Post-processing Time Research Articles

Large Scale in vivo Acquisition, Segmentation and 3D Reconstruction of Cortical Vasculature using μDoppler Ultrasound Imaging

The brain is composed of a dense and ramified vascular network of arteries, veins and capillaries of various sizes. One way to assess the risk of cerebrovascular pathologies is to use computational models to predict the physiological effects of reduced blood supply and correlate these responses with observations of brain damage. Therefore, it is crucial to establish a detailed 3D organization of the brain vasculature, which could be used to develop more accurate in silico models. To this end, we have adapted our functional ultrasound imaging platform, previously designed for recording large scale activity, to enable rapid and reproducible acquisition, segmentation and reconstruction of the cortical vasculature. For the first time, it allows us to digitize the cortical ∼100-μm3 spatial resolution. Unlike most available strategies, our approach can be performed in vivo within minutes. Moreover, it is easy to implement since it requires neither exogenous contrast agents nor long post-processing time. Therefore, we performed a cortex-wide reconstruction of the vasculature and its quantitative analysis, including i) classification of descending arteries versus ascending veins in more than 1500 vessels/animal and ii) rapid estimation of their length. Importantly, we confirmed the relevance of our approach in a model of cortical stroke, which allows rapid visualization of the ischemic lesion. This development contributes to extending the capabilities of ultrasound neuroimaging to better understand cerebrovascular pathologies such as stroke, vascular cognitive impairment and brain tumors, and is highly scalable for the clinic.

Synchroextracting Transform Based on the Novel Short-Time Fractional Fourier Transform

As a generalization of the short-time Fourier transform (STFT), the novel short-time fractional Fourier transform (NSTFRFT) has been introduced recently. In order to improve the concentration of the time–frequency representation (TFR) generated by the NSTFRFT, two post-processing time–frequency analysis methods, two synchroextracting transforms based on the NSTFRFT with two different fractional Fourier transform (FRFT) angles, are proposed in this paper. One is achieved via an equation where the instantaneous frequency satisfies the condition where the FRFT angle takes π2, and the other one is obtained using the instantaneous frequency estimator in the case that the FRFT angle takes a value related to the chirp rate of the signal. Although the conditions of the two synchroextracting transforms are different, their implementation can be unified into the same algorithm. The proposed synchroextracting transforms supplement existing post-processing time–frequency analysis methods which are based on the NSTFRFT. Experiments are conducted to verify the performance and superiority of the proposed methods.

Single-pixel orbital angular momentum detection in the temporal domain

Abstract Orbital angular momentum (OAM) beam’s spatial information captured using a camera, employs millions of single-pixel detectors arranged in a two-dimensional matrix. Poses challenges in OAM detection due to significant storage requirements, reduced speed due to low frame rate, and increased post-processing time. To address this bottleneck, a novel OAM detection technique utilizing only a diffuser and a single-pixel fast photodiode (FPD) is reported. The sixteen OAM beams, l = [ − 8 + 8 ] interact with the rotating diffuser resulting in a temporally varying speckle field. The 1D temporal speckle information (TSI) has been segregated from the temporally varying 2D speckle pattern images for each beam. The segregated 1D TSI has been classified by employing a machine learning model and achieved a classification accuracy of 97.1% and 97.3% on simulated and experimental data respectively. To further validate the proposed single-pixel OAM detection method, the 1D TSI is captured directly using a FPD. The experimentally captured 1D TSI captured via photodiode has been classified by a lightweight custom-designed 1D convolutional neural network and achieved an increased classification accuracy of 96%. This approach redefines OAM detection, operating in the temporal domain with a single-pixel FPD, thereby significantly reducing storage and computational costs while promising high-speed OAM detection.

Automatic Vessel Segmentation and Reformation of Non-contrast Coronary Magnetic Resonance Angiography Using Transfer Learning-based 3D U-net with Attention Mechanism

BackgroundCoronary Magnetic Resonance Angiography (CMRA) presents distinct advantages, but its reliance on manual image post-processing is labor-intensive and requires specialized knowledge. This study aims to design and test an efficient artificial intelligence (AI) model capable of automating coronary artery segmentation and reformation from CMRA images for coronary artery disease (CAD) diagnosis. MethodsBy leveraging transfer learning from a pre-existing coronary CT angiography model, a 3D attention-aware U-Net was established, trained, and validated on a dataset of 104 subjects’ CMRA. Furthermore, an independent clinical evaluation was conducted on an additional cohort of 70 patients. The AI model's performance in segmenting coronary arteries was assessed using the Dice Similarity Coefficient (DSC) and Recall. The comparison between the AI model and manual processing by experienced radiologists on vessel reformation was based on reformatted image quality (rIQ) scoring, post-processing time, and the number of necessary user interactions. The diagnostic performance of AI-segmented CMRA for significant stenosis (≥50% diameter reduction) was evaluated using conventional coronary angiography (CAG) as a reference in sub-set data. ResultsThe DSC of the AI model achieved on the training and validation sets were 0.952 and 0.944, with recalls of 0.936 and 0.923, respectively. In the clinical evaluation, the model outperformed manual processes by reducing vessel post-processing time, from 632.6±17.0seconds to 77.4±8.9seconds, and the number of user interactions from 221±59 to 8±2. The AI postprocessed images maintained high rIQ scores comparable to those processed manually (2.7±0.8 vs 2.7±0.6; P=0.4806). In subjects with CAG, the prevalence of CAD was 71%. The sensitivity, specificity, and accuracy at patient-based analysis were 94%, 71%, and 88%, respectively, by AI post-processed whole-heart CMRA. ConclusionThe AI auto-segmentation system can effectively facilitate CMRA vessel reformation and reduce the time consumption for radiologists. It has the potential to become a standard component of daily workflows, optimizing the clinical application of CMRA in the future.

Research on filling missing GNSS precipitable water vapor time series data using the PSORF model combined with reanalysis datasets

Abstract The inversion of precipitable water vapor (PWV) using the Global Navigation Satellite System (GNSS) has advantages such as all-weather observation, high precision, low cost, and high temporal resolution. Currently, long-term GNSS-PWV data has become an important data source for studying climate change. However, due to factors such as equipment failures, observation technology limitations, and estimation model errors, missing data and outliers often occur in real-time or post-processed PWV time series data. Furthermore, the main sources of GNSS-PWV errors are influenced by the atmospheric weighted mean temperature and surface meteorological data (pressure and temperature). The results indicate that the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (ERA5) dataset exhibits high accuracy in the Chinese region, making it suitable for GNSS-PWV inversion. By utilizing ERA5 meteorological data to calculate hourly GNSS-PWV and conducting accuracy assessments, it is demonstrated that the PWV inverted based on GNSS and ERA5 meteorological parameters possesses high precision. Based on this, this study selects GNSS stations from the Crustal Movement Observation Network of China where the proportion of missing measured data is less than 8%. By combining ERA5, random forest (RF), and particle swarm optimization (PSO) algorithms, a new model called PSORF is proposed to fill in missing values in GNSS-PWV time series data. The research findings reveal that the R 2 and root mean square error (RMSE) of PSORF-PWV are 0.98 and 2.16 mm, respectively. Additionally, GNSS stations with more than 8% missing measured data are utilized to validate the accuracy of the PSORF model. A comparative analysis is conducted between the results obtained through the PSORF model and the ERA5-PWV acquired via traditional interpolation methods. The MAE and RMSE of PSORF-PWV are reduced by 21% and 17%, respectively, indicating that the PSORF model excels in filling missing data and effectively enhances the accuracy and reliability of PWV time series analysis. This study not only presents an effective approach for processing missing PWV data but also evaluates the applicability and accuracy of the ERA5 dataset in PWV inversion. This provides crucial technical support and data security for climate change research, short-term humidity field forecasting, and studies in related fields.

Cardiovascular magnetic resonance semi-automated threshold-based post-processing of right ventricular volumes in repaired tetralogy of Fallot.

Cardiovascular magnetic resonance (CMR) is the gold-standard to estimate right ventricular (RV) volumes, which are key for clinical management of patients with repaired tetralogy of Fallot (rTOF). Semi-automated threshold-based methods (SAT) have been proposed for CMR post-processing as alternatives to fully manual standard tracing. We investigated the impact of SAT on RV analysis using different thresholds in rTOF patients. RV volumes and mass were estimated using SAT and standard fully manual tracing methods in rTOF patients. Two threshold levels were set for SAT, i.e., default 50 (SAT-50) and 30 (SAT-30). RV stroke volumes (SV) were compared to main pulmonary artery forward flow (MPA-FF). Post-processing time, intra- and interobserver variabilities were compared across methods. Sixty-two CMRs of rTOF patients were analyzed. Compared to the standard fully manual tracing, no significant differences in RV mass, volumes and ejection fraction were observed using SAT-30, whereas SAT-50 significantly underestimated RV end-diastolic-volume index (EDVi) by 10.4% (mean difference of - 11.8 ± 6.2ml/m2, p 0.03) and overestimated RV mass index by 21.8% (mean difference of 14.2 ± 11.9g/m2, p 0.002). Compared to MPA-FF, RVSV by standardfully manual method and SAT-30 showed minor biases, respectively, 0.03ml/m2 and 0.7ml/m2, while SAT-50 underestimated RVSV by 6.86ml/m2 (p < 0.001). In six patients, the degree of RV EDVi underestimation by SAT-50 determined a change of category from dilated to non-dilated RV. Intra- and interobserver variabilities were good to excellent for all methods. Post-processing duration was shorter for SAT compared to standardmanual segmentation (5.5 ± 1.7min vs. 19.5 ± 4.4min, p < 0.001). CMR SAT-30 post-processing is a precise, accurate and time-saving method for biventricular assessment of volumes, ejection fraction and mass in rTOF.

Application of a novel local and automatic PCA algorithm for diffraction pattern denoising in TEM-ASTAR analysis in microelectronics

This paper introduces a novel denoising method for TEM-ASTAR™ Diffraction Pattern (DP) datasets, termed LAT–PCA (Local Automatic Thresholding – Principal Component Analysis). This approach enhances the established PCA algorithm by partitioning the 4D dataset (a 2D map of 2D DPs) into localized windows. Within these windows, PCA identifies a basis where the physical signal predominantly resides in the higher-order principal components. By thresholding lower-order components, the method effectively reduces noise while retaining the essential features of the DPs. The locality of the approach, focusing on small windows, enhances computational efficiency and aligns with the localized nature of the crystallographic grain signals in ASTAR. The automatic aspect of the method employs a theoretical pure noise distribution, i.e. a Marchenko-Pastur Distribution, to set a threshold, beyond which the components are mostly noise.The LAT–PCA method offers significant reductions in acquisition and post-processing times. With denoised data, selecting the correct parameters for accurate phase maps and grain orientations becomes more straightforward, facilitating robust quantitative grain analysis. Experiments performed on a silicon-germanium-carbon sample validate the method's efficacy. The sample was analyzed with varying acquisition times to produce a high signal-to-noise ratio reference dataset and a lower ratio test dataset. The LAT–PCA algorithm's denoising results on the test dataset were benchmarked against the reference, demonstrating considerable improvements and reliability.In summary, LAT–PCA is an effective, automatic solution for denoising TEM DP datasets. Its adaptability to different noise levels and local processing capability makes it a valuable tool for enhancing dataset quality and reducing the time required for data acquisition and analysis. This method can minimize acquisition time, conserve microscope usage, and reduce sample drift and deterioration, leading to more accurate characterizations with fewer deformation artifacts.

Single vs. multi-slice assessments of in vivo placental T2∗ measurements

IntroductionPlacental health is vital for maternal and fetal well-being, and placental T2∗ has been suggested to identify in vivo placental dysfunction prior to delivery. However, ideal regions of interest to best inform functional assessments of the placenta remain unknown. The aim of this study is to compare global and slice-wise measures of in-vivo placental T2∗ assessments. MethodsThis prospective study recruited pregnant people with singleton pregnancies between December 2017 and February 2022.3D multi-echo RF-spoiled gradient echo sequences were acquired, and placental T2∗ values were derived from global and slice-wise approaches. Statistical analyses included Pearson correlation coefficients, concordance correlation coefficients (CCC), intraclass correlation coefficients (ICC), and Bland-Altman analyses. ResultsOf 115 participants (mean gestational age, 29.25 ± 5.05 weeks), 68 were healthy controls, and 47 were high-risk pregnancies. Global and slice-wise placental T2∗ assessments for the entire cohort showed no significant difference nor for individual subgroups (healthy controls or high-risk). Pearson correlation values ranged between 0.88 and 0.99 for mean global and slice-wise placental T2∗. CCC analyses ranged from 0.88 to 0.99 for mean T2∗, and ICC analyses ranged between 0.88 and 0.99 for mean T2∗, showing a strong agreement between measurements. Bland-Altman analyses depicted T2∗ differences across coverage methods, and groups resided within the 95 % limits of agreement. DiscussionSingle-slice placental assessments offer robust, comparable T2∗ values to global assessments, with the added benefit of reducing post-processing time and SAR exposure. This supports slice-wise approaches as valid alternatives for assessing placental health in various pregnancies.

Robust post-processing time frequency technology and its application to mechanical fault diagnosis

Post-processing synchrosqueezing transform and synchroextracting transform methods can improve TFR resolution for fault diagnosis. The normal and fault signal can be described by infinite variance process, and 1 < α ≤ 2, even the background noise belongs to the process under complex conditions. The effect of traditional SST and SET methods is greatly reduced and even lost in infinite variance process environment. Several robust post-processing methods are proposed including FSET, FSSET, FSOSET and FMSST technology employing infinite variance process statistical model and FLOS, and their mathematical derivation are completed in this paper. The proposed methods are compared with the conventional methods, and the results show that the proposed methods achieve better results than the existing methods. In addition, the new methods are applied to diagnose the bearing outer race DE signals polluted by infinite variance process, the result demonstrates that they have performance advantages. Finally, the characteristics, shortcomings and application scenarios of the improved algorithms are summarized.

An Overview of Pre-Analytical Factors Impacting Metabolomics Analyses of Blood Samples.

Discrepant sample processing remains a significant challenge within blood metabolomics research, introducing non-biological variation into the measured metabolome and biasing downstream results. Inconsistency during the pre-analytical phase can influence experimental processes, producing metabolome measurements that are non-representative of in vivo composition. To minimize variation, there is a need to create and adhere to standardized pre-analytical protocols for blood samples intended for use in metabolomics analyses. This will allow for reliable and reproducible findings within blood metabolomics research. In this review article, we provide an overview of the existing literature pertaining to pre-analytical factors that influence blood metabolite measurements. Pre-analytical factors including blood tube selection, pre- and post-processing time and temperature conditions, centrifugation conditions, freeze-thaw cycles, and long-term storage conditions are specifically discussed, with recommendations provided for best practices at each stage.

YOLO-SAG: An improved wildlife object detection algorithm based on YOLOv8n

Wildlife conservation is crucial for maintaining biodiversity, ensuring ecosystem balance and stability, and fostering sustainable development. Currently, the use of infrared camera traps to monitor and capture photos of wildlife is a vital methodology in protecting and researching wildlife, and automatic detection and identification of animals within captured photographs are paramount. However, factors such as the complexity of the field environment and the varying sizes of animal targets lead to low detection accuracy, while high-precision detection models are hindered by high computational complexity and sluggish training speeds. This paper proposes a wildlife target detection algorithm based on improved YOLOv8n - YOLO-SAG, which aims to balance accuracy and speed. Training stability is enhanced by introducing the Softplus activation function, which increases detection accuracy; incorporating the AIFI enhances intra-scale feature interaction, reducing missed and false detections. Integrating the GSConv and VoV-GSCSP module lightens neck convolutions, reducing computational redundancy and balancing the computational and parametric quantities brought by the AIFI. Experimental results on a self-made wildlife dataset indicate that the YOLO-SAG achieves 94.9%, 90.9%, 96.8%, and 79.9% in Precision, Recall, mAP@0.5, and mAP@0.5–0.95, respectively, which are 3.4%, 3.3%, 3.2%, and 4.9% higher than the original YOLOv8n. Inference and post-processing times reach 1.2 ms and 0.5 ms, a speedup of 25% and 54.5%, respectively, and the computation volume is only 7.2 GFLOPs, an 11.1% decrease.

Grape detection in natural environment based on improved YOLOv8 network

In the pursuit of intelligent and efficient grape picking, rapid and precise detection of grape locations serves as the fundamental cornerstone. However, amidst the natural environment, grape detection encounters various interference factors, such as fluctuating light intensity, grape leaf obstructions, and grape overlap, all of which can undermine detection accuracy. To address these challenges, this study proposes a grape detection method leveraging an enhanced YOLOv8 network, wherein the conventional CIoU is replaced with Wise-IoU to augment network precision. Additionally, an efficient multi-scale attention module is introduced to heighten the network's focus on grapes. To expedite detection, the original network backbone is substituted with the CloFormer_xxs network. The collected grape images undergo preprocessing to ensure image quality, forming the basis of the dataset. Furthermore, the dataset is augmented using disadvantages-enhance, a novel data enhancement mode, thereby enhancing the robustness and generalization of network. The comprehensive comparison and ablation experiments are conducted to demonstrate the advantageous effects of the proposed modules on the network. Subsequently, the improved network's superiority in grape detection is validated through comparative analyses with other networks, showcasing superior accuracy and faster detection speeds. The network achieves a remarkable accuracy of 92.1%, average accuracy of 94.7%, with preprocessing and post-processing times of 15ms and 0.8ms, respectively. Consequently, the enhanced network presented in this study offers a viable solution for facilitating intelligent and efficient grape picking operations.

An efficient PanAir integrated framework for automated analysis

The work proposed here is an automated pre and post-processor integrated to PanAir that is is a high-order aerodynamic panel method-based software for flow analysis developed in 70s but still in active use especially for preliminary aircraft design. With the integrated environment proposed in this work, manipulation of input and output data to and from PanAir is bypassed successfully that is otherwise requires manual manipulations and use of third party software. The integrated environment is validated over a Cessna 210 aircraft with a modified NLF (1)-0414 airfoil. The flow around the aircraft is analyzed using PanAir together with the integrated environment and results show that pre and post processing times reduced and ease in PanAir use is increased significantly.

Simultaneous soot parameters fields predictions accuracy improvements in laminar sooting flames from soot radiation measurements — a multi-task learning approach

Machine learning assisted optical diagnostics could reduce conventional extensive experimental data collecting and post-processing costs and time in academic and industrial combustion measurements. In this paper, a novel Trident-Net (T-Net) architecture is designed and assisted for retrieval of high-fidelity soot temperature, volume fraction (SVF), and diameter fields simultaneously from soot radiation measurements in laminar sooting flames. Uniquely, the T-net is subtly fabricated in one branch of the encoder and three branches of decoders, which enables three adjustable cost functions and the corresponding decoder branch for soot three respective parameter fields. Contrasted with previous Back-propagation (BP) and U-net models, the T-Net is more efficient and achieves a higher entire score in terms of individual decoder manipulation. In addition, owing to the generalization performance improvement of multi-task learning, the T-Net model demonstrates decent prediction performance under few-shot learning. Thus, this proposed T-Net model showcases the great opportunities for real-time, in situ, monitoring of practical combustion pollutant emission, by embedded in a response strategy of the detection devices.

Deep learning reconstruction for coronary CT angiography in patients with origin anomaly, stent or bypass graft.

To develop and validate a deep learning (DL)-model for automatic reconstruction for coronary CT angiography (CCTA) in patients with origin anomaly, stent or bypass graft. In this retrospective study, a DL model for automatic CCTA reconstruction was developed with training and validation sets from 6063 and 1962 patients. The algorithm was evaluated on an independent external test set of 812 patients (357 with origin anomaly or revascularization, 455 without). The image quality of DL reconstruction and manual reconstruction (using dedicated cardiac reconstruction software provided by CT vendors) was compared using a 5-point scale. The successful reconstruction rates and post-processing time for two methods were recorded. In the external test set, 812 patients (mean age, 64.0 ± 11.6, 100 with origin anomalies, 152 with stents, 105 with bypass grafts) were evaluated. The successful rates for automatic reconstruction were 100% (455/455), 97% (97/100), 100% (152/152), and 76.2% (80/105) in patients with native vessel, origin anomaly, stent, and bypass graft, respectively. The image quality scores were significantly higher for DL reconstruction than those for manual approach in all subgroups (4 vs. 3 for native vessel, 4 vs. 4 for origin anomaly, 4 vs. 3 for stent and 4 vs. 3 for bypass graft, all p < 0.001). The overall post-processing time was remarkably reduced for DL reconstruction compared to manual method (11s vs. 465s, p < 0.001). The developed DL model enabled accurate automatic CCTA reconstruction of bypass graft, stent and origin anomaly. It significantly reduced post-processing time and improved clinical workflow.

Autofluorescence lifetime flow cytometry with time-correlated single photon counting.

Autofluorescence lifetime imaging microscopy (FLIM) is sensitive to metabolic changes in single cells based on changes in the protein-binding activities of the metabolic co-enzymes NAD(P)H. However, FLIM typically relies on time-correlated single-photon counting (TCSPC) detection electronics on laser-scanning microscopes, which are expensive, low-throughput, and require substantial post-processing time for cell segmentation and analysis. Here, we present a fluorescence lifetime-sensitive flow cytometer that offers the same TCSPC temporal resolution in a flow geometry, with low-cost single-photon excitation sources, a throughput of tens of cells per second, and real-time single-cell analysis. The system uses a 375 nm picosecond-pulsed diode laser operating at 50 MHz, alkali photomultiplier tubes, an FPGA-based time tagger, and can provide real-time phasor-based classification (i.e., gating) of flowing cells. A CMOS camera produces simultaneous brightfield images using far-red illumination. A second PMT provides two-color analysis. Cells are injected into the microfluidic channel using a syringe pump at 2-5 mm/s with nearly 5 ms integration time per cell, resulting in a light dose of 2.65 J/cm2 that is well below damage thresholds (25 J/cm2 at 375 nm). Our results show that cells remain viable after measurement, and the system is sensitive to autofluorescence lifetime changes in Jurkat T cells with metabolic perturbation (sodium cyanide), quiescent versus activated (CD3/CD28/CD2) primary human T cells, and quiescent versus activated primary adult mouse neural stem cells, consistent with prior studies using multiphoton FLIM. This TCSPC-based autofluorescence lifetime flow cytometer provides a valuable label-free method for real-time analysis of single-cell function and metabolism with higher throughput than laser-scanning microscopy systems.

Post-processing of quantitative 4D-CT for initial evaluation of scapholunate Instability: Assessment of simplified approaches to data analysis

ObjectivesTo evaluate the diagnostic performance of simplified post-processing approaches for quantitative wrist 4D-CT in the assessment of scapholunate instability (SLI). MethodsA prospective monocentric case-control study included 60 patients with suspected post-traumatic scapholunate ligament (SLL) tears and persistent pain. Of these, 40 patients exhibited SLL tears, subdivided into two groups of 20 each: one group with completely torn ligaments and the other with partially torn ligaments. The remaining 20 patients, whose SLLs were intact, served as controls. 4D-CT and CT arthrography were performed, and post-processed by two readers using three approaches: the standard method with full data assessment and dedicated software, partial data assessment with post-processing software (bone locking), and partial data assessment without post-processing software (no bone locking). The scapholunate gap (SLG) parameter was measured in millimeters to evaluate scapholunate diastasis during radioulnar deviation (RUD). The scapholunate ligament status on CT arthrography was considered the gold standard. ResultsThe SLG-derived parameters (range, mean, and maximal values) were significantly increased in patients with both intact and torn scapholunate ligaments across all post-processing approaches (P values ranging from 0.001 to 0.004). SLG range was the best parameter for diagnosing SLL tears, with ROC AUC values ranging from 0.7 to 0.88 across the three post-processing methods. The interobserver reproducibility was better with the alternative approaches (ICC values 0.93–0.96) compared to the standard approach (ICC values 0.65–0.72). Additionally, post-processing time was shorter with the alternative approaches, especially when specific software was not used (reduced from 10 to three minutes). ConclusionSimpler approaches to wrist 4D-CT data analysis yielded acceptable diagnostic performances and improved interobserver reproducibility compared to the standard approach.

Liquid Metal Electrode Ink for Printable Lithium‐Ion Batteries

Recent advancements have significantly improved the flexibility of lithium‐ion batteries (LIBs). The battery electrode contains a reaction layer and a current collector layer. Flexible battery electrodes, in which these layers are integrated, are fabricated in a single process, simplifying the electrode fabrication process. However, to date, integrated electrodes have lower conductivity than conventional two‐layer electrodes, and the process that maintains conductivity has not been simplified. This study proposes a liquid metal electrode ink that simultaneously plays the role of the reaction and current collector layers with high conductivity and can be formed in a single process via printing. It employs a gallium‐based liquid metal as a liquid current collector, embedded with Li4Ti5O12 or Li2TiS3 as active materials. These liquid metal electrodes exhibit a high electronic conductivity of 104 S cm−1 and can be fabricated without requiring the lengthy postprocessing time associated with conventional inks. Moreover, the ink can be applied directly to various insulating and nonflat curved substrates, demonstrating its charge–discharge performance, even when bent to a 1 mm radius to maintain flexibility. Liquid metal‐based electrode inks simplify the LIB fabrication process, contributing to efficient production methods for electrodes in future LIBs.

Evaluating the consistency in different methods for measuring left atrium diameters

BackgroundThe morphological information of the pulmonary vein (PV) and left atrium (LA) is of immense clinical importance for effective atrial fibrillation ablation. The aim of this study is to examine the consistency in different LA diameter measurement techniques.MethodsRetrospective imaging data from 87 patients diagnosed with PV computed tomography angiography were included. The patients consisted of 50 males and 37 females, with an average age of (60.74 ± 8.70) years. Two physicians independently measured the anteroposterior diameter, long diameter, and transverse diameter of the LA using six different methods. Additionally, we recorded the post-processing time of the images. Physician 1 conducted measurements twice with a one-month interval between the measurements to assess intra-rater reliability. Using the intraclass correlation coefficient (ICC), the consistency of each LA diameter measurement by the two physicians was evaluated. We compared the differences in the LA diameter and the time consumed for measurements using different methods. This was done by employing the rank sum test of a randomized block design (Friedman M test) and the q test for pairwise comparisons among multiple relevant samples.Results(1) The consistency of the measured LA diameter by the two physicians was strong or very strong. (2) There were statistical differences in the anteroposterior diameter, long diameter, and transverse diameter of LA assessed using different methods (χ2 = 222.28, 32.74, 293.83, P < 0.001). (3) Different methods for measuring the diameters of LA required different amounts of time (χ2 = 333.10, P < 0.001).ConclusionThe results of left atrium (LA) diameter measurements conducted by different physicians were found to be reliable. However, the LA diameters obtained through various techniques exhibited variations. It was observed that measuring LA long diameters using only the VR (volume rendering) picture was the most clinically applicable method.

Plasma electrolytic polishing of additively manufactured metal parts: Optimizing the post processing workflow

Additive manufacturing of metal parts is gaining acceptance in industry due to its unique manufacturing capabilities. Higher surface roughness of printed parts is one of the major challenges of this advanced manufacturing technique, which requires a crucial post-processing stage before the components can be used in practice. This study investigates the influence of plasma electrolytic polishing (PeP) and the combination of PeP and particle blasting as a post processing technique for additively manufactured stainless steel samples. The results show a 70% improvement in surface roughness after 10 minutes of particle blasting and 3 minutes of PeP in sequence and presents the possibility of optimizing processing time to achieve better surface qualities with minimal post processing time.