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In addition to offering methionine, 2-hydroxy-4-methylthiobutyric acid (HMTBa) is also an organic acid and shows excellent bacteriostasis. Therefore, 3 experiments were conducted to determine the influence of drinking water supplemented HMTBa in combination with acidifier on performance, intestinal development, and microflora in broilers. The addition of different concentration (0.02–0.20%) of the blend of HMTBa and other acids significantly reduced the pH of water and exerted antimicrobial activity in dose-dependent manner in vitro. The outcomes from animal trial consisting of the drinking water with blended acidifier at 0.00, 0.05, 0.10, 0.15, and 0.20% indicated that the water with 0.15 or 0.20% acidifier resulted in linear and quadratic higher body weight at 42 d, gain and water consumption during 1 to 42 d (P < 0.05). In experiment 3, responding to graded blended acidifier in drinking water, birds receiving 0.10, 0.15, and 0.20% acidifier decreased the internal pH of gastrointestinal tract and muscle, and exhibited increased duodenal weight, length, villus high, and the ratio of villus high to crypt depth. Drinking water with 0.2% blended acidifier increased the abundance of probiotics (Bacteroidaceae, Ruminococcaceae, and Lachnospiraceae) and decreased the account of pathogenic bacteria such as Desulfovibrionaceae. Alternations in gut microflora were closely related to the metabolism of carbohydrate, amino acid, and vitamins. These findings, therefore, suggest that drinking water with 0.10 to 0.13% the combination HMTBa with acidifier might benefit to intestinal development and gut microbiota, and the subsequent produce a positive effect on the performance of broilers.

In recent years, educational resources have gradually been digitized, and digital education platforms have gradually become popular. We use AI to accurately assist people in performing daily tasks through a machine learning process. In education, we can use AI in many situations, such as predicting student's learning outcome and discovering student's learning strategies. However, most solutions have not yet utilized modern AI capabilities, such as natural language processing. This research aims to help teachers use machines to automatically generate short answer questions to reduce the time for teachers to write exam questions. In addition, the main reason we focus on short answers is that many studies prove that short answer exercises can enhance student's long-term memory, thereby improving their learning performance. We propose an automatic question generation (AQG) system that combines syntax-base and semantics-base, in order to prove that the system is highly available and improve student's learning performance, we conducted experiments with 41 students. The experimental results show that student’s learning performance has been significantly improved, which means that by repeatedly testing the machine question generation system, students can deepen their long-term memory of course knowledge.

Nowadays, there are more and more researches focused on prediction of learning outcome, and most of them applied quantitate type of analysis approaches. Thus, we want to apply another type of analysis approach to do early prediction. In this research, we applied temporal features and analysis approach to predict students’ learning outcomes and identify at-risk students. The result shows that using temporal features is effective on early prediction of learning outcome and there exists differences of learning behaviors between students which have different learning background.

In order to enhance the experience of learning, many educators applied learning analytics in a classroom, the major principle of learning analytics is targeting at-risk student and given timely int...

Different lipid sources differ in the fatty acid profiles and differently affect growth performance as well as immune function of broilers. The influences of different dietary lipid sources on growth performance, nutrient digestibility, blood T lymphocyte population, and cardiac antioxidant status were investigated of broilers. A total of 360 one-day-old male broilers (BW = 44 ± 3 g) were randomized into 3 treatment groups, consisting of 6 replicates with 20 birds in each group. Broilers received standard diets supplemented with 5% (wt/wt) of lard (LD, as a control diet), sesame oil (SO), or flaxseed oil (FO). Broilers in both SO and FO treatment groups had lower (P

This study is based on a novel conceptual framework, precision education, and takes a blended Python programming course as an example to explore how to implement precision education that includes diagnosis, prediction, prevention and treatment. Precision education follows the principles of personalized services for precision medicine. Its purpose is to strengthen the learning risk prediction and early intervention mentioned in emerging learning analytics through big data, artificial intelligence and other emerging technologies, thereby improving teacher teaching quality and student learning efficiency. This study is based on the design of the e-book learning dashboard, so that teachers can quickly understand students’ learning status, and improve the e-book through students’ feedback on the dashboard to achieve precision diagnosis. Next, this study uses machine learning algorithms to predict students' learning performance, and thus determine whether students are at-risk to achieve precision prediction. Finally, through the correspondence between reading strategy and reading sequence, and then clearly distinguish the types of students by grouping, and use it as a treatment target for precision treatment and prevention. It is hoped that this empirical study can be used as a case study for implementing precision education.

In this paper, a compact torus (CT) injector system is designed for experimental advanced superconducting tokamak (EAST). The injector system consists of a coaxial electrode and four pulsed power systems and generates self-organized CT with high density and velocity. In addition, the performance of EAST-CTI is evaluated using a series of diagnostics systems. The experimental results show that the velocity, electron density, and number of particles of the CT are 56.0 km/s, 8.73 × 1020 m-3, and 2.4 × 1018, respectively. The detailed description and experimental results are presented in this paper.

Edge localized modes (ELMs) are completely suppressed by ion cyclotron resonant heating (ICRH) during H-mode discharge, which is first observed in the Experimental Advanced Superconducting Tokamak (EAST). The electromagnetic turbulence model in the BOUT++ framework is applied to the simulations, with the aim of obtaining the key physics mechanisms of ELM suppression by ICRH. The simulations, for the EAST experiment with no ELMs after ICRH, reveal that the radio frequency (RF) sheath plays a key role in the suppression of ELMs. The simulation results show that: (a) using the flow-balanced electric field, the ELM sizes are about 3.4% before ICRH, and 2.1% after ICRH. Although the smaller ELM is obtained with ICRH, it still belongs to relatively large ELMs; (b) instead of the flow-balance condition, the radial electric field E r is calculated using the RF sheath model, and the ELM size is about 0.36% with ICRH, much closer to the small/no ELM regime. After considering the effect of ICRH, the larger E × B shear rate in the pedestal and scrape-off layer induced by the RF sheath reduces the linear growth rate, and enhances the nonlinear mode coupling. Furthermore, we find that there is an effective range of the sheath potential, in which the ELM can be well suppressed, but beyond this range the ELM may be only mitigated rather than suppressed. This can reasonably explain why the probability of ELM suppression by ICRH appearing in the experiments is low.

A general phenomenon that the edge localized modes (ELMs) can be effectively mitigated with the enhanced coherent modes (CMs) has been observed on EAST. For this phenomenon, the experimental statistical analysis and electromagnetic (EM) simulations have been performed. There is a threshold value of the CM intensity in the experiments, which plays a key role in ELMs mitigation. Through the ELITE and conventional BOUT++ analysis, we found that when the insignificant ELM and enhanced CM co-exist, the pedestal is located in unstable P–B region and the ELM is relatively large. The simulation results only using the experimental profiles without considering other factors cannot reproduce the no significant ELM experiment. The CM enhances the edge turbulence, which can control ELMs. Therefore, the effects of CM are considered to explain the ELM mitigation. Modifying the three-field reduced model in BOUT++, an imposed perturbation is added as the CM. The simulation results indicate that: without the CM, the ELM size belongs to the relative large ELM region; after considering the CM, the ELM is mitigated and the energy loss is reduced by about 44.5%. Analysis shows that the CM enhances the three-wave nonlinear interactions in the pedestal and reduces the phase coherence time (PCT) between the pressure and potential, which lead the perturbation to tend to be ‘multiple-mode’ coupling. The competition of free energy between the multiple modes leads to the lack of obvious filament structures and the decreased energy loss. The above reveals that there is a competitive relationship between turbulence and ELMs, and the CM-enhanced turbulence can effectively reduce ELM energy loss. In addition, through the parameter scanning, there is a threshold of the amplitude A, which is consistent with the statistical results in the experiments.

To help students’ learning, finding at-risk students is an important issue in education. This study has collected students’ learning logs in ebook reading environment for 17 blended learning courses in Taiwan. Then, we have construct students’ learning performance prediction model based on learning logs by using eight classification methods. To improve prediction performance, we have also propose grading policy rules which include of grading on stringency rule, grading on moderate rule, and grading on leniency rule to define the labels of students’ learning performance. According to the prediction performance results, the LR is the suitable classification method for predicting students’ learning performance. Besides, the grading on leniency rule has obtained the higher prediction performance. For exploring the influence factors on classification performance, we investigate the relationship between students’ online learning actions and students’ academic performance by Spearman coefficient analysis the open file (f1), the number of delete marker (f6), the number of next pages (f12), and the number of previous pages (f13) features are the critical factors for affecting the prediction performance of learning logs.

The flipped classroom pedagogy has been widely used recently. Despite many researches have paid attention with the learning outcome of flipped classroom, there has been limited attention in regard ...

Optimal treatment for advanced cervical cancer after first line chemotherapy remains undefined. Immune checkpoint inhibition with pembrolizumab, a programmed cell death protein 1(PD-1) inhibitor, is under investigation. We analyzed the micro-environmental and molecular genetic profile of tumors from 4 patients with metastatic cervical cancer treated with off-label second-line pembrolizumab in an effort to identify predictive biomarkers. All patients received 2 mg/kg of pembrolizumab, 3-weekly until disease progression. Immunohistochemistry(IHC) for PD-1, PD-L1, CD3 and CD8, as well as next generation sequencing (NGS) for 50 cancer-related genes were performed on tumor samples. All patients tolerated treatment well with no discontinuation of treatment due to toxicity. One patient experienced dramatic and prolonged partial response, and remains stable on pembrolizumab with a progression free survival (PFS) of 21 months at the time of reporting of this series. Three patients experienced disease progression as best response. In the exceptional responder, there was no tumoral expression of PD-L1, however, combined positive score (CPS) for PD-L1 was 1 and we identified somatic mutations in ERBB4(R612W), PIK3CA(E542K) and RB1(E365K). In 2 patients, despite progressive disease defined by RECIST v1.1, symptom stabilization on pembrolizumab was observed. The tumors of both patients had PD-1 expression in ≥1% of stromal lymphocytes. All patients with response or clinical benefit had CPS for PD-L1 ≥ 1. NGS revealed PIK3CA mutations in 3 tumors. Pembrolizumab is a promising therapeutic option in advanced cervical cancer. Further evaluation of biomarkers may guide optimal patient selection., Highlights • 1 of 4 patients receiving second line pembrolizumab, experienced dramatic and durable response. • All patients with response or clinical benefit had CPS for PD-L1 ≥ 1. • Germline BRCA1 mutation was detected in 1 patient, but she was a non-responder. • All tumors had somatic mutation in PIK3CA or PTEN.

With the rise of big data analytics, learning analytics has become a major trend for improving the quality of education. Learning analytics is a methodology for helping students to succeed in the classroom; the principle is to predict student's academic performance at an early stage and thus provide them with timely assistance. Accordingly, this study used multiple linear regression (MLR), a popular method of predicting students' academic performance, to establish a prediction model. Moreover, we combined MLR with principal component analysis (PCA) to improve the predictive accuracy of the model. Traditional MLR has certain drawbacks; specifically, the coefficient of determination (R²) and mean square error (MSE) measures and the quantile-quantile plot (Q-Q plot) technique cannot evaluate the predictive performance and accuracy of MLR. Therefore, we propose predictive MSE (pMSE) and predictive mean absolute percentage correction (pMAPC) measures for determining the predictive performance and accuracy of the regression model, respectively. Analysis results revealed that the proposed model for predicting students' academic performance could obtain optimal pMSE and pMAPC values by using six components obtained from PCA.

As information technology continues to evolve rapidly, programming skills become increasingly crucial. To be able to construct superb programming skills, the training must begin before college or even senior high school. However, when developing comprehensive training programmers, the learning and teaching processes must be considered. In order to improve the students' learning outcome and engagement in programming course, this study applied learning analytics into the proposed massive online open courses (MOOCs) enabled collaborative programming course. Through the proposed learning activity, instructors receive a monthly report that explains which students are at risk and in need of timely intervention. This study conducted an experiment to evaluate the effectiveness of the proposed learning activity. Students in the experimental group received learning interventions from an instructor according to the result of learning analytics, and students in the control group received interventions accordi...

A real-time disruption predictor using random forest was developed for high-density disruptions and used in the plasma control system (PCS) of the EAST tokamak for the first time. The disruption predictor via random forest (DPRF) ran in piggyback mode and was actively exploited in dedicated experiments during the 2019–2020 experimental campaign to test its real-time predictive capabilities in oncoming high-density disruptions. During dedicated experiments, the mitigation system was triggered by a preset alarm provided by DPRF and neon gas was injected into the plasma to successfully mitigate disruption damage. DPRF’s average computing time of ∼250 μs is also an extremely relevant result, considering that the algorithm provides not only the probability of an impending disruption, i.e. the disruptivity, but also the so-called feature contributions, i.e. explainability estimates to interpret in real time the drivers of the disruptivity. DPRF was trained with a dataset of disruptions in which the electron density reached at least 80% of the Greenwald density limit, using the zero-dimensional signal routinely available to the EAST PCS. Through offline analysis, an optimal warning threshold on the DPRF disruptivity signal was found, which allows for a successful alarm rate of 92% and a false alarm rate of 9.9%. By analyzing the false alarm causes, we find that a fraction (∼15%) of the misclassifications are due to sudden transitions of plasma confinement from H- to L-mode, which often occur during high-density discharges in EAST. By analyzing DPRF feature contributions, it emerges that the loop voltage signal is that main cause of such false alarms: plasma signals more apt to characterize the confinement back-transition should be included to avoid false alarms.

The purpose of learning analytics is to promote student success in the classroom. To implement the framework of learning analytics, researchers have adopted machine learning methodologies to identify at-risk students at an early stage. In theory, machine learning is a mathematical algorithm that improves automation through experience. The experience is the data collected from online learning platforms, and in general, the data contain various features such as the number of times that a student accesses the learning material each week. Relevant studies have demonstrated extremely high accuracy in identifying at-risk students using identification models trained by machine learning. However, numerous details and data challenges have been overlooked in prior studies, calling into question the accuracy of past contributions. In this study, we focused on one type of data challenge: data imbalance. The data imbalance problems in education are usually the result of teachers’ grading policy. To highlight the seriousness of this issue, we collected data from 12 blended learning courses and summarized 3 types of grading policies: discrimination, stringency, and leniency. We then provided evidence that the leniency strategy causes the illusion of high accuracy of at-risk student identification. Finally, we verified a robust method to address the effectiveness of the leniency strategy, and using these results, we summarized the characteristics of students who tend to be misidentified by machine learning methodology.

The purpose of time-interval sequential pattern mining is to help superstore business managers promote product sales. Sequential pattern mining discovers the time interval patterns for items: for example, if most customers purchase product item A, and then buy items B and C after r to s and t to u days respectively, the time interval between r to s and t to u days can be provided to business managers to facilitate informed marketing decisions. We treat these time intervals as patterns to be mined, to predict the purchasing time intervals between A and B, as well as B and C. Nevertheless, little work considers the significance of product items while mining these time-interval sequential patterns. This work extends previous work and retains high-utility time interval patterns during pattern mining. This type of mining is meant to more closely reflect actual business practice. Experimental results show the differences between three mining approaches when jointly considering item utility and time intervals for purchased items. In addition to yielding more accurate patterns than the other two methods, the proposed UTMining_A method shortens execution times by delaying join processing and removing unnecessary records.

The objective of the study was to investigate the distribution of LPIN2 variants and haplotypes among breeds and perform an association analysis of the variants and haplotypes with the broiler traits in chickens. Six breeds were used to study the variation and distribution of chicken LPIN2, and an F2 resource population was used to measure growth traits, carcass traits, meat quality traits and serum biochemistry parameters. A c.-599G>A variant was located in the promoter region of LPIN2 and c.444G>A and c.1730A>T (E577D) coding variant mutations were detected. Linkage disequilibrium tests showed that these three variants were under moderate linkage disequilibrium in the 6 breeds and 7 haplotypes were constructed. The distribution of variation/haplotypes presented clear differences among breeds. Association analysis showed that c.-599G>A was associated with leg muscle weight, jejunum length, ileum length, leg muscle fibre density and leg muscle fibre diameter; c.444G>A was associated with spleen weight, ileum length, body weight at hatch and metatarsus length at 8 weeks; c.1730T>A had significant effects on chicken liver weight, heart weight, body weight at 10 weeks, serum albumin and glucose. Diplotypes were significantly associated with body weight at hatch, heart weight, pancreas weight, duodenum length, leg muscle fibre density and lactate dehydrogenase.

The aim of this study was to evaluate the effects of geese’s maternal diet supplemented with flaxseed on the fatty acid profiles of egg yolks and the antioxidant status of their offspring. A total of 288 female Huoyan geese (42 weeks old) were randomly allotted to four experimental groups in this 56-day experiment and fed on diets containing flaxseed at 0% (control), 5%, 10% and 15%, respectively. There were nine replicate pens per treatment, with eight geese per replicate pen. The concentration of α-linolenic acid (linear, P

Effects of dietary flaxseed meal (FSM) on production performance, egg quality, hatchability, and fatty acids profiles in egg yolks of geese and thigh meat from their offspring were studied in a 35-d study. A total of 288 female Huoyan geese (28-wk-old) were divided into 4 experimental diets containing FSM at 0 (control), 50, 100, and 150 g/kg. There were 9 replicate pens per treatment with 8 geese per replicate pen. The total number of eggs produced (linear, P =0.04) and feed efficiency were greater (linear, P =0.04) in FSM containing groups than control diet. The inclusion of FSM in the geese diet linearly decreased the concentration of palmitic acid (linear, P =0.02) and saturated fatty acid (linear, P =0.05) in egg yolk, whereas the contents of C18:3n-3 (linear, P =0.04), C22:6n-3 (quadratic, P =0.05), total polyunsaturated fatty acids (PUFA; linear, P =0.01), and total n-3 PUFA (linear, P =0.01) were enhanced as the FSM levels increased. The arachidonic acid concentration ( P =0.05) and n-6/n-3 ratio ( P P =0.03) and total saturated fatty acid ( P =0.04) concentrations in thigh meat. The contents of C18:3n-3 increased (linear, P =0.01), whereas C20:4n-6 decreased (linear, P =0.01) in FSM supplemented groups compared to control group. The concentrations of PUFA and total n-3 PUFA were linearly increased ( P =0.04 and 0.01, respectively) and the n-6/n-3 ratio decreased linearly ( P =0.02) as the FSM levels increased. In conclusion, dietary addition of FSM up to 150 g/kg levels improved the egg production without any effect on egg quality and breeding performance. Moreover, feeding FSM to geese can increase the n-3 fatty acids concentration in egg yolk and deposition in the offspring in a dose-dependent manner.

In the previous simulations on the divertor heat fluxes of C-Mod and EAST, the inverse proportional to the plasma current or poloidal magnetic field trend of Eich's Scaling is well reproduced by the BOUT+ + simulations. However, the simulated SOL width is only half of the value realized in EAST measurements. The reason is believed to be the lack of RF heating scheme in the simulations. The RF heating on EAST, especially lower hybrid wave (LHW), is considered to change the boundary topology and increase the flux expansion. To prove the topology change effects of LHW in the scraped-off layer (SOL), a modeled helical current filament (HCF) due to LHW in SOL, which has the same amplitude of 13 kA to the experiments, is added as the force-free form into the 6-field 2-fluid module of BOUT++. The radial magnetic field induced by this HCF could be much smaller than the perturbed field induced by the edge tubulence or ELM, but it is able to force the perturbations of the turbulence or ELM with the same toroidal mode number to grow up at the start of the linear phase during the simulations. This forced mode is effective to compete with the spontaneous fluctuations and change their linear properties, which leads to the suppression of the divertor heat flux and the broadening of SOL width. The modeling results imply that the HCF with the toroidal mode number is able to increase the SOL width by ~, and the peak parallel heat flux towards divertor target is decreased by ~. The scan of the toroidal mode number of HCF indicates that the smaller could generate the broader SOL width and lower peak heat flux. The modeled broadening of the particle flux by HCF clearly shows the secondary striate filaments on divertor target, which is similar to the splitting of the strike point observed in the measurements by the divertor probes.

The aim of this study was to investigate the effect of Bacillus subtilis var. natto N21 (BAC) and Saccharomyces cerevisiae Y10 (SAC) fermented liquid feed (FLF) during different incubation times on the growth performance, relative organ weight, intestinal microflora, and organ antioxidative status in Landes geese. Two hundred forty male Landes geese (10 wk old) with the BW of 4.163 ± 0.108 kg were selected for a 3-wk trial and randomly allotted to 3 treatments according to their BW (10 replicates/treatment and 8 geese/replicate). The treatments included 1) CON, dry basal feed (corn-soybean basal diet mixed with water) before feeding (2:1 wt/wt), 2) FLF24, 24 h FLF, and 3) FLF48, 48 h FLF. The FLF diet was prepared by storing basal diet with 10(9) cfu/g feed of each BAC and SAC and water (2:1 wt/wt) in a closed tank at 20°C fermented for 24 or 48 h. The BW gain and feed intake of geese fed FLF24 and FLF48 was greater (P < 0.05) than CON treatment. Feeding geese with FLF24 and FLF48 feeds increased (P < 0.05) the relative weight of leg muscle whereas the liver was heavier (P < 0.05) in FLF48 treatment than CON and FLF24 treatments. The FLF24 and FLF48 increased (P < 0.05) the Lactobacillus population and depressed (P < 0.05) Escherichia coli population in small and large intestine. The high-density lipoprotein cholesterol concentration was greatest (P < 0.05) in FLF48 whereas the total cholesterol and low-density lipoprotein cholesterol was less (P < 0.05) in FLF24 and FLF48 treatments than CON. Geese fed FLF48 diet had greater glutathione peroxidase activity and less malondialdehyde content in heart and liver than those fed CON diet. In breast muscle, the superoxide dismutase activity were increased (P < 0.05) by FLF24 and FLF48 treatments than CON diet. In conclusion, the results indicated that feeding geese with BAC and SAC mix FLF can improve growth and feed intake, modulate the intestine ecology, and decrease the blood cholesterol concentrations; it also can improve the antioxidative status of organs and breast muscle.

This study was conducted to assess the effect of modifying the n-6:n-3 polyunsaturated fatty acid ratio (FAR) of diet using linseed, soybean, and corn oil on growth performance, cardiac antioxidant status, T-cell and cytokine mRNA expression in the thymus, and blood T lymphocyte subset characteristics in broilers. A total of 480 one-day-old male broilers (BW=46±4 g) were randomly allotted into 4 treatments consisting of dietary n-6 to n-3 FAR of 12:1, 9:1, 6:1, and 3:1. There were 6 replicates per treatment with 20 birds per replicate. After feeding for 42 d, chicks were slaughtered, and the blood and tissue samples were collected. Decreasing n-6 to n-3 FAR did not affect feed intake, but increased BW gain (linear, P=0.05) and improved feed conversion ratio (quadratic, P=0.03) during the first 3 weeks. Cardiac hydroxyl radical (P=0.03) and malondialdehyde (MDA) (P=0.01) concentration reduced linearly with decreasing dietary n-6:n-3 FAR, whereas superoxide dismutase (SOD) (quadratic, P=0.04), glutathione S-transferase (GSH-ST) (quadratic, P=0.02) and glutathione peroxidase (GSH-Px) (linear, P=0.05) levels were increased. The T lymphocyte subsets were also analyzed using monoclonal antibodies and flow cytometry simultaneously. The messenger RNA (mRNA) expression of cluster of differentiation antigens (CD)4+ receptor and toll-like receptor (TLR)-3 in thymus were depressed (quadratic, P

Computer-supported Collaborative Learning (CSCL) is a pedagogical strategy associated with how learners construct knowledge with a group by computer-based learning system. In recent years, most of the computer-based learning systems record the interaction log of each learner when developing course assignments. However, the recorded data is facing a challenge to expose the learners behaviors during the course and to design a computer-supported collaborative learning activity. To address those challenges in this paper, a novel collaborative programming tool called Software Project Development and Insight Learning Environment (SPDI Learning Environment) is described. The SPDI Learning environment allows learners of computer science to develop course assignments collaboratively. Besides, it allows instructors to investigate the learners behaviors by associating a web-based integrated development environment (IDE) with Big Data analysis pipeline and Visualization Dashboard. In addition to collect real data from courses, we designed learning activities to help teachers to engage the field of CSCL and Learning Analytics.

This chapter will address the reason that MOOCs brought our interests (MOE, The Ministry of Education, Taiwan), and the purpose and core mission of MOOCs in Taiwan, as well as MOOCs mean to the future of education in Taiwan. Taiwan’s MOOCs movement has been developed in two directions, one is led by MOE’s MOOCs project, government-funding projects; the other is inspired by volunteers who come from various universities, K12 schools, and private sectors. Taiwan has very strong research activities of digital learning, and now it is the right time that MOOCs project may benefit from the achievements of previous digital learning activities. Taiwan’s MOOCs movement should cover from K12 to higher education to lifelong learning, and to enterprise learning as well. MOOCs will reform or even revolutionize Taiwan’s education by enabling Taiwan’s Chinese courses on the world’s landscape, and improving pedagogical methods and structures. When MOOCs evolve, the way of learning evolve too. MOOCs are one of the choices students can have. Face-to-face and other online learning will evolve along with MOOCs. For the residential education, it will be changed because of MOOCs. We expect to see more and more blended learning occur especially from campus courses, and more and more collaborative learning, and flipped classrooms. In fact, this pedagogical theory has been practiced in Taiwan from K-12 to higher education. We see more and more progress and look forward to improvements in Taiwan’s education.

According to the phenomenon of MOOCs gained widespread exposure in the U. S. academic world, the infusion of MOOCs into traditional education has become a new trend in Taiwan since 2013. By February 2016, more than 170,000 learners have registered for various MOOC Chinese course platforms, and these platforms have collected more than 500 MOOC Chinese courses. Given that large number of partners and registers are using various MOOC Chinese course platforms, MOOCs have greatly affected education in Taiwan. Besides the government-funding projects, the industry in Taiwan also devoted to the development of MOOCs through providing free MOOCs platforms. The cooperation among industry and government have focused on reforming the spirit of Taiwan's education by applying MOOC courses at all education levels to realize flipped classroom. Observing the MOOCs movement in Taiwan, MOOCs pose a new means of self-regulated learning from K-12 education to higher education levels.

We aimed to evaluate the effectiveness and safety of bismuth-containing quadruple therapy plus postural change after dosing for Helicobacter pylori eradication in gastrectomized patients. We compared 76 gastric stump patients with H. pylori infection (GS group) with 50 non-gastrectomized H. pylori-positive patients who met the treatment indication (controls). The GS group was divided into GS group 1 and GS group 2. All groups were administered bismuth potassium citrate (220 mg), esomeprazole (20 mg), amoxicillin (1.0 g), and furazolidone (100 mg) twice daily for 14 days. GS group 1 maintained a left lateral horizontal position for 30 min after dosing. H. pylori was detected using rapid urease testing and histologic examination of gastric mucosa before and 3 months after therapy. Mucosal histologic manifestations were evaluated using visual analog scales of the updated Sydney System. GS group 1 had a higher prevalence of eradication than the GS group 2 (intention-to-treat [ITT]: P=0.025; per-protocol [PP]: P=0.030), and the control group had a similar prevalence. GS group 2 had a lower prevalence of eradication than controls (ITT: P=0.006; PP: P=0.626). Scores for chronic inflammation and activity declined significantly (P

In this paper, we adopt the first-order shear deformation theory in the moving least squares differential quadrature (MLSDQ) procedure for predicting the free vibration behavior of moderately thick symmetrically laminated composite plates. The transverse deflection and two rotations of the laminate are independently approximated with the moving least squares (MLS) approximation. The weighting coefficients used in the MLSDQ approximation are obtained through the fast computation of the MLS shape functions and their partial derivatives. The natural frequencies of vibration are computed for various laminated plates and compared with the available published results. Through numerical experiments, the capability and efficiency of the MLSDQ method for eigenvalue problems are demonstrated, and the numerical accuracy and convergence are thoughtfully examined. Effects of the size of support, order of completeness of the basis functions and node irregularity on the numerical accuracy are also investigated.

In this paper, the moving least-squares differential quadrature (MLSDQ) method is applied to the bending and buckling analyses of moderately thick symmetric laminates based on the first-order shear deformation theory (FSDT). The transverse deflection and two rotations of the laminate are independently assumed with the moving least-squares (MLS) approximation. The weighting coefficients used in the MLSDQ approximation are obtained through a fast computation of the MLS shape functions and their partial derivatives. Numerical examples are illustrated to study the accuracy, stability and convergence of the MLSDQ method. The typical displacements, stresses and critical buckling loads of various laminated plates are presented and compared with the analytical values. Effects of support size, order of the complete basis functions and node irregularity on the numerical accuracy are investigated.

The cracked piezoelectric problem is observed numerically. To simulate the characteristic singularity at the crack tip, a plane piezoelectric hybrid element is derived. The new model involves displacement u, stress σ, electric displacement D and electric potential ϕ as independent variables. The electromechanical coupling behavior of the cracked piezoelectric ceramics PZT-4 and PZT-5 is investigated. Under impermeable crack condition, the 1/ r -singularity at the crack-tip zone is exactly represented for σ and D. The efficiency of implementing the permeable crack condition is also inspected. To examine current energy release rate formulas, the path-independent integral is computed, and then a fitting formula for the energy release rate is obtained. In this paper, all the numerical results are compared with the previously reported theoretical solutions.

1. A total of 720 1-d-old broilers were used in a 28 d experiment to determine the effects of probiotic supplementation in diets with different dietary nutrient densities. 2. Birds were randomly allotted to one of the 4 treatments in a 2 × 2 factorial arrangement (12 replicateswith 15 broilers per replicate) with two levels of nutrient density [high nutrient density (metabolisable energy (ME) 12.7 MJ/kg and crude protein (CP) 230.3 g/kg for 1-7 d; ME 13.2 MJ/kg and CP 220.3 g/kg for 8-28 d) or low nutrient density (ME 12.1 MJ/kg and CP 220.2 g/kg for 0-7 d; ME 12.6 MJ/kg and CP 209.8 g/kg for 8-28 d)] and 0 or 2 g/kg probiotics (1.0 × 10(10) viable spores/g of Bacillus subtilis endospores and 1.0 × 10(9) viable spores/g of Clostridium butyricum). 3. The high-nutrient-density diet increased body weight gain (BWG), feed conversion ratio (FCR), serum cholesterol and triglyceride concentration relative to the low-nutrient-density diet. High-nutrient-density diet reduced water loss ratio of breast muscle, liver and fat relative to body weight compared to low-nutrient density-diet. The inclusion of probiotics increased BWG and feed intake throughout the experiment. Dietary probiotics increased the percentage of blood lymphocytes and relative weight of spleen and bursa of Fabricius when compared to the non-probiotic treatment. The inclusion of probiotics decreased serum cholesterol and triglyceride concentrations and lightness (L*) value of breast meat compared to the non-probiotic-supplemented diet. 4. In conclusion, high dietary nutrient density increased growth performance and serum cholesterol and triglyceride concentrations in broiler chickens. The inclusion of probiotics increased growth performance but reduced serum cholesterol and triglyceride concentrations. The positive effect of probiotic supplementation on growth performance was reduced by the high-nutrient-density diet during the first week of life.

Wogonin, one of flavonoid compounds isolated from Chinese herbal plants Scutellaria baicalensis Georgi, has been recognized as a potent anti-cancer agent acting through control of growth, differentiation and apoptosis. However, the underlying molecular mechanism of its anti-cancer activity remains to be further elucidated. In this study, we investigated the potential role of wogonin in the induced-apoptosis of human breast cancer cells MCF-7. Wogonin was found to inhibit the proliferation of MCF-7 in a concentration and time-dependent manner, notably wogonin could induce G1 phase arrest of MCF-7 cells. Wogonin-induced apoptosis was accompanied by a significant decrease of the Bcl-2 and survivin and increase of Bax and p53. Wogonin also increased active apoptosis forms of caspases-3, -8, -9 significantly. Z-DEVD-fmk, a specific caspase-3 inhibitor, significantly inhibited wogonin-induced cell apoptosis. Wogonin also suppressed the phosphorylation of PI3K/Akt and induced phosphorylation of ERK. PD98059, a specific ERK inhibitor, significantly blocked wogonin-induced apoptosis. On the other hand, LY294002, a specific PI3K inhibitor, significantly increased wogonin-induced cell apoptosis. Further study indicated that LY294002 not only down-regulated the expression of survivin alone, but also enhanced the inhibition of survivin expression combined with wogonin. In conclusion, the pro-apoptotic effect of wogonin is mediated through the activation of ERK and the activation of caspases, and is correlated with the block of the PI3K/Akt/survivin signal pathways in MCF-7 cells.