Modeling Toolkit Research Articles

A free and open-source toolkit of three-dimensional models and software to study face perception

A free and open-source toolkit of three-dimensional models and software to study face perception

Short-Term Inflation Projection Based on Marker Models

The article proposes a hybrid model for inflation projection that combines econometric and neural network models. At the same time, both factor variables and market markers of consumer price index growth are used as explanatory variables. It has been shown that such an approach makes it possible both to maintain the theoretical fullness of the model and to ensure high accuracy of calculations, which is unattainable when using only one type of model toolkit.

Efficient Search Mechanism from Large Scale Corpora for Domain-Specific Language Modeling in Speech Recognition

With the Internet and the World Wide Web revolution, large corpora in variety of forms are germinating ceaselessly that can be manifested as big data. One obligatory area for the usage of such large corpora is language modeling for large vocabulary continuous speech recognition. Language modeling is an indispensable module in speech recognition architecture, which plays a vital role in reducing the search space during the recognition process. Additionally, the language model that is contiguous to the domain of the speech can dwindle the search space and escalate the recognition accuracy. In this paper, an efficient searching mechanism for domain-specific document retrieval from the large corpora has been elucidated using Elasticsearch which is a distributed and an efficient search engine for big data. This assisted us in tuning the language model in accordance with the domain and also by reducing the search time by more than 90% in comparison to conventional search and retrieval mechanism used in our earlier work. A word level and a phrase level retrieval process for creating domain-specific language model has been implemented. The evaluation of the system is performed on the basis of word error rate (WER) and perplexity (PPL) of the speech recognition system. The results shows nearly 10% decrease on WER and a major reduction in the PPL that helped in boosting the performance of the speech recognition process. From the results, it can be consummated that Elasticsearch is an efficient mechanism for domain specific document retrieval from large corpora rather than using topic modeling toolkits

An innovation Systems Approach to Sustainability Transitions Between Qatar and Jordan by Using SAP Software System


 
 
 
 This paper aimed to discuss how and where SAP software system can be used. It addresses relevant areas including linear innovation model, multi-level perspective, innovation systems and factors of innovation, factors of innovation, innovation management, circular economy, sustainability transitions, and SAP software system in terms of history, use, digital circular economy business model toolkit, and the difference between linear SAP and circular SAP solutions. It also compares the use of SAP in Qatar and Jordan.
 
 
 

Operations Management in the Age of the Sharing Economy: What Is Old and What Is New?

The sharing economy, a term we use to refer to business models built around on-demand access to products and services mediated by online platforms that match many small suppliers or service providers to many small buyers, has emerged as an important area of study in operations management. We first describe three “canonical” applications that have garnered much attention from the operations management community. We use these applications to highlight distinguishing features of sharing economy business models and to point out research questions that are new. Then we draw connections between classical operations management theory and models and those that have been used to study sharing economy applications. We do so to put in context some of the recent work on the sharing economy and to showcase the underlying modeling toolkit and identify opportunities for future research.

Creative Learning Model Toolkit: An Essential Element of Science Learning to Develop Learning Skills in Students

This study aims to (1) produce Creative Learning Model Toolkit (CLMT) on science learning which is appropriate to develop learning skills in students, (2) know the students learning skill that can be developed when using CLMT in science learning, (3) know the validity of CLMT that is used to develop students’ learning skills in science learning. The research method uses Research and Development (R&D), by pointing to Four-D model with 4 main phases namely define, design, develop, disseminate. The instruments used in this study were product validation questionnaire, learning skills observation sheet, and test of cognitive knowledge. Result data of validation toolkit and observation sheets were analysed descriptively while N-gain for test of cognitive knowledge. The result showed that: (1) CLMT have been produced by several approach namely Contextual Teaching Learning, Collaborative Learning and Web based Learning; (2) CLMT could develop student’s learning skills for instance critical thinking, social skill, and ICT literacy; (3) CLMT that was developed was considered as very good category both by expert and N-gain. Moreover, based on the analysis CLMT was appropriate to use in science learning process for develop students’ learning skills.

Assessing the CPTPP Impacts Against Canada's Progressive Trade Agenda

This report reviews Canada’s progressive trade agenda (PTA), situates it in a broader global context of policies addressing the distributional and sustainability issues raised by international trade and investment, and outlines enhancements to the standard general equilibrium modelling toolkit to enable a systematic, quantitative assessment of the impacts of a trade agreement on Canada’s labour market, including job impacts by age, sex, and profession, and transitions into and out of the labour force and into and out of employment. The methodology is illustrated through an assessment of impact on Canada of the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) against the objectives of the PTA. Annex 1 to the report describes the modelling framework in technical terms.

Real‐Time Fiscal Forecasting Using Mixed‐Frequency Data*

AbstractThe sovereign debt crisis has increased the importance of monitoring budgetary execution. We employ real‐time data using a mixed data sampling (MiDaS) methodology to demonstrate how budgetary slippages can be detected early on. We show that in spite of using real‐time data, the year‐end forecast errors diminish significantly when incorporating intra‐annual information. Our results show the benefits of forecasting aggregates via subcomponents, in this case total government revenue and expenditure. Our methodology could significantly improve fiscal surveillance and could therefore be an important part of the European Commission's model toolkit.

Exploring Reader-Generated Language to Describe Multicultural Literature

How do readers describe multicultural fiction works? While in library and information science (LIS) we have the language of appeal factorsand genre trendsto describe works of fiction, these linguistic choices may not be used by readers to describe their own responses and reactions to works that provide cultural affirmation of one’s own culture or exposure to learning different cultures. In this research, text mining processes are employed to harvest reader-generated book reviews and subsequently analyze the words readers use to describe award-winning multicultural fiction on the retailer site Amazon.com. Our goal with this study is to provide LIS professionals an insight into readers’ perspectives related to multicultural fiction. We describe our methodology of engaging in topic modeling as described by Jockers and Mimno (2013) as applied to multicultural fiction reviews. First, we explore the construction and processing of a corpus of reader reviews of multicultural fiction titles, then we model topics using a topic modeling toolkit to generate topics from these reviews. Through this analysis, we determine consistent terms used to describe multicultural fiction that can be used to indicate common reader experience and identify topics. Closing discussion reflects on whether librarians can use text mining of reader reviews to enhance their reader advisory services for readers seeking books that represent multiple and/or diverse cultures.

Patient‐Specific Hemodynamic Analysis of Coronary Arteries

In cases of severe cardiac stenosis, the selection of treatment options including angioplasty, stent insertion, and bypass surgery are based on cardiac diagnosis provided by electrocardiograms, stress tests, and angiograms. Although these diagnostic tools are all vital, it is still difficult to determine the true extent to which normal blood flow has been compromised. To date, a clear guideline for aggressive intervention does not exist, as stenosis characteristics can vary greatly between patients. Plaque composition, compressibility, and shape are all factors that can modify a patient's risk of incurring a life‐threatening cardiac event in the near future. Our work aims to establish a streamlined computational model that can provide accurate estimation of dynamic shear stress and tensile strain on a plaque based on a patient's specific geometry. Together with flow information provided by 4D cardiac MRI, information from the computational model will allow a cardiologist to make better informed decisions regarding therapeutic options for patient cardiac health.The goal of this undergraduate research project was to determine the most effective method for 3D reconstruction and segmentation of the heart and coronary arteries from patient‐specific CTA (computed tomography angiography) data. The resulting 3D geometry will be imported into COMSOL, a computational fluid dynamics software, for hemodynamics and stress‐strain analysis.Patients' CTA data was provided by the Cardiac Imaging Department of the St. Francis Hospital (with IRB approval). A set of 2097 images were imported into a 3D reconstruction software, 3D Slicer (http://www.slicer.org), and visualized in sagittal, axial, and coronal views. By scanning through images in each plane, major components of the heart (i.e., atria, ventricles, aorta, coronary arteries) were identified manually. 3D reconstruction and segmentation were accomplished using a seeding procedure in which each part of the heart was carefully selected and identified as an individual component. 3D Slicer then generated a 3D‐reconstructed geometry of the heart from seeds. Proper seeding allowed different components of the heart to be visualized individually in the 3D viewer, enabling isolation of the coronary arteries from the whole heart model.Preliminary results showed a 3D model of the whole heart from CTA data with the left main coronary isolated. Manual segmentation was found to be challenging because coronary artery branches are embedded within the cardiac muscle. It was demonstrated to be efficient to refine seeds for coronary artery identification and segmentation by carefully examining the 3D model and performing several iterations of correction. The coronary artery geometry generated in 3D Slicer is then processed by a Vascular Modeling Tool Kit (VMTK, an open source software) to generate the proper geometries (in .stl format) needed for COMSOL, for hemodynamics modeling and analysis.As methods for reconstruction and segmentation are further refined, computational modeling will be used as a high throughput tool to generate useful information on stress/strain conditions within the coronary arteries, moving toward our goal of streamlined personalized cardiac treatment.This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

RETRACTED ARTICLE: VRML-based real estate 3D virtual scene display and its impact on property prices

In the fierce sales competition environment of real estate, virtual reality, a new computer technology with strong immersion and interactivity, is applied to this industry and exerts the best effect. This paper designs and implements a real estate display system based on virtual reality technology, and can browse and access through the Internet. Implement programming modeling using the VRML language and modeling with visual modeling toolkit software. The modeling of the geometry, physics and behavior of the real estate community environment and related objects is completed. Compare various modeling methods and find a balance between the model’s fidelity and browsing speed. In addition, the SpVAR model is used to study the real estate 3D virtual scene display between three representative urban agglomerations and its relationship with real estate prices. The establishment of a new model guidance based on the SpVAR model for future option pricing has a larger Reference value.

Model and toolkit for the formation of the production enterprise digital platform

The relevance of this topic is determined by the necessary to develop and study the model and mathematical toolkit for the production enterprise digital platform. The model of ecosystem is the territory where enterprises manufacture products and form the life services balanced with the production. This balance is fundamental for the digital platform of production enterprise controlling system. In its turn, the controlling system includes production organization, management and accounting systems and innovation activity which continuously increase market added value of manufactured and sold products and life services of the territory. Digital platform of a manufacturing-technological system includes the model of production and its graphical and digital mathematical toolkit. We suggest that the model of production can be presented as a closed operation cycle converting manufacturing capital into monetary capital in the form of expenses and added market value of products. Mathematical toolkit of this model is the system of two vector equations including the vector equation of manufacturing capital and of monetary capital. We identified the regularities of changing operation cycle parameters and their proportions. The main condition of territory strategic planning should be oriented on the equality of sales value of manufactured products and sales value of life activity services. Physical basis of technological processes should correspond to products consumer properties. Further research will be devoted to the improvement of the digital platform mathematical toolkit ensuring efficient management accounting and innovation activity of enterprises.

Ordinal regression models for zero-inflated and/or over-dispersed count data

Count data commonly arise in natural sciences but adequately modeling these data is challenging due to zero-inflation and over-dispersion. While multiple parametric modeling approaches have been proposed, unfortunately there is no consensus regarding how to choose the best model. In this article, we propose a ordinal regression model (MN) as a default model for count data given that this model is shown to fit well data that arise from several types of discrete distributions. We extend this model to allow for automatic model selection (MN-MS) and show that the MN-MS model generates superior inference when compared to using the full model or more traditional model selection approaches. The MN-MS model is used to determine how human biting rate of mosquitoes, known to be able to transmit malaria, are influenced by environmental factors in the Peruvian Amazon. The MN-MS model had one of the best fit and out-of-sample predictive skill amongst all models. While A. darlingi is strongly associated with highly anthropized landscapes, all the other mosquito species had higher mean biting rates in landscapes with a lower fraction of exposed soil and urban area, revealing a striking shift in species composition. We believe that the MN and MN-MS models are valuable additions to the modelling toolkit employed by environmental modelers and quantitative ecologists.

A Latency-Aware Multiple Data Replicas Placement Strategy for Fog Computing

With the rapid increase of the number of IoT devices, transmitting big amount of data from these devices to data centers which are far away will cause problems like high latency or network congestions. Fog Computing provides a better solution for Fog-enabled latency sensitive data services to place data on Fog nodes which are closer to the data generators. However, recent studies only focus on the data placement problem of placing one single data replica to the proper Fog node. Under the situation that there are several data consumers whose topology positions are different subscribing the same data, one single data replica cannot meet the latency requirement of all the consumers. Hence, we build a multi-replica data placement model iFogStorM for Fog Computing to formulate the problem of how many data replicas need to be placed on Fog nodes and how to optimize the data placement. Furthermore, we propose a greedy algorithm based data replica placement strategy, MultiCopyStorage, to reduce the overall latency. MultiCopyStorage uses a pruning method to filter the inferior solutions calculates the overall latency and chooses the solution with the minimum overall latency as the final solution. We conducted experiments on iFogSim, a toolkit for modeling and simulation of Fog Computing, evaluated the proposed strategy with the CloudStorage strategy, Closest Node strategy, iFogStor strategy, and two kinds of heuristic strategy, iFogStorZ, and iFogStorG. The experiment result demonstrates that MultiCopyStorage strategy reduces the overall latency by 6% and 10% compared to iFogStor and iFogStorG strategy respectively. Meanwhile, execution time of the MultiCopyStorage is less than the heuristic strategy, iFogStorG and iFogStorZ, which proves that the proposed strategy can support real-time scheduling.

The silicate weathering feedback in the context of ophiolite emplacement: Insights from an inverse model of global weathering proxies

Silicate weathering provides a stabilizing feedback in the global carbon cycle and has played a key role in keeping surface environments clement for the majority of Earth9s history. It was recently proposed that ophiolite emplacement at low latitude at ∼90 to 70 Ma and ∼50 to 40 Ma enhanced the rate of silicate weathering and led to global cooling that terminated the Early Eocene Climatic Optimum. In this study, we revisit this proposal with a series of models of varying complexities to investigate the likelihood that Earth9s climate was driven largely by ophiolite emplacement in the Paleogene. Our models are constrained by either marine Os isotopic records or the combination of carbon cycle, Sr and Os isotopic records, and solid Earth degassing rates. The proposed changes in ophiolite weathering are inconsistent with our Os isotope mass balance model. With our coupled carbon cycle and weathering proxy model, total global silicate weathering fluxes (including ophiolite weathering) slightly decreased or remained relatively constant from 52 to 42 Ma. This constancy in global silicate weathering fluxes is inconsistent with the idea that enhanced ophiolite weathering directly led to global climate cooling. However, our model results support the idea that the strength of the silicate weathering feedback (weatherability) has played a significant role in regulating the pCO2 levels through the Cenozoic. Further, the modeling toolkit developed here can be used to explore the role of mafic rock weathering in driving climate change in other intervals through Earth9s history.

A symbolic model checking approach in formal verification of distributed systems

Model checking is an influential method to verify complex interactions, concurrent and distributed systems. Model checking constructs a behavioral model of the system using formal concepts such as operations, states, events and actions. The model checkers suffer some weaknesses such as state space explosion problem that has high memory consumption and time complexity. Also, automating temporal logic is the main challenge to define critical specification rules in the model checking. To improve the model checking weaknesses, this paper presents Graphical Symbolic Modeling Toolkit (GSMT) to design and verify the behavioral models of distributed systems. A behavioral modeling framework is presented to design the system behavior in the forms of Kripke structure (KS) and Labeled Transition System (LTS). The behavioral models are created and edited using a graphical user interface platform in four layers that include a design layer, a modeling layer, a logic layer and a symbolic code layer. The GSMT generates a graphical modeling diagram visually for creating behavioral models of the system. Also, the temporal logic formulas are constructed according to some functional properties automatically. The executable code is generated according to the symbolic model verifier that user can choose the original model or reduced model with respect to a recursive reduced model. Finally, the generated code is executed using the NuSMV model checker for evaluating the constructed temporal logic formulas. The code generation time for transforming the behavioral model is compared to other model checking platforms. The proposed GSMT platform has outperformed evaluation than other platforms.

CONCEPTION OF MODELING THE SYSTEM OF ENSURING FINANCIAL ECONOMIC SECURITY

Urgency of the research. Functioning of the mechanism of ensuring financial economic security within the system of strategic management of a business entity is based on the use of information and analytical support along with economic mathematical modeling. Target setting. Economic mathematical modeling has a significant cognitive potential to describe the laws and patterns of the studied systems functioning. Its use leads to the formation of the main tasks for ensuring the financial economic security system and the creation of the concept of modeling such a system as a theoretical foundation to shape a model basis of its description. Actual scientific researches and issues analysis. The issues of the managerial mechanisms for the financial economic security formation and the modeling tools use are highlighted in the scientific publications by O. Zyhriy, T. Vasyltsiv, L. Menggang, D. Nanto, J. Jürjens, O. Illiashenko, V. Heiets, and many others. Uninvestigated parts of general matters defining. The issue of creating a holistic system of scientific and theoretical positions regarding the use of means of economic mathematical modeling is not sufficiently studied now. The research objective of this article is to identify the main tasks of modeling the composing parts of the financial economic security system and to shape conceptual provisions aimed at designing the appropriate modeling toolkit. The statement of basic materials. A description of the conceptual provisions regarding the modeling of the system of financial economic security in the context of the solution of the main tasks is presented. Conclusions. The presented conception allows identifying the structure of the model basis as a part of the mechanism of ensuring financial economic security. This contributes to the selection of models for solving the issues of forming managerial decisions to provide the required level of security.

Computational fluid dynamics methods applied to intracranial stenosis imaging

Background Stenosis of the major intracranial arteries is an important cause of stroke, accounting for 8% to 10% of ischemic strokes in US. It was demonstrated that Computational Fluid Dynamics (CFD) modelling may be a promising technique in evaluating and understanding the hemodynamic characteristics of ICAS lesions. Previously, it has been shown that creating CFD models from routinely acquired computed tomographic angiography (CTA) and digital subtraction angiography (DSA) source images is feasible. However, it has not been evaluated if the results from CFD analysis of CTA and DSA source images correlate. Methods This retrospective study evaluated 80 patients with symptomatic ICAS lesions treated at Tiantan Hospital in Beijing. All patients had a symptomatic stenosis of the M1 segment of the middle cerebral artery (MCA-M1) with DSA and CTA imaging acquired at the time of treatment. The Vascular Modelling Toolkit software was used to generate 3D models from CTA source images. A region of interest containing the stenotic vessel was manually selected on the CTA source images, and then segmented using a gradient-driven level set method to generate an initial vessel volume. 3D reconstructions were generated from DSA images using Coronary3D software. The reconstructions were generated by using an anteroposterior and a lateral view of the stenotic vessel. Blood flow simulation was performed on both DSA and CTA generated meshes using ANSYS FLUENT SOLVER (ANSYS Inc.). A two-sample t test for paired data was used to compare the pressure and velocity measurements across imaging modalities. Results CTA and DSA imaging were evaluated for 20 patients with symptomatic ICAS. Although 3D reconstruction was possible for all 80 CTA scans, faint signals and convoluted vasculature in 60 of the 80 DSA scans prevented 3D reconstruction and excluded these cases from the study. When it was plotted the change in pressures of CTA versus the change in pressures of DSA, we received a R2 of 0.001; meaning that the linear model explains almost none of the variability in between the data in terms of changes in velocity and pressure. Conclusion It was found no noticeable correlation between the information regarding pressure and velocity as given by DSA and by CTA. Therefore, we could not say that the information from DSA is on par with that of CTA. Improvements in software that can robustly reconstruct DSA scans of brain vasculature is needed to computationally analyse a larger sample size of DSA data.

Operations Management in the Age of the Sharing Economy: What Is Old and What Is New?

The sharing economy, a term we use to refer to business models built around on-demand access to products and services mediated by online platforms that match many small suppliers or service providers to many small buyers, has emerged as an important area of study in operations management. We first describe three canonical applications that have garnered much attention from the operations management community. We use these applications to highlight distinguishing features of sharing economy business models and to point out research questions that are new. Then we draw connections between classical operations management theory/models and theory/models that have been used to study sharing economy applications. We do so to put in context some of the recent work on sharing economy, and to showcase the underlying modeling toolkit and identify opportunities for future research.

MODEL ASPECTS OF EVALUATING EFFICIENCY OF THE PERSONNEL MANAGEMENT AND MOTIVATION

This paper proposes methodological aspects of evaluation of organization efficiency of personnel management and motivation on the bases of application of economic and mathematical tools by implementing a three-component algorithmic model. The structural divisions heads’ work effectiveness evaluation is carried out on the basis of econometric systems of structural equations that allow you to rank personality, communicative, innovative, social and corporate managers characteristics that have the greatest impact on the overall performance indicators of the bank staff among which are considered amount of attracted funds; assessment of employee's compliance with the standard; activity efficiency (number of contracts); contribution to development. The obtained modeling results confirm the hypothesis that structural unit managers need to work on the development of their communicative, innovative, personal and social characteristics. Models for analyzing the personnel motivation factors on the bases of the methodology of multivariate statistical factor analysis with initial list of 13 main indicators of staff motivation are proposed; as a result, we set up the aggregated factors that reflect the properties of several motivational components such as financial, social incentives and medical insurance for employees. The model for estimating influence of motivation factors on results of bank's activity on the bases of nonlinear econometric functions is constructed. The regression equation of the motivation factors influence on the bank total profit is obtained. The proposed model toolkit for evaluation of efficiency of management and staff motivation will allow the bank's management to improve motivation and incentives mechanisms in order to raise efficiency of stuff productivity in direction of increasing bank profitability and competitiveness.