Typical load profiles in the smart grid context – a clustering methods comparison

The present research paper presents five different clustering methods to identify typical load profiles of medium voltage (MV) electricity consumers. These methods are intended to be used in a smart grid environment to extract useful knowledge about customer’s behaviour. The obtained knowledge can be used to support a decision tool, not only for utilities but also for consumers. Load profiles can be used by the utilities to identify the aspects that cause system load peaks and enable the development of specific contracts with their customers. The framework presented throughout the paper consists in several steps, namely the pre-processing data phase, clustering algorithms application and the evaluation of the quality of the partition, which is supported by cluster validity indices. The process ends with the analysis of the discovered knowledge. To validate the proposed framework, a case study with a real database of 208 MV consumers is used.

Demand response programs definition supported by clustering and classification techniques

The growing importance and influence of new resources connected to the power systems has caused many changes in their operation. Environmental policies and several well know advantages have been made renewable based energy resources largely disseminated. These resources, including Distributed Generation (DG), are being connected to lower voltage levels where Demand Response (DR) must be considered too. These changes increase the complexity of the system operation due to both new operational constraints and amounts of data to be processed. Virtual Power Players (VPP) are entities able to manage these resources. Addressing these issues, this paper proposes a methodology to support VPP actions when these act as a Curtailment Service Provider (CSP) that provides DR capacity to a DR program declared by the Independent System Operator (ISO) or by the VPP itself. The amount of DR capacity that the CSP can assure is determined using data mining techniques applied to a database which is obtained for a large set of operation scenarios. The paper includes a case study based on 27,000 scenarios considering a diversity of distributed resources in a 33 bus distribution network.

Detectives with pimples: how teen noir is crossing the frontiers of the traditional noir films

In the last ten years, teen noir movies and series — such as Donnie Darko (2001), Brick (2005), or Veronica Mars (2004-2007) — have become increasingly popular among audiences, both in the USA and in Europe, and aroused the curiosity of critics. These teen noir adventures present darker themes and technical features that distinguish them from numerous productions aiming at young adults. Their narrative and aesthetic characteristics reinvent and subvert the tradition of classic noir movies of the forties and fifties, thus generating a sense of novelty. In this article, I focus my attention on Veronica Mars, a famous teen noir series, created by Rob Thomas, to examine: a) the teen noir themes; b) the new profile and role of the private investigator; c) the empowerment of girls/young women; d) razor-sharp dialogues; e) intertextual references to old- school noir movies. In order to do so, resort to the research of specialists in the field of neo noir, such as Mark Conrad, Foster Hirsch, or Roz Kaveney. My main goal is to prove that a new (sub)genre is slowly emerging and revivifying teen cinema.

Mould and yeast identification in archival settings: preliminary results on the use of traditional methods and molecular biology options in Portuguese archives

This project was developed to fully assess the indoor air quality in archives and libraries from a fungal flora point of view. It uses classical methodologies such as traditional culture media – for the viable fungi – and modern molecular biology protocols, especially relevant to assess the non-viable fraction of the biological contaminants. Denaturing high-performance liquid chromatography (DHPLC) has emerged as an alternative to denaturing gradient gel electrophoresis (DGGE) and has already been applied to the study of a few bacterial communities. We propose the application of DHPLC to the study of fungal colonization on paper-based archive materials. This technology allows for the identification of each component of a mixture of fungi based on their genetic variation. In a highly complex mixture of microbial DNA this method can be used simply to study the population dynamics, and it also allows for sample fraction collection, which can, in many cases, be immediately sequenced, circumventing the need for cloning. Some examples of the methodological application are shown. Also applied is fragment length analysis for the study of mixed Candida samples. Both of these methods can later be applied in various fields, such as clinical and sand sample analysis. So far, the environmental analyses have been extremely useful to determine potentially pathogenic/toxinogenic fungi such as Stachybotrys sp., Aspergillus niger, Aspergillus fumigatus, and Fusarium sp. This work will hopefully lead to more accurate evaluation of environmental conditions for both human health and the preservation of documents.

Patterns, mathematics and culture : the search for symmetry in Azorean sidewalks and traditional crafts

In the hustle and bustle of daily life, how often do we stop to pay attention to the tiny details around us, some of them right beneath our feet? Such is the case of interesting decorative patterns that can be found in squares and sidewalks beautified by the traditional Portuguese pavement. Its most common colors are the black and the white of the basalt and the limestone used; the result is a large variety and richness in patterns. No doubt, it is worth devoting some of our time enjoying the lovely Portuguese pavement, a true worldwide attraction. The interesting patterns found on the Azorean handicrafts are as fascinating and substantial from the cultural point of view. Patterns existing in the sidewalks and crafts can be studied from the mathematical point of view, thus allowing a thorough and rigorous cataloguing of such heritage. The mathematical classification is based on the concept of symmetry, a unifying principle of geometry. Symmetry is a unique tool for helping us relate things that at first glance may appear to have no common ground at all. By interlacing different fields of endeavor, the mathematical approach to sidewalks and crafts is particularly interesting, and an excellent source of inspiration for the development of highly motivated recreational activities. This text is an invitation to visit the nine islands of the Azores and to identify a wide range of patterns, namely rosettes and friezes, by getting to know different arts and crafts and sidewalks.

On clustering interval data with different scales of measures : experimental results

This article is is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. Attribution-NonCommercial (CC BY-NC) license lets others remix, tweak, and build upon work non-commercially, and although the new works must also acknowledge & be non-commercial.

Clustering of variables with a three-way approach for health sciences

TPM Vol. 21, No. 4, December 2014, 435-447 – Special Issue © 2014 Cises.

Clustering an interval data set : are the main partitions similar to a priori partition?

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Fractional dynamics and MDS visualization of earthquake phenomena

This paper analyses earthquake data in the perspective of dynamical systems and fractional calculus (FC). This new standpoint uses Multidimensional Scaling (MDS) as a powerful clustering and visualization tool. FC extends the concepts of integrals and derivatives to non-integer and complex orders. MDS is a technique that produces spatial or geometric representations of complex objects, such that those objects that are perceived to be similar in some sense are placed on the MDS maps forming clusters. In this study, over three million seismic occurrences, covering the period from January 1, 1904 up to March 14, 2012 are analysed. The events are characterized by their magnitude and spatiotemporal distributions and are divided into fifty groups, according to the Flinn–Engdahl (F–E) seismic regions of Earth. Several correlation indices are proposed to quantify the similarities among regions. MDS maps are proven as an intuitive and useful visual representation of the complex relationships that are present among seismic events, which may not be perceived on traditional geographic maps. Therefore, MDS constitutes a valid alternative to classic visualization tools for understanding the global behaviour of earthquakes.

Pulmonary tuberculosis in Continental Portugal 2000-2010: temporal trends clustering as a tool for control evaluation

Clustering analysis is a useful tool to detect and monitor disease patterns and, consequently, to contribute for an effective population disease management. Portugal has the highest incidence of tuberculosis in the European Union (in 2012, 21.6 cases per 100.000 inhabitants), although it has been decreasing consistently. Two critical PTB (Pulmonary Tuberculosis) areas, metropolitan Oporto and metropolitan Lisbon regions, were previously identified through spatial and space-time clustering for PTB incidence rate and risk factors. Identifying clusters of temporal trends can further elucidate policy makers about municipalities showing a faster or a slower TB control improvement.

Feature selection for clustering categorical data with an embedded modelling approach

Research on the problem of feature selection for clustering continues to develop. This is a challenging task, mainly due to the absence of class labels to guide the search for relevant features. Categorical feature selection for clustering has rarely been addressed in the literature, with most of the proposed approaches having focused on numerical data. In this work, we propose an approach to simultaneously cluster categorical data and select a subset of relevant features. Our approach is based on a modification of a finite mixture model (of multinomial distributions), where a set of latent variables indicate the relevance of each feature. To estimate the model parameters, we implement a variant of the expectation-maximization algorithm that simultaneously selects the subset of relevant features, using a minimum message length criterion. The proposed approach compares favourably with two baseline methods: a filter based on an entropy measure and a wrapper based on mutual information. The results obtained on synthetic data illustrate the ability of the proposed expectation-maximization method to recover ground truth. An application to real data, referred to official statistics, shows its usefulness.

Categorical data clustering using a minimum message length criterion

Research on cluster analysis for categorical data continues to develop, new clustering algorithms being proposed. However, in this context, the determination of the number of clusters is rarely addressed. We propose a new approach in which clustering and the estimation of the number of clusters is done simultaneously for categorical data. We assume that the data originate from a finite mixture of multinomial distributions and use a minimum message length criterion (MML) to select the number of clusters (Wallace and Bolton, 1986). For this purpose, we implement an EM-type algorithm (Silvestre et al., 2008) based on the (Figueiredo and Jain, 2002) approach. The novelty of the approach rests on the integration of the model estimation and selection of the number of clusters in a single algorithm, rather than selecting this number based on a set of pre-estimated candidate models. The performance of our approach is compared with the use of Bayesian Information Criterion (BIC) (Schwarz, 1978) and Integrated Completed Likelihood (ICL) (Biernacki et al., 2000) using synthetic data. The obtained results illustrate the capacity of the proposed algorithm to attain the true number of cluster while outperforming BIC and ICL since it is faster, which is especially relevant when dealing with large data sets.

Clustering and selecting categorical features

In data clustering, the problem of selecting the subset of most relevant features from the data has been an active research topic. Feature selection for clustering is a challenging task due to the absence of class labels for guiding the search for relevant features. Most methods proposed for this goal are focused on numerical data. In this work, we propose an approach for clustering and selecting categorical features simultaneously. We assume that the data originate from a finite mixture of multinomial distributions and implement an integrated expectation-maximization (EM) algorithm that estimates all the parameters of the model and selects the subset of relevant features simultaneously. The results obtained on synthetic data illustrate the performance of the proposed approach. An application to real data, referred to official statistics, shows its usefulness.

Nutritional value and influence of the thermal processing on a traditional Portuguese fermented sausage (alheira)

Alheiras are a traditional, smoked, fermented meat sausage, produced in Portugal, with an undeniable cultural and gastronomic legacy. In this study, we assessed the nutritional value of this product, as well as the influence of different types of thermal processing. Alheiras from Mirandela were submitted to six different procedures: microwave, skillet, oven, charcoal grill, electric fryer and electric grill. Protein, fat, carbohydrate, minerals, NaCl, and cholesterol contents, as well as fatty acid profile were evaluated. The results show that alheiras are not hypercaloric but an unbalanced foodstuff (high levels of proteins and lipids) and the type of processing has a major impact on their nutritional value. Charcoal grill is the healthiest option: less fat (12.5 g/100 g) and cholesterol (29.3 mg/100 g), corresponding to a lower caloric intake (231.8 kcal, less 13% than the raw ones). Inversely, fried alheiras presented the worst nutritional profile, with the highest levels of fat (18.1 g/100 g) and cholesterol (76.0 g/100 g).

Traditional knowledge for promotion of socioeconomic inclusion of local communities

This paper discusses the key role played by public research institutes for promoting socioeconomic inclusion of local communities based on traditional knowledge and traditional medicine. Nongovernmental organizations and cooperatives have had an important role in raising financial resources, being involved with advocacy of local communities and advancing legislation changes. But strict best manufacturing practices regulations imposed by the Brazilian National Health Surveillance Agency on the requirements for approval and commercialization of drugs based on herbal medicine products call for the involvement of strong public research institutes capable of supporting community-based pharmacies. Thus, public research institutes are pivotal as they can conduct scientific research studies to evidence the efficacy of herbal medicine products and help building the capacity of local communities to comply with current regulations.