Listeners normalize speech for contextual speech rate even without an explicit recognition task

Speech can be produced at different rates. Listeners take this rate variation into account by normalizing vowel duration for contextual speech rate: An ambiguous Dutch word /m?t/ is perceived as short /mAt/ when embedded in a slow context, but long /ma:t/ in a fast context. Whilst some have argued that this rate normalization involves low-level automatic perceptual processing, there is also evidence that it arises at higher-level cognitive processing stages, such as decision making. Prior research on rate-dependent speech perception has only used explicit recognition tasks to investigate the phenomenon, involving both perceptual processing and decision making. This study tested whether speech rate normalization can be observed without explicit decision making, using a cross-modal repetition priming paradigm. Results show that a fast precursor sentence makes an embedded ambiguous prime (/m?t/) sound (implicitly) more /a:/-like, facilitating lexical access to the long target word "maat" in a (explicit) lexical decision task. This result suggests that rate normalization is automatic, taking place even in the absence of an explicit recognition task. Thus, rate normalization is placed within the realm of everyday spoken conversation, where explicit categorization of ambiguous sounds is rare

Baseline tests of the EVA change-of-pace coupe electric passenger vehicle

The EVA Change-of-Pace Coupe, is an electric passenger vehicle, to characterize the state-of-the-art of electric vehicles. The EVA Change-of-Pace Coupe is a four passenger sedan that has been coverted to an electric vehicle. It is powered by twenty 6 volt traction batteries through a silicon controlled rectifier chopper controller actuated by a foot throttle to change the voltage applied to the series wound, direct current motor. Braking is accomplished with a vacuum assist hydraulic braking system. Regenerative braking is also provided

Baseline tests of the battronic Minivan electric delivery van

An electric passenger vehicle was tested to develop data characterizing the state of the art of electric and hybrid vehicles. The test measured vehicle maximum speed, range at constant speed, range over stop-and-go driving schedules, maximum acceleration, gradeability and limit, road energy consumption, road power, indicated energy consumption, braking capability and battery charge efficiency. The data obtained are to serve as a baseline to compare improvements in electric and hybrid vehicle technologies and to assist in establishing performance standards

Baseline tests of the C. H. Waterman DAF electric passenger vehicle

An electric vehicle was tested as part of an Energy Research Development Administration (ERDA) project to characterize the state-of-the-art of electric vehicles. The Waterman vehicle performance test results are presented in this report. The vehicle is a converted four-passenger DAF 46 sedan. It is powered by sixteen 6-volt traction batteries through a three-step contactor controller actuated by a foot throttle to change the voltage applied to the 6.7 kW motor. The braking system is a conventional hydraulic braking system

Analysis of solutions dedicated to non-conformity prevention

Research financed from the NCN research project no. UMO-2012/05 / B / HS4 / 04139.Purpose: The purpose of the work is to analyze the use of solutions aimed at preventing incompatibilities and assess the effectiveness of these solutions to minimize the risk associated with the quality of manufactured products. The analysis presented in the work is based on the Poka-Yoke error prevention concept used in the selected enterprise. The paper presents the state of the company before and after the introduction of Poka-Yoke method. Approach/Methodology/Design: The research methods used in the paper were the analysis of company data, analysis of literature states, as well as methods using the Poka-Yoke concept. The company was surveyed in 2017/2018 during the system implementation. Findings: The results on the control card for nonconforming units of type "p" were also presented, where the analysis showed that the introduction of the Poka-Yoke system to the company reduced the number of nonconformities by 6.69%. Practical Implications: Implementation of the Poka-Yoke system will allow enterprises to create conditions in which an error cannot happen, or will be immediately visible. The result of such an action may be distracting operators from repetitive operations, reducing the number of deficiencies and induce immediate action when a problem occurs. Originality/Value: Showing how the use of the Poka-Yoke system affects the prevention of inadvertent error in enterprises, by determining the differences between the state of the enterprise before and after the introduction of the Poka-Yoke system, there is an opportunity to improve the economy and contribute to the social and economic sphere.peer-reviewe

Supervisory control and data acquisition system as a means of safety at work formation in a manufacturing company

Purpose: The aim of the paper is to present an application of the Supervisory Control and Data Acquisition system (SCADA) in the shaping of occupational safety by raising workers’ awareness concerning importance of maintaining high personal safety standards in a manufacturing company. Approach/Methodology/Design: Shaping work safety covers all activities that increase employee awareness concerning compliance with safety regulations and work practices with particular respect to the rules of conduct in specific situations. This article presents the use of the SCADA system in shaping of the workplace safety. It also underlines its influence on raising employee awareness of the importance of complying with health and safety regulations. Findings: It can be stated that the safety days screen meets the company’s expectations. Moreover, it can be a valuable support in shaping workplace safety and raising employee awareness of safe work. The crucial thing is to carry out a detailed analysis and then choose such a solution that is adequate for the needs of a particular enterprise. Practical Implications: It is noted that the use of any solution that optimizes processes in the enterprise (including manufacturing processes) and the opportunities that IT tools provide, greatly contributes to the growth of safety. Providing safe working conditions by employers is usually closely linked to the organization's management strategy. Originality/Value: Currently we can observe the growing use of distributed control and visualization systems (SCADA systems) in the automation of industrial processes.peer-reviewe

Investigation of the Summer Kara Sea Circulation Employing a Variational Data Assimilation Technique

[ 1] The summer circulations and hydrographic fields of the Kara Sea are reconstructed for mean, positive and negative Arctic Oscillation regimes employing a variational data assimilation technique which provides the best fit of reconstructed fields to climatological data and satisfies dynamical and kinematic constraints of a quasi-stationary primitive equation ocean circulation model. The reconstructed circulations agree well with the measurements and are characterized by inflow of 0.63, 0.8, 0.51 Sv through Kara Gate and 1.18, 1.1, 1.12 Sv between Novaya Zemlya and Franz Josef Land, for mean climatologic conditions, positive and negative AO indexes, respectively. The major regions of water outflow for these regimes are the St. Anna Trough (1.17, 1.21, 1.34 Sv) and Vilkitsky/ Shokalsky Straits (0.52, 0.7, 0.51 Sv). The optimized velocity pattern for the mean climatological summer reveals a strong anticyclonic circulation in the central part of the Kara Sea ( Region of Fresh Water Inflow, ROFI zone) and is confirmed by ADCP surveys and laboratory modeling. This circulation is well pronounced for both high and low AO phases, but in the positive AO phase it is shifted approximately 200 km west relatively to its climatological center. During the negative AO phase the ROFI locaion is close to its climatological position. The results of the variational data assimilation approach were compared with the simulated data from the Hamburg Shelf Ocean Model (HAMSOM) and Naval Postgraduate School 18 km resolution (NPS-18) model to validate these models

3D Dose-Driven, Automatic VMAT Machine Parameter Generation with Deep Learning

Purpose/Objective(s): Recent research efforts utilizing knowledge-based treatment planning for the prediction of 3D radiation dose distributions from planning structure sets have achieved positive results. Most ongoing efforts to generate deliverable plans from the predicted doses rely on full inverse optimizations using dose-volume histogram (DVH) objectives derived from these doses. In this study, we aim to leverage deep learning (DL) to rapidly generate machine delivery parameters for volumetric modulated arc therapy (VMAT) from predicted desired doses. Materials/Methods: Data of 50 previously treated patients at our institution with prostate adenocarcinoma who received definitive radiotherapy were retrospectively obtained. All plans were generated with a one-arc VMAT technique, with conventional fractionation (78 Gy in 39 fx or 79.2 Gy in 44 fx to the prostate gland +/- seminal vesicles). A multi-task U-Net was constructed: it takes the 2D projections of the 3D dose and planning structures as inputs, and it predicts the numerical multi-leaf collimator (MLC) sequence and weights for the 178 control points. Five cases were randomly selected for testing only, and the remaining 45 formed the training set. The algorithm was implemented in Python 3.8 with PyTorch 1.7 as the DL framework. Model training was performed on a GPU. The DL-predicted plans underwent further inverse optimization with the 3D-dose-derived DVH objectives, utilizing only the last step of the Photon Optimizer (PO) in a treatment planning system. The optimization time and plan quality were compared to plans generated with one full PO optimization with the same objectives and clinical plans (all normalized to D95%=100% Rx dose). Results: The DL model was trained for 200 epochs. On average, DL-predicted plans could be optimized in 22% (range, 18-26%) of the time required for full optimization plans. Dosimetric comparison (Table 1) demonstrated that the quality of the DL-predicted plans was comparable with clinical plans and full optimization plans, but the DL-predicted plans tended to have increased dose inhomogeneity within the PTVs. Conclusion: We demonstrated the feasibility of rapidly generating deliverable VMAT plans from desired 3D doses with deep learning. Further work is needed to improve PTV dose homogeneity and generalize the method to multi-arc VMAT delivery