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This paper presents regulation of an asynchronous induction motor so as to create a stable vacuum milk pump using Variable Frequency Drive (VFD). Contribution includes providing information about the usage of the VFD, which regulates the activity of an asynchronous induction motor so that the vacuum pump milking machine creates stable vacuum. The paper describes the functional and time dependence of input values and output parameters of frequency converters at changing electric motor speed. For simulation and verification the milking process a mathematical model of the milking machine was created. The simulation was verified in Matlab/Simulink software. The constructed mathematical model showed symmetric regulation. Control model symmetry was verified at the laboratory of milking machine. The possibility to remove the control valve from milking equipment was proven using the measured data. It was found that constant vacuum values can be maintained. A constant vacuum can be maintained by changing vacuum pump speed. This control is of an accepted standard (ISO 5707: 2007). The power saving control values (on the milking equipment) of the VFD were positive throughout the measuring range. The performance of the milking vacuum pump is normally designed from the maximum air consumption of the milking machine at nominal vacuum (50 kPa), and a performance reserve is added to this. This means that the pump is operated between the ranges 7.53 and 15.06 dm3 s−1. By using a vacuum pump controlled by a VFD, power savings can be achieved from 32.50% to 54.02% compared to a control valve.

[Abstract] Our current knowledge on the crocodyliform evolution is strongly biased towards the skull morphology, and the postcranial skeleton is usually neglected in many taxonomic descriptions. However, it is logical to expect that it can contribute with its own phylogenetic signal. In this paper, the changes in the tree topology caused by the addition of the postcranial information are analysed for the family Allodaposuchidae, the most representative eusuchians in the latest Cretaceous of Europe. At present, different phylogenetic hypotheses have been proposed for this group without reaching a consensus. The results of this paper evidence a shift in the phylogenetic position when the postcranium is included in the dataset, pointing to a relevant phylogenetic signal in the postcranial elements. Finally, the phylogenetic relationships of allodaposuchids within Eusuchia are reassessed; and the internal relationships within Allodaposuchidae are also reconsidered after an exhaustive revision of the morphological data. New and improved diagnoses for each species are here provided. This work was supported by postdoctoral grants funded by the program “Axudas á etapa postdoutoral da Xunta de Galicia 2017 – Modalidade A” (ED481B 2017/027) (https://www.xunta.gal/) and the program “Ayudas Juan de la Cierva – Formación 2019” (FJC2019-042583-I) (https://www.ciencia.gob.es/). Additional support to visit palaeontological collections was received from the program “Axudas á investigación 2017 e 2018” of the Universidade da Coruña (https://www.udc.es/), and from the SYNTHESYS+ Project (DE-TAF-2604) which is financed by the European Commission via the H2020 Research Infrastructure programme (https://www.synthesys.info/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript Xunta de Galicia; ED481B 2017/027

Objective Digital nudging has been mooted as a tool to alter user privacy behavior. However, empirical studies on digital nudging have yielded divergent results: while some studies found nudging to be highly effective, other studies found no such effects. Furthermore, previous studies employed a wide range of digital nudges, making it difficult to discern the effectiveness of digital nudging. To address these issues, we performed a systematic review of empirical studies on digital nudging and information disclosure as a specific privacy behavior. Method The search was conducted in five digital libraries and databases: Scopus, Google Scholar, ACM Digital Library, Web of Science, and Science Direct for peer-reviewed papers published in English after 2006, examining the effects of various nudging strategies on disclosure of personal information online. Results The review unveiled 78 papers that employed four categories of nudge interventions: presentation, information, defaults, and incentives, either individually or in combination. A meta-analysis on a subset of papers with available data (n = 54) revealed a significant small-to-medium sized effect of the nudge interventions on disclosure (Hedges’ g = 0.32). There was significant variation in the effectiveness of nudging (I2 = 89%), which was partially accounted for by interventions to increase disclosure being more effective than interventions to reduce disclosure. No evidence was found for differences in the effectiveness of nudging with presentation, information, defaults, and incentives interventions. Conclusion Identifying ways to nudge users into making more informed and desirable privacy decisions is of significant practical and policy value. There is a growing interest in digital privacy nudges for disclosure of personal information, with most empirical papers focusing on nudging with presentation. Further research is needed to elucidate the relative effectiveness of different intervention strategies and how nudges can confound one another.

The interaction between phenotypic plasticity, e.g. learning, and evolution is an important topic both in Evolutionary Biology and Machine Learning. The evolution of learning is commonly studied in Evolutionary Biology, while the use of an evolutionary process to improve learning is of interest to the field of Machine Learning. This paper takes a different point of view by studying the effect of learning on the evolutionary process, the so-called Baldwin effect. A well-studied result in the literature about the Baldwin effect is that learning affects the speed of convergence of the evolutionary process towards some genetic configuration, which corresponds to the environment-induced plastic response. This paper demonstrates that learning can change the outcome of evolution, i.e., lead to a genetic configuration that does not correspond to the plastic response. Results are obtained both analytically and experimentally by means of an agent-based model of a foraging task, in an environment where the distribution of resources follows seasonal cycles and the foraging success on different resource types is conditioned by trade-offs that can be evolved and learned. This paper attempts to answer a question that has been overlooked: whether learning has an effect on what genotypic traits are evolved, i.e. the selection of a trait that enables a plastic response changes the selection pressure on a different trait, in what could be described as co-evolution between different traits in the same genome., PLoS ONE, 14 (9), ISSN:1932-6203

PDkit is an open source software toolkit supporting the collaborative development of novel methods of digital assessment for Parkinson’s Disease, using symptom measurements captured continuously by wearables (passive monitoring) or by high-use-frequency smartphone apps (active monitoring). The goal of the toolkit is to help address the current lack of algorithmic and model transparency in this area by facilitating open sharing of standardised methods that allow the comparison of results across multiple centres and hardware variations. PDkit adopts the information-processing pipeline abstraction incorporating stages for data ingestion, quality of information augmentation, feature extraction, biomarker estimation and finally, scoring using standard clinical scales. Additionally, a dataflow programming framework is provided to support high performance computations. The practical use of PDkit is demonstrated in the context of the CUSSP clinical trial in the UK. The toolkit is implemented in the python programming language, the de facto standard for modern data science applications, and is widely available under the MIT license., Author summary Parkinson’s Disease is the fastest growing neurological condition affecting millions of people across the world. People with Parkinson’s suffer from a variety of symptoms that result in diminished ability to move, eat, remember or sleep. Research in new treatments are limited because the clinical tools used to assess its symptoms are subjective, require considerable time to perform and specialised skills and can only detect coarse-grain changes. To address this situation, clinicians are turning to smartphone apps and wearables to create new ways to assess symptoms that are more sensitive to change and can be applied frequently at home by patients and their carers. In this paper, we discuss PDkit, an open source toolkit that we developed to help address this current lack of algorithmic and model transparency. Adopting PDkit facilitates the open sharing of standardised methods and can accelerate the development of new methods and system to assess Parkinson’s and enables research groups to innovate. The toolkit provides funcionality that support data ingestion, quality of information augmentation, feature extraction, biomarker estimation and finally, scoring using standard clinical scales. The practical use of PDkit is demonstrated via its use by the CUSSP clinical trial conducted in the UK.

The purpose of this paper is to examine, whether and under which conditions humans are able to predict the putting distance of a robotic device. Based on the "flash-lag effect" (FLE) it was expected that the prediction errors increase with increasing putting velocity. Furthermore, we hypothesized that the predictions are more accurate and more confident if human observers operate under full vision (F-RCHB) compared to either temporal occlusion (I-RCHB) or spatial occlusion (invisible ball, F-RHC, or club, F-B). In two experiments, 48 video sequences of putt movements performed by a BioRob robot arm were presented to thirty-nine students (age: 24.49±3.20 years). In the experiments, video sequences included six putting distances (1.5, 2.0, 2.5, 3.0, 3.5, and 4.0 m; experiment 1) under full versus incomplete vision (F-RCHB versus I-RCHB) and three putting distances (2. 0, 3.0, and 4.0 m; experiment 2) under the four visual conditions (F-RCHB, I-RCHB, F-RCH, and F-B). After the presentation of each video sequence, the participants estimated the putting distance on a scale from 0 to 6 m and provided their confidence of prediction on a 5-point scale. Both experiments show comparable results for the respective dependent variables (error and confidence measures). The participants consistently overestimated the putting distance under the full vision conditions; however, the experiments did not show a pattern that was consistent with the FLE. Under the temporal occlusion condition, a prediction was not possible; rather a random estimation pattern was found around the centre of the prediction scale (3 m). Spatial occlusion did not affect errors and confidence of prediction. The experiments indicate that temporal constraints seem to be more critical than spatial constraints. The FLE may not apply to distance prediction compared to location estimation.

A major tenet in theoretical neuroscience is that cognitive and behavioral processes are ultimately implemented in terms of the neural system dynamics. Accordingly, a major aim for the analysis of neurophysiological measurements should lie in the identification of the computational dynamics underlying task processing. Here we advance a state space model (SSM) based on generative piecewise-linear recurrent neural networks (PLRNN) to assess dynamics from neuroimaging data. In contrast to many other nonlinear time series models which have been proposed for reconstructing latent dynamics, our model is easily interpretable in neural terms, amenable to systematic dynamical systems analysis of the resulting set of equations, and can straightforwardly be transformed into an equivalent continuous-time dynamical system. The major contributions of this paper are the introduction of a new observation model suitable for functional magnetic resonance imaging (fMRI) coupled to the latent PLRNN, an efficient stepwise training procedure that forces the latent model to capture the ‘true’ underlying dynamics rather than just fitting (or predicting) the observations, and of an empirical measure based on the Kullback-Leibler divergence to evaluate from empirical time series how well this goal of approximating the underlying dynamics has been achieved. We validate and illustrate the power of our approach on simulated ‘ground-truth’ dynamical systems as well as on experimental fMRI time series, and demonstrate that the learnt dynamics harbors task-related nonlinear structure that a linear dynamical model fails to capture. Given that fMRI is one of the most common techniques for measuring brain activity non-invasively in human subjects, this approach may provide a novel step toward analyzing aberrant (nonlinear) dynamics for clinical assessment or neuroscientific research., PLoS Computational Biology, 15 (8), ISSN:1553-734X, ISSN:1553-7358

Smartphones and other mobile devices have fundamentally changed patterns of Internet use in everyday life by making online access constantly available. The present paper offers a theoretical explication and empirical assessment of the concept of online vigilance, referring to users' permanent cognitive orientation towards online content and communication as well as their disposition to exploit these options constantly. Based on four studies, a validated and reliable self-report measure of online vigilance was developed. In combination, the results suggest that the Online Vigilance Scale (OVS) shows a stable factor structure in various contexts and user populations and provides future work in communication, psychology, and other social sciences with a new measure of the individual cognitive orientation towards ubiquitous online communication.

Media, and particularly TV media, have a great impact on the general public. In recent years, spatial patterns of information and the relevance of intangible geographies have become increasingly important. Gatekeeping plays a critical role in the selection of information that is transformed into media. Therefore, gatekeeping, through national media, also co-forms the generation of mental maps. In this paper, correspondence analysis (a statistical method) combined with cloud lines (a new visual analytics technique) is used to analyze how individual major regional events in one of the post-communist countries, the Czech Republic, penetrate into the media on a national scale. Although national news should minimize distortions about regions, this assumption has not been verified by our research. Impressions presented by the media of selected regions that were markedly influenced by one or several events in those regions demonstrate that gatekeepers, especially news reporters, functioned as a filter by selecting only a few specific, and in many cases, unusual events for dissemination. Web of Science 11 11 art. no. e0165527

Next-generation sequencing (NGS) technologies have led to a huge amount of genomic data that need to be analyzed and interpreted. This fact has a huge impact on the DNA sequence alignment process, which nowadays requires the mapping of billions of small DNA sequences onto a reference genome. In this way, sequence alignment remains the most time-consuming stage in the sequence analysis workflow. To deal with this issue, state of the art aligners take advantage of parallelization strategies. However, the existent solutions show limited scalability and have a complex implementation. In this work we introduce SparkBWA, a new tool that exploits the capabilities of a big data technology as Spark to boost the performance of one of the most widely adopted aligner, the Burrows-Wheeler Aligner (BWA). The design of SparkBWA uses two independent software layers in such a way that no modifications to the original BWA source code are required, which assures its compatibility with any BWA version (future or legacy). SparkBWA is evaluated in different scenarios showing noticeable results in terms of performance and scalability. A comparison to other parallel BWA-based aligners validates the benefits of our approach. Finally, an intuitive and flexible API is provided to NGS professionals in order to facilitate the acceptance and adoption of the new tool. The source code of the software described in this paper is publicly available at https://github.com/citiususc/SparkBWA, with a GPL3 license This work was supported by Ministerio de Economía y Competitividad (Spain) (http://www.mineco.gob.es) grants TIN2013-41129-P and TIN2014-54565-JIN. There was no additional external funding received for this study SI

In this paper, we propose an emotion separated method(SeTF·IDF) to assign the emotion labels of sentences with different values, which has a better visual effect compared with the values represented by TF·IDF in the visualization of a multi-label Chinese emotional corpus Ren_CECps. Inspired by the enormous improvement of the visualization map propelled by the changed distances among the sentences, we being the first group utilizes the Word Mover's Distance(WMD) algorithm as a way of feature representation in Chinese text emotion classification. Our experiments show that both in 80% for training, 20% for testing and 50% for training, 50% for testing experiments of Ren_CECps, WMD features get the best f1 scores and have a greater increase compared with the same dimension feature vectors obtained by dimension reduction TF·IDF method. Compared experiments in English corpus also show the efficiency of WMD features in the cross-language field.