42,092 research outputs found
Solving multiple-criteria R&D project selection problems with a data-driven evidential reasoning rule
In this paper, a likelihood based evidence acquisition approach is proposed
to acquire evidence from experts'assessments as recorded in historical
datasets. Then a data-driven evidential reasoning rule based model is
introduced to R&D project selection process by combining multiple pieces of
evidence with different weights and reliabilities. As a result, the total
belief degrees and the overall performance can be generated for ranking and
selecting projects. Finally, a case study on the R&D project selection for the
National Science Foundation of China is conducted to show the effectiveness of
the proposed model. The data-driven evidential reasoning rule based model for
project evaluation and selection (1) utilizes experimental data to represent
experts' assessments by using belief distributions over the set of final
funding outcomes, and through this historic statistics it helps experts and
applicants to understand the funding probability to a given assessment grade,
(2) implies the mapping relationships between the evaluation grades and the
final funding outcomes by using historical data, and (3) provides a way to make
fair decisions by taking experts' reliabilities into account. In the
data-driven evidential reasoning rule based model, experts play different roles
in accordance with their reliabilities which are determined by their previous
review track records, and the selection process is made interpretable and
fairer. The newly proposed model reduces the time-consuming panel review work
for both managers and experts, and significantly improves the efficiency and
quality of project selection process. Although the model is demonstrated for
project selection in the NSFC, it can be generalized to other funding agencies
or industries.Comment: 20 pages, forthcoming in International Journal of Project Management
(2019
Development of titanium dioxide nanoparticles/nanosolution for photocatalytic activity
Biological and chemical contaminants by man-made activities have been serious
global issue. Exposure of these contaminants beyond the limits may result in serious
environmental and health problem. Therefore, it is important to develop an effective
solution that can be easily utilized by mankind. One of the effective ways to
overcome this problem is by using titanium dioxide (TiO2). TiO2 is a well-known
photocatalyst that widely used for environmental clean-up due to its ability to
decompose organic pollutant and kill bacteria. Although it is proven TiO2 has an
advantage to solve this concern, its usefulness unfortunately is limited only under
UV light irradiation. Therefore, the aim of this work was to investigate the potential
of TiO2 that can be activated under visible light by the incorporation of metal ions
(Fe, Ag, Zr and Ag-Zr). In this study, sol-gel method was employed for the synthesis
of metal ions incorporated TiO2. XRD analysis revealed that all samples content
biphasic anatase-brookite TiO2 of size 3 nm to 5 nm. It was found that the
incorporation of these metal ions did not change the morphology of TiO2 but the
crystallinity and optical properties were affected. The crystallinity of anatase in the
biphasic TiO2 was found to be decreased and favored brookite formation. PL analysis
showed metal ions incorporation suppressed the recombination of electron-hole pairs
while the band gap energy of TiO2 (3.2 eV) was decreased by the incorporation of Fe
(2.46 eV) and Ag (2.86 eV). Among this incorporation, Ag-Zr incorporated TiO2
showed highest performance for methyl orange degradation (93%) under fluorescent
xxv
light irradiation for 10 h. This follows by Zr-TiO2 (82%), Fe-TiO2 (75%) and Ag�TiO2 (43%). Meanwhile, the highest antibacterial performance was exhibited by Ag�TiO2. TEM images showed that E.coli bacterium was killed within 12 h after treated
with Ag-TiO2. The results obtained from the fieldwork study established that Ag-Zr
incorporation have excellent performances for VOC removal and antibacterial test.
The VOC content after treated with Ag-Zr-TiO2 fulfilled the Industry Code of
Practice on Indoor Air Quality 2010 which is lower than 3 ppm. In addition, the
percentage of microbes also found to be decrease around 45 % within 5 days of
monitoring
Robust Classification for Imprecise Environments
In real-world environments it usually is difficult to specify target
operating conditions precisely, for example, target misclassification costs.
This uncertainty makes building robust classification systems problematic. We
show that it is possible to build a hybrid classifier that will perform at
least as well as the best available classifier for any target conditions. In
some cases, the performance of the hybrid actually can surpass that of the best
known classifier. This robust performance extends across a wide variety of
comparison frameworks, including the optimization of metrics such as accuracy,
expected cost, lift, precision, recall, and workforce utilization. The hybrid
also is efficient to build, to store, and to update. The hybrid is based on a
method for the comparison of classifier performance that is robust to imprecise
class distributions and misclassification costs. The ROC convex hull (ROCCH)
method combines techniques from ROC analysis, decision analysis and
computational geometry, and adapts them to the particulars of analyzing learned
classifiers. The method is efficient and incremental, minimizes the management
of classifier performance data, and allows for clear visual comparisons and
sensitivity analyses. Finally, we point to empirical evidence that a robust
hybrid classifier indeed is needed for many real-world problems.Comment: 24 pages, 12 figures. To be published in Machine Learning Journal.
For related papers, see http://www.hpl.hp.com/personal/Tom_Fawcett/ROCCH
Advancing Alternative Analysis: Integration of Decision Science.
Decision analysis-a systematic approach to solving complex problems-offers tools and frameworks to support decision making that are increasingly being applied to environmental challenges. Alternatives analysis is a method used in regulation and product design to identify, compare, and evaluate the safety and viability of potential substitutes for hazardous chemicals.Assess whether decision science may assist the alternatives analysis decision maker in comparing alternatives across a range of metrics.A workshop was convened that included representatives from government, academia, business, and civil society and included experts in toxicology, decision science, alternatives assessment, engineering, and law and policy. Participants were divided into two groups and prompted with targeted questions. Throughout the workshop, the groups periodically came together in plenary sessions to reflect on other groups' findings.We conclude the further incorporation of decision science into alternatives analysis would advance the ability of companies and regulators to select alternatives to harmful ingredients, and would also advance the science of decision analysis.We advance four recommendations: (1) engaging the systematic development and evaluation of decision approaches and tools; (2) using case studies to advance the integration of decision analysis into alternatives analysis; (3) supporting transdisciplinary research; and (4) supporting education and outreach efforts
Environmental Risk Assessment of Produced Water Discharges on the Dutch Continental Shelf
The OSPAR Offshore Industry Committee (OIC) has decided, in its meeting of 2008, to evaluate the possibility of implementing a risk based approach towards produced water management. Currently, Norway has made most progress in this field as it has fully implemented the Environmental Impact Factor as the basis of their biannual reporting obligations. The Netherlands has for as yet mainly followed a source (immission) based approach, and therefore did not adopt a specific risk based approach. In this study an overview is provided of current approaches to assess the ecological risk of produced water discharges and it is investigated how these approaches can be used in the Dutch situation for produced water management as intended by the OIC
Recommended from our members
Uncertainty explicit assessment of off-the-shelf software: A Bayesian approach
Assessment of software COTS components is an essential part of component-based software development. Poorly chosen components may lead to solutions of low quality and that are difficult to maintain. The assessment may be based on incomplete knowledge about the COTS component itself and other aspects (e.g. vendor’s credentials, etc.), which may affect the decision of selecting COTS component(s). We argue in favor of assessment methods in which uncertainty is explicitly represented (‘uncertainty explicit’ methods) using probability distributions. We provide details of a Bayesian model, which can be used to capture the uncertainties in the simultaneous assessment of two attributes, thus, also capturing the dependencies that might exist between them. We also provide empirical data from the use of this method for the assessment of off-the-shelf database servers which illustrate the advantages of ‘uncertainty explicit’ methods over conventional methods of COTS component assessment which assume that at the end of the assessment the values of the attributes become known with certainty
A new and efficient intelligent collaboration scheme for fashion design
Technology-mediated collaboration process has been extensively studied for over a decade. Most applications with collaboration concepts reported in the literature focus on enhancing efficiency and effectiveness of the decision-making processes in objective and well-structured workflows. However, relatively few previous studies have investigated the applications of collaboration schemes to problems with subjective and unstructured nature. In this paper, we explore a new intelligent collaboration scheme for fashion design which, by nature, relies heavily on human judgment and creativity. Techniques such as multicriteria decision making, fuzzy logic, and artificial neural network (ANN) models are employed. Industrial data sets are used for the analysis. Our experimental results suggest that the proposed scheme exhibits significant improvement over the traditional method in terms of the time–cost effectiveness, and a company interview with design professionals has confirmed its effectiveness and significance
- …