Custom Model Research Articles

Estimated Human and Economic Burden of Four Major Adult Vaccine-Preventable Diseases in the United States, 2013.

Low uptake of routinely recommended adult immunizations is a public health concern. Using data from the peer-reviewed literature, government disease-surveillance programs, and the US Census, we developed a customizable model to estimate human and economic burden caused by four major adult vaccine-preventable diseases (VPD) in 2013 in the United States, and for each US state individually. To estimate the number of cases for each adult VPD for a given population, we multiplied age-specific incidence rates obtained from the literature by age-specific 2013 Census population data. We then multiplied the estimated number of cases for a given population by age-specific, estimated medical and indirect (non-medical) costs per case. Adult VPDs examined were: (1) influenza, (2) pneumococcal disease (both invasive disease and pneumonia), (3) herpes zoster (shingles), and (4) pertussis (whooping cough). Sensitivity analyses simulated the impact of various epidemiological scenarios on the total estimated economic burden. Estimated US annual cost for the four adult VPDs was $26.5 billion (B) among adults aged 50 years and older, $15.3B (58 %) of which was attributable to those 65 and older. Among adults 50 and older, influenza, pneumococcal disease, herpes zoster, and pertussis made up $16.0B (60 %), $5.1B (19 %), $5.0B (19 %), and $0.4B (2 %) of the cost, respectively. Among those 65 and older, they made up $8.3B (54 %), $3.8B (25 %), $3.0B (20 %), and 0.2B (1 %) of the cost, respectively. Most (80–85 %) pneumococcal costs stemmed from nonbacteremic pneumococcal pneumonia (NPP). Cost attributable to adult VPD in the United States is substantial. Broadening adult immunization efforts beyond influenza only may help reduce the economic burden of adult VPD, and a pneumococcal vaccination effort, primarily focused on reducing NPP, may constitute a logical starting place. Sensitivity analyses revealed that a pandemic influenza season or change in size of the US elderly population could increase these costs dramatically.

Latency-aware publish/subscribe systems on MANET

In this paper, we proposed an analytical approach for latency-aware publish/subscribe systems on MANET. The proposed customisable model ensures the user a good quality of service requirements and combines both proactive and reactive statistical analyses. In fact, the reactive analysis, suitable for multimedia applications, aims to detect failures by approximating latency series with a Gumbel distribution. Besides, the proactive analysis, suitable for crisis management applications, forecasts failure occurrence relying on autoregression or autoregressive integrated moving average formulas. Finally, a hybrid analysis was proposed by dynamically switching from reactive to proactive forms of analysis whenever quality of service violations are noticed. The correlation method was also used to identify source failure causes once they were detected or predicted. The efficiency and accuracy of the proposed scheme were validated by performing simulations.

실제착의와 3D 가상착의의 외관 유사도 평가에 관한 연구 - 여성복 바지원형을 중심으로 -

Several retailers such as Target and Kohle's and their vendors have piloting the 3D clothing simulation programs to produce garment samples. However, few studies have verified the virtual fit information and 3d visualization process for pants, and no study compared the commercial 3D virtual programs. This study is designed to analyze similarity of fit and appearance between real pants with 3D virtual pants based on three 3D virtual programs (Optitex, CLO 3D, and i-Designer), three lower body types (slim, normal, and thick waist type), and fit status. We selected a representative model for each lower body type, produced their custom pants according to Lee and Nam's method(2007), and took photos of front, side and back view for visual analysis. Then, we virtually tried each model's custom pants on her parametric avatar developed by manually inputting their body measurements using the three 3D virtual program. Thirty fit experts compared the real fit to virtual fit. This study found that 'Optitex' and 'i-Designer' can visualize more effectively than 'CLO 3D' in many fit locations. Regarding the body types, 3D virtual program can visualize pants fit for 'thick waist body type' more preciously than the other body types. With respect to fit status, it does not affect much on the similarity overall.

Three-dimensional variability of the mesentery and the superior mesenteric artery: application to virtual trauma modeling

Trauma is a major cause of death worldwide, mainly affecting a young male population. Blunt trauma of the abdomen can cause a trauma of the mesentery in 5 % of cases. Rapid decelerations and injuries by seat belts are the most common pathophysiological mechanisms. Three-dimensional anatomical scanning of the mesentery and gastrointestinal tract is the first essential step in modeling abdominal trauma in an attempt to understand the pathophysiology of mesenteric lesions and to improve the safety features of vehicles. To analyze the individual variability of the mesentery and the superior mesenteric artery (SMA) from medical imaging and to develop a three-dimensional customizable finite element model. In this retrospective study, one hundred abdominopelvic injected CT scans were analyzed from healthy patients. The evaluation criteria of the mesentery were its volume (total and the distribution of adipose tissue/non adipose tissue), the length of the SMA and the distance between duodenojejunal angle (DJA) and the ileocecal junction (ICJ). The variability of these measures has been studied by demographic (age and gender) and morphologic (height evaluated by the T11-L4 distance, the waist circumference and the thickness of the subcutaneous adipose tissue). Mean mesenteric volume was 644 cm(3) (ranges from 89 to 1,869 cm(3)), and the mean length of the SMA was 224.9 mm (ranges from 138.4 to 312.3). There was a statistically significant association between waist circumference and the total volume of the mesentery, its fat component and non fat component (p < 0.001). Waist circumference was the only morphological parameter associated with the length of the superior mesenteric artery and the length of the DJA to ICJ (p < 0.001). Subcutaneous adipose tissue and female sex were statistically associated with total mesentery volume (respectively, p = 0.005 and p = 0.001). Age was an independent predictor of the increased volume of the mesentery and the length of the SMA. The height of the subject changes the length of the SMA (p = 0.001). The assessment of the mesenteric variability highlighted three factors associated with its size and length: age, sex, and waist circumference. These parameters have to be taken into account to personalize numerical model in the area of virtual trauma.

Theorising a model for teaching and assessing reflective learning in higher education

The importance of reflection in higher education and across disciplinary fields is widely recognised and it is generally included in university graduate attributes, professional standards and programme objectives. Furthermore, reflection is commonly embedded into assessment requirements in higher education subjects, often without necessary scaffolding or clear expectations for students. Despite the rhetoric around the importance of reflection for ongoing learning, there is scant literature on any systematic, developmental approach to teaching reflective learning across higher education programmes/courses. Given that professional or academic reflection is not intuitive, and requires specific pedagogic intervention to do well, a programme/course-wide approach is essential. This paper draws on current literature to theorise a new, transferable and customisable model for teaching and assessing reflective learning across higher education, which foregrounds and explains the pedagogic field of higher education as a multi-dimensional space. We argue that explicit and strategic pedagogic intervention, supported by dynamic resources, is necessary for successful, broad-scale approaches to reflection in higher education.

Computer simulation of energy use, greenhouse gas emissions, and process economics of the fluid milk process

Energy-savings measures have been implemented in fluid milk plants to lower energy costs and the energy-related carbon dioxide (CO2) emissions. Although these measures have resulted in reductions in steam, electricity, compressed air, and refrigeration use of up to 30%, a benchmarking framework is necessary to examine the implementation of process-specific measures that would lower energy use, costs, and CO2 emissions even further. In this study, using information provided by the dairy industry and equipment vendors, a customizable model of the fluid milk process was developed for use in process design software to benchmark the electrical and fuel energy consumption and CO2 emissions of current processes. It may also be used to test the feasibility of new processing concepts to lower energy and CO2 emissions with calculation of new capital and operating costs. The accuracy of the model in predicting total energy usage of the entire fluid milk process and the pasteurization step was validated using available literature and industry energy data. Computer simulation of small (40.0 million L/yr), medium (113.6 million L/yr), and large (227.1 million L/yr) processing plants predicted the carbon footprint of milk, defined as grams of CO2 equivalents (CO2e) per kilogram of packaged milk, to within 5% of the value of 96g of CO2e/kg of packaged milk obtained in an industry-conducted life cycle assessment and also showed, in agreement with the same study, that plant size had no effect on the carbon footprint of milk but that larger plants were more cost effective in producing milk. Analysis of the pasteurization step showed that increasing the percentage regeneration of the pasteurizer from 90 to 96% would lower its thermal energy use by almost 60% and that implementation of partial homogenization would lower electrical energy use and CO2e emissions of homogenization by 82 and 5.4%, respectively. It was also demonstrated that implementation of steps to lower non-process-related electrical energy in the plant would be more effective in lowering energy use and CO2e emissions than fuel-related energy reductions. The model also predicts process-related water usage, but this portion of the model was not validated due to a lack of data. The simulator model can serve as a benchmarking framework for current plant operations and a tool to test cost-effective process upgrades or evaluate new technologies that improve the energy efficiency and lower the carbon footprint of milk processing plants.

Intelligent Decision-Making on Modular Products Using Fuzzy Algorithms

In daily life, it is unavoidable to make a lot of different decisions. Unfortunately, people are not optimal decision makers and do not act rationally when selecting the desired product [1]. To help people select the “right” product to meet their needs, a customizable model based on the fuzzy algorithms is proposed in this study. The linguistic triangular fuzzy variables are used to quantify the judgment intensities to establish the customizable model. The relationship between individual’s needs and product alternatives are constructed by using the fuzzy inference and fuzzy distance algorithms. With the program created in this research, the user will be able to customize the product and obtain the suggested combination of alternatives that fits the user’s needs by selecting the required linguistic terms. A bicycle is chosen as the objective product for a case study in this research and this proposed model can also be used with other products.

Documentation for model transparency

Documentation for model transparency

A control engineering framework for managing whole hospital occupancy

As factors such as population growth, nation-wide closure of hospitals, and an aging population combine to strain the healthcare system of the United States (US), the demand for better resource and capacity planning increases. This paper proposes a five-step methodology to model and control whole hospital occupancy. The hospital system is viewed using a continuous-time, fluid tank analogy. The system is subsequently discretized and a framework using control theory and Model Predictive Control (MPC) is developed to assist in tactical decision making, while maintaining occupancy targets. The result is a customizable modeling approach that represents interactions between different hospital areas, and interactions between the hospital and the outside world, or the population seeking hospital services.

Rights Well: An Authors’ Rights Workshop for Librarians

This article seeks to make a compelling case for authors’ rights training through emphasis on academic librarians’ dual roles as both authors and as liaisons to research and teaching faculty. Using the example of the Rights Well Workshop developed at Oregon State University Libraries, the article demonstrates the value of training librarians as authors in order to further develop their own understanding of copyright transfer and negotiation. The workshop provides a customizable model that librarians can use when educating faculty in other disciplines about author rights, with emphasis on the practices of the publishers relevant to the targeted discipline.

Simulating Social Network Formation

Social networks depict complex systems as graph theoretic models. The study of the formation of such systems (or networks) and the subsequent analysis of the network structures are of great interest. For information systems research and its impact on business practice, the ability to model and simulate a system of individuals interacting to achieve a certain socio-economic goal holds much promise for proper design and use of cyber networks. We use case-based decision theory to formulate a customizable model of information gathering in a social network. In this model, the agents in the network have limited awareness of the social network in which they operate and of the fixed, underlying payoff structure. Agents collect payoff information from neighbors within the prevailing social network, and they base their networking decisions on this information. Along with the introduction of the decision theoretic model, we developed software to simulate the formation of such networks in a customizable context to examine how the network structure can be influenced by the parameters that define social relationships. We present computational experiments that illustrate the growth and stability of the simulated social networks ensuing from the proposed model. The model and simulation illustrates how network structure influences agent behavior in a social network and how network structures, agent behavior, and agent decisions influence each other.

Employer Model of Workplace Impacts of Anti-TNF Therapy for Rheumatoid Arthritis

Rheumatoid arthritis (RA) greatly affects patients' abilities to perform work, which can translate into substantial employer costs. We developed a customizable model that allows employers to calculate workplace impacts of RA therapies in employees with RA. Costs of medical leave (absenteeism)/disability, reduced productivity, job turnover, and work-equipment adaptations for employees with RA were calculated. Costs of the tumor necrosis factor antagonist adalimumab were compared with those of other RA treatments. Default parameters were based on literature, clinical trials, government sources, and employers' data. Annual per-employee workplace cost was $9071 for adalimumab versus $16,335 for other RA therapies. Costs included reduced productivity (57%), absenteeism/disability (21%), and job turnover (21%). RA imposes a large financial burden on employers, predominantly owing to lost productivity. When compared with other RA therapies, adalimumab substantially reduced employers' costs.

Python scripting in the Nengo simulator

Nengo (http://nengo.ca) is an open-source neural simulator that has been greatly enhanced by the recent addition of a Python script interface. Nengo provides a wide range of features that are useful for physiological simulations, including unique features that facilitate development of population-coding models using the neural engineering framework (NEF). This framework uses information theory, signal processing, and control theory to formalize the development of large-scale neural circuit models. Notably, it can also be used to determine the synaptic weights that underlie observed network dynamics and transformations of represented variables. Nengo provides rich NEF support, and includes customizable models of spike generation, muscle dynamics, synaptic plasticity, and synaptic integration, as well as an intuitive graphical user interface. All aspects of Nengo models are accessible via the Python interface, allowing for programmatic creation of models, inspection and modification of neural parameters, and automation of model evaluation. Since Nengo combines Python and Java, it can also be integrated with any existing Java or 100% Python code libraries. Current work includes connecting neural models in Nengo with existing symbolic cognitive models, creating hybrid systems that combine detailed neural models of specific brain regions with higher-level models of remaining brain areas. Such hybrid models can provide (1) more realistic boundary conditions for the neural components, and (2) more realistic sub-components for the larger cognitive models.

A distributed approach to enabling privacy-preserving model-based classifier training

This paper proposes a novel approach for privacy-preserving distributed model-based classifier training. Our approach is an important step towards supporting customizable privacy modeling and protection. It consists of three major steps. First, each data site independently learns a weak concept model (i.e., local classifier) for a given data pattern or concept by using its own training samples. An adaptive EM algorithm is proposed to select the model structure and estimate the model parameters simultaneously. The second step deals with combined classifier training by integrating the weak concept models that are shared from multiple data sites. To reduce the data transmission costs and the potential privacy breaches, only the weak concept models are sent to the central site and synthetic samples are directly generated from these shared weak concept models at the central site. Both the shared weak concept models and the synthetic samples are then incorporated to learn a reliable and complete global concept model. A computational approach is developed to automatically achieve a good trade off between the privacy disclosure risk, the sharing benefit and the data utility. The third step deals with validating the combined classifier by distributing the global concept model to all these data sites in the collaboration network while at the same time limiting the potential privacy breaches. Our approach has been validated through extensive experiments carried out on four UCI machine learning data sets and two image data sets.

Proto-Plasm : parallel language for adaptive and scalable modelling of biosystems

This paper discusses the design goals and the first developments of Proto-Plasm, a novel computational environment to produce libraries of executable, combinable and customizable computer models of natural and synthetic biosystems, aiming to provide a supporting framework for predictive understanding of structure and behaviour through multiscale geometric modelling and multiphysics simulations. Admittedly, the Proto-Plasm platform is still in its infancy. Its computational framework—language, model library, integrated development environment and parallel engine—intends to provide patient-specific computational modelling and simulation of organs and biosystem, exploiting novel functionalities resulting from the symbolic combination of parametrized models of parts at various scales. Proto-Plasm may define the model equations, but it is currently focused on the symbolic description of model geometry and on the parallel support of simulations. Conversely, CellML and SBML could be viewed as defining the behavioural functions (the model equations) to be used within a Proto-Plasm program. Here we exemplify the basic functionalities of Proto-Plasm, by constructing a schematic heart model. We also discuss multiscale issues with reference to the geometric and physical modelling of neuromuscular junctions.

Modelling and simulation of social systems with INGENIAS

Most agent-based simulation toolkits are based on the Java programming language. This makes their use difficult for social scientists, who are usually not skilled in computer programming. However, agent modelling concepts are not unlike those which could be used for the modelling of social systems. This assumption is considered in proposing the use of a graphical agent-oriented language for the specification of social simulation models, and for transforming (automatically) these models to code for an agent-based simulation toolkit. In this manner, a social scientist could prepare social models in a more convenient way, and execute simulations on existing simulation toolkits, getting results back in terms of the model. This framework is built with a set of agent development tools, specifically, the INGENIAS Development Kit (IDK), which provides a customisable model editor and modules for automatic code generation.

Customer-driven Collaborative Product Assembler for Internet-based Commerce

In this study, a real-time collaborative 3D assembling system (Co-assembler) has been described that allows casual customers to interactively and virtually configure the 3D products over the Internet. It provides a framework for a web-based 3D assembly system that uses a customizable product model to significantly simplify the configuration and the assembly process, and a customer-driven recommendation helper to assist the customers make their design decisions. Two assembly-specific data formats - Assembly ML and Parametric Product ML are developed for archival and data transfer. The system aims to capture the customers’ needs and convert them into technical specifications, indirectly helping to promote e-commerce for manufacturing enterprises.

A conceptual model of service customization and its implementation

With the development of Internet and next generation networks in telecommunications, more and more new services are required to be introduced into networks. Introducing new services into traditional network is always associated with standardizing new protocols. The progress of protocol standardization usually takes several years, which cannot meet the increasing demands of the applications in Internet and next generation networks. Service customization in network systems may be one possible solution to cope with this problem. Based on the principle that network service is provided by interactions among protocol entities, this paper proposes a conceptual model of service customization (SECUM) by separating the service logic from protocol interactive logic within existing network architecture. The theory of Communicating Sequential Processes (CSP) is used to formalize the SECUM in order to locate exactly the service logic and to define precisely the SECUM. For validating the SECUM's usability in practical network systems, this paper also proposes an implementation model for SECUM: a component-based protocol implementation model (CPIM). CPIM discomposes protocol entity into application component, service component, message component and communication component. Service component associates application component with message component. Users or network managers can customize network services by configuring service component. The paper shows respectively the applications of SECUM and CPIM by proposing a customizable IP service model based on SECUM and describing an implementation of Session Initiation Protocol (SIP) based on CPIM. Compared with the existing service-customization techniques, SECUM is a service customization model internal to network system and may provide more powerful capabilities of service customization.

Mobile patient data management for psychiatry: a network-based, multiple user, customizable model and implementation for consultation physician

Mobile patient data management for psychiatry: a network-based, multiple user, customizable model and implementation for consultation physician

Spice up your MOSFET modeling

A customizable and predictive BSIM model has been generated for devices with L/sub eff/ down to 12 nm and a wide range of interconnect sizes. Extensive evaluations demonstrate the validity of this approach. The main advantages of this approach over previous work are its applicability to generic technologies as well as the ease of use provided by a Web-based distribution model. These predictive technology models will be useful for circuit design research aimed at processes that are not yet available.