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Despite the broad interest and use of sentiment analysis nowadays, most of the conclusions in current literature are driven by simple statistical representations of sentiment scores. On that basis, the generated sentiment evaluation consists nowadays of encoding and aggregating emotional information from a number of individuals and their populational trends. We hypothesized that the stochastic processes aimed to be measured by sentiment analysis systems will exhibit nontrivial statistical and temporal properties. We established an experimental setup consisting of analyzing the short text messages (tweets) of 6 user groups with different nature (universities, politics, musicians, communication media, technological companies, and financial companies), including in each group ten high-intensity users in their regular generation of traffic on social networks. Statistical descriptors were checked to converge at about 2000 messages for each user, for which messages from the last two weeks were compiled using a custom-made tool. The messages were subsequently processed for sentiment scoring in terms of different lexicons currently available and widely used. Not only the temporal dynamics of the resulting score time series per user was scrutinized, but also its statistical description as given by the score histogram, the temporal autocorrelation, the entropy, and the mutual information. Our results showed that the actual dynamic range of lexicons is in general moderate, and hence not much resolution is given within their end-of-scales. We found that seasonal patterns were more present in the time evolution of the number of tweets, but to a much lesser extent in the sentiment intensity. Additionally, we found that the presence of retweets added negligible effects over standard statistical modes, while it hindered informational and temporal patterns. The innovative Compounded Aggregated Positivity Index developed in this work proved to be characteristic for industries and at the same time an interesting way to identify singularities among peers. We conclude that temporal properties of messages provide with information about the sentiment dynamics, which is different in terms of lexicons and users, but commonalities can be exploited in this field using appropriate temporal digital processing tools.

The search for an unbiased company valuation method to reduce uncertainty, whether or not it is automatic, has been a relevant topic in social sciences and business development for decades. Many methods have been described in the literature, but consensus has not been reached. In the companion paper we aimed to review the assessment capabilities of traditional company valuation model, based on company&rsquo, s intrinsic value using the Discounted Cash Flow (DCF). In this paper, we capitalized on the potential of exogenous information combined with Machine Learning (ML) techniques. To do so, we performed an extensive analysis to evaluate the predictive capabilities with up to 18 different ML techniques. Endogenous variables (features) related to value creation (DCF) were proved to be crucial elements for the models, while the incorporation of exogenous, industry/country specific ones, incrementally improves the ML performance. Bagging Trees, Supported Vector Regression, Gaussian Process Regression methods consistently provided the best results. We concluded that an unbiased model can be created based on endogenous and exogenous information to build a reference framework, to price and benchmark Enterprise Value for valuation and credit risk assessment.

The Discounted Cash Flow (DCF) method is probably the most extended approach used in company valuation, its main drawbacks being probably the known extreme sensitivity to key variables such as Weighted Average Cost of Capital (WACC) and Free Cash Flow (FCF) estimations not unquestionably obtained. In this paper we propose an unbiased and systematic DCF method which allows us to value private equity by leveraging on stock markets evidences, based on a twofold approach: First, the use of the inverse method assesses the existence of a coherent WACC that positively compares with market observations, second, different FCF forecasting methods are benchmarked and shown to correspond with actual valuations. We use financial historical data including 42 companies in five sectors, extracted from Eikon-Reuters. Our results show that WACC and FCF forecasting are not coherent with market expectations along time, with sectors, or with market regions, when only historical and endogenous variables are taken into account. The best estimates are found when exogenous variables, operational normalization of input space, and data-driven linear techniques are considered (Root Mean Square Error of 6.51). Our method suggests that FCFs and their positive alignment with Market Capitalization and the subordinate enterprise value are the most influencing variables. The fine-tuning of the methods presented here, along with an exhaustive analysis using nonlinear machine-learning techniques, are developed and discussed in the companion paper.

During the last decades there has been a rapidly growing elderly population and the number of patients with chronic heart-related diseases has exploded. Many of them (such as those with congestive heart failure or some types of arrhythmias) require close medical supervision, thus imposing a big burden on healthcare costs in most western economies. Specifically, continuous or frequent Arterial Blood Pressure (ABP) and electrocardiogram (ECG) monitoring are important tools in the follow-up of many of these patients. In this work, we present a novel remote non-ambulatory and clinically validated heart self-monitoring system, which allows ABP and ECG monitoring to effectively identify clinically relevant arrhythmias. The system integrates digital transmission of the ECG and tensiometer measurements, within a patient-comfortable support, easy to recharge and with a multi-function software, all of them aiming to adapt for elderly people. The main novelty is that both physiological variables (ABP and ECG) are simultaneously measured in an ambulatory environment, which to our best knowledge is not readily available in the clinical market. Different processing techniques were implemented to analyze the heart rhythm, including pause detection, rhythm alterations and atrial fibrillation, hence allowing early detection of these diseases. Our results achieved clinical quality both for in-lab hardware testing and for ambulatory scenario validations. The proposed active assisted living (AAL) Sensor-based system is an end-to-end multidisciplinary system, fully connected to a platform and tested by the clinical team from beginning to end.

El departamento de Ingeniería de la Universidad Miguel Hernández del Campus de Orihuela, lleva más de catorce años investigando sobre el desarrollo de nuevos materiales y la forma de aditivarlos y su aplicación a la sociedad.El aditivo en cuestión, se trata de un fluidificante, superplastificante y retardante del fraguado del yeso, compuesto por dióxido de silicio coloidal más ácido cítrico, con proporciones de 1-3 % de dióxido de silicio y de 99-97 % de ácido cítrico.Este nuevo aditivo patentado se puede adicionar, en el proceso de amasado, tanto en fase sólida como en fase líquida actuando sobre el proceso de fraguado del yeso y obteniéndose un buen resultado.Es de aplicación en yeso blanco, yeso negro ó moreno (Black paster) y escayola. Las dosificaciones, por kg de yeso, están entre 0.06 gr/kg hasta 1,2 gr/kg de yeso, con unos tiempos de fraguado entre 15 minutos y 240 minutos.La dosificación de agua para el amasado es variable, siendo una ventaja la reducción de agua en el masado. Se establece en 0,2 kg de agua por 1 kg de yeso de este modo se obtiene un yeso amasado que convertido en pequeñas bolas de diámetro entre 2 y 8 mm es utilizable en la fabricación de prefabricados mediante prensado.En la fabricación de ladrillos se produce una reducción de emisiones de CO2 en torno al 80 %, ya que el proceso de calcinación del yeso se puede realizar a una temperatura de unos 250 ºC (la temperatura puede ser menor, o mayor si se quiere reducir tiempo de cocción). Se evita el tiempo de secado del ladrillo cerámico (unas 8 horas a 100 ºC) más el de cocción del mismo (unas 8-14 horas a 900 ºC).El desarrollo del material obtenido lleva aparejado la aplicación directa en la fabricación de ladrillos huecos o semihuecos para su uso industria debido a su óptimo comportamiento a compresión (resistencia media de 19,23N/mm2), además de obtenerse un beneficio medioambiental y económico.

Recent research has proven the existence of statistical relation among fragmented QRS and several highly prevalence diseases, such as cardiac sarcoidosis, acute coronary syndrome, arrythmogenic cardiomyopathies, Brugada syndrome, and hypertrophic cardiomyopathy. One out of five hundred people suffer from hypertrophic cardiomyopathies. The relation among the fragmentation and arrhythmias drives the objective of this work, which is to propose a valid method for QRS fragmentation detection. With that aim, we followed a two-stage approach. First, we identified the features that better characterize the fragmentation by analyzing the physiological interpretation of multivariate approaches, such as principal component analysis (PCA) and independent component analysis (ICA). Second, we created an invariant transformation method for the multilead electrocardiogram (ECG), by scrutinizing the statistical distributions of the PCA eigenvectors and of the ICA transformation arrays, in order to anchor the desired elements in the suitable leads in the feature space. A complete database was compounded incorporating real fragmented ECGs, surrogate registers by synthetically adding fragmented activity to real non-fragmented ECG registers, and standard clean ECGs. Results showed that the creation of beat templates together with the application of PCA over eight independent leads achieves 0.995 fragmentation enhancement ratio and 0.07 dispersion coefficient. In the case of ICA over twelve leads, the results were 0.995 fragmentation enhancement ratio and 0.70 dispersion coefficient. We conclude that the algorithm presented in this work constructs a new paradigm, by creating a systematic and powerful tool for clinical anamnesis and evaluation based on multilead ECG. This approach consistently consolidates the inconspicuous elements present in multiple leads onto designated variables in the output space, hence offering additional and valid visual and non-visual information to standard clinical review, and opening the door to a more accurate automatic detection and statistically valid systematic approach for a wide number of applications. In this direction and within the companion paper, further developments are presented applying this technique to fragmentation detection.

With the emergence of information and communication technologies, a large amount of data has turned available for the organizations, which creates expectations on their value and content for management purposes. However, the exploratory analysis of available organizational data based on emerging Big Data technologies are still developing in terms of operative tools for solid and interpretable data description. In this work, we addressed the exploratory analysis of organization databases at early stages where little quantitative information is available about their efficiency. Categorical and metric single-variable tests are proposed and formalized in order to provide a mass criterion to identify regions in forms with clusters of significant variables. Bootstrap resampling techniques are used to provide nonparametric criteria in order to establish easy-to-use statistical tests, so that single-variable tests are represented each on a visual and quantitative statistical plot, whereas all the variables in a given form are jointly visualized in the so-called chromosome plots. More detailed profile plots offer deep comparison knowledge for categorical variables across the organization physical and functional structures, while histogram plots for numerical variables incorporate the statistical significance of the variables under study for preselected Pareto groups. Performance grouping is addressed by identifying two or three groups according to some representative empirical distribution of some convenient grouping feature. The method is applied to perform a Big-Data exploratory analysis on the follow-up forms of Spanish Red Cross, based on the number of interventions and on a by-record basis. Results showed that a simple one-variable blind-knowledge exploratory Big-Data analysis, as the one developed in this paper, offers unbiased comparative graphical and numerical information that characterize organizational dynamics in terms of applied resources, available capacities, and productivity. In particular, the graphical and numerical outputs of the present analysis proved to be a valid tool to isolate the underlying overloaded or under-performing resources in complex organizations. As a consequence, the proposed method allows a systematic and principled way for efficiency analysis in complex organizations, which combined with organizational internal knowledge could leverage and validate efficient decision-making.

Hypertrophic cardiomyopathy (HCM) is a myocardial disorder that affects 0.2% of the population and it is genetically transmitted. Several ECG findings have been related to the presence of fibrosis in other cardiac diseases, but data for HCM in this setting are lacking. Our hypothesis is that fibrosis affects the electrical cardiac propagation in patients with HCM in a relatively specific way and that this effect may be detected with suitable postprocessing applied to the ECG signals. We used 43 standard 12-lead ECGs from patients with previous clinical diagnosis of HCM. Principal Component Analysis (PCA) was applied by combining the ECG-leads oriented to different anatomic regions, hence assessing the potential fibrosis effects in the resulting leads for postprocessing convenience. Linear classifier of Support Vector Machine type were used with several statistics extracted from the resulting PCA-components, including normalized power, standard deviation, kurtosis, skewness, and local maxima. Results reached 75.0% sensitivity, 80.0% specificity, 85.7% positive predictive value, 66.7% negative predictive value, and 76.9% accuracy in our database. There is evidence that myocardial fibrosis can be detected in patients with HCM by postprocessing their ECG signals.

Despite the wide literature on R-wave detection algorithms for ECG Holter recordings, the long-term monitoring applications are bringing new requirements, and it is not clear that the existing methods can be straightforwardly used in those scenarios. Our aim in this work was twofold: First, we scrutinized the scope and limitations of existing methods for Holter monitoring when moving to long-term monitoring; Second, we proposed and benchmarked a beat detection method with adequate accuracy and usefulness in long-term scenarios. A longitudinal study was made with the most widely used waveform analysis algorithms, which allowed us to tune the free parameters of the required blocks, and a transversal study analyzed how these parameters change when moving to different databases. With all the above, the extension to long-term monitoring in a database of 7-day Holter monitoring was proposed and analyzed, by using an optimized simultaneous-multilead processing. We considered both own and public databases. In this new scenario, the noise-avoid mechanisms are more important due to the amount of noise that exists in these recordings, moreover, the computational efficiency is a key parameter in order to export the algorithm to the clinical practice. The method based on a Polling function outperformed the others in terms of accuracy and computational efficiency, yielding 99.48% sensitivity, 99.54% specificity, 99.69% positive predictive value, 99.46% accuracy, and 0.85% error for MIT-BIH arrhythmia database. We conclude that the method can be used in long-term Holter monitoring systems.

This article belongs to the Special Issue Sensors for Health Monitoring and Disease Diagnosis., Noise and artifacts are inherent contaminating components and are particularly present in Holter electrocardiogram (ECG) monitoring. The presence of noise is even more significant in long-term monitoring (LTM) recordings, as these are collected for several days in patients following their daily activities; hence, strong artifact components can temporarily impair the clinical measurements from the LTM recordings. Traditionally, the noise presence has been dealt with as a problem of non-desirable component removal by means of several quantitative signal metrics such as the signal-to-noise ratio (SNR), but current systems do not provide any information about the true impact of noise on the ECG clinical evaluation. As a first step towards an alternative to classical approaches, this work assesses the ECG quality under the assumption that an ECG has good quality when it is clinically interpretable. Therefore, our hypotheses are that it is possible (a) to create a clinical severity score for the effect of the noise on the ECG, (b) to characterize its consistency in terms of its temporal and statistical distribution, and (c) to use it for signal quality evaluation in LTM scenarios. For this purpose, a database of external event recorder (EER) signals is assembled and labeled from a clinical point of view for its use as the gold standard of noise severity categorization. These devices are assumed to capture those signal segments more prone to be corrupted with noise during long-term periods. Then, the ECG noise is characterized through the comparison of these clinical severity criteria with conventional quantitative metrics taken from traditional noise-removal approaches, and noise maps are proposed as a novel representation tool to achieve this comparison. Our results showed that neither of the benchmarked quantitative noise measurement criteria represent an accurate enough estimation of the clinical severity of the noise. A case study of long-term ECG is reported, showing the statistical and temporal correspondences and properties with respect to EER signals used to create the gold standard for clinical noise. The proposed noise maps, together with the statistical consistency of the characterization of the noise clinical severity, paves the way towards forthcoming systems providing us with noise maps of the noise clinical severity, allowing the user to process different ECG segments with different techniques and in terms of different measured clinical parameters., This work was supported by the PRINCIPIAS project (TEC2013-48439-C4-1-R and TEC2013-48439-C4-2-R), FINALE project (TEC2016-75161-C2-1-R and TEC2016-75161-C2-2-R), KERMES project (TEC2016-81900-REDT), and project TEC2015-64835-C3-1-R with FEDER fundings. These grants allow to cover open access costs for publication.

In the past few years, the presence of fragmentation in the QRS complex has been demonstrated to be related to diseases such as myocardial fibrosis, cardiac sarcoidosis, arrythmogenic cardiopathies, acute coronary syndrome, and Brugada syndrome, among others. The detection of fragmentation in the QRS is usually carried out manually, which represents a subjective pattern recognition task that demands an effort by the clinician, increasing with the number of patients. These problems have made the process of fragmentation detection a good candidate to its automatization. In this work, we used a database with over six-thousand 12-lead ECG from Hospital Virgen de la Arrixaca de Murcia (Spain), which where digitally recorded with GE MAC5000. Affected and non-affected patients records were extracted for computerized analysis. Clinical supervision was performed for gold-standard development and for signal classification. Fragmentation detection algorithms were developed using first and second derivatives calculation in the pre-qualified segments of the signal, after fiducial point detection. The obtained results were 96.88% sensitivity, 72.92% specificity, and 82.50% accuracy. These results confirm that it is possible to automatically detect fragmentation, constituting a relevant tool to pre-qualify patients for further diagnostic-tests, and it also opens new opportunities for computerized diagnosis.

Objective: Heart rate turbulence (HRT) has been successfully explored for cardiac risk stratification. While HRT is known to be influenced by the heart rate (HR) and the coupling interval (CI), nonconcordant results have been reported on how the CI influences HRT. The purpose of this study is to investigate HRT changes in terms of CI and HR by means of an especially designed protocol. Methods: A dataset was acquired from 11 patients with structurally normal hearts for which CI was altered by different pacing trains and HR by isoproterenol during electrophysiological study (EPS). The protocol was designed so that, first, the effect of HR changes on HRT and, second, the combined effect of HR and CI could be explored. As a complement to the EPS dataset, a database of 24-h Holters from 61 acute myocardial infarction (AMI) patients was studied for the purpose of assessing risk. Data analysis was performed by using different nonlinear ridge regression models, and the relevance of model variables was assessed using resampling methods. The EPS subjects, with and without isoproterenol, were analyzed separately. Results: The proposed nonlinear regression models were found to account for the influence of HR and CI on HRT, both in patients undergoing EPS without isoproterenol and in low-risk AMI patients, whereas this influence was absent in high-risk AMI patients. Moreover, model coefficients related to CI were not statistically significant, $p > 0.05$ , on EPS subjects with isoproterenol. Conclusion: The observed relationship between CI and HRT, being in agreement with the baroreflex hypothesis, was statistically significant ( $p ), when decoupling the effect of HR and normalizing the CI by the HR. Significance: The results of this study can help to provide new risk indicators that take into account physiological influence on HRT, as well as to model how this influence changes in different cardiac conditions.

Hypertrophic cardiomyopathy, according to its prevalence, is a comparatively common disease related to the risk of suffering sudden cardiac death, heart failure and stroke. This illness is characterized by the excessive deposition of collagen among healthy myocardium cells. This situation, which is medically known as fibrosis, constitutes effective conduction obstacles in the myocardium electrical path, and when severe enough, it can be outlined as additional peaks or notches in the QRS, clinically entitled as fragmentation. Nowadays, the fragmentation detection is performed by visual inspection, but the fragmented QRS can be confused with the noise present in the electrocardiogram (ECG). On the other hand, fibrosis detection is performed by magnetic resonance imaging with late gadolinium enhancement, the main drawback of this technique being its cost in terms of time and money. In this work, we propose two automatic algorithms, one for fragmented QRS detection and another for fibrosis detection. For this purpose, we used four different databases, including the subrogated database described in the companion paper and incorporating three additional ones, one compounded by more accurate subrogated ECG signals and two compounded by real and affected subjects as labeled by expert clinicians. The first real-world database contains QRS fragmented records and the second one contains records with fibrosis and both were recorded in Hospital Cl&iacute, nico Universitario Virgen de la Arrixaca (Spain). To deeply analyze the scope of these datasets, we benchmarked several classifiers such as Neural Networks, Support Vector Machines (SVM), Decision Trees and Gaussian Na&iuml, ve Bayes (NB). For the fragmentation dataset, the best results were 0.94 sensitivity, 0.88 specificity, 0.89 positive predictive value, 0.93 negative predictive value and 0.91 accuracy when using SVM with Gaussian kernel. For the fibrosis databases, more limited accuracy was reached, with 0.47 sensitivity, 0.91 specificity, 0.82 predictive positive value, 0.66 negative predictive value and 0.70 accuracy when using Gaussian NB. Nevertheless, this is the first time that fibrosis detection is attempted automatically from ECG postprocessing, paving the way towards improved algorithms and methods for it. Therefore, we can conclude that the proposed techniques could offer a valuable tool to clinicians for both fragmentation and fibrosis diagnoses support.

The assessment of promotional sales with models constructed by machine learning techniques is arousing interest due, among other reasons, to the current economic situation leading to a more complex environment of simultaneous and concurrent promotional activities. An operative model diagnosis procedure was previously proposed in the companion paper, which can be readily used both for agile decision making on the architecture and implementation details of the machine learning algorithms, and for differential benchmarking among models. In this paper, a detailed example of model analysis is presented for two representative databases with different promotional behaviour, namely, a non-seasonal category (milk) and a heavily seasonal category (beer). The performance of four well-known machine learning techniques with increasing complexity is analyzed in detail here. In particular, k-Nearest Neighbours, General Regression Neural Networks, Multilayer Perceptron (MLP), and Support Vector Machines (SVM), are differentially compared. Present paper evaluates these techniques along the experiments described for both categories when applying the methodological findings obtained in the companion paper. We conclude that some elements included in the architecture are not essential for a good performance of the machine learning promotional models, such as the semiparametric nature of the kernel in SVM models, whereas other can be strongly dependent of the database, such as the convenience of multiple output models in MLP regression schemes. Additionally, the specificity of the behaviour of certain categories and product ranges determines the need to establish suitable and specific procedures for a better prediction and feature extraction.

Common reed ( Arundo donax ) is a fast growing perennial plant, considered in many countries as a weed or invasive plant. New material developments in recent years have led to common cane proliferation and an invasion in crops and water channels, significantly increasing elimination and control costs. In this study, it is proposed that particleboard be manufactured of common reed ( A. donax ). The objective is twofold. On the one hand it allows control of a weed, and secondly, it helps reduce the high dependence of imported wood timber and boards by using a readily and annually renewable resource. The fibers of common reed A. donax , possess high tensile strength (200 N/mm 2 between knots), providing much higher values than wood. This fact together with an important moisture and aging resistance, encourage us to propose it as a suitable raw material for the production of particleboards. In this study we propose the manufacture of particle board with urea formaldehyde resin, which is currently used in the wood chipboard industry. To perform this study, A. donax fibers obtained from a shredding hammer, were classified attending to size and slenderness. Following the standard industrial chipboard manufacturing processes, boards were developed to compare their behaviour against wood chipboards. Fifteen test-tube batches with different proportions of pre-selected fibers were manufactured. New boards were subjected to a mechanical stress test, and other physical properties were also measured. UNE EN—specific wood chipboard regulations were applied. Results showed that particleboard could be manufactured using A. donax adjusting the particle size to a maximum of 4 mm sieves. Finally it can be stated that making common reed panels also involves the reuse of a plant actually considered a weed, to become a raw material for industrial applications, leaving a door open to future development and research areas.

Present work elaborates on systems and engineering procedures to create particleboards based on Common Reed. Particleboard was manufactured using the shredding blades, usually applied by wood-particleboard industry. Diverse types of boards were manufactured keeping the same proportion of urea formaldehyde resin (for indoor use), but with different particle proportions according to ISO standard sieves, and varying the pressure from 3 N/mm2 to 25 N/mm2. Mechanical Density, bending resistance and elasticity tests were performed and results were compared against commercial wood particleboards. Results allowed us to state that particle size and pressure plays a significant role in common reed particleboard properties. Common reed particleboards were classified according to UNE EN standards, taking into account relevant standard tests carried out as well as additional classification standard-rules.

Vasovagal Syncope (VVS) represents the most frequent cause of loss of consciousness. Additionally to its clinical usefulness, the tilt test is a good quality physiological gold standard for the spectral analysis of Heart Rate Variability (HRV). Noise removal in HRV signals is problematic, due to the presence of ectopic beats and non-stationary short-term trends. Given current Tilt Test systems simultaneously record several physiological signals, we hypothesize that independent component analysis (ICA) may separate physiological from mostly-noise components, and denoising can be properly done. Four-dimensional recordings (HR, systolic/diastolic blood pressure, and ejection volume) were obtained during 50 Tilt Test. After ICA decomposition, a 5th order median filter was applied to the noisiest component, prior to signal reconstruction. In order to check the denoising performance, a gold-standard was made by manually removing ectopic beats and artifacts from the original signals by an expert. For comparison purposes, a 5th order median filter was also applied separately to the HR signal. The spectrum analysis showed that denoising of multidimensional recordings with ICA during Tilt Test yields HRV signals with lower distortion at HF band.