SONET Toolkit: A Decision Support System for Designing Robust and Cost-Effective Fiber-Optic Networks

1995 ◽  
Vol 25 (1) ◽  
pp. 20-40 ◽  
Author(s):  
Steven Cosares ◽  
David N. Deutsch ◽  
Iraj Saniee ◽  
Ondria J. Wasem
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Fiber Optic ◽  
Cost Effective ◽  
Fiber Optic Networks

A decision support system for cost-effective diagnosis

2010 ◽  
Vol 50 (3) ◽  
pp. 149-161 ◽  
Author(s):  
Chih-Lin Chi ◽  
W. Nick Street ◽  
David A. Katz
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Cost Effective ◽  
Effective Diagnosis

scholarly journals Improving design methodologies and assessment tools for building on permafrost in a warming climate

2020 ◽  
Author(s):  
Kevin Bjella ◽  
Yuri Shur ◽  
Misha Kanevskiy ◽  
Paul Duvoy ◽  
Bruno Grunau ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Critical Infrastructure ◽  
Cost Effective ◽  
Field Investigation ◽  
Assessment Tools ◽  
Borehole Data ◽  
Foundation Design ◽  
Site Assessment

The U.S. Department of Defense (DoD) operates numerous Arctic and Subarctic installations, including Alaska. Changes to permafrost can threaten critical built infrastructure. It is critical to accurately characterize and compare site conditions in permafrost regions to enable the efficient, cost-effective design and construction of an infrastructure well suited to the permafrost environment and that meets DoD requirements. This report describes three research efforts to establish (1) field investigation approaches for ground ice detection and delineation, (2) methods and modeling for early warning detection of thawing permafrost under infrastructure, and (3) an outline of a decision support system that determines the most applicable foundation design for warming and degrading permafrost. Outcomes of these interrelated efforts address needs to improve construction of DoD mission critical infrastructure on Arctic and Subarctic permafrost terrains. Field investigation processes used systematic methodologies including borehole data and geophysical measurements to effectively characterize subsurface permafrost information. The Permafrost Foundation Decision Support System (PFFDSS) tool implements and logically links field survey information and foundation type assessments. The current version of PFFDSS is designed to be accessible to design-engineers of a broad range of experience, that will reduce the effort and cost, and improve the effectiveness of site assessment.

scholarly journals A Web-based Spatial Decision Support System of Wastewater Surveillance for COVID-19 Monitoring: A Case Study of a University Campus

2022 ◽  
Author(s):  
Wenwu Tang ◽  
Tianyang Chen ◽  
Zachery Slocum ◽  
Yu Lan ◽  
Eric Delmelle ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Virus Transmission ◽  
Cost Effective ◽  
Spatiotemporal Data ◽  
Spatial Decision Support System ◽  
Spatial Decision Support ◽  
Time Data ◽  
Web Based

The ongoing COVID-19 pandemic has produced substantial impacts on our society. Wastewater surveillance has increasingly been introduced to support the monitoring, and thus mitigation, of COVID-19 outbreaks and transmission. Monitoring of buildings and sub-sewershed areas via a wastewater surveillance approach has been a cost-effective strategy for mass testing of residents in congregate living situations such as universities. A series of spatial and spatiotemporal data are involved with wastewater surveillance, and these data must be interpreted and integrated with other information to better serve as guidance on response to a positive wastewater signal. The management and analysis of these data poses a significant challenge, in particular, for the need of supporting timely decision making. In this study, we present a web-based spatial decision support system framework to address this challenge. Our study area is the main campus of the University of North Carolina at Charlotte. We develop a spatiotemporal data model that facilitates the management of space-time data related to wastewater surveillance. We use spatiotemporal analysis and modeling to discover spatio-temporal patterns of COVID-19 virus abundance at wastewater collection sites that may not be readily apparent in wastewater data as they are routinely collected. Web-based GIS dashboards are implemented to support the automatic update and sharing of wastewater testing results. Our web-based SDSS framework enables the efficient and automated management, analytics, and sharing of spatiotemporal data of wastewater testing results for our study area. This framework provides substantial support for informing critical decisions or guidelines for the prevention of COVID-19 outbreak and the mitigation of virus transmission on campus.

A decision support system for the control of Campylobacter in chickens at farm level using data from Denmark

2016 ◽  
Vol 154 (4) ◽  
pp. 720-731
Author(s):  
A. B. GARCIA ◽  
A. L. MADSEN ◽  
H. VIGRE
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Control Strategies ◽  
Cost Benefit ◽  
Cost Effective ◽  
Epidemiological Data ◽  
Sensitivity Analyses ◽  
Potential Control ◽  
Financial Variables

SUMMARYThe control ofCampylobacterin poultry is considered a public health priority and some intervention strategies have been implemented in Denmark. Nonetheless,Campylobacterinfection in poultry can still be considerable particularly during the summer when the most promisingCampylobactercontrol strategy seems to be the use of fly screens. The use of cost-effective vaccines againstCampylobacteris also desirable. In order to controlCampylobacter, poultry producers need to make crucial decisions under conditions of uncertainty. With the aim of assisting poultry producers in decision making regardingCampylobactercontrol strategies, the objective of the present study was to produce a decision support system that integrated knowledge and used a Bayesian approach to handle uncertainty. This decision support system integrated epidemiological data, microbiological considerations, financial information and potential control strategies (the use of fly screens and hypothetical vaccines). In conclusion, results from model and sensitivity analyses indicated that the financial variables (cost–benefit functions) and the effectiveness of the different control strategies drove the results.

A numerical approach for inventory pre-positioning in emergency management

2020 ◽  
Vol 10 (2) ◽  
pp. 205-230
Author(s):  
Valentina Di Pasquale ◽  
Fabio Fruggiero ◽  
Raffaele Iannone
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Response Times ◽  
Humanitarian Aid ◽  
Cost Effective ◽  
Numerical Approach ◽  
Economic Benefits ◽  
Seismic Events ◽  
Content Type

PurposeThe increasing number of natural disasters has increased the attention on emergency plans aimed at providing fast support to affected communities. In this context, inventory pre-positioning management, which involves positioning the materials required to meet the affected community's needs early, has been increasingly acknowledged, but many challenges persist. The purpose of the paper is to provide a decision support system for the optimal quantification and location of humanitarian aid, trying to enhance and extend the existing literature on this topic.Design/methodology/approachThe paper develops a numerical model for inventory pre-positioning of humanitarian aid to reduce both emergency response times and costs connected to goods procurement for seismic events. By examining the characteristics of the territory and the affected population, the model defines the optimal stock levels for four basic need items (hygienic sanitary kits, beds, blankets and camp tents) to be pre-allocated in the territory.FindingsThe model was validated using data obtained from the two severe earthquakes that occurred in Italy. The case study showed how the simulated outputs differ from the real case data and the economic benefits of adopting inventory pre-positioning considering the cost reductions (purchase, storage, transport and fulfilment of requirements).Originality/valueThe proposed decision support system allows the pre-positioning of emergency supplies in local areas in order to reduce response times and increase the speed of intervention in the event of seismic events, exploiting the advantages of a simulation model. Numerical and graphical results can easily support improvements in humanitarian logistics, providing those affected with rapid, cost-effective and better-adapted responses.

scholarly journals FARMAPRICE: A Pharmacogenetic Clinical Decision Support System for Precise and Cost-Effective Therapy

Genes ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 276 ◽  
Author(s):  
Rossana Roncato ◽  
Lisa Dal Cin ◽  
Silvia Mezzalira ◽  
Francesco Comello ◽  
Elena De Mattia ◽  
...  
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Decision Support ◽  
Support System ◽  
Clinical Decision Support System ◽  
Cost Effective ◽  
Clinical Decision ◽  
Pharmacological Therapy ◽  
High Tech ◽  
Health Providers

Pharmacogenetic (PGx) guidelines for the precise dosing and selection of drugs remain poorly implemented in current clinical practice. Among the barriers to the implementation process is the lack of clinical decision support system (CDSS) tools to aid health providers in managing PGx information in the clinical context. The present study aimed to describe the first Italian endeavor to develop a PGx CDSS, called FARMAPRICE. FARMAPRICE prototype was conceived for integration of patient molecular data into the clinical prescription process in the Italian Centro di Riferimento Oncologico (CRO)-Aviano Hospital. It was developed through a coordinated partnership between two high-tech companies active in the computerization of the Italian healthcare system. Introducing FARMAPRICE into the clinical setting can aid physicians in prescribing the most efficacious and cost-effective pharmacological therapy available.

Decision support system enabled by depth imaging sensor data for intelligent automation of moving assemblies

2016 ◽  
Vol 232 (1) ◽  
pp. 51-66 ◽  
Author(s):  
Vinayak Ashok Prabhu ◽  
Narendi Muhandri ◽  
Boyang Song ◽  
Ashutosh Tiwari
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Manufacturing Industry ◽  
Cost Effective ◽  
Sensor Data ◽  
Vehicle Body ◽  
Manufacturing Companies ◽  
Wheel Loading ◽  
Assembly Operations

Automation, driven by informatics, enables manufacturing companies to increase productivity and meet market demands for cost-effective and high-quality products. However, many manufacturing operations across industry verticals continue to be manual even today. One such example is the manual assembly of the final trim and wheels in an automotive production line where there is heavy reliance on human decision-making pertaining to when, where and how to install components on and inside a constantly moving vehicle body. The main aim of this work is to develop a rule-based decision support system that will enable an automation solution to make human-like decisions in moving assembly operations. The wheel loading operation is chosen as a case study and a decision support framework and tool is developed and successfully tested using multiple assembly scenarios generated from experimental data provided by gaming interface sensors installed on the laboratory-based shopfloor. The resulting decision support system has the potential to enable the automation of moving assembly operations in various sectors of the manufacturing industry.

Decision Support System Assisted E-Recruiting System

2019 ◽  
Vol 16 (2) ◽  
pp. 335-340 ◽  
Author(s):  
Abdullah Alqahtani ◽  
Fahd Abdulsalam Alhaidari ◽  
Atta-ur-Rahman ◽  
Maqsood Mahmud ◽  
Kiran Sultan
Keyword(s):  
Fuzzy Logic ◽  
Decision Support ◽  
Decision Support System ◽  
Significant Role ◽  
Support System ◽  
Cost Effective ◽  
Recruitment Process ◽  
Recruitment System

Individuals play a significant role to enhance and accelerate the performance of the organizations and hiring right employees, is indeed a challenging task. It is because many people show interest in the single job opening. HR in large organizations, recruits and explores such people by following proper recruitment process. This recruitment process has been proven to be lengthy, tidy and costly because of a substantial number of applicants. Recruitment of suitable individuals becomes more difficult for smaller organizations as they do not have HR department. Automation can improve the process though. By doing this, the process becomes shorter, easier, and cost effective. In that respect several automated recruiting systems have been proposed in the literature. The primary limitation of the existing recruitment systems is that it simply picks out those candidates who meet the (100%) skills set and turns down the rest of them yet if they are meeting the partial criteria. It intends that as these programs have been projected to pick only those who are gathering all the prerequisites. In fact, the recruitment system can select the candidates that are even meeting the near about or close requirements. It is because if a candidate does not meet (100%), then the candidate with the lesser requirement should be kept under consideration. There can be chances where a person meeting 9 out of 10 requirements could have proved to be the best choice, but because of the system we lose such candidates. The current research proposes a Fuzzy logic-based decision support system (DSS), to overcome the above-mentioned limitation. Abiding by the Fuzzy logic, the proposed system can view the partial skills of the applicants. The system has been validated by implementing the prototype. The outcomes depict a significant improvement to overcome the limitation of the existing recruitment systems.

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