An adaptive neighborhood search algorithm for optimizing stochastic mining complexes

Les métaheuristiques sont très utilisées dans le domaine de l'optimisation discrète. Elles permettent d’obtenir une solution de bonne qualité en un temps raisonnable, pour des problèmes qui sont de grande taille, complexes, et difficiles à résoudre. Souvent, les métaheuristiques ont beaucoup de paramètres que l’utilisateur doit ajuster manuellement pour un problème donné. L'objectif d'une métaheuristique adaptative est de permettre l'ajustement automatique de certains paramètres par la méthode, en se basant sur l’instance à résoudre. La métaheuristique adaptative, en utilisant les connaissances préalables dans la compréhension du problème, des notions de l'apprentissage machine et des domaines associés, crée une méthode plus générale et automatique pour résoudre des problèmes. L’optimisation globale des complexes miniers vise à établir les mouvements des matériaux dans les mines et les flux de traitement afin de maximiser la valeur économique du système. Souvent, en raison du grand nombre de variables entières dans le modèle, de la présence de contraintes complexes et de contraintes non-linéaires, il devient prohibitif de résoudre ces modèles en utilisant les optimiseurs disponibles dans l’industrie. Par conséquent, les métaheuristiques sont souvent utilisées pour l’optimisation de complexes miniers. Ce mémoire améliore un procédé de recuit simulé développé par Goodfellow & Dimitrakopoulos (2016) pour l’optimisation stochastique des complexes miniers stochastiques. La méthode développée par les auteurs nécessite beaucoup de paramètres pour fonctionner. Un de ceux-ci est de savoir comment la méthode de recuit simulé cherche dans le voisinage local de solutions. Ce mémoire implémente une méthode adaptative de recherche dans le voisinage pour améliorer la qualité d'une solution. Les résultats numériques montrent une augmentation jusqu'à 10% de la valeur de la fonction économique.

Aplicação do modelo de Pólya na resolução de problemas de processo : um estudo envolvendo alunos do 2.º ano do 1.º Ciclo do Ensino Básico

Relatório de Estágio apresentado à Escola Superior de Educação do Instituto Politécnico de Castelo Branco para cumprimento dos requisitos necessários à obtenção do grau de Mestre em Educação Pré-Escolar e Ensino do 1.º Ciclo do Ensino Básico.

Violência conjugal percebida por um menor em contexto familiar e suas consequências psicossociais

A violência como fenómeno social e familiar, não é um problema atual, desde sempre esteve presente, sendo que a sua exposição apresenta diferentes intensidades em diferentes momentos da sua evolução. Como fenómeno mundial, percorreu todas as culturas, etnias, tipos de economia e regimes políticos (Sagim, 2003). O objectivo da presente investigação é a violência conjugal percebida por um menor em contexto familiar e suas consequências psicossociais: estudo de caso. Método: A metodologia escolhida é qualitativa e é designada por naturalista. O método utilizado foi o estudo de caso e a recolha de dados foi a entrevista (semi-estruturada) e fez-se a descodificação desta através da análise de conteúdo, que foi organizada em várias categorias . Instrumentos: Questionário sócio demográficos (filha); Guião de Entrevista para adultos(mãe), semi-estruturada; Entrevista Clínica Semi–Estruturada (SCICA);A Escala de Sinalização do Ambiente Natural Infantil (S.A.N.I.); O teste projectivo Pata Negra de Corman (filha). Participantes: R de 12 anos de idade, sexo feminino, caucasiana, possui o 9º ano de escolaridade Resultados: Verificou-se que R tem uma boa capacidade de coping e resolução de problemas, indo do encontro referido por alguns autores nos meus estudos, sendo que noutros não se enquadra no perfil defendido pela literatura. Referindo segundo o DSM-5, R apresenta alguma sintomatologia clínica como a ansiedade de separação, revelando insegurança e medo da perda dos afetos por parte dos progenitores. Conclusão: : Concluiu-se ainda que alguns estudos referem que nem todas as crianças expostas à violência intrafamiliar responderão negativamente, uma vez que a presença de fatores de proteção tèm um papel fundamental. Entre estes, o ambiente escolar, o relacionamento com a vizinhança e o suporte advindo de demais membros familiares, entre outros (Sani, 2008).

El poder del debate ciudadano: procesos de gobernanza en la agenda interlocal de desarrollo Kennedy-Bosa

El objetivo de este artículo es analizar cómo el debate ciudadano incide en la gestación de procesos de gobernanza en la Agenda Interlocal de Desarrollo Kennedy-Bosa, proyecto de participación ciudadana en Bogotá. A través del documento se pretende demostrar que el debate: 1) permitió que las organizaciones sociales, presentes en la Agenda Interlocal, entablaran relaciones horizontales; 2) posibilitó la interacción de múltiples actores de la ciudad, entre ellos Secretarías Distritales y entidades privadas; 3) promocionó el diálogo y el intercambio de ideas como medio para la resolución de problemáticas identificadas en las localidades de Bosa y Kennedy. Para demostrar lo anterior, se realizó observación participante y entrevistas en las que se evidenció el proceso de toma de decisiones y la interacción de los actores presentes en la Agenda.

Innovación social: una reflexión sobre los beneficios en su implementación en el Estado colombiano.

La innovación social es un tipo de innovación que promueve la búsqueda de soluciones enfocadas a los problemas que se presentan en la sociedad. Estas soluciones generan además un valor agregado que ayudan al crecimiento del país. En Colombia este tipo de iniciativas han empezado a tener una gran importancia y han empezado a promover condiciones de vida más favorables y justas que buscan generar un beneficio a la sociedad y contribuir al desarrollo del país. A partir de la historia de la innovación social, ejemplos y grandes literatos en la administración y la sociología, comprenderemos la importancia de la innovación en nuestro país.

Interdisciplinary project-based learning: technology for improving student cognition

The article studies a way of enhancing student cognition by using interdisciplinary project-based learning (IPBL) in a higher education institution. IPBL is a creative pedagogic approach allowing students of one area of specialisation to develop projects for students with different academic profiles. The application of this approach in the Ural State University of Economics resulted in a computer-assisted learning system (CALS) designed by IT students. The CALS was used in an analytical chemistry course with students majoring in Commodities Management and Expertise (‘expert’ students). To test how effective the technology was, the control and experimental groups were formed. In the control group, learning was done with traditional methods. In the experimental group, it was reinforced by IPBL. A statistical analysis of the results, with an application of Pearson χ 2 test, showed that the cognitive levels in both IT and ‘expert’ experimental groups improved as compared with the control groups. The findings demonstrated that IPBL can significantly enhance learning. It can be implemented in any institution of higher or secondary education that promotes learning, including the CALS development and its use for solving problems in different subject areas.

Bio-QA-GNN: Reasoning with Language Models and Knowledge Graphs for Interpretable Biomedical Question Answering

Nowadays the idea of injecting world or domain-specific structured knowledge into pre-trained language models (PLMs) is becoming an increasingly popular approach for solving problems such as biases, hallucinations, huge architectural sizes, and explainability lack—critical for real-world natural language processing applications in sensitive fields like bioinformatics. One recent work that has garnered much attention in Neuro-symbolic AI is QA-GNN, an end-to-end model for multiple-choice open-domain question answering (MCOQA) tasks via interpretable text-graph reasoning. Unlike previous publications, QA-GNN mutually informs PLMs and graph neural networks (GNNs) on top of relevant facts retrieved from knowledge graphs (KGs). However, taking a more holistic view, existing PLM+KG contributions mainly consider commonsense benchmarks and ignore or shallowly analyze performances on biomedical datasets. This thesis start from a propose of a deep investigation of QA-GNN for biomedicine, comparing existing or brand-new PLMs, KGs, edge-aware GNNs, preprocessing techniques, and initialization strategies. By combining the insights emerged in DISI's research, we introduce Bio-QA-GNN that include a KG. Working with this part has led to an improvement in state-of-the-art of MCOQA model on biomedical/clinical text, largely outperforming the original one (+3.63\% accuracy on MedQA). Our findings also contribute to a better understanding of the explanation degree allowed by joint text-graph reasoning architectures and their effectiveness on different medical subjects and reasoning types. Codes, models, datasets, and demos to reproduce the results are freely available at: \url{https://github.com/disi-unibo-nlp/bio-qagnn}.

An improved novel method for solving nonlinear boundary value problems

A modified formula for the integral transform of a nonlinear function is proposed for a class of nonlinear boundary value problems. The technique presented in this paper results in analytical solutions. Iterations and initial guess, which are needed in other techniques, are not required in this novel technique. The analytical solutions are found to agree surprisingly well with the numerically exact solutions for two examples of power law reaction and Langmuir-Hinshelwood reaction in a catalyst pellet.

An efficient numerical scheme for solving multi-dimensional fractional optimal control problems with a quadratic performance index

The shifted Legendre orthogonal polynomials are used for the numerical solution of a new formulation for the multi-dimensional fractional optimal control problem (M-DFOCP) with a quadratic performance index. The fractional derivatives are described in the Caputo sense. The Lagrange multiplier method for the constrained extremum and the operational matrix of fractional integrals are used together with the help of the properties of the shifted Legendre orthonormal polynomials. The method reduces the M-DFOCP to a simpler problem that consists of solving a system of algebraic equations. For confirming the efficiency and accuracy of the proposed scheme, some test problems are implemented with their approximate solutions.

Solving large 0–1 multidimensional knapsack problems by a new simplified binary artificial fish swarm algorithm

The artificial fish swarm algorithm has recently been emerged in continuous global optimization. It uses points of a population in space to identify the position of fish in the school. Many real-world optimization problems are described by 0-1 multidimensional knapsack problems that are NP-hard. In the last decades several exact as well as heuristic methods have been proposed for solving these problems. In this paper, a new simpli ed binary version of the artificial fish swarm algorithm is presented, where a point/ fish is represented by a binary string of 0/1 bits. Trial points are created by using crossover and mutation in the different fi sh behavior that are randomly selected by using two user de ned probability values. In order to make the points feasible the presented algorithm uses a random heuristic drop item procedure followed by an add item procedure aiming to increase the profit throughout the adding of more items in the knapsack. A cyclic reinitialization of 50% of the population, and a simple local search that allows the progress of a small percentage of points towards optimality and after that refines the best point in the population greatly improve the quality of the solutions. The presented method is tested on a set of benchmark instances and a comparison with other methods available in literature is shown. The comparison shows that the proposed method can be an alternative method for solving these problems.

A firefly dynamic penalty approach for solving engineering design problems

Firefly Algorithm is a recent swarm intelligence method, inspired by the social behavior of fireflies, based on their flashing and attraction characteristics [1, 2]. In this paper, we analyze the implementation of a dynamic penalty approach combined with the Firefly algorithm for solving constrained global optimization problems. In order to assess the applicability and performance of the proposed method, some benchmark problems from engineering design optimization are considered.

Robust learning in safety-related domains : machine learning methods for solving safety-related application problems

Magdeburg, Univ., Fak. für Informatik, Diss., 2009

A parameter-free approach for solving combinatorial optimization problems through biased randomization of efficient heuristics

This paper discusses the use of probabilistic or randomized algorithms for solving combinatorial optimization problems. Our approach employs non-uniform probability distributions to add a biased random behavior to classical heuristics so a large set of alternative good solutions can be quickly obtained in a natural way and without complex conguration processes. This procedure is especially useful in problems where properties such as non-smoothness or non-convexity lead to a highly irregular solution space, for which the traditional optimization methods, both of exact and approximate nature, may fail to reach their full potential. The results obtained are promising enough to suggest that randomizing classical heuristics is a powerful method that can be successfully applied in a variety of cases.

Solving Large Location-Allocation problems by Clustering and Simulated Annealing

Globalization involves several facility location problems that need to be handled at large scale. Location Allocation (LA) is a combinatorial problem in which the distance among points in the data space matter. Precisely, taking advantage of the distance property of the domain we exploit the capability of clustering techniques to partition the data space in order to convert an initial large LA problem into several simpler LA problems. Particularly, our motivation problem involves a huge geographical area that can be partitioned under overall conditions. We present different types of clustering techniques and then we perform a cluster analysis over our dataset in order to partition it. After that, we solve the LA problem applying simulated annealing algorithm to the clustered and non-clustered data in order to work out how profitable is the clustering and which of the presented methods is the most suitable

Algorithm optimization for solving crew scheduling problems

In this paper, we are proposing a methodology to determine the most efficient and least costly way of crew pairing optimization. We are developing a methodology based on algorithm optimization on Eclipse opensource IDE using the Java programming language to solve the crew scheduling problems.