Tracking Quality Research Articles

Improving time-efficiency and accuracy in echocardiography: real-time automated measurements of left ventricular global longitudinal strain using deep learning

Abstract Background Accurate quantification of left ventricular (LV) systolic function is fundamental in echocardiography. LV Global Longitudinal Strain (GLS) offers advantages over LV ejection fraction, being more sensitive, reproducible and offering better prognostic value. However, existing semi-automatic methods are time-consuming and heavily operator-dependent. Additionally, poor image quality and foreshortening decrease accuracy and reproducibility of GLS. As a result, these challenges lead to suboptimal efficiency and underutilization of GLS quantification in clinical practice. Purpose Our objective was to develop a deep learning (DL) application for real-time automated measurements of GLS, capable of providing tracking quality and foreshortening feedback during scanning. Subsequently, we aimed to validate the novel method in a prospective cohort study. Methods Deep learning networks were trained to identify apical views, identify timing of end-systole and end-diastole, conduct cardiac segmentation, estimate myocardial motion, assess LV length in each view for foreshortening detection, and present real-time visualization of tracking quality and GLS values for three cardiac cycles to the operator for review (Figure 1). The DL method was validated in a prospective cohort of 40 patients included regardless of image quality. Agreement with manual measurements obtained using semi-automatic software (EchoPAC version 204, GE HealthCare, Horten, Norway) was evaluated. Bland-Altman statistics and Pearson's correlation coefficient were used to evaluate agreement and correlation. Results Feasibility for real-time DL measurements of GLS was 98%, with one patient excluded due to poor image quality. There was good agreement (Bias 0.7%, LoA: -1.6 to 3.1%, figure 2) and excellent correlation (Pearson’s correlation coefficient of 0.93, p<0.001) between the real-time DL application and manual reference measurements for GLS. Using the DL application during scanning resulted in a 58% (95% CI 56%-61%) reduction in time required for scanning and post-processing to obtain GLS values, compared to using the semi-automatic method. Average time consumption was 2 minutes and 16 seconds for the DL method and 5 minutes and 27 seconds for the reference method. Conclusion Using deep learning, real-time fully automated measurement of GLS during scanning is feasible, time-saving, and provides good agreement with reference methods. Real-time measurements also offer the potential to reduce operator-related variability by integrating tools to provide image and tracking quality feedback to the user, ultimately benefiting patients.Application user interfaceAgreement between DL and reference

WeCare – Mental healthcare for health professionals

Abstract Issue/Problem Health professionals face physical and mental stressors (risk factors) due to challenging working conditions. Description of the Problem Despite high levels of health literacy among healthcare workers, maintaining healthy habits is challenging due to variable shifts, including nights, high workloads, and intense emotional and mental demands. Adopting a healthier lifestyle requires personal changes that can be difficult to implement, particularly in nursing and caregiving. This is critical, as retaining and attracting new staff in these fields is essential for the healthcare sector. Results/Effects/Changes To address these challenges, it’s crucial to boost intrinsic motivation and provide healthcare workers with practical tools to support their well-being and encourage them to remain in the sector. The WeCare project offers a mobile app designed as a ‘pocket coach,’ accessible at any time. Healthcare professionals can use the app to assess their current mental well-being through self-evaluation. With built-in assessments and predefined thresholds, the app tracks stress levels and quality of life. Depending on the outcomes, users are automatically guided to appropriate information, gamified activities, and exercises tailored to their specific needs. The project, running from 2022 to 2024, is co-funded by the European Union’s Erasmus+ programme. Lessons The app is beta-tested during 2024 in five countries: Germany, Austria, Serbia, Poland, and Italy, with local language support. The test group will consist of healthcare professionals and related professions. The results, including user engagement and motivational factors are analyzed. As the project concludes in autumn, comprehensive recommendations and lessons learned will be compiled and presented at the European Public Health (EPH) Conference. This will also include sharing the app and its solution with the broader healthcare community. Key messages • The WeCare app supports healthcare workers’ mental health with a mobile “pocket coach” that provides personalized exercises. Through gamification, it promotes healthy habits and boosts job motivation. • It is critical to support healthcare workers, as retaining and attracting new persons is essential for the healthcare sector.

Improving the Quality of Individual-Level Web Tracking: Challenges of Existing Approaches and Introduction of a New Content and Long-Tail Sensitive Academic Solution

This article evaluates the quality of data collection in individual-level desktop web tracking used in the social sciences and shows that the existing approaches face sampling issues, validity issues due to the lack of content-level data and their disregard for the variety of devices and long-tail consumption patterns as well as transparency and privacy issues. To overcome some of these problems, the article introduces a new academic web tracking solution, WebTrack, an open-source tracking tool maintained by a major European research institution, GESIS. The design logic, the interfaces, and the backend requirements for WebTrack are discussed, followed by a detailed examination of the strengths and weaknesses of the tool. Finally, using data from 1,185 participants, the article empirically illustrates how an improvement in data collection through WebTrack leads to innovative shifts in the use of tracking data. As WebTrack allows for collecting the content people are exposed to beyond the classical news platforms, it can greatly improve the detection of politics-related information consumption in tracking data through automated content analysis compared to traditional approaches that rely on the source-level analysis.

Tracking quality standard test for tissue culture ‎pigeon pox virus Vaccine

Tracking quality standard test for tissue culture ‎pigeon pox virus Vaccine

Enhanced vertical railway track quality index with dynamic responses from moving trains

The conventional vertical track quality index (TQI) based on the standard deviation of longitudinal levels yields standardized railway track condition assessment. Nevertheless, its capability to identify problems is limited, particularly in the ballast and substructure layers when abrupt changes affect train-track interaction. Previous research shows that dynamic responses from moving trains via axle box acceleration (ABA) measurements can quantify abrupt changes in the vertical dynamic responses. Thus, this paper proposes a framework to design an enhanced vertical TQI, called EnVTQI, by integrating track longitudinal levels and dynamic responses from ABA measurements. First, measured ABA signals are processed to mitigate the influence of variation in measurement speed. Then, substructure and ballast-related features are extracted, including scale average wavelet power (SAWP) in the ranges 0.04 m-1 to 0.33 m-1 (substructure) and 1.25 m-1 to 2.50 m-1 (ballast). This enables identifying track conditions at different track layers. Finally, EnVTQI is determined by weight averaging between the conventional vertical TQI and the ABA features from moving trains. The performance of EnVTQI is evaluated based on 48 segments of a 200-m track on a Dutch railway line. The results indicate that EnVTQI helps to distinguish track segments that cause poor train-track interaction, which the conventional TQI does not indicate. EnVTQI can supplement the conventional TQI, improving the effectiveness of track maintenance decision-making.

Model optimization and acceleration method based on meta-learning and model pruning for laser vision weld tracking system

Purpose This paper aims to propose a lightweight, high-accuracy object detection model designed to enhance seam tracking quality under strong arcs and splashes condition. Simultaneously, the model aims to reduce computational costs. Design/methodology/approach The lightweight model is constructed based on Single Shot Multibox Detector (SSD). First, a neural architecture search method based on meta-learning and genetic algorithm is introduced to optimize pruning strategy, reducing human intervention and improving efficiency. Additionally, the Alternating Direction Method of Multipliers (ADMM) is used to perform structural pruning on SSD, effectively compressing the model with minimal loss of accuracy. Findings Compared to state-of-the-art models, this method better balances feature extraction accuracy and inference speed. Furthermore, seam tracking experiments on this welding robot experimental platform demonstrate that the proposed method exhibits excellent accuracy and robustness in practical applications. Originality/value This paper presents an innovative approach that combines ADMM structural pruning and meta-learning-based neural architecture search to significantly enhance the efficiency and performance of the SSD network. This method reduces computational cost while ensuring high detection accuracy, providing a reliable solution for welding robot laser vision systems in practical applications.

Investigation of the impact of local properties of 3D data on the accuracy of block matching-based speckle tracking echocardiography

Abstract Introduction: Due to its simplicity, block-matching is a popular motion-tracking method used in speckle-tracking echocardiography. Improvement of its robustness and accuracy is thus of prime interest. Although it seems plausible that the quality of block matching-based tracking depends on the local properties of image data, and thus, it should be possible to assess in advance how well certain portions of the image data are suited for displacement estimation, the potential relationship has not been studied extensively. Material and methods: This study aimed to search for a relationship between selected features of echocardiographic data and the quality of local displacement estimation. The study used a 3D synthetic B-mode imaging sequence data with known ground truth. Frame-to-frame displacements were estimated for 9856 points in five different frame pairs with mean inter-frame displacements of 0.15, 0.87, 2, 3.02, and 3.84 mm. In each case, tracking errors were evaluated against thirteen grayscale image features, the displacement’s magnitudes, and the normalized cross-correlation (NCX) values. Additionally, a multi-variable regression model was applied to test the combined ability of the proposed features to predict tracking quality. Results: Median tracking error magnitudes were 0.06, 0.13, 0.28, 0.74, and 1.5 mm for each image pair. Weak correlation between errors and individual data features was found only in the case of 3 features: NCX (Pearson’s correlation coefficients in the range of −0.366 to −0.223), number of speckles within the kernel (−0.283, −0.282, and −0.214 for three lowest deformations) and mean of the 3D gradient (−0.252, −0.237 and −0.25). The regression model, however, provided significant prediction improvement with R2 exceeding 0.5. Conclusions: In conclusion, only a weak relationship between the individual investigated kernel features and tracking accuracy has been established, but their combined strength can be assessed as at least moderate.

A sensitive wavelength-enhanced reconstruction algorithm for track quality assessment based on measured data and vehicle dynamics

High-speed railways are extensively utilized worldwide. However, with prolonged operation, track irregularities increasingly pose significant challenges, adversely affecting the safety and stability of train operations. This paper proposes a sensitive wavelength-enhanced reconstruction algorithm based on the cmplete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method to address this issue. This method evaluates the influence of reconstructed track irregularities on vehicle dynamic response, considering sensitive wavelengths and amplification factors. The algorithm effectively enhances these sensitive wavelengths while maintaining the integrity of the input signal, utilizing measured track irregularities. After applying the algorithm, the maximum vertical body acceleration rises by 206.09%. The study validates a coupled train model and analyzes the mapping relationship between single-wave irregularities and vertical body acceleration. Compared to random irregularities, the maximum vertical acceleration can amplify up to 122.80% under various operating conditions. The periodic sensitive wavelengths of track irregularities significantly impact the train’s dynamic response.

Curving and running resistance of freight trains: current experience with on-track measurements

Curving resistance affects the railway vehicle running through curves. For its estimation in train dynamics calculations, different empirical formulas are used in various countries. It is practically impossible to determine its exact value because of its dependency on various – to some extent random – parameters. In the framework of the experimental research of running resistance of freight trains at the Faculty of Transport Engineering of the University of Pardubice, measurements in regular operation conditions on the Czech railway network were realised in recent years. Because some of these results can be used to quantify the curving resistance, the applied evaluation methods and the relevant results are presented in this paper. Besides that, a new general formula of running resistance for freight trains, based on the measurement results, is proposed. Attention is also paid to the effect of track quality on the vehicle running resistance.

Detecting wheel slip from railway operational data through a combined wavelet, long short-term memory and neural network classification method

— Detecting wheel slip is important in railway operations, to prevent damage to wheels and tracks, reduce maintenance costs, improve safety and enhance passenger comfort. Slip activity is characterised by reduced adhesion between the wheel and the rail and limits effective braking or acceleration, causing also operational risks. It is influenced by environmental conditions, vehicle load, track and axle quality, contaminants, inclines, rail oxidation, and braking forces. This paper introduces an innovative method for wheel slip detection in operational trains, utilizing wavelet analysis combined with Long-Short Term Memory (LSTM) modelling. This method analyzes operational data to effectively identify wheel slip, showing promising results when compared to traditional classification-based machine learning methods such as decision trees, forests, logistic regression, naïve Bayes, and support vector machines. This novel approach addresses the complexities of wheel slip detection and is capable of identifying the conditions leading to slip several seconds prior to the commencement of the slip event, offering a practical solution for real-world railway systems.

Global Strategies for Postoperative Care and Bowel Management in Patients With Anorectal Malformations: Varied Practices and Barriers

BackgroundAnorectal malformations (ARMs) commonly result in constipation and fecal incontinence following primary surgical reconstruction. This study investigates global variations in postoperative care and resources. MethodsA survey was distributed via the International Pediatric Endosurgery Group (IPEG) and snowball sampling. Geographically, respondents were categorized into high-income countries (HICs) and low—or middle-income countries (LMICs). Results233 surveys were received, 64% from LMICs and 36% from HICs. Of these, 51% reported monitoring ARM patients for over a year, while 23% utilized digital technologies. Access to anesthesia for anorectal exams was available to 70% of respondents. Only 29% had established a one-week Bowel Management Program (BMP). Collaboration with urologists and gynecologists was more prevalent in HICs (59%) compared to LMICs (37%, p < 0.01). In HICs, nurses and advanced practice providers were significantly more involved in BMP (71% vs. 39% in LMICs; p < 0.01), and abdominal radiographs for regimen adjustments were used more frequently (80% vs. 69% in LMICs; p = 0.03). Treatment regimens were more varied in HICs, and quality of life tracking was more consistent (19% vs. 9% in LMICs; p = 0.02). LMICs reported significant shortages of medications and equipment (75% vs. 58% in HICs; p = 0.01), inadequate sanitation (48% vs. 24%; p < 0.01), and insufficient insurance coverage (58% vs. 44%; p = 0.04). ConclusionsThere are notable global disparities in the postoperative care of ARM patients, particularly in BMP protocols and treatment regimens, with LMICs facing severe socioeconomic challenges. This emphasizes the urgent need for targeted strategies and resources to enhance outcomes for ARM patients across different regions. Level of EvidenceLevel III.

Mechanical characteristics of track slab and clamps during the pouring of self-compacting concrete in the China Railway Track System III slab track

The pouring of self-compacting concrete (SCC) is crucial for ensuring the construction quality and comfort of slab tracks. In this study, a fluid–structure coupling pouring model of SCC was established based on the coupled Eulerian–Lagrangian (CEL) approach. It was applied to trace the forces and displacements of track slab and clamps during SCC pouring. The influences of some key parameters, such as the pouring equipment, technology, and rheological properties, were investigated, including the height and diameter of the funnel, pouring velocity, and plastic viscosity of the SCC. The results indicated that during pouring, the floating force and maximum vertical displacement of the track slab, as well as the vertical forces of the clamps, initially remained stable before undergoing rapid changes, eventually reaching stable values. The flow velocity of SCC in the pouring hole and its rebound primarily influenced these mechanical indicators. The funnel height positively affected the floating force of the track slab, negatively affected the vertical forces of the clamps, and slightly affected the peak values of the maximum vertical displacement of the track slab. By contrast, the funnel diameter had little influence on these mechanical behaviour indicators. As the pouring velocity increased, the floating force of the track slab and vertical forces of the clamps significantly increased. Moreover, the peak values of the maximum vertical displacement of the track slab increased to 0.47 mm at a pouring velocity of 0.03 m/s. In the case of low plastic viscosity, an increase in plastic viscosity led to a visible decline in the floating and clamp forces. However, when the plastic viscosity exceeded 80 Pa•s, further changes had little effect on the mechanical indicators owing to the combined effects of excessively low flow velocity and rebounding.

CCBA-NMS-YD: A Vehicle Pedestrian Detection and Tracking Method Based on Improved YOLOv7 and DeepSort

In this paper, we propose a vehicle pedestrian detection and tracking method based on the improved YOLOv7 and DeepSort algorithms. We aim to improve the quality of vehicle pedestrian detection and tracking, addressing the challenges that current commercially available autonomous driving technologies face in complex and changing road traffic situations. First, the NMS (non-maximum suppression) algorithm in YOLOv7 is replaced with a modified Soft-NMS algorithm to ensure that targets can be accurately detected at high densities, and second, the CCBA (coordinate channel attention module) attention mechanism is incorporated to improve the feature extraction and perception capabilities of the network. Finally, a multi-scale feature network is introduced to extract features of small targets more accurately. Finally, the MobileNetV3 lightweight module is introduced into the feature extraction network of DeepSort, which not only reduces the number of model parameters and network complexity, but also improves the tracking performance of the target. The experimental results show that the improved YOLOv7 algorithm improves the average detection accuracy by 3.77% compared to that of the original algorithm; on the MOT20 dataset, the refined DeepSort model achieves a 1.6% increase in MOTA and a 1.9% improvement in MOTP; in addition, the model volume is one-eighth of the original algorithm. In summary, our model is able to achieve the desired real-time and accuracy, which is more suitable for autonomous driving.

Transfer learning-based drive-by monitoring of railway tracks

Track quality significantly impacts passenger comfort and safety. The maintenance of track quality involves routine monitoring of track geometry and track component defects. In a drive-by monitoring framework, vibration data are collected from operational trains providing an effective and low-cost track monitoring system. This framework enables each train to inspect multiple lines without the need to install and maintain sensors on each line. In conventional data-driven frameworks that utilize machine learning or statistical models, geometrical defects diagnosis model is often constructed using supervised learning. Supervised learning requires response data and corresponding damage states (i.e., labels) for each railway line to learn the line-specific damage diagnosis model. However, labelled data are frequently unavailable for many lines in practical situations, making it challenging to construct a damage diagnosis model. Furthermore, using data directly from source line to construct a damage diagnosis model for the target line can lead to inaccurate results. To this end, in this paper, a novel drive-by monitoring framework is proposed based on Transfer Learning (TL) concept. This approach transfers the geometrical defects diagnosis model learned from one line to a new line. Firstly, condition-sensitive time domain features are extracted from the raw data. Then, a deep learning model based on the long short-term memory (LSTM) network is trained on a dataset from one line (source domain) and fine-tuned for a new dataset using a small number of samples from another line (target domain). The effectiveness of this framework is compared through the classification performance using real-world monitoring datasets collected from two different lines of the France railways network. The results demonstrate up to a 22% increase in defect detection accuracy compared to the basic method.

Влияние режимов лазерной наплавки на геометрические размеры стального трека

Introduction. Laser surfacing is one of the leading trends in the field of additive technologies, which consists in layer-by-layer build of material using a laser as an energy source. To obtain a high-quality product, it is necessary to select the optimal building parameters correctly. The problem is that such optimization is necessary for all equipment, since minor differences in its characteristics can make significant changes in the parameters of layer-by-layer build. In order to determine the optimal build mode, it is enough to analyze the effect of various equipment parameters on the characteristics of single tracks. Therefore, the purpose of this work is to determine the most important parameters of laser radiation that affect the surfacing process and the optimal mode for building a single track of chromium-nickel steel. The work investigated single tracks obtained by laser surfacing of powder from austenitic chromium-nickel steel AISI 316L. The optimization factors included such characteristics as laser power, beam speed, flow rate of supplied powder and laser spot size. The wavelength of laser radiation was 1.07 μm. Research methods. To determine the quality and geometric dimensions of single tracks, the macrostructure of cross sections of specimens was studied using metallography and scanning electron microscopy methods. Results and discussion. It is established that the optimal mode for growing single tracks of steel AISI 316L is characterized by a laser radiation power of 1,250 W and a scanning speed of 25 mm/s. In this case, the optimal powder consumption rate is 12 g/min, and the laser spot size is 4.1 mm. The work shows that the powder consumption and laser spot size have the greatest influence on the coefficient of effective use of powder material. By changing it, the surfacing performance can be increased by 10–15 %.

Investigating the use of comprehensive motion monitoring for intrafraction 3D drift assessment of hypofractionated prostate cancer patients on a 1.5T magnetic resonance imaging radiotherapy system

This work investigates the use of a multi-2D cine magnetic resonance imaging-based comprehensive motion monitoring (CMM) system for the assessment of prostate intrafraction 3D drifts. The data of six healthy volunteers were analyzed and the values of a clinically-relevant registration quality factor metric exported by CMM were presented. Additionally, the CMM-derived prostate motion was compared to a 3D-based reference and the 2D-3D tracking agreement was reported. Due to the low quality of SI motion tracking (often >2 mm tracking mismatch between anatomical planes) we conclude that further improvements are desirable prior to clinical introduction of CMM for prostate drift corrections.

Correlating ballast resilient modulus with particle movement through SmartRock sensing

The resilient modulus of ballast is a key parameter in assessing track quality and is influenced by various loading conditions, such as deviator stress, bulk stress, loading frequency, and confining pressure. These factors, when incorporated into different models, can sometimes complicate their application in track maintenance planning. Therefore, a simplified model that collectively considers these factors would be beneficial in practical applications for estimating the ballast resilient modulus. This study utilizes smart sensing technology, SmartRock, to track particle movement during large-scale triaxial cyclic loading tests, exploring the potential correlation between ballast resilient modulus and particle motion. The findings indicate that the settling acceleration of ballast particles significantly impacts the ballast resilient modulus. This effect is dependent on the applied cyclic loading pulses, an aspect previously overlooked in research. Consequently, an index termed the Cyclic Loading and Acceleration Index (CLAI) has been introduced. CLAI integrates the effects of deviator stress, loading frequency, loading pulses, and settling acceleration. Based on CLAI, a simplified model for ballast resilient modulus has been developed and validated.

Adaptive synchronous sliding control for a robot manipulator based on neural networks and fuzzy logic

Robot manipulators have become important equipment in production lines, medical fields, and transportation. Improving the quality of trajectory tracking for robot hands is always an attractive topic in the research community. This is a challenging problem because robot manipulators are complex nonlinear systems and are often subject to fluctuations in loads and external disturbances. This article proposes an adaptive synchronous sliding control scheme to improve trajectory tracking performance for a robot manipulator. The proposed controller ensures that the positions of the joints track the desired trajectory, synchronize the errors, and significantly reduces chattering. First, the synchronous tracking errors and synchronous sliding surfaces are presented. Second, the synchronous tracking error dynamics are determined. Third, a robust adaptive control law is designed, the unknown components of the model are estimated online by the neural network, and the parameters of the switching elements are selected by fuzzy logic. The built algorithm ensures that the tracking and approximation errors are ultimately uniformly bounded (UUB). Finally, the effectiveness of the constructed algorithm is demonstrated through simulation and experimental results. Simulation and experimental results show that the proposed controller is effective with small synchronous tracking errors, and the chattering phenomenon is significantly reduced.

Anti-Occlusion Visual Tracking Algorithm for UAVs with Multi-Feature Adaptive Fusion

Most of the existing trackers based on discriminative correlation filters use only one feature or a simple linear fusion of multiple features for object tracking, and most of them lack a mechanism to handle occlusions. This leads to poor tracking performance in rapidly changing and easily occluded scenarios, especially on unmanned aerial vehicle (UAV) platforms. To address this issue, this paper proposes an anti-occlusion visual tracking algorithm for UAVs with multi-feature adaptive fusion named multi-feature adaptive fusion and anti-occlusion tracker (MAFAOT). It introduces a novel approach for implementing an adaptive fusion of multiple features. This method transforms the multi-feature fusion problem into a maximization issue by designing a tracking quality evaluation index. It successfully achieves an adaptive fusion of gradient direction histogram and color histogram feature responses. MAFAOT also introduces an anti-occlusion update pool strategy, enabling the tracker to adapt dynamically to various complex scenarios, including occlusion and motion blur. The experimental results on the OTB100 and UAV123 datasets confirm the significant advantages of MAFAOT in terms of precision and success rate compared to other correlation filter-based algorithms. The proposed methods further enhance the expressiveness of the features and effectively avoid the problem of tracker contamination caused by occlusion. Furthermore, this paper applies the proposed methods to the kernelized correlation filters (KCF) algorithm. On the OTB100 dataset, the improved KCF algorithm shows an improvement of 10.94% in precision and 11.11% in success rate. On the UAV123 dataset, it shows an improvement of 14.53% in precision and 16.62% in success rate, further verifying the effectiveness and versatility of the proposed methods.

Study on the Impact of RCEP National Logistics Performance on Vietnam's Cross-Border E-Commerce Export Trade

In the era of global economic integration, the Regional Comprehensive Economic Partnership (RCEP) plays a pivotal role in shaping trade dynamics, particularly by enhancing Vietnam's cross-border e-commerce exports through improved logistics. This study meticulously reviews a vast array of both domestic and international literature and employs Stata 17.0 software to analyze panel data spanning from 2014 to 2022 across RCEP member countries. It focuses on six core logistics performance indicators: customs and logistics management, logistics infrastructure, timeliness of shipments, quality of logistics services, tracking and tracing capabilities, and ease of international shipments.The findings from this analysis reveal that these logistics performance indicators significantly influence Vietnam's e-commerce exports. Infrastructure quality and the ease of shipments have the most substantial impact, suggesting that robust logistics infrastructure and efficient shipment processes are crucial for enhancing trade efficiency. Despite the overall positive trends, the study notes significant variations in logistics development within the RCEP region. While countries like Singapore exhibit advanced logistics capabilities, ASEAN countries generally lag behind, indicating a disparate level of logistics progress across the region.The research highlights the potential for targeted logistics improvements to directly boost Vietnam's cross-border e-commerce exports. By advancing logistics performance, especially in areas lagging behind, there is an opportunity to not only enhance trade but also to level the playing field within the RCEP. This study underscores the critical importance of developing cohesive and efficient logistics systems to support the burgeoning e-commerce sector, thereby fostering economic growth and deeper regional integration within the RCEP framework.