Performance Improvement Research Articles

A collaborative multidisciplinary trauma program improvement team improves VTE chemoprophylaxis guideline compliance in non-operative stable TBI.

Delays in initiating venous thromboembolism (VTE) prophylaxis in patients suffering from traumatic brain injury (TBI) persist despite guidelines recommending early initiation. We hypothesized that the expansion of a Trauma Program Performance Improvement (PI) team will improve compliance of early (24-48 hours) initiation of VTE prophylaxis and will decrease VTE events in TBI patients. We performed a single-center retrospective review of all TBI patients admitted to a Level I trauma center before (2015-2016,) and after (2019-2020,) the expansion of the Trauma Performance Improvement and Patient Safety (PIPS) team and the creation of trauma process and outcome dashboards. Exclusion criteria included discharge or death within 48 hours of admission, expanding intracranial hemorrhage on CT scan, and a neurosurgical intervention (craniotomy, pressure monitor, or drains) prior to chemoprophylaxis initiation. A total of 1,112 patients met the inclusion criteria, of which 54% (n = 604) were admitted after Trauma PIPS expansion. Following the addition of a dedicated PIPS nurse in the trauma program and creation of process dashboards, the time from stable CT to VTE prophylaxis initiation decreased (52 hours to 35 hours; p < 0.001) and more patients received chemoprophylaxis at 24 hours to 48 hours (59% from 36%, p < 0.001) after stable head CT. There was no significant difference in time from first head CT to stable CT (9 vs. 9 hours; p = 0.15). The Contemporary group had a lower rate of VTE events (1% vs. 4%; p < 0.001) with no increase in bleeding events (2% vs. 2%; p = 0.97). On multivariable analysis, being in the Early cohort was an independent predictor of VTE events (adjusted odds ratio, 3.74; 95% confidence interval, 1.45-6.16). A collaborative multidisciplinary Trauma PIPS team improves guideline compliance. Initiation of VTE chemoprophylaxis within 24 hours to 48 hours of stable head CT is safe and effective. Therapeutic/Care Management; Level IV.

Gender-related differences in kinematic parameters of the start and performance in a 50m breaststroke swimming

Introduction: In swimming, the competitions are divided by age categories, with each swimming event beginning at the start. For optimal improvements in swimming performance in the sprint events, the performance at the start and during swimming in each age category should be analyzed. Aim: The aim of our study is to show the differences in kinematic parameters of the start and performance in the sprint event of 50 m breaststroke from the perspective of gender in different age groups at international competitions organized in Slovakia. Materials and Methods: The females were categorized by age into K3 (2010-2011; n=36; 282 WA points), K2 (2008-2009; n=26; 409 WA points) and K1 (2007 and older; n=34; 503 WA points) groups. Males were categorized into K3 (2009-2010; n=27; 265 WA points), K2 (2007-2008; n=33; 372 WA points) and K1 (2006 and older; n=39; 207 WA points) groups. We used SwimPro cameras and Dartgish software to monitor kinematic parameters in the above-water, underwater, and swim phases. The parameters monitored were block time (BT), time (FT), and length (FD) of flight, time (UWT) and length (UWD) underwater, swim time (ST) and distance (SD) to 15 m, time to 15 m (T15), 25 m (T25), and 50 m (T50) distance. Data were tested by Shapiro-Wilk, Kruskal-Wallis ANOVA, and Mann-Whitney U test in Statistica software (version 13.5). Results: Statistically significant differences (p&lt;0.01) in females were found in the above-water phase in BT, FT, and FD between K3 and K1. The underwater phase in UWT showed differences between K3 to K1, K2 to K1 whereas for UWD - between K3 and K2 and between K2 and K1. For T15 and T25, there were differences (p&lt;0.01) between all categories, but in the final time (T50), the differences occurred between K3 and K2 and between K3 and K1. In males, there was a significant difference in the above-water phase between K3 and K1 and between K2 and K1. In UWD, ST, SD, T15, T25, and T50, there were significant differences between all categories. Differences (p&gt;0.01) between males and females by category in the above-water phase were measured in FT (K1) and FD (K2, K1). In the underwater phase, the differences were found in UWT (K2) and UWD (K1). For T15, T25, and T50, significant differences were shown in all categories. Conclusions: The kinematic analysis revealed differences not only in the start phases and the 50 m breaststroke swimming in the male and female categories but also between genders. Differences in swimming performance may be due to performance at the start and therefore more attention should be paid to the start phases in the different categories of swimmers.

Cooperative Control and Performance Evaluation of a Linear MIMO Parabolic Spatiotemporal Dynamic System on Directed and Switching Topologies.

The current work concerns on cooperative control of exponential stabilization and control performance improvement in the spatial domain for a linear spatiotemporal dynamic system associated with multiple control actuators and multiple collaborative measurement sensors. By assuming that the system dynamics is modeled by a MIMO parabolic partial differential equation (PPDE) and each sensor can share measurement information with its topological neighbors in directed and switching topological networks, a cooperative control protocol is proposed to achieve the control aim of this article. With the help of a combination of multiagent consensus theory, Lyapunov's method, and integral inequality technique, sufficient conditions are presented for the closed-loop exponential stability of the PPDE in the norm |·|2 . Moreover, some performance indexes are defined to evaluate the closed-loop control performance improvement in the spatial domain. Extensive simulation results are finally presented for a simple numerical example and a practical heat treatment process to verify the effectiveness and performance improvement capability of the proposed cooperative control protocol.

Durability of Effects of Cognitive Remediation on Cognition and Psychosocial Functioning in Schizophrenia: A Systematic Review and Meta-Analysis of Randomized Clinical Trials.

Cognitive remediation provides substantial improvements in cognitive performance and real-world functioning for people living with schizophrenia, but the durability of these benefits needs to be reassessed and better defined. The aims of this study were to provide a comprehensive assessment of the durability of the benefits of cognitive remediation for cognition and functioning in people living with schizophrenia and evaluating potential moderators of effects. A systematic search was conducted in PubMed, Scopus, and PsycINFO, and reference lists of included articles and Google Scholar were inspected. Eligible studies were randomized clinical trials of cognitive remediation in patients diagnosed with schizophrenia spectrum disorders in which follow-up assessments were included. Screening and data extraction were performed by at least two independent reviewers. Cohen's d was used to measure outcomes. Primary outcomes were changes in cognition and functioning from baseline to conclusion of follow-up. Moderators of the durability of effects were assessed. Of 2,840 identified reports, 281 full texts were assessed and 130 reports on 67 studies with 5,334 participants were included. Cognitive remediation produced statistically significant positive effects that persisted at the end of follow-up in global cognition (d=0.23) and in global functioning (d=0.26). Smaller study samples and single-center studies were associated with better cognitive outcomes; longer treatment and follow-up duration, techniques for transferring cognitive gains to the real world, integration with psychiatric rehabilitation, group format of delivery, and more female participants in the sample were associated with better functional outcomes. Cognitive remediation provides durable improvements in cognition and functioning in schizophrenia. This finding corroborates the notion that cognitive remediation should be implemented more widely in clinical and rehabilitation practice.

Fast Incomplete Multi-View Clustering With View-Independent Anchors.

Multi-view clustering (MVC) methods aim to exploit consistent and complementary information among each view and achieve encouraging performance improvement than single-view counterparts. In practical applications, it is common to obtain instances with partially available information, raising researches of incomplete multi-view clustering (IMC) issues. Recently, several fast IMC methods have been proposed to process the large-scale partial data. Though with considerable acceleration, these methods seek view-shared anchors and ignore specific information among single views. To tackle the above issue, we propose a fast IMC with view-independent anchors (FIMVC-VIA) method in this article. Specifically, we learn individual anchors based on the diversity of distribution among each incomplete view and construct a unified anchor graph following the principle of consistent clustering structure. By constructing an anchor graph instead of pairwise full graph, the time and space complexities of our proposed FIMVC-VIA are proven to be linearly related to the number of samples, which can efficiently solve the large-scale task. The experiment performed on benchmarks with different missing rate illustrates the improvement in complexity and effectiveness of our method compared with other IMC methods. Our code is publicly available at https://github.com/Tracesource/ FIMVC-VIA.

A Survey of Visual Transformers.

Transformer, an attention-based encoder-decoder model, has already revolutionized the field of natural language processing (NLP). Inspired by such significant achievements, some pioneering works have recently been done on employing Transformer-liked architectures in the computer vision (CV) field, which have demonstrated their effectiveness on three fundamental CV tasks (classification, detection, and segmentation) as well as multiple sensory data stream (images, point clouds, and vision-language data). Because of their competitive modeling capabilities, the visual Transformers have achieved impressive performance improvements over multiple benchmarks as compared with modern convolution neural networks (CNNs). In this survey, we have reviewed over 100 of different visual Transformers comprehensively according to three fundamental CV tasks and different data stream types, where taxonomy is proposed to organize the representative methods according to their motivations, structures, and application scenarios. Because of their differences on training settings and dedicated vision tasks, we have also evaluated and compared all these existing visual Transformers under different configurations. Furthermore, we have revealed a series of essential but unexploited aspects that may empower such visual Transformers to stand out from numerous architectures, e.g., slack high-level semantic embeddings to bridge the gap between the visual Transformers and the sequential ones. Finally, two promising research directions are suggested for future investment. We will continue to update the latest articles and their released source codes at https://github.com/liuyang-ict/awesome-visual-transformers.

Pure large kernel convolutional neural network transformer for medical image registration

Deformable medical image registration is a fundamental and critical task in medical image analysis. Recently, deep learning-based methods have rapidly developed and have shown impressive results in deformable image registration. However, existing approaches still suffer from limitations in registration accuracy or generalization performance. To address these challenges, in this paper, we propose a pure convolutional neural network module (CVTF) to implement hierarchical transformers and enhance the registration performance of medical images. CVTF has a larger convolutional kernel, providing a larger global effective receptive field, which can improve the network’s ability to capture long-range dependencies. In addition, we introduce the spatial interaction attention (SIA) module to compute the interrelationship between the target feature pixel points and all other points in the feature map. This helps to improve the semantic understanding of the model by emphasizing important features and suppressing irrelevant ones. Based on the proposed CVTF and SIA, we construct a novel registration framework named PCTNet. We applied PCTNet to generate displacement fields and register medical images, and we conducted extensive experiments and validation on two public datasets, OASIS and LPBA40. The experimental results demonstrate the effectiveness and generality of our method, showing significant improvements in registration accuracy and generalization performance compared to existing methods. Our code has been available at https://github.com/fz852/PCTNet.

Multi-apodization with cross-correlation combined with generalized sidelobe canceller applied to ultrasound imaging.

Beamforming is vital for medical ultrasound imaging systems. The generalized sidelobe canceller (GSC) beamforming can improve the image quality of lateral resolution, but its performance improvement in contrast and robustness is limited. This paper proposes an improved generalized sidelobe canceller algorithm based on multi-apodization with cross-correlation (MAXB-IGSC), which aims to improve the contrast and robustness of ultrasound imaging while maintaining the high image resolution and background speckle quality of GSC. The proposed MAXB-IGSC uses multiple pairs of complementary received apodization functions to process the echo data individually to obtain multiple pairs of data sets. The average of their normalized cross-correlation coefficients is then calculated and utilized to determine the adaptive subarray length of the GSC covariance matrix and weights the output of the improved GSC. The MAXB-IGSC improves the contrast ratio (CR) by 171.18% in anechoic cyst simulation and by 91.23%/130.97%/171.76% in geabr_0 (a dataset from the University of Michigan) experiment compared with GSC, respectively. Furthermore, MAXB-IGSC exhibits significant noise immunity, which greatly improves the robustness of the imaging. The technology also maintains the brightness and uniformity of the background speckle. The proposed MAXB-IGSC has potential for obtaining high-quality ultrasound images in clinical applications.

Enhancement of combustion, performance and emission characteristics of diesel engines fuelled with jatropha-karanja biodiesel using EGM and TGME as additive

The oxygenated additives can improve the physical properties of biodiesel blends to achieve enhanced characteristics of a diesel engine. The present work has investigated ethylene glycol monoacetate (EGM) and tri-ethylene glycol mono-methyl ether (TGME) as potential additives for the jatropha-karanja dual blended biodiesel for improved characteristics of diesel engines. The experiments have been conducted with a single cylinder four stroke diesel engine operating at constant speed of 1500 rpm. The performance, emission and combustion parameters of the engine were investigated with pure diesel, jatropha-karanja dual blended biodiesel having 20 % of methyl ester and the biodiesel added with 2, 4, and 6 % of both the additives respectively. The doping of additives improved the brake thermal efficiency by 4.1 and 4.7 % with brake specific fuel consumption dropped by 0.18 and 0.2 kg/kWh. The additives reduced the hydrocarbon emission by 29.8 % and carbon monoxide by 40 % with a slight increase in NOx emission by 5.2 %. The cylinder pressure attained value near to pure diesel while the heat release rate increased by 12.6 % indicating proper fuel combustion inside the engine cylinder. Thus, the oxygenated additives can suitably be adopted as performance improvers in biodiesel fuels for automotive engines.

DHFS-ECM: Design of a Dual Heuristic Feature Selection-based Ensemble Classification Model for the Identification of Bamboo Species from Genomic Sequences

Background: Analyzing genomic sequences plays a crucial role in understanding biological diversity and classifying Bamboo species. Existing methods for genomic sequence analysis suffer from limitations such as complexity, low accuracy, and the need for constant reconfiguration in response to evolving genomic datasets Aim: This study addresses these limitations by introducing a novel Dual Heuristic Feature Selection- based Ensemble Classification Model (DHFS-ECM) for the precise identification of Bamboo species from genomic sequences. Methods: The proposed DHFS-ECM method employs a Genetic Algorithm to perform dual heuristic feature selection. This process maximizes inter-class variance, leading to the selection of informative N-gram feature sets. Subsequently, intra-class variance levels are used to create optimal training and validation sets, ensuring comprehensive coverage of class-specific features. The selected features are then processed through an ensemble classification layer, combining multiple stratification models for species-specific categorization. Results: Comparative analysis with state-of-the-art methods demonstrate that DHFS-ECM achieves remarkable improvements in accuracy (9.5%), precision (5.9%), recall (8.5%), and AUC performance (4.5%). Importantly, the model maintains its performance even with an increased number of species classes due to the continuous learning facilitated by the Dual Heuristic Genetic Algorithm Model. Conclusion: DHFS-ECM offers several key advantages, including efficient feature extraction, reduced model complexity, enhanced interpretability, and increased robustness and accuracy through the ensemble classification layer. These attributes make DHFS-ECM a promising tool for real-time clinical applications and a valuable contribution to the field of genomic sequence analysis.

Self-Guided DMT: Exploring a Novel Paradigm of Dance Movement Therapy in Mixed Reality for Children with ASD.

Children diagnosed with Autism Spectrum Disorder (ASD) often exhibit motor disorders. Dance Movement Therapy (DMT) has shown great potential for improving the motor control ability of children with ASD. However, traditional DMT methods often lack vividness and are difficult to implement effectively. To address this issue, we propose a Mixed Reality DMT approach, utilizing interactive virtual agents. This approach offers immersive training content and multi-sensory feedback. To improve the training performance of children with ASD, we introduce a novel training paradigm featuring a self-guided mode. This paradigm enables the rapid creation of a virtual twin agent of the child with ASD using a single photo to embody oneself, which can then guide oneself during training. We conducted an experiment with the participation of 24 children diagnosed with ASD (or ASD propensity), recording their training performance under various experimental conditions. Through expert rating, behavior coding of training sessions, and statistical analysis, our findings revealed that the use of the twin agent for self-guidance resulted in noticeable improvements in the training performance of children with ASD. These improvements were particularly evident in terms of enhancing movement quality and refining overall target-related responses. Our study holds clinical potential in the field of medical treatment and rehabilitation for children with ASD.

Noise-Generating and Imaging Mechanism Inspired Implicit Regularization Learning Network for Low Dose CT Reconstrution.

Low-dose computed tomography (LDCT) helps to reduce radiation risks in CT scanning while maintaining image quality, which involves a consistent pursuit of lower incident rays and higher reconstruction performance. Although deep learning approaches have achieved encouraging success in LDCT reconstruction, most of them treat the task as a general inverse problem in either the image domain or the dual (sinogram and image) domains. Such frameworks have not considered the original noise generation of the projection data and suffer from limited performance improvement for the LDCT task. In this paper, we propose a novel reconstruction model based on noise-generating and imaging mechanism in full-domain, which fully considers the statistical properties of intrinsic noises in LDCT and prior information in sinogram and image domains. To solve the model, we propose an optimization algorithm based on the proximal gradient technique. Specifically, we derive the approximate solutions of the integer programming problem on the projection data theoretically. Instead of hand-crafting the sinogram and image regularizers, we propose to unroll the optimization algorithm to be a deep network. The network implicitly learns the proximal operators of sinogram and image regularizers with two deep neural networks, providing a more interpretable and effective reconstruction procedure. Numerical results demonstrate our proposed method improvements of > 2.9 dB in peak signal to noise ratio, > 1.4% promotion in structural similarity metric, and > 9 HU decrements in root mean square error over current state-of-the-art LDCT methods.

Characterization and Performance Improvement of Face-to-Face Double Stack REBCO Coated Conductors

Characterization and Performance Improvement of Face-to-Face Double Stack REBCO Coated Conductors

Comprehensive Performance Improvement of SRG by Turn-ON Angle Optimization Using Linear Normalized Model

Comprehensive Performance Improvement of SRG by Turn-ON Angle Optimization Using Linear Normalized Model

Multiobjective Multitasking Optimization With Decomposition-Based Transfer Selection.

Multiobjective multitasking optimization (MTO) needs to solve a set of multiobjective optimization problems simultaneously, and tries to speed up their solution by transferring useful search experiences across tasks. However, the quality of transfer solutions will significantly impact the transfer effect, which may even deteriorate the optimization performance with an improper selection of transfer solutions. To alleviate this issue, this article suggests a new multiobjective multitasking evolutionary algorithm (MMTEA) with decomposition-based transfer selection, called MMTEA-DTS. In this algorithm, all tasks are first decomposed into a set of subproblems, and then the transfer potential of each solution can be quantified based on the performance improvement ratio of its associated subproblem. Only high-potential solutions are selected to promote knowledge transfer. Moreover, to diversify the transfer of search experiences, a hybrid transfer evolution method is designed in this article. In this way, more diverse search experiences are transferred from high-potential solutions across different tasks to speed up their convergence. Three well-known benchmark suites suggested in the competition of evolutionary MTO and one real-world problem suite are used to verify the effectiveness of MMTEA-DTS. The experiments validate its advantages in solving most of the test problems when compared to five recently proposed MMTEAs.

Data Augmentation for Sample Efficient and Robust Document Ranking

Contextual ranking models have delivered impressive performance improvements over classical models in the document ranking task. However, these highly over-parameterized models tend to be data-hungry and require large amounts of data even for fine-tuning. In this article, we propose data-augmentation methods for effective and robust ranking performance. One of the key benefits of using data augmentation is in achieving sample efficiency or learning effectively when we have only a small amount of training data. We propose supervised and unsupervised data augmentation schemes by creating training data using parts of the relevant documents in the query-document pairs. We then adapt a family of contrastive losses for the document ranking task that can exploit the augmented data to learn an effective ranking model. Our extensive experiments on subsets of the MS MARCO and TREC-DL test sets show that data augmentation, along with the ranking-adapted contrastive losses, results in performance improvements under most dataset sizes. Apart from sample efficiency, we conclusively show that data augmentation results in robust models when transferred to out-of-domain benchmarks. Our performance improvements in in-domain and more prominently in out-of-domain benchmarks show that augmentation regularizes the ranking model and improves its robustness and generalization capability.

Fake Voice Detection System

Fake Voice Detection System

Modified calibration strategies and parameter regionalization potential for streamflow estimation using a hydrological model

ABSTRACT Calibration and regionalization are two key tasks in hydrological modelling, and the availability of increased data and computing resources has enhanced our ability to complete these tasks. This paper addresses both tasks, i.e. (i) it proposes novel model structure-based calibration strategies with the goal to improve estimation of high streamflows, and (ii) it explores the association of soil hydraulic properties with optimal parameters to estimate streamflows in ungauged catchments. Both tasks are demonstrated by employing a conceptual hydrological model, Sacramento Soil Moisture Accounting (SAC-SMA), on multiple catchments in the Narmada River Basin. The initial calibrated model exhibited good model performance, with high flows being underestimated. Seven calibration strategies via revising parameter space are explored, and improvement in high flow performance is observed at nine out of 12 catchments. Estimated streamflows based on soil hydraulic parameters demonstrate regionalization potential. The results indicate successful application of the proposed methods which are transferable to other basins.

Bioinspired Fluid Dynamic Designs of Vertical-Axis Turbines: State-of-the-Art Review and the Way Forward

Abstract With the increasing popularity of vertical axis turbines (VATs), researchers are now focusing on their performance improvement. Instead of adopting conventional means of performance improvements such as augmentation techniques and exhaustive parametric design optimization, the bio-inspired turbine designs have become a center of attraction, especially during the last decade. This review article attempts to compile the bio-inspired designs belonging to the VATs. Bio-inspired designs implemented in Savonius, Darrieus, Nautilus, and Seed-inspired turbines are elaborated besides giving a detailed explanation of the corresponding bio-organism and natural phenomenon. How the working principles of bio-organisms emulated in the form of fluid dynamic design are explained thoroughly in this paper. The bio-inspired designs for VATs are then classified pragmatically for the future designs. Research gaps are highlighted for the aspiring researchers, and this is followed by the important strategies and allied challenges.

A hybrid approach for driver drowsiness detection utilizing practical data to improve performance system and applicability.

Numerous systems for detecting driver drowsiness have been developed; however, these systems have not yet been widely used in real-time. The purpose of this study was to investigate at the feasibility of detecting alert and drowsy states in drivers using an integration of features from respiratory signals, vehicle lateral position, and reaction time and out-of-vehicle ways of data collection in order to improve the system's performance and applicability in the real world. Data was collected from 25 healthy volunteers in a driving simulator-based study. Their respiratory activity was recorded using a wearable belt and their reaction time and vehicle lateral position were measured using tests developed on the driving simulator. To induce drowsiness, a monotonous driving environment was used. Different time domain features have been extracted from respiratory signals and combined with the reaction time and lateral position of the vehicle for modeling. The observer of rating drowsiness (ORD) scale was used to label the driver's actual states. The t-tests and Man-Whitney test was used to select only statistically significant features (p < 0.05), that can differentiate between the alert and drowsy states effectively. Significant features then combined to investigate the improvement in performance using the Multilayer Perceptron (MLP), the Support Vector Machines (SVMs), the Decision Trees (DTs), and the Long Short Term Memory (LSTM) classifiers. The models were implemented in Python library 3.6. The experimental results illustrate that the support vector machine classifier achieved accuracy of 88%, precision of 85%, recall of 83%, and F1 score of 84% using selected features. These results indicate the possibility of very accurate detection of driver drowsiness and a viable solution for a practical driver drowsiness system based on combined measurement using less-intrusive and out-of-vehicle recording methods.