Greenhouse gases (mainly carbon dioxide) is one of the important factors contributing to extreme weather we face today. The carbon cap-and trade and carbon offset are common but important carbon ...emission reduction policies in many countries. Further, carbon emissions generated by corporate activities can be effectively reduced through specific capital investment in green technology. However, such kind of capital investment is rather costly and is unlikely for a single company to solely invest in it. Moreover, most decision-making situations in business are correlated instead of independent. Therefore, this paper explores potential competitive and cooperative issues of the sustainable product-inventory models with collaborative investment in carbon emission reduction technology under carbon cap-and trade and carbon offset polies. A Stackelberg approach of game theory is utilized for determining the optimal equilibrium solution between the buyer and the vendor under different carbon emission reductions. Realistic data examples are used to demonstrate the solution and sensitivity analysis on the main variables. The results indicate that proportions of investment by the vendor and buyer in carbon emission reduction technology play a critical role in both parties’ shipping, ordering strategies and profits. This role becomes more prominent as the proportion of investment increases. In addition, an increased proportion of investment in carbon emission reduction technology involves increased investment and thus contributes to the fulfillment of the carbon reduction goal. Finally, a comparison between the carbon cap-and-trade and carbon offset policies reveals that, although increases in the carbon trading price and carbon offset price are both conducive to carbon emissions inhibition, they exert different effects on the total profits of the vendor and buyer.
The great value of applying digital games in language learning has been highlighted. However, there has been a lack of attention paid to the effects of prior knowledge in a contextual game-based ...language learning environment. To this end, this study developed an MMORPG-based educational game to facilitate English learning, aiming at investigating how students' different levels of prior knowledge, in terms of their prior English ability and online gaming experience, affect their learning performance and anxiety. The results showed that the high English ability students significantly outperformed those with low English ability, the low online gaming experience students significantly outperformed those with high online gaming experience, and the low English ability students experienced significantly higher degrees of anxiety than those with high English ability. Furthermore, the results showed that prior English ability was positively correlated to learning performance but negatively correlated to anxiety, and anxiety was negatively correlated to learning performance. These findings suggest that a contextual game-based learning environment can be exploited as a useful tool to support language learning; however, the learning performance and anxiety created by the environment were affected to varying degrees by the levels of students' prior knowledge.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The proposed model is based on a three-stage multinational supply chain production-inventory model, considering the deterioration of both raw materials and finished products. The application of ...cross-border carbon policy combinations is an innovation of this study, and we provide assumptions for two situations.
Display omitted
•A cross-border production-inventory model for deteriorating items is developed.•Two carbon policy combinations in a global supply chain system are considered.•Comparison between proposed model and previous related studies is provided.•The concavity of the optimal solution has been verified by theoretical analysis.•Numerical examples and sensitivity analysis are done to gain managerial insights.
This study is the first to propose a cross-border production-inventory model for deteriorating items under different carbon emission policy combinations by considering a global supply chain system with a single manufacturer from abroad and a single domestic retailer. An integrated three-stage supply chain that includes material supply, manufacturer’s production and delivery, and retailer’s ordering and sales is designed. Two carbon policy combinations are considered: (1) the manufacturer faces a carbon cap-and-trade policy, and the retailer faces a carbon tax policy; (2) the manufacturer faces a carbon tax policy and the retailer faces a carbon cap-and-trade policy. The main purpose is to determine the optimal material supply, production, and delivery strategies for the manufacturer and the optimal replenishment strategies for the retailer to maximize the joint total profit of the supply chain in each situation, while observing the solutions’ carbon reduction benefits. This study uses mathematical programming to find optimal solutions for the manufacturer and the retailer, with numerical examples presented to demonstrate the solution procedures for comparing different carbon policy combinations. The results show that under different policy combinations, fluctuations in carbon prices, carbon taxes, and exchange rates have different effects on the profits of the entire supply chain and each member, as well as the carbon reduction’s social benefits. This is expected to provide enterprise or supply chain decision makers, especially in multinational enterprises, with valuable references for opting for a green supply chain.
The continuing attention to the educational value of digital games highlights the need for more focused literature reviews in order to identify critical gaps and opportunities in domain-specific ...areas. The current study thus set out to provide a scoping overview of empirical evidence on the use and impacts of digital games in language education from 2007 to 2016, as a means to advance the emerging research on digital game-based language learning (DGBLL). A total of 50 selected studies were systematically analyzed, revealing the following findings: (1) Most of the selected DGBLL studies adopted mixed methods to examine the educational use of digital games; (2) Immersive games, notably massively multiplayer online role-playing games, were the most common genre in the current DGBLL literature; (3) Most of the games for language learning were custom-built by DGBLL researchers; (4) Personal computers were the most common platforms for playing games to support language learning; (5) Most of the DGBLL studies adopted games to facilitate the learning of English as a second or foreign language; (6) Most of the research on DGBLL investigated learners with mixed levels of language proficiency; (7) University students were the most frequently selected samples in the existing DGBLL literature; and (8) The majority of DGBLL studies featured positive outcomes in regard to student learning, with the most frequently reported ones being related to affective or psychological states, closely followed by language acquisition. Taken as a whole, these findings reflect the diverse nature of this field and suggest the overall feasibility of using digital games for promoting the language and literacy learning of both native and non-native speakers in various aspects. Several promising but under-researched areas were also identified in this review, along with discussions on their implications for future investigations.
•A review of empirical studies on digital games for language learning was conducted.•A variety of game genres were identified, with immersive games being most popular.•Most of the studies adopted games to facilitate the teaching and learning of English.•The reviewed studies featured positive student learning outcomes in various aspects.•Several promising but under-researched areas were discussed with suggestions.
Since the advent of new technology for learning, innovative language instructors have been constantly seeking new pedagogy to match the potential of technology-enhanced instruction. While previous ...studies have supported the adoption of technologies to facilitate language teaching and learning, research into enhancing English as a foreign language (EFL) learners' oral proficiency by creating an online learning community in a flipped classroom remains insufficient. Therefore, the current study examined the impact of an online learning community in a flipped classroom, specifically via mobile platforms, on EFL learners' oral proficiency and student perceptions. Fifty English-majored sophomores enrolled in two oral training classes at a four-year comprehensive university in central Taiwan participated in this study. A mixed method was employed to analyze multiple sources of data, including pre- and post-tests on oral reading and comprehension questions, a ＂Community of Inquiry＂ (CoI) questionnaire, and semi-structured focus-group interviews. The results from multiple sources indicated that the online learning community not only facilitated meaningful and positive collaboration but also significantly improved the participants' oral proficiency, thus leading to more active engagement in highly interactive learning activities, such as storytelling, dialogue collaboration, class discussion, and group presentations.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, IZUM, KILJ, NMLJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Egg white proteins were hydrolysed separately using five different proteases to obtain antioxidant peptides. The antioxidant activity of egg white protein hydrolysates was influenced by the time of ...hydrolysis and the type of enzyme. Of the various hydrolysates produced, papain hydrolysate obtained by 3-h hydrolysis (PEWPH) displayed the highest DPPH radical scavenging activity. PEWPH could also quench the superoxide anion and hydroxyl radicals, effectively inhibit lipid peroxidation and exhibit reducing power. Then, PEWPH was purified sequentially by ultrafiltration, gel filtration, RP-HPLC and two fractions with relatively strong antioxidant activity were subsequently subjected to LC–MS/MS for peptide sequence identification. The sequences of the two antioxidant peptides were identified to be Tyr-Leu-Gly-Ala-Lys (551.54 Da) and Gly-Gly-Leu-Glu-Pro-Ile-Asn-Phe-Gln (974.55 Da), and they were identified for the first time from food-derived protein hydrolysates. Last, the two purified peptides were synthesized and they showed 7.48- and 6.02-fold higher DPPH radical scavenging activity compared with the crude PEWPH, respectively. These results indicate that PEWPH and/or its isolated peptides may be useful ingredients in food and nutraceutical applications.
Cryptocurrency, particularly Bitcoin, is a significant financial asset for investors, but predicting its price is challenging due to its volatile and erratic nature. In this study, we suggest a novel ...ternary-frequency (TF) prediction scheme for Bitcoin prices, which combines complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), a time series clustering method, and the reconstruction of intrinsic mode functions (IMFs). In the proposed scheme, CEEMDAN was utilized to decompose Bitcoin’s daily price into IMFs, then prototypes of time series clustering were used to construct robust ensemble clusters. The IMFs in the ensemble clusters were reconstructed into ensemble time series and then identified as three different frequencies, which were respectively used in a prediction model to generate different predicted values, and then aggregated to produce the final prediction results. To generate three different TF Bitcoin price prediction schemes, this study employed three prominent prediction algorithms: autoregressive integrated moving average with exogenous variables (ARIMAX), multivariate adaptive regression splines (MARS), and extreme gradient boosting (XGB); these resulted in three distinct models, named TF-ARIMAX, TF-MARS, and TF-XGB. Empirical results from the two daily Bitcoin and one daily Ethereum closing price datasets showed that the proposed TF prediction scheme outperformed other benchmark approaches. Moreover, among the three TF models, TF-MARS produced superior prediction accuracy compared to both TF-ARIMAX and TF-XGB models, and proved to be an effective alternative for cryptocurrency price prediction.
The American Heart Association has recently established seven ideal cardiovascular health metrics for cardiovascular health promotion and disease reduction (i.e., non-smoking, normal body mass index, ...physically active, healthy diet, and normal levels of cholesterol, blood pressure and fasting blood glucose). The present study seeks to evaluate how well these metrics predict mortality from all causes and cardiovascular diseases in adult Chinese living in a northern industrial city.
Data of 95,429 adults who participated in the Kailuan cohort study from June 2006 to October 2007 was analyzed. All participants underwent questionnaire assessment, clinical examination, laboratory assessments and were followed up biannually. During a median follow-up of 4.02 years, 1,843 deaths occurred, with 597 deaths resulting from cardiovascular diseases. Lower mortality rates from all causes and cardiovascular diseases were observed among the subjects who met a higher number of the ideal health metrics. Compared to the participants who met none or one ideal health metric, those meeting ≥5 ideal health metrics had a lower risk of all-cause mortality by 30% (adjusted hazard ratio, 0.70; 95% confidence interval, 0.56-0.88) and a lower risk of mortality from cardiovascular diseases by 39% (adjusted hazard ratio, 0.61; 95% confidence interval, 0.41-0.89) . Four metrics (smoking status, physical activity, blood pressure and fasting blood glucose) were significantly associated with all-cause mortality. Three metrics (physical activity, blood pressure and fasting blood glucose) were significantly associated with mortality from cardiovascular diseases.
The number of ideal health metrics is negatively associated with mortality rates from all causes and cardiovascular diseases among adults in a Northern Chinese industrial city. The data supports the AHA recommendation of ideal health metrics for adults from Northern China.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Longitudinal data, while often limited, contain valuable insights into features impacting clinical outcomes. To predict the progression of chronic kidney disease (CKD) in patients with metabolic ...syndrome, particularly those transitioning from stage 3a to 3b, where data are scarce, utilizing feature ensemble techniques can be advantageous. It can effectively identify crucial risk factors, influencing CKD progression, thereby enhancing model performance. Machine learning (ML) methods have gained popularity due to their ability to perform feature selection and handle complex feature interactions more effectively than traditional approaches. However, different ML methods yield varying feature importance information. This study proposes a multiphase hybrid risk factor evaluation scheme to consider the diverse feature information generated by ML methods. The scheme incorporates variable ensemble rules (VERs) to combine feature importance information, thereby aiding in the identification of important features influencing CKD progression and supporting clinical decision making. In the proposed scheme, we employ six ML models-Lasso, RF, MARS, LightGBM, XGBoost, and CatBoost-each renowned for its distinct feature selection mechanisms and widespread usage in clinical studies. By implementing our proposed scheme, thirteen features affecting CKD progression are identified, and a promising AUC score of 0.883 can be achieved when constructing a model with them.
Purpose: Cardiovascular disease (CVD) is a major worldwide health burden. As the risk factors of CVD, hypertension, and hyperlipidemia are most mentioned. Early stage hypertension in the population ...with dyslipidemia is an important public health hazard. This study was the application of data-driven machine learning (ML), demonstrating complex relationships between risk factors and outcomes and promising predictive performance with vast amounts of medical data, aimed to investigate the association between dyslipidemia and the incidence of early stage hypertension in a large cohort with normal blood pressure at baseline. Methods: This study analyzed annual health screening data for 71,108 people from 2005 to 2017, including data for 27 risk-related indicators, sourced from the MJ Group, a major health screening center in Taiwan. We used five machine learning (ML) methods—stochastic gradient boosting (SGB), multivariate adaptive regression splines (MARS), least absolute shrinkage and selection operator regression (Lasso), ridge regression (Ridge), and gradient boosting with categorical features support (CatBoost)—to develop a multi-stage ML algorithm-based prediction scheme and then evaluate important risk factors at the early stage of hypertension, especially for groups with high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) levels within or out of the reference range. Results: Age, body mass index, waist circumference, waist-to-hip ratio, fasting plasma glucose, and C-reactive protein (CRP) were associated with hypertension. The hemoglobin level was also a positive contributor to blood pressure elevation and it appeared among the top three important risk factors in all LDL-C/HDL-C groups; therefore, these variables may be important in affecting blood pressure in the early stage of hypertension. A residual contribution to blood pressure elevation was found in groups with increased LDL-C. This suggests that LDL-C levels are associated with CPR levels, and that the LDL-C level may be an important factor for predicting the development of hypertension. Conclusion: The five prediction models provided similar classifications of risk factors. The results of this study show that an increase in LDL-C is more important than the start of a drop in HDL-C in health screening of sub-healthy adults. The findings of this study should be of value to health awareness raising about hypertension and further discussion and follow-up research.
PDF
