João Romano Torres e Cia: hermenêutica social de uma editora

Em 29 de Outubro de 1885 era publicado o primeiro número d’O Recreio, Publicação Semanal, Litteraria e Charadistica, criado e dirigido por Ignacio Moreira. No número 26, de 9 de Agosto de 1886, na primeira página, sob o título “Expediente”, dá-se conta aos leitores e aos colaboradores que “d’este numero em deante toda a correspondencia deve ser dirigida a João Romano Torres, rua Nova de S. Mamede, aos Caldas, 26, 3.º - Lisboa” (p. 201). Para João Romano Torres, que acabara de adquirir a publicação, trata-se de um acto refundacional, que significará para o editor o início de um percurso editorial através do qual se dará origem a uma editora cuja actividade chegará ao último quartel do século seguinte, estabelecendo um catálogo que a tornou reconhecível e reconhecida no espaço do livro em Portugal. Desta editora se falará aqui apenas de um período situado entre o ano de 1885 e o fim da primeira década de 1900. ABSTRACT - On the 29th October 1885, the first issue of O Recreio, Publicação Semanal, Litteraria e Charadística (proposed English translation: The Playground, Weekly, Literary and Charades Publication) was published, created and headed by Ignacio Moreira. On the 26th issue, issued on the 9th August 1886, in the front page, under the headline “Dispatch”, it is imparted with readers and collaborators that “from this issue forward, all correspondence should be addressed to João Romano Torres, Nova de S. Mamede street, at Caldas, 26, 3rd - Lisbon” (p. 201). For João Romano Torres, who had just acquired the publication, this was a re-foundational act, which will represent for this publisher the beginning of a publishing trajectory through which a new publishing house will emerge, whose activity will reach the final quarter of the next century, establishing a catalogue which made it recognizable and recognized in the book field in Portugal. This publishing house will be addressed here regarding only the period spanning from the year 1885 to the end of the first decade of the 1900s.

Acções prescritivas e estratégicas: a edição como espaço social

O propósito deste artigo é o de contribuir para um entendimento da edição como espaço social complexo. Este espaço social é constituído por um conjunto de agentes que actuam como construtores activos na esfera das ideias e da cultura escrita através de uma matriz prescritiva e selectiva da sua intervenção no livro, infundindo-lhe uma identidade própria que extravasa o texto na sua estrita acepção autoral. Por outro lado, o campo editorial e os agentes que o habitam integram processos mais vastos, configuradores de uma indústria específica e governados por interesses relacionados com a constituição de mercados de bens culturais, traduzindo-se este segundo aspecto nos pressupostos estratégicos de ressonância mercantil que suportam a actividade de mediação e prescrição de sentidos.

Investment optimization in distribution network based on fuzzy outage parameters

This paper presents a methodology that aims to increase the probability of delivering power to any load point of the electrical distribution system by identifying new investments in distribution components. The methodology is based on statistical failure and repair data of the distribution power system components and it uses fuzzy-probabilistic modelling for system component outage parameters. Fuzzy membership functions of system component outage parameters are obtained by statistical records. A mixed integer non-linear optimization technique is developed to identify adequate investments in distribution networks components that allow increasing the availability level for any customer in the distribution system at minimum cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a real distribution network.

Alcohol drinking patterns by gender, ethnicity, and social class in Bahia, Brazil

OBJECTIVE: To study patterns of alcohol consumption and prevalence of high-risk drinking. METHODS: A household survey was carried out in a sample of 2,302 adults in Salvador, Brazil. Cases of High-Risk Drinking (HRD) were defined as those subjects who referred daily or weekly binge drinking plus episodes of drunkenness and those who reported any use of alcoholic beverages but with frequent drunkenness (at least once a week). RESULTS: Fifty-six per cent of the sample acknowledged drinking alcoholic beverages. Overall consumption was significantly related with gender (male), marital status (single), migration (non-migrant), better educated (college level), and social class (upper). No significant differences were found regarding ethnicity, except for cachaça (Brazilian sugarcane liquor) and other distilled beverages. Overall 12-month prevalence of high-risk drinking was 7%, six times more prevalent among males than females (almost 13% compared to 2.4%). A positive association of HRD prevalence with education and social class was found. No overall relationship was found between ethnicity and HRD. Male gender and higher socioeconomic status were associated with increased odds of HRD. Two-way stratified analyses yielded consistent gender effects throughout all strata of independent variables. CONCLUSIONS: The findings suggest that social and cultural elements determine local patterns of alcohol-drinking behavior. Additional research on long-term and differential effects of gender, ethnicity, and social class on alcohol use and misuse is needed in order to explain their role as sources of social health inequities.

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.

Self-optimization module for scheduling using case-based reasoning

Metaheuristics performance is highly dependent of the respective parameters which need to be tuned. Parameter tuning may allow a larger flexibility and robustness but requires a careful initialization. The process of defining which parameters setting should be used is not obvious. The values for parameters depend mainly on the problem, the instance to be solved, the search time available to spend in solving the problem, and the required quality of solution. This paper presents a learning module proposal for an autonomous parameterization of Metaheuristics, integrated on a Multi-Agent System for the resolution of Dynamic Scheduling problems. The proposed learning module is inspired on Autonomic Computing Self-Optimization concept, defining that systems must continuously and proactively improve their performance. For the learning implementation it is used Case-based Reasoning, which uses previous similar data to solve new cases. In the use of Case-based Reasoning it is assumed that similar cases have similar solutions. After a literature review on topics used, both AutoDynAgents system and Self-Optimization module are described. Finally, a computational study is presented where the proposed module is evaluated, obtained results are compared with previous ones, some conclusions are reached, and some future work is referred. It is expected that this proposal can be a great contribution for the self-parameterization of Metaheuristics and for the resolution of scheduling problems on dynamic environments.

Self-optimization for dynamic scheduling in manufacturing systems

Scheduling is a critical function that is present throughout many industries and applications. A great need exists for developing scheduling approaches that can be applied to a number of different scheduling problems with significant impact on performance of business organizations. A challenge is emerging in the design of scheduling support systems for manufacturing environments where dynamic adaptation and optimization become increasingly important. In this paper, we describe a Self-Optimizing Mechanism for Scheduling System through Nature Inspired Optimization Techniques (NIT).

Combined heat and power and consumption optimization in a SCADA-based system

The operation of power systems in a Smart Grid (SG) context brings new opportunities to consumers as active players, in order to fully reach the SG advantages. In this context, concepts as smart homes or smart buildings are promising approaches to perform the optimization of the consumption, while reducing the electricity costs. This paper proposes an intelligent methodology to support the consumption optimization of an industrial consumer, which has a Combined Heat and Power (CHP) facility. A SCADA (Supervisory Control and Data Acquisition) system developed by the authors is used to support the implementation of the proposed methodology. An optimization algorithm implemented in the system in order to perform the determination of the optimal consumption and CHP levels in each instant, according to the Demand Response (DR) opportunities. The paper includes a case study with several scenarios of consumption and heat demand in the context of a DR event which specifies a maximum demand level for the consumer.

Virtual power players dealing with excessive wind power situations using real time pricing

The increasing importance given by environmental policies to the dissemination and use of wind power has led to its fast and large integration in power systems. In most cases, this integration has been done in an intensive way, causing several impacts and challenges in current and future power systems operation and planning. One of these challenges is dealing with the system conditions in which the available wind power is higher than the system demand. This is one of the possible applications of demand response, which is a very promising resource in the context of competitive environments that integrates even more amounts of distributed energy resources, as well as new players. The methodology proposed aims the maximization of the social welfare in a smart grid operated by a virtual power player that manages the available energy resources. When facing excessive wind power generation availability, real time pricing is applied in order to induce the increase of consumption so that wind curtailment is minimized. The proposed method is especially useful when actual and day-ahead wind forecast differ significantly. The proposed method has been computationally implemented in GAMS optimization tool and its application is illustrated in this paper using a real 937-bus distribution network with 20310 consumers and 548 distributed generators, some of them with must take contracts.

A decision-support system based on particle swarm optimization for multiperiod hedging in electricity markets

This paper proposes a particle swarm optimization (PSO) approach to support electricity producers for multiperiod optimal contract allocation. The producer risk preference is stated by a utility function (U) expressing the tradeoff between the expectation and variance of the return. Variance estimation and expected return are based on a forecasted scenario interval determined by a price range forecasting model developed by the authors. A certain confidence level is associated to each forecasted scenario interval. The proposed model makes use of contracts with physical (spot and forward) and financial (options) settlement. PSO performance was evaluated by comparing it with a genetic algorithm-based approach. This model can be used by producers in deregulated electricity markets but can easily be adapted to load serving entities and retailers. Moreover, it can easily be adapted to the use of other type of contracts.

Distributed energy resource short-term scheduling using signaled particle swarm optimization

Distributed Energy Resources (DER) scheduling in smart grids presents a new challenge to system operators. The increase of new resources, such as storage systems and demand response programs, results in additional computational efforts for optimization problems. On the other hand, since natural resources, such as wind and sun, can only be precisely forecasted with small anticipation, short-term scheduling is especially relevant requiring a very good performance on large dimension problems. Traditional techniques such as Mixed-Integer Non-Linear Programming (MINLP) do not cope well with large scale problems. This type of problems can be appropriately addressed by metaheuristics approaches. This paper proposes a new methodology called Signaled Particle Swarm Optimization (SiPSO) to address the energy resources management problem in the scope of smart grids, with intensive use of DER. The proposed methodology’s performance is illustrated by a case study with 99 distributed generators, 208 loads, and 27 storage units. The results are compared with those obtained in other methodologies, namely MINLP, Genetic Algorithm, original Particle Swarm Optimization (PSO), Evolutionary PSO, and New PSO. SiPSO performance is superior to the other tested PSO variants, demonstrating its adequacy to solve large dimension problems which require a decision in a short period of time.

A short-term risk management tool applied to OMEL electricity market using particle swarm optimization

Short-term risk management is highly dependent on long-term contractual decisions previously established; risk aversion factor of the agent and short-term price forecast accuracy. Trying to give answers to that problem, this paper provides a different approach for short-term risk management on electricity markets. Based on long-term contractual decisions and making use of a price range forecast method developed by the authors, the short-term risk management tool presented here has as main concern to find the optimal spot market strategies that a producer should have for a specific day in function of his risk aversion factor, with the objective to maximize the profits and simultaneously to practice the hedge against price market volatility. Due to the complexity of the optimization problem, the authors make use of Particle Swarm Optimization (PSO) to find the optimal solution. Results from realistic data, namely from OMEL electricity market, are presented and discussed in detail.

Os padrões de vinculação estabelecidos e indicadores de resilência (autonomia e capacidades de interação social) em crianças de idade pré-escolar em função da frequência em rede pública ou privada

A dissertação procura compreender de que maneira a frequência da criança numa escola pública ou numa escola privada, poderá afetar o desenvolvimento dos indicadores de resiliência definidos (autonomia e capacidades de interação social) e a sua vinculação aos pais. O presente estudo realizou-se em três escolas do primeiro ciclo com jardim de infância, duas das quais pertenciam ao ensino público e uma ao ensino privado. A revisão da literatura, exposta nos vários capítulos, apresenta a visão de diversos investigadores sobre a problemática da relação entre o desenvolvimento dos indicadores de resiliência definidos, a maneira como as crianças criam as suas relações de vinculação, focando as diferenças relativamente à frequência em rede pública ou rede privada. Por se tratar de um estudo comparativo, foi escolhida uma metodologia quantitativa. Procedeu-se, em seguida, a uma análise qualitativa dos resultados obtidos em algumas variáveis. O estudo conclui que não se verificam diferenças significativas no que diz respeito aos resultados obtidos pelas crianças relativamente às variáveis em estudo, com exceção do indicador de resiliência “capacidades de interação social”. Este estudo também nos mostra que existem várias variáveis a ter em conta para compreender a resiliência e a relação entre as variáveis, nomeadamente, as qualificações dos prestadores de cuidados. Sugere-se a continuação do trabalho iniciado, avaliando a vinculação e os indicadores de resiliência estudados (autonomia e capacidades de interação social) destas mesmas crianças, no futuro. Sugere-se ainda a realização de outros estudos, dentro da mesma área, que possam aprofundar a influência das diversas variáveis que dizem respeito ao contexto socioeconómico e sociodemográfico onde as crianças estão inseridas e ver de que maneira estes afetam as variáveis estudadas neste trabalho. - Abstract The dissertation sought to understand how child‟s frequency in a public or a private school, can affect the development of resilience indicators defined (autonomy and capabilities social interaction capabilities) and their connection to parents. This study was conducted in three primary schools with kindergarten, two of which belonged to public education and one to private education. The literature review, exposed in several chapters, presents the view of many researchers on the issue of the relationship between the development of resilience indicators defined, the way children create their linking relations, focusing on the differences in the frequency on public or private school. Since this is a comparative study, a quantitative methodology was chosen. Then we‟ve proceeded to a qualitative analysis of the results obtained on some variables. The study concludes that there are no significant differences with regard to the results obtained by children, for the variables under study except for the indicator of resilience “capabilities of social interaction”. This study also shows that there are several variables to take into account to understand the resilience and the relation between variables, namely, the qualifications of care providers. It is suggested to continue the work begun by evaluating the binding and resilience indicators studied (autonomy and social interaction skills) of these same children in the future. It is also suggested in other studies within the same area, which may deepen the influence of several variables that relate to socio-demographic and socio-economic context where children are located and see how they affect the variables studied in this work

Optimization model for medium term natural gas commitment

In this paper we study the optimal natural gas commitment for a known demand scenario. This study implies the best location of GSUs to supply all demands and the optimal allocation from sources to gas loads, through an appropriate transportation mode, in order to minimize total system costs. Our emphasis is on the formulation and use of a suitable optimization model, reflecting real-world operations and the constraints of natural gas systems. The mathematical model is based on a Lagrangean heuristic, using the Lagrangean relaxation, an efficient approach to solve the problem. Computational results are presented for Iberian and American natural gas systems, geographically organized in 65 and 88 load nodes, respectively. The location model results, supported by the computational application GasView, show the optimal location and allocation solution, system total costs and suggest a suitable gas transportation mode, presented in both numerical and graphic supports.

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.