Method For Building Models Research Articles

Applying Critical Race Theory to Group Model Building Methods to Address Community Violence.

Group model building (GMB) is an approach to building qualitative and quantitative models with stakeholders to learn about the interrelationships among multilevel factors causing complex public health problems over time. Scant literature exists on adapting this method to address public health issues that involve racial dynamics. This study's objectives are to (1) introduce GMB methods, (2) present a framework for adapting GMB to enhance cultural responsiveness, and (3) describe outcomes of adapting GMB to incorporate differences in racial socialization during a community project seeking to understand key determinants of community violence transmission. An academic-community partnership planned a 1-day session with diverse stakeholders to explore the issue of violence using GMB. We documented key questions inspired by critical race theory (CRT) and adaptations to established GMB "scripts" (i.e., published facilitation instructions). The theory's emphasis on experiential knowledge led to a narrative-based facilitation guide from which participants created causal loop diagrams. These early diagrams depict how violence is transmitted and how communities respond, based on participants' lived experiences and mental models of causation that grew to include factors associated with race. Participants found these methods useful for advancing difficult discussion. The resulting diagrams can be tested and expanded in future research, and will form the foundation for collaborative identification of solutions to build community resilience. GMB is a promising strategy that community partnerships should consider when addressing complex health issues; our experience adapting methods based on CRT is promising in its acceptability and early system insights.

Study on Vibration of Marine Diesel-Electric Hybrid Propulsion System

This study analyzes the characteristics of hybrid propulsion shafting and builds mathematical models and vibration equations of shafting using the lumped parameter method. Main focus is on the asymmetric double diesel propulsion shafting operation process and the impact of the phase angle and motor excitation on torsional vibration of shafting. Model result is validated by testing results conducted on double diesel propulsion shafting bench. Mathematical model and model-building methods of shafting are correct.

Comparsion Study on Two Methods of Water Inflow Prediction of Coal Mining

Because of the complex geologic space body and the underground seepage conditions, it is difficult to predict groundwater water inflow in prevention and treatment control work of coal mine. To predict the groundwater flow of the coal seam under the roof sandstone aquifer in Nanshan coal, the big water well method and three-dimensional numerical model were used at the same time. The quantity of predicted normal water inflow was 553.5 m3/h by using numerical model and the big-well method is about 591.6 m3/h. The result of big-well methods was relatively close to the result of numerical simulation method, which proved the rationality of the two methods in parameters selection and model building, and greatly improved the reliability and accuracy of prediction results. Compared with large-well method, by establishing the complex 3D geological structure of underground water and fitting the parameters based on the actual data of hydrology, numerical method could simulate the process of groundwater seepage and dewatering engineering, overcome the parameter uncertainty of large-well method, improve the predictive accuracy of the mine water inflow, and has important application value.

Control Analysis Using Tuning Methods for a Designed, Developed and Modeled Cross Flow Water Tube Heat Exchanger

Cross flow water tube heat exchanger can be designed and made operational using methods of model building and simulation of the system. This paper projects the design and development of a model of cross flow water tube heat-exchanger system, simulation and validation of control analysis of different tuning methods. Feedback and override control system is developed using inputs acquired with the help of sensory system. A mathematical model is formulated for analysis of system behaviour. The temperature is regulated at the desired set point automatically. Keywords—Heat Exchanger, Feedback, Override, Temperature, PID.

Proteoglycan-induced arthritis model in mice: research progress

Proteoglycan-induced arthritis(PGIA) is a unique systemic autoimmune disease model of spondyloarthritis(SpA)induced by intraperitoneal immunization of either BALB / cor certain C3H colonies with cartilage proteoglycan.This model is currently the only systemic autoimmune mice model with axial skeleton manifestation,which has become a focus of study in Sp A modeling.Here in this review we summarize the methods of animal model building,phenotype identification,immunological characteristics and pathways involved in bone remodeling of PGIA based on previous studies,hoping to provide references for study on SpA animal model.

A New Multiple UAVs Cooperative Search Model Building and Route Planning Method

In multiple UAVs cooperative search tasks, the traditional environment model was based on squares, which caused the problem of different cost in each UAV’s single step. Therefore, we divide the battlefield by regular hexagons to make the UAVs’ steps more coordinate. At the same time, the cell’s encoding method was improved, which can greatly reduce the computing complexity of the coordinates. Route planning is an important part in cooperative search to decide where to go and how to search. By analyzing the method of route planning with several steps, Model Predictive Control (MPC) was proved to be an easy and effective method. However it became a complex probem to selecte a best route from so big amount of planning routes. Then the method of Discrete Particle Swarm Optimal (DPSO) was proposed to solve this problem. In order to evaluate the value of the route, an evaluate function was designed, containing the reward function of the route, the repulsion function of the no-fly zone and the force function of other UAVs. The reward function was designed based on the targets’ existence probabilities and the areas’ information uncertainty values. Then the no-fly zone was designed based on the artificial potential field method. However, due to each UAV can fly in different height, that is, collision would not happen, so the force function of other UAVs didn’t need concerned. In the end, a simulation experiment with some static targets and a no-fly zone was carried out, in which, a greed method was put forward to be a comparison to MPC. The results showed that the methodology proposed can ensure that the UAVs detected the targets safely and covered as more area as possible.

Dynamic time warping — An improved method for 4D and tomography time shift estimation?

Time shift estimation is a key issue in many areas of seismic exploration such as time-lapse studies and traveltime tomography. Automated estimation is useful because it enables applications such as time strain analysis and automated velocity model building. A commonly used automated estimation algorithm is the windowed crosscorrelation. Unfortunately, this method can be inaccurate in areas where time shifts vary significantly over short intervals. Algorithms based on mismatch minimization have been proposed to remedy this problem. All the mentioned methods suffer from cycle skipping in the face of large time shifts. I, therefore, used the dynamic time warping (DTW) algorithm as an alternative method to estimate time shifts. I claimed the time shift estimates produced by DTW are of higher quality than those produced by the previous methods, especially with regard to the cycle-skipping problem. I found that DTW always is able to perfectly restore time shifts applied to traces when the changes are purely temporal. I investigated its accuracy when amplitude changes are present, and found that the algorithm is well suited for time-lapse analysis. Finally, we determined that DTW is resilient against cycle-skipping errors, in the presence of large time shifts and with significant changes in the data, and thus a candidate for use in automated velocity model-building methods, such as waveform tomography or full-waveform inversion.

Treatment selections using risk-benefit profiles based on data from comparative randomized clinical trials with multiple endpoints.

In a typical randomized clinical study to compare a new treatment with a control, oftentimes each study subject may experience any of several distinct outcomes during the study period, which collectively define the "risk-benefit" profile. To assess the effect of treatment, it is desirable to utilize the entirety of such outcome information. The times to these events, however, may not be observed completely due to, for example, competing risks or administrative censoring. The standard analyses based on the time to the first event, or individual component analyses with respect to each event time, are not ideal. In this paper, we classify each patient's risk-benefit profile, by considering all event times during follow-up, into several clinically meaningful ordinal categories. We first show how to make inferences for the treatment difference in a two-sample setting where categorical data are incomplete due to censoring. We then present a systematic procedure to identify patients who would benefit from a specific treatment using baseline covariate information. To obtain a valid and efficient system for personalized medicine, we utilize a cross-validation method for model building and evaluation and then make inferences using the final selected prediction procedure with an independent data set. The proposal is illustrated with the data from a clinical trial to evaluate a beta-blocker for treating chronic heart failure patients.

THE INSTANCE INDIVIDUAL INFORMATIVITY EVALUATION FOR THE SAMPLING IN NEURAL NETWORK MODEL SYNTHESIS

The problem of mathematical support development is solved to automate the sampling at diagnostic and recognizing model building by precedents. The object of study is the process of diagnostic and recognizing neural network model building by precedents. The subject of study is the sampling methods for neural network model building by precedents. The purpose of the work is to increase the speed and quality of the formation process of selected training samples for neural network model building by precedents. The method of training sample selection is proposed which for a given initial sample of precedents and given feature space partition determines the weights characterizing the term and feature usefulness. It characterizes the individual absolute and relative informativity of instances relative to the centers and the boundaries of feature intervals based on the weight values. This allows to automate the sample analysis and its division into subsamples, and, as a consequence, to reduce the training data dimensionality. This in turn reduces the time and provides an acceptable accuracy of neural model training. The software implementing proposed indicators is developed. The experiments to study their properties are conducted. The experimental results allow to recommend the proposed indicators for use in practice, as well as to determine effective conditions for the application of the proposed indicators.

Nonlinear modeling, estimation and predictive control in APMonitor

This paper describes nonlinear methods in model building, dynamic data reconciliation, and dynamic optimization that are inspired by researchers and motivated by industrial applications. A new formulation of the ℓ1-norm objective with a dead-band for estimation and control is presented. The dead-band in the objective is desirable for noise rejection, minimizing unnecessary parameter adjustments and movement of manipulated variables. As a motivating example, a small and well-known nonlinear multivariable level control problem is detailed that has a number of common characteristics to larger controllers seen in practice. The methods are also demonstrated on larger problems to reveal algorithmic scaling with sparse methods. The implementation details reveal capabilities of employing nonlinear methods in dynamic applications with example code in both Matlab and Python programming languages.

Visual Modeling and Simulation Research of a Kind of On-Orbit Servicing Spacecraft

On-orbit servicing technology is a novel aerospace technology, which has important economic and military value. Reference to two on-orbit servicing programs named Orbital Express and Phoenix, the paper provides visual modeling and on-orbit module replacement simulation approach for a type of spacecraft based on satellite tool kit (STK). A kind of universal method for STK model building is presented, which includes multi-software model transformation and manual encoding. Some key problems about model integration and post processing, that helps complex STK model building, has also been summarized. An on-orbit replacement task simulation system based on co-simulation of MATLAB and STK can provide effective technical support for the aerospace program.

Group Model-Building to identify potential sources of environmental impacts outside the scope of LCA studies

Specific methodologies that consider a more comprehensive/diverse set of parameters must be explored by the LCA community. This study utilises the Group Model-Building (GMB) method to identify, and ...

Radiation Therapy Oncology Group (RTOG) Protocol 0915: A Randomized Phase 2 Study Comparing 2 Stereotactic Body Radiation Therapy (SBRT) Schedules for Medically Inoperable Patients With Stage I Peripheral Non-Small Cell Lung Cancer

Purpose/Objective(s): Multiple studies based on 18F-flourodeoxyglucose positron-emission tomography (FDG-PET) have reported correlations between simple SUV-based measures and outcomes in oropharyngeal cancer (OC). We investigated the use of shape and intensity feature datamining (‘radiomics’) and machine learning techniques to develop predictive models for local failure (LF), regional failure (RF), or distant metastasis (DM) following definitive chemoradiation therapy (CRT). Materials/Methods: From December 2002 to March 2009, all stage III-IV OC patients treated at our institution with CRT with retrievable pretreatment and post-treatment PET scans were identified (N Z 174). Pretreatment PET scans were converted to Computational Environment for Radiotherapy Research (CERR) format and 24 representative shape and intensity features of FDG-avid disease regions were extracted for each scan. Using machine learning feature selection methods, multiparameter logistic regression models were built incorporating clinical factors and imaging features. Rates of LF, RF, and DM were analyzed using logistic regression and Cox proportional-hazards regression with competing-risks analysis. Confidence intervals are 95%. Models were characterized by the Spearman rank correlation coefficient (Rs) and area under the ROC curve (AUC). All model-building methods were tested for significant correlation by leave-one-out cross-validation (LOOCV), and for LF with an independent patient cohort from a collaborating institution. Results: Median follow-up time was 55 months (5-112 months). Five-year estimated LF, RF, and DM rates were 7.4% (CI 3.2-11.6%), 6.1% (CI 2.39.9%), and 19.6% (13.4-25.8%), respectively. Smoking history (69% were long time smokers) was not significant. LF correlated with image intensity skewness and metabolic tumor volume (MTV), model Rs Z 0.277 (p < .001), AUC Z 0.826. RF correlated with shape heterogeneity and MTV, model Rs Z 0.283 (p < .001), AUC Z 0.866. DM correlated with image intensity kurtosis and MTV, model Rs Z 0.334 (p < .001), AUC Z 0.744. All model building methods produced significant correlations on LOOCV, with model Rs Z 0.207, 0.248, and 0.238 (p Z .007, .001, and .002) and AUC Z 0.743, 0.820, and 0.674 for LF, RF, and DM; the model for LF retained significant correlation in the independent patient cohort, Rs Z .186 (p Z .046), AUC 0.625. Conclusions: In the largest study of its kind to date, predictive models constructed using FDG-PET intensity and shape features significantly correlated to LF, RF, and DM in the test cohort and on LOOCV, and with LF with an independent population. Such models could assist in patient selection for dose reduction or escalation, or to identify high-risk patients for additional adjuvant therapy, but further understanding of the biological basis of image features will be necessary. Author Disclosure: M.R. Folkert: None. J.H. Oh: None. J. Setton: None. A.P. Apte: None. W.L. Thorstad: None. H. Schoder: None. N.Y. Lee: None. J.O. Deasy: E. Research Grant; Varian Medical Systems.

The observer's observer's paradox

Perspectives are central to paradox, from perceptual illusions (Bruno, N. (2001). When does action resist visual illusions? Trends in Cognitive Sciences, 5(9), 379–382), to dialetheism (Dietrich, E. (2008). The Bishop and Priest: Toward a point-of-view based epistemology of true contradictions. Logos Architekton. Journal of Logic and Philosophy of Science, 2, 35–58.; Dietrich & Webber, this issue). But why? We argue that apparent inconsistencies among perspectives are driven by the observer's methods, goals, etc., of inquiry: The manner in which the observer is observing. It is well known in various areas of science that observers inherently disrupt the system that is being studied. But even after we have measurement schemes (or theoretical apparatus) for collecting maximally observer-untainted behavioural data, there remains an inextricable observer-centred influence. We consider the case of cognitive science, in which diverse theoretical frameworks on offer continue to be pitted against one another as if one were going to be churned out as true, and the others falsified. A simulation is developed in which the observer's observer's paradox is demonstrated in a highly idealised computational model of a scientist studying a system. We use a model-building method referred to as ‘ε-machine reconstruction’ (Crutchfield, 1994) and provide readers a basic introduction to it. By using this framework to model the observer–measurement process, we explore how different measurement schemes impact resultant theories. The end product is a kind of existence proof, which we liken to more complicated circumstances of cognitive science. We argue that progress in cognitive science – overcoming apparently paradoxical persistence of several theoretical frameworks – may come from rendering the observer as an active aspect of these cognitive theories. The upshot would be one of two possibilities. Either theories are inconsistent because they ‘access’ divergent levels of organisation in the human cognitive system, or they may be integrated in an as-yet unknown theoretical framework.

Best Practices in Population Modeling Should Always Be Evolving

CPT: Pharmacometrics &amp; Systems Pharmacology (2013) 2, e52; doi:10.1038/psp.2013.37; published online 3 July 2013

Quantitative Structure - Activity Relationships Study of Carbonic Anhydrase Inhibitors Using Logistic Regression Model

Binary Logistic Regression (BLR) has been developed as non-linear models to establish quantitative structure- activity relationships (QSAR) between structural descriptors and biochemical activity of carbonic anhydrase inhibitors. Using a training set consisted of 21 compounds with known ki values, the model was trained and tested to solve two-class problems as active or inactive on the basis of the predicted value for IC50. Many quantitative descriptors were generated to express the physicochemical properties of 21 compounds with optimized structures. After filtration of these descriptors, 39 of descriptors for carbonic anhydrase (CA, EC 4.2.1.1) isozyme IX (CAIX) and 45 for isozymeXII (CAXII) remained and were selected for QSAR study. Logistic regression was then used to non-linearly select the most important descriptors and to develop a model for prediction of IC50. To evaluate the performance of the established models, Jjackknife and self consistency tests were performed during implementation of the two model-building methods. The applied indices including accuracy, sensitivity, and specificity were 85%, 82% and 100% for CAIX and also 71%, 68% and 80% for CAXII respectively. The primary advantage of such an approach is the reduction of redundant variables and the consequent improvement in the efficiency of modeling.

Building Information Modeling in Architectural Digital Design Education: Questions and Answers

The paper presents the results of two last years research in digital education computing using object-oriented building modeling methods including parametric modeling, component-based design approaching to the concept of building information modeling (BIM) as a base concept of contemporary architectural digital design. This paper summarizes results of research in BIM application in architectural design, attempts to determine the problems, capture the trends of object-oriented modeling in computer aided architectural design (CAAD) education. The results of the analysis of various aspects of BIM using surveys of architecture postgraduate students and undergraduate students of the last year are discussed. The methods of statistics for analysis of data are used. The interrelations between various factors of building information modeling as a part of CAAD education are investigated. The results of the work could be important for the definition of the guidelines for the future of architectural digital design education. DOI: http://dx.doi.org/10.5755/j01.sace.1.2.3281

The Research about the Method of Establishing the Model of Limited Unit about the Push-Extend Multi-under-Reamed Pile

This paper mainly introduces how to use the method of combining plane coordinates and polar coordinate, and the method of using ANSYS software to establish the calculating model of the limited units about the Push-extend Multi-under-reamed Pile, at the same time, it is successfully provided that a method of building model of the limited units for computer simulation analysis of irregular rotator body.

Non-Destructive Technique for Determination of Elastic Material Properties

An inverse technique, based on the planning of experiments and response surface methodology have been applied to determine elastic properties of unidirectional carbon fiber composite plate and carbon nanotubes reinforced aluminium plate. The nuances of application of investigated technique for orthotropic material properties determination are considered on the example of a composite plate. In turn, possibility of using of this technique on specimens with small dimensions (80x60x2 mm) is checked up and estimated on an example of an isotropic nanocomposite plate. The results obtained were verified by comparing the experimentally measured eigenfrequencies with numerical ones obtained by FEM using determinated elastic material properties. has been employed in (4), but the minimization method was different. Instead of the direct minimization of the functional, the experiment design and response surface approach are employed for approximation of the numerical (finite element) model. Such an approach can reduce the computational efforts significantly. In order to reduce the computational efforts, methods based on the approximation concepts were used in the structural optimization for the first time (5). The development of approximation functions has become a separate problem in optimum structural design. Approximating models can be built in different ways. To construct a more general model of the original function, the methods of experiment design (6) and approximate model building (7) can be used. A simplified model, called a meta-model (8), is elaborated using results of the numerical experiment at a sample point of the experiment design. The response analysis using the simplified model is computationally much cheaper than the solution employing the original model. II. NUMERICAL-EXPERIMENTAL METHOD

Study on Pedestrian Red-Time Crossing Behavior

In many cities in China, the problems of low efficiency and pedestrian risk taking at signalized intersections are mainly attributed to pedestrian red-time crossing. Pedestrian red-time crossing has many causes, such as traffic design and signal control, traffic flow, and the psychology of pedestrians’ decision making. In this study, data were collected on pedestrian red-time crossing, signal control, crossing facility design, and vehicle traffic flow with field observations and on pedestrians’ decision-making psychology data by using an intercept questionnaire administered after the crossing. A model for pedestrian red-time crossing choice was proposed on the basis of integrated field observations and questionnaire data. The model was compared with models based on either observational data alone or questionnaire data alone and proved to be well fit and to yield better prediction accuracy. The duration of red signal time was found to be the biggest influence on pedestrian red-time crossing. Suggestions for intersection design and signal control are proposed on the basis of these findings. Model building and data collection methods of pedestrian red-time crossing are also discussed.