"I’ll call you when we are ready" (One family’s experience of critical care and end of life decision making)

The purpose of this paper is to explore through narrative accounts one family's expérience of critical care, after the admission of a family member to an Intensive Care Unit (ICU) and their subséquent death five weeks later. Numerous studies support the need for effective communication and clear information to be given to the family. In this instance it was évident from their stories that there were numerous barriers to communication, including language and a lack of insight into the needs of the family. Many families do not understand the complexities of nursing care in an ICU so lack of communication by nursing staff was identified as uncaring behavior and encounters. Facilitating a family's proximity to a dying patient and encouraging them to participate in care helps to maintain some sensé of personal control. Despite a commitment to involving family members in care, which was enshrined in the Unit Philosophy, relatives were banished to the waiting room for hours. They experienced feelings of powerlessness and helplessness as they waited with other relatives for news following investigations or until 'the doctor had completed his rounds'. Explanations of "we must make 'the patient' comfortable" was no consolation for those who wished to be involved in care. The words "I'il call you when we are ready" became a mantra to the forgotten families who waited patiently for those with power to admit them to the ICU. Implications are this family felt they were left alone to cope with the traumatic expériences leading up to and surrounding the death. They felt mainly supported by the priest, who not only administered the last rites but provided spiritual support to the family and dealt sensitively with many issues. Paternalism in décision making when there is a moral obligation to ensure that discussions on end of life dilemmas are an inclusive process with families, doctors, nurses was not understood, therefore it caused conflict within the family over EOL décision making. The family felt that the opportunity to share expériences through telling and retelling their stories would enable them to reconfigure the past and create purpose in the future.

Vientiä käynnistävän yrityksen jakelurakennevaihtoehdot

Tämän kandidaatintyön tavoitteena on tarjota vientiä aloittelevalle yritykselle avaimia viennin jakeluketjun suunnitteluun sekä sen rakentamiseksi. Päämääränä työssä onkin selvittää mitkä päätökset ovat keskeisimpiä jakeluketjun suunnittelun kannalta viennin aloitusvaiheessa sekä analysoida mitkä tekijät ja minkälaisessa suhteessa vaikuttavat näihin päätöksiin. Työssä tarkastellaan jakeluketjun eri osia, niihin liittyviä erityispiirteitä sekä jakeluketjun rakennetta koskevaa päätöksentekoa. Jakeluketjua käsitellään kolmessa osassa, jakeluteiden, varastoinnin sekä kuljetusten osalta, keskittyen ensin jokaisen perusmalleihin ja sen jälkeen niihin liittyviin päätöksenteon ajureihin. Tutkimuksessa löydettiin selkeät päätöksenteon vaikutustekijät ja havaittiin niiden pohjalta kannattavaksi aloittaa jakeluketjun suunnittelu jakeluteiden valinnasta, edeten varastoinnin suunnitteluun ja lopuksi kuljetusratkaisujen valintaan. Samalla kullekin osa-alueelle löydettiin olennaisimmat vaikuttimet, joiden avulla jakeluketjun suunnittelu voidaan rakenteellisesti toteuttaa.

Detecção e classificação de faltas de curto-circuito em sistemas de distribuição de energia elétrica usando lógica nebulosa

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Case-based reasoning to support decision making for managing drinking water quality events in distribution systems

Peer reviewed

Environmental Decision-making utilizing a Web GIS to Monitor Hazardous Industrial Emissions in the Valencian community of Spain

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies.

Spatial predictions of phylogenetic diversity in conservation decision making.

Considering genetic relatedness among species has long been argued as an important step toward measuring biological diversity more accurately, rather than relying solely on species richness. Some researchers have correlated measures of phylogenetic diversity and species richness across a series of sites and suggest that values of phylogenetic diversity do not differ enough from those of species richness to justify their inclusion in conservation planning. We compared predictions of species richness and 10 measures of phylogenetic diversity by creating distribution models for 168 individual species of a species-rich plant family, the Cape Proteaceae. When we used average amounts of land set aside for conservation to compare areas selected on the basis of species richness with areas selected on the basis of phylogenetic diversity, correlations between species richness and different measures of phylogenetic diversity varied considerably. Correlations between species richness and measures that were based on the length of phylogenetic tree branches and tree shape were weaker than those that were based on tree shape alone. Elevation explained up to 31% of the segregation of species rich versus phylogenetically rich areas. Given these results, the increased availability of molecular data, and the known ecological effect of phylogenetically rich communities, consideration of phylogenetic diversity in conservation decision making may be feasible and informative.

The induced 2-tuple linguistic generalized OWA operator and its application in linguistic decision making

[spa] Se presenta el operador de media ponderada ordenada generalizada lingüística de 2 tuplas inducida (2-TILGOWA). Es un nuevo operador de agregación que extiende los anteriores modelos a través de utilizar medias generalizadas, variables de ordenación inducidas e información lingüística representada mediante el modelo de las 2 tuplas lingüísticas. Su principal ventaja se encuentra en la posibilidad de incluir a un gran número de operadores de agregación lingüísticos como casos particulares. Por eso, el análisis puede ser visto desde diferentes perspectivas de forma que se obtiene una visión más completa del problema considerado y seleccionar la alternativa que parece estar en mayor concordancia con nuestros intereses o creencias. A continuación se desarrolla una generalización mayor a través de utilizar medias cuasi-aritméticas, obteniéndose el operador Quasi-2-TILOWA. El trabajo finaliza analizando la aplicabilidad del nuevo modelo en un problema de toma de decisiones sobre gestión de la producción.

The induced 2-tuple linguistic generalized OWA operator and its application in linguistic decision making

[spa] Se presenta el operador de media ponderada ordenada generalizada lingüística de 2 tuplas inducida (2-TILGOWA). Es un nuevo operador de agregación que extiende los anteriores modelos a través de utilizar medias generalizadas, variables de ordenación inducidas e información lingüística representada mediante el modelo de las 2 tuplas lingüísticas. Su principal ventaja se encuentra en la posibilidad de incluir a un gran número de operadores de agregación lingüísticos como casos particulares. Por eso, el análisis puede ser visto desde diferentes perspectivas de forma que se obtiene una visión más completa del problema considerado y seleccionar la alternativa que parece estar en mayor concordancia con nuestros intereses o creencias. A continuación se desarrolla una generalización mayor a través de utilizar medias cuasi-aritméticas, obteniéndose el operador Quasi-2-TILOWA. El trabajo finaliza analizando la aplicabilidad del nuevo modelo en un problema de toma de decisiones sobre gestión de la producción.

Training primary care physicians in shared decision making for colorectal cancer screening : Insights from the first statewide colorectal cancer screening program in Switzerland

Background: The State of Vaud has launched the first population-based, organized, colorectal cancer screening program in Switzerland for the population aged 50 to 69. Each primary care physician (PCP) has been invited to participate in an interactive session preparing them to enroll patients in the screening program. We aimed at testing the impact of an interactive seminar for PCPs on their intention to discuss the options of no screening, screening with the fecal-immunological test (FIT) and colonoscopy. We measured attitude, intentions and knowledge through questionnaires filled by PCPs before and after a 2.5 hour-long interactive seminar. The main outcome was the proportion of physicians foreseeing to offer coloscopy vs FIT on an equal basis. Physicians estimated the proportion of their patients prescribed a fecal occult blood test (FOBT) vs coloscopy over the months before the seminar and after the interactive seminar. We used a clinical vignette to test for knowledge about screening indications. The interactive seminar included powerpoint presentations with quizzes and clickers, an 8-minute video presenting a shared decision making (SDM) consultation around CRC screening and distribution of educational materials such as a SDM decision aid and background epidemiological information.

Identification of imaging selection patterns in acute ischemic stroke patients and the influence on treatment and clinical trial enrollment decision making.

BACKGROUND AND PURPOSE: For the STroke Imaging Research (STIR) and VISTA-Imaging Investigators The purpose of this study was to collect precise information on the typical imaging decisions given specific clinical acute stroke scenarios. Stroke centers worldwide were surveyed regarding typical imaging used to work up representative acute stroke patients, make treatment decisions, and willingness to enroll in clinical trials. METHODS: STroke Imaging Research and Virtual International Stroke Trials Archive-Imaging circulated an online survey of clinical case vignettes through its website, the websites of national professional societies from multiple countries as well as through email distribution lists from STroke Imaging Research and participating societies. Survey responders were asked to select the typical imaging work-up for each clinical vignette presented. Actual images were not presented to the survey responders. Instead, the survey then displayed several types of imaging findings offered by the imaging strategy, and the responders selected the appropriate therapy and whether to enroll into a clinical trial considering time from onset, clinical presentation, and imaging findings. A follow-up survey focusing on 6 h from onset was conducted after the release of the positive endovascular trials. RESULTS: We received 548 responses from 35 countries including 282 individual centers; 78% of the centers originating from Australia, Brazil, France, Germany, Spain, United Kingdom, and United States. The specific onset windows presented influenced the type of imaging work-up selected more than the clinical scenario. Magnetic Resonance Imaging usage (27-28%) was substantial, in particular for wake-up stroke. Following the release of the positive trials, selection of perfusion imaging significantly increased for imaging strategy. CONCLUSIONS: Usage of vascular or perfusion imaging by Computed Tomography or Magnetic Resonance Imaging beyond just parenchymal imaging was the primary work-up (62-87%) across all clinical vignettes and time windows. Perfusion imaging with Computed Tomography or Magnetic Resonance Imaging was associated with increased probability of enrollment into clinical trials for 0-3 h. Following the release of the positive endovascular trials, selection of endovascular only treatment for 6 h increased across all clinical vignettes.

Searching for consensus in ahp-group decision making. A bayesian perspective

This paper sets out to identify the initial positions of the different decisionmakers who intervene in a group decision making process with a reducednumber of actors, and to establish possible consensus paths between theseactors. As a methodological support, it employs one of the most widely-knownmulticriteria decision techniques, namely, the Analytic Hierarchy Process(AHP). Assuming that the judgements elicited by the decision makers follow theso-called multiplicative model (Crawford and Williams, 1985; Altuzarra et al.,1997; Laininen and Hämäläinen, 2003) with log-normal errors and unknownvariance, a Bayesian approach is used in the estimation of the relative prioritiesof the alternatives being compared. These priorities, estimated by way of themedian of the posterior distribution and normalised in a distributive manner(priorities add up to one), are a clear example of compositional data that will beused in the search for consensus between the actors involved in the resolution ofthe problem through the use of Multidimensional Scaling tools

Searching for consensus in ahp-group decision making. A bayesian perspective

This paper sets out to identify the initial positions of the different decision makers who intervene in a group decision making process with a reduced number of actors, and to establish possible consensus paths between these actors. As a methodological support, it employs one of the most widely-known multicriteria decision techniques, namely, the Analytic Hierarchy Process (AHP). Assuming that the judgements elicited by the decision makers follow the so-called multiplicative model (Crawford and Williams, 1985; Altuzarra et al., 1997; Laininen and Hämäläinen, 2003) with log-normal errors and unknown variance, a Bayesian approach is used in the estimation of the relative priorities of the alternatives being compared. These priorities, estimated by way of the median of the posterior distribution and normalised in a distributive manner (priorities add up to one), are a clear example of compositional data that will be used in the search for consensus between the actors involved in the resolution of the problem through the use of Multidimensional Scaling tools

Systematic Approach for the selection of monitoring technologies in CO2 geological storage projects. Application of multicriteria decision making

Geologic storage of carbon dioxide (CO2) has been proposed as a viable means for reducing anthropogenic CO2 emissions. Once injection begins, a program for measurement, monitoring, and verification (MMV) of CO2 distribution is required in order to: a) research key features, effects and processes needed for risk assessment; b) manage the injection process; c) delineate and identify leakage risk and surface escape; d) provide early warnings of failure near the reservoir; and f) verify storage for accounting and crediting. The selection of the methodology of monitoring (characterization of site and control and verification in the post-injection phase) is influenced by economic and technological variables. Multiple Criteria Decision Making (MCDM) refers to a methodology developed for making decisions in the presence of multiple criteria. MCDM as a discipline has only a relatively short history of 40 years, and it has been closely related to advancements on computer technology. Evaluation methods and multicriteria decisions include the selection of a set of feasible alternatives, the simultaneous optimization of several objective functions, and a decision-making process and evaluation procedures that must be rational and consistent. The application of a mathematical model of decision-making will help to find the best solution, establishing the mechanisms to facilitate the management of information generated by number of disciplines of knowledge. Those problems in which decision alternatives are finite are called Discrete Multicriteria Decision problems. Such problems are most common in reality and this case scenario will be applied in solving the problem of site selection for storing CO2. Discrete MCDM is used to assess and decide on issues that by nature or design support a finite number of alternative solutions. Recently, Multicriteria Decision Analysis has been applied to hierarchy policy incentives for CCS, to assess the role of CCS, and to select potential areas which could be suitable to store. For those reasons, MCDM have been considered in the monitoring phase of CO2 storage, in order to select suitable technologies which could be techno-economical viable. In this paper, we identify techniques of gas measurements in subsurface which are currently applying in the phase of characterization (pre-injection); MCDM will help decision-makers to hierarchy the most suitable technique which fit the purpose to monitor the specific physic-chemical parameter.

Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector

Guest editorial Ali Emrouznejad is a Senior Lecturer at the Aston Business School in Birmingham, UK. His areas of research interest include performance measurement and management, efficiency and productivity analysis as well as data mining. He has published widely in various international journals. He is an Associate Editor of IMA Journal of Management Mathematics and Guest Editor to several special issues of journals including Journal of Operational Research Society, Annals of Operations Research, Journal of Medical Systems, and International Journal of Energy Management Sector. He is in the editorial board of several international journals and co-founder of Performance Improvement Management Software. William Ho is a Senior Lecturer at the Aston University Business School. Before joining Aston in 2005, he had worked as a Research Associate in the Department of Industrial and Systems Engineering at the Hong Kong Polytechnic University. His research interests include supply chain management, production and operations management, and operations research. He has published extensively in various international journals like Computers & Operations Research, Engineering Applications of Artificial Intelligence, European Journal of Operational Research, Expert Systems with Applications, International Journal of Production Economics, International Journal of Production Research, Supply Chain Management: An International Journal, and so on. His first authored book was published in 2006. He is an Editorial Board member of the International Journal of Advanced Manufacturing Technology and an Associate Editor of the OR Insight Journal. Currently, he is a Scholar of the Advanced Institute of Management Research. Uses of frontier efficiency methodologies and multi-criteria decision making for performance measurement in the energy sector This special issue aims to focus on holistic, applied research on performance measurement in energy sector management and for publication of relevant applied research to bridge the gap between industry and academia. After a rigorous refereeing process, seven papers were included in this special issue. The volume opens with five data envelopment analysis (DEA)-based papers. Wu et al. apply the DEA-based Malmquist index to evaluate the changes in relative efficiency and the total factor productivity of coal-fired electricity generation of 30 Chinese administrative regions from 1999 to 2007. Factors considered in the model include fuel consumption, labor, capital, sulphur dioxide emissions, and electricity generated. The authors reveal that the east provinces were relatively and technically more efficient, whereas the west provinces had the highest growth rate in the period studied. Ioannis E. Tsolas applies the DEA approach to assess the performance of Greek fossil fuel-fired power stations taking undesirable outputs into consideration, such as carbon dioxide and sulphur dioxide emissions. In addition, the bootstrapping approach is deployed to address the uncertainty surrounding DEA point estimates, and provide bias-corrected estimations and confidence intervals for the point estimates. The author revealed from the sample that the non-lignite-fired stations are on an average more efficient than the lignite-fired stations. Maethee Mekaroonreung and Andrew L. Johnson compare the relative performance of three DEA-based measures, which estimate production frontiers and evaluate the relative efficiency of 113 US petroleum refineries while considering undesirable outputs. Three inputs (capital, energy consumption, and crude oil consumption), two desirable outputs (gasoline and distillate generation), and an undesirable output (toxic release) are considered in the DEA models. The authors discover that refineries in the Rocky Mountain region performed the best, and about 60 percent of oil refineries in the sample could improve their efficiencies further. H. Omrani, A. Azadeh, S. F. Ghaderi, and S. Abdollahzadeh presented an integrated approach, combining DEA, corrected ordinary least squares (COLS), and principal component analysis (PCA) methods, to calculate the relative efficiency scores of 26 Iranian electricity distribution units from 2003 to 2006. Specifically, both DEA and COLS are used to check three internal consistency conditions, whereas PCA is used to verify and validate the final ranking results of either DEA (consistency) or DEA-COLS (non-consistency). Three inputs (network length, transformer capacity, and number of employees) and two outputs (number of customers and total electricity sales) are considered in the model. Virendra Ajodhia applied three DEA-based models to evaluate the relative performance of 20 electricity distribution firms from the UK and the Netherlands. The first model is a traditional DEA model for analyzing cost-only efficiency. The second model includes (inverse) quality by modelling total customer minutes lost as an input data. The third model is based on the idea of using total social costs, including the firm’s private costs and the interruption costs incurred by consumers, as an input. Both energy-delivered and number of consumers are treated as the outputs in the models. After five DEA papers, Stelios Grafakos, Alexandros Flamos, Vlasis Oikonomou, and D. Zevgolis presented a multiple criteria analysis weighting approach to evaluate the energy and climate policy. The proposed approach is akin to the analytic hierarchy process, which consists of pairwise comparisons, consistency verification, and criteria prioritization. In the approach, stakeholders and experts in the energy policy field are incorporated in the evaluation process by providing an interactive mean with verbal, numerical, and visual representation of their preferences. A total of 14 evaluation criteria were considered and classified into four objectives, such as climate change mitigation, energy effectiveness, socioeconomic, and competitiveness and technology. Finally, Borge Hess applied the stochastic frontier analysis approach to analyze the impact of various business strategies, including acquisition, holding structures, and joint ventures, on a firm’s efficiency within a sample of 47 natural gas transmission pipelines in the USA from 1996 to 2005. The author finds that there were no significant changes in the firm’s efficiency by an acquisition, and there is a weak evidence for efficiency improvements caused by the new shareholder. Besides, the author discovers that parent companies appear not to influence a subsidiary’s efficiency positively. In addition, the analysis shows a negative impact of a joint venture on technical efficiency of the pipeline company. To conclude, we are grateful to all the authors for their contribution, and all the reviewers for their constructive comments, which made this special issue possible. We hope that this issue would contribute significantly to performance improvement of the energy sector.

Unsupervised KDD to creatively support managers' decision making with fuzzy association rules:a distribution channel application

To be competitive in contemporary turbulent environments, firms must be capable of processing huge amounts of information, and effectively convert it into actionable knowledge. This is particularly the case in the marketing context, where problems are also usually highly complex, unstructured and ill-defined. In recent years, the development of marketing management support systems has paralleled this evolution in informational problems faced by managers, leading to a growth in the study (and use) of artificial intelligence and soft computing methodologies. Here, we present and implement a novel intelligent system that incorporates fuzzy logic and genetic algorithms to operate in an unsupervised manner. This approach allows the discovery of interesting association rules, which can be linguistically interpreted, in large scale databases (KDD or Knowledge Discovery in Databases.) We then demonstrate its application to a distribution channel problem. It is shown how the proposed system is able to return a number of novel and potentially-interesting associations among variables. Thus, it is argued that our method has significant potential to improve the analysis of marketing and business databases in practice, especially in non-programmed decisional scenarios, as well as to assist scholarly researchers in their exploratory analysis. © 2013 Elsevier Inc.