Custom Model Research Articles

TxSim: Modeling Training of Deep Neural Networks on Resistive Crossbar Systems

Deep neural networks (DNNs) have gained tremendous popularity in recent years due to their ability to achieve superhuman accuracy in a wide variety of machine learning tasks. However, the compute and memory requirements of DNNs have grown rapidly, creating a need for energy-efficient hardware. Resistive crossbars have attracted significant interest in the design of the next generation of DNN accelerators due to their ability to natively execute massively parallel vector-matrix multiplications within dense memory arrays. However, crossbar-based computations face a major challenge due to device and circuit-level nonidealities, which manifest as errors in the vector-matrix multiplications and eventually degrade DNN accuracy. To address this challenge, there is a need for tools that can model the functional impact of nonidealities on DNN training and inference. Existing efforts toward this goal are either limited to inference or are too slow to be used for large-scale DNN training. We propose TxSim, a fast and customizable modeling framework to functionally evaluate DNN training on crossbar-based hardware considering the impact of nonidealities. The key features of TxSim that differentiate it from prior efforts are: 1) it comprehensively models nonidealities during all training operations (forward propagation, backward propagation, and weight update) and 2) it achieves computational efficiency by mapping crossbar evaluations to well-optimized Basic Linear Algebra Subprograms (BLAS) routines and incorporates speedup techniques to further reduce simulation time with minimal impact on accuracy. TxSim achieves 6×- 108× improvement in simulation speed over prior works, and thereby makes it feasible to evaluate the training of large-scale DNNs on crossbars. Our experiments using TxSim reveal that the accuracy degradation in DNN training due to nonidealities can be substantial (3%-36.4%) for large-scale DNNs and data sets, underscoring the need for further research in mitigation techniques. We also analyze the impact of various device and circuit-level parameters and the associated nonidealities to provide key insights that can guide the design of crossbar-based DNN training accelerators.

Learnings from Developing a Context-Specific LCA Tool for Buildings—The Case of LCAbyg 4

Buildings are a major cause of global resource consumption, greenhouse gas (GHG) emissions and other impacts on the environment, originating from both operational energy and material use. Informed design decisions can help mitigate potential impacts on the environment, by the use of life cycle assessment (LCA) in the early project stages. In order to mitigate building environment impacts throughout the industry, architects and engineers need tools that are integrated in the design workflow and based on the information available. Existing LCA tools for building design professionals are predominantly embedded in a specific context such as a country or a sustainability scheme. This paper provides learnings for the development of context-tailored tools for building-LCA using the case of Denmark, with specific focus on GHG-emissions that are in focus worldwide. Based on stakeholder involvement, four key areas were defined: Default information, flexibility, environmental design feedback and transparent results. Tool functions include a component library and a quantity estimator for bridging incomplete building information. A comparison monitor displays the performance of design solutions selected in the model, while a number of graphs and tables provide analysis of inventory and results. Finally, a customisable model data export, a complete input/output file for revision and custom analysis are among key functions for transparency.

Modeling the movement of customers in retail space under conditions of social distancing

The paper considers the problem of modeling the movement of customers in retail space under conditions of social distancing, which is caused by the requirements to prevent infection and further spread of the coronavirus infection COVID-19. A discrete model of a retail space, a model of a customer, a model of customers' movement according to the principles of functioning of a two-dimensional cellular automaton without memory have been developed. The retail space model allows to take into account the key objects of a retail space such as a supermarket/hypermarket: racks, cash desks, entrance, exit from the premises. In contrast to the classical models of representing a person using cellular automata, the customers' model ensures that social distance is taken into account by expanding the Moore neighborhood to about 2. In this case, the Moore neighborhood of order 1 is used to directly move the cell of the automaton representing the customer, and the cells of the neighborhood of order 2 are used to determining compliance with social distancing restrictions. The model of the customer behavior takes into account real-life situations: just like people, the cells of an automaton can “violate” the rules of social distancing. The set of rules for the customer's model is based on the general strategy of bypassing the retail space and contains additional restrictions regarding conflict situations for violating social distance, as well as a description of behavior in normal traffic and movement in the event of extreme situations. These models are implemented in software, which allows to control the settings of the simulation parameters. Heatmaps are used to visually represent the simulation results, showing the zones of pollution and zones of violation of social distance. The simulation results show that even with a slight increase in the probability of customers violating social distance, the number of distance violation cases grows exponentially.

INCREASING THE ROUGHNESS OF THE PROCEDURE FOR EVALUATING A VECTOR OF THE CONDITION OF OBJECTS TO THE INFLUENCE OF UNCERTAINTY FACTORS

Algorithms for increasing the roughness of the procedure for assessing the state vector of control objects to the influence of uncertainty factors are given. Expressions are obtained for extended state vectors and observations. Stable inversion algorithms are given for a nondegenerate block matrix with the allocation of its left and right zero divisors of maximum rank. The presented stable computational procedures allow us to regularize the problem of synthesis of algorithms for estimating the parameters of regulators in adaptive control systems with a customizable model and to improve the quality indicators of control processes under conditions of parametric uncertainty

The Effects of Quantitative Trait Architecture on Detection Power in Short-Term Artificial Selection Experiments.

Evolve and resequence (E&R) experiments, in which artificial selection is imposed on organisms in a controlled environment, are becoming an increasingly accessible tool for studying the genetic basis of adaptation. Previous work has assessed how different experimental design parameters affect the power to detect the quantitative trait loci (QTL) that underlie adaptive responses in such experiments, but so far there has been little exploration of how this power varies with the genetic architecture of the evolving traits. In this study, we use forward simulation to build a more realistic model of an E&R experiment in which a quantitative polygenic trait experiences a short, but strong, episode of truncation selection. We study the expected power for QTL detection in such an experiment and how this power is influenced by different aspects of trait architecture, including the number of QTL affecting the trait, their starting frequencies, effect sizes, clustering along a chromosome, dominance, and epistasis patterns. We show that all of these parameters can affect allele frequency dynamics at the QTL and linked loci in complex and often unintuitive ways, and thus influence our power to detect them. One consequence of this is that existing detection methods based on models of independent selective sweeps at individual QTL often have lower detection power than a simple measurement of allele frequency differences before and after selection. Our findings highlight the importance of taking trait architecture into account when designing and interpreting studies of molecular adaptation with temporal data. We provide a customizable modeling framework that will enable researchers to easily simulate E&R experiments with different trait architectures and parameters tuned to their specific study system, allowing for assessment of expected detection power and optimization of experimental design.

Customized Low-Cost Model for Hands-on Training in Intraoperative Ultrasound for Neurosurgeons: Our Experience and Review of Literature

Practical ultrasound (US) training is essential to overcome operator dependence and optimize image acquisition. For intraoperative neurosurgical application, in addition to hand-eye coordination, ultrasound training should incorporate training for visuomotor and visuospatial skills, as well as 3-dimensional depth orientation. Our agar-based, low-cost model has been developed keeping these skill sets in mind. We have described preparation of an agar-based, low-cost customizable model using commonly available echogenic objects as targets, which allows the clinician to perform various training tasks like depth insonation, target localization, and biopsy and resection cavity insonation. This low-cost model was implemented for internal training and validated at an international training course. The cost of the model was 4 USD, and its preparation time was <1 hour. It can be used for performing multiple US training tasks and provides realistic images and good tactile feedback. However, the model is perishable and artifacts are occasionally visible. Feedback survey results showed that >80% of participants felt the model was useful for US training. Our customizable low-cost US training model is an effective and efficient tool for US training with high acceptance by neurosurgeons. It faithfully mimics various intraoperative tasks and helps clinicians gain confidence to use intraoperative ultrasound as an adjunct during the procedures. This model can be used by individual surgeons/departments for ongoing training, as well as for larger training courses and workshops.

PNS139 VALUE ASSESSMENT FRAMEWORKS: HAVE THEY REACHED THEIR DESTINATION?

Value Assessment frameworks are the tools that assist healthcare decision makers to assess the value of healthcare intervention. They have the potential to have a remarkable impact on patient treatment decisions, either through their use by patients and their doctors by using them as a shared decision-making tool or by the payers to make reimbursement choices. The aim was to analyse the development of value assessment frameworks in the US and evaluate how far these frameworks have advanced We analysed the six value assessment frameworks, used by different professional organizations in US, American Society of Clinical Oncology (ASCO), American Heart Association (AHA)/American College of Cardiology (ACC), Institute for Clinical and Economic Review (ICER), Memorial Sloan Kettering Cancer Center (MSKCC) (DrugAbacus), National Comprehensive Cancer Network (NCCN) and Innovation and Value Initiative (IVI), against the six framework elements, the assessment process, methodology, benefits, costs, evidence, and dissemination and utilization, as suggested by the National Pharmaceutical Council. The domain of value assessment frameworks have advanced over the last few years. All the assessment frameworks do not address all the six framework elements. Each consideration has been addressed by at least one framework in some or the other way. Except ICER and IVI, no other framework has patient centric metrics. The sensitivity analysis is missing from all the frameworks except IVI where customizable models with option to change parameters are provided. One of the common concerns regarding their execution is the absence of transparency in the way they orchestrate clinical evidence to arrive at their recommendations. The framework developers should focus on the areas they have not addressed. If done appropriately, value assessment frameworks have the capacity to bring about a revolutionary change in patient treatment decisions. Since these frameworks have limitations, wholesome adoption, as of today, is not feasible.

Numerical simulation of steel-concrete composite beams: updated strategies of finite element modeling

abstract: The use of steel-concrete composite beams allows the best properties of these materials to be explored, resulting in more economical solutions. Many researchers have studied the behavior of composite beams from different strategies of numerical modeling, and some of these are presented in this article. In this context, the present work proposes the construction of a tridimensional numerical model using ANSYS software, version 19.2, with current-technology elements and compatible material models. For the simulation of concrete behavior, two models have been used: the first, denominated DP-CONCRETE, is a native ANSYS model, available in the more recent versions of this software; and the second, denominated USERMAT, is a customizable model that was developed based on Ottosen criterion. The results obtained with these models for the analyzed beams presented a good correlation with the experimental results and with numerical results from previous works.

Quantitative Microbial Risk Assessment of Swimming in Sewage Impacted Waters Using CrAssphage and Pepper Mild Mottle Virus in a Customizable Model

Fecal indicator bacteria currently employed for microbial water quality management are poor representatives of viruses. Viral water quality indicators have recently been proposed based on the human gut bacteriophage crAssphage and the food virus pepper mild mottle virus (PMMoV) due to their high abundance in sewage and association to human waste. Here, we develop a model relating crAssphage and PMMoV abundance to the risk of swimmer illness in a recreational water contaminated with fresh, untreated domestic sewage. This model, entitled QMRAswim, is available via a Web-based user interface and is generalizable to any indicator or pathogen. The majority of predicted illnesses from exposure to untreated domestic sewage-contaminated water were attributable to viruses, primarily norovirus. The mean crAssphage and PMMoV concentrations correlating with 30 illnesses per 1000 bathers were 4648 GC/100 mL and 5054 GC/100 mL, respectively, approximately 50 times their standard detection limit. This study reaffirms th...

Making Heterogeneous Specimen Data ‘FAIR’: Implementing a digital specimen repository

The definition of a digital specimen is proposed to encompass the digital representation(s) of physical specimens from natural science collections. The digital specimen concept is intended to define a representation (digital object) that brings together an array of heterogeneous data types, which are themselves alternative physical specimen representations. In this case, the digital specimen (DS) holds references to specimen data from a collection management system, images, 3D models, research articles, DNA sequences, collector information, among many other data types. The proposal is to create persistent relationships between the DS and other categories of digital objects (e.g. resource types mentioned above, collections, storage platforms, organisations, databases, and provenance data). Complying with FAIR data principles (findability, accessibility, interoperability, and reuse), i.e., achieving data ‘FAIRness’, eases data integration, which is needed for cross-disciplinary linking and combination of data from different domains, making the DS as a comprehensive package of information about a specimen. Implementation and access to a digital specimen repository (DSR) as a Digital Object Architecture (Sharp 2016) component demonstrates the alignment of the DS concept and FAIR data principles (Wilkinson et al. 2016, Kahn and Wilensky 2006). The DSR fulfills four roles: data producer, resource manager, data publisher, and collaboration space. As data producer, the DSR allows acquisition and curation (indexing, storage) of DSs linking primary data, models, analyses, and other digital object types. As resource manager, the DSR manages access to distributed platforms, ranging from acquisition networks (digitisation stations, museums, herbariums) to processing services, advanced computational resources, data asset storage systems, and specialised servers. As data publisher, the DSR provides access to data assets from national and transnational data archives. As collaboration space, the DSR supports users’ accessing, sharing and (re)using data assets, and derived data products and services. Adopting the collaboration space and data publisher roles, the DSR implements interfaces that expose the DSs to the research community, fulfilling the FAIR findability, accessibility, and reuse principles. Adopting the data producer and resource manager roles, the DSR creates meaningful and persistent relationships required to link DSs and other types of digital objects, fulfilling the FAIR interoperability principle. A prototype DSR based on the Cordra digital object repository has been deployed (Corporation for National Research Initiatives (CNRI) 2018, Reilly and Tupelo-Schneck 2010). The advantages of Cordra are: rapid deployment, customisable object model, creation of relations between digital objects, and application program interfaces for programmatic access. Rapid deployment of the DSR provides a tangible target for discussing the implementation of the DS concept. The customisable object model enables the refinement and enhancing of the definition of DS in response to feedback from colleagues who have accessed the DSR and used its contents. Creating relations between digital objects enables flexible linking to digital objects stored in different repositories. Accessing the DSR programmatically through APIs enables extending the use of the repository in different platforms (e.g. mobile devices) as well as integration with other repositories and services. As well as supporting a HTTP-oriented API, Cordra implements Digital Object Interface Protocol (DONA Foundation 2018), allowing the definition of operations to act directly on selected DSs in the repository. The DSR prototype has been demonstrated by providing access to the repository administrative interface and with a custom interface designed to facilitate access by different user groups, such as collection curators, researchers, teachers, and students. The client interface has been designed to demonstrate a subset of the functionalities derived from user stories, which describe software features from the end-user perspective. Demonstrating the DSR capabilities as proposed, will inform the refinement of the design of the DS model and provide early feedback about the needed software features.

Optimal Deployment of Configurable Business Processes in Cloud Federations

Configurable processes are increasingly being adopted by enterprises that seek experience sharing and best practice adoption. A configurable process is a customizable model that specifies how different enterprises perform similar processes. At the modeling level, a configurable process model provides for flexible business process (BP) reuse by (de)selecting the (ir)relevant parts to derive a particular process variant. At the exploitation level, it offers flexibility and agility to an enterprise looking to outsource its BP to different providers cooperating in a cloud federation. More specifically, an enterprise can use a configurable process model to derive particular process variants that it outsources depending on its objectives. In particular, it may opt for outsourcing the variant that results in the optimal deployment , e.g., having the minimal cost of allocated cloud services that fulfill the user quality of service (QoS) requirements. However, identifying the optimal deployment variants is a complex problem because of the heterogeneity of services within a cloud federation and the number of possible variants that can be derived from a configurable process model. In addition, the complexity of this problem increases for variable user QoS requirements. In this paper, we propose an approach to derive, from a configurable process model, the variant that has the optimal deployment in a cloud federation. We propose a linear programming approach that accounts for the variability of both the BP model and the user QoS requirements, and that ensures an optimal time-aware cloud service allocation. We experimentally show the effectiveness and flexibility of our approach on a generated testbed.

PMH24 - COST IMPLICATIONS OF ANTIPSYCHOTICS DRUGS: A HOSPITAL BUDGET IMPACT MODEL

the primary objective was to create an interactive model to analyze the budget impact of maintenance therapy with different antipsychotics (APs) from the Russian hospital perspective. an interactive, customizable model in Excel was built to calculate the costs and to provide pharmacoeconomic assessment of antipsychotic therapy. Cost-minimization (CMA), cost-effectiveness (CEA) and budget impact analysis (BIA) were used to conduct economic evaluation of standard doses therapy (according to national guidelines) of APs. Data on antipsychotics used in the long-term treatment of schizophrenia were derived from meta-analysis. Mean standard treatment duration was 60 days in inpatients facilities. Costs for drugs were estimated from the national hospitals sources (2015). Exchange rate in the analyzed period was €1 = 71.8 RUB. CMA showed, that clozapine demonstrated the lowest costs per patient (€25) followed by olanzapine (€202) with the assumption that they have similar effectiveness. Olanzapine was associated with higher drug costs relative to haloperidol (€202 vs. €2.3), but also had statistically significantly greater total effectiveness - mean relapse prevention rate was 83% and 72% respectively (OR=0.50, 95% CI 0.30–0.82). The ICER for olanzapine (€1,818) was 15 times less than willingness to pay the threshold for Russia (3-times GDP). The developed evaluation instrument enabled to estimate the budget impact of olanzapine (greater effectiveness) for two identical cohorts (n=200) of patients with schizophrenia: the model predicted a 18.6% increase in net costs for olanzapine in case of increase by 5% its utilization with an alternative haloperidol decreases in the same proportion (with base case proportion 24 and 76% respectively). results suggest that direct inpatients costs on mental healthcare could be minimized by using clozapine instead of olanzapine. In comparison with haloperidol the use of olanzapine in the treatment of schizophrenia is the cost-effective alternative for the Russian National health service.

GraphVar 2.0: A user-friendly toolbox for machine learning on functional connectivity measures

BackgroundWe previously presented GraphVar as a user-friendly MATLAB toolbox for comprehensive graph analyses of functional brain connectivity. Here we introduce a comprehensive extension of the toolbox allowing users to seamlessly explore easily customizable decoding models across functional connectivity measures as well as additional features. New MethodGraphVar 2.0 provides machine learning (ML) model construction, validation and exploration. Machine learning can be performed across any combination of graph measures and additional variables, allowing for a flexibility in neuroimaging applications. ResultsIn addition to previously integrated functionalities, such as network construction and graph-theoretical analyses of brain connectivity with a high-speed general linear model (GLM), users can now perform customizable ML across connectivity matrices, graph measures and additionally imported variables. The new extension also provides parametric and nonparametric testing of classifier and regressor performance, data export, figure generation and high quality export. Comparison with existing methodsCompared to other existing toolboxes, GraphVar 2.0 offers (1) comprehensive customization, (2) an all-in-one user friendly interface, (3) customizable model design and manual hyperparameter entry, (4) interactive results exploration and data export, (5) automated queue system for modelling multiple outcome variables within the same session, (6) an easy to follow introductory review. ConclusionsGraphVar 2.0 allows comprehensive, user-friendly exploration of encoding (GLM) and decoding (ML) modelling approaches on functional connectivity measures making big data neuroscience readily accessible to a broader audience of neuroimaging investigators.

IT in the workspace – The need for digital transformation

Abstract We live today in a world irreversibly and comprehensively impacted by the digital economy, either by its ability to transform traditional markets into digital ones or by creating new ones altogether. In such a digitized environment, agility, speed, adaptability and innovation through disruption are key attributes for organizations to maximize, in order to enable their workforce to be effective and efficient and achieve the organizational goals. The paper aims to outline that organizations have, as part of their digital transformation journey, opportunities to embrace the benefits that the new tools of the digital economy, such as cloud, mobility and big data analytics bring. The most immediate advantage of such a change is the ability to reduce the total IT spend, making the organization more frugal and allowing resources to be diverted into other ( potentially revenue generating ) parts of the business. As a method of proving that, we’ve taken several case study examples of enterprises both achieving savings by implementing new tools into their business. However, businesses often do not settle just for that but instead use part of the newly freed up resources to enhance their digital environments. Digital tools enable teams to work easily together and allow the workforce to access any and all of the organization’s resources from anywhere and at any time they wish. This “consumerized” demand model, where IT service is customized every step of the way is what lead enterprises to change their internal IT work frame and thus migrate from the legacy “one size fits all “ model, to the new, flexible, scalable and customizable model, through the usage of new capabilities like Cloud, Big Data, Analytics, Internet of Things (IoT), Mobility.

3:45 PM Abstract No. 297 Custom 3D-printed ultrasound-compatible vascular access models: training medical students for vascular access

Commercially available US-guided vascular access models (CM) simulate anatomic and US-visible landmarks; however, they are expensive and do not simulate variant anatomy. The purpose of this study was to compare training efficacy between CM and a novel 3D-printed US-compatible vascular access model (3DPVAM) using a simulation experience (SE) directed at medical students. We hypothesized that student comfort with US-guided femoral artery (FA) access increases after SE, and that 3DPVAM and CM SE efficacy are comparable. A 3DPVAM of normal FA anatomy was developed from an anonymized CT exam. After IRB approval, students were randomized to 3DPVAM or FemoraLineMan CM (Simulab) SE. Students completed a pre-SE questionnaire ranking comfort with FA access on a Likert scale. A standardized SE was administered by IR faculty. Students completed a post-SE questionnaire ranking comfort with FA access on a Likert scale. Student questionnaire results were compared between 3DPVAM and CM groups using Chi-square, Wilcoxon signed-rank, and noninferiority analyses. 15 and 17 students were randomized to 3DPVAM and CM training, respectively. 93.3% of 3DPVAM trainees and 100% of CM trainees had never attempted FA access. 73.3% of 3DPVAM trainees and 76.5% of CM trainees did not feel confident performing FA access. After training and SE, most 3DPVAM and CM trainees agreed that the model was easy to use (93.3% and 94.1%, respectively) and helpful for practice (93.3% and 94.1%). In both groups, training increased subjective student confidence by 2 Likert points (3DPVAM p = 0.001; CM p = <0.0001). The subjective confidence increase in 3DPVAM trainees was noninferior to that in CM trainees (noninferiority margin 1 Likert point, p<0.0001). Generation of an inexpensive custom 3DPVAM and SE is feasible and produces comparable subjective training outcomes to CM. We aim to extend our SE to resident and fellow trainees and to assess objective improvements in the future. We also aim to develop complex 3DPVAMs of variant anatomy and other body regions with the goal of creating customizable models to improve vascular access outcomes and patient safety across multiple disciplines.

Grid code compliant controllers for Multi-terminal HVDC grids aimed to integrate wind power: assessing their impact on the operational security of a real-world system

Exploiting multi-terminal High Voltage Direct Current (MTDC) grids for offshore wind park integration into continental bulk AC system raises several technical challenges, in particular the verification of the compliance of MTDC grids and offshore wind parks to the performance requirements (in terms of frequency, voltage support and fault ride through) which are being established by the grid codes, and the availability of general and customizable models to simulate their response. This work is aimed to provide a set of “open access” general models, able to fulfil the grid code requirements if suitably tuned, enabling the TSOs to perform their analyses without relying exclusively on vendor-specific models. In order to verify the realistic response of these models, an example of MTDC grid is simulated, connecting it to plausible operating scenarios of a real world AC system (the West Denmark grid) in target year 2020: some indications to properly tune control parameters are provided together with the results of several simulations aimed to check the fulfilment of grid code requirements in case of typical contingencies.

A cost-benefit analysis to select the most effective method for positional cloning: genotyping by sequencing versus allele-specific PCR

Next-generation sequencing technologies have enabled the rapid generation of high-resolution genetic maps to enable the identification of genomic regions associated with traits of interest. In certain cases these regions of interest need to be narrowed down via fine mapping to increase resolution to the level of the individual gene. In those cases, there is a choice of genotyping options, whereby cost, time, and information content need to be considered. We developed detailed, customizable models to compare the cost of genotyping by sequencing and allele-specific PCR for fine-mapping genomic regions of interest. The models were validated experimentally with data from a fine-mapping experiment designed to identify candidate genes within a 3.2-Mb disease resistance locus in the cereal crop sorghum (Sorghum bicolor (L.). Moench), which has a sequenced genome. The size of the mapping population, genetic diversity of the parental lines, choice of reagents, and labor cost were shown to influence the overall cost of the two methods, and, consequently, which method would be most cost-effective for a given experiment. The model can be easily customized to reflect experiments with different species, genetic populations, experimental and personnel costs to determine the most cost-effective procedure for a new positional cloning project.

Multi-resolution geometric modeling of the mitral heart valve leaflets.

An essential element of cardiac function, the mitral valve (MV) ensures proper directional blood flow between the left heart chambers. Over the past two decades, computational simulations have made marked advancements toward providing powerful predictive tools to better understand valvular function and improve treatments for MV disease. However, challenges remain in the development of robust means for the quantification and representation of MV leaflet geometry. In this study, we present a novel modeling pipeline to quantitatively characterize and represent MV leaflet surface geometry. Our methodology utilized a two-part additive decomposition of the MV geometric features to decouple the macro-level general leaflet shape descriptors from the leaflet fine-scale features. First, the general shapes of five ovine MV leaflets were modeled using superquadric surfaces. Second, the finer-scale geometric details were captured, quantified, and reconstructed via a 2D Fourier analysis with an additional sparsity constraint. This spectral approach allowed us to easily control the level of geometric details in the reconstructed geometry. The results revealed that our methodology provided a robust and accurate approach to develop MV-specific models with an adjustable level of spatial resolution and geometric detail. Such fully customizable models provide the necessary means to perform computational simulations of the MV at a range of geometric accuracies in order to identify the level of complexity required to achieve predictive MV simulations.

A Customizable Model for Chronic Disease Coordination: Lessons Learned From the Coordinated Chronic Disease Program.

In 2012, the Centers for Disease Control and Prevention provided funding and technical assistance to all states and territories to implement the Coordinated Chronic Disease Program, marking the first time that all state health departments had federal resources to coordinate chronic disease prevention and control programs. This article describes lessons learned from this initiative and identifies key elements of a coordinated approach. We analyzed 80 programmatic documents from 21 states and conducted semistructured interviews with 7 chronic disease directors. Six overarching themes emerged: 1) focused agenda, 2) identification of functions, 3) comprehensive planning, 4) collaborative leadership and expertise, 5) managed resources, and 6) relationship building. These elements supported 4 essential activities: 1) evidence-based interventions, 2) strategic use of staff, 3) consistent communication, and 4) strong program infrastructure. On the basis of these elements and activities, we propose a conceptual model that frames overarching concepts, skills, and strategies needed to coordinate state chronic disease prevention and control programs.

Development of a direct-dynamic model for a passenger vehicle on MATLAB Simulink

The development of full longitudinal simulations for vehicles, which are customizable and offer good results, can be time-consuming, whereas most software programs present to their users closed “black boxes” that fail to provide full control and understanding of their mechanisms and calculations. This article provides engineers and researchers with an open and customizable model. By starting from the vehicle’s equation of motion and utilizing MATLAB Simulink’s basic toolbox it was possible to build a direct-dynamic model that allows the user to tweak all the main longitudinal forces while also maintaining repeatability through a PID controlled auto-pilot. The graphics obtained from the simulator show that it is able to calculate the vehicle’s longitudinal forces, verify the driver’s inputs and test gear selection algorithms, becoming a powerful tool for future studies.