Comparasion of road traffic emission models in Madrid (Spain)

Many cities in Europe have difficulties to meet the air quality standards set by the European legislation, most particularly the annual mean Limit Value for NO2. Road transport is often the main source of air pollution in urban areas and therefore, there is an increasing need to estimate current and future traffic emissions as accurately as possible. As a consequence, a number of specific emission models and emission factors databases have been developed recently. They present important methodological differences and may result in largely diverging emission figures and thus may lead to alternative policy recommendations. This study compares two approaches to estimate road traffic emissions in Madrid (Spain): the COmputer Programme to calculate Emissions from Road Transport (COPERT4 v.8.1) and the Handbook Emission Factors for Road Transport (HBEFA v.3.1), representative of the ‘average-speed’ and ‘traffic situation’ model types respectively. The input information (e.g. fleet composition, vehicle kilometres travelled, traffic intensity, road type, etc.) was provided by the traffic model developed by the Madrid City Council along with observations from field campaigns. Hourly emissions were computed for nearly 15 000 road segments distributed in 9 management areas covering the Madrid city and surroundings. Total annual NOX emissions predicted by HBEFA were a 21% higher than those of COPERT. The discrepancies for NO2 were lower (13%) since resulting average NO2/NOX ratios are lower for HBEFA. The larger differences are related to diesel vehicle emissions under “stop & go” traffic conditions, very common in distributor/secondary roads of the Madrid metropolitan area. In order to understand the representativeness of these results, the resulting emissions were integrated in an urban scale inventory used to drive mesoscale air quality simulations with the Community Multiscale Air Quality (CMAQ) modelling system (1 km2 resolution). Modelled NO2 concentrations were compared with observations through a series of statistics. Although there are no remarkable differences between both model runs, the results suggest that HBEFA may overestimate traffic emissions. However, the results are strongly influenced by methodological issues and limitations of the traffic model. This study was useful to provide a first alternative estimate to the official emission inventory in Madrid and to identify the main features of the traffic model that should be improved to support the application of an emission system based on “real world” emission factors.

Radar track segmentation with cubic splines for collision risk models in high density terminal areas

This paper presents a method to segment airplane radar tracks in high density terminal areas where the air traffic follows trajectories with several changes in heading, speed and altitude. The radar tracks are modelled with different types of segments, straight lines, cubic spline function and shape preserving cubic function. The longitudinal, lateral and vertical deviations are calculated for terminal manoeuvring area scenarios. The most promising model of the radar tracks resulted from a mixed interpolation using straight lines for linear segments and spline cubic functions for curved segments. A sensitivity analysis is used to optimise the size of the window for the segmentation process.

Some recent applications of non-linear models to the engineering practice

The Department of Structural Analysis of the University of Santander has been for a longtime involved in the solution of the country´s practical engineering problems. Some of these have required the use of non-conventional methods of analysis, in order to achieve adequate engineering answers. As an example of the increasing application of non-linear computer codes in the nowadays engineering practice, some cases will be briefly presented. In each case, only the main features of the problem involved and the solution used to solve it will be shown

Distributed models in P-Systems architectures to reduce computation time

Membrane systems are computational equivalent to Turing machines. However, their distributed and massively parallel nature obtains polynomial solutions opposite to traditional non-polynomial ones. At this point, it is very important to develop dedicated hardware and software implementations exploiting those two membrane systems features. Dealing with distributed implementations of P systems, the bottleneck communication problem has arisen. When the number of membranes grows up, the network gets congested. The purpose of distributed architectures is to reach a compromise between the massively parallel character of the system and the needed evolution step time to transit from one configuration of the system to the next one, solving the bottleneck communication problem. The goal of this paper is twofold. Firstly, to survey in a systematic and uniform way the main results regarding the way membranes can be placed on processors in order to get a software/hardware simulation of P-Systems in a distributed environment. Secondly, we improve some results about the membrane dissolution problem, prove that it is connected, and discuss the possibility of simulating this property in the distributed model. All this yields an improvement in the system parallelism implementation since it gets an increment of the parallelism of the external communication among processors. Proposed ideas improve previous architectures to tackle the communication bottleneck problem, such as reduction of the total time of an evolution step, increase of the number of membranes that could run on a processor and reduction of the number of processors.

Comparison of methods for downscaling runoff from regional climate models in Spanish basins

At present there is much literature that refers to the advantages and disadvantages of different methods of statistical and dynamical downscaling of climate variables projected by climate models. Less attention has been paid to other indirect variables, like runoff, which play a significant role in evaluating the impact of climate change on hydrological systems. Runoff presents a much greater bias in climate models than other climate variables, like temperature or precipitation. It is very important to identify the methods that minimize bias while downscaling runoff from the gridded results of climate models to the basin scale

The Analysis of Airline Business Models in the Development of Possible Future Business Options

Innovations in the current interconnected world of organizations have lead to a focus on business models as a fundamental statement of direction and identity. Although industry transformations generally emanate from technological changes, recent examples suggest they may also be due to the introduction of new business models. In the past, different types of airline business models could be clearly separated from each other. However, this has changed in recent years partly due to the concentration process and partly to reaction caused by competitive pressure. At least it can be concluded that in future the distinction of different business models will remain less clear. To advance the use of business models as a concept, it is essential to be able to compare and perform analyses to identify the business models that may have the highest potential. This can essentially contribute to understanding the synergies and incompatibilities in the case of two airlines that are going in for a merger. This is illustrated by the example of Swiss Air-Lufthansa merger analysis. The idea is to develop quantitative methods and tools for comparing and analyzing Aeronautical/Airline business models. The paper identifies available methods of comparing airline business models and lays the ground work for a quantitative model of comparing airline business models. This can be a useful tool for business model analysis when two airlines are merged

A review on probabilistic graphical models in evolutionary computation

Thanks to their inherent properties, probabilistic graphical models are one of the prime candidates for machine learning and decision making tasks especially in uncertain domains. Their capabilities, like representation, inference and learning, if used effectively, can greatly help to build intelligent systems that are able to act accordingly in different problem domains. Evolutionary algorithms is one such discipline that has employed probabilistic graphical models to improve the search for optimal solutions in complex problems. This paper shows how probabilistic graphical models have been used in evolutionary algorithms to improve their performance in solving complex problems. Specifically, we give a survey of probabilistic model building-based evolutionary algorithms, called estimation of distribution algorithms, and compare different methods for probabilistic modeling in these algorithms.

Looking for business models in digital media and content industries

International Conference on Dynamics of the Media and Content Industry. European Forum for Science and Industry.

Analysis and validation of CFD wind farm models in complex terrain. Effects induced by topography and wind turbines

Wind farms have been extensively simulated through engineering models for the estimation of wind speed and power deficits inside wind farms. These models were designed initially for a few wind turbines located in flat terrain. Other models based on the parabolic approximation of Navier Stokes equations were developed, making more realistic and feasible the operational resolution of big wind farms in flat terrain and offshore sites. These models have demonstrated to be accurate enough when solving wake effects for this type of environments. Nevertheless, few analyses exist on how complex terrain can affect the behaviour of wind farm wake flow. Recent numerical studies have demonstrated that topographical wakes induce a significant effect on wind turbines wakes, compared to that on flat terrain. This circumstance has recommended the development of elliptic CFD models which allow global simulation of wind turbine wakes in complex terrain. An accurate simplification for the analysis of wind turbine wakes is the actuator disk technique. Coupling this technique with CFD wind models enables the estimation of wind farm wakes preserving the extraction of axial momentum present inside wind farms. This paper describes the analysis and validation of the elliptical wake model CFDWake 1.0 against experimental data from an operating wind farm located in complex terrain. The analysis also reports whether it is possible or not to superimpose linearly the effect of terrain and wind turbine wakes. It also represents one of the first attempts to observe the performance of engineering models compares in large complex terrain wind farms.

Damping models in the truncated derivative nonlinear Schrödinger equation

Four-dimensional flow in the phase space of three amplitudes of circularly polarized Alfven waves and one relative phase, resulting from a resonant three-wave truncation of the derivative nonlinear Schrödinger equation, has been analyzed; wave 1 is linearly unstable with growth rate , and waves 2 and 3 are stable with damping 2 and 3, respectively. The dependence of gross dynamical features on the damping model as characterized by the relation between damping and wave-vector ratios, 2 /3, k2 /k3, and the polarization of the waves, is discussed; two damping models, Landau k and resistive k2, are studied in depth. Very complex dynamics, such as multiple blue sky catastrophes and chaotic attractors arising from Feigenbaum sequences, and explosive bifurcations involving Intermittency-I chaos, are shown to be associated with the existence and loss of stability of certain fixed point P of the flow. Independently of the damping model, P may only exist as against flow contraction just requiring.In the case of right-hand RH polarization, point P may exist for all models other than Landau damping; for the resistive model, P may exist for RH polarization only if 2+3/2.

Inference Models in DNA Computing

La computación molecular es una disciplina que se ocupa del diseño e implementación de dispositivos para el procesamiento de información sobre un sustrato biológico, como el ácido desoxirribonucleico (ADN), el ácido ribonucleico (ARN) o las proteínas. Desde que Watson y Crick descubrieron en los años cincuenta la estructura molecular del ADN en forma de doble hélice, se desencadenaron otros descubrimientos, como las enzimas de restricción o la reacción en cadena de la polimerasa (PCR), contribuyendo de manera determinante a la irrupción de la tecnología del ADN recombinante. Gracias a esta tecnología y al descenso vertiginoso de los precios de secuenciación y síntesis del ADN, la computación biomolecular pudo abandonar su concepción puramente teórica. El trabajo presentado por Adleman (1994) logró resolver un problema de computación NP-completo (El Problema del Camino de Hamilton dirigido) utilizando únicamente moléculas de ADN. La gran capacidad de procesamiento en paralelo ofrecida por las técnicas del ADN recombinante permitió a Adleman ser capaz de resolver dicho problema en tiempo polinómico, aunque a costa de un consumo exponencial de moléculas de ADN. Utilizando algoritmos de fuerza bruta similares al utilizado por Adleman se logró resolver otros problemas NP-completos, como por ejemplo el de Satisfacibilidad de Fórmulas Lógicas / SAT (Lipton, 1995). Pronto se comprendió que la computación biomolecular no podía competir en velocidad ni precisión con los ordenadores de silicio, por lo que su enfoque y objetivos se centraron en la resolución de problemas con aplicación biomédica (Simmel, 2007), dejando de lado la resolución de problemas clásicos de computación. Desde entonces se han propuesto diversos modelos de dispositivos biomoleculares que, de forma autónoma (sin necesidad de un bio-ingeniero realizando operaciones de laboratorio), son capaces de procesar como entrada un sustrato biológico y proporcionar una salida también en formato biológico: procesadores que aprovechan la extensión de la polimerasa (Hagiya et al., 1997), autómatas que funcionan con enzimas de restricción (Benenson et al., 2001) o con deoxiribozimas (Stojanovic et al., 2002), o circuitos de hibridación competitiva (Yurke et al., 2000). Esta tesis presenta un conjunto de modelos de dispositivos de ácidos nucleicos capaces de implementar diversas operaciones de computación lógica aprovechando técnicas de computación biomolecular (hibridación competitiva del ADN y reacciones enzimáticas) con aplicaciones en diagnóstico genético. El primer conjunto de modelos, presentados en el Capítulo 5 y publicados en Sainz de Murieta and Rodríguez-Patón (2012b), Rodríguez-Patón et al. (2010a) y Sainz de Murieta and Rodríguez-Patón (2010), define un tipo de biosensor que usa hebras simples de ADN para codificar reglas sencillas, como por ejemplo "SI hebra-ADN-1 Y hebra-ADN-2 presentes, ENTONCES enfermedad-B". Estas reglas interactúan con señales de entrada (ADN o ARN de cualquier tipo) para producir una señal de salida (también en forma de ácido nucleico). Dicha señal de salida representa un diagnóstico, que puede medirse mediante partículas fluorescentes técnicas FRET) o incluso ser un tratamiento administrado en respuesta a un conjunto de síntomas. El modelo presentado en el Capítulo 5, publicado en Rodríguez-Patón et al. (2011), es capaz de ejecutar cadenas de resolución sobre fórmulas lógicas en forma normal conjuntiva. Cada cláusula de una fórmula se codifica en una molécula de ADN. Cada proposición p se codifica asignándole una hebra simple de ADN, y la correspondiente hebra complementaria a la proposición ¬p. Las cláusulas se codifican incluyendo distintas proposiciones en la misma hebra de ADN. El modelo permite ejecutar programas lógicos de cláusulas Horn aplicando múltiples iteraciones de resolución en cascada, con el fin de implementar la función de un nanodispositivo autónomo programable. Esta técnica también puede emplearse para resolver SAP sin ayuda externa. El modelo presentado en el Capítulo 6 se ha publicado en publicado en Sainz de Murieta and Rodríguez-Patón (2012c), y el modelo presentado en el Capítulo 7 se ha publicado en (Sainz de Murieta and Rodríguez-Patón, 2013c). Aunque explotan métodos de computación biomolecular diferentes (hibridación competitiva de ADN en el Capítulo 6 frente a reacciones enzimáticas en el 7), ambos modelos son capaces de realizar inferencia Bayesiana. Funcionan tomando hebras simples de ADN como entrada, representando la presencia o la ausencia de un indicador molecular concreto (una evidencia). La probabilidad a priori de una enfermedad, así como la probabilidad condicionada de una señal (o síntoma) dada la enfermedad representan la base de conocimiento, y se codifican combinando distintas moléculas de ADN y sus concentraciones relativas. Cuando las moléculas de entrada interaccionan con las de la base de conocimiento, se liberan dos clases de hebras de ADN, cuya proporción relativa representa la aplicación del teorema de Bayes: la probabilidad condicionada de la enfermedad dada la señal (o síntoma). Todos estos dispositivos pueden verse como elementos básicos que, combinados modularmente, permiten la implementación de sistemas in vitro a partir de sensores de ADN, capaces de percibir y procesar señales biológicas. Este tipo de autómatas tienen en la actualidad una gran potencial, además de una gran repercusión científica. Un perfecto ejemplo fue la publicación de (Xie et al., 2011) en Science, presentando un autómata biomolecular de diagnóstico capaz de activar selectivamente el proceso de apoptosis en células cancerígenas sin afectar a células sanas.

Business models in the Smart Grid: challenges, opportunities and proposals for prosumer profitability.

Considering that non-renewable energy resources are dwindling, the smart grid turns out to be one of the most promising and compelling systems for the future of energy. Not only does it combine efficient energy consumption with avant-garde technologies related to renewable energies, but it is also capable of providing several beneficial utilities, such as power monitoring and data provision. When smart grid end users turn into prosumers, they become arguably the most important value creators within the smart grid and a decisive agent of change in terms of electricity usage. There is a plethora of research and development areas related to the smart grid that can be exploited for new business opportunities, thus spawning another branch of the so-called ?green economy? focused on turning smart energy usage into a profitable business. This paper deals with emerging business models for smart grid prosumers, their strengths and weaknesses and puts forward new prosumer-oriented business models, along with their value propositions.

Region-based moving object detection using spatially conditioned nonparametric models in a GPU

A novel GPU-based nonparametric moving object detection strategy for computer vision tools requiring real-time processing is proposed. An alternative and efficient Bayesian classifier to combine nonparametric background and foreground models allows increasing correct detections while avoiding false detections. Additionally, an efficient region of interest analysis significantly reduces the computational cost of the detections.

Autonomous classification models in ubiquitous environments

Stream-mining approach is defined as a set of cutting-edge techniques designed to process streams of data in real time, in order to extract knowledge. In the particular case of classification, stream-mining has to adapt its behaviour to the volatile underlying data distributions, what has been called concept drift. Moreover, it is important to note that concept drift may lead to situations where predictive models become invalid and have therefore to be updated to represent the actual concepts that data poses. In this context, there is a specific type of concept drift, known as recurrent concept drift, where the concepts represented by data have already appeared in the past. In those cases the learning process could be saved or at least minimized by applying a previously trained model. This could be extremely useful in ubiquitous environments that are characterized by the existence of resource constrained devices. To deal with the aforementioned scenario, meta-models can be used in the process of enhancing the drift detection mechanisms used by data stream algorithms, by representing and predicting when the change will occur. There are some real-world situations where a concept reappears, as in the case of intrusion detection systems (IDS), where the same incidents or an adaptation of them usually reappear over time. In these environments the early prediction of drift by means of a better knowledge of past models can help to anticipate to the change, thus improving efficiency of the model regarding the training instances needed. By means of using meta-models as a recurrent drift detection mechanism, the ability to share concepts representations among different data mining processes is open. That kind of exchanges could improve the accuracy of the resultant local model as such model may benefit from patterns similar to the local concept that were observed in other scenarios, but not yet locally. This would also improve the efficiency of training instances used during the classification process, as long as the exchange of models would aid in the application of already trained recurrent models, that have been previously seen by any of the collaborative devices. Which it is to say that the scope of recurrence detection and representation is broaden. In fact the detection, representation and exchange of concept drift patterns would be extremely useful for the law enforcement activities fighting against cyber crime. Being the information exchange one of the main pillars of cooperation, national units would benefit from the experience and knowledge gained by third parties. Moreover, in the specific scope of critical infrastructures protection it is crucial to count with information exchange mechanisms, both from a strategical and technical scope. The exchange of concept drift detection schemes in cyber security environments would aid in the process of preventing, detecting and effectively responding to threads in cyber space. Furthermore, as a complement of meta-models, a mechanism to assess the similarity between classification models is also needed when dealing with recurrent concepts. In this context, when reusing a previously trained model a rough comparison between concepts is usually made, applying boolean logic. The introduction of fuzzy logic comparisons between models could lead to a better efficient reuse of previously seen concepts, by applying not just equal models, but also similar ones. This work faces the aforementioned open issues by means of: the MMPRec system, that integrates a meta-model mechanism and a fuzzy similarity function; a collaborative environment to share meta-models between different devices; a recurrent drift generator that allows to test the usefulness of recurrent drift systems, as it is the case of MMPRec. Moreover, this thesis presents an experimental validation of the proposed contributions using synthetic and real datasets.

Automatic physical activity pattern recognition and meal intake modelling in type 1 diabetes for their inclusion in artificial pancreas systems

La diabetes comprende un conjunto de enfermedades metabólicas que se caracterizan por concentraciones de glucosa en sangre anormalmente altas. En el caso de la diabetes tipo 1 (T1D, por sus siglas en inglés), esta situación es debida a una ausencia total de secreción endógena de insulina, lo que impide a la mayoría de tejidos usar la glucosa. En tales circunstancias, se hace necesario el suministro exógeno de insulina para preservar la vida del paciente; no obstante, siempre con la precaución de evitar caídas agudas de la glucemia por debajo de los niveles recomendados de seguridad. Además de la administración de insulina, las ingestas y la actividad física son factores fundamentales que influyen en la homeostasis de la glucosa. En consecuencia, una gestión apropiada de la T1D debería incorporar estos dos fenómenos fisiológicos, en base a una identificación y un modelado apropiado de los mismos y de sus sorrespondientes efectos en el balance glucosa-insulina. En particular, los sistemas de páncreas artificial –ideados para llevar a cabo un control automático de los niveles de glucemia del paciente– podrían beneficiarse de la integración de esta clase de información. La primera parte de esta tesis doctoral cubre la caracterización del efecto agudo de la actividad física en los perfiles de glucosa. Con este objetivo se ha llevado a cabo una revisión sistemática de la literatura y meta-análisis que determinen las respuestas ante varias modalidades de ejercicio para pacientes con T1D, abordando esta caracterización mediante unas magnitudes que cuantifican las tasas de cambio en la glucemia a lo largo del tiempo. Por otro lado, una identificación fiable de los periodos con actividad física es un requisito imprescindible para poder proveer de esa información a los sistemas de páncreas artificial en condiciones libres y ambulatorias. Por esta razón, la segunda parte de esta tesis está enfocada a la propuesta y evaluación de un sistema automático diseñado para reconocer periodos de actividad física, clasificando su nivel de intensidad (ligera, moderada o vigorosa); así como, en el caso de periodos vigorosos, identificando también la modalidad de ejercicio (aeróbica, mixta o de fuerza). En este sentido, ambos aspectos tienen una influencia específica en el mecanismo metabólico que suministra la energía para llevar a cabo el ejercicio y, por tanto, en las respuestas glucémicas en T1D. En este trabajo se aplican varias combinaciones de técnicas de aprendizaje máquina y reconocimiento de patrones sobre la fusión multimodal de señales de acelerometría y ritmo cardíaco, las cuales describen tanto aspectos mecánicos del movimiento como la respuesta fisiológica del sistema cardiovascular ante el ejercicio. Después del reconocimiento de patrones se incorpora también un módulo de filtrado temporal para sacar partido a la considerable coherencia temporal presente en los datos, una redundancia que se origina en el hecho de que en la práctica, las tendencias en cuanto a actividad física suelen mantenerse estables a lo largo de cierto tiempo, sin fluctuaciones rápidas y repetitivas. El tercer bloque de esta tesis doctoral aborda el tema de las ingestas en el ámbito de la T1D. En concreto, se propone una serie de modelos compartimentales y se evalúan éstos en función de su capacidad para describir matemáticamente el efecto remoto de las concetraciones plasmáticas de insulina exógena sobre las tasas de eleiminación de la glucosa atribuible a la ingesta; un aspecto hasta ahora no incorporado en los principales modelos de paciente para T1D existentes en la literatura. Los datos aquí utilizados se obtuvieron gracias a un experimento realizado por el Institute of Metabolic Science (Universidad de Cambridge, Reino Unido) con 16 pacientes jóvenes. En el experimento, de tipo ‘clamp’ con objetivo variable, se replicaron los perfiles individuales de glucosa, según lo observado durante una visita preliminar tras la ingesta de una cena con o bien alta carga glucémica, o bien baja. Los seis modelos mecanísticos evaluados constaban de: a) submodelos de doble compartimento para las masas de trazadores de glucosa, b) un submodelo de único compartimento para reflejar el efecto remoto de la insulina, c) dos tipos de activación de este mismo efecto remoto (bien lineal, bien con un punto de corte), y d) diversas condiciones iniciales. ABSTRACT Diabetes encompasses a series of metabolic diseases characterized by abnormally high blood glucose concentrations. In the case of type 1 diabetes (T1D), this situation is caused by a total absence of endogenous insulin secretion, which impedes the use of glucose by most tissues. In these circumstances, exogenous insulin supplies are necessary to maintain patient’s life; although caution is always needed to avoid acute decays in glycaemia below safe levels. In addition to insulin administrations, meal intakes and physical activity are fundamental factors influencing glucose homoeostasis. Consequently, a successful management of T1D should incorporate these two physiological phenomena, based on an appropriate identification and modelling of these events and their corresponding effect on the glucose-insulin balance. In particular, artificial pancreas systems –designed to perform an automated control of patient’s glycaemia levels– may benefit from the integration of this type of information. The first part of this PhD thesis covers the characterization of the acute effect of physical activity on glucose profiles. With this aim, a systematic review of literature and metaanalyses are conduced to determine responses to various exercise modalities in patients with T1D, assessed via rates-of-change magnitudes to quantify temporal variations in glycaemia. On the other hand, a reliable identification of physical activity periods is an essential prerequisite to feed artificial pancreas systems with information concerning exercise in ambulatory, free-living conditions. For this reason, the second part of this thesis focuses on the proposal and evaluation of an automatic system devised to recognize physical activity, classifying its intensity level (light, moderate or vigorous) and for vigorous periods, identifying also its exercise modality (aerobic, mixed or resistance); since both aspects have a distinctive influence on the predominant metabolic pathway involved in fuelling exercise, and therefore, in the glycaemic responses in T1D. Various combinations of machine learning and pattern recognition techniques are applied on the fusion of multi-modal signal sources, namely: accelerometry and heart rate measurements, which describe both mechanical aspects of movement and the physiological response of the cardiovascular system to exercise. An additional temporal filtering module is incorporated after recognition in order to exploit the considerable temporal coherence (i.e. redundancy) present in data, which stems from the fact that in practice, physical activity trends are often maintained stable along time, instead of fluctuating rapid and repeatedly. The third block of this PhD thesis addresses meal intakes in the context of T1D. In particular, a number of compartmental models are proposed and compared in terms of their ability to describe mathematically the remote effect of exogenous plasma insulin concentrations on the disposal rates of meal-attributable glucose, an aspect which had not yet been incorporated to the prevailing T1D patient models in literature. Data were acquired in an experiment conduced at the Institute of Metabolic Science (University of Cambridge, UK) on 16 young patients. A variable-target glucose clamp replicated their individual glucose profiles, observed during a preliminary visit after ingesting either a high glycaemic-load or a low glycaemic-load evening meal. The six mechanistic models under evaluation here comprised: a) two-compartmental submodels for glucose tracer masses, b) a single-compartmental submodel for insulin’s remote effect, c) two types of activations for this remote effect (either linear or with a ‘cut-off’ point), and d) diverse forms of initial conditions.