Deploying the Maintenance Decision Support System (MDSS) in Iowa, 2003

Adverse weather conditions dramatically affect the nation’s surface transportation system. The development of a prototype winter Maintenance Decision Support System (MDSS) is part of the Federal Highway Administration’s effort to produce a prototype tool for decision support to winter road maintenance managers to help make the highways safer for the traveling public. The MDSS is based on leading diagnostic and prognostic weather research capabilities and road condition algorithms, which are being developed at national research centers. In 2003, the Iowa Department of Transportation was chosen as a field test bed for the continuing development of this important research program. The Center for Transportation Research and Education assisted the Iowa Department of Transportation by collecting and analyzing surface condition data. The Federal Highway Administration also selected five national research centers to participate in the development of the prototype MDSS. It is anticipated that components of the prototype MDSS system developed by this project will ultimately be deployed by road operating agencies, including state departments of transportation, and generally supplied by private vendors.

Evaluation of the multicriteria approval method for timber-harvesting group decision support

Abstract

A decision support system for selective cleaning

[Abstract]

R&D Performance Analysis: Case Studies on the Challenges and Promotion of the Evaluation and Measurement of R&D

Due to the intense international competition, demanding, and sophisticated customers, and diverse transforming technological change, organizations need to renew their products and services by allocating resources on research and development (R&D). Managing R&D is complex, but vital for many organizations to survive in the dynamic, turbulent environment. Thus, the increased interest among decision-makers towards finding the right performance measures for R&D is understandable. The measures or evaluation methods of R&D performance can be utilized for multiple purposes; for strategic control, for justifying the existence of R&D, for providing information and improving activities, as well as for the purposes of motivating and benchmarking. The earlier research in the field of R&D performance analysis has generally focused on either the activities and considerable factors and dimensions - e.g. strategic perspectives, purposes of measurement, levels of analysis, types of R&D or phases of R&D process - prior to the selection of R&Dperformance measures, or on proposed principles or actual implementation of theselection or design processes of R&D performance measures or measurement systems. This study aims at integrating the consideration of essential factors anddimensions of R&D performance analysis to developed selection processes of R&D measures, which have been applied in real-world organizations. The earlier models for corporate performance measurement that can be found in the literature, are to some extent adaptable also to the development of measurement systemsand selecting the measures in R&D activities. However, it is necessary to emphasize the special aspects related to the measurement of R&D performance in a way that make the development of new approaches for especially R&D performance measure selection necessary: First, the special characteristics of R&D - such as the long time lag between the inputs and outcomes, as well as the overall complexity and difficult coordination of activities - influence the R&D performance analysis problems, such as the need for more systematic, objective, balanced and multi-dimensional approaches for R&D measure selection, as well as the incompatibility of R&D measurement systems to other corporate measurement systems and vice versa. Secondly, the above-mentioned characteristics and challenges bring forth the significance of the influencing factors and dimensions that need to be recognized in order to derive the selection criteria for measures and choose the right R&D metrics, which is the most crucial step in the measurement system development process. The main purpose of this study is to support the management and control of the research and development activities of organizations by increasing the understanding of R&D performance analysis, clarifying the main factors related to the selection of R&D measures and by providing novel types of approaches and methods for systematizing the whole strategy- and business-based selection and development process of R&D indicators.The final aim of the research is to support the management in their decision making of R&D with suitable, systematically chosen measures or evaluation methods of R&D performance. Thus, the emphasis in most sub-areas of the present research has been on the promotion of the selection and development process of R&D indicators with the help of the different tools and decision support systems, i.e. the research has normative features through providing guidelines by novel types of approaches. The gathering of data and conducting case studies in metal and electronic industry companies, in the information and communications technology (ICT) sector, and in non-profit organizations helped us to formulate a comprehensive picture of the main challenges of R&D performance analysis in different organizations, which is essential, as recognition of the most importantproblem areas is a very crucial element in the constructive research approach utilized in this study. Multiple practical benefits regarding the defined problemareas could be found in the various constructed approaches presented in this dissertation: 1) the selection of R&D measures became more systematic when compared to the empirical analysis, as it was common that there were no systematic approaches utilized in the studied organizations earlier; 2) the evaluation methods or measures of R&D chosen with the help of the developed approaches can be more directly utilized in the decision-making, because of the thorough consideration of the purpose of measurement, as well as other dimensions of measurement; 3) more balance to the set of R&D measures was desired and gained throughthe holistic approaches to the selection processes; and 4) more objectivity wasgained through organizing the selection processes, as the earlier systems were considered subjective in many organizations. Scientifically, this dissertation aims to make a contribution to the present body of knowledge of R&D performance analysis by facilitating dealing with the versatility and challenges of R&D performance analysis, as well as the factors and dimensions influencing the selection of R&D performance measures, and by integrating these aspects to the developed novel types of approaches, methods and tools in the selection processes of R&D measures, applied in real-world organizations. In the whole research, facilitation of dealing with the versatility and challenges in R&D performance analysis, as well as the factors and dimensions influencing the R&D performance measure selection are strongly integrated with the constructed approaches. Thus, the research meets the above-mentioned purposes and objectives of the dissertation from the scientific as well as from the practical point of view.

ASTRAL-R score predicts non-recanalisation after intravenous thrombolysis in acute ischaemic stroke.

Intravenous thrombolysis (IVT) as treatment in acute ischaemic strokes may be insufficient to achieve recanalisation in certain patients. Predicting probability of non-recanalisation after IVT may have the potential to influence patient selection to more aggressive management strategies. We aimed at deriving and internally validating a predictive score for post-thrombolytic non-recanalisation, using clinical and radiological variables. In thrombolysis registries from four Swiss academic stroke centres (Lausanne, Bern, Basel and Geneva), patients were selected with large arterial occlusion on acute imaging and with repeated arterial assessment at 24 hours. Based on a logistic regression analysis, an integer-based score for each covariate of the fitted multivariate model was generated. Performance of integer-based predictive model was assessed by bootstrapping available data and cross validation (delete-d method). In 599 thrombolysed strokes, five variables were identified as independent predictors of absence of recanalisation: Acute glucose > 7 mmol/l (A), significant extracranial vessel STenosis (ST), decreased Range of visual fields (R), large Arterial occlusion (A) and decreased Level of consciousness (L). All variables were weighted 1, except for (L) which obtained 2 points based on β-coefficients on the logistic scale. ASTRAL-R scores 0, 3 and 6 corresponded to non-recanalisation probabilities of 18, 44 and 74 % respectively. Predictive ability showed AUC of 0.66 (95 %CI, 0.61-0.70) when using bootstrap and 0.66 (0.63-0.68) when using delete-d cross validation. In conclusion, the 5-item ASTRAL-R score moderately predicts non-recanalisation at 24 hours in thrombolysed ischaemic strokes. If its performance can be confirmed by external validation and its clinical usefulness can be proven, the score may influence patient selection for more aggressive revascularisation strategies in routine clinical practice.

Integrating Group Support Systems to innovation process of a forest company

The objective of this study was to analyze the effects of Group Support Systems (GSS) to overall efficiency of innovation process. Overall efficiency was found to be a sum of meeting efficiency, product effectiveness, and learning efficiency. These components were studied in various working situations common in early stages of innovation process. In the empirical part of this study, the suitability of GSS at the forest company was assessed. The basics for this study were idea generation meetings held at LUT and results from the surveys done after the sessions. This data combined with the interviews and theoretical background was used to analyze suitability of this technology to organizational culture at the company. The results of this study are divided to theory and case level. On theory level GSS was found to be a potentially valuable tool for innovation managers, especially at the first stages of the process. On case level, GSS was found to be a suitable tool at Stora Enso for further utilization. A five step implementation proposal was built to illustrate what would be the next stages of GSS implementation, if technology was chosen for further implementation.

Evaluation of a revision of the BSPcast decision support system for control of brown spot of pear

Control of brown spot of pear requires fungicide treatments of pear trees during the growing season. Scheduling fungicide sprays with the Brown spot of pear forecasting system (BSPcast) provides significantfungicide savings but does not increase the efficacy of disease control. Modifications in BSPcast wereintroduced in order to increase system performance. The changes consisted of: (1) the use of a daily infectionrisk (Rm≥0.2) instead of the 3-day cumulative risk (CR≥0.4) to guide the fungicide scheduling, and (2) theinclusion of the effect of relative humidity during interrupted wetness periods. Trials were performed during2 years in an experimental pear orchard in Spain. The modifications introduced did not result in increaseddisease control efficacy, compared with the original BSPcast system. In one year, no reduction in the numberof fungicide applications was obtained using the modified BSPcast system in comparison to the original system, but in the second year the number of treatments was reduced from 15 to 13. The original BSPcast model overestimated the daily infection risk in 6.5% of days with wetness periods with low relative humidity during the wetness interruption, and in these cases the modified version was more adequate

A decision support system for cost determination in grain storage facility operations

Many research works have being carried out on analyzing grain storage facility costs; however a few of them had taken into account the analysis of factors associated to all pre-processing and storage steps. The objective of this work was to develop a decision support system for determining the grain storage facility costs and utilization fees in grain storage facilities. The data of a CONAB storage facility located in Ponta Grossa - PR, Brazil, was used as input of the system developed to analyze its specific characteristics, such as amount of product received and stored throughout the year, hourly capacity of drying, cleaning, and receiving, and dispatch. By applying the decision support system, it was observed that the reception and expedition costs were exponentially reduced as the turnover rate of the storage increased. The cleaning and drying costs increased linearly with grain initial moisture. The storage cost increased exponentially as the occupancy rate of the storage facility decreased.

Corporate Social Responsibility in the Oil and Gas Industry: Environmental Management Systems as a Source of Sustainable Development

The environmental aspect of corporate social responsibility (CSR) expressed through the process of the EMS implementation in the oil and gas companies is identified as the main subject of this research. In the theoretical part, the basic attention is paid to justification of a link between CSR and environmental management. The achievement of sustainable competitive advantage as a result of environmental capital growth and inclusion of the socially responsible activities in the corporate strategy is another issue that is of special significance here. Besides, two basic forms of environmental management systems (environmental decision support systems and environmental information management systems) are explored and their role in effective stakeholder interaction is tackled. The most crucial benefits of EMS are also analyzed to underline its importance as a source of sustainable development. Further research is based on the survey of 51 sampled oil and gas companies (both publicly owned and state owned ones) originated from different countries all over the world and providing reports on sustainability issues in the open access. To analyze their approach to sustainable development, a specifically designed evaluation matrix with 37 indicators developed in accordance with the General Reporting Initiative (GRI) guidelines for non-financial reporting was prepared. Additionally, the quality of environmental information disclosure was measured on the basis of a quality – quantity matrix. According to results of research, oil and gas companies prefer implementing reactive measures to the costly and knowledge-intensive proactive techniques for elimination of the negative environmental impacts. Besides, it was identified that the environmental performance disclosure is mostly rather limited, so that the quality of non-financial reporting can be judged as quite insufficient. In spite of the fact that most of the oil and gas companies in the sample claim the EMS to be embedded currently in their structure, they often do not provide any details for the process of their implementation. As a potential for the further development of EMS, author mentions possible integration of their different forms in a single entity, extension of existing structure on the basis of consolidation of the structural and strategic precautions as well as development of a unified certification standard instead of several ones that exist today in order to enhance control on the EMS implementation.

Linguistic models for decision support

Linguistic modelling is a rather new branch of mathematics that is still undergoing rapid development. It is closely related to fuzzy set theory and fuzzy logic, but knowledge and experience from other fields of mathematics, as well as other fields of science including linguistics and behavioral sciences, is also necessary to build appropriate mathematical models. This topic has received considerable attention as it provides tools for mathematical representation of the most common means of human communication - natural language. Adding a natural language level to mathematical models can provide an interface between the mathematical representation of the modelled system and the user of the model - one that is sufficiently easy to use and understand, but yet conveys all the information necessary to avoid misinterpretations. It is, however, not a trivial task and the link between the linguistic and computational level of such models has to be established and maintained properly during the whole modelling process. In this thesis, we focus on the relationship between the linguistic and the mathematical level of decision support models. We discuss several important issues concerning the mathematical representation of meaning of linguistic expressions, their transformation into the language of mathematics and the retranslation of mathematical outputs back into natural language. In the first part of the thesis, our view of the linguistic modelling for decision support is presented and the main guidelines for building linguistic models for real-life decision support that are the basis of our modeling methodology are outlined. From the theoretical point of view, the issues of representation of meaning of linguistic terms, computations with these representations and the retranslation process back into the linguistic level (linguistic approximation) are studied in this part of the thesis. We focus on the reasonability of operations with the meanings of linguistic terms, the correspondence of the linguistic and mathematical level of the models and on proper presentation of appropriate outputs. We also discuss several issues concerning the ethical aspects of decision support - particularly the loss of meaning due to the transformation of mathematical outputs into natural language and the issue or responsibility for the final decisions. In the second part several case studies of real-life problems are presented. These provide background and necessary context and motivation for the mathematical results and models presented in this part. A linguistic decision support model for disaster management is presented here – formulated as a fuzzy linear programming problem and a heuristic solution to it is proposed. Uncertainty of outputs, expert knowledge concerning disaster response practice and the necessity of obtaining outputs that are easy to interpret (and available in very short time) are reflected in the design of the model. Saaty’s analytic hierarchy process (AHP) is considered in two case studies - first in the context of the evaluation of works of art, where a weak consistency condition is introduced and an adaptation of AHP for large matrices of preference intensities is presented. The second AHP case-study deals with the fuzzified version of AHP and its use for evaluation purposes – particularly the integration of peer-review into the evaluation of R&D outputs is considered. In the context of HR management, we present a fuzzy rule based evaluation model (academic faculty evaluation is considered) constructed to provide outputs that do not require linguistic approximation and are easily transformed into graphical information. This is achieved by designing a specific form of fuzzy inference. Finally the last case study is from the area of humanities - psychological diagnostics is considered and a linguistic fuzzy model for the interpretation of outputs of multidimensional questionnaires is suggested. The issue of the quality of data in mathematical classification models is also studied here. A modification of the receiver operating characteristics (ROC) method is presented to reflect variable quality of data instances in the validation set during classifier performance assessment. Twelve publications on which the author participated are appended as a third part of this thesis. These summarize the mathematical results and provide a closer insight into the issues of the practicalapplications that are considered in the second part of the thesis.

Evaluating land-use related environmental impacts of biomass value chains for decision-support : Comparison and testing of methodologies proposed for environmental life cycle assessment

Life cycle assessment (LCA) is one of the most established quantitative tools for environmental impact assessment of products. To be able to provide support to environmentally-aware decision makers on environmental impacts of biomass value-chains, the scope of LCA methodology needs to be augmented to cover landuse related environmental impacts. This dissertation focuses on analysing and discussing potential impact assessment methods, conceptual models and environmental indicators that have been proposed to be implemented into the LCA framework for impacts of land use. The applicability of proposed indicators and impact assessment frameworks is tested from practitioners' perspective, especially focusing on forest biomass value chains. The impacts of land use on biodiversity, resource depletion, climate change and other ecosystem services is analysed and discussed and the interplay in between value choices in LCA modelling and the decision-making situations to be supported is critically discussed. It was found out that land use impact indicators are necessary in LCA in highlighting differences in impacts from distinct land use classes. However, many open questions remain on certainty of highlighting actual impacts of land use, especially regarding impacts of managed forest land use on biodiversity and ecosystem services such as water regulation and purification. The climate impact of energy use of boreal stemwood was found to be higher in the short term and lower in the long-term in comparison with fossil fuels that emit identical amount of CO2 in combustion, due to changes implied to forest C stocks. The climate impacts of energy use of boreal stemwood were found to be higher than the previous estimates suggest on forest residues and stumps. The product lifetime was found to have much higher influence on the climate impacts of woodbased value chains than the origin of stemwood either from thinnings or final fellings. Climate neutrality seems to be likely only in the case when almost all the carbon of harvested wood is stored in long-lived wooden products. In the current form, the land use impacts cannot be modelled with a high degree of certainty nor communicated with adequate level of clarity to decision makers. The academia needs to keep on improving the modelling framework, and more importantly, clearly communicate to decision-makers the limited certainty on whether land-use intensive activities can help in meeting the strict mitigation targets we are globally facing.

KAIRON PROJECT : assessing the role of expressive arts in community-based breast cancer support systems in the Niagara Region

Research Report Written for the Canadian Breast Cancer Foundation.

Soil organic carbon management for sustainable land use in Sudano-Sahelian West Africa

Judged by their negative nutrient balances, low soil cover and low productivity, the predominant agro-pastoral farming systems in the Sudano-Sahelian zone of West Africa are highly unsustainable for crop production intensification. With kaolinite as the main clay type, the cation exchange capacity of the soils in this region, often less than 1 cmol_c kg^-1 soil, depends heavily on the organic carbon (Corg) content. However, due to low carbon sequestration and to the microbe, termite and temperature-induced rapid turnover rates of organic material in the present land-use systems, Corg contents of the topsoil are very low, ranging between 1 and 8 g kg^-1 in most soils. For sustainable food production, the availability of phosphorus (P) and nitrogen (N) has to be increased considerably in combination with an improvement in soil physical properties. Therefore, the adoption of innovative management options that help to stop or even reverse the decline in Corg typically observed after cultivating bush or rangeland is of utmost importance. To maintain food production for a rapidly growing population, targeted applications of mineral fertilisers and the effective recycling of organic amendments as crop residues and manure are essential. Any increase in soil cover has large effects in reducing topsoil erosion by wind and water and favours the accumulation of wind-blown dust high in bases which in turn improves P availability. In the future decision support systems, based on GIS, modelling and simulation should be used to combine (i) available fertiliser response data from on-station and on-farm research, (ii) results on soil productivity restoration with the application of mineral and organic amendments and (iii) our present understanding of the cause-effect relationships governing the prevailing soil degradation processes. This will help to predict the effectiveness of regionally differentiated soil fertility management approaches to maintain or even increase soil Corg levels.