194 research outputs found
Genetische kovarianz zwischen umnittelbarem und mütterlichem effekt je nach der richtung der innerfamilienmässigen selektion für das körpergewicht im alter von 2I tagen bei mäusen
International audienc
Fuzzy system identification by generating and evolutionary optimizing fuzzy rule bases consisting of relevant fuzzy rules
One approach forsystem identification among many othersis the fuzzy identification approach. The advantage of this approach compared to other analytical approaches is, that it is not necessary to make an assumption for the model to be used for the identification. In addition, the fuzzy approach can handle nonlinearities easier than analytical approaches. The Fuzzy-ROSA method is a method for data-based generation of fuzzy rules. This is the first step of a two step identification process. The second step is the optimization of the remaining free parameters, i.e. the composition of the rule base and the linguistic terms, to further improve the quality of the model and obtain small interpretable rule bases. In this paper, a new evolutionary strategy for the optimization of the linguistic terms of the output variable is presented. The effectiveness of the two step fuzzy identification is demonstrated on the benchmark problem 'kin dataset' of the Delve dataset repository and the results are compared to analytical and neural network approaches
A Hybrid Evolutionary Search Concept for Data-based Generation of Relevant Fuzzy Rules in High Dimensional Spaces
In this paper we propose a hybrid fuzzy-evolutionary system for fuzzy modelling in high dimensional spaces. The system architecture is based on a Michigan-style approach (one individual represents one fuzzy rule). The design of the evolutionary algorithm makes use of a distance measure in the search space that in turn reflects some heuristic assumptions about the fitness landscape. Additionally, strategy parameters are dynamically adapted by means of a fuzzy controller. The approach is successfully applied to a complex benchmark problem as well as to several real-world modelling tasks such as the cancellation behaviour of insurance clients and the classification of automatic gearboxes
Parallel Evolutionary Algorithms for Optimizing Data{based Generated Fuzzy Systems
Abstract. In the eld of data{based fuzzy modeling, the complexity of applications and the amount of data to be processed have grown continuously. Thus, the computational eort for solving these applications has also increased drastically. In order to meet this challenge, parallel computing approaches are applied. The task here is the optimization of data{based generated fuzzy rule bases. For this kind of application the tness evaluation of an individual is very time consuming. Here, a parallel genetic algorithm is applied to solve the optimization problem in an acceptable amount of time. Furthermore, it will be analyzed how the quality of the results changes with the use of multi{population models or neighborhood models. This will be illustrated by two example applications
Managing Carbon Aspirations: The Influence of Corporate Climate Change Targets on Environmental Performance
Addressing climate change is among the most challenging ethical issues facing contemporary business and society. Unsustainable business activities are causing significant distributional and procedural injustices in areas such as public health and vulnerability to extreme weather events, primarily because of a distinction between primary emitters and those already experiencing the impacts of climate change. Business, as a significant contributor to climate change and beneficiary of externalizing environmental costs, has an obligation to address its environmental impacts. In this paper, we explore the role of firms’ climate change targets in shaping their emissions trends in the context of a large multi-country sample of companies. We contrast two intentions for setting emissions reductions targets: symbolic attempts to manage external stakeholder perceptions via “greenwashing” and substantive commitments to reducing environmental impacts. We argue that the attributes of firms’ climate change targets (their extent, form, and time horizon) are diagnostic of firms’ underlying intentions. Consistent with our hypotheses, while we find no overall effect of setting climate change targets on emissions, we show that targets characterized by a commitment to more ambitious emissions reductions, a longer target time frame, and absolute reductions in emissions are associated with significant reductions in firms’ emissions. Our evidence suggests the need for vigilance among policy-makers and environmental campaigners regarding the underlying intentions that accompany environmental management practices and shows that these can to some extent be diagnosed analytically
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The Role of Short-Termism and Uncertainty Avoidance in Organizational Inaction on Climate Change: A Multi-Level Framework
Despite increasing pressure to deal with climate change, firms have been slow to respond with effective action. This article presents a multi-level framework for a better understanding of why many firms are failing to reduce their absolute greenhouse gas emissions, which contribute to climate change. The concepts of short-termism and uncertainty avoidance from research in psychology, sociology, and organization theory can explain the phenomenon of organizational inaction on climate change. Antecedents related to short-termism and uncertainty avoidance reinforce one another at three levels—individual, organizational, and institutional—and result in organizational inaction on climate change. The article also discusses the implications of this multi-level framework for research on corporate sustainability
Utilisation of an operative difficulty grading scale for laparoscopic cholecystectomy
Background
A reliable system for grading operative difficulty of laparoscopic cholecystectomy would standardise description of findings and reporting of outcomes. The aim of this study was to validate a difficulty grading system (Nassar scale), testing its applicability and consistency in two large prospective datasets.
Methods
Patient and disease-related variables and 30-day outcomes were identified in two prospective cholecystectomy databases: the multi-centre prospective cohort of 8820 patients from the recent CholeS Study and the single-surgeon series containing 4089 patients. Operative data and patient outcomes were correlated with Nassar operative difficultly scale, using Kendall’s tau for dichotomous variables, or Jonckheere–Terpstra tests for continuous variables. A ROC curve analysis was performed, to quantify the predictive accuracy of the scale for each outcome, with continuous outcomes dichotomised, prior to analysis.
Results
A higher operative difficulty grade was consistently associated with worse outcomes for the patients in both the reference and CholeS cohorts. The median length of stay increased from 0 to 4 days, and the 30-day complication rate from 7.6 to 24.4% as the difficulty grade increased from 1 to 4/5 (both p < 0.001). In the CholeS cohort, a higher difficulty grade was found to be most strongly associated with conversion to open and 30-day mortality (AUROC = 0.903, 0.822, respectively). On multivariable analysis, the Nassar operative difficultly scale was found to be a significant independent predictor of operative duration, conversion to open surgery, 30-day complications and 30-day reintervention (all p < 0.001).
Conclusion
We have shown that an operative difficulty scale can standardise the description of operative findings by multiple grades of surgeons to facilitate audit, training assessment and research. It provides a tool for reporting operative findings, disease severity and technical difficulty and can be utilised in future research to reliably compare outcomes according to case mix and intra-operative difficulty
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A paradox approach to organizational tensions during the pandemic crisis
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