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Background Nurses working in Critical Care areas must obtain a certain skillset, this is a requirement from the RCPCH (2014) and PICS (2015). Traditionally theory is delivered within a classroom environment and skills acquired through clinical practice. It is important that skills can be learnt and practiced in a safe environment through simulation. Our aim was to introduce simulation into our critical care course as part of a flipped classroom to build on the theory learnt in a practical way. Summary of work In September 2018 we designed our Critical Care Course cardiovascular session around a flipped class model to allow more time for practical scenarios. Nurses utilise online education resources prior to entering a two-hour theory session. During this session knowledge is built on the pre-session work and nurses are prepared with the theory required for cardiac scenarios such as SVT, Sinus bradycardia and cardiac arrest. Specific learning outcomes are set for each scenario for example administration of adenosine or safe cardioversion with the defibrillator. Further learning is consolidated during a supportive teaching debrief. Summary of results We have delivered 3 courses so far in this way. 55 nurses from 8 hospitals attended the session, from 21 different ward areas Feedback was obtained using post session questionnaire to establish the usefulness of the session to their practice. 80% (n=44) of nurses completed the feedback. 91% (n=40) found the scenarios useful in embedding the theory taught. 9% (n=4) found the group size to be too large which impacted on their experience. Changes were made following this result in September, and since then the results have improved. Nurses attending the session said they found the session interesting and enjoyable, they liked putting the theory into practice and found the scenarios really helpful. Following the course nurses have reported that they now feel more confident in dealing with similar situations in practice. Conclusion A learning environment that uses Simulation following theory allows nurses to safely develop the skills required work in a Critical Care environment. This has shown to be an enjoyable and effective method of adult learning. Reference RCPCH: Back to Facing the Future 2014 http://www.rcpch.ac.uk/system/files/protected/news/Back%20to%20Facing%20the%20 Future%20FINAL_1.pdf The Paediatric Intensive Care Society. Standards for the care of critically ill children. [4th Edition]. 2015. http://www.ukpics.org.uk/documents/PICS_standards.pdf

In this paper we focus our attention on multi-dimensional copula models of returns of the indexes of selected prominent international financial markets. Our modeling results, based on elliptic copulas, 7-dimensional vine copulas and hierarchical Archimedean copulas demonstrate a dominant role of the SPX index among the considered major stock indexes (mainly at the first tree of the optimal vine copulas). Some interesting weaker conditional dependencies can be detected at it’s highest trees. Interestingly, while global optimal model (for the whole period of 277 months) belong to the Student class, the optimal local models can be found (with very minor differences in the values of GoF test statistic) in the classes of vine and hierarchical Archimedean copulas. The dominance of these models is most striking over the interval of the financial market crisis, where the quality of the best Student class model was providing a substantially poorer fit.

Aiming at the problem of irregular volatility in financial markets, this paper studies prediction algorithms based on GARCH class model and BP neural network model. In this paper, we compare the prediction accuracy between three kinds of GARCH base models and seven neural networks models. Results show that GARCH class model has a new neural network which can enhance volatility prediction accuracy; GARCH class model better, its variables as input value to neural network combination model help larger; additional introduction similar model variables reduce neural network combination model performance unless the new model variable brings information improvement advantages cover its data redundancy shortcomings.

This paper estimates and forecasts West Texas Intermediate (WTI) crude oil market volatility with GARCH, EGARCH models. We find that, in terms of Root Mean Squared Error (RMSE) criteria, EGARCH model outperformances GARCH model in both short term and long term forecast length. Then, we apply Markov Switching model on oil volatility term and find that we can improve forecast accuracy of oil market volatility further with MSGARCH model, when compared with normal GARCH and normal EGARCH model. Besides, empirical results confirm that two regimes consist of regime 1 with low volatility and regime 2 with high volatility exist in WTI crude oil market. Also, the smoothed probabilities indicate that before 2004, WTI crude oil market stays in a low volatility regime most of time, while after 2004, WTI crude oil market switches to high volatility regime and stays in it until present most of time.

«Resource class», or «Resource zone», is a model of inclusive education for children with autism spectrum disorders that has proved its effectivity in integraing children to comprehensive school system for the past five years. A resource class is a place where the resources for inclusion of a child with autism of other mental disor¬ders into the school community are gathered. Here, the point of ‘resource class’ model is explained, described are the experience of introduction of resource zone technology, risks and problems appearing when creating a resource class in school. Member of the council of the Regional public organization to help children with autism spectrum disorders «Contact», educators, shares her experience in creating a resource class for autistic children with parents’ effort in «School № 2009» in Moscow.

The CLASS model is a top-down capital stress testing framework that uses public data, simple econometric models and auxiliary assumptions to project the effect of macroeconomic scenarios on U.S. banking firms. Through the lens of the model, we find that the total banking system capital shortfall under stressful macroeconomic conditions began to rise 4 years before the financial crisis, peaking in the fourth quarter of 2008. The capital gap has since fallen sharply, and is now significantly below pre-crisis levels. In the cross-section, banking firms estimated to be most sensitive to macroeconomic conditions also have higher capital ratios, consistent with a “precautionary” view of bank capital, though this behavior is evident only since the crisis. We interpret our results as evidence that the resiliency of the U.S. banking system has improved since the financial crisis, and also as an illustration of the value of stress testing as a macroprudential policy tool.

The purpose of the current study is to present a class model of SQ3R instruction, which consists of 5 stages of reading strategy, Survey, Question, Read, Recite, Review for foreign language learning and to investigate its effects on learners’ self-efficacy and their reading comprehension. Five reading steps of reading strategy were instructed associated with skill practices, annotating, note-taking and summary outlining. A total of 23 students in a college academic reading class participated in the study. Students’ self-selected academic reading materials were used for the course. Reading self-efficacy survey and the academic reading part of IELTS practice test were conducted before and after the instruction. Student products such as annotated texts and reaction writing as well as portfolios were collected and analyzed. The survey and academic reading test results indicated that the learners’ reading self-efficacy and their reading ability were improved and that the students seemed to have gained more confidence in academic reading.

Sanqi is a widely used traditional Chinese medicines (TCM) for its outstanding efficacy. In Chinese market, Sanqi powder is the goal of counterfeiting for a long time. Investigation of Sanqi authenticity is very important in both economic and public health terms. The present work aims at studying the feasibility of combining near-infrared (NIR) spectroscopy with relief-based variable selection and class-modeling for identifying adulterated Sanqi powder. A total of 209 samples including pure and mixed samples, were prepared. Principal component analysis (PCA) was applied for exploratory analysis. The relief algorithm was used to rank all variables, based on which only the first 100 most informative variables were picked out for subsequent class-modeling. By optimizing the parameters such as the number of components, type I and type II errors, the final one-class models were constructed on the training set and evaluated on the test set. Such a procedure is simple and is more in line with actual need. The performance of the models is acceptable. The results indicate that NIR spectroscopy combined with class-modeling and relief-based variable selection is feasible for identifying the adulteration of Sanqi powder.

[EN] In order to compensate the power quality issues that arise in distribution grids with high penetration of renewable energy sources, an active power filter device controlled by a novel model-based predictive controller, i.e. the linear state signal shaping model predictive controller, is implemented. This paper proposes the use of a Multiobjective Optimization evolutionary algorithm, i.e. the Multiobjective Differential Evolution with Spherical Pruning X, for the tuning of this novel controller. An application example for power quality compensation of a grid modeled as a switched system with four modes is given. The model includes nonlinear loads that introduce harmonic distortion and multiple consumer loads that enable the existence of conflicting objectives, typical of multiobjective optimization problems. A decision making strategy is developed in order to find the best controller parameters in a reasonable amount of time that enable the provision of optimal power quality services by balancing multiple objectives that can conflict with each other., This contribution was partly developed within the project NEW 4.0 (North German Energy Transition 4.0) which is funded by the German Federal Ministry for Economic Affairs and Energy (BMWI). This paper was also partly funded by the Free and Hanseatic City of Hamburg (Hamburg City Parliament publication 20/11568).

PDO (protected designation of origin) Zafferano dell’Aquila (AQ), Iranian (IR), and commercial (CS) saffron samples were analyzed by inductively coupled plasma mass spectrometry (ICP-MS). The ICP-MS data of 30 elements were handled by unsupervised (principal component analysis (PCA)) and supervised (linear discriminant analysis (LDA)) multivariate statistical methods to identify a subset of discriminant variables useful for geographical classification. Moreover, class modeling of AQ saffron was performed by both UNEQ (unequal disperses classes) and SIMCA (soft independent modeling of class analogy) methods. A good differentiation of the AQ, IR, and CS samples was obtained by the LDA based on four selected elements: Sr, Ca, Mo, and Fe. A UNEQ class model for the PDO AQ saffron based on the above four elements provided 100% sensitivity (all authentic AQ saffron were accepted) and 100% specificity (all IR and CS were rejected), while slightly worse results were obtained using the SIMCA (89% sensitivity and 96% specificity).

There are lots of countries’ government to start the so-called digital (e-learning) learning. In the first few sections, this author tries to analyse those barriers that prevent our schools from implementing digital learning. In the following sections, this paper compares the effectiveness of traditional learning versus a digital one. It shows that the class size is the determining factor. At the same time, the flipped class model is now the newest teaching and learning pedagogy that should be focused. The result tells us that when there is a suitable teacher training, the flipped model can help students achieve higher academic achievement. This scholar believes the implementation of such a model may finally come up with a better-improved learning method in the future that combines these three pedagogies. However, an improvement in our education may lead to a rise in government expenditure. The problem may finally affect various fields of spending. Thus, education becomes a political issue and may require some extra resources in order to fulfil the expanding needs of education manpower and her future development.

Writing is of great importance in college English teaching. However, the limitation of time in learning English makes it impossible to deal with both the teaching and the practicing of writing inside the classroom. This paper tries to apply the flipped class model to deal with this obstacle. With the combination of students' autonomous learning and practicing in online course and teacher's guidance in offline course, the students would not only be able to figure out "how to write" but also "what to write". The difficulties of both language and content of writing would be solved under the flipped class model.

With the widespread popularity of MOOC, SPOC generally becomes a hot spot of educational reform. This paper introduces the development processes of SPOC at first, then analyzes the teaching process of SPOC according to some famous SPOC experiments, next designs a flipped class model based on SPOC, finally discusses the teaching effects of flipped class model at a western university in China. The results show (1) students are easier to ignore the self-previewing before class and self-reviewing after class at the beginning of term; (2) students pay greater attentions to the self-previewing before class and self-reviewing after class at the end of the term; (3) students like watching videos more than reading literature; (4) students’ grades are obviously improved in flipped class model.

El objetivo de este artículo es realizar una propuesta teórico-práctica con objeto de analizar el impacto que un adecuado diseño e implementación del modelo de Clase Inversa /Aprendizaje Inverso (Flipped Classroom /Learning) puede tener en el desarrollo de las Inteligencias Múltiples como estrategias de aprendizaje activo y diferenciador en el aula. Para ello, se comienza por definir en qué consiste la clase inversa (también aprendizaje inverso) centrándose en los conceptos de espacio individual y espacio grupal y la importancia de un adecuado diseño metodológico para atender a la diversidad del alumnado en general y sus diferencias en cuanto a Inteligencias Múltiples en particular. En este contexto, se realiza un análisis previo sobre cuál puede ser el mejor espacio de aprendizaje en cada una de las Inteligencias Múltiples identificadas por Gardner, para concluir con una serie de recomendaciones y una propuesta práctica sobre su implementación en el aula a la luz de la taxonomía de Bloom.

We describe a flipped class proposal in the Teacher in Physical Education of the Universidad Nacional of Rio Cuarto, Argentina. The experience was designed and implemented using the social network Facebook and the selection of short videos, it was carried out in two groups with different teachers. The perceptions andsuggestions on the flipped class model were recovered with an ad hoc questionnaire of closed and open questions, frequencies of exchanges, publications and comments on Facebook were also analyzed. The results show highly positive aspects such as the autonomy of the students, the guiding role and facilitator of the teacher. It is concluded that the planning of flipped classes and the sustained accompaniment topromote self-regulation in students is important.

One problem of exploiting information captured by an OO system model such as class models is mapping user queries on the model. This is particularly due to the informal character of the user queries. This paper presents an approach based on text mining for identifying and interpreting user queries. This would enable a matching between the interpreted user query and the corresponding entities specified by the system model. Therefore, navigation expressions in OCL can be derived from the system model that have a formal character and are able to answer the user questions against the class model.

Sixty-five samples of red garlic (Allium sativum L.) coming from four different production territories of Italy were analysed by means of inductively coupled plasma optical emission spectrometry. The garlic samples were discriminated according to the geographical origin using the content of seven elements (Ba, Ca, Fe, Mg, Mn, Na and Sr). Both classification and class modelling methods by using linear discriminant analysis (LDA) and soft independent model class analogy (SIMCA), respectively, were applied. Classification ability and modelling efficiency were evaluated on an external prediction set (21 garlic samples) designed by application of duplex Kennard-Stone algorithm. All the calibration and prediction samples were correctly classified by means of LDA. The class models developed using SIMCA exhibited high sensitivity (almost all the calibration and external samples were accepted by the respective classes) and good specificity (the majority of extraneous samples were refused by each class model).

Presently, rice type is identified manually by humans, which is time consuming and error prone. Therefore, there is a need to do this by machine which makes it faster with greater accuracy. This paper proposes a deep learning based method for classification of rice types. We propose two methods to classify the rice types. In the first method, we train a deep convolutional neural network (CNN) using the given segmented rice images. In the second method, we train a combination of a pretrained VGG16 network and the proposed method, while using transfer learning in which the weights of a pretrained network are used to achieve better accuracy. Our approach can also be used for classification of rice grain as broken or fine. We train a 5-class model for classifying rice types using 4000 training images and another 2- class model for the classification of broken and normal rice using 1600 training images. We observe that despite having distinct rice images, our architecture, pretrained on ImageNet data boosts classification accuracy significantly.

A method based on one class support vector machine (OCSVM) is proposed for class incremental learning. Several OCSVM models divide the input space into several parts. Then, the 1VS1 classifiers are constructed for the confuse part by using the support vectors. During the class incremental learning process, the OCSVM of the new class is trained at first. Then the support vectors of the old classes and the support vectors of the new class are reused to train 1VS1 classifiers for the confuse part. In order to bring more information to the certain support vectors, the support vectors are at the boundary of the distribution of samples as much as possible when the OCSVM is built. Compared with the traditional methods, the proposed method retains the original model and thus reduces memory consumption and training time cost. Various experiments on different datasets also verify the efficiency of the proposed method., 6 pages, 4 figures, Under review as a conference paper at International Conference on Pattern Recognition 2018

In this paper we consider the exogenous indifference classes model of Barbera and Ehlers (2011) and Sato (2009) and analyze further the relationship between the structure of indifference classes across agents and dictatorship results. The key to our approach is the pairwise partition graph. We provide necessary conditions on these graphs for strategy-proofness and unanimity (or efficiency) to imply dictatorship. These conditions are not sufficient; we also provide separate stronger conditions that are sufficient. A full characterization is obtained in the case of two agents for domains where strategy-proofness and efficiency imply dictatorship.

The number of common neighbor between nodes is applied to the modeling of resting-state brain function network in order to analyze the effect of anatomical distance on the modeling of resting-state brain function network. Three models based on anatomical distance, the number of common neighbor, or anatomical distance and the number of common neighbor are designed. Basing on residuals creates the evaluation criteria for selecting the optimal brain function model network in each class model. The model is selected to simulate the human real brain function network by comparison with real data functional magnetic resonance imaging (fMRI) network. Finally, the result shows that the best model only is based on anatomical distance.

In this article we chronicle the development of the undergraduate statistical consulting course at Miami University, from canned to client-based projects, and argue that if the course is well designed with suitable mentoring, students can perform remarkably sophisticated analyses of real-world data problems that require solutions beyond the methods encountered in previous classes. We review the historical context in which the consulting class evolved, describe the logistics of implementing it, and review assessment and student reaction to the course. We also illustrate the types of challenging projects the students are confronted with via two case studies and relate the skills learned and reinforced in this consulting class model to the skills demanded in the modern statistical work force. This course also provides an opportunity to strengthen and nurture key points from the new American Statistical Association guidelines for undergraduate programs: namely, communicating analyses of real and complex data ...

Traditional analysis of food adulterations is usually focused on detection of specific adulterants that are known or suspected to exist. However, because there are various ways of adulterations and new adulterants are constantly emerging, the usefulness of the traditional analysis methods is limited. This paper investigated the feasibility of using near infrared (NIR) spectroscopy and chemometrics to detect a range of adulterants in a Chinese functional food, kudzu starch (KS). Pure KS objects ( n = 125) were collected from five main producing areas. Five potential adulterants were investigated, including an illegal but commonly used whitening agent, talcum powder, and four cheaper plant starches, namely, sweet potato, potato, maize and cassava starches. The doping levels were 2%, 4%, 6%, 10% and 20% (w/w) for plant starches and 0.5%, 1%, 2%, 5% and 10% (w/w) for talcum powder. A recently proposed one-class classifier, partial least squares class model (PLSCM) was used to estimate the distribution of authentic KS samples based on NIR spectra. The results indicate that taking second-order derivative (D2) spectra can effectively improve the PLSCM model specificity for detecting adulterants. D2-PLSCM can detect 0.5% or more of talcum powder and 4% or more of the four cheaper starches.

The short- and long-term efficacy of the Adlerian parent education program Systematic Training for Effective Parenting (STEP), which is the group-based parenting class model, was the focus of the current study in Lithuania. In the quasi-experimental research study, the outcome measures included parenting style, child behavior as perceived by parents, and knowledge related to parenting. In 2011–2012, 44 parenting groups were organized, which resulted in 348 program and 299 comparison group participants at posttest. In 3- to 4-month period, the follow-up assessment was completed by 38.8% of parents. Specifically, the findings indicated that the STEP program was efficacious for increasing maternal and paternal knowledge on parenting, decreasing maternal authoritarian and permissive parenting styles and parental negative perceptions of the target child’s behavior. The changes listed were found to be stable as reflected in the follow-up. The researchers believe that the current study with the sufficient empirical rigor expands the generalizability of the STEP program outcomes on mothers and fathers in North America, Lithuania as well as Europe. Furthermore, the results have finally provided the Lithuanian therapists, parent educators, and parents with an evidence-based parenting program as recommended by the Council of Europe.

In the present study, near-infrared spectroscopy (NIRS) was explored as a fast and reliable screening method for the detection of adulteration of skim milk powder (SMP). Sixty genuine SMP were adulterated with acid whey (1–25 % w/w), starch (2 and 5 %) and maltodextrin (2 and 5 %) for a total of 348 adulterated samples. Two chemometric approaches were employed. In the first approach, an untargeted one class model for genuine skim milk powder was developed by Soft Independent Modelling of Class Analogy. In the second approach, adulterant-specific regression models were developed to assess the amount of each adulterant by partial least square regression and principal component regression. The class modelling approach had the advantage that several adulterants could be detected with the same chemometric model, including situations where multiple adulterants are present in the test sample or where yet unknown adulterants are present. Regression models showed a better sensitivity with genuine SMP samples completely discriminated from samples adulterated with 5 % acid whey and 2 % of starch or maltodextrin. NIRS proved to be a useful tool for the rapid and cost-efficient untargeted and/or targeted detection of adulterations in SMP.

National audience; In information systems, model integration consists in grouping into a single model all the business entities of several thematically connected models. In this paper, five operations are proposed to assist this integration: an alignment model which highlights the correspondences between the models; two union models and two intersection models built up with the common elements. The union and intersection models are calculated according to either a mechanism based on the semantics of business entities or a mechanism based on the binary description of these entities. Whatever the mechanism used, the union models are the smallest of the model unions and the intersection models, kernel of all models, are the greatest intersections of the models. These five operations are achieved with the help of Formal Concept Analysis (FCA).; En système d'information, l'intégration de modèles consiste à regrouper au sein d'un unique modèle l'ensemble des entités métiers de plusieurs modèles connectés d'un point de vue thématique. Dans cette communication, cinq opérations sont proposées afin d'assister cette in-tégration et améliorer les modèles structurels : la première crée un modèle alignement montrant les correspondances entre les modèles, la seconde produit deux modèles union et la troisième produit deux modèles intersection avec les seuls éléments communs à tous les modèles. Les mo-dèles union et intersection sont calculés suivant soit un mécanisme reposant sur la sémantique des entités métiers soit un mécanisme fondé sur la description binaire de ces entités. Quel que soit le mécanisme utilisé, les modèles union sont les plus petits des unions des modèles et les modèles intersection, noyau de tous les modèles, sont les plus grands des intersections des mo-dèles. Ces cinq opérations sont réalisées à l'aide de l'Analyse Formelle de Concepts (AFC).

This letter explores the behavior of conditional correlations among main cryptocurrencies, stock and bond indices, and gold, using a generalized DCC class model. From a portfolio management point of view, asset correlation is a key metric in order to construct efficient portfolios. We find that: (i) correlations among cryptocurrencies are positive, albeit varying across time; (ii) correlations with Monero are more stable across time; (iii) correlations between cryptocurrencies and traditional financial assets are negligible.

Computational thinking is to use heuristic reasoning to seek the solution of the problem, and has a strong ability to innovate. In the teaching of computer courses, it is one of the important subjects of teaching research to cultivate students’ computational thinking ability. The traditional teaching model ignores the cognitive role of the students, which is not conducive to the cultivation of students’ innovative thinking and creative ability. Blending learning is an inevitable trend of development in the field of higher education, and a hot research topic in the field of education. Inspired by “Database Principles” course model at University of Missouri, Kansas City, a multi-dimensional blending learning flipped class model of computer courses in colleges and universities based on students’ computational thinking ability is proposed to construct teaching model and design theoretical and practical teaching system and diversified teaching process evaluation system. In the course of “Database Principles”, the teaching practice is carried out to verify the teaching effect.

Introduction, 349. — I. The two class model, 351. — II. The one class model: diminishing returns, 358. — III. The one class model: infinite growth with constant returns, 365. — Concluding remarks, 370

National audience; In information systems, model integration consists in grouping into a single model all the business entities of several thematically connected models. In this paper, three transformations are proposed to assist this integration: an alignment model which highlights the correspondences between the models; a union model (Least Common Multiple Model or LCM) and the intersection model (Greatest Common Model or GCM) built up with the common elements. The LCM is the smallest union of the models and the GCM, kernel of all models, is the greatest intersection of the models. These transformations are achieved with the help of Formal Concept Analysis (FCA).; En système d'information, l'intégration de modèles consiste à regrouper au sein d'un unique modèle l'ensemble des entités métiers de plusieurs modèles connectés d'un point de vue thématique. Dans cette communication, trois transformations sont proposées afin d'assister cette intégration et d'améliorer les modèles : la première produit un modèle d'alignement mon-trant les correspondances entre les modèles, la seconde produit un modèle union (Least Common Multiple Model ou LCM) et la troisième produit un modèle intersection (Greatest Common Model ou GCM) constitué des seuls éléments communs à tous les modèles. Le LCM est la plus petite des unions des modèles et le GCM, noyau de tous les modèles, est la plus grande des intersections des modèles. Ces transformations sont réalisées à l'aide de l'Analyse Formelle de Concepts (AFC) en basant les transformations sur des opérations portant sur les contextes formels.

This article presents the authors' experience with flipping a fundamental concepts of nursing course for students in an undergraduate baccalaureate program. Authors describe implementing a flipped class, practical strategies to transform students' learning experience, and lessons learned. This article serves as a guide to faculty and programs seeking to develop and implement the flipped class model in nursing education.

Teacher as a profession, has its own specialty. To make teachers have strong professional, the quality of teacher education is the key. The teaching mode called "flip class" is beneficial to the development of the teachers' theory quality and technological literacy. If we bring it into the teacher education curriculum teaching, it’ll promote teachers' professional specialization development.

The physical class is always out of the door. This class model is different from the traditional class. As this special characteristic, how to ensure the weight of the sports test course achievement evaluation becomes necessary and important. The application of AHP makes it possible to carry out the quantity evaluation in the sports research. The study makes a comprehensive evaluation with AHP and puts forward a method for relative analysis and a thereunder for decision-maker. From the necessary and sufficient conditions of completely consistent matrix, the criterion of consistency has been improved. We create a new improved AHP method, namely MCAHP (matrix correction analytic hierarchy process). And we apply it to examine the physical class in determining the weights of evaluation indicators by students. Examples show that the improved algorithm is better than the original AHP in significantly reduced computation. And this method improves the running speed. The first part is the introduction. The second part is the steps of AHP. The third part is the study on the MCAHP algorithm. The fourth part is the exploration of the MCAHP algorithm. The fifth part is the numerical experiment and the last part is the conclusion.

b ulk- s ynchronous p arallel ( bsp ) is a bridging model for hpc ( h igh p erformance c omputing) algorithm design. It provides a conceptual bridge between the physical implementation of the machine and the abstraction available to a programmer, while having portable and scalable performance predictions of bsp algorithms on most hpc systems. Two questions may come to mind. What are formally bsp algorithms? And how to ensure that the programmer can effectively program every bsp algorithm with a bsp language, especially with the right cost? g urevich proved that three convincing postulates for the sequential algorithms are equivalent to what is called a bstract s tate m achines ( asm s ), and thus that asm s capture the sequential algorithms. Firstly, we extend these sequential postulates and asm s in order to intuitively and realistically capture the bsp algorithms (and not more). Secondly, by using an operational semantics and an algorithmic simulation, we prove that is equivalent to , a minimal imperative bsp programming language. Therefore, bsp programming languages (extending at least ) are bsp algorithmically complete, involving the definition of a class model of the bsp algorithms.

Currently, the higher education curriculum has developed two systems, namely the block-based curriculum in the field of medicine and the semester credit-based curriculum. The block-based curriculum is currently applied in medical schools while the semester credit-based curriculum is applied in non-medical faculties. The presence of these two curriculum systems raises problems in the management of academic information systems. One of the problems that occur is that the existing information system has not been able to manage the block curriculum and semester credit simultaneously in one system. So that this causes the academic information system unable to manage the implementation of assessments on the block-based curriculum. This paper proposes a new model in the design of academic information systems that are able to integrate the needs of block-based curriculum and credit. The method used to solve this problem is an object-oriented approach using class diagrams as a model to describe the structure of academic information systems. The results of the development of the new model show that the structure of the block-based curriculum is a general form (generalization) of semester-based credit. Therefore the implementation of the two curriculums can use a similar model class structure to be more efficient and easier to maintain information system software in the future.

This study aims to develop a Modular Object-Oriented Dynamic Learning Environment (Moodle)-based online learning system and derive a class model to enhance learners&rsquo, creative collaboration capabilities. The research method for this is first to analyze the functional elements of the online learning system to enhance creative collaboration among the functions of the Moodle online learning system. Second, a class model is derived to enhance creative collaboration by applying the constructivist teaching&ndash, learning principle. Third, we conduct a verification of the effectiveness of the Moodle-based class model that was developed to enhance creative collaboration. The results of the study show that, among the functions of the Moodle online learning system, forms, real-time conferencing, reciprocal evaluation, Wiki, and blogs have the ability to enhance creative collaboration. Second, the class model for promoting creative collaboration based on a mood-based online learning system consists of self-reflections, learner-driven learning, professional learning, practical task, and the role of the processor. Third, after verifying the application effect of the Moodle-based class model for university students, a significant effect is found in the creative collaboration of university students. The results of this study suggest that: First, the Moodle online learning system should be utilized to provide learners with a variety of online learning opportunities to promote creative collaboration. Second, the teaching&ndash, learning operation manual needs to be developed later in order for the class model to be effectively implemented at the classroom site to promote creative collaboration based on the Moodle online learning system.