Cardiorespiratory fitness is negatively associated with metabolic risk factors independently of the adherence to a healthy dietary pattern

Background and aim: Cardiorespiratory fitness (CRF) and diet have been involved as significant factors towards the prevention of cardio-metabolic diseases. This study aimed to assess the impact of the combined associations of CRF and adherence to the Southern European Atlantic Diet (SEADiet) on the clustering of metabolic risk factors in adolescents. Methods and Results: A cross-sectional school-based study was conducted on 468 adolescents aged 15-18, from the Azorean Islands, Portugal. We measured fasting glucose, insulin, total cholesterol (TC), HDL-cholesterol, triglycerides, systolic blood pressure, waits circumference and height. HOMA, TC/HDL-C ratio and waist-to-height ratio were calculated. For each of these variables, a Z-score was computed by age and sex. A metabolic risk score (MRS) was constructed by summing the Z scores of all individual risk factors. High risk was considered when the individual had 1SD of this score. CRF was measured with the 20 m-Shuttle-Run- Test. Adherence to SEADiet was assessed with a semi-quantitative food frequency questionnaire. Logistic regression showed that, after adjusting for potential confounders, unfit adolescents with low adherence to SEADiet had the highest odds of having MRS (OR Z 9.4; 95%CI:2.6e33.3) followed by the unfit ones with high adherence to the SEADiet (OR Z 6.6; 95% CI: 1.9e22.5) when compared to those who were fit and had higher adherence to SEADiet.

Assessment of nutritional and metabolic profiles of pea shoots: The new ready-to-eat baby-leaf vegetable

Pea-shoots are a new option as ready-to-eat baby-leaf vegetable. However, data about the nutritional composition and the shelf-life stability of these leaves, especially their phytonutrient composition is scarce. In this work, the macronutrient, micronutrient and phytonutrients profile of minimally processed pea shoots were evaluated at the beginning and at the end of a 10-day storage period. Several physicochemical characteristics (color, pH, total soluble solids, and total titratable acidity) were also monitored. Standard AOAC methods were applied in the nutritional value evaluation, while chromatographic methods with UV–vis and mass detection were used to analyze free forms of vitamins (HPLC-DAD-ESI-MS/MS), carotenoids (HPLC-DAD-APCI-MSn) and flavonoid compounds (HPLC-DAD-ESI-MSn). Atomic absorption spectrometry (HR-CS-AAS) was employed to characterize the mineral content of the leaves. As expected, pea leaves had a high water (91.5%) and low fat (0.3%) and carbohydrate (1.9%) contents, being a good source of dietary fiber (2.1%). Pea shoots showed a high content of vitamins C, E and A, potassium and phosphorous compared to other ready-to-eat green leafy vegetables. The carotenoid profile revealed a high content of β-carotene and lutein, typical from green leafy vegetables. The leaves had a mean flavonoid content of 329 mg/100 g of fresh product, mainly composed by glycosylated quercetin and kaempferol derivatives. Pea shoots kept their fresh appearance during the storage being color maintained throughout the shelf-life. The nutritional composition was in general stable during storage, showing some significant (p < 0.05) variation in certain water-soluble vitamins.

A Data Mining Framework for Electric Load Profiling

This paper consists in the characterization of medium voltage (MV) electric power consumers based on a data clustering approach. It is intended to identify typical load profiles by selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The best partition is selected using several cluster validity indices. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ behavior. The data-mining-based methodology 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 partitions. To validate our approach, a case study with a real database of 1.022 MV consumers was used.

Short-term Load Forecasting Based on Load Profiling

Load forecasting has gradually becoming a major field of research in electricity industry. Therefore, Load forecasting is extremely important for the electric sector under deregulated environment as it provides a useful support to the power system management. Accurate power load forecasting models are required to the operation and planning of a utility company, and they have received increasing attention from researches of this field study. Many mathematical methods have been developed for load forecasting. This work aims to develop and implement a load forecasting method for short-term load forecasting (STLF), based on Holt-Winters exponential smoothing and an artificial neural network (ANN). One of the main contributions of this paper is the application of Holt-Winters exponential smoothing approach to the forecasting problem and, as an evaluation of the past forecasting work, data mining techniques are also applied to short-term Load forecasting. Both ANN and Holt-Winters exponential smoothing approaches are compared and evaluated.

Load Profiling Tool to Support Smart Grid Operation Scenarios

This paper presents the characterization of high voltage (HV) electric power consumers based on a data clustering approach. The typical load profiles (TLP) are obtained selecting the best partition of a power consumption database among a pool of data partitions produced by several clustering algorithms. The choice of the best partition is supported using several cluster validity indices. The proposed data-mining (DM) based methodology, that includes all steps presented in the process of knowledge discovery in databases (KDD), presents an automatic data treatment application in order to preprocess the initial database in an automatic way, allowing time saving and better accuracy during this phase. These methods are intended to be used in a smart grid environment to extract useful knowledge about customers’ consumption behavior. To validate our approach, a case study with a real database of 185 HV consumers was used.

User Profiling and Knowledge Extraction For Tourism

O sector do turismo é uma área francamente em crescimento em Portugal e que tem desenvolvido a sua divulgação e estratégia de marketing. Contudo, apenas se prende com indicadores de desempenho e de oferta instalada (número de quartos, hotéis, voos, estadias), deixando os indicadores estatísticos em segundo plano. De acordo com o “ Travel & tourism Competitiveness Report 2013”, do World Economic Forum, classifica Portugal em 72º lugar no que respeita à qualidade e cobertura da informação estatística, disponível para o sector do Turismo. Refira-se que Espanha ocupa o 3º lugar. Uma estratégia de mercado, sem base analítica, que sustente um quadro de orientações específico e objetivo, com relevante conhecimento dos mercados alvo, dificilmente é compreensível ou até mesmo materializável. A implementação de uma estrutura de Business Intelligence que permita a realização de um levantamento e tratamento de dados que possibilite relacionar e sustentar os resultados obtidos no sector do turismo revela-se fundamental e crucial, para que sejam criadas estratégias de mercado. Essas estratégias são realizadas a partir da informação dos turistas que nos visitam, e dos potenciais turistas, para que possam ser cativados no futuro. A análise das características e dos padrões comportamentais dos turistas permite definir perfis distintos e assim detetar as tendências de mercado, de forma a promover a oferta dos produtos e serviços mais adequados. O conhecimento obtido permite, por um lado criar e disponibilizar os produtos mais atrativos para oferecer aos turistas e por outro informá-los, de uma forma direcionada, da existência desses produtos. Assim, a associação de uma recomendação personalizada que, com base no conhecimento de perfis do turista proceda ao aconselhamento dos melhores produtos, revela-se como uma ferramenta essencial na captação e expansão de mercado.