Choosing cost-effective locations for conservation fences in the local landscape

2012 ◽  
Vol 39 (3) ◽  
pp. 192 ◽  
Author(s):  
Michael Bode ◽  
Karl E. C. Brennan ◽  
Keith Morris ◽  
Neil Burrows ◽  
Neville Hague
Keyword(s):  
Systematic Approach ◽  
Decision Support Tool ◽  
Cost Effective ◽  
Small Scale ◽  
Decision Making Process ◽  
Large Set ◽  
Expected Return ◽  
Support Tool ◽  
Funding Sources ◽  
Predator Exclusion

Context Exclosure fences are widely used to reintroduce locally extinct animals. These fences function either as permanent landscape-scale areas free from most predators, or as small-scale temporary acclimatisation areas for newly translocated individuals to be ‘soft released’ into the wider landscape. Existing research can help managers identify the best design for their exclosure fence, but there are currently no methods available to help identify the optimal location for these exclosures in the local landscape (e.g. within a property). Aims We outline a flexible decision-support tool that can help managers choose the best location for a proposed exclosure fence. We applied this method to choose the site of a predator-exclusion fence within the proposed Lorna Glen (Matuwa) Conservation Park in the rangelands of central Western Australia. Methods The decision was subject to a set of economic, ecological and political constraints that were applied sequentially. The final exclosure fence location, chosen from among those sites that satisfied the constraints, optimised conservation outcomes by maximising the area enclosed. Key results From a prohibitively large set of potential exclosure locations, the series of constraints reduced the number of candidates down to 32. When ranked by the total area enclosed, one exclosure location was clearly superior. Conclusions By describing the decision-making process explicitly and quantitatively, and systematically considering each of the candidate solutions, our approach identifies an efficient exclosure fence location via a repeatable and transparent process. Implications The construction of an exclusion fence is an expensive management option, and therefore needs to convincingly demonstrate a high expected return-on-investment. A systematic approach for choosing the location of an exclosure fence provides managers with a decision that can be justified to funding sources and stakeholders.

Development of a decision support tool for sustainable renovation – a case study

2016 ◽  
Vol 34 (1) ◽  
pp. 3-11 ◽  
Author(s):  
Linus Malmgren ◽  
Stefan Elfborg ◽  
Kristina Mjörnell
Keyword(s):  
Design Methodology ◽  
Energy Use ◽  
Systematic Approach ◽  
Decision Support Tool ◽  
Cost Effective ◽  
Social Benefits ◽  
Content Type ◽  
Support Tool ◽  
Sustainability Criteria

Purpose – Building owners are encouraged to reduce the energy consumption both in order to contribute to national energy saving goals and to reduce the costs of heating and operation of buildings. However, it is important to pursue the most optimal strategy to achieve cost-effective energy use while maintaining other values. Building managers often do not have the time or expertise to make a proper evaluation of the available options, hence there is a need for practical methodologies for evaluation of renovation alternatives. The paper aims to discuss these issues. Design/methodology/approach – Renobuild, a systematic approach for evaluating renovation alternatives based on sustainability criteria are under development. It aims to compare renovation alternatives from an environmental, economic and social perspective. To verify the methodology it has been used to evaluate different renovation alternatives for a multi-family apartment building in Sweden. Findings – The study showed that building managers are provided with a clear comparison between different renovation alternatives from a sustainability perspective. Using Renobuild or similar methodologies may highlight renovation measures which marginally increases the investments, but can lead to significant environmental and social benefits. Research limitations/implications – So far the methodology has only been tested on a limited number of buildings in Sweden. More buildings will need to be tested for further verification. Originality/value – Not many tools or methodologies exist which address several aspects of sustainability. Renobuild is a practical methodology that can help building owners to increase focus on sustainability.

Neural Network Ensemble Approach to Pushed Convoys Dispatching Problems

2020 ◽  
Vol 27 (1) ◽  
pp. 70-82 ◽  
Author(s):  
Aleksandar Radonjić ◽  
Danijela Pjevčević ◽  
Vladislav Maraš
Keyword(s):  
Neural Network ◽  
Decision Support Tool ◽  
Primary Objective ◽  
Danube River ◽  
Decision Making Process ◽  
Neural Network Ensemble ◽  
Support Tool ◽  
Shipping Company ◽  
The Republic ◽  
Power Measurements

AbstractThis paper investigates the use of neural networks (NNs) for the problem of assigning push boats to barge convoys in inland waterway transportation (IWT). Push boat–barge convoy assignmentsare part of the daily decision-making process done by dispatchers in IWT companiesforwhich a decision support tool does not exist. The aim of this paper is to develop a Neural Network Ensemble (NNE) model that will be able to assist in push boat–barge convoy assignments based on the push boat power.The primary objective of this paper is to derive an NNE model for calculation of push boat Shaft Powers (SHPs) by using less than 100% of the experimental data available. The NNE model is applied to a real-world case of more than one shipping company from the Republic of Serbia, which is encountered on the Danube River. The solution obtained from the NNE model is compared toreal-world full-scale speed/power measurements carried out on Serbian push boats, as well as with the results obtained from the previous NNE model. It is found that the model is highly accurate, with scope for further improvements.

Multidimensional Phase I Dose Ranging Trials for Stroke Recovery Interventions: Key Challenges and How to Address Them

2021 ◽  
pp. 154596832110193
Author(s):  
Emily J. Dalton ◽  
Leonid Churilov ◽  
Natasha A. Lannin ◽  
Dale Corbett ◽  
Bruce C. V. Campbell ◽  
...  
Keyword(s):  
Decision Support ◽  
Phase I ◽  
Early Phase ◽  
Phase I Trial ◽  
Systematic Approach ◽  
Decision Support Tool ◽  
Maximum Tolerated Dose ◽  
Stroke Recovery ◽  
Support Tool ◽  
Dose Ranging

Despite an increase in the amount of published stroke recovery research, interventions have failed to markedly affect the trajectory of recovery poststroke. We argue that early-phase research to systematically investigate dose is an important contributor to advance the science underpinning stroke recovery. In this article, we aim to ( a) define the problem of insufficient use of a systematic approach to early-phase, multidimensional dose articulation research and ( b) propose a solution that applies this approach to design a multidimensional phase I trial to identify the maximum tolerated dose (MTD). We put forward a design template as a decision support tool to increase knowledge of how to develop a phase I dose-ranging trial for nonpharmaceutical stroke recovery interventions. This solution has the potential to advance the development of efficacious stroke recovery interventions, which include activity-based rehabilitation interventions.

Multivariable Automatisierungsentscheidungen*/Multivariate automation decisions – A tool for cost-benefit assessments as a basis for decision-making

2019 ◽  
Vol 109 (03) ◽  
pp. 134-139
Author(s):  
P. Burggräf ◽  
J. Wagner ◽  
M. Dannapfel ◽  
K. Müller ◽  
B. Koke
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Environmental Conditions ◽  
Cost Benefit ◽  
Decision Support Tool ◽  
Decision Making Process ◽  
Support Tool ◽  
Cost And Benefit ◽  
Benefit Evaluation ◽  
Level Of Automation

Der wachsende Bedarf an Wandlungsfähigkeit führt zu einer höheren Frequenz in der Umplanung von Montagesystemen und erfordert eine kontinuierliche Überprüfung und Anpassung des Automatisierungsgrades. Um auch die komplexen Umgebungsbedingungen abzubilden, sollen nicht-monetäre Faktoren in den Entscheidungsprozess eingebunden werden. Um die Entscheidung zu unterstützen, stellt dieser Beitrag ein Tool zur Identifizierung und Bewertung von Automatisierungsszenarien mittels einer Nutzwert-Aufwand-Analyse vor.   The increasing need for adaptability in assembly leads to a higher planning frequency of the system and requires continuous checks and adaptations of the appropriate level of automation. To account for the complex environmental conditions, non-monetary factors are included in the decision-making process. This paper presents a decision support tool to identify and evaluate automation scenarios by means of cost and benefit evaluation.

A structured model for evaluating countermeasures at highway–railway grade crossings

2005 ◽  
Vol 32 (4) ◽  
pp. 627-635 ◽  
Author(s):  
Young-Jin Park ◽  
Frank F Saccomanno
Keyword(s):  
Data Mining ◽  
Statistical Models ◽  
Decision Support Tool ◽  
Cost Effective ◽  
Expected Number ◽  
Structured Model ◽  
Support Tool ◽  
Data Mining Techniques ◽  
Use Of Data ◽  
Grade Crossings

Various countermeasures can be introduced to reduce collisions at highway–railway grade crossings. These countermeasures may take different forms, such as passive and (or) active driver warning devices, supplementary traffic controls (four quadrant barriers, wayside horn, closed circuit television (CCTV) monitoring, etc.), illumination, signage and highway speed limit, etc. In this research, we present a structured model that makes use of data mining techniques to estimate the effect of changes in countermeasures on the expected number of collisions at a given crossing. This model serves as a decision-support tool for the evaluation and development of cost-effective and practicable safety program at highway–railway grade crossings. The use of data mining techniques helps to resolve many of the problems associated with conventional statistical models used to predict the expected number of collisions for a given type of crossing. Statistical models introduce biases that limit their ability to fully represent the relationship between selected countermeasures and resultant collisions for a mix of crossing attributes. This paper makes use of Canadian inventory and collision data to illustrate the potential merits of the proposed model to provide decision support.Key words: highway–railway grade crossing, collision prediction model, countermeasures, Poisson regression.

A Fuzzy-Based Decision Support Tool for Appraisement of Supplier's Quality Assurance Practices

2016 ◽  
pp. 255-290
Author(s):  
Chitrasen Samantra ◽  
Saurav Datta ◽  
Siba Sankar Mahapatra
Keyword(s):  
Quality Assurance ◽  
Evaluation Model ◽  
Decision Support Tool ◽  
Cost Effective ◽  
Working Relationships ◽  
Global Marketplace ◽  
Support Tool ◽  
Effective Components ◽  
Present Context ◽  
Interactive Relationship

Recently competition in the global marketplace has stimulated immense attention being paid by the enterprises towards securing highest quality, cost effective components and materials, consistently delivered on time. This objective can only be achieved by establishing long term, close working relationships with suppliers, who adopt a proper quality philosophy. Supplier Quality Assurance is the confidence in a supplier's ability to deliver a commodity or service towards satisfying customer's needs. Supplier Quality Assurance can be achieved through interactive relationship between the customer and the supplier; it aims at ensuring the product's ‘suitably fit' to the customer's requirements with little or no adjustment or inspection. In the present context, the study develops a decision-making framework to assure as well as to assess suppliers' existing quality philosophy, current policy and related practices. An Interval-Valued Fuzzy Set (IVFS) theory has been adopted to develop such an evaluation model.

Incident Occurrence Models for Freeway Incident Management

2003 ◽  
Vol 1856 (1) ◽  
pp. 125-135 ◽  
Author(s):  
Sravanthi Konduri ◽  
Samuel Labi ◽  
Kumares C. Sinha
Keyword(s):  
Negative Binomial ◽  
Prediction Models ◽  
Negative Binomial Regression ◽  
Decision Support Tool ◽  
Cost Effective ◽  
Traffic Volume ◽  
Incident Management ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Support Tool

Incident prediction models are presented for the Interstate 80/Interstate 94 (Borman Expressway in northwestern Indiana) and Interstate 465 (northeastern Indianapolis, Indiana) freeway sections developed as a function of traffic volume, truck percentage, and weather. Separate models were developed for all incidents and noncrash incidents. Three model types were considered (Poisson regression, negative binomial regression, and nonlinear regression), and the results were compared based on magnitudes and signs of model parameter estimates and t-statistics. Least-squares estimation and maximum-likelihood methods were used to estimate the model parameters. Data from the Indiana Department of Transportation and the Indiana Climatology Database were used to establish the relationships. For a given session and incident category, the results from the Poisson and negative binomial models were found to be consistent. It was observed that, unlike section length, traffic volume is nonlinearly related to incidents, and therefore these two variables have to be considered as separate terms in the modeling process. Truck percentage was found to be a statistically significant factor affecting incident occurrence. It was also found that the weather variable (rain and snow) was negatively correlated to incidents. The freeway incident models developed constitute a useful decision support tool for implementation of new freeway patrol systems or for expansion of existing ones. They are also useful for simulating incident occurrences with a view to identifying elements of cost-effective freeway patrol strategies (patrol deployment policies, fleet size, crew size, and beat routes).

scholarly journals The benefits of action to reduce household air pollution (BAR-HAP) model: A new decision support tool

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245729
Author(s):  
Ipsita Das ◽  
Jessica J. Lewis ◽  
Ramona Ludolph ◽  
Melanie Bertram ◽  
Heather Adair-Rohani ◽  
...  
Keyword(s):  
Air Pollution ◽  
Decision Support ◽  
Decision Support Tool ◽  
Global Scale ◽  
Household Air Pollution ◽  
Large Set ◽  
Costs And Benefits ◽  
Decision Support Model ◽  
Support Tool ◽  
Policy Interventions

Cooking with polluting and inefficient fuels and technologies is responsible for a large set of global harms, ranging from health and time losses among the billions of people who are energy poor, to environmental degradation at a regional and global scale. This paper presents a new decision-support model–the BAR-HAP Tool–that is aimed at guiding planning of policy interventions to accelerate transitions towards cleaner cooking fuels and technologies. The conceptual model behind BAR-HAP lies in a framework of costs and benefits that is holistic and comprehensive, allows consideration of multiple policy interventions (subsidies, financing, bans, and behavior change communication), and realistically accounts for partial adoption and use of improved cooking technology. It incorporates evidence from recent efforts to characterize the relevant set of parameters that determine those costs and benefits, including those related to intervention effectiveness. Practical aspects of the tool were modified based on feedback from a pilot testing workshop with multisectoral users in Nepal. To demonstrate the functionality of the BAR-HAP tool, we present illustrative calculations related to several cooking transitions in the context of Nepal. In accounting for the multifaceted nature of the issue of household air pollution, the BAR-HAP model is expected to facilitate cross-sector dialogue and problem-solving to address this major health, environment and development challenge.

scholarly journals Development and validation of a neuroimaging biomarker for electroconvulsive therapy outcome in depression: a multicenter machine learning analysis

2021 ◽  
Author(s):  
Willem B Bruin ◽  
Leif Oltedal ◽  
Hauke Bartsch ◽  
Christopher C Abbott ◽  
Miklos Argyelan ◽  
...  
Keyword(s):  
Decision Support ◽  
Functional Mri ◽  
Clinical Decision Support ◽  
Electroconvulsive Therapy ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Support Vector ◽  
Small Scale ◽  
Support Tool ◽  
Clinical Decision Support Tool

Electroconvulsive therapy (ECT) is the most effective intervention for patients with treatment resistant depression. A clinical decision support tool could guide patient selection to improve the overall response rate and avoid ineffective treatments with adverse effects. Initial small-scale, mono-center studies indicate that both structural magnetic resonance imaging (MRI) and functional MRI biomarkers may predict ECT outcome, but it is not known whether those results can generalize to data from other centers. Here, we used MRI data of 189 depressed patients from seven participating centers of the Global ECT-MRI Research Collaboration (GEMRIC) to develop and validate neuroimaging biomarkers for ECT outcome in a multi-center setting. We used multimodal data (i.e., clinical, structural MRI and resting-state functional MRI) and evaluated which data modalities or combinations thereof could provide the best predictions for treatment response (≥50% symptom reduction) or remission (minimal symptoms after treatment) using a support vector machine (SVM) classifier. Remission classification using a combination of gray matter volume with functional connectivity led to good performing models with 0.82-0.84 area under the curve (AUC) when trained and tested on samples coming from all centers, and remained acceptable when validated on other centers with 0.71-0.73 AUC. These results show that multimodal neuroimaging data is able to provide good prediction of remission with ECT for individual patients across different treatment centers, despite significant variability in clinical characteristics across centers. This suggests that these biomarkers are robust, indicating that future development of a clinical decision support tool applying these biomarkers may be feasible.

SMART COVID Navigator: A Clinical Decision Support for COVID-19 (Preprint)

2021 ◽  
Author(s):  
Varun Suraj ◽  
Catherine Del Vecchio Fitz ◽  
Laura B. Kleiman ◽  
Jeremy Warner ◽  
Gil Alterovitz
Keyword(s):  
Decision Support ◽  
Clinical Decision Support ◽  
Web Application ◽  
Decision Support Tool ◽  
Clinical Decision ◽  
Observational Research ◽  
Large Set ◽  
Health Records ◽  
Support Tool ◽  
Research Findings

UNSTRUCTURED The coronavirus disease 2019 (COVID-19) caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has infected 83 million individuals at the time of writing. In this paper, we describe the creation of a clinical decision support tool, the SMART COVID Navigator, a web application to assist clinicians in treating COVID-19 patients. A large volume of research findings from observational studies about disease interactions with COVID-19 are being produced almost daily. Our app allows clinicians to access a patient’s electronic health records and identify disease interactions from a large set of observational research studies that affect severity and fatality due to COVID-19. We also analyze the results of the collected studies to determine which medical conditions result in an increased chance of severity and/or fatality of COVID-19 progression.

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