An equivalent discharge model of electrostatics spark

2021 ◽  
Vol 111 ◽  
pp. 103576
Author(s):  
Zhiyu Wang ◽  
Suijun Yang ◽  
Shuliang Ye
Keyword(s):  
Discharge Model

scholarly journals Improving continuity of patient care across sectors: study protocol of the process evaluation of a quasi-experimental multi-centre study regarding an admission and discharge model in Germany (VESPEERA)

BMJ Open ◽  
2019 ◽  
Vol 9 (11) ◽  
pp. e031245
Author(s):  
Johanna Forstner ◽  
Aline Kunz ◽  
Cornelia Straßner ◽  
Lorenz Uhlmann ◽  
Stephanie Kuemmel ◽  
...  
Keyword(s):  
General Practice ◽  
Patient Care ◽  
Process Evaluation ◽  
Study Protocol ◽  
Care Providers ◽  
Total N ◽  
Outcomes Evaluation ◽  
Quasi Experimental ◽  
Discharge Model ◽  
General Practices

IntroductionHospital stays are critical events as they often disrupt continuity of care. This process evaluation aims to describe and explore the implementation of the VESPEERA programme (Improving continuity of patient care across sectors: An admission and discharge model in general practices and hospitals, Versorgungskontinuitaet sichern: Patientenorientiertes Einweisungs- und Entlassmanagement in Hausarztpraxen und Krankenhauesern). The evaluation concerns the intervention fidelity, reach in targeted populations, perceived effects, working mechanisms, feasibility, determinants for implementation, including contextual factors, and associations with the outcomes evaluation. The aim of the VESPEERA programme is the development, implementation and evaluation of a structured admission and discharge programme in general practices and hospitals.Methods and analysisThe process evaluation is linked to the VESPEERA outcomes evaluation, which has a quasi-experimental multi-centre design with four study arms and is conducted in hospitals and general practices in Germany. The VESPEERA programme comprises several components: an assessment before admission, an admission letter, a telephonic discharge conversation between hospital and general practice before discharge, discharge information for patients, structured planning of follow-up care after discharge in the general practice and a telephone monitoring for patients with a risk of rehospitalisation. The process evaluation has a mixed-methods design, incorporating interviews (patients, both care providers who do and do not participate in the VESPEERA programme, total n=75), questionnaires (patients and care providers who participate in the VESPEERA programme, total n=475), implementation plans of hospitals, data documented in general practices, claims-based data and hospital process data. Data analysis is descriptive and explorative. Qualitative data will be transcribed and analysed using framework analysis based on the Consolidated Framework for Implementation Research. Associations between the outcomes of the program and measures in the process evaluation will be explored in regression models.Ethics and disseminationEthics approval has been obtained by the ethics committee of the Medical Faculty Heidelberg prior to the start of the study (S-352/2018). Results will be disseminated through a final report to the funding agency, articles in peer-reviewed journals and conferences.Trial registration numberhttp://www.drks.de/DRKS00015183.Trial statusThe study protocol on hand is the protocol V.1.1 from 18 June 2018. Recruitment for interviews started on 3 September 2018 and will approximately be completed by the end of May 2019.

scholarly journals Self-Discharge Model of a Nickel-Hydrogen Cell

2000 ◽  
Vol 147 (3) ◽  
pp. 902 ◽  
Author(s):  
B. Wu ◽  
R. E. White
Keyword(s):  
Discharge Model

scholarly journals Generalized Heat Flow Model of a Forced Air Electric Thermal Storage Heater Core

2017 ◽  
Vol 9 (4) ◽  
Author(s):  
Nicholas T. Janssen ◽  
Rorik A. Peterson ◽  
Richard W. Wies
Keyword(s):  
Three Dimensional ◽  
Flow Characteristics ◽  
Thermal Storage ◽  
Element Model ◽  
Cold Climate ◽  
Heater Core ◽  
Discharge Model ◽  
Load Leveling ◽  
Forced Air ◽  
Three Dimensional Finite Element

Electric thermal storage (ETS) devices can be used for grid demand load-leveling and off-peak domestic space heating (DSH). A high-resolution three-dimensional finite element model of a forced air ETS heater core is developed and employed to create a general charge/discharge model. The effects of thermal gradients, air flow characteristics, material properties, and core geometry are simulated. A simplified general stove discharge model with a single time constant is presented based on the results of the numerical simulations. This simplified model may be used to stimulate economic/performance case studies for cold climate communities interested in distributed thermal energy storage.

Apparent and True Charges in the Partial Discharge Model at Different Defective Domain Sizes

2021 ◽  
Vol 64 (6) ◽  
pp. 94-100
Author(s):  
Nikolay Ignatev ◽  
◽  
Sergey Tetiora ◽  
Dmitry Turkin ◽  
◽  
...  
Keyword(s):  
Equivalent Circuit ◽  
Partial Discharge ◽  
Diagnostic Value ◽  
Partial Discharges ◽  
Active Resistance ◽  
Air Cavity ◽  
Discharge Ignition ◽  
Discharge Model ◽  
Air Cavities ◽  
Solid Insulation

A model of the partial discharges in a sample of solid insulation with air cavity is presented. The material of the insulation is cross-linked polyethylene. The model is based on an active-capacitive equivalent circuit, in which the resistance of the air cavity at the instant of a partial discharge ignition is shunted by the active resistance of the spark. The model takes into consideration the delay of the discharge development. The evaluation of the diagnostic value of the apparent and true charges is carried out. The results of the apparent and true charges relationship with the dimensions and proportions of the air cavity are presented. It is shown that the same magnitude of both true and apparent charges can correspond to the different volumes and shapes of the air cavities in the insulation.

scholarly journals Identifying Children at Readmission Risk: At-Admission Versus Traditional At-Discharge Readmission Prediction Model

Healthcare ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1334
Author(s):  
Hasan Symum ◽  
José Zayas-Castro
Keyword(s):  
Prediction Model ◽  
Information Exchange ◽  
High Risk Patient ◽  
Machine Learning Algorithms ◽  
Polynomial Kernel ◽  
Gradient Boosting ◽  
Support Vector ◽  
Hospital Discharges ◽  
Discharge Model ◽  
Readmission Risk

The timing of 30-day pediatric readmissions is skewed with approximately 40% of the incidents occurring within the first week of hospital discharges. The skewed readmission time distribution coupled with delay in health information exchange among healthcare providers might offer a limited time to devise a comprehensive intervention plan. However, pediatric readmission studies are thus far limited to the development of the prediction model after hospital discharges. In this study, we proposed a novel pediatric readmission prediction model at the time of hospital admission which can improve the high-risk patient selection process. We also compared proposed models with the standard at-discharge readmission prediction model. Using the Hospital Cost and Utilization Project database, this prognostic study included pediatric hospital discharges in Florida from January 2016 through September 2017. Four machine learning algorithms—logistic regression with backward stepwise selection, decision tree, Support Vector machines (SVM) with the polynomial kernel, and Gradient Boosting—were developed for at-admission and at-discharge models using a recursive feature elimination technique with a repeated cross-validation process. The performance of the at-admission and at-discharge model was measured by the area under the curve. The performance of the at-admission model was comparable with the at-discharge model for all four algorithms. SVM with Polynomial Kernel algorithms outperformed all other algorithms for at-admission and at-discharge models. Important features associated with increased readmission risk varied widely across the type of prediction model and were mostly related to patients’ demographics, social determinates, clinical factors, and hospital characteristics. Proposed at-admission readmission risk decision support model could help hospitals and providers with additional time for intervention planning, particularly for those targeting social determinants of children’s overall health.

Sequentially Updated Discharge Model for Optimizing Hospital Resource Use and Surgical Patients’ Satisfaction

2015 ◽  
Vol 100 (6) ◽  
pp. 2174-2181 ◽  
Author(s):  
Michael Z. Tong ◽  
Gregory Pattakos ◽  
Jiayan He ◽  
Jeevanantham Rajeswaran ◽  
Michael W. Kattan ◽  
...  
Keyword(s):  
Resource Use ◽  
Surgical Patients ◽  
Hospital Resource ◽  
Patients Satisfaction ◽  
Discharge Model

A Dark Discharge Model of Earthquake Lightning

1998 ◽  
Vol 37 (Part 1, No. 9A) ◽  
pp. 5016-5020 ◽  
Author(s):  
Shunji Takaki ◽  
Motoji Ikeya
Keyword(s):  
Discharge Model

Patient Outcomes after Discharge from a Geriatric Day Hospital

2005 ◽  
Vol 24 (3) ◽  
pp. 305-309 ◽  
Author(s):  
Richard G. Crilly ◽  
Sonya Lylwynec ◽  
Marita Kloseck ◽  
Jan M. Smith ◽  
Tyler Olsen ◽  
...  
Keyword(s):  
Health System ◽  
Patient Outcomes ◽  
Older Patients ◽  
Goal Attainment ◽  
Telephone Survey ◽  
Continuum Of Care ◽  
Goal Attainment Scaling ◽  
Day Hospital ◽  
Medical Diagnoses ◽  
Discharge Model

ABSTRACTEvidence suggests that frailer older patients benefit from a continuum of care rather than the admit/discharge model of our health system. This study examined patient outcomes after discharge from a geriatric day hospital (GDH) to determine what proportion continues to do well, what proportion declines, how the two groups differ, and if factors predictive of deterioration can be identified. Using telephone survey and Goal Attainment Scaling methodologies, the goals of 151 patients discharged from a GDH between 6 and 18 months previously were examined to determine whether GDH achievements were maintained or lost. All but 5 patients improved between GDH admission and discharge; after discharge, 39 per cent deteriorated. The need for more support in the community was predictive of deterioration, probably reflecting patient frailty. Number of medical diagnoses or medications were not predictive. Frailer older patients tend not to maintain goals achieved in a GDH after discharge and may benefit from ongoing maintenance.

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