Integrating Proprioception with the Perceptual-Behavioral Pilot Model

2022 ◽  
Author(s):  
Edward N. Bachelder ◽  
Bimal L. Aponso
Keyword(s):  
Pilot Model

Laboratory study and computer model development related to gross solids' movement in combined sewers

1996 ◽  
Vol 33 (9) ◽  
pp. 39-47 ◽  
Author(s):  
John W. Davies ◽  
Yanli Xu ◽  
David Butler
Keyword(s):  
Computer Simulation ◽  
Pipe Flow ◽  
Model Development ◽  
Simulation Models ◽  
Laboratory Studies ◽  
Pilot Model ◽  
Sewer Systems ◽  
Flow Quality ◽  
Computer Simulation Models ◽  
Theoretical Considerations

Significant problems in sewer systems are caused by gross solids, and there is a strong case for their inclusion in computer simulation models of sewer flow quality. The paper describes a project which considered methods of modelling the movement of gross solids in combined sewers. Laboratory studies provided information on advection and deposition of typical gross solids in part-full pipe flow. Theoretical considerations identified aspects of models for gross solids that should differ from those for dissolved and fine suspended pollutants. The proposed methods for gross solids were incorporated in a pilot model, and their effects on simple simulations were considered.

scholarly journals Clinical laboratory services for primary healthcare centers in urban cities: a pilot ACO model of ten primary healthcare centers

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Soha A. Tashkandi ◽  
Ali Alenezi ◽  
Ismail Bakhsh ◽  
Abdullah AlJuryyan ◽  
Zahir H AlShehry ◽  
...  
Keyword(s):  
Saudi Arabia ◽  
Quality Improvement ◽  
Primary Healthcare ◽  
Laboratory Testing ◽  
Gap Analysis ◽  
Reference Laboratory ◽  
Pilot Model ◽  
Laboratory Services ◽  
Analytical Phase ◽  
Primary Healthcare Centers

Abstract Background Primary healthcare centers (PHC) ensure that patients receive comprehensive care from promotion and prevention to treatment, rehabilitation, and palliative care in a familiar environment. It is designed to provide first-contact, continuous, comprehensive, and coordinated patient care that will help achieve equity in the specialty healthcare system. The healthcare in Saudi Arabia is undergoing transformation to Accountable Care Organizations (ACO) model. In order for the Kingdom of Saudi Arabia (KSA) to achieve its transformational goals in healthcare, the improvement of PHCs’ quality and utilization is crucial. An integral part of this service is the laboratory services. Methods This paper presents a pilot model for the laboratory services of PHC's in urban cities. The method was based on the FOCUS-PDCA quality improvement method focusing on the pre-analytical phase of the laboratory testing as well as the Saudi Central Board for Accreditation of Healthcare Institutes (CBAHI) gap analysis and readiness within the ten piloted primary healthcare centers. Results The Gap analysis, revealed in-consistency in the practice, lead to lower the quality of the service, which was seen in the low performance of the chosen key performance indicators (KPI's) (high rejection rates, lower turn-around times (TAT) for test results) and also in the competency of the staff. Following executing the interventions, and by using some of the ACO Laboratory strategies; the KPI rates were improved, and our results exceeded the targets that we have set to reach during the first year. Also introducing the electronic connectivity improved the TAT KPI and made many of the processes leaner. Conclusions Our results revealed that the centralization of PHC's laboratory service to an accredited reference laboratory and implementing the national accreditation standards improved the testing process and lowered the cost, for the mass majority of the routine laboratory testing. Moreover, the model shed the light on how crucial the pre-analytical phase for laboratory quality improvement process, its effect on cost reduction, and the importance of staff competency and utilization.

Hidden Markov Model of Lane-Changing-Based Car-Following Behavior on Freeways using Naturalistic Driving Data

2021 ◽  
pp. 036119812199938
Author(s):  
Li Zhao ◽  
Laurence Rilett ◽  
Mm Shakiul Haque
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Gaussian Mixture ◽  
Vehicle Body ◽  
Lane Change ◽  
Lane Changing ◽  
Car Following ◽  
Pilot Model ◽  
Naturalistic Driving

This paper develops a methodology for simultaneously modeling lane-changing and car-following behavior of automated vehicles on freeways. Naturalistic driving data from the Safety Pilot Model Deployment (SPMD) program are used. First, a framework to process the SPMD data is proposed using various data analytics techniques including data fusion, data mining, and machine learning. Second, pairs of automated host vehicle and their corresponding front vehicle are identified along with their lane-change and car-following relationship data. Using these data, a lane-changing-based car-following (LCCF) model, which explicitly considers lane-change and car-following behavior simultaneously, is developed. The LCCF model is based on Gaussian-mixture-based hidden Markov model theory and is disaggregated into two processes: LCCF association and LCCF dissociation. These categories are based on the result of the lane change. The overall goal is to predict a driver’s lane-change intention using the LCCF model. Results show that the model can predict the lane-change event in the order of 0.6 to 1.3 s before the moment of the vehicle body across the lane boundary. In addition, the execution times of lane-change maneuvers average between 0.55 and 0.86 s. The LCCF model allows the intention time and execution time of driver’s lane-change behavior to be forecast, which will help to develop better advanced driver assistance systems for vehicle controls with respect to lane-change and car-following warning functions.

Multimodal Pilot Model Identification in Real Flight

2009 ◽  
Author(s):  
Peter Zaal ◽  
Daan Pool ◽  
Max Mulder ◽  
Marinus van Paassen ◽  
Jan Mulder
Keyword(s):  
Model Identification ◽  
Pilot Model

scholarly journals Climate-related migration and population health: social science-oriented dynamic simulation model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Rafael Reuveny
Keyword(s):  
Public Health ◽  
Social Science ◽  
Simulation Model ◽  
Environmental Health ◽  
Dynamic Simulation ◽  
Health Problems ◽  
Ecological Impacts ◽  
Sensitivity Analyses ◽  
Pilot Model ◽  
Dynamic Simulation Model

Abstract Background Social science models find the ecological impacts of climate change (EICC) contribute to internal migration in developing countries and, less so, international migration. Projections expect massive climate-related migration in this century. Nascent research calls to study health, migration, population, and armed conflict potential together, accounting for EICC and other factors. System science offers a way: develop a dynamic simulation model (DSM). We aim to validate the feasibility and usefulness of a pilot DSM intended to serve as a proof-of-concept and a basis for identifying model extensions to make it less simplified and more realistic. Methods Studies have separately examined essential parts. Our DSM integrates their results and computes composites of health problems (HP), health care (HC), non-EICC environmental health problems (EP), and environmental health services (ES) by origin site and by immigrants and natives in a destination site, and conflict risk and intensity per area. The exogenous variables include composites of EICC, sociopolitical, economic, and other factors. We simulate the model for synthetic input values and conduct sensitivity analyses. Results The simulation results refer to generic origin and destination sites anywhere on Earth. The effects’ sizes are likely inaccurate from a real-world view, as our input values are synthetic. Their signs and dynamics are plausible, internally consistent, and, like the sizes, respond logically in sensitivity analyses. Climate migration may harm public health in a host area even with perfect HC/ES qualities and full access; and no HP spillovers across groups, conflict, EICC, and EP. Deviations from these conditions may worsen everyone’s health. We consider adaptation options. Conclusions This work shows we can start developing DSMs to understand climate migration and public health by examining each case with its own inputs. Validation of our pilot model suggests we can use it as intended. We lay a path to making it more realistic for policy analysis.

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