. We attempted to clarify whether the Charlson comorbidity index (CCI) overall or itemized is predictive for overall survival, and we compared newly developed comorbidities between abiraterone and enzalutamide groups. We extracted data about expenses and comorbidities for patients who have mCRPC, received abiraterone and enzalutamide and met pre-examination operation criteria between September 2016 and December 2017 from the Taiwan National Health Insurance database. A total of 1153 patients with mCRPC who received abiraterone (n = 782) or enzalutamide (n = 371) with or without previous chemotherapy were included. We used the propensity score to match confounding factors, including age, pre-existing comorbidities, and precipitating factors for comorbidity (e.g., hypertension, hyperlipidemia), to eliminate selection bias in the comparison of newly developed comorbidities. Cox regression analysis was used for overall survival. We found that enzalutamide is superior to abiraterone with regard to overall survival. Our study revealed that there is no statistically significant difference in development of new comorbidities between abiraterone and enzalutamide group. Moreover, the CCI score, rather than any single item of the CCI, was a statistically significant predictor for overall survival. [ABSTRACT FROM AUTHOR]

Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults' survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study's advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients' survival risk before a CABG operation, early prevention and disease management would be possible. [ABSTRACT FROM AUTHOR]

The sports market has grown rapidly over the last several decades. Sports outcomes prediction is an attractive sports analytic challenge as it provides useful information for operations in the sports market. In this study, a hybrid basketball game outcomes prediction scheme is developed for predicting the final score of the National Basketball Association (NBA) games by integrating five data mining techniques, including extreme learning machine, multivariate adaptive regression splines, k-nearest neighbors, eXtreme gradient boosting (XGBoost), and stochastic gradient boosting. Designed features are generated by merging different game-lags information from fundamental basketball statistics and used in the proposed scheme. This study collected data from all the games of the NBA 2018–2019 seasons. There are 30 teams in the NBA and each team play 82 games per season. A total of 2460 NBA game data points were collected. Empirical results illustrated that the proposed hybrid basketball game prediction scheme achieves high prediction performance and identifies suitable game-lag information and relevant game features (statistics). Our findings suggested that a two-stage XGBoost model using four pieces of game-lags information achieves the best prediction performance among all competing models. The six designed features, including averaged defensive rebounds, averaged two-point field goal percentage, averaged free throw percentage, averaged offensive rebounds, averaged assists, and averaged three-point field goal attempts, from four game-lags have a greater effect on the prediction of final scores of NBA games than other game-lags. The findings of this study provide relevant insights and guidance for other team or individual sports outcomes prediction research. [ABSTRACT FROM AUTHOR]