Aspects of the Surface-to-Air Missile Systems Modelling and Simulation

2018 ◽  
pp. 324-339 ◽  
Author(s):  
Jan Farlik ◽  
Ferdinand Tesar
Keyword(s):  
Modelling And Simulation ◽  
Systems Modelling

scholarly journals Reducing youth suicide: systems modelling and simulation to guide targeted investments across the determinants

BMC Medicine ◽  
2021 ◽  
Vol 19 (1) ◽  
Author(s):  
Jo-An Occhipinti ◽  
Adam Skinner ◽  
Frank Iorfino ◽  
Kenny Lawson ◽  
Julie Sturgess ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Determinants ◽  
Suicide Attempts ◽  
Expert Knowledge ◽  
Population Level ◽  
Modelling And Simulation ◽  
Decision Makers ◽  
Public Health Issue ◽  
Coordinated Care ◽  
Systems Modelling

Abstract Background Reducing suicidal behaviour (SB) is a critical public health issue globally. The complex interplay of social determinants, service system factors, population demographics, and behavioural dynamics makes it extraordinarily difficult for decision makers to determine the nature and balance of investments required to have the greatest impacts on SB. Real-world experimentation to establish the optimal targeting, timing, scale, frequency, and intensity of investments required across the determinants is unfeasible. Therefore, this study harnesses systems modelling and simulation to guide population-level decision making that represent best strategic allocation of limited resources. Methods Using a participatory approach, and informed by a range of national, state, and local datasets, a system dynamics model was developed, tested, and validated for a regional population catchment. The model incorporated defined pathways from social determinants of mental health to psychological distress, mental health care, and SB. Intervention scenarios were investigated to forecast their impact on SB over a 20-year period. Results A combination of social connectedness programs, technology-enabled coordinated care, post-attempt assertive aftercare, reductions in childhood adversity, and increasing youth employment projected the greatest impacts on SB, particularly in a youth population, reducing self-harm hospitalisations (suicide attempts) by 28.5% (95% interval 26.3–30.8%) and suicide deaths by 29.3% (95% interval 27.1–31.5%). Introducing additional interventions beyond the best performing suite of interventions produced only marginal improvement in population level impacts, highlighting that ‘more is not necessarily better.’ Conclusion Results indicate that targeted investments in addressing the social determinants and in mental health services provides the best opportunity to reduce SB and suicide. Systems modelling and simulation offers a robust approach to leveraging best available research, data, and expert knowledge in a way that helps decision makers respond to the unique characteristics and drivers of SB in their catchments and more effectively focus limited health resources.

Methodology in Systems Modelling and Simulation

1982 ◽  
Vol 12 (1) ◽  
pp. 97-97 ◽  
Author(s):  
B. Zeigler ◽  
M. Elzas ◽  
G. Klir ◽  
T. Oren ◽  
S. G. Tzafestas
Keyword(s):  
Modelling And Simulation ◽  
Systems Modelling

DFG Research Training Group 1387/1: dIEM oSiRiS – Integrative Development of Modelling and Simulation Methods for Regenerative Systems (DFG Graduiertenkolleg 1387/1: dIEM oSiRiS – Die integrative Entwicklung von Modellierungs- und Simulationsmethoden für regenerative Systeme)

2007 ◽  
Vol 49 (6) ◽  
Author(s):  
Adelinde Uhrmacher ◽  
Arndt Rolfs ◽  
Jana Frahm
Keyword(s):  
Large Scale ◽  
Research Training ◽  
Training Group ◽  
Modelling And Simulation ◽  
Neural Cells ◽  
Simulation Methods ◽  
Uncertain Environments ◽  
New Methodologies ◽  
Systems Modelling ◽  
Regenerative Systems

Regenerative systems are able to overcome significant perturbations, and maintain autonomously their functionality in dynamic and uncertain environments. To analyse or develop these types of systems modelling and simulation play a crucial role. However, due to the fact of being large scale and of embracing many heterogeneously acting and interacting sub-systems, they require the development of new methodologies to support a flexible modelling at different levels of organization and abstraction and an efficient execution of experiments. These methodological developments are at the core of the DFG Research Training Group dIEM oSiRiS (The Integrative Development of Modelling and Simulation Methods for Regenerative Systems). Thereby, the analysis of characteristics and requirements of regenerative systems and the evaluation of the developed concepts are based on a concrete biological regenerative system: the exploration of signalling pathways that play a significant role in the differentiation of neural cells.

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