Selective Industrial and Trade Policies in Developing Countries: Theoretical and Empirical Issues

This paper analyses the case for selective industrial and trade policies in Africa, drawing upon the lessons of East Asia. It reviews the theoretical arguments for government intervention in the context of technological learning, and relates this to the new environment of rapid technical change and globalisation of production. It also considers the risks of government failure in mounting selective policies, and concludes that the degree of selectivity has to be much less than in East Asia. The case for selective policies nevertheless remains strong, if Africa is to make any industrial progress.

A new ant colony optimization model for complex graph-based problems

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid. Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de lectura: julio de 2014Nowadays, there is a huge number of problems that due to their complexity have employed heuristic-based algorithms to search for near-to-optimal (or even optimal) solutions. These problems are usually NP-complete, so classical algorithms are not the best candidates to address these problems because they need a large amount of computational resources, or they simply cannot find any solution when the problem grows. Some classical examples of these kind of problems are the Travelling Salesman Problem (TSP) or the N-Queens problem. It is also possible to find examples in real and industrial domains related to the optimization of complex problems, like planning, scheduling, Vehicle Routing Problems (VRP), WiFi network Design Problem (WiFiDP) or behavioural pattern identification, among others. Regarding to heuristic-based algorithms, two well-known paradigms are Swarm Intelligence and Evolutionary Computation. Both paradigms belongs to a subfield from Artificial Intelligence, named Computational Intelligence that also contains Fuzzy Systems, Artificial Neural Networks and Artificial Immune Systems areas. Swarm Intelligence (SI) algorithms are focused on the collective behaviour of selforganizing systems. These algorithms are characterized by the generation of collective intelligence from non-complex individual behaviour and the communication schemes amongst them. Some examples of SI algorithms are particle swarm optimization, ant colony optimization (ACO), bee colony optimization o bird flocking. Ant Colony Optimization (ACO) are based on the foraging behaviour of these insects. In these kind of algorithms, the ants take different decisions during their execution that allows them to build their own solution to the problem. Once any ant has finished its execution, the ant goes back through the followed path and it deposits, in the environment, pheromones that contains information about the built solution. These pheromones will influence the decision of future ants, so there is an indirect communication through the environment called stigmergy. When an ACO algorithm is applied to any of the optimization problems just described, the problem is usually modelled into a graph. Nevertheless, the classical graph-based representation is not the best one for the execution of ACO algorithms because it presents some important pitfalls. The first one is related to the polynomial, or even exponential, growth of the resulting graph. The second pitfall is related to those problems that needs from real variables because these problems cannot be modelled using the classical graph-based representation. On the other hand, Evolutionary Computation (EC) are a set of population-based algorithms based in the Darwinian evolutionary process. In this kind of algorithms there is one (or more) population composed by different individuals that represent a possible solution to the problem. For each iteration, the population evolves by the use of evolutionary procedures which means that better individuals (i.e. better solutions) are generated along the execution of the algorithm. Both kind of algorithms, EC and SI, have been traditionally applied in previous NP-hard problems. Different population-based strategies have been developed, compared and even combined to design hybrid algorithms. This thesis has been focused on the analysis of classical graph-based representations and its application in ACO algorithms into complex problems, and the development of a new ACO model that tries to take a step forward in this kind of algorithms. In this new model, the problem is represented using a reduced graph that affects to the ants behaviour, which becomes more complex. Also, this size reduction generates a fast growth in the number of pheromones created. For this reason, a new metaheuristic (called Oblivion Rate) has been designed to control the number of pheromones stored in the graph. In this thesis different metaheuristics have been designed for the proposed system and their performance have been compared. One of these metaheuristics is the Oblivion Rate, based on an exponential function that takes into account the number of pheromones created in the system. Other Oblivion Rate function is based on a bioinspired swarm algorithm that uses some concepts extracted from the evolutionary algorithms. This bio-inspired swarm algorithm is called Coral Reef Opmization (CRO) algorithm and it is based on the behaviour of the corals in a reef. Finally, to test and validate the proposed model, different domains have been used such as the N-Queens Problem, the Resource-Constraint Project Scheduling Problem, the Path Finding problem in Video Games, or the Behavioural Pattern Identification in users. In some of these domains, the performance of the proposed model has been compared against a classical Genetic Algorithm to provide a comparative study and perform an analytical comparison between both approaches.En la actualidad, existen un gran número de problemas que debido a su complejidad necesitan algoritmos basados en heurísticas para la búsqueda de solucionas subóptimas (o incluso óptimas). Normalmente, estos problemas presentan una complejidad NP-completa, por lo que los algoritmos clásicos de búsqueda de soluciones no son apropiados ya que necesitan una gran cantidad de recursos computacionales, o simplemente, no son capaces de encontrar alguna solución cuando el problema crece. Ejemplos clásicos de este tipo de problemas son el problema del vendedor viajero (o TSP del inglés Travelling Salesman Problem) o el problema de las N-reinas. También se pueden encontrar ejemplos en dominios reales o industriales que generalmente están ligados a temas de optimización de sistemas complejos, como pueden ser problemas de planificación, scheduling, problemas de enrutamiento de vehículos (o VRP del inglés Vehicle Routing Problem), el diseño de redes Wifi abiertas (o WiFiDP del inglés WiFi network Design Problem), o la identificación de patrones de comportamiento, entre otros. En lo referente a los algoritmos basados en heuristicas, dos paradigmas muy conocidos son los algoritmos de enjambre (Swarm Intelligence) y la computación evolutiva (Evolutionary Computation). Ambos paradigmas pertencen al subárea de la Inteligencia Artificial denominada Inteligencia Computacional, que además contiene los sistemas difusos, redes neuronales y sistemas inmunológicos artificiales. Los algoritmos de inteligencia de enjambre, o Swarm Intelligence, se centran en el comportamiento colectivo de sistemas auto-organizativos. Estos algoritmos se caracterizan por la generación de inteligencia colectiva a partir del comportamiento, no muy complejo, de los individuos y los esquemas de comunicación entre ellos. Algunos ejemplos son particle swarm optimization, ant colony optimization (ACO), bee colony optimization o bird flocking. Los algoritmos de colonias de hormigas (o ACO del inglés Ant Colony Optimization) se basan en el comportamiento de estos insectos en el proceso de recolección de comida. En este tipo de algoritmos, las hormigas van tomando decisiones a lo largo de la simulación que les permiten construir su propia solución al problema. Una vez que una hormiga termina su ejecución, deshace el camino andado depositando en el entorno feronomas que contienen información sobre la solución construida. Estas feromonas influirán en las decisiones de futuras hormigas, por lo que produce una comunicación indirecta utilizando el entorno. A este proceso se le llama estigmergia. Cuando un algoritmo de hormigas se aplica a alguno de los problemas de optimización descritos anteriormente, se suele modelar el problema como un grafo sobre el cual se ejecutarán las hormigas. Sin embargo, la representación basada en grafos clásica no parece ser la mejor para la ejecución de algoritmos de hormigas porque presenta algunos problemas importantes. El primer problema está relacionado con el crecimiento polinómico, o incluso expnomencial, del grafo resultante. El segundo problema tiene que ver con los problemas que necesitan de variables reales, o de coma flotante, porque estos problemas, con la representación tradicional basada en grafos, no pueden ser modelados. Por otro lado, los algoritmos evolutivos (o EC del inglés Evolutionary Computation) son un tipo de algoritmos basados en población que están inspirados en el proceso evolutivo propuesto por Darwin. En este tipo de algoritmos, hay una, o varias, poblaciones compuestas por individuos diferentes que representan problems solutiones al problema modelado. Por cada iteración, la población evoluciona mediante el uso de procedimientos evolutivos, lo que significa que mejores individuos (mejores soluciones) son creados a lo largo de la ejecución del algoritmo. Ambos tipos de algorithmos, EC y SI, han sido tradicionalmente aplicados a los problemas NPcompletos descritos anteriormente. Diferentes estrategias basadas en población han sido desarrolladas, comparadas e incluso combinadas para el diseño de algoritmos híbridos. Esta tesis se ha centrado en el análisis de los modelos clásicos de representación basada en grafos de problemas complejos para la posterior ejecución de algoritmos de colonias de hormigas y el desarrollo de un nuevo modelo de hormigas que pretende suponer un avance en este tipo de algoritmos. En este nuevo modelo, los problemas son representados en un grafo más compacto que afecta al comportamiento de las hormigas, el cual se vuelve más complejo. Además, esta reducción en el tamaño del grafo genera un rápido crecimiento en el número de feronomas creadas. Por esta razón, una nueva metaheurística (llamada Oblivion Rate) ha sido diseñada para controlar el número de feromonas almacenadas en el grafo. En esta tesis, varias metaheuristicas han sido diseñadas para el sistema propuesto y sus rendimientos han sido comparados. Una de estas metaheurísticas es la Oblivion Rate basada en una función exponencial que tiene en cuenta el número de feromonas creadas en el sistema. Otra Oblivion Rate está basada en un algoritmo de enjambre bio-inspirado que usa algunos conceptos extraídos de la computación evolutiva. Este algoritmo de enjambre bio-inspirado se llama Optimización de arrecifes de corales (o CRO del inglés Coral Reef Optimization) y está basado en el comportamiento de los corales en el arrecife. Finalmente, para validar y testear el modelo propuesto, se han utilizado diversos dominios de aplicación como son el problema de las N-reinas, problemas de planificación de proyectos con restricciones de recursos, problemas de búsqueda de caminos en entornos de videojuegos y la identificación de patrones de comportamiento de usuarios. En algunos de estos dominios, el rendimiento del modelo propuesto ha sido comparado contra un algoritmo genético clásico para realizar un estudio comparativo, y analítico, entre ambos enfoques

National strategies for technology adoption in the industrial sector: Lessons of recent experience in the developing regions

human development, technology

Mechanisms of Homeostatic Control of Neuronal Intrinsic Excitability

A neuron’s identity and function are dictated by its electrophysiological signature. The firing pattern of a neuron emerges from the particular combination of ion channels in its membrane. A neuron can “tune” the combination of ionic conductances that it expresses to return back to its target excitability when faced with changing conditions. While this phenomenon of firing rate homeostasis (FRH) is well-established, the mechanisms underlying it have remained mysterious. A prevalent theory proposes that firing rates are maintained through regulatory feedback relying on the detection and stabilization of a single variable, calcium. Within the framework of this theory, all perturbations with equivalent effects on neuronal activity should invoke the same homeostatic response. In a direct test of this hypothesis, we compared two independent experimental manipulations to the Shal potassium ion channel. While we observed FRH following either a conductance-blocking mutation or complete elimination of the Shal protein, the compensating currents and the molecular mechanisms underlying the homeostatic response differed between the two conditions. Neurons lacking the Shal protein enacted transcriptional upregulation of the ion channels Slo, Shab, and Shaker, in part through the transcription factor Krüppel. In contrast, neurons with a non-conducting Shal channel compensated through non-transcriptional modification of a different set of conductances. We propose that neurons have multiple, separable homeostatic signaling systems, including proteostatic and activity-sensitive feedback systems. We then further expand on the mechanisms of FRH to include a role for the Notch signaling system. This canonical pathway for neural development is reactivated following loss of Shal and is necessary for stabilization of firing rates. We propose a model in which the loss of the transcription factor Nerfin-1 de-represses the Notch, and Notch cleavage by presenilin followed by cooperation of NICD with Su(H) results in transcriptional rebalancing of ion channels. These findings have implications for the pathophysiology of human channelopathies and Alzheimer’s disease

Evolutionary underpinnings of microgeographic adaptation in song sparrows distributed along a steep climate gradient

2021 Summer.Includes bibliographical references.Understanding how evolutionary processes interact to maintain adaptive variation in natural populations has been a fundamental goal of evolutionary biology. Yet, despite adaptation remaining at the forefront of evolutionary theory and empirical studies, there remains a lack of consensus about the evolutionary conditions that enable adaptation to persist in natural populations, especially when considering complex phenotypes in response to multivariate selection regimes. In my dissertation, I disentangle the evolutionary mechanisms that shape adaptive divergence in song sparrows (Melospiza melodia) distributed along a climate gradient on the California Channel Islands and nearby coastal California. First, I found evidence that climate, and neither vegetation nor selection for increased foraging efficiency, likely drive adaptive divergence in bill morphology among insular populations. Second, I used an integrated population and landscape genomics approach to infer that bill variation is indicative of microgeographic local adaptation to temperature. Lastly, I tested whether the distinct climate gradient facilitates adaptative divergence in other thermoregulatory traits and found evidence to support environmental temperatures result in fixed population differences in many complementary phenotypes, including plumage color, feather microstructure, and thermal physiology. Collectively, these results find support for microgeographic climate adaptation in a suite of complex phenotypes and demonstrate the utility of integrative approaches to infer local adaptation in natural populations. Finally, by developing a more holistic understanding of climate adaptation in natural populations, my results inform conservation management of this species of special concern

Self-efficacy and Performance Relationships: Examining the Roles of Personality, Bias, and Effort

The relationship between self-efficacy (a situation or task specific form of confidence) and performance has long been accepted as positive and reciprocal. However, recent challenges in the literature have demonstrated that it is only under certain conditions that the relationship remains positive, and that a number of boundary conditions affect the direction of the relationship when examined at the within-person level of analysis. One consistent factor throughout the within-person research is that overconfidence (confidence levels above that of actual performance), is related a decrease in effort, which in turn may contribute to poorer performance. Thus, a negative relationship between self-efficacy and performance at the within-person level is observed. Despite the surge in research examining the within-person relationship between self-efficacy and performance and moderating variables, the potential moderating role of the self (e.g., individual differences) seems to have been neglected and the role of effort is yet to be fully understood. Thus, the aims of the thesis are twofold. The primary aim of the thesis is to explore the role of the self within the within-person self-efficacy and performance relationship. In chapter 2, participants performed a golf putting task in front of an on-looking peer, in order to examine the positive bias often found in self-predictions of performance (but not in peer predictions). Contrary to previous research, results revealed that the self was no more biased than the on-looking peer, perhaps due to the presence of objective performance feedback. Chapter three examined subclinical narcissism as a moderator of the within-person relationship between self-efficacy and performance, since individuals high in subclinical narcissism have demonstrated overly positive views of the self. Results revealed that narcissism moderated the relationship between previous performance and subsequent self-efficacy but not the relationship between self-efficacy and subsequent performance. Chapters three and four addressed the secondary aim of the thesis, to further explore the role of effort. Chapter three adopted a 2 psychophysiological measure of effort, and found tentative evidence that individuals high in subclinical narcissism may have engaged in ego-protecting strategies (under-reporting their self-report effort). Chapter four sought to find evidence for the argument that the relationship between preparatory self-efficacy and preparatory effort would be an inverted ‘U’ (Feltz et al., 2008), however no evidence was found. Overall, the results demonstrate the importance of considering the role of the self within the self-efficacy and performance relationship, and suggests that advances in the measurement of effort are needed in order to understand the role of effort as an underlying mechanism further

Conservation and synteny of long non-coding RNAs invertebrate genomes and their identification in novel transcriptomes

Long non-coding RNAs (IncRNAs) are a biological entity defined by what they are not, rather than by what they are. This indicates that our knowledge about them is sensibly limited. The aim of my PhD is to gain insights into the evolution and the functions of IncRNAs through computational approaches and the usage of large scale functional genomics dataset. I developed an annotation pipeline, which can effectively identify IncRNAs in entire transcriptomes. The pipeline is able to accurately annotate the coding genes while predicting a conservative estimate of the IncRNA population. It allowed me to show, for the first time, the presence of lncRNA transcription in a diverse range of organisms. Further, I analysed sequence and positional conservation of lncRNAs, demonstrating the presence of short segments of conserved sequence in IncRNAs and the existence of several syntenically conserved non-coding transcripts over large evolutionary distances. However, I also demonstrate that positional conservation of lncRNAs with a flanking coding gene is generally independent from the conservation of the lncRNA expression with respect to the coding gene. Finally, I have characterised the diversity of lncRNA transcription in specific cells and developmental stages of two teleost fishes. In summary, the work presented in the thesis provides novel findings and contributions in the field of lncRNAomics

An exploration and validation of computer modeling of evolution, natural selection, and evolutionary biology with cellular automata for secondary students.

The Evolutionary Tool Kit, a new software package, is the prototype of a concept simulator providing an environment for students to create microworlds of populations of artificial organisms. Its function is to model processes, concepts and arguments in natural selection and evolutionary biology, using either Mendelian asexual or sexual reproduction, or counterfactual systems such as \u27paint pot\u27 or blending inheritance. In this environment students can explore a conceptual What if? in evolutionary biology, test misconceptions and deepen understanding of inheritance and changes in populations. Populations can be defined either with typological, or with populational thinking, to inquire into the role and necessity of variation in natural selection. The approach is generative not tutorial. The interface is highly graphic with twenty traits set as icons that are moved onto the \u27phenotypes\u27. Activities include investigations of evolutionary theory of aging, reproductive advantage, sexual selection and mimicry. Design of the activities incorporates Howard Gardner\u27s Theory of Multiple Intelligences. Draft of a teacher and student manual are included