Wind turbine database for intelligent operation and maintenance strategies.

With the aim of helping researchers to develop intelligent operation and maintenance strategies, in this manuscript, an extensive 3-years Supervisory Control and Data Acquisition database of five Fuhrländer FL2500 2.5 MW wind turbines is presented. The database contains 312 analogous variables recorded at 5-minute intervals, from 78 different sensors.

Special Issue "Machine Learning Methods for Biomedical Data Analysis".

Machine learning is an effective method for developing automatic algorithms for analysing sophisticated biomedical data [...

EMD-based data augmentation method applied to handwriting data for the diagnosis of Essential Tremor using LSTM networks.

The increasing capacity of today's technology represents great advances in diagnosing diseases using standard procedures supported by computer science. Deep learning techniques are able to extract the characteristics of temporal signals to study their patterns and diagnose diseases such as essential tremor. However, these techniques require a large amount of data to train the neural network and achieve good results, and the more data the network has, the more accurate the final model implemented.

A framework for mapping and monitoring human-ocean interactions in near real-time during COVID-19 and beyond.

The human response to the COVID-19 pandemic set in motion an unprecedented shift in human activity with unknown long-term effects. The impacts in marine systems are expected to be highly dynamic at local and global scales. However, in comparison to terrestrial ecosystems, we are not well-prepared to document these changes in marine and coastal environments.

Insights on the environmental management system of the European port sector.

This research focusses on assessing the environmental performance of European Ports based on a wide representation of EcoPorts members. The data is extracted from the Self-Diagnosis Method (SDM), a concise checklist against which port managers can self-assess the environmental management of their port in relation to the performance of the EcoPorts membership. A total number of 97 ports from 18 different European Maritime States contributed to this evaluation.

A Fast Approach to Removing Muscle Artifacts for EEG with Signal Serialization Based Ensemble Empirical Mode Decomposition.

An electroencephalogram (EEG) is an electrophysiological signal reflecting the functional state of the brain. As the control signal of the brain-computer interface (BCI), EEG may build a bridge between humans and computers to improve the life quality for patients with movement disorders. The collected EEG signals are extremely susceptible to the contamination of electromyography (EMG) artifacts, affecting their original characteristics.

Performance trends of environmental management in European ports.

This paper presents the current status and trends over time in the environmental situation of European ports, based on the results of a wide representation of EcoPorts members (90 ports). All the information presented in this research comes from the Self-Diagnosis Method (SDM), a concise checklist managed by European Sea Ports Organisation (ESPO), against which ports can self-assess their environmental management. The results provide data on a total number of 54 indicators, being the existence of an inventory of environmental legislation the indicator with the highest implementation (96,7%), followed by the existence of an environmental policy (95,7%).

Characterization of deep-sea benthic invertebrate megafauna of the Galapagos Islands.

The deep sea represents the largest and least explored biome on the planet. Despite the iconic status of the Galapagos Islands and being considered one of the most pristine locations on earth, the deep-sea benthic ecosystems of the archipelago are virtually unexplored in comparison to their shallow-water counterparts. In 2015, we embarked on a multi-disciplinary scientific expedition to conduct the first systematic characterization of deep-sea benthic invertebrate communities of the Galapagos, across a range of habitats.

Automatic ham classification method based on support vector machine model increases accuracy and benefits compared to manual classification.

The thickness of the subcutaneous fat (SFT) is a very important parameter in the ham, since determines the process the ham will be submitted. This study compares two methods to predict the SFT in slaughter line: an automatic system using an SVM model (Support Vector Machine) and a manual measurement of the fat carried out by an experienced operator, in terms of accuracy and economic benefit. These two methods were compared to the golden standard obtained by measuring SFT with a ruler in a sample of 400 hams equally distributed within each SFT class.

Discrete Cosine Transform for the Analysis of Essential Tremor.

Essential tremor (ET) is the most common movement disorder. In fact, its prevalence is about 20 times higher than that of Parkinson's disease. In addition, studies have shown that a high percentage of cases, between 50 and 70%, are estimated to be of genetic origin.

On-line Ham Grading using pattern recognition models based on available data in commercial pig slaughterhouses.

The thickness of the subcutaneous fat in hams is one of the most important factors for the dry-curing process and largely determines its final quality. This parameter is usually measured in slaughterhouses by a manual metrical measure to classify hams. The aim of the present study was to propose an automatic classification method based on data obtained from a carcass automatic classification equipment (AutoFom) and intrinsic data of the pigs (sex, breed, and weight) to simulate the manual classification system.

Benchmark dynamics in the environmental performance of ports.

This paper analyses the 2016 environmental benchmark performance of the port sector, based on a wide representation of EcoPorts members. This is the fifth time that this study has been conducted as an initiative of the European Sea Ports Organisation (ESPO). The data and results are derived from the Self-Diagnosis Method (SDM), a concise checklist against which port managers can self-assess the environmental management of their port in relation to the performance of the EcoPorts membership.

Identification and selection of Environmental Performance Indicators for sustainable port development.

In this paper an identification and selection of Environmental Performance Indicators (EPIs) in port areas has been conducted. A comprehensive inventory of existing EPIs in use in the seaport sector has been identified for monitoring performance of operational (e.g.