Specific Acquisition Research Articles

Enhancing English For Nursing: A Comprehensive Needs Analysis For Effective Communication In Emergency Nursing Study Programs

students enrolled in nursing study programs with a focus on emergency characteristics. The investigation encompasses both target needs and learning needs, emphasizing the specific language requirements in the nursing field. English for Nursing falls under the domain of English for Specific Purposes (ESP), designed to cater to learners' specific language needs, fostering more effective and comprehensive teaching and learning. By aligning English instruction with the demands of the nursing discipline, non-English program students will gain familiarity with English tailored to nursing contexts. The ESP approach aims to address the specific language acquisition challenges faced by learners with distinct objectives. Data collection involves questionnaires administered to 45 students and direct interviews with emergency nursing lecturers. Findings reveal the significance of learning English for improved communication within the context of conversations or practical applications. The identified English materials concentrate on terms relevant to nursing study programs, particularly those associated with emergency characteristics, becoming the focal points of instruction. Additionally, materials focusing on enhancing listening and speaking skills emerge as key priorities in the learning process. The outcomes of this study are anticipated to serve as a valuable reference for the design and development of syllabi, specifically tailored to English for Nursing courses.

Embedded software of the KM3NeT central logic board

The KM3NeT Collaboration is building and operating two deep sea neutrino telescopes at the bottom of the Mediterranean Sea. The telescopes consist of latices of photomultiplier tubes housed in pressure-resistant glass spheres, called digital optical modules and arranged in vertical detection units. The two main scientific goals are the determination of the neutrino mass ordering and the discovery and observation of high-energy neutrino sources in the Universe. Neutrinos are detected via the Cherenkov light, which is induced by charged particles originated in neutrino interactions. The photomultiplier tubes convert the Cherenkov light into electrical signals that are acquired and timestamped by the acquisition electronics. Each optical module houses the acquisition electronics for collecting and timestamping the photomultiplier signals with one nanosecond accuracy. Once finished, the two telescopes will have installed more than six thousand optical acquisition nodes, completing one of the more complex networks in the world in terms of operation and synchronization. The embedded software running in the acquisition nodes has been designed to provide a framework that will operate with different hardware versions and functionalities. The hardware will not be accessible once in operation, which complicates the embedded software architecture. The embedded software provides a set of tools to facilitate remote manageability of the deployed hardware, including safe reconfiguration of the firmware. This paper presents the architecture and the techniques, methods and implementation of the embedded software running in the acquisition nodes of the KM3NeT neutrino telescopes. Program summaryProgram title: Embedded software for the KM3NeT CLBCPC Library link to program files:https://doi.org/10.17632/s847hpsns4.1Licensing provisions: GNU General Public License 3Programming language: CNature of problem: The challenge for the embedded software in the KM3NeT neutrino telescope lies in orchestrating the Digital Optical Modules (DOMs) to achieve the synchronized data acquisition of the incoming optical signals. The DOMs are the crucial component responsible for capturing neutrino interactions deep underwater. The embedded software must configure and precisely time the operation of each DOM. Any deviation or timing mismatch could compromise data integrity, undermining the scientific value of the experiment. Therefore, the embedded software plays a critical role in coordinating, synchronizing, and operating these modules, ensuring they work in unison to capture and process neutrino signals accurately, ultimately advancing our understanding of fundamental particles in the Universe.Solution method: The embedded software on the DOMs provides a solution based on a C-based bare-metal application, operating without a real-time embedded OS. It is loaded into the RAM during FPGA configuration, consuming less than 256 kB of RAM. The software architecture comprises two layers: system software and application. The former offers OS-like features, including a multitasking scheduler, firmware updates, peripheral drivers, a UDP-based network stack, and error handling utilities. The application layer contains a state machine ensuring consistent program states. It is navigated via slow control events, including external inputs and autonomous responses. Subsystems within the application code control specific acquisition electronics components via the associated driver abstractions.Additional comments including restrictions and unusual features: Due to the operation conditions of the neutrino telescope, where access is restricted, the embedded software implements a fail-safe procedure to reconfigure the firmware where the embedded software runs.

Meta-Analysis of Pulse Transition Features in Non-Invasive Blood Pressure Estimation Systems: Bridging Physiology and Engineering Perspectives.

The pulse transition features (PTFs), including pulse arrival time (PAT) and pulse transition time (PTT), hold significant importance in estimating non-invasive blood pressure (NIBP). However, the literature showcases considerable variations in terms of PTFs' correlation with blood pressure (BP), accuracy in NIBP estimation, and the comprehension of the relationship between PTFs and BP. This inconsistency is exemplified by the wide-ranging correlations reported across studies investigating the same feature. Furthermore, investigations comparing PAT and PTT have yielded conflicting outcomes. Additionally, PTFs have been derived from various bio-signals, capturing distinct characteristic points like the pulse's foot and peak. To address these inconsistencies, this study meticulously reviews a selection of such research endeavors while aligning them with the biological intricacies of blood pressure and the human cardiovascular system (CVS). Each study underwent evaluation, considering the specific signal acquisition locale and the corresponding recording procedure. Moreover, a comprehensive meta-analysis was conducted, yielding multiple conclusions that could significantly enhance the design and accuracy of NIBP systems. Grounded in these dual aspects, the study systematically examines PTFs in correlation with the specific study conditions and the underlying factors influencing the CVS. This approach serves as a valuable resource for researchers aiming to optimize the design of BP recording experiments, bio-signal acquisition systems, and the fine-tuning of feature engineering methodologies, ultimately advancing PTF-based NIBP estimation.

Learnings From Impact and Implications of Signal-To-Noise in NMR T1-T2 Logging of Unconventional Reservoirs

We study the impact of signal-to-noise ratio (SNR) on nuclear magnetic resonance (NMR) T1-T2 maps across data sets acquired in multiple wells of an unconventional field under various logging and processing conditions. The mean and standard deviation of NMR porosity error between continuous moving-pass and stationary measurements are used to obtain insights into the impact of SNR on accuracy and precision. In a proof-of-concept experiment, we introduce a novel semi-analytical smeared-peak (SASP) technique that compensates for the over-regularized smearing due to poor SNR, of T1-T2 relaxation responses of different fluids. The SASP approximation to de-smear volumes of different fluid types is validated with field measurements from multiple wells. The uplift of the SASP technique in improving fluid volume interpretations is apparent in the in-situ calibration of low-SNR moving-pass NMR measurements with high-quality stationary measurements. The learnings show that logging protocols that are designed to increase SNR by combining specific acquisition parameters with processing strategies, within acceptable compromises, are mandatory for reliable NMR characterization of unconventional reservoirs.

Longitudinal MRI in the context of in utero surgery for open spina bifida: A descriptive study.

Fetal surgery for open spina bifida (OSB) requires comprehensive preoperative assessment using imaging for appropriate patient selection and to evaluate postoperative efficacy and complications. We explored patient access and conduct of fetal magnetic resonance imaging (MRI) for prenatal assessment of OSB patients eligible for fetal surgery. We compared imaging acquisition and reporting to the International Society of Ultrasound in Obstetrics and Gynecology MRI performance guidelines. We surveyed access to fetal MRI for OSB in referring fetal medicine units (FMUs) in the UK and Ireland, and two NHS England specialist commissioned fetal surgery centers (FSCs) at University College London Hospital, and University Hospitals KU Leuven Belgium. To study MRI acquisition protocols, we retrospectively analyzed fetal MRI images before and after fetal surgery for OSB. MRI for fetal OSB was accessible with appropriate specialists available to supervise, perform, and report scans. The average time to arrange a fetal MRI appointment from request was 4 ± 3 days (range, 0-10), the average scan time available was 37 ± 16 min (range, 20-80 min), with 15 ± 11 min (range, 0-30 min) extra time to repeat sequences as required. Specific MRI acquisition protocols, and MRI reporting templates were available in only 32% and 18% of units, respectively. Satisfactory T2-weighted (T2W) brain imaging acquired in three orthogonal planes was achieved preoperatively in all centers, and 6 weeks postoperatively in 96% of FSCs and 78% of referring FMUs. However, for T2W spine image acquisition referring FMUs were less able to provide three orthogonal planes presurgery (98% FSC vs. 50% FMU, p < 0.001), and 6 weeks post-surgery (100% FSC vs. 48% FMU, p < 0.001). Other standard imaging recommendations such as T1-weighted (T1W), gradient echo (GE) or echoplanar fetal brain and spine imaging in one or two orthogonal planes were more likely available in FSCs compared to FMUs pre- and post-surgery (p < 0.001). There was timely access to supervised MRI for OSB fetal surgery assessment. However, the provision of images of the fetal brain and spine in sufficient orthogonal planes, which are required for determining eligibility and to determine the reversal of hindbrain herniation after fetal surgery, were less frequently acquired. Our evidence suggests the need for specific guidance in relation to fetal MRI for OSB. We propose an example guidance for MRI acquisition and reporting.

Spatial and Temporal Resolution Needs for Volcano Topographic Change Data Sets Based on Past Eruptions (1980–2019)

AbstractUp‐to‐date topography data sets are essential for forecasting volcanic hazards and monitoring deformation. Digital elevation models are used to quantify eruption rates, used in flow modeling programs, and are necessary to accurately process interferometric synthetic aperture radar data for surface deformation. We can track topographic change at volcanoes through fieldwork, airborne instruments, and satellite data, with the last providing the greatest potential for global coverage. Despite this global coverage, we do not know the characteristics of topographic change at volcanoes over a given time interval. We define the specific acquisition needs for topography data using topographic change detected from recent eruptions. We review existing literature and compile a data set of eruptive products (121 lava flows, 99 domes and 163 pyroclastic density currents (PDCs)) from eruptions between 1980 and 2019. We find that different sensing capabilities are required for different use cases. A vertical accuracy of 1 m would detect 92% of all eruptive products including 100% of lava domes and lava flows, but only 78% of PDCs. A horizontal resolution of 13 × 13 m pixels is the minimum necessary to detect 90% of all eruptive products. Explosive eruptions (with PDC products) typically lasted less than 1 day and would need a temporal resolution of 1 day while a longer repeat interval is acceptable at effusive eruptions (lava domes and flows), which could last weeks to years. We find a lack of consistent data acquisition, with 45% of the 383 eruptive products reported not having published spatial dimensions.

The transcription factor NFIL3/E4BP4 regulates the developmental stage–specific acquisition of basophil function

Basophils are rare but important effector cells in many allergic disorders. Contrary to their early progenitors, the terminal developmental processes of basophils in which they gain their unique functional properties are unknown. We sought to identify a novel late-stage basophil precursor and a transcription factor regulating the terminal maturation of basophils. Using flow cytometry, transcriptome analysis, and functional assays, we investigated the identification and functionality of the basophil precursors as well as basophil development. We generated mice with basophil-specific deletion of nuclear factor IL-3 (NFIL3)/E4BP4 and analyzed the functional impairment of NFIL3/E4BP4-deficient basophils invitro and invivo using an oxazolone-induced murine model of allergic dermatitis. We report a new mitotic transitional basophil precursor population (referred to as transitional basophils) that expresses the FcεRIα chain at higher levels than mature basophils. Transitional basophils are less responsive to IgE-linked degranulation but produce more cytokines in response to IL-3, IL-33, or IgE cross-linking than mature basophils. In particular, we found that the expression of NFIL3/E4BP4 gradually rises as cells mature from the basophil progenitor stage. Basophil-specific deletion of NFIL3/E4BP4 reduces the expression of genes necessary for basophil function and impairs IgE receptor signaling, cytokine secretion, and degranulation in the context of murine atopic dermatitis. We discovered transitional basophils, a novel late-stage mitotic basophil precursor cell population that exists between basophil progenitors and postmitotic mature basophils. We demonstrated that NFIL3/E4BP4 augments the IgE-mediated functions of basophils, pointing to a potential therapeutic regulator for allergic diseases.

Benchmarking Probabilistic Deep Learning Methods for License Plate Recognition

Learning-based algorithms for automated license plate recognition implicitly assume that the training and test data are well aligned. However, this may not be the case under extreme environmental conditions, or in forensic applications where the system cannot be trained for a specific acquisition device. Predictions on such out-of-distribution images have an increased chance of failing. But this failure case is oftentimes hard to recognize for a human operator or an automated system. Hence, in this work we propose to model the prediction uncertainty for license plate recognition explicitly. Such an uncertainty measure allows to detect false predictions, indicating an analyst when not to trust the result of the automated license plate recognition. In this paper, we compare three methods for uncertainty quantification on two architectures. The experiments on synthetic noisy or blurred low-resolution images show that the predictive uncertainty reliably finds wrong predictions. We also show that a multi-task combination of classification and super-resolution improves the recognition performance by 109% and the detection of wrong predictions by 29%.

Calibration phantom-based prediction of CT lung nodule volume measurement performance.

A calibration phantom-based method has been developed for predicting small lung nodule volume measurement bias and precision that is specific to a particular computed tomography (CT) scanner and acquisition protocol. The approach involves CT scanning a simple reference object with a specific acquisition protocol, analyzing the scan to estimate the fundamental imaging properties of the CT acquisition system, generating numerous simulated images of a target geometry using the fundamental imaging properties, measuring the simulated images with a standard nodule volume segmentation algorithm, and calculating bias and precision performance statistics from the resulting volume measurements. We evaluated the ability of this approach to predict volume measurement bias and precision of Teflon spheres (diameters =4.76, 6.36, and 7.94 mm) placed within an anthropomorphic chest phantom when using 3M Scotch Magic™ tape as the reference object. CT scanning of the spheres was performed with 0.625, 1.25, and 2.5 mm slice thickness and spacing. The study demonstrated good agreement between predicted volumetric performance and observed volume measurement performance for both volumetric measurement bias and precision. The predicted and observed volume mean for all slice thicknesses was found to be 28% and 13% lower on average than the manufactured sphere volume, respectively. When restricted to 0.625 and 1.25 mm slice thickness scans, which are recommended for small lung nodule volume measurement, we found that the difference between predicted and observed volume coefficient of variation was less than 1.0 %. The approach also showed a resilience to varying CT image acquisition protocols, a critical capability when deploying in a real-world clinical setting. This is the first report of a calibration phantom-based method's ability to predict both small lung nodule volume measurement bias and precision. Volume measurement bias and precision for small lung nodules can be predicted using simple low-cost reference objects to estimate fundamental CT image characteristics and modeling and simulation techniques. The approach demonstrates an improved method for predicting task specific, clinically relevant measurement performance using advanced and fully automated image analysis techniques and low-cost reference objects.

3-D Printing of Calibrated Germanium-68 PET Phantoms Without Inactive Walls

We aimed to print a solid-state germanium-68 phantom for positron emission tomography (PET) with excellent control of the activity concentration in the phantom material, with safe handling characteristics, and without inactive walls. To this end, we developed a novel method for incorporating germanium-68 into hydrophobic stereo-lithography monomers. The spontaneous phase transfer process provides complexation and induces phase transfer of aqueous ionic germanium-68 into the hydrophobic monomers, ensures homogenous isotope distribution in the rigid polymer matrix, and prevents leaching in aqueous environments. With this method, we additively manufactured radioactive spheres following the International Electrotechnical Commission design with the addition of a small sphere of 7.7 mm diameter. Extended leaching testing demonstrated near perfect source tightness. The same phantom can be repeatedly used in the quality management of multicenter clinical trials, without preparative errors. Long-lived solid-state phantoms produced with this method will provide consistent and reproducible validation of total body PET/CT systems. PET images with these phantoms acquired within a decaying gallium-68 background at different signal to background ratios showed homogenous activity distribution and no fill-related artefacts. The varying background allowed the assessment of trial specific acquisition protocols that match a given clinical question much better than todays predefined standard protocol.

Fabric defect detection using ACS-based thresholding and GA-based optimal Gabor filter

Fabric defect detection is closely related to quality control. Therefore, it is an important topic in the modern textile field. In this article, a new fabric defect detection method using ACS-based thresholding and GA-based optimal Gabor filter is proposed. The multi-level gray-scale image thresholding is improved by using Adaptive cuckoo search (ACS) to calculate the best threshold. In order to suppress texture interference, the optimal Gabor filter calculated by genetic algorithm (GA) is used to achieve the optimal feature extraction of fabric defects. A specific image acquisition system is set up to obtain clear defective fabric images and corresponding defect-free images which are used to build the database. The performance of the method is evaluated by extensive experiments on various types of fabric defects. Experimental results show that the proposed method, effective and robust, is superior than the other six methods.

Motion analysis systems in research and for practicing orthopedists

Complex biomechanical motion analysis can provide relevant information for avariety of orthopedic problems. When purchasing motion analysis systems, in addition to the classical measurement quality criteria (validity, reliability, objectivity), spatial and temporal conditions, as well as the requirements for the qualification of the measuring personnel should be considered. In complex movement analysis, systems are used to determine kinematics, kinetics and muscle activity (electromyography). This article gives an overview of methods of complex biomechanical motion analysis for use in orthopaedic research or for individual patient care. In addition to the use for pure movement analysis, the use of movement analysis methods in the field of biofeedback training is discussed. For the specific acquisition of motion analysis systems, it is recommended to contact professional societies (e.g., the German Society for Biomechanics),universities with existing motion analysis facilities or distributors in the field of biomechanics.

Determination of cannabinoids in 50 μL whole blood samples by online extraction using turbulent flow chromatography and LC-HRAM-Orbitrap-MS: Application on driving under the influence of drugs cases.

The analysis of cannabinoids in whole blood is usually done by traditional mass spectrometry (MS) techniques, after offline cleanup or derivatization steps which can be lengthy, laborious, and expensive. We present a simple, fast, highly specific, and sensitive method for the determination of Δ9 -tetrahydrocannabinol (THC), cannabidiol (CBD), cannabinol (CBN), 11-hydroxy-Δ9 -tetrahydrocannabinol (11-OH-THC), and 11-nor-9-carboxy-Δ9 -tetrahydrocannabinol (THC-COOH) in 50 μL whole blood samples. After the addition of deuterated internal standards (IS) and a simple protein precipitation step, an online extraction of sample supernatants using turbulent flow chromatography (TurboFlow-Thermo Scientific) was carried out. Analytes were separated on a C18 analytical column and detected by LC-HRAM-Orbitrap-MS using a Thermo Scientific Q Exactive Focus MS system. MS detection was performed in polarity switching and selected ion monitoring (SIM) modes using five specific acquisition windows, at a resolution of 70,000 (FWHM). Total run time was about 10min including preanalytical steps. Method validation was carried out by determining limit of detection (LOD), lower limit of quantitation (LLOQ), linearity range, analytical accuracy, intra-assay and interassay precision, carry-over, matrix effect, extraction recovery, and selectivity, for all analytes. Measurement uncertainties were also evaluated, and a decision rule was set with confidence for forensic purposes. The method may become suitable for clinical and forensic toxicology applications, taking advantage of the small matrix volume required, the simple and cost-effective sample preparation procedure, and the fast analytical run time. Performances were monitored over a long-term period and tested on 7620 driving under the influence of drugs (DUID) samples, including 641 positive samples.

Coronary artery calcium identified on non-gated chest CT scans - A wasted opportunity to avoid the tragedy

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Coronary artery calcium (CAC) is an independent predictor of cardiovascular events. While it is traditionally performed utilizing gating with specific acquisition parameters, CAC can be identified in non-gated standard chest computed tomography (CT). This study aimed to assess CAC on chest CTs, evaluating its correlation with coronary lesions on coronary angiography (CAG) and prognosis. Methods We retrospectively reviewed patients (pts) who underwent CAG due to acute coronary syndrome (ACS) who had undergone a prior non-gated non-contrast chest CT. CAC was qualitatively evaluated by visual assessment (mild/moderate/severe) and quantitatively assessed using Agatson score and stratified by terciles. Evaluation was performed by an investigator blinded to CAG report. Results We included 114 pts after reviewing 1000 CAGs: 67% male, mean age 68 years, 78% hypertension, 62% dyslipidemia, 38% chronic kidney disease, 38% diabetes. The mean time difference between CT and CAG was 23 months. CAG was performed due to unstable angina in 33% of pts, NSTEMI in 52% and STEMI in 16%. Significant lesions were found in 57% (69% performed PCI and 17% surgical revascularization). CAC was visual classified as mild, moderate and severe in 31%, 33% and 16% of pts, respectively. Moderate or severe CAC was an independent predictor of significant lesions on CAG [OR 22, 95%CI 8–61, p&amp;lt;0.001] and all-cause mortality [OR 4, 95%CI 2–9, p = 0.001]. Pts with severe CAC had higher peak troponin than those with mild/moderate CAC (1780 vs 315 ng/L, p = 0.024). Quantitative CAC score accurately predicted significant lesions (AUC 0.81, p&amp;lt;0.001; figure 1A), with higher scores in this subgroup (1308 vs 120, p&amp;lt;0.001) and strongly correlated with SYNTAX score (p&amp;lt;0.001). Survival analysis stratified by severity of CAC assessment is shown in figure 1B and 1C. The most severely calcified artery in the CT often matched the culprit vessel of future ACS, with 79%, 60% and 50% concordance for left anterior descending, circumflex, and right coronary artery, respectively. While significant CAC was identified in 80% of CTs, formal reporting was as low as 25%, even with severe CAC, where only 2/18 reports mentioned it. Furthermore, only 62% pts were on statin therapy at the time of CAG. Conclusion CAC evaluation in chest CTs was feasible and strongly associated with the extent/severity of coronary artery disease on CAG, as well as mortality. Notwithstanding, CAC underreporting was frequent and statin therapy underused, suggesting a simple and common opportunity for preventive care.

Adapting model-based deep learning to multiple acquisition conditions: Ada-MoDL.

The aim of this work is to introduce a single model-based deep network that can provide high-quality reconstructions from undersampled parallel MRI data acquired with multiple sequences, acquisition settings, and field strengths. A single unrolled architecture, which offers good reconstructions for multiple acquisition settings, is introduced. The proposed scheme adapts the model to each setting by scaling the convolutional neural network (CNN) features and the regularization parameter with appropriate weights. The scaling weights and regularization parameter are derived using a multilayer perceptron model from conditional vectors, which represents the specific acquisition setting. The perceptron parameters and the CNN weights are jointly trained using data from multiple acquisition settings, including differences in field strengths, acceleration, and contrasts. The conditional network is validated using datasets acquired with different acquisition settings. The comparison of the adaptive framework, which trains a single model using the data from all the settings, shows that it can offer consistently improved performance for each acquisition condition. The comparison of the proposed scheme with networks that are trained independently for each acquisition setting shows that it requires less training data per acquisition setting to offer good performance. The Ada-MoDL framework enables the use of a single model-based unrolled network for multiple acquisition settings. In addition to eliminating the need to train and store multiple networks for different acquisition settings, this approach reduces the training data needed for each acquisition setting.

A National Needs Assessment in Simulation Based Training in Vascular Surgery

The aim of this research was to ascertain the highest need areas for vascular simulation, in order to tailor training for the highest impact. A needs assessment was conducted according to best practices using the Delphi method. All consultant vascular surgeons/trainers in the training jurisdiction (n=33) were approached through an independent intermediary to contribute and generate a prioritized list of procedures for training. The research team were blinded to participant identities. Three rounds were conducted according to the Delphi process and scored according to the Copenhagen Needs Assessment Formula (CAMES-NAF). A final list of 34 vascular procedures was selected and prioritized by surgical trainers. Principles of arterial repair and endarterectomy/patching were considered the highest priority. Complex major interventions such as open abdominal aortic aneurysm (AAA) repair, carotid endarterectomy, and endovascular aortic repair (EVAR) consistently ranked higher than rarer, such as first rib resection and more basic procedures, such as foam injection for varicose veins. Major lower limb amputations typically ranked lower overall compared to arterial interventions. Surgical trainers generally agreed with the ranking according to the CAMES-NAF. There was some disagreement for a select few procedures such as iliac stenting (which rose 13 places) and open radial artery exposure (which fell 6 places) on subsequent iterations. Core operative principles and common major operative cases should remain the priority for vascular technical skills training. Other procedures which may be less invasive, but have the potential for major complications should also not be overlooked. In designing simulators the main focus should center on specific skill acquisition for commonly performed major procedures and management of the recognized potential complications. Lower limb amputations are considered adequately taught in clinical practice, or are too challenging to simulate in simulator models apart from cadaveric models.

Development and validation of a scale to measure the simulation-based learning outcomes in teacher education

IntroductionSimulation-based learning (SBL) is gradually being integrated into the field of teacher education. However, beyond specific content acquisition, comprehensive knowledge of SBL outcomes is limited. This research aimed to systematically develop a scale to measure SBL outcomes in teacher education.MethodsA mixed-methods sequential exploratory design was implemented, to develop the Simulation-based Learning Outcomes in Teacher Education (SLOTE) scale. Data were collected in two phases: a qualitative (N = 518) phase followed by a quantitative (N = 370) phase.ResultsThe qualitative analysis revealed three overarching themes: communication skills, collaborative-learning-related insights, and emotional self-awareness. The scale items were prepared based on key quotes from the qualitative data. To define, quantify, and validate learning outcomes of SBL in teacher education, EFA and CFA were used to test the relationship among 29 items of the scale.DiscussionThe study provides a theoretical conceptualization of SBL’s multifaceted learning outcomes in teacher education. These findings allow for a better understanding of the observers’ role in SBL, suggesting that enacting the simulation is not inherently more emotionally demanding and, consequently, participants in both roles are apt to benefit equally from the experiential learning afforded by the SBL process. Theoretical and practical implications of using SBL in teacher education are discussed.

The Definition, Presentation and Automatic Generation of Contextual Data in Lexicography

Abstract This paper deals with several aspects of context in lexicography. Section 1 briefly mentions some different approaches to the concept context in various fields. Section 2 puts the focus on different uses and perceptions of the concept context in lexicography, contrasting it with related concepts, such as cotext, contextualization and contextual information. A more comprehensive discussion also covers different aspects of the occurrence of the concept context in dictionary research, with specific reference to central aspects of the so-called inner and outer context. Various portals, dictionaries and dictionary entries will illustrate the above-mentioned approaches. Section 3 approaches the subject from a user perspective. Section 4 addresses the question How can contextual data be extracted or generated? To answer this question, some methods and tools for (automatic) acquisition and analysis of contextual data, – in particular of the local contextual data in terms of Faber and León-Araúz (2016) – are introduced. Examples of these are lexical databases or semantic networks, like WordNet, and corpora, like Sketch Engine, or predictive methods, like Word2vec and similar ones. Some advantages and disadvantages of specific data acquisition tools used for the analysis of local contextual data are indicated. This section also contributes to a more detailed discussion of the automatic generation of the so-called local syntactic-semantic context or word environment, specifically of the building of syntactic-semantic argument patterns and their examples.

Stochastic ACF-ZCL model of time-series signals with an application to restfulness assessment

In this paper, a stochastic model of a time-series signal has been derived using the first zero-crossing lag (ZCL) of the sample autocorrelation function (ACF). For this, the order of a power law series is obtained deterministically from its ZCL. This is extended to polynomial regression models and stochastic models of time-series signals. The residuals of both the stochastic ACF-ZCL model and the conventional PACF model represent the phasic component and noise but the former follow the beta distribution more consistently. The proposed model is applied for restfulness assessment of 10-minutes long differential dermal potentials (DDP) acquired simultaneously from all 4 limbs of subjects using a specific 4-channel data acquisition system. The findings clearly show the manner in which stages of restfulness affect the features of the deterministic (or tonic) and the stochastic (or phasic) components of the stochastic ACF-ZCL model.

Sources of residual autocorrelation in multiband task fMRI and strategies for effective mitigation.

Analysis of task fMRI studies is typically based on using ordinary least squares within a voxel- or vertex-wise linear regression framework known as the general linear model. This use produces estimates and standard errors of the regression coefficients representing amplitudes of task-induced activations. To produce valid statistical inferences, several key statistical assumptions must be met, including that of independent residuals. Since task fMRI residuals often exhibit temporal autocorrelation, it is common practice to perform "prewhitening" to mitigate that dependence. Prewhitening involves estimating the residual correlation structure and then applying a filter to induce residual temporal independence. While theoretically straightforward, a major challenge in prewhitening for fMRI data is accurately estimating the residual autocorrelation at each voxel or vertex of the brain. Assuming a global model for autocorrelation, which is the default in several standard fMRI software tools, may under- or over-whiten in certain areas and produce differential false positive control across the brain. The increasing popularity of multiband acquisitions with faster temporal resolution increases the challenge of effective prewhitening because more complex models are required to accurately capture the strength and structure of autocorrelation. These issues are becoming more critical now because of a trend toward subject-level analysis and inference. In group-average or group-difference analyses, the within-subject residual correlation structure is accounted for implicitly, so inadequate prewhitening is of little real consequence. For individual subject inference, however, accurate prewhitening is crucial to avoid inflated or spatially variable false positive rates. In this paper, we first thoroughly examine the patterns, sources and strength of residual autocorrelation in multiband task fMRI data. Second, we evaluate the ability of different autoregressive (AR) model-based prewhitening strategies to effectively mitigate autocorrelation and control false positives. We consider two main factors: the choice of AR model order and the level of spatial regularization of AR model coefficients, ranging from local smoothing to global averaging. We also consider determining the AR model order optimally at every vertex, but we do not observe an additional benefit of this over the use of higher-order AR models (e.g. (AR(6)). To overcome the computational challenge associated with spatially variable prewhitening, we developed a computationally efficient R implementation using parallelization and fast C++ backend code. This implementation is included in the open source R package BayesfMRI. We find that residual autocorrelation exhibits marked spatial variance across the cortex and is influenced by many factors including the task being performed, the specific acquisition protocol, mis-modeling of the hemodynamic response function, unmodeled noise due to subject head motion, and systematic individual differences. We also find that local regularization is much more effective than global averaging at mitigating autocorrelation. While increasing the AR model order is also helpful, it has a lesser effect than allowing AR coefficients to vary spatially. We find that prewhitening with an AR(6) model with local regularization is effective at reducing or even eliminating autocorrelation and controlling false positives. Our analysis revealed dramatic spatial differences in autocorrelation across the cortex. This spatial topology is unique to each session, being influenced by the task being performed, the acquisition technique, various modeling choices, and individual differences. If not accounted for, these differences will result in differential false positive control and power across the cortex and across subjects.