The Application of a Systems Methodology to the Design and Specification of an Intelligent Telecare System

1998 ◽  
pp. 787-792
Author(s):  
G Williams ◽  
DA Bradley ◽  
K Doughty
Keyword(s):  
2020 ◽  
Vol 10 (1) ◽  
pp. 86-99
Author(s):  
Lewis Tsuro ◽  
Stan Hardman

The Soft Systems Methodology (SSM) was developed as a set of tools for identifying and making incremental steps to improve situations with poorly defined causes or solutions. The supply chain forms a key process of any construction project; however, on any given construction site, supply chain inefficiencies could arise from many different avenues. Opinions vary, though, on which of these avenues is more important for increasing supply chain efficiencies; whether any problem even exist across the different aspects of the supply chain; as well as what steps should be taken to resolve them. It was therefore studied, here, whether SSM could be employed as a useful tool to systematically apply in the supply chains of a construction project in South Africa, for understanding and targeting the problematic situations that arise. Following thorough cyclical open-ended interviews with 17 workers, supervisors, foremen, site clerks, senior managers, and the CEO of the principal contractor at a new office park construction project in Rosebank, Johannesburg, and a thematic analysis of the data, SSM was performed to understand the existing challenges, and develop a suitable model for improvement. The study found that SSM was a good tool for understanding the ‘messy’ circumstances surrounding the chosen construction project supply chain, as well as actions that could be taken to improve the supply chain’s efficiency on site. The findings add weight to the argument that SSM could be a good tool for project managers to systematically introduce into their project planning regimens


2009 ◽  
Vol 3 (2) ◽  
pp. 11-34 ◽  
Author(s):  
Radu Neagu ◽  
Sean Keenan ◽  
Kete Chalermkraivuth

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1353
Author(s):  
Hai Sun ◽  
Lanling Hu ◽  
Wenchi Shou ◽  
Jun Wang

Predicting evacuation patterns is useful in emergency management situations such as an earthquake. To find out how pre-trained individuals interact with one another to achieve their own goal to reach the exit as fast as possible firstly, we investigated urban people’s evacuation behavior under earthquake disaster coditions, established crowd response rules in emergencies, and described the drill strategy and exit familiarity quantitatively through a cellular automata model. By setting different exit familiarity ratios, simulation experiments under different strategies were conducted to predict people’s reactions before an emergency. The corresponding simulation results indicated that the evacuees’ training level could affect a multi-exit zone’s evacuation pattern and clearance time. Their exit choice preferences may disrupt the exit options’ balance, leading to congestion in some of the exits. Secondly, due to people’s rejection of long distances, congestion, and unfamiliar exits, some people would hesitant about the evacuation direction during the evacuation process. This hesitation would also significantly reduce the overall evacuation efficiency. Finally, taking a community in Zhuhai City, China, as an example, put forward the best urban evacuation drill strategy. The quantitative relation between exit familiar level and evacuation efficiency was obtained. The final results showed that the optimized evacuation plan could improve evacuation’s overall efficiency through the self-organization effect. These studies may have some impact on predicting crowd behavior during evacuation and designing the evacuation plan.


2014 ◽  
Vol 8 (10) ◽  
pp. 1294-1300
Author(s):  
Tseng Chu-Chun ◽  
Yang Che-Ming

Introduction: In Taiwan, severe enteroviral infections must be reported to the government within 24 hours to ensure that severe enterovirus 71 (EV71) infections can be detected early. The objective of this research was to ascertain whether over-reporting is a problem in mandatory disease-reporting systems. Methodology: A multiyear cross-sectional study methodology was applied based on secondary data analyses. Data from the national notifiable communicable disease surveillance system of Taiwan Centers for Disease Control were analyzed to assess the trends and factors influencing reporting accuracy. Results: From July 1999 to December 2008, 2,611 cases of severe enteroviral infection were reported in Taiwan. Among these cases, 1,516 were confirmed to be EV71 cases, and the remaining 1,095 were confirmed to be non-EV71 infections. The overall accuracy rate was 58%. The accuracy rate was 60%–70% higher during epidemics (2000–2002, 2005, and 2008) and high seasons than it was in other seasons. The accuracy rate was highest among medical centers and lowest among district hospitals. Conclusions: The results indicated that reports are more accurate during high seasons and peak years than during other periods. This might be attributable to the adequate level of specific educational programs for professionals when more cases occur, which could facilitate identification. Based on experiences in Taiwan, optimal training can ensure that surveillance systems are not inundated by false-positive reports.


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