Correction Of Errors Research Articles

78 Preliminary Exploration of a Novel Speech Analysis Algorithm to Detect Cognitive Impairment in a Spanish Population

Objective:Early detection of mild cognitive impairment (MCI) and dementia is crucial for initiation of treatment and access to appropriate care. While comprehensive neuropsychological assessment is often an intrinsic part of the diagnostic process, access to services may be limited and cannot be utilized effectively on a large scale. For these reasons, cognitive screening instruments are used as brief and cost-effective methods to identify individuals who require further evaluation. Novel technologies and automated software systems to screen for cognitive changes in older individuals are evolving as new avenues for early detection. The present study presents preliminary data on a new technology that uses automated linguistic analysis software to screen for MCI and dementia.Participants and Methods:Data were collected from 148 Spanish-speaking individuals recruited in Spain (MAge=74.4, MEducation=12.93, 56.7% females) of whom 78 were diagnosed as cognitively normal [CN; Mmmse = 28.51 (1.39)], 49 as MCI [MMMSE = 25.65 (2.94)], and 21 as all-cause dementia [MMMSE = 22.52 (2.06)]. Participants were recorded performing various verbal tasks [Animal fluency, phonemic (F) fluency, Cookie Theft Description, and CERAD list learning task]. Recordings were processed via text-transcription and sound signal processing techniques to capture neuropsychological variables and audio characteristics. Features from each task were used in the development of an algorithm (for that task) to compute a score between 0 or 1 (healthy to more impairment), and a fifth algorithm was constructed using audio characteristics from all tasks. These five classifiers were combined algorithmically to provide the final algorithm. Receiver Operating Characteristic (ROC) analysis was conducted to determine sensitivity and specificity of predicted algorithm performance [CN vs. impaired (MCI or dementia)] against clinical diagnoses, and additional general linear modeling was used to test whether age, sex, education, and multilingualism significantly predicted logistically transformed weighted algorithm scores.Results:Scores were transformed to logit scores, with significant differences in mean logit scores between all groups (p <.001). Logit-inverse transformation of mean logit scores (possible range 0 -1) resulted in values of 0.06 for CN, 0.90 for MCI, and 0.99 for all-cause dementia groups. ROC curve analyses revealed the algorithm obtained a total area under the curve of 0.92, with an overall accuracy of 86.8%, a sensitivity of 0.92, and specificity of 0.82. Age was identified as a significant predictor (beta = 0.22; p <0.01) of algorithm output, whereas years of education (beta = -0.04; p = 0.64), sex (beta = 0.38; p = 0.02, did not survive correction for type-1 error), and multilingualism (beta = -0.24; p = 0.22) were non-significant.Conclusions:These findings provide initial support for the utility of an automated speech analysis algorithm to detect cognitive impairment quickly and efficiently in a Spanish-speaking population. Although sociodemographic variables were not included in the algorithm, age significantly predicted algorithm output, and should be further explored to determine if age-adjusted formulas would improve algorithm accuracy for younger versus older individuals. Additional research is needed to validate this novel methodology in other languages, as this may represent a promising cross-cultural screening method for MCI and dementia detection.

Fuzzy prediction of the mine's ventilation structure's tunnel air volume

For the problem that it is difficult to accurately measure the air volume in the tunnel where the mine ventilation structure is located. Firstly, the tunnel air volume value is expressed by the interval number, and the air volume value of the tunnel where the ventilation structure is located is obtained by the law of nodal air balance. Then the interval number is transformed into a triangular fuzzy number to represent the tunnel air volume, the generalized induced ordered weighted average operator is introduced, the minimum-maximum closeness is used as the optimal quasi-measure, and the preference coefficient is introduced to transform the multi-objective nonlinear optimization problem into a single-objective optimization problem. And consider the temporal characteristics of the error series and the trend of the error value change for the error correction of air volume prediction error, and construct the interval combination type air volume fuzzy prediction model. Using the air volume data of a mine tunnel in Inner Mongolia, the example calculation of the model shows that the interval combination type prediction method can effectively improve the accuracy of air volume prediction in the tunnel of ventilation structures. Keywods: Error correction, Interval combination type, Interval number, Preference coefficient, Triangular fuzzy number

The impact of rapid cycle simulation deliberate practice on nursing student’s resuscitation self-efficacy: A quasi-experimental study

BackgroundNursing students often report anxiety about the performance of resuscitation in a placement context. Rapid cycle deliberate practice which involves re-running the scenario after de-brief allowing for the correction of errors and improved practice has been widely used to develop skills in resuscitation. Few studies have examined the use of rapid cycle deliberate practice to improve resuscitation confidence and self-efficacy. Objectiveto assess if rapid cycle deliberate practice leads to improvements in resuscitation self-efficacy in pre-registration nursing students. DesignQuasi-experimental pre and post-test design measuring self-efficacy using the Basic Resuscitation Skills – Self Efficacy Scale. SettingUniversity, United Kingdom. ParticipantsStudents were invited to participate (n = 120) and 106 consented to take part in the study. Participants were in pre-determined practical groups with 56 in the experimental group and 50 in the control group. MethodsA pre and post-test of nursing students’ self-efficacy during a resuscitation simulation scenario. The scenario will relate to a patient admitted to the emergency room with chest pain who then goes into cardiac arrest. The control group undertake the simulation exercise and then received a de-brief whereas the experimental group participated in a rerun of the scenario following the de-brief (deliberate practice). Both groups completed the Basic Resuscitation Skills Self-efficacy scale pre and post the session. Data were analysed using a paired sample t-test. ResultsBoth groups showed improved self-efficacy as a result of the simulation session. The difference in the post-test mean scores between the control and the experimental group was marginal and not statistically significant. Conclusionrapid cycle deliberate practice simulation does not lead to improved resuscitation self-efficacy amongst pre-registration nursing students when compared with a single session. AbstractNursing students are often anxious about performing resuscitation in practice. Can rapid cycle deliberate practice improve resuscitation self-efficacy? Pre and post-test study (n=106) showed improved self-efficacy with no statistical difference between standard simulation and deliberate practice.

Comparison of machine learning methods for the prediction of type 2 diabetes in primary care setting using EHR data

Diabetes remains a major global public health challenge, thus the need for better methods for managing diabetes. Machine learning could provide reliable solutions to the need for early detection and management of diabetes. This study conducted experiments to compare a number of selected machine learning approaches to determine their suitability for early detection of diabetes in the primary care setting. A retrospective study was conducted using EHR dataset of confirmed cases of diabetes collected during routine care at Nairobi Hospital. Institutional ethical approvals were obtained, and data were retrieved from the database through stratified sampling based on gender. Diagnoses were confirmed using the ICD-10 codes. Records with 5% or so of missing values were excluded from this analysis. Data were processed by correction of errors and replacement of missing values using measures of central tendency. The data were transformed through normalization using the decimal-scaling method. Data analysis was conducted using selected supervised and unsupervised learning algorithms. Model performances were validated using metrics for the evaluation of classification and clustering results, respectively. Random Forest had the highest accuracy (0.95) and error rate (0.05), while Gradient Boosting and Multilayer Perceptron (MLP) with 3 hidden layers obtained accuracy (0.94) and error rate (0.06), respectively. The process of selecting machine learning algorithms needs to explore both supervised and unsupervised learning techniques. In addition, an appropriate architectural design of an MLP could present astounding results for classification tasks in primary care settings.

Semi-supervised learning and bidirectional decoding for effective grammar correction in low-resource scenarios

The correction of grammatical errors in natural language processing is a crucial task as it aims to enhance the accuracy and intelligibility of written language. However, developing a grammatical error correction (GEC) framework for low-resource languages presents significant challenges due to the lack of available training data. This article proposes a novel GEC framework for low-resource languages, using Arabic as a case study. To generate more training data, we propose a semi-supervised confusion method called the equal distribution of synthetic errors (EDSE), which generates a wide range of parallel training data. Additionally, this article addresses two limitations of the classical seq2seq GEC model, which are unbalanced outputs due to the unidirectional decoder and exposure bias during inference. To overcome these limitations, we apply a knowledge distillation technique from neural machine translation. This method utilizes two decoders, a forward decoder right-to-left and a backward decoder left-to-right, and measures their agreement using Kullback-Leibler divergence as a regularization term. The experimental results on two benchmarks demonstrate that our proposed framework outperforms the Transformer baseline and two widely used bidirectional decoding techniques, namely asynchronous and synchronous bidirectional decoding. Furthermore, the proposed framework reported the highest F1 score, and generating synthetic data using the equal distribution technique for syntactic errors resulted in a significant improvement in performance. These findings demonstrate the effectiveness of the proposed framework for improving grammatical error correction for low-resource languages, particularly for the Arabic language.

Super-resolution macroscopic imaging via unknown speckle illumination using sparse aperture transmitter

Optical sparse aperture imaging technology has promising applications in the next generation of high-resolution imaging systems, but the phasing errors correction of sparse aperture is an urgent problem to be addressed. In this paper, we propose a blind structured-illumination super-resolution imaging system using a sparse aperture as the transmitter to overcome phasing errors and obtain higher resolution. In our scheme, an unknown pattern is projected onto the object by a sparse aperture system. By translating the unknown pattern, a sequence of modulated raw images is obtained. Based on these low-resolution raw images, the super-resolution object and the unknown illumination pattern are jointly estimated by the Adam algorithm. Here we show, both in the simulation and experiment, the resolution of reconstructions using sparse aperture as transmitter are similar to those using monolithic lens. Furthermore, we validate that the proposed method can protect the reconstruction effect from piston error and widen the tolerance range of tip/tilt error (peak-valley values) up to 0.5λ. At the same time, our proposed system can also alleviate to some extent the problem of mid-frequency modulation transfer function (MTF) attenuation that exists in sparse aperture imaging. Our approach not only simplifies the process of constructing synthetic aperture system, but also provides a novel application for synthetic aperture systems.

Application of Artificial Neural Networks and Adaptive Neuro‐Fuzzy Inference Systems for Attenuating Effects of Temperature and Humidity on Sensor Accuracy

AbstractThe novelty of this work is the correction of measurement errors in a galvanic cell sensor, which might occur due to temperature and humidity variations, using artificial neural networks (ANN) and adaptive neuro‐fuzzy inference systems (ANFIS). The training process for the ANN and ANFIS employed 540 data sets consisting of voltage responses of the galvanic cell to known oxygen concentrations, temperatures, and relative humidities. The trained ANFIS was embedded within a central processing unit (CPU) and tested on another 144 data sets with various oxygen concentrations, temperatures, and relative humidities. The deviations for the sensor with ANFIS were less than 0.5 % for 109 data points (75 %), and there was no deviation greater than 2.5 %.

Accuracy of an objective binocular automated phoropter for providing spectacle prescriptions

ABSTRACT Clinical relevance Currently eye examinations are usually based on autorefraction followed by subjective refraction (SR) with a phoropter. An automated phoropter that can also perform autorefraction may facilitate the optometric workflow. Background The efficiency and feasibility of an objective autorefraction and correction system are assessed by comparing objective refractive measurements with SR on the same subjects and evaluating the visual acuity (VA) values obtained after the objective refractive measurement and correction. Methods Objective autorefraction and correction was performed on 41 subjects using an automated binocular phoropter system. The auto-phoropter performs autorefraction by wavefront measurement and corrects the spherical and cylindrical errors with tunable fluidic lenses while the patient looks at a visual display inside the instrument. The instrument outputs are optometric constants of spherical and cylindrical aberrations. After measurement and automated correction of the refractive errors, the VA values were assessed by having the subjects look at an integrated Snellen chart. The objective measurement results were statistically compared with their SR. Results The correlations between SR and objective autorefraction and correction spherical equivalents (M) were 0.98 (0.97–0.99) and 0.96 (0.93–0.98), the vertical Jackson cross cylinder (J0) were 0.96 (0.92–0.98) and 0.95 (0.91–0.97), and the oblique Jackson cross cylinder (J45) were 0.73 (0.55–0.85) and 0.82 (0.69–0.90), for the right and left eyes, respectively, with the 95% confidence interval (CI) values in parentheses. 89.0% of the 82 eyes had at least 6/7.5 VA. Conclusions A significant agreement between the SR and objective autorefraction and correction was observed. An all-objective refractive assessment with instantaneous verification may improve the precision of eye prescriptions and possibly reduce the procedure time.

Infectious Complications of Contact Lenses: A Review of the Literature

The use of contact lenses is very common, they are prescribed for the correction of refractive errors that cannot be treated by glasses such as aphakia, keratoconus and strong anisometropia, or as alternatives to glasses. Contact lenses can cause serious complications that are not always easy for patients to manage [1]. Infectious complications in contact lens wearers are a real diagnostic and therapeutic emergency. These infections can be bacterial, amoebic (due to wearing contact lenses in swimming pools, rinsing lenses with tap water or saliva), fungal (due to wearing therapeutic, cosmetic or aphakic lenses, diabetes, alcoholism, immunosuppression, corticosteroids). Infectious complications require emergency treatment. The first line of treatment consists of removing the contact lens and sending it, along with the lens case and lens care product, to the laboratory along with corneal samples and appropriate medical treatment. Infectious keratitis related to contact lens wear is serious and can permanently affect the visual prognosis. The prevention requires a fitting under medical supervision and an awareness of the patients with potential risks of infection.

Simulation-driven machine learning approach for high-speed correction of slope-dependent error in coherence scanning interferometry.

Slope-dependent error often occurs in the coherence scanning interferometry (CSI) measurement of functional engineering surfaces with complex geometries. Previous studies have shown that these errors can be corrected through the characterization and phase inversion of the instrument's three-dimensional (3D) surface transfer function. However, since CSI instrument is usually not completely shift-invariant, the 3D surface transfer function characterization and correction must be repeated for different regions of the full field of view, resulting in a long computational process and a reduction of measurement efficiency. In this work, we introduce a machine learning approach based on a deep neural network that is trainable for slope-dependent error correction in CSI. Our method leverages a deep neural network to directly learn errors characteristics from simulated surface measurements provided by a previously validated physics-based virtual CSI method. The experimental results demonstrate that the trained network is capable of correcting the surface height map with 1024 × 1024 sampling points within 0.1 seconds, covering a 178 µm field of view. The accuracy is comparable to the previous phase inversion approach while the new method is two orders of magnitude faster under the same computational condition.

Application of double-error correction codes to protect configuration programmable logic memory against space radiation

Objectives. Programmable logic integrated circuits of the field programmable gate array (FPGA) type based on static configuration memory are widely used in the electronics of onboard spacecraft systems. Under the influence of space radiation, errors may occur in the FPGA configuration memory. The main methods of protection against such errors involve various options for reservation triggers, as well as the use of error-correcting codes in special error detection and correction circuits. The purpose of the present work is to determine which error-correcting codes are best suited to the implementation of internal scrubbing of the FPGA configuration memory taking redundancy into account.Methods. The paper analyses various methods for scrubbing FPGA configuration memory, which are used to correct errors caused by the action of space radiation. It is proposed to increase the efficiency of internal scrubbing of the FPGA configuration memory using codes that correct both single- and double-adjacent SEC-DED-DAEC errors. In this case, the need to perform external scrubbing of the configuration memory is reduced by overwriting it with a reference configuration from non-volatile radiation-resistant memory; in this way, FPGA downtime caused by the external scrubbing procedure is reduced. Due to the known SEC-DED-DAEC codes having a non-zero probability of erroneous detection and subsequent erroneous correction of a double non-adjacent error, as well as various redundancy and implementation complexities, a study was made of the most efficient code for internal scrubbing.Results. The results showed that the Datta, Neale and Hoyoon–Yongsurk codes are optimal from the indicated positions. Recommendations are given for selecting a specific code depending on the specific requirements for a particular planned space mission.Conclusions. The study confirms the effectiveness of protecting the memory of programmable logic by using two-error-correcting codes.

Successful chatbot design for polytechnic students in Singapore

ABSTRACT In Temasek Polytechnic, Singapore, several AI chatbots acting as digital teaching assistants were trialed between July 2021 and August 2022. As these were developed for different purposes, it is important to learn if these AI chatbots help achieve the desired learning outcomes. This paper focuses on lessons learnt from three chatbots designed for different learning experiences: the independent learning of EXCEL, the correction of errors in the application of APA citation style, and the provision of supplementary theoretical support for software usage for Social Media Analytics. The research team studied the students’ reaction to these educational chatbots, their motivation to use them, as well as the efficacy of the chatbot features to support specific purposes. This paper offers a design model that describes the three-stage approach for learning with an AI chatbot, with examples elucidating them. These sequential stages are foundational in the chatbot design to provide focused, personalised, student-controlled online learning environment. The application of this model is detailed in a fourth AI chatbot designed for the subject, Airline Management.

GA-Net: A geographical attention neural network for the segmentation of body torso tissue composition

PurposeBody composition analysis (BCA) of the body torso plays a vital role in the study of physical health and pathology and provides biomarkers that facilitate the diagnosis and treatment of many diseases, such as type 2 diabetes mellitus, cardiovascular disease, obstructive sleep apnea, and osteoarthritis. In this work, we propose a body composition tissue segmentation method that can automatically delineate those key tissues, including subcutaneous adipose tissue, skeleton, skeletal muscle tissue, and visceral adipose tissue, on positron emission tomography/computed tomography scans of the body torso. MethodsTo provide appropriate and precise semantic and spatial information that is strongly related to body composition tissues for the deep neural network, first we introduce a new concept of the body area and integrate it into our proposed segmentation network called Geographical Attention Network (GA-Net). The body areas are defined following anatomical principles such that the whole body torso region is partitioned into three non-overlapping body areas. Each body composition tissue of interest is fully contained in exactly one specific minimal body area. Secondly, the proposed GA-Net has a novel dual-decoder schema that is composed of a tissue decoder and an area decoder. The tissue decoder segments the body composition tissues, while the area decoder segments the body areas as an auxiliary task. The features of body areas and body composition tissues are fused through a soft attention mechanism to gain geographical attention relevant to the body tissues. Thirdly, we propose a body composition tissue annotation approach that takes the body area labels as the region of interest, which significantly improves the reproducibility, precision, and efficiency of delineating body composition tissues. ResultsOur evaluations on 50 low-dose unenhanced CT images indicate that GA-Net outperforms other architectures statistically significantly based on the Dice metric. GA-Net also shows improvements for the 95% Hausdorff Distance metric in most comparisons. Notably, GA-Net exhibits more sensitivity to subtle boundary information and produces more reliable and robust predictions for such structures, which are the most challenging parts to manually mend in practice, with potentially significant time-savings in the post hoc correction of these subtle boundary placement errors. Due to the prior knowledge provided from body areas, GA-Net achieves competitive performance with less training data. Our extension of the dual-decoder schema to TransUNet and 3D U-Net demonstrates that the new schema significantly improves the performance of these classical neural networks as well. Heatmaps obtained from attention gate layers further illustrate the geographical guidance function of body areas for identifying body tissues. Conclusions(i) Prior anatomic knowledge supplied in the form of appropriately designed anatomic container objects significantly improves the segmentation of bodily tissues. (ii) Of particular note are the improvements achieved in the delineation of subtle boundary features which otherwise would take much effort for manual correction. (iii) The method can be easily extended to existing networks to improve their accuracy for this application.

Ultra reliability and massive connectivity provision in integrated internet of military things (IoMT) based on tactical datalink

One of the major challenges arising in internet of military things (IoMT) is accommodating massive connectivity while providing guaranteed quality of service (QoS) in terms of ultra-high reliability. In this regard, this paper presents a class of code-domain nonorthogonal multiple accesses (NOMAs) for uplink ultra reliable networking of massive IoMT based on tactical datalink such as Link-16 and joint tactical information distribution system (JTIDS). In the considered scenario, a satellite equipped with Nr antennas servers K devices including vehicles, drones, ships, sensors, handset radios, etc. Nonorthogonal coded modulation, a special form of multiple input multiple output (MIMO)-NOMA is proposed. The discussion starts with evaluating the output signal to interference-plus-noise (SINR) of receiver filter, leading to the unveiling of a closed-form expression for overloading systems as the number of users is significantly larger than the number of devices admitted such that massive connectivity is rendered. The expression allows for the development of simple yet successful interference suppression based on power allocation and phase shaping techniques that maximizes the sum rate since it is equivalent to fixed-point programming as can be proved. The proposed design is exemplified by nonlinear modulation schemes such as minimum shift keying (MSK) and Gaussian MSK (GMSK), two pivotal modulation formats in IoMT standards such as Link-16 and JITDS. Numerical results show that near capacity performance is offered. Fortunately, the performance is obtained using simple forward error corrections (FECs) of higher coding rate than existing schemes do, while the transmit power is reduced by 6 dB. The proposed design finds wide applications not only in IoMT but also in deep space communications, where ultra reliability and massive connectivity is a keen concern.

Shoulder and Neck Pain in Swimmers: Front Crawl Stroke Analysis, Correlation with the Symptomatology in 61 Masters Athletes and Short Literature Review.

Background: Swimming and, specifically, front crawl, can be included among the "overhead" sports. Overhead sports are a risk factor for some problems of the musculoskeletal system, especially the shoulder. The aim of this study was to assess the incidence of shoulder and neck pain in a Masters Swimming Team and its correlation with the crawl stroke. Methods: This is an observational study through video-analysis of the stroke and a questionnaire. The participants selected for the present study were 61 athletes of a Masters team, whose prevailing training stroke was the front crawl. Their stroke was analyzed during training using a go-pro camera mounted on a sliding trolley on a track, evaluating their technical defects with their trainer. A questionnaire about frequency of shoulder and neck pain during the last five years was administered to all the participants at the study. Results: From the questionnaire, 45 and 55 out of 61 athletes had suffered from shoulder pain and cervical pain, respectively. Both types of pain were correlated with the weekly swimming volume. The swimmers with hyperflexion of the wrist and prolonged internal rotation in the pulling phase had shoulder problems. Those who suffered from current shoulder pain reduced the underwater time. The four swimmers with an excessive body roll during breathing and those who kept their heads extended, reported cervical pain. Conclusions: Shoulder and neck pain could be prevented with the correction of specific technical errors in crawl stroke.

Excited-state van der Waals potential energy surfaces for the NO A2Σ+ + CO2X1Σg+ collision complex.

Excited state van der Waals (vdW) potential energy surfaces (PESs) of the NO A2Σ+ + CO2X1Σg+ system are thoroughly investigated using coupled cluster theory and complete active space perturbation theory to second order (CASPT2). First, it is shown that pair natural orbital coupled cluster singles and doubles with perturbative triples yields comparable accuracy compared to CCSD(T) for molecular properties and vdW-minima at a fraction of computational cost of the latter. Using this method in conjunction with highly diffuse basis sets and counterpoise correction for basis set superposition error, the PESs for different intermolecular orientations are investigated. These show numerous vdW-wells, interconnected for all geometries except one, with a maximum depth of up to 830 cm-1; considerably deeper than those on the ground state surface. Multi-reference effects are investigated with CASPT2 calculations. The long-range vdW-surfaces support recent experimental observations relating to rotational energy transfer due the anisotropy in the potentials.

THE ROLE OF SIMULATION TRAINING FOR TEACHING (IN TEACHING) ANESTHESIOLOGIST INTERNS: A TWO-WAY SURVEY

Resume. In the modern system of training intern anesthesiologists, simulation training is becoming an increasingly common tool for achieving educational goals. The simulation ensures the acquisition of professional competencies without the risk of harm to the patient and enables teachers to objectively assess the level of professional training of each intern. There is a growing need to review traditional approaches to the interaction between a teacher and an intern with the increasing use of simulation technologies.
 The purpose of the study was to determine the role of simulation training in the teaching of anesthesiologist interns.
 Materials and method. A prospective audit was conducted at the Department of Surgery, Anesthesiology and Intensive Care of the O.O. Bogomolets National Medical University in the period from April to June 2023. Interns of the 2nd year of training in 2023 in the speciality «Anesthesiology and intensive care» were trained in a 1-day simulation training «Critical incidents during anaesthesia". We examine data (lower-level outcomes) assessing the role of simulation training on learning outcomes and willingness to work independently from the perspective of interns and faculty using an anonymous questionnaire at the end of the training.
 Results: the audit included 36 responses from interns and 10 from teachers. Many intern doctors noted an increase in their motivation to study, achievement of educational goals and correction of errors as a result of debriefing thanks to the conducted simulation training. Intern doctors noted that such training was very close to real conditions, it increased confidence in working with patients and readiness for emergencies, helped identify knowledge gaps and was more effective compared to traditional training (lectures, seminars). The results of the teachers' survey confirm the importance of simulation training as an effective means of training interns, which ensures the achievement of educational goals without risks for patients, accelerates the process of acquisition of competencies by learners, increases their motivation to study and objectively reflects the level of skills and knowledge of interns.
 Conclusions. Our research has proven that simulation training should be a mandatory component of the training program of the clinical internship in the speciality «Anesthesiology and intensive care» with an increase in their share in the training program of specialists. Creating conditions of mutual trust between interns and teachers is the key to the social mission of ensuring the quality of postgraduate education. Conducting a debriefing after working out each simulation scenario lays the foundation for the formation of critical thinking in future doctors and helps to avoid mistakes in real clinical practice.
 The role of systematic simulation training at the postgraduate level to improve outcomes in real clinical practice may be the subject of further research.

Exploring a Cadaver-Based Model for Teaching Emergency Medicine Residents Ultrasound-Guided Serratus Anterior Plane Blocks.

Background Ultrasound-guided regional anesthesia (USGRA) is increasingly being incorporated into ED clinical practice to provide pain control for a variety of traumatic injuries. The serratus anterior plane block (SAPB) has been shown to be effective at reducing intravenous opioid use and improving pulmonary function for patients with rib fractures, but there is limited prior research about how to safely teach this procedure to emergency medicine (EM) residents. Our goal was to examine the effect of a cadaver-based education model on EM residents' confidence in performing USGRA and provide a review of commonly encountered errors. Methods EM residents participated in a half-day cadaver-based education session that included a variety of less-commonly performed procedures including SAPB and fascia iliaca compartment block (FICB) USGRA. Residents received a didactic lecture and hands-on simulation practice during the month prior to the session. During the session, residents performed a SAPB and FICB on the cadaver patient using the same nerve block kit and ultrasound machine they would use for a living patient, with 1:1 supervision by an emergency ultrasound fellowship-trained physician who provided real-time feedback during the procedure. Representative ultrasound video clips were saved and reviewed. Surveys that were completed by residents after the session were analyzed. Results There were 23 residents who participated, and most had not performed any FICB (74%) or SAPB (87%) previously. The percentage of residents comfortable with general USGRA increased from 8.7% to 91.3% (p<0.001). Comfort with FICB increased from 9.1% to 77.3% (p<0.001), and comfort with SAPB increased from 9.1% to 77.3% (p<0.001). Instructors identified and corrected several common errors, such as overly aggressive needle insertion, instillation of air, and instillation of anesthetic into muscle rather than the fascial plane. Conclusion We found that a cadaver-based education model for teaching EM residents the SAPB and the FICB was associated with significant increases in resident confidence in performing the procedure and facilitated identification and correction of common procedural errors that may otherwise have gone undetected.

Insdel codes from subspace and rank-metric codes

Insertion and deletion (insdel in short) codes are designed to deal with synchronization errors in communication channels caused by insertions and deletions of message symbols. These codes have received a lot of attention due to their applications in diverse areas such as computational biology, DNA data storage, race-track memory error corrections, language processing, and synchronous digital communication networks. In the present work, we study constructions and limitations of insdel codes from rank metric and subspace codes. This paper studies and improves the idea of the work [4] by Hao Chen on the connection between insdel codes and subspace codes.We discuss why subspace code is a natural choice for constructing insdel codes and show that the interleaved Gabidulin codes can be used to construct nonlinear insdel codes approaching the Singleton bound. Then we show that the indexing scheme of transforming efficient Hamming metric codes to efficient insdel codes can be adapted for the class of rank metric codes. And that improves the base field size of the construction of insdel codes from lifted rank-metric codes. It is also shown that the size of the insdel code from a subspace code can be improved significantly than in the previously proposed construction. We give an algebraic condition for a linear Gabidulin rank metric code to be optimal insdel code adapting the condition proved for Reed-Solomon codes. Moreover, we give constructions of linear and nonlinear insdel codes from Sidon spaces.

Cathodal high-definition transcranial direct current stimulation (HD-tDCS) of the left ventral prefrontal cortex (vPFC) interferes with conscious error correction

Precise motor timing requires the ability to flexibly adapt one’s own movements with respect to changes in the environment. Previous studies suggest that the correction of perceived as compared to non-perceived timing errors involves at least partially distinct brain networks. The dorsolateral prefrontal cortex (dPFC) has been linked to the correction of perceived timing errors and evidence for a contribution of the ventrolateral PFC (vPFC) specifically to the correction of non-perceived errors exists. The present study aimed at clarifying the functional contribution of the left vPFC for the correction of timing errors by adopting high-definition transcranial direct current stimulation (HD-tDCS). Twenty-one young healthy volunteers synchronized their right index finger taps with respect to an isochronous auditory pacing signal. Perceivable and non-perceivable step-changes of the metronome were interspersed, and error correction was analyzed by means of the phase-correction response (PCR). In subsequent sessions anodal and cathodal HD-tDCS was applied to the left vPFC to establish a brain-behavior relationship. Sham stimulation served as control condition. Synchronization accuracy as well as error correction were determined immediately prior to and after HD-tDCS. The analysis suggests a detrimental effect of cathodal HD-tDCS distinctively on error correction in trials with perceived timing errors. The data support the significance of the left vPFC for error correction in the temporal domain but contradicts the view of a role in the correction of non-perceived errors.