An energy and environmental meta-model for strategic sustainable planning

2009 ◽  
Author(s):  
D. S. Zachary ◽  
U. Leopold ◽  
L. Aleluia Reis ◽  
C. Braun ◽  
G. Kneip ◽  
...  
Keyword(s):  
Meta Model ◽  
Sustainable Planning

BALANCING OF ROTATING MACHINES USING A KRIGING META-MODEL

2019 ◽  
Author(s):  
Vinícius Carvalho ◽  
Leonardo Sicchieri ◽  
Marcus Filipe Sousa Reis ◽  
Aldemir Ap Cavalini Jr ◽  
Valder Steffen Jr
Keyword(s):  
Rotating Machines ◽  
Meta Model

scholarly journals Designing Multi-Agent System Organisations for Flexible Runtime Behaviour

2020 ◽  
Vol 10 (15) ◽  
pp. 5335
Author(s):  
Kathleen Keogh ◽  
Liz Sonenberg
Keyword(s):  
Design Method ◽  
Multi Agent System ◽  
Improve Performance ◽  
Uncertain Environments ◽  
Agent System ◽  
Meta Model ◽  
Design Time ◽  
Organisational Support ◽  
Multi Agent ◽  
Associated Design

We address the challenge of multi-agent system (MAS) design for organisations of agents acting in dynamic and uncertain environments where runtime flexibility is required to enable improvisation through sharing knowledge and adapting behaviour. We identify behavioural features that correspond to runtime improvisation by agents in a MAS organisation and from this analysis describe the OJAzzIC meta-model and an associated design method. We present results from simulation scenarios, varying both problem complexity and the level of organisational support provided in the design, to show that increasing design time guidance in the organisation specification can enable runtime flexibility afforded to agents and improve performance. Hence the results demonstrate the usefulness of the constructs captured in the OJAzzIC meta-model.

scholarly journals Real-Time Identification of Patients Included in the CMS Bundled Payment Care Improvement (BPCI) Program

2020 ◽  
Vol 41 (S1) ◽  
pp. s367-s368
Author(s):  
Michael Korvink ◽  
John Martin ◽  
Michael Long
Keyword(s):  
Performance Evaluation ◽  
Real Time ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Care Providers ◽  
Meta Model ◽  
Bundled Payment ◽  
Clinical Cohort ◽  
Real Time Identification ◽  
Care Improvement

Background: The Bundled Payment Care Improvement Program is a CMS initiative designed to encourage greater collaboration across settings of care, especially as it relates to an initial set of targeted clinical episodes, which include sepsis and pneumonia. As with many CMS incentive programs, performance evaluation is retrospective in nature, resulting in after-the-fact changes in operational processes to improve both efficiency and quality. Although retrospective performance evaluation is informative, care providers would ideally identify a patient’s potential clinical cohort during the index stay and implement care management procedures as necessary to prevent or reduce the severity of the condition. The primary challenges for real-time identification of a patient’s clinical cohort are CMS-targeted cohorts are based on either MS-DRG (grouping of ICD-10 codes) or HCPCS coding—coding that occurs after discharge by clinical abstractors. Additionally, many informative data elements in the EHR lack standardization and no simple and reliable heuristic rules can be employed to meaningfully identify those cohorts without human review. Objective: To share the results of an ensemble statistical model to predict patient risks of sepsis and pneumonia during their hospital (ie, index) stay. Methods: The predictive model uses a combination of Bernoulli Naïve Bayes natural language processing (NLP) classifiers, to reduce text dimensionality into a single probability value, and an eXtreme Gradient Boosting (XGBoost) algorithm as a meta-model to collectively evaluate both standardized clinical elements alongside the NLP-based text probabilities. Results: Bernoulli Naïve Bayes classifiers have proven to perform well on short text strings and allow for highly explanatory unstructured or semistructured text fields (eg, reason for visit, culture results), to be used in a both comparative and generalizable way within the larger XGBoost model. Conclusions: The choice of XGBoost as the meta-model has the benefits of mitigating concerns of nonlinearity among clinical features, reducing potential of overfitting, while allowing missing values to exist within the data. Both the Bayesian classifier and meta-model were trained using a patient-level integrated dataset extracted from both a patient-billing and EHR data warehouse maintained by Premier. The data set, joined by patient admission-date, medical record number, date of birth, and hospital entity code, allows the presence of both the coded clinical cohort (derived from the MS-DRG) and the explanatory features in the EHR to exist within a single patient encounter record. The resulting model produced F1 performance scores of .65 for the sepsis population and .61 for the pneumonia population.Funding: NoneDisclosures: None

Pattern Recognition of Travel Mobility in a City Destination: Application of Network Motif Analytics

2021 ◽  
pp. 004728752110247
Author(s):  
Sangwon Park ◽  
Ren Ridge Zhong
Keyword(s):  
Network Science ◽  
Network Motif ◽  
Interdisciplinary Approach ◽  
Movement Patterns ◽  
Urban Tourism ◽  
Lengths Of Stay ◽  
Sustainable Planning ◽  
Total Movement ◽  
City Network ◽  
Travel Behaviors

Urban tourism is considered a complex system. Tourists who visit cities have diverse purposes, leading to multifaceted travel behaviors. Understanding travel movement patterns is crucial in developing sustainable planning for urban tourism. Built on network science, this article discusses 12 key topologies of travel patterns/flow occurring in a city network by applying network motif analytics. The 12 significant types of travel mobility can account for approximately 50% of the total movement patterns. In addition, this study presents variations in travel movement patterns depending on not only different lengths of stay in topological structures of travel mobility, but also relative proportions of each type. As a result, this article suggests an interdisciplinary approach that adopts the network science method to better understand city travel behaviors. Important methodological and practical implications that could be useful for city destination planners are suggested.

scholarly journals Towards sustainable management of the stock and ecosystem services of urban trees. From theory to model and application

Trees ◽  
2021 ◽  
Author(s):  
H. Pretzsch ◽  
A. Moser-Reischl ◽  
M. A. Rahman ◽  
S. Pauleit ◽  
T. Rötzer
Keyword(s):  
Ecosystem Services ◽  
Carbon Sequestration ◽  
Leaf Area ◽  
Ecosystem Service ◽  
Sustainable Management ◽  
Environmental Changes ◽  
Urban Trees ◽  
Urban Tree ◽  
Age Class ◽  
Sustainable Planning

Abstract Key message A model for sustainable planning of urban tree stocks is proposed, incorporating growth, mortality, replacement rates and ecosystem service provision, providing a basis for planning of urban tree stocks. Abstract Many recent studies have improved the knowledge about urban trees, their structures, functions, and ecosystem services. We introduce a concept and model for the sustainable management of urban trees, analogous to the concept of sustainable forestry developed by Carl von Carlowitz and others. The main drivers of the model are species-specific tree diameter growth functions and mortality rates. Based on the initial tree stock and options for the annual replanting, the shift of the distribution of the number of trees per age class can be predicted with progressing time. Structural characteristics such as biomass and leaf area are derived from tree dimensions that can be related to functions such as carbon sequestration or cooling. To demonstrate the potential of the dynamic model, we first show how different initial stocks of trees can be quantitatively assessed by sustainability indicators compared to a target stock. Second, we derive proxy variables for ecosystem services (e.g. biomass for carbon sequestration, leaf area for deposition and shading) from a given distribution of the number of trees per age class. Third, we show by scenario analyses how selected ecosystem services and functions may be improved by combining complementary tree species. We exercise one aspect (cooling) of one ecosystem service (temperature mitigation) as an example. The approach integrates mosaic pieces of knowledge about urban trees, their structures, functions, and resulting ecosystem services. The presented model makes this knowledge available for a sustainable management of urban tree stocks. We discuss the potential and relevance of the developed concept and model for ecologically and economically sustainable planning and management, in view of progressing urbanization and environmental changes.

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