Search results for "Variable"
showing 10 items of 1674 documents
Who Is the Social Entrepreneur?
2021
Social entrepreneurs are understood as individuals integrated within a society, being continually influenced by the environment. This chapter explains that the constitution of a social entrepreneur is determined by external variables (environmental influences) and internal variables (characteristics of the individual). Among the external influences, we should highlight the Triple Bottom Line phenomenon, the Corporate Social Responsibility trend, technological advances and the Sustainable Development Goals. The intrinsic motivators would be divided into three different dimensions: attitude and intention, personality traits and leadership skills. The synergies produced as a consequence of the…
Designing of microcontroller based Syringe Pump with variable and low delivery rates for the administration of small volumes
2015
Delivering medications and fluids intravenously is a common practice in modern medical procedures. Administering medications or fluids directly into a patient's blood circulation results in a predictable and immediate absorption of the drug or fluid administered, this may play a vital role in the treatment of certain acute conditions which require immediate action by drugs or fluids. Syringe Pumps are highly useful in delivering a precise quantity of the substance at specific periods of time as required. This research presents a proposal to create a cost effective working prototype of syringe pump for variable and low delivery rates for the administration of small volumes. The presented Syr…
Comparative study of several programs used in the potentiometric evaluation of equilibrium constants including an error sensitivity analysis
1989
Abstract The programs MINIQUAD, MINIPOT, SUPERQUAD and PHCONST are applied to the evaluation of the protonation constants of several hypothetical polyprotic substances using simulated titrations, and the precision and accuracy of the results are discussed and compared. When statistical weights are used the results are very similar, the accuracy being better with PHCONST. Error sensitivity analysis was used as a means of establishing the influence of a systematic error of an experimental variable on the accuracy of the determination. An algorithm for the accurate calculation of error sensitivities is proposed and checked. Error sensitivities can be used to make a choice between the various m…
Modelling the Interaction between Air Pollutant Emissions and Their Key Sources in Poland
2021
The main purpose of this study is to investigate the relationships between key sources of air pollutant emissions (sources of energy production, factories which are particularly harmful to the environment, the fleets of cars, environmental protection expenditure) and the main environmental air pollution (SO2, NOx, CO and PM) in Poland. Models based on MLP neural networks were used as predictive models. Global sensitivity analysis was used to demonstrate the significant impact of individual network input variables on the output variable. To verify the effectiveness of the models created, the actual data were compared with the data obtained through modelling. Projected courses of changes in t…
Learning non-linear time-scales with kernel -filters
2009
A family of kernel methods, based on the @c-filter structure, is presented for non-linear system identification and time series prediction. The kernel trick allows us to develop the natural non-linear extension of the (linear) support vector machine (SVM) @c-filter [G. Camps-Valls, M. Martinez-Ramon, J.L. Rojo-Alvarez, E. Soria-Olivas, Robust @c-filter using support vector machines, Neurocomput. J. 62(12) (2004) 493-499.], but this approach yields a rigid system model without non-linear cross relation between time-scales. Several functional analysis properties allow us to develop a full, principled family of kernel @c-filters. The improved performance in several application examples suggest…
The Hierarchical Continuous Pursuit Learning Automation for Large Numbers of Actions
2018
Part 10: Learning - Intelligence; International audience; Although the field of Learning Automata (LA) has made significant progress in the last four decades, the LA-based methods to tackle problems involving environments with a large number of actions are, in reality, relatively unresolved. The extension of the traditional LA (fixed structure, variable structure, discretized, and pursuit) to problems within this domain cannot be easily established when the number of actions is very large. This is because the dimensionality of the action probability vector is correspondingly large, and consequently, most components of the vector will, after a relatively short time, have values that are smal…
New Representations for Multidimensional Functions Based on Kolmogorov Superposition Theorem. Applications on Image Processing
2012
Mastering the sorting of the data in signal (nD) can lead to multiple applications like new compression, transmission, watermarking, encryption methods and even new processing methods for image. Some authors in the past decades have proposed to use these approaches for image compression, indexing, median filtering, mathematical morphology, encryption. A mathematical rigorous way for doing such a study has been introduced by Andrei Nikolaievitch Kolmogorov (1903-1987) in 1957 and recent results have provided constructive ways and practical algorithms for implementing the Kolmogorov theorem. We propose in this paper to present those algorithms and some preliminary results obtained by our team…
Proportional Small Sample Bias in Pricing Kernel Estimations
2014
Numerous empirical studies find pricing kernels that are not-monotonically decreasing; the findings are at odds with the pricing kernel being marginal utility of a risk-averse, so-called representative agent. We study in detail the common procedure which estimates the pricing kernel as the ratio of two separate density estimations. In a first step, we analyze theoretically the functional dependence for the ratio of a density to its estimated density; this cautions the reader of potential computational issues coupled with statistical techniques. In a second step, we study this quantitatively; we show that small sample biases shape the estimated pricing kernel, and that estimated pricing kern…
Methods for evaluating causality in observational studies.
2019
BACKGROUND: In clinical medical research, causality is demonstrated by randomized controlled trials (RCTs). Often, however, an RCT cannot be conducted for ethical reasons, and sometimes for practical reasons as well. In such cases, knowledge can be derived from an observational study instead. In this article, we present two methods that have not been widely used in medical research to date. METHODS: The methods of assessing causal inferences in observational studies are described on the basis of publications retrieved by a selective literature search. RESULTS: Two relatively new approaches—regression-discontinuity methods and interrupted time series—can be used to demonstrate a causal relat…
Visual data mining with self-organising maps for ventricular fibrillation analysis
2012
Detection of ventricular fibrillation (VF) at an early stage is being deeply studied in order to lower the risk of sudden death and allows the specialist to have greater reaction time to give the patient a good recovering therapy. Some works are focusing on detecting VF based on numerical analysis of time-frequency distributions, but in general the methods used do not provide insight into the problem. However, this study proposes a new methodology in order to obtain information about this problem. This work uses a supervised self-organising map (SOM) to obtain visually information among four important groups of patients: VF (ventricular fibrillation), VT (ventricular tachycardia), HP (healt…