Методология учебной технологии математического моделирования физических систем

The article briefly presents a systematic approach to model development and corresponding modeling methodology. It is based on the use of energy and bound graph submodels of physical systems. These submodels provide useful means of transition from description of physical objects commonly accepted in courses of general physics of the technical universities to qualitative description in mathematical modeling project

Developing Model-Based Design Evaluation for Pipelined A/D Converters

This paper deals with a prospective approach of modeling, design evaluation and error determination applied to pipelined A/D converter architecture. In contrast with conventional ADC modeling algorithms targeted to extract the maximum ADC non-linearity error, the innovative approach presented allows to decompose magnitudes of individual error sources from a measured or simulated response of an ADC device. Design Evaluation methodology was successfully applied to Nyquist rate cyclic converters in our works [13]. Now, we extend its principles to pipelined architecture. This qualitative decomposition can significantly contribute to the ADC calibration procedure performed on the production line in term of integral and differential nonlinearity. This is backgrounded by the fact that the knowledge of ADC performance contributors provided by the proposed method helps to adjust the values of on-chip converter components so as to equalize (and possibly minimize) the total non-linearity error. In this paper, the design evaluation procedure is demonstrated on a system design example of pipelined A/D converter. Significant simulation results of each stage of the design evaluation process are given, starting from the INL performance extraction proceeded in a powerful Virtual Testing Environment implemented in Maple™ software and finishing by an error source simulation, modeling of pipelined ADC structure and determination of error source contribution, suitable for a generic process flow

STRENGTHS AND LIMITATIONS OF QUALITATIVE AND QUANTITATIVE RESEARCH METHODS

Scientific research adopts qualitative and quantitative methodologies in the modeling and analysis of numerous phenomena. The qualitative methodology intends to understand a complex reality and the meaning of actions in a given context. On the other hand, the quantitative methodology seeks to obtain accurate and reliable measurements that allow a statistical analysis. Both methodologies offer a set of methods, potentialities and limitations that must be explored and known by researchers. This paper concisely maps a total of seven qualitative methods and five quantitative methods. A comparative analysis of the most relevant and adopted methods is done to understand the main strengths and limitations of them. Additionally, the work developed intends to be a fundamental reference for the accomplishment of a research study, in which the researcher intends to adopt a qualitative or quantitative methodology. Through the analysis of the advantages and disadvantages of each method, it becomes possible to formulate a more accurate, informed and complete choice.  Article visualizations

Flood risk modeling of urbanized estuarine areas under uncertainty: a case study for Whitesands, UK

Aims: The impacts of catastrophic flooding have steadily increased over the last few decades. This work investigated the effectiveness of flood modeling, with low dimensionality models along with a wealth of soft (qualitative) and hard (quantitative) data. In the presence of very low resolution or qualitative data this approach has the potential of assessing a plethora of different scenarios with little computational cost, without compromise in prediction accuracy. Study Design: A flood risk modeling approach was implemented for the urbanized and flood prone region of Whitesands, at the Scottish town of Dumfries. This involved collection of a wide range of data: a) topographical maps and data from field visits were used to complement existing cross-sectional data, for building the river’s geometry, b) appropriate hydrological data were employed to run the simulations, while historical information about the extent, depth and impacts of flooding were utilized for calibrating the hydraulic model, and c) a wealth of photographic data obtained during the most recent December 2013 flood, were used for the model’s validation. Place and Duration of Study: Desk study: School of Engineering, University of Glasgow; September 2013 to May 2014. Field study: Dumfries; November 2013 to January 2014. Methodology: The HEC-RAS 1D model has been used to represent the hydraulics of the system. Flood maps were produced considering the local topography and predicted inundation depths. Flood cost and risk takes further into account the type and value of inundated property as well as the extent and depth of flooding. Results: The model predictions (inundation depths and flood extents presented in the flood maps) were in fairly good agreement with the observed results along the studied section of the river. Conclusion: This study presented a flood modeling approach that utilized an appropriate range of accessible data in the absence of detailed information. As the level of performance was comparable to other inundation models the results can be used for identification of flood mitigation measures and to inform best management strategies for waterways and floodplains

Computerization of workflows, guidelines and care pathways: a review of implementation challenges for process-oriented health information systems

There is a need to integrate the various theoretical frameworks and formalisms for modeling clinical guidelines, workflows, and pathways, in order to move beyond providing support for individual clinical decisions and toward the provision of process-oriented, patient-centered, health information systems (HIS). In this review, we analyze the challenges in developing process-oriented HIS that formally model guidelines, workflows, and care pathways. A qualitative meta-synthesis was performed on studies published in English between 1995 and 2010 that addressed the modeling process and reported the exposition of a new methodology, model, system implementation, or system architecture. Thematic analysis, principal component analysis (PCA) and data visualisation techniques were used to identify and cluster the underlying implementation ‘challenge’ themes. One hundred and eight relevant studies were selected for review. Twenty-five underlying ‘challenge’ themes were identified. These were clustered into 10 distinct groups, from which a conceptual model of the implementation process was developed. We found that the development of systems supporting individual clinical decisions is evolving toward the implementation of adaptable care pathways on the semantic web, incorporating formal, clinical, and organizational ontologies, and the use of workflow management systems. These architectures now need to be implemented and evaluated on a wider scale within clinical settings

The Effect of Mathematical Habits of Mind and Early Mathematical Ability on Modeling Ability of High School Students

Modeling mathematics serves as a bridge between the processes of translating real-world problems into mathematics. In reality, however, students' mathematical modeling skills, including their knowledge of derivative applications, remain subpar. This study is intended to determine the relationship between mathematical modeling skills, mathematical habits of mind (MHoM), and early mathematical ability (EMA). This research employed a quantitative methodology with mix method sequential explanatory design. The method used a quantitative and qualitative approach. The first quantitative phase is used, then explained more deeply through the qualitative phase. Sample in this study were 36 eleventh-grade students from one of Tasikmalaya's senior high schools. In this investigation, the EMA was the previous semester's math report card grade. A mathematical modeling ability test question and an MHoM questionnaire were administered to students, and the quantitative analysis of the results followed. This study demonstrates that the relationship between MHoM and EMA has a 31.2% modeling capability. In addition, a one-point increase in MHoM and EMA increases the average mathematical modeling ability of pupils by 1,570 and 2,241. Therefore, it can be concluded that MHoM and EMA have a positive effect on mathematical modeling ability

A Framework for Developing the Structure of Public Health Economic Models

Background: A conceptual modeling framework is a methodology that assists modelers through the process of developing a model structure. Public health interventions tend to operate in dynamically complex systems. Modeling public health interventions requires broader considerations than clinical ones. Inappropriately simple models may lead to poor validity and credibility, resulting in suboptimal allocation of resources. Objective: This article presents the first conceptual modeling framework for public health economic evaluation. Methods: The framework presented here was informed by literature reviews of the key challenges in public health economic modeling and existing conceptual modeling frameworks; qualitative research to understand the experiences of modelers when developing public health economic models; and piloting a draft version of the framework. Results: The conceptual modeling framework comprises four key principles of good practice and a proposed methodology. The key principles are that 1) a systems approach to modeling should be taken; 2) a documented understanding of the problem is imperative before and alongside developing and justifying the model structure; 3) strong communication with stakeholders and members of the team throughout model development is essential; and 4) a systematic consideration of the determinants of health is central to identifying the key impacts of public health interventions. The methodology consists of four phases: phase A, aligning the framework with the decision-making process; phase B, identifying relevant stakeholders; phase C, understanding the problem; and phase D, developing and justifying the model structure. Key areas for further research involve evaluation of the framework in diverse case studies and the development of methods for modeling individual and social behavior. Conclusions: This approach could improve the quality of Public Health economic models, supporting efficient allocation of scarce resources

Verification and Validation in GERAM Framework for Modeling of Information Systems

The main aim of this article is to propose a methodology for using verification and validation tools in a framework for modeling of an Industrial Enterprise Information Systems. The first part of this paper introduces the Generalized Enterprise Reference Architecture and Methodology (GERAM) framework and its parts that are used for modeling of industrial enterprise information systems. The second part introduces the verification and validation concepts and tools. The third part of this article proposes the use of the verification and validation tools in GERAM framework to improve the coherency, correctness, error-free, qualitative aspects and efficiency of an enterprise information system