Teaching drawing: an interdisciplinary pedagogical experience in a graphic design course

Various authors have written about the importance of drawing in design methodology. Their general conclusion points drawing as an essential tool for design research, as it allows investigation of several alternative solutions in design process (Cross, 2007). The recent profound changes in design nature (Norman, 2011), justify a discussion on the purpose of drawing in design courses. As a consequence of this new reality, the educational institutions face the challenge of the definition of their curricular structures and teaching methodologies. Among others, concepts such as collaboration and multidisciplinary design approaches have been discussed as strategies for design education (Heller and Talarico, 2011, pp. 82-85). In this context, and using our teaching activity experience in Drawing and Design areas, the authors discuss:  how can drawing methods be included in the current design teaching?  can drawing be considered as an interdisciplinary approach?  what contributions can these methodologies provide to the educational/learning process? Based on these concerns, we developed an interdisciplinary project in the Graphic Design Course with two curricular units: Drawing 1 and Aesthetic and Design Theory 1. In this article the authors present the aims and process developed, and discuss the outcomes of this pedagogical experience.

Fads and fashions in management: the case of ERP

This article discusses the Enterprise Resource Planning (ERP) rage. ERP represents immense investments for companies around the globe and has been promoted as a management panacea. Not surprisingly, many implementations fail to match expectations. In this study, we propose a broader perspective to comprehend the substantive, institutional, and political factors involved in the ERP phenomenon, as an alternative to the "techno-reductionism" that has characterized the prevailing approach on the subject, and present an exploratory survey of 28 implementation experiences, concentrating on the process of adoption, chosen implementation approaches and outcome assessment.

Extreme Value Theory versus traditional GARCH approaches applied to financial data: a comparative evaluation

Although stock prices fluctuate, the variations are relatively small and are frequently assumed to be normal distributed on a large time scale. But sometimes these fluctuations can become determinant, especially when unforeseen large drops in asset prices are observed that could result in huge losses or even in market crashes. The evidence shows that these events happen far more often than would be expected under the generalized assumption of normal distributed financial returns. Thus it is crucial to properly model the distribution tails so as to be able to predict the frequency and magnitude of extreme stock price returns. In this paper we follow the approach suggested by McNeil and Frey (2000) and combine the GARCH-type models with the Extreme Value Theory (EVT) to estimate the tails of three financial index returns DJI,FTSE 100 and NIKKEI 225 representing three important financial areas in the world. Our results indicate that EVT-based conditional quantile estimates are much more accurate than those from conventional AR-GARCH models assuming normal or Student’s t-distribution innovations when doing out-of-sample estimation (within the insample estimation, this is so for the right tail of the distribution of returns).

Moodle and multiple-choice tests

Multiple-Choice items are used in many different kinds of tests in several areas of knowledge. They can be considered an interesting tool to the self-assessing or as an alternative or complementary instrument to the traditional methods for assessing knowledge. The objectivity and accuracy of the multiple-choice tests is an important reason to think about. They are especially useful when the number of students to evaluate is too large. Moodle (Modular Object-Oriented Dynamic Learning Environment) is an Open Source course management system centered around learners' needs and designed to support collaborative approaches to teaching and learning. Moodle offers to the users a rich interface, context-specific help buttons, and a wide variety of tools such as discussion forums, wikis, chat, surveys, quizzes, glossaries, journals, grade books and more, that allow them to learn and collaborate in a truly interactive space. Come together the interactivity of the Moodle platform and the objectivity of this kind of tests one can easily build manifold random tests. The proposal of this paper is to relate our journey in the construction of these tests and share our experience in the use of the Moodle platform to create, take advantage and improve the multiple-choices tests in the Mathematic area.

Particle swarm optimization based approaches to vehicle-to-grid scheduling

This paper addresses the problem of energy resources management using modern metaheuristics approaches, namely Particle Swarm Optimization (PSO), New Particle Swarm Optimization (NPSO) and Evolutionary Particle Swarm Optimization (EPSO). The addressed problem in this research paper is intended for aggregators’ use operating in a smart grid context, dealing with Distributed Generation (DG), and gridable vehicles intelligently managed on a multi-period basis according to its users’ profiles and requirements. The aggregator can also purchase additional energy from external suppliers. The paper includes a case study considering a 30 kV distribution network with one substation, 180 buses and 90 load points. The distribution network in the case study considers intense penetration of DG, including 116 units from several technologies, and one external supplier. A scenario of 6000 EVs for the given network is simulated during 24 periods, corresponding to one day. The results of the application of the PSO approaches to this case study are discussed deep in the paper.

Research on using the decision tree method in order to select the best alternative for the pharmacies supply

Introduction / Aims: Adopting the important decisions represents a specific task of the manager. An efficient manager takes these decisions during a sistematic process with well-defined elements, each with a precise order. In the pharmaceutical practice and business, in the supply process of the pharmacies, there are situations when the medicine distributors offer a certain discount, but require payment in a shorter period of time. In these cases, the analysis of the offer can be made with the help of the decision tree method, which permits identifying the decision offering the best possible result in a given situation. The aims of the research have been the analysis of the product offers of many different suppliers and the establishing of the most advantageous ways of pharmacy supplying. Material / Methods: There have been studied the general product offers of the following medical stores: A&G Med, Farmanord, Farmexim, Mediplus, Montero and Relad. In the case of medicine offers including a discount, the decision tree method has been applied in order to select the most advantageous offers. The Decision Tree is a management method used in taking the right decisions and it is generally used when one needs to evaluate the decisions that involve a series of stages. The tree diagram is used in order to look for the most efficient means to attain a specific goal. The decision trees are the most probabilistic methods, useful when adopting risk taking decisions. Results: The results of the analysis on the tree diagrams have indicated the fact that purchasing medicines with discount (1%, 10%, 15%) and payment in a shorter time interval (120 days) is more profitable than purchasing without a discount and payment in a longer time interval (160 days). Discussion / Conclusion: Depending on the results of the tree diagram analysis, the pharmacies would purchase from the selected suppliers. The research has shown that the decision tree method represents a valuable work instrument in choosing the best ways for supplying pharmacies and it is very useful to the specialists from the pharmaceutical field, pharmaceutical management, to medicine suppliers, pharmacy practitioners from the community pharmacies and especially to pharmacy managers, chief – pharmacists.

Natural gas system operation: lagrangean optimization techniques

To comply with natural gas demand growth patterns and Europe´s import dependency, the gas industry needs to organize an efficient upstream infrastructure. The best location of Gas Supply Units – GSUs and the alternative transportation mode – by phisical or virtual pipelines, are the key of a successful industry. In this work we study the optimal location of GSUs, as well as determining the most efficient allocation from gas loads to sources, selecting the best transportation mode, observing specific technical restrictions and minimizing system total costs. For the location of GSUs on system we use the P-median problem, for assigning gas demands nodes to source facilities we use the classical transportation problem. The developed model is an optimisation-based approach, based on a Lagrangean heuristic, using Lagrangean relaxation for P-median problems – Simple Lagrangean Heuristic. The solution of this heuristic can be improved by adding a local search procedure - the Lagrangean Reallocation Heuristic. These two heuristics, Simple Lagrangean and Lagrangean Reallocation, were tested on a realistic network - the primary Iberian natural gas network, organized with 65 nodes, connected by physical and virtual pipelines. Computational results are presented for both approaches, showing the location gas sources and allocation loads arrangement, system total costs and gas transportation mode.

Chitosan-alginate microparticulate delivery system for an alternative route of administration of BCG vaccine

Immunisation against M. tuberculosis with current available BCG vaccine lacks efficacy in preventing adult pulmonary tuberculosis. Targeting nasal mucosa is an attractive option for a more effective immunization. The delivery of BCG via the intranasal route involves overcoming barriers such as crossing the physical barrier imposed by the mucus layer and ciliar remotion, cellular uptake and intracellular trafficking by antigen presenting cells. Due to its biodegradable, immunogenic and mucoadhesive properties, chitosan particulate delivery systems can act both as vaccine carrier and adjuvant, improving the elicited immune response. In this study, different combinations of Chitosan/Alginate/TPP microparticles with BCG were produced as vaccine systems. The developed microparticle system successfully modulates BCG surface physicochemical properties and promotes effective intracellular uptake by human macrophage cell lines Preliminary immune responses were evaluated after s.c. and intranasal immunisation of BALB/c mice. BCG vaccination successfully stimulated the segregation of IgG2a and IgG1, where intranasal immunisation with chitosan/alginate particulate system efficiently elicited a more equilibrated cellular/humoral immune response.

Ancillary services dispatch using linear programming and genetic algorithm approaches

Electricity market players operating in a liberalized environment requires access to an adequate decision support tool, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. For this, decision support tools must include ancillary market simulation. This paper proposes two different methods (Linear Programming and Genetic Algorithm approaches) for ancillary services dispatch. The methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A test case concerning the dispatch of Regulation Down, Regulation Up, Spinning Reserve and Non-Spinning Reserve services is included in this paper.

Determination of chitin content in fungal cell wall: an alternative flow cytometric method

The conventional methods used to evaluate chitin content in fungi, such as biochemical assessment of glucosamine release after acid hydrolysis or epifluorescence microscopy, are low throughput, laborious, time-consuming, and cannot evaluate a large number of cells. We developed a flow cytometric assay, efficient, and fast, based on Calcofluor White staining to measure chitin content in yeast cells. A staining index was defined, its value was directly related to chitin amount and taking into consideration the different levels of autofluorecence. Twenty-two Candida spp. and four Cryptococcus neoformans clinical isolates with distinct susceptibility profiles to caspofungin were evaluated. Candida albicans clinical isolate SC5314, and isogenic strains with deletions in chitin synthase 3 (chs3Δ/chs3Δ) and genes encoding predicted Glycosyl Phosphatidyl Inositol (GPI)-anchored proteins (pga31Δ/Δ and pga62Δ/Δ), were used as controls. As expected, the wild-type strain displayed a significant higher chitin content (P < 0.001) than chs3Δ/chs3Δ and pga31Δ/Δ especially in the presence of caspofungin. Ca. parapsilosis, Ca. tropicalis, and Ca. albicans showed higher cell wall chitin content. Although no relationship between chitin content and antifungal drug susceptibility phenotype was found, an association was established between the paradoxical growth effect in the presence of high caspofungin concentrations and the chitin content. This novel flow cytometry protocol revealed to be a simple and reliable assay to estimate cell wall chitin content of fungi.

Alternative regimens for prostate cancer treatment using radiation therapy

Purpose/Objective: The purpose of this work was to determine biologically equivalent alternative regimens for the treatment of prostate cancer using External Beam Radiotherapy (EBRT) and Low Dose-Rate Brachytherapy (LDRBT) with 125I implants and to evaluate the sensitivity of these regimens to different sets of radiobiological parameters of the Linear-Quadratic (LQ) model.

Inexact restoration approaches to solve mathematical program with complementarity constraints

Mathematical Program with Complementarity Constraints (MPCC) finds applica- tion in many fields. As the complementarity constraints fail the standard Linear In- dependence Constraint Qualification (LICQ) or the Mangasarian-Fromovitz constraint qualification (MFCQ), at any feasible point, the nonlinear programming theory may not be directly applied to MPCC. However, the MPCC can be reformulated as NLP problem and solved by nonlinear programming techniques. One of them, the Inexact Restoration (IR) approach, performs two independent phases in each iteration - the feasibility and the optimality phases. This work presents two versions of an IR algorithm to solve MPCC. In the feasibility phase two strategies were implemented, depending on the constraints features. One gives more importance to the complementarity constraints, while the other considers the priority of equality and inequality constraints neglecting the complementarity ones. The optimality phase uses the same approach for both algorithm versions. The algorithms were implemented in MATLAB and the test problems are from MACMPEC collection.

Eficácia do Constraint-Induced Movement Therapy, em utentes com sequelas de Acidente Vascular Encefálico, até 9 meses

O Acidente Vascular Encefálico é uma das principais causas de morte, tornando-se cada vez mais iminente processos de reabilitação que minimizem as sequelas, nomeadamente as limitações do membro superior que dificultam o envolvimento em atividades da vida diária. O Constraint-Induced Movement Therapy, surge como uma abordagem que incrementa o uso do membro superior mais afetado. A presente investigação trata-se de um estudo de casos múltiplos. Pretende-se verificar se existem melhorias na funcionalidade do membro superior mais afetado, analisar em que atividades da vida diária são visíveis melhorias funcionais e compreender se o maior envolvimento nas atividades diárias está diretamente relacionado com a melhoria na capacidade funcional. Pretende-se ainda que os valores obtidos no Wolf Motor Function Test sejam um contributo para a sua validação para a população portuguesa. Utilizou-se um questionário para recolha de dados pessoais e clínicos (amplitudes de movimento, dor e espasticidade); o Wolf Motor Function Test e o Action Research Arm Test para verificar a funcionalidade do membro superior mais afetado; e a Motor Activity Log que avalia o envolvimento em atividades da vida diária. O grupo é constituído por 3 utentes que sofreram um primeiro Acidente Vascular Encefálico até 9 meses de evolução, internados na Santa Casa da Misericórdia de Monção e que cumpriam os critérios de inclusão. O programa foi implementado três horas/dia, durante 10 dias, mantendo a restrição no membro superior menos afetado durante 90% do dia acordado. Como se trata de um estudo de casos múltiplos, analisou-se cada participante individualmente e verificou-se a diferença entre os resultados finais e iniciais para cada uma das variáveis. Os resultados obtidos revelam ganhos na amplitude de movimento, velocidade de execução e capacidade funcional do membro superior mais afetado, nomeadamente nas funções de preensão e pinça da mão, bem como se testemunhou minimização do fenómeno learned nonuse. Verificaram-se ganhos funcionais em todos os participantes nas atividades da vida diária apesar de serem diferentes de participante para participante. Dois participantes afirmaram que voltariam a participar no programa.Conclui-se, assim que a técnica resulta em ganhos funcionais nestes utentes, indicando um caminho alternativo a outras abordagens de reabilitação.

Energy resource management in smart grids considering an Intensive use of electric vehicles

The introduction of electricity markets and integration of Distributed Generation (DG) have been influencing the power system’s structure change. Recently, the smart grid concept has been introduced, to guarantee a more efficient operation of the power system using the advantages of this new paradigm. Basically, a smart grid is a structure that integrates different players, considering constant communication between them to improve power system operation and management. One of the players revealing a big importance in this context is the Virtual Power Player (VPP). In the transportation sector the Electric Vehicle (EV) is arising as an alternative to conventional vehicles propel by fossil fuels. The power system can benefit from this massive introduction of EVs, taking advantage on EVs’ ability to connect to the electric network to charge, and on the future expectation of EVs ability to discharge to the network using the Vehicle-to-Grid (V2G) capacity. This thesis proposes alternative strategies to control these two EV modes with the objective of enhancing the management of the power system. Moreover, power system must ensure the trips of EVs that will be connected to the electric network. The EV user specifies a certain amount of energy that will be necessary to charge, in order to ensure the distance to travel. The introduction of EVs in the power system turns the Energy Resource Management (ERM) under a smart grid environment, into a complex problem that can take several minutes or hours to reach the optimal solution. Adequate optimization techniques are required to accommodate this kind of complexity while solving the ERM problem in a reasonable execution time. This thesis presents a tool that solves the ERM considering the intensive use of EVs in the smart grid context. The objective is to obtain the minimum cost of ERM considering: the operation cost of DG, the cost of the energy acquired to external suppliers, the EV users payments and remuneration and penalty costs. This tool is directed to VPPs that manage specific network areas, where a high penetration level of EVs is expected to be connected in these areas. The ERM is solved using two methodologies: the adaptation of a deterministic technique proposed in a previous work, and the adaptation of the Simulated Annealing (SA) technique. With the purpose of improving the SA performance for this case, three heuristics are additionally proposed, taking advantage on the particularities and specificities of an ERM with these characteristics. A set of case studies are presented in this thesis, considering a 32 bus distribution network and up to 3000 EVs. The first case study solves the scheduling without considering EVs, to be used as a reference case for comparisons with the proposed approaches. The second case study evaluates the complexity of the ERM with the integration of EVs. The third case study evaluates the performance of scheduling with different control modes for EVs. These control modes, combined with the proposed SA approach and with the developed heuristics, aim at improving the quality of the ERM, while reducing drastically its execution time. The proposed control modes are: uncoordinated charging, smart charging and V2G capability. The fourth and final case study presents the ERM approach applied to consecutive days.

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.