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Electricity is an indicator that shows the progress of a civilization; it is a product that has greatly changed the way we think about the world. Electricity price forecasting became a fundamental task in all countries due to the deregulation of the electricity market in the 1990s. This work examines the effectiveness of using multiple variables for price prediction given the large number of factors that could influence the price of the electricity market. The tests were carried out over four periods using data from Spain and deep learning models. Two different attribute selection methods based on Pearson's correlation coefficient have been used to improve the efficiency of the training process. The variables used as input to the different prediction models were chosen, considering those most commonly used previously in the literature. This study attempts to test whether using time series lags improves the non-use of lags. The results obtained have shown that lags improve the results compared to a previous work in which no lags were used. [ABSTRACT FROM AUTHOR]

Buena Pinta Cave (Pinilla del Valle, Madrid) has been interpreted as a hyena den with sporadic occupations of Homo neanderthalensis in the western part of the site (level 23). In order to identify the different formation processes in this area of the site, spatial analyses have been carried out with GIS and spatial statistics based on the taphonomic analysis of the faunal remains. Based on the vertical and sectional analyses of the assemblage, it has been possible to determine that level 23 actually corresponds to three archaeological levels with well-differentiated characteristics: a lower level with few faunal remains and fossil-diagenetic alterations related to humid environments associated with clays; an intermediate level with a high percentage of remains with water-related modifications and evidences of transport; and an upper level delimited mainly thanks to by a paraconformity evidenced by the concentration of weathered remains in this area and a significant reduction in remains with water-related alterations above. The results obtained show the necessity to redefine field layers and the usefulness of integrating taphonomic data and spatial studies. [ABSTRACT FROM AUTHOR]

Sepsis is a life-threatening condition whose early recognition is key to improving outcomes for patients in intensive care units (ICUs). Artificial intelligence can play a crucial role in mining and exploiting health data for sepsis prediction. However, progress in this field has been impeded by a lack of comparability across studies. Some studies do not provide code, and each study independently processes a dataset with large numbers of missing values. Here, we present a comparative analysis of early sepsis prediction in the ICU by using machine learning (ML) algorithms and provide open-source code to the community to support future work. We reviewed the literature and conducted two phases of experiments. In the first phase, we analyzed five imputation strategies for handling missing data in a clinical dataset (which is often sampled irregularly and requires hand-crafted preprocessing steps). We used the MIMIC-III dataset, which includes more than 5,800 ICU hospital admissions from 2001 to 2012. In the second phase, we conducted an extensive experimental study using five ML methods and five popular deep learning models. We evaluated the performance of the methods by using the area under the precision-recall curve, a standard metric for clinical contexts. The deep learning methods (TCN and LSTM) outperformed the other methods, particularly in early detection tasks more than 4 hours before sepsis onset. The motivation for this work was to provide a benchmark framework for future research, thus enabling advancements in this field. [ABSTRACT FROM AUTHOR]

Spatial analysis has been much used to examine the distribution of archaeological remains at Pleistocene sites. However, little is known about the distribution patterns at sites identified as hunting camps, i.e., places occupied over multiple short periods for the capture of animals later transported to a base camp. The present work examines a Neanderthal hunting camp (the Navalmaíllo Rock Shelter in Pinilla del Valle, Madrid, Spain) to determine whether different activities were undertaken in different areas of the site. A spatial pattern was detected with a main cluster of materials (lithic tools, faunal remains, and coprolites) clearly related to the presence of nearby hearths—the backbone of the utilised space. This main cluster appears to have been related to collaborative and repetitive activities undertaken by the hunting parties that used the site. Spatial analysis also detected a small, isolated area perhaps related to carcasses processing at some point in time and another slightly altered by water. [ABSTRACT FROM AUTHOR]

The generation of synthetic data is becoming a fundamental task in the daily life of any organization due to the new protection data laws that are emerging. Because of the rise in the use of Artificial Intelligence, one of the most recent proposals to address this problem is the use of Generative Adversarial Networks (GANs). These types of networks have demonstrated a great capacity to create synthetic data with very good performance. The goal of synthetic data generation is to create data that will perform similarly to the original dataset for many analysis tasks, such as classification. The problem of GANs is that in a classification problem, GANs do not take class labels into account when generating new data, it is treated as any other attribute. This research work has focused on the creation of new synthetic data from datasets with different characteristics with a Conditional Generative Adversarial Network (CGAN). CGANs are an extension of GANs where the class label is taken into account when the new data is generated. The performance of our results has been measured in two different ways: firstly, by comparing the results obtained with classification algorithms, both in the original datasets and in the data generated; secondly, by checking that the correlation between the original data and those generated is minimal. [ABSTRACT FROM AUTHOR]

The importance of electricity in people's daily lives has made it an indispensable commodity in society. In electricity market, the price of electricity is the most important factor for each of those involved in it, therefore, the prediction of the electricity price has been an essential and very important task for all the agents involved in the purchase and sale of this good. The main problem within the electricity market is that prediction is an arduous and difficult task, due to the large number of factors involved, the non-linearity, non-seasonality and volatility of the price over time. Data Science methods have proven to be a great tool to capture these difficulties and to be able to give a reliable prediction using only price data, i.e., taking the problem from an univariate point of view in order to help market agents. In this work, we have made a comparison among known models in the literature, focusing on Deep Learning architectures by making an extensive tuning of parameters using data from the Spanish electricity market. Three different time periods have been used in order to carry out an extensive comparison among them. The results obtained have shown, on the one hand, that Deep Learning models are quite effective in predicting the price of electricity and, on the other hand, that the different time periods and their particular characteristics directly influence the final results of the models. [ABSTRACT FROM AUTHOR]

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Traditional computational techniques are recently being improved with the use of prior biological knowledge from open-access repositories in the area of gene expression data analysis. In this work, we propose the use of prior knowledge as heuristic in an inference method of gene-gene associations from gene expression profiles. In this paper, we use Gene Ontology, which is an open-access ontology where genes are annotated using their biological functionality, as a source of prior knowledge together with a gene pairwise Gene-Ontology-based measure. The performance of our proposal has been compared to other benchmark methods for the inference of gene networks, outperforming in some cases and obtaining similar and competitive results in others, but with the advantage of providing simple and interpretable models, which is a desired feature for the Artificial Intelligence Health related models as stated by the European Union. [ABSTRACT FROM AUTHOR]

Studying lithic raw material sourcing, processing and distribution is helpful when trying to reconstruct the territory, ecology, and cultural practices of Neanderthal groups. The use of multiple methods in such analyses allows for more refined characterizations to be made, helping to distinguish between materials better than any single method. Although 85% of the raw materials making up the Mousterian assemblage at the Navalmaíllo Rock Shelter (Pinilla del Valle, Madrid Region of Spain) correspond to the available local geological resources, 10% is made of white/beige chert, which is not registered in regional geological cartography. Petrographic and scanning electron microscopy with energy dispersive X‐ray spectroscopy analyses were performed to determine the origin of this white/beige chert and thus shed light on the procurement strategies of the Neanderthal groups that occupied the rock shelter. The results show this chert to correspond to three different types of rock: quartz–kaolinite rocks from dykes, cretaceous marine cherts, and quartz fillings of cavities/fractures. These findings are in accordance with the geological features and formations present in the Lozoya valley, as recorded during a geoarchaeological survey, and indicate that the Neanderthals occupying the center of the Iberian Peninsula possessed a detailed knowledge of the landscape that allowed them to exploit its resources during MIS5a–early MIS4. [ABSTRACT FROM AUTHOR]

In recent years, reports on bone breakage at archaeological sites have become more common in the taphonomic literature. The present work tests a recently published method, based on the use of machine learning algorithms for analysing the processes involved in bone breakage, to identify the agent that broke the bones of medium-sized animals at the Mousterian Navalmaíllo Rock Shelter (Pinilla del Valle, Madrid). This is the first time this method has been used in an archaeological setting. The results show that these bones were mostly broken by anthropic action, while some were slightly ravaged by carnivores, probably hyaenas. These findings agree very well with published interpretations of the site, and show the method used to be useful in taphonomic studies of archaeological materials with poorly preserved cortical surfaces. [ABSTRACT FROM AUTHOR]

Navalmaíllo Rock Shelter is an Upper Pleistocene archaeological site in the Lozoya River Valley (Madrid, Spain) with a quartz-based Mousterian lithic assemblage. To understand the reasons behind an intense use of quartz over flint and quartzite, a mechanical experiment was carried out. Flakes from flint, quartzite, and local quartz were tested under controlled conditions and quantifiable variables. The mechanical action consisted in a standardised linear repetitive cutting protocol over antler and pine wood. Results allowed to differentiate flake resistance between raw materials through mass and edge angle material loss statistics. Results also showed that the edges produced on flint are sharper allowing to create deeper cuts, but the thin working edges break more easily meaning that they would need a higher maintenance by retouch. Quartzite and quartz have similar performances, but quartzite suffers a more intense modification of the edge angle, while quartz edges present a higher endurance. When compared with flint, quartzite and quartz are more suitable for those tasks where heavier force is applied. Based on that, we concluded that there was no functional disadvantage in using a quartz-based toolkit. Therefore, the quartz assemblages recovered throughout the sequence of Navalmaíllo Rock Shelter show that it was intensely explored not just because of its availability in the landscape but also for its suitability to the development of the different activities taking place at Navalmaíllo Rock Shelter such as big sized herbivore butchering, for a long time span. [ABSTRACT FROM AUTHOR]

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