Compression Rate Research Articles

Investigating BLS instructors’ ability to evaluate CPR performance: focus on compression depth, rate, and recoil

BackgroundOut-of-hospital cardiac arrest (OHCA) presents significant challenges with low survival rates, emphasizing the need for effective bystander CPR training. In Basic Life Support (BLS) training, the role of instructors is pivotal as they assess and correct learners’ cardiopulmonary resuscitation (CPR) techniques to ensure proficiency in life-saving skills. This study evaluates the concordance between CPR quality assessments by Basic Life Support (BLS) instructors and those determined through Quantitative CPR (QCPR) devices, utilizing data from BLS courses conducted at National Cheng Kung University Hospital from October 2017 to April 2018.MethodsThe study analyzed existing data from BLS courses, comparing CPR quality assessments made by instructors with those recorded by QCPR devices. Key metrics such as chest compression speed, depth, and recoil were examined to identify the degree of consistency between human and automated evaluations.ResultsIn this study, CPR performance was analyzed using QCPR devices and BLS instructors across metrics like speed, depth, and recoil. Employing the Cohen kappa statistic revealed moderate to low interrater reliability, the kappa value is 0.65 (95% C.I. 0.65–0.65) for depth, 0.56 (95% C.I. 0.33–0.79) for speed, and 0.50 (95% C.I.0.28–0.71) for recoil. Correlation analysis visualized in a heatmap indicated a higher consistency in depth evaluations (correlation coefficient = 0.7) compared to speed and recoil, suggesting a need for improved alignment in CPR training assessments.ConclusionsThe study underscores the importance of refining CPR training methods and adopting advanced technological aids to enhance the reliability of CPR skill assessments. By improving the accuracy of these evaluations, the training can be better tailored to increase the effectiveness of life-saving interventions, potentially boosting survival rates in out-of-hospital cardiac arrest scenarios.

An exploratory study on the effect of rescuer team size on basic and advanced life support technical skills in a high-fidelity simulation of canine cardiopulmonary arrest.

To evaluate the effect of rescuer team size on objective skill measures of basic life support (BLS) and advanced life support (ALS) using high-fidelity canine CPR simulation. Prospective, experimental study. Veterinary clinical simulation center. Forty-eight Reassessment Campaign on Veterinary Resuscitation CPR-certified veterinary students. Five groups of participants each conducted 3 CPR simulations in configurations of 4, 6, and 8 rescuers. Simulations represented a shock patient declining into asystole, followed by ventricular fibrillation and return of spontaneous circulation. Resuscitation efforts were video-recorded to evaluate BLS and ALS tasks. Mean (±SD) was derived and data were compared among team sizes using ANOVA and Tukey's post hoc analysis. Significance was set at P<0.05. Among teams of 4, 6, and 8 rescuers, time to first chest compression (13s [±6], 9s [±2], 8s [±4]; P=0.24) and positive-pressure breath (101s [±37], 56s [±15], 67s [±24]; P=0.05) were not significantly different. Chest compression (100/min [±5], 108/min [±6], 107/min [±6]; P=0.12) and ventilatory rates (9/min [±1], respectively, P=0.52) were not significantly different. Time without chest compressions/total length of CPR was not significantly different (72s [±16], 61s [±16], 54s [±8]; P=0.15). Capnography and ECG monitoring were used by all teams. Time to first vasopressor administration was significantly different among team sizes (268s [±70], 164s [±65], 174s [±34]; P=0.04), with vasopressors being most quickly administered by teams of 6 rescuers. Time to electrical defibrillation was not significantly different (486s [±45], 424s [±22], 488s [±181]; P=0.57). Incorrect ALS interventions occurred in 60%, 0%, and 40% of CPR events in 4, 6, and 8 rescuer teams, respectively. Although the achievement of BLS tasks was comparable in teams of 4 rescuers, teams of 6 rescuers may be preferable based on differences in the rate of guideline-incompliant treatments and ALS task efficiency. Teams of 8 rescuers were neither more efficient nor more accurate at conducting BLS and ALS tasks.

An a priori method for estimating the informativeness of the configuration of sensor placement when solving inverse problems of remote sensing

Abstract The question of finding the optimal location and number of sensors is important when solving applied inverse problems of remote sensing. An incorrect answer to this question can significantly affect (1) the accuracy of reconstructing the unknown parameters of the object under study, and/or (2) the computational complexity of the inverse problem being solved. The work proposes an economical algorithm that allows among all equivalent (in the sense of the computational complexity of obtaining a solution to the inverse problem) sensor configurations, to select the one that will give a more accurate result when processing experimental data. This algorithm also allows to make a conclusion about whether it is really necessary to process all the experimental data obtained in the experiment, or whether it is possible to limit ourselves to only part of them without a significant loss of accuracy in the reconstructed solution. The algorithm is based on (1) the application of the mosaic-skeleton approximation method to the matrix of the system of equations to which the inverse problem being solved is reduced, and (2) the calculation of the compression rate of the approximating matrix relative to the original dense matrix for a given permissible relative approximation error. Thus, the algorithm is a priori, that is, it does not require a preliminary search for a solution to the inverse problem for the sensor configuration under study. Moreover, if the formulas that define the elements of the system matrix are known, then the algorithm requires the calculation of not all of these elements, but only part of them.

A fast region of interest algorithm for efficient data compression and improved peak detection in high-resolution mass spectrometry.

Liquid chromatography coupled to high-resolution mass spectrometry (LC-HRMS) is commonly used for identification of compounds in complex samples due to the high chromatographic and mass spectral resolution provided. In subsequent data processing workflows, it is imperative to preserve this resolution to fully exploit the data. "Region of interest" (ROI) algorithms were introduced as a better alternative to equidistant binning for compressing HRMS data because they better preserve the mass spectral resolution. In this paper, we present a new ROI algorithm that improves on the selection of contiguous m/z traces, amongst others by introducing the concept of chromatographic filter, allows for an automated approach to optimise the admissible mass-to-charge deviation (δm/z) and can be used to match ROIs across multiple samples. The algorithm was tested on a LC-HRMS dataset comprised of 21 replicate injections of a wastewater effluent extract and assessed on its ability to correctly retrieve the ROI's relative to 57 compounds and match them across all injections. In summary, it achieved a ten-fold compression rate in on-disk storage at a noise threshold of 200 counts, and the median ROI length matched the observed chromatographic peak width (12-23 points). Correct ROI matching with a mass accuracy of 9ppm was observed for 52 compounds across all 21 injections with only one compound split between two adjacent m/z traces in six runs. Overall, the new algorithm performed favourably compared to the ROI algorithm currently used in the well-established ROI-MCR (multivariate curve resolution) workflow for deconvolution of HRMS chromatographic data.

Multi-Component Temporal-Correlation Seismic Data Compression Algorithm Based on the PCA and DWT

Industrial application data acquisition systems can be sources of vast amounts of data. The seismic surveys conducted by oil and gas companies result in enormous datasets, often exceeding terabytes of data. The storage and communication demands these data require can only be achieved through compression. Careful consideration must be given to minimize the reconstruction error of compressed data caused by lossy compression. This paper investigates the combination of principal component analysis (PCA), discrete wavelet transform (DWT), thresholding, quantization, and entropy encoding to compress such datasets. The proposed method is a lossy compression algorithm tuned by evaluating the reconstruction error in frequency ranges of interest, namely 0–20 Hz and 15–65 Hz. The PCA compression and decompression acts as a noise filter while the DWT drives the compression. The proposed method can be tuned through threshold and quantization percentages and the number of principal components to achieve compression rates of up to 31:1 with reconstruction residues energy of less than 4% in the frequency ranges of 0–20 Hz, 15–65 Hz, and 60–105 Hz.

Comparison of different surfaces in resuscitation quality using a real-time feedback device: A manikin study.

Delivering chest compressions (CCs) at the targeted depth and rate is a crucial aspect of maintaining the quality of cardiopulmonary resuscitation (CPR). Although administering CCs on a firm surface is recommended, it may not always be feasible. This study aimed to determine whether the underlying surface affects CC depth and rate using a real-time feedback device. An observational study was conducted on a manikin (ResusciAnne; Laerdal). 25 volunteer emergency medicine physicians performed 2 min of continuous CCs without feedback on the floor, emergency department stretcher (EDS), and ambulance stretcher (AS). The following day, all participants performed an additional 2 min of CCs while receiving audiovisual real-time feedback (ZOLL M2 series). Compression depths and rates were measured and recorded in a real-time feedback device. A total of 150 CC intervals were analyzed. The mean values of compression depths and rates on all surfaces are within the targeted range for high-quality CPR, except for the mean depth without feedback on the EDS (mean: 6.37 cm). There were a statistically significant difference, with both AS and EDS were achieved deeper compressions than those on the floor (P < 0.05). When examining the mean compression depths on three different surfaces with feedback, no statistically significant difference was observed. However, CCs performed without feedback on both AS and EDS were statistically significantly deeper than those on the floor. The mean compression rates both on the floor and the AS were statistically significantly faster compared to EDS. When examining the mean compression rates during CCs performed on three different surfaces with feedback, no statistically significant difference was observed but in the without feedback compressions, both on AS and floor were found to be statistically significantly faster than EDS. CC's depth are influenced by the underlying surface. It appears more feasible to minimize surface-related differences while maintaining appropriate targets for depth using real-time feedback devices. The mean compression rate could be kept within the targeted range regardless of the surface.

Shear thickening inside elastic open-cell foams under dynamic compression.

We measure the response of open-cell polyurethane foams filled with a dense suspension of fumed silica particles in polyethylene glycol at compression speeds spanning several orders of magnitude. The gradual compressive stress increase of the composite material indicates the existence of shear rate gradients in the interstitial suspension caused by wide distributions in pore sizes in the disordered foam network. The energy dissipated during compression scales with an effective internal shear rate, allowing for the collapse of three data sets for different pore-size foams. When scaled by this effective shear rate, the most pronounced energy increase coincides with the effective shear rate corresponding to the onset of shear thickening in our bulk suspension. Optical measurements of the radial deformation of the foam network and of the suspension flow under compression provide additional insight into the interaction between shear thickening fluid and foam. This optical data, combined with a simple model of a spring submerged in viscous flow, illustrates the dynamic interaction of viscous drag with foam elasticity as a function of compression rate, and identifies the foam pore size distribution as a critically important model parameter. Taken together, the stress measurements, dissipated energy, and relative motion of the fluid and the foam can be rationalized by knowing the pore size distribution and the average pore size of the foam.

The impact of rhythm perception on chest compression rate during CPR: Insights from a pilot simulation study.

This pilot study aims to assess how individuals with rhythm perception, particularly musicians, are able to maintain the predefined chest compression rate during cardiopulmonary resuscitation compared to people without rhythm perception. The study was conducted at the Pilsen Emergency Medicine Conference (Czechia) using a simulation-based cohort design. Participants performed chest compressions on a manikin for 120 s, with the first 10 s guided by a metronome. Participants were grouped based on self-reported rhythmic perception, such as playing a musical instrument. The primary outcome was the average chest compression rate per minute. A total of 67 participants were included. Both groups provided chest compression rate within the recommended limits. Musicians maintained a better chest compression rate (mean 110.56 compressions per minute) compared to nonmusicians (mean 107.31; T-test, p = 0.00074). Those with any rhythmic perception experience also performed better (T-test, p = 0.036931). Secondary factors, including gender, clinical experience, and prior resuscitation training, did not significantly affect the results. This study demonstrates that individuals with rhythm perception, especially musicians, follow the predefined frequency of chest compressions more effectively.

Design and Development of Schema for Schemaless Databases

A schema database functions as a repository for interconnected data points, facilitating comprehension of data structures by organizing information into tables with rows and columns. These databases utilize established connections to arrange data, with attribute values linking related tuples. This integrated approach to data management and distributed processing enables schema databases to maintain models even when the working set size surpasses available RAM. However, challenges such as data quality, storage, scarcity of data science professionals, data validation, and sourcing from diverse origins persist. Notably, while schema databases excel at reviewing transactions, they often fall short in updating them effectively. To address these issues, a Chimp-based radial basis neural model (CbRBNM) is employed. Initially, the Schemaless database was considered and integrated into the Python system. Subsequently, compression functions were applied to both schema and schema-less databases to optimize relational data size by eliminating redundant files. Performance validation involved calculating compression parameters, with the proposed method achieving memory usage of 383.37[Formula: see text]Mb, a computation time of 0.455[Formula: see text]s, a training time of 167.5[Formula: see text]ms, and a compression rate of 5.60%. Extensive testing demonstrates that CbRBNM yields a favorable compression ratio and enables direct searching on compressed data, thereby enhancing query performance.

Device endothelialization and morphology assessments at 1 year using computed tomography angiography: comparison of traditional with novel puncturable atrial septal defect occluders

BackgroundComplete endothelialization is often not achieved within 6 months after implantation of an atrial septal defect (ASD) occluder, which may lead to microthrombus or thrombosis. This study aimed to assess the endothelialization and morphology of a novel puncturable ASD occluder (ReAces) compared with traditional occluders 1 year post-implantation using computed tomography angiography (CTA).MethodsFifteen patients from each group in a randomized controlled trial comparing ReAces with traditional occluders were included at the 1-year follow-up at Zhongshan Hospital, Fudan University. Baseline characteristics and procedural data were recorded. CTA was performed to assess the device morphology and degree of endothelialization. Independent samples t-test and Fisher's exact test were primarily used to compare the above data.ResultsEach patient received a single device and had no residual shunts. There were no differences in defect size (15.3 ± 4.0 mm vs. 15.3 ± 4.7 mm, p = 1.00) or occluder size (21.2 ± 4.4 mm vs. 21.5 ± 5.3 mm, p = 0.882) between the two groups. At the 1-year follow-up CTA, the central region thickness (4.2 mm ± 0.9 mm vs. 7.8 mm ± 2.4 mm, p < 0.0001), left atrial device-occupied volume (1863.3 mm2 ± 404.4 mm2 vs. 4764.4 mm2 ± 2321.2 mm2, p < 0.001), and device compression rates (10.1 ± 4.8% vs. 17.5 ± 5.6%, p = 0.001) were significantly lower in the experimental group. All occluders in the experimental group achieved complete endothelialization, whereas in the control group, 7 patients did not (100% vs. 53.3%, p = 0.006).ConclusionsCompared with traditional occluders, ReAces presented a significantly greater complete endothelialization rate, lower central region thickness, lower left atrial device-occupied volume, and lower device compression rates at 1 year post-implantation by CTA.Trial registrationTrial registry: ClinicalTrials.gov.Unique identifying number: NCT05371366.Date of registration: 04/05/2022.Graphical

Exploring Automated Summarization: From Extraction to Abstraction

This paper provides a review of AI-powered automated summarization models, with a focus on two principal approaches: extractive and abstractive. The study aims to evaluate the capabilities of these models in generating concise yet meaningful summaries and analyze their lexical proficiency and linguistic fluidity. The compression rates are assessed using quantitative metrics such as page, word, and character counts, while language fluency is described in terms of ability to manipulate grammar and lexical patterns without compromising meaning and content. The study draws on a selection of scientific publications across various disciplines, testing the functionality and output quality of automated summarization tools such as Summate.it, WordTune, SciSummary, Scholarcy, and OpenAI ChatGPT-4. The findings reveal that the selected models employ a hybrid strategy, integrating both extractive and abstractive techniques. Summaries produced by these tools exhibited varying degrees of completeness and accuracy, with page compression rates ranging from 50 to 95%, and character count reductions reaching up to 98%. Qualitative evaluation indicated that while the models generally captured the main ideas of the source texts, some summaries suffered from oversimplification or misplaced emphasis. Despite these limitations, automated summarization models exhibit significant potential as effective tools for both text compression and content generation, highlighting the need for continued research, particularly from the perspective of linguistic analysis. Summaries generated by AI models offer new opportunities for analyzing machine-generated language and provide valuable data for studying how algorithms process, condense, and restructure human language.

Autoencoder-Based DIFAR Sonobuoy Signal Transmission and Reception Method Incorporating Residual Vector Quantization and Compensation Module: Validation Through Air Channel Modeling

This paper proposes a novel autoencoder-based neural network for compressing and reconstructing underwater acoustic signals collected by Directional Frequency Analysis and Recording sonobuoys. To improve both signal compression rates and reconstruction performance, we integrate Residual Vector Quantization and a Compensation Module into the decoding process to effectively compensate for quantization errors. Additionally, an unstructured pruning technique is applied to the encoder to minimize computational load and parameters, addressing the battery limitations of sonobuoys. Experimental results demonstrate that the proposed method reduces the data transmission size by approximately 31.25% compared to the conventional autoencoder-based method. Moreover, the spectral mean square errors are reduced by 60.58% for continuous wave signals and 55.25% for linear frequency modulation signals under realistic air channel simulations.

Evaluation of the Effectiveness of Advanced Technology Clinical Simulation Manikins in Improving the Capability of Australian Paramedics to Deliver High-Quality Cardiopulmonary Resuscitation: Pre- and Postintervention Study.

Emergency medical services attend out-of-hospital cardiac arrests all across Australia. Resuscitation by emergency medical services is attempted in nearly half of all cases. However, resuscitation skills can degrade over time without adequate exposure, which negatively impacts patient survival. Consequently, for paramedics working in areas with low out-of-hospital cardiac arrest case volumes, ambulance services and professional bodies recognize the importance of alternative ways to maintain resuscitation skills. Simulation-based training via resuscitation manikins offers a potential solution for maintaining paramedic clinical practice skills. The aim of the study is to examine the effectiveness of advanced technology clinical simulation manikins and accompanying simulation resources (targeted clinical scenarios and debriefing tools) in improving the demonstrable capability of paramedics to deliver high-quality patient care, as measured by external cardiac compression (ECC) performance. A pre- and postintervention study design without a control group was used. Data were collected at the start of the manikin training forum (baseline), immediately following the training forum (time 2), and 6 to 11 months after the training forum (time 3). The study was conducted with paramedics from 95 NSW Ambulance locations (75 regional locations and 20 metropolitan locations). Eligible participants were paramedics who were employed by NSW Ambulance (N=106; 100% consent rate). As part of the intervention, paramedics attended a training session on the use of advanced technology simulation manikins. Manikins were then deployed to locations for further use. The main outcome measure was an overall compression score that was automatically recorded and calculated by the simulator manikin in 2-minute cycles. This score was derived from compressions that were fully released and with the correct hand position, adequate depth, and adequate rate. A total of 106 (100% consent rate) paramedics participated, primarily representing regional ambulance locations (n= 75, 78.9%). ECC compression scores were on average 95% or above at all time points, suggesting high performance. No significant differences over time (P>.05) were identified for the overall ECC performance score, compressions fully released, compressions with adequate depth, or compressions with the correct hand position. However, paramedics had significantly lower odds (odds ratio 0.30, 95% CI 0.12-0.78) of achieving compressions with adequate rate at time 3 compared to time 2 (P=.01). Compressions were of a slower rate, with an average difference of 2.1 fewer compressions every minute. Despite this difference in compression rate over time, this did not cause significant detriment to overall ECC performance. Training and deployment of simulator manikins did not significantly change paramedics' overall ECC performance. The high baseline performance (ceiling effect) of paramedics in this sample may have prevented the potential increase in skills and performance.

Efficacy of Percutaneous Kyphoplasty and Percutaneous Pedicle Screw Fixation in Managing Osteoporotic Vertebral Compression Fractures in Middle-Aged and Elderly Individuals: A Comparative Study.

This study is aims to compare the clinical efficacy and safety of percutaneous kyphoplasty (PKP) and percutaneous pedicle screw fixation (PPSF) in managing osteoporotic vertebral compression fractures (OVCFs) among middle-aged and elderly individuals. A total of 142 patients aged 55-65 years were selected retrospectively from the Department of Orthopedics of our hospital from June 2021 to June 2023 and classified into PKP (n = 68) and PPSF (n = 74) groups. General data of patients were collected, and related perioperative indicators, Visual Analog Scale (VAS) scores, Oswestry Disability Index (ODI), Activities of Daily Living (ADL) scores, changes in Cobb angle of the fractured vertebrae, vertebral compression rate, as well as postoperative complications were compared between the two groups. The PKP group showed shorter operation duration, reduced intraoperative blood loss, shorter hospital stay, and earlier compared to the PPSF group (p < 0.001). In comparison with the PPSF group, the PKP group endured much reduced pain, with improvements in spinal function and quality of life. On postoperative day 1, the PPSF group demonstrated superior restoration of vertebral height and deformity correction in relative to the other group (p < 0.05), with no significant difference in preoperative Cobb angle and vertebral compression rate (p > 0.05). The incidence of complications showed no significant differences between the two groups (p > 0.05). The advantages of PKP lie in its ability to effectively relieve pain, improve spinal function, and enhance the quality of life. PPSF, meanwhile, is well-suited for the recovery of vertebral height and the correction of deformity in the early stage.

A study on identifying representative trips for mobility service design

AbstractRecently, with growing interest in urban mobility patterns, the demand for collecting and analysing origin‐destination (OD) data is increasing. Due to the large scale and dimensionality of OD data, there are two issues in analysing the data: big‐data storage and major pattern extraction. To deal with two issues at the same time, this study suggests a principal control analysis‐based major demand identification method to improve the usability of microscopic OD data. Especially, this study focuses on finding principal components that preserve major patterns from OD data with small random noise so that the data can be effectively used for mobility service design. The proposed method is applied to smart card data of Seoul and Sejong and extracted major demand patterns from peak‐ and non‐peak hour data of these cities. The degree of daily regularity, reconstruction accuracy, and compression rate of the reconstructed data is analysed varying sets of principal components. The obtained results show that the major demands contain a low volume and a large volume of demand and with lower‐order principal components, major demands can be efficiently extracted by removing randomly appearing small‐volume demand. In addition, the trade‐off behaviour is observed between the degree of daily regularity and reconstruction accuracy depending on the compression rate. Based on the observations, it can be found that the loss of major demand patterns could be prevented when targeting a reconstruction accuracy of 90–95% and the proposed method can reduce the data size while preserving major mobility patterns.

Research on reciprocating sealing performance and leakage rate prediction based on GA-PSO-BPNN hybrid algorithm

Purpose This study aims to investigate the sealing performance of reciprocating seals under the effect of rubber abrasion using ABAQUS simulation software, and to propose a prediction framework based on a hybrid algorithm (GA-PSO-BPNN) to predict the leakage of reciprocating seals of downhole gauging instrumentation under different working condition parameters. Design/methodology/approach The authors combined the UMESHMOTION user program with the improved Archard wear model to investigate reciprocating seal performance. GA and a PSO were proposed as ways to enhance the BPNN’s predictive model. Findings The results show that the impact of fluid pressure fluctuations on the wear of the seal lip is more pronounced during the rapid wear phase compared to the steady wear phase. Similarly, variations in compression rate have a greater impact on seal lip wear at different stages of wear. The GA-PSO-BPNN prediction model outperforms the single-prediction model in terms of prediction accuracy. Originality/value The authors investigated sealing performance through simulation software and propose a GA-PSO-BPNN-based fault diagnosis method for rotating machinery. To verify the accuracy of the prediction model, a reciprocating sealing test platform for gauge work cylinders is constructed. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-08-2024-0293/

Implementation of text summarization on indonesian scientific articles using textrank algorithm with TF-IDF web-based

The development of information technology has significantly changed how information is accessed, necessitating readers to absorb content efficiently and make quick decisions. To address this challenge, this research developed a text summarization system specifically for Indonesian scientific articles using a web-based implementation of the TextRank and TF-IDF algorithms. TextRank was selected for its capability to identify key sentences without requiring training data, while TF-IDF was employed to weight words based on their frequency within the document. The dataset comprised 100 scientific articles in Indonesian from the Unimed Kode Journal, covering the years 2022-2024. The summarization process included several critical stages: text preprocessing, TF-IDF weighting, cosine similarity calculation, and sentence ranking. The resulting summaries were rigorously evaluated by language experts and website specialists using a Likert scale to assess both the quality of the summaries and the usability of the system. The findings demonstrated that the system effectively generated summaries that retained essential information from the original articles, with the highest accuracy observed at a 50% compression rate (88.533%). Additionally, the system achieved good performance at 40% compression (85.133%) and 30% compression (81.26%). The web-based system allows users to input article text and quickly obtain a summary, offering a practical tool for researchers and readers to efficiently comprehend academic content.

Comparative effectiveness of self-learning and instructor-assisted pediatric cardiopulmonary resuscitation training: A prospective randomized study.

Unlike that for adults, training for cardiopulmonary resuscitation of infant and child is scarce, and warrants efforts for greater accessibility. Effective self-learning could expand training accessibility and facilitate the development of effective infant and child cardiopulmonary resuscitation training methods. This study was conducted to develop a pediatric cardiopulmonary resuscitation self-learning training program, implement nurse training, and evaluate training effectiveness by comparing trainees' achievement of self-efficacy in pediatric cardiopulmonary resuscitation, with or without instructor assistance. Prospective randomized controlled study. General hospital. Nurses working in a general hospital. Participants were randomly and sequentially assigned to either an instructor-assisted or a self-learning group and completed a pre-training survey, pediatric cardiopulmonary resuscitation training, post-training survey and test, and a 1-year follow-up test. Pediatric cardiopulmonary resuscitation training was divided into infant and child cardiopulmonary resuscitation training. Among the 97 participants, instructor-assisted and self-learning groups trained in pediatric cardiopulmonary resuscitation showed no significant difference in correct chest compressions, ventilation performance, and self-efficacy at the post-training assessment. However compared to the pre-training assessment, these parameters increased significantly in both groups at the post-training assessment (self-efficacy: infant cardiopulmonary resuscitation: pre-training 15.48, post-training: 17.25 vs pre-training 19.74, post-training 20.05; child cardiopulmonary resuscitation: pre-training 15.78, post-training 17.90 vs pre-training 19.48, post-training 20.55; both p<0.001), respectively. In the self-learning group, at the 1-year follow-up, the rate of correct infant resuscitation compression decreased significantly from 89 (immediate post-training score) to 76 (p=0.07), without significant intergroup difference. Regarding the time of measurement, although the main effect was significant (p<0.001), the interaction effect of instructor-assisted training and time of measurement on pediatric cardiopulmonary resuscitation self-efficacy was not. Pediatric cardiopulmonary resuscitation training did not differ significantly with training modality and improved self-efficacy, which changed significantly over time. These results aid the design of effective self-learning training programs for pediatric cardiopulmonary resuscitation training.

A ViT‐Based Adaptive Recurrent Mobilenet With Attention Network for Video Compression and Bit‐Rate Reduction Using Improved Heuristic Approach Under Versatile Video Coding

ABSTRACTVideo compression received attention from the communities of video processing and deep learning. Modern learning‐aided mechanisms use a hybrid coding approach to reduce redundancy in pixel space across time and space, improving motion compensation accuracy. The experiments in video compression have important improvements in past years. The Versatile Video Coding (VVC) is the primary enhancing standard of video compression which is also referred to as H. 226. The VVC codec is a block‐assisted hybrid codec, making it highly capable and complex. Video coding effectively compresses data while reducing compression artifacts, enhancing the quality and functionality of AI video technologies. However, the traditional models suffer from the incorrect compression of the motion and ineffective compensation frameworks of the motion leading to compression faults with a minimal trade‐off of the rate distortion. This work implements an automated and effective video compression task under VVC using a deep learning approach. Motion estimation is conducted using the Motion Vector (MV) encoder‐decoder model to track movements in the video. Based on these MV, the reconstruction of the frame is carried out to compensate for the motions. The residual images are obtained by using Vision Transformer‐based Adaptive Recurrent MobileNet with Attention Network (ViT‐ARMAN). The parameters optimization of the ViT‐ARMAN is done using the Opposition‐based Golden Tortoise Beetle Optimizer (OGTBO). Entropy coding is used in the training phase of the developed work to find the bit rate of residual images. Extensive experiments were conducted to demonstrate the effectiveness of the developed deep learning‐based method for video compression and bit rate reduction under VVC.

Impact of Chest Compression Fraction on Outcomes in In-Hospital and Emergency Department Cardiac Arrests at a Tertiary Care Center in South India

ABSTRACT Background: The study aimed to determine the average chest compression fraction (CCF) and its correlation with influencing factors to optimize blood flow during resuscitation, aiming for a minimum of 60%. Materials and Methods: Prospective observational study on 50 cardiac arrest patients, analyzing chest compression fraction (CCF) and identifying associations using Spearman’s correlation coefficient and individual student t-tests. Results: The study revealed a satisfactory average Chest Compression Fraction (CCF) of 71.66 ± 7.79, with diabetes mellitus being the most prevalent medical condition, and no significant correlation between compression depth, rate, and recoil. Conclusion: The average cardiac arrest (CCF) in ED patients decreased with longer code durations, daytime events, defibrillator use, increased team members, male providers, and chest compressions.