Enhancing Electrode Performance through Triple Distribution Modulation of Active Material, Conductive Agent, and Porosity.

The increasing demand for large-scale energy storage propels the development of lithium-ion batteries with high energy and high power density. Low tortuosity electrodes with aligned straight channels have proved to be effective in building such batteries. However, manufacturing such low tortuosity electrodes in large scale remains extremely challenging. In contrast, high-performance electrodes with customized gradients of materials and porosity are possible to be made by industrial roll-to-roll coating process. Yet, the desired design of gradients combining materials and porosity is unclear for high-performance gradient electrodes. Here, triple gradient LiFePO4 electrodes (TGE) are fabricated featuring distribution modulation of active material, conductive agent, and porosity by combining suction filtration with the phase inversion method. The effects and mechanism of active material, conductive agent, and porosity distribution on electrode performance are analyzed by experiments. It is found that the electrode with a gradual increase of active material content from current collector to separator coupled with the distribution of conductive agent and porosity in the opposite direction, demonstrates the best rate capability, the fastest electrochemical reaction kinetics, and the highest utilization of active material. This work provides valuable insights into the design of gradient electrodes with high performance and high potential in application.

Enhancing the Reversibility of Lithium Cobalt Oxide Phase Transition in Thick Electrode via Low Tortuosity Design.

Lithium cobalt oxide (LCO) is a widely used cathode material for lithium-ion batteries. However, it suffers from irreversible phase transition during cycling because of high cutoff voltage or huge concentration polarization in thick electrode, resulting in deteriorated cyclability. Here, we design a low tortuous LiCoO2 (LCO-LT) electrode by ice-templating method and investigate the reversibility of LCO phase transition. LCO-LT thick electrode shows accelerated lithium-ion transport and reduced concentration polarization, achieving excellent rate capability and homogeneous actual operating voltage. Moreover, LCO-LT thick electrode exhibits a durable phase transition between O2 and H1-3, mitigated volume expansion, and suppressed microcrack formation. LCO-LT electrode (25 mg cm-2) delivers improved capacity retentions of 94.4% after 200 cycles and 93.3% after 150 cycles at cutoff voltages of 4.3 and 4.5 V, respectively. This strategy provides a new concept to improve the reversibility of LCO phase transition in thick electrode by low tortuosity design.

The influence of radiation dose on taste impairment in a prospective observational study cohort of oropharyngeal cancer patients.

BACKGROUND: To analyze the influence of radiation dose on late radiation-associated taste impairment in oropharyngeal cancer (OPC) patients treated with intensity-modulated radiotherapy (IMRT) using the taste bud bearing tongue mucosa as organ at risk. MATERIAL AND METHODS: This study is part of an ongoing, prospective observational study. Cancer-free OPC survivors with at least 24 months from IMRT were included in this analysis. Scores for taste impairment and dry mouth were extracted from the MD Anderson Symptom Inventory Head and Neck module (MDASI-HN) with scores of ≥5 considered as moderate-to-severe symptoms. The mean dose, minimum and maximum dose to the taste bud bearing tongue mucosa, the ipsi- and contralateral parotid and submandibular glands were extracted and analyzed for correlation with moderate-to-severe taste impairment. RESULTS: One hundred sixteen T1-4 OPC patients were included (81% males, median age: 55). The primary tumor was in the tonsil in 92 cases (79%) and in the base of tongue in 21 cases (18%). Patients were treated with 64.2-72.0 Gy; 37 patients (32%) received concurrent chemotherapy and 22 (19%) concurrent targeted therapy. After a median of 58 months from RT (IQR: 43-68) 38 patients (33%) suffered from moderate-to-severe long-term radiation-associated taste impairment. No dose volume parameter of the taste bud bearing tongue mucosa and the salivary glands was significantly associated with moderate-to-severe taste impairment for the whole patient cohort. For patients without concurrent chemotherapy, the minimum and mean dose to the ipsilateral parotid gland, and the maximum dose to the submandibular gland was significantly associated with late taste impairment (all p < 0.05). A significant correlation was found between taste impairment and dry mouth (p < 0.001). CONCLUSION: The dose to the ipsilateral parotid gland seems to play an important role in the development of late taste impairment. The influence of dose to the taste bud bearing tongue mucosa remains unclear and needs further investigation.

SIDE-A Unified Framework for Simultaneously Dehazing and Enhancement of Nighttime Hazy Images.

Single image dehazing is a difficult problem because of its ill-posed nature. Increasing attention has been paid recently as its high potential applications in many visual tasks. Although single image dehazing has made remarkable progress in recent years, they are mainly designed for haze removal in daytime. In nighttime, dehazing is more challenging where most daytime dehazing methods become invalid due to multiple scattering phenomena, and non-uniformly distributed dim ambient illumination. While a few approaches have been proposed for nighttime image dehazing, low ambient light is actually ignored. In this paper, we propose a novel unified nighttime hazy image enhancement framework to address the problems of both haze removal and illumination enhancement simultaneously. Specifically, both halo artifacts caused by multiple scattering and non-uniformly distributed ambient illumination existing in low-light hazy conditions are considered for the first time in our approach. More importantly, most current daytime dehazing methods can be effectively incorporated into nighttime dehazing task based on our framework. Firstly, we decompose the observed hazy image into a halo layer and a scene layer to remove the influence of multiple scattering. After that, we estimate the spatially varying ambient illumination based on the Retinex theory. We then employ the classic daytime dehazing methods to recover the scene radiance. Finally, we generate the dehazing result by combining the adjusted ambient illumination and the scene radiance. Compared with various daytime dehazing methods and the state-of-the-art nighttime dehazing methods, both quantitative and qualitative experimental results on both real-world and synthetic hazy image datasets demonstrate the superiority of our framework in terms of halo mitigation, visibility improvement and color preservation.

[Research on calculation of the regional cerebral blood volume based on minimum mean square error method].

In this paper, the Fourier transform based minimum mean square error (FT-based MMSE) method is used to calculate the regional cerebral blood volume (rCBV) in magnetic resonance (MR) perfusion imaging, and the method is improved to handle the existing noise in the imaging process. In the experiments with signal-to-noise ratio (SNR) of 50 dB, the rCBV values were compared with the results using MMSE method. The effects of different SNRs on the estimation of rCBV were analyzed. The experimental results showed that MMSE was a simple way to filter the measurement noise, and could calculate rCBV accurately. Compared with other existing methods, the present method is not sensitive to environment, and furthermore, it is suitable to deal with the perfusion images acquired from the environment with larger SNR.

FDG Uptake Caused by Right Varicocele.

ABSTRACT: A 67-year-old man underwent 18 F-FDG PET/CT for lung cancer staging. Interestingly, the PET scan revealed strip-shaped FDG uptake in the right inguinal contoured area, which was later confirmed as a right varicocele through ultrasound imaging.

Mechanical regulation to interfacial thermal transport in GaN/diamond heterostructures for thermal switch.

Gallium nitride offers an ideal material platform for next-generation high-power electronics devices, which enable a spectrum of applications. The thermal management of the ever-growing power density has become a major bottleneck in the performance, reliability, and lifetime of the devices. GaN/diamond heterostructures are usually adopted to facilitate heat dissipation, given the extraordinary thermal conduction properties of diamonds. However, thermal transport is limited by the interfacial conductance at the material interface between GaN and diamond, which is associated with significant mechanical stress at the atomic level. In this work, we investigate the effect of mechanical strain perpendicular to the GaN/diamond interface on the interfacial thermal conductance of heterostructures using full-atom non-equilibrium molecular dynamics simulations. We found that the heterostructure exhibits severe mechanical stress at the interface in the absence of loading, which is due to lattice mismatch. Upon tensile/compressive loading, the interfacial stress is more pronounced, and the strain is not identical across the interface owing to the contrasting elastic moduli of GaN and diamond. In addition, the interfacial thermal conductance can be notably enhanced and suppressed by tensile and compressive strains, respectively, leading to a 400% variation in thermal conductance. More detailed analyses reveal that the change in interfacial thermal conductance is related to the surface roughness and interfacial bonding strength, as described by a generalized relationship. Moreover, phonon analyses suggest that the unequal mechanical deformation under compressive strain in GaN and diamond induces different frequency shifts in the phonon spectra, leading to an enhancement in phonon overlapping energy, which promotes phonon transport at the interface and elevates the thermal conductance and vice versa for tensile strain. The effect of strain on interface thermal conductance was investigated at various temperatures. Based on the mechanical tunability of thermal conductance, we propose a conceptual design for a mechanical thermal switch that regulates thermal conductance with excellent sensitivity and high responsiveness. This study offers a fundamental understanding of how mechanical strain can adjust interface thermal conductance in GaN/diamond heterostructures with respect to mechanical stress, deformation, and phonon properties. These results and findings lay the theoretical foundation for designing thermal management devices in a strain environment and shed light on developing intelligent thermal devices by leveraging the interplay between mechanics and thermal transport.

ZnO/g-C3N4 photocatalyst activated by low-pressure ultraviolet for restoring the SWASV signals: A fast pretreatment method for electrochemically detecting Cd2+ and Pb2+ in soil extracts.

Soil organic matter (SOM) significantly impacts the detection accuracy of Cd2+ and Pb2+ using square wave anodic stripping voltammetry (SWASV) due to the complexation of SOM to heavy metal ions (HMIs), thereby attenuating SWASV signals. This study explored an effective pretreatment method that combined low-pressure ultraviolet (LPUV) photolysis with the ZnO/g-C3N4 photocatalyst, activating the photocatalyst to generate highly oxidative •OH radicals and O2•- radicals, which effectively disrupted this complexation, consequently restoring the electroactivity of HMIs and achieving high-fidelity SWASV signals. The parameters of the LPUV-ZnO/g-C3N4 photocatalytic system were meticulously optimized, including the pH of photolysis, duration of photolysis, g-C3N4 mass fraction, and concentration of the photocatalyst. Furthermore, the ZnO/g-C3N4 photocatalyst was thoroughly characterized, with an in-depth investigation on the synergistic interaction between ZnO and g-C3N4 and the mechanisms contributing to the restoration of SWASV signals. This synergistic interaction effectively separated charge carriers and reduced charge transfer resistance, enabling photogenerated electrons (e-) from the conduction band of g-C3N4 to be quickly transferred to the conduction band of ZnO, preventing the recombination of e- and hole (h+) and generating more radicals to disrupt complexation and restore the SWASV signals. Finally, the analysis of HMIs in real soil extracts using the proposed pretreatment method demonstrated high detection accuracy of 94.9% for Cd2+ and 99.8% for Pb2+, which validated the feasibility and effectiveness of the proposed method in environmental applications.

An ultra-efficient pretreatment method adopted LPUV/CoFe2O4/PMS-based photolysis for accurate detection of Cd(II) and Pb(II) in water via SWASV.

Dissolved organic matter (DOM) is a widely occurring substance in rivers that can strongly complex with heavy metal ions (HMIs), severely interfering with the electrochemical signal of anodic stripping voltammetry (ASV) and reducing the detection accuracy of HMIs in water. In this study, we investigated a novel advanced oxidation process (AOP) that involves the activation of peroxymonosulfate (PMS) using low-pressure ultraviolet (LPUV) radiation and CoFe2O4 photocatalysis. This novel AOP was used for the first time as an effective pretreatment method to break or weaken the complexation between HMIs and DOM, thereby restoring the electrochemical signals of HMIs. The key parameters, including the PMS concentration, CoFe2O4 concentration, and photolysis time, were optimized to be 6 mg/L, 12 mg/L, and 30 s for eliminating DOM interference during the electrochemical analysis of HMIs via LPUV/CoFe2O4-based photolysis. Investigations of the microstructure, surface morphology, specific surface area, and pore volume of CoFe2O4 were conducted to reveal the exceptional signal recovery capability of LPUV/CoFe2O4/PMS-based photolysis in mitigating interference from DOM during HMIs analysis. The PMS activation mechanism, which is critical to the signal recovery process, was elucidated by analyzing the reactive oxygen species (ROS) and the surface elemental composition of CoFe2O4. Additionally, the degradation and transformation behavior of humus-HMIs complexes were analyzed to study the mechanism of ASV signal recovery further. Notably, the detection results of HMIs in actual water samples obtained using the proposed pretreatment method were compared with those obtained from ICP-MS, yielding an RMSE less than 0.04 µg/L, which indicated the satisfactory performance of the proposed pretreatment method for the ASV detection of HMIs in complex actual samples.

Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with Bayesian deep learning.

BACKGROUND: Radiotherapy is a core treatment modality for oropharyngeal cancer (OPC), where the primary gross tumor volume (GTVp) is manually segmented with high interobserver variability. This calls for reliable and trustworthy automated tools in clinician workflow. Therefore, accurate uncertainty quantification and its downstream utilization is critical. METHODS: Here we propose uncertainty-aware deep learning for OPC GTVp segmentation, and illustrate the utility of uncertainty in multiple applications. We examine two Bayesian deep learning (BDL) models and eight uncertainty measures, and utilize a large multi-institute dataset of 292 PET/CT scans to systematically analyze our approach. RESULTS: We show that our uncertainty-based approach accurately predicts the quality of the deep learning segmentation in 86.6% of cases, identifies low performance cases for semi-automated correction, and visualizes regions of the scans where the segmentations likely fail. CONCLUSIONS: Our BDL-based analysis provides a first-step towards more widespread implementation of uncertainty quantification in OPC GTVp segmentation.

Radiotherapy is used as a treatment for people with oropharyngeal cancer. It is important to distinguish the areas where cancer is present so the radiotherapy treatment can be targeted at the cancer. Computational methods based on artificial intelligence can automate this task but need to be able to distinguish areas where it is unclear whether cancer is present. In this study we compare these computational methods that are able to highlight areas where it is unclear whether or not cancer is present. Our approach accurately predicts how well these areas are distinguished by the models. Our results could be applied to improve the computational methods used during radiotherapy treatment. This could enable more targeted treatment to be used in the future, which could result in better outcomes for people with oropharyngeal cancer.

Dataset of weekly intra-treatment diffusion weighted imaging in head and neck cancer patients treated with MR-Linac.

Radiation therapy (RT) is a crucial treatment for head and neck squamous cell carcinoma (HNSCC); however, it can have adverse effects on patients' long-term function and quality of life. Biomarkers that can predict tumor response to RT are being explored to personalize treatment and improve outcomes. While tissue and blood biomarkers have limitations, imaging biomarkers derived from magnetic resonance imaging (MRI) offer detailed information. The integration of MRI and a linear accelerator in the MR-Linac system allows for MR-guided radiation therapy (MRgRT), offering precise visualization and treatment delivery. This data descriptor offers a valuable repository for weekly intra-treatment diffusion-weighted imaging (DWI) data obtained from head and neck cancer patients. By analyzing the sequential DWI changes and their correlation with treatment response, as well as oncological and survival outcomes, the study provides valuable insights into the clinical implications of DWI in HNSCC.

Artificial Intelligence and Machine Learning in Cancer Pain: A Systematic Review.

BACKGROUND/OBJECTIVES: Pain is a challenging multifaceted symptom reported by most cancer patients. This systematic review aims to explore applications of artificial intelligence/machine learning (AI/ML) in predicting pain-related outcomes and pain management in cancer. METHODS: A comprehensive search of Ovid MEDLINE, EMBASE and Web of Science databases was conducted using terms: "Cancer", "Pain", "Pain Management", "Analgesics", "Artificial Intelligence", "Machine Learning", and "Neural Networks" published up to September 7, 2023. AI/ML models, their validation and performance were summarized. Quality assessment was conducted using PROBAST risk-of-bias andadherence to TRIPOD guidelines. RESULTS: Forty four studies from 2006-2023 were included. Nineteen studies used AI/ML for classifying pain after cancer therapy [median AUC 0.80 (range 0.76-0.94)]. Eighteen studies focused on cancer pain research [median AUC 0.86 (range 0.50-0.99)], and 7 focused on applying AI/ML for cancer pain management, [median AUC 0.71 (range 0.47-0.89)]. Median AUC (0.77) of models across all studies. Random forest models demonstrated the highest performance (median AUC 0.81), lasso models had the highest median sensitivity (1), while Support Vector Machine had the highest median specificity (0.74). Overall adherence to TRIPOD guidelines was 70.7%. Overall, high risk-of-bias (77.3%), lack of external validation (14%) and clinical application (23%) was detected. Reporting of model calibration was also missing (5%). CONCLUSION: Implementation of AI/ML tools promises significant advances in the classification, risk stratification, and management decisions for cancer pain. Further research focusing on quality improvement, model calibration, rigorous external clinical validation in real healthcare settings is imperative for ensuring its practical and reliable application in clinical practice.

Comparison of Machine Leaning Models for Prediction of Acute Pain Severity and On-Treatment Opioid Utilization in Oral Cavity and Oropharyngeal Cancer Patients Receiving Radiation Therapy: Exploratory Analysis from a Large-Scale Retrospective Cohort.

Background: Acute pain is a common and debilitating symptom experienced by oral cavity and oropharyngeal cancer (OC/OPC) patients undergoing radiation therapy (RT). Uncontrolled pain can result in opioid overuse and increased risks of long-term opioid dependence. The specific aim of this exploratory analysis was the prediction of severe acute pain and opioid use in the acute on-treatment setting, to develop risk-stratification models for pragmatic clinical trials. Materials and Methods: A retrospective study was conducted on 900 OC/OPC patients treated with RT during 2017 to 2023. Clinical data including demographics, tumor data, pain scores and medication data were extracted from patient records. On-treatment pain intensity scores were assessed using a numeric rating scale (0-none, 10-worst) and total opioid doses were calculated using morphine equivalent daily dose (MEDD) conversion factors. Analgesics efficacy was assessed based on the combined pain intensity and the total required MEDD. ML models, including Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and Gradient Boosting Model (GBM) were developed and validated using ten-fold cross-validation. Performance of models were evaluated using discrimination and calibration metrics. Feature importance was investigated using bootstrap and permutation techniques. Results: For predicting acute pain intensity, the GBM demonstrated superior area under the receiver operating curve (AUC) (0.71), recall (0.39), and F1 score (0.48). For predicting the total MEDD, LR outperformed other models in the AUC (0.67). For predicting the analgesics efficacy, SVM achieved the highest specificity (0.97), and best calibration (ECE of 0.06), while RF and GBM achieved the same highest AUC, 0.68. RF model emerged as the best calibrated model with ECE of 0.02 for pain intensity prediction and 0.05 for MEDD prediction. Baseline pain scores and vital signs demonstrated the most contributed features for the different predictive models. Conclusion: These ML models are promising in predicting end-of-treatment acute pain and opioid requirements and analgesics efficacy in OC/OPC patients undergoing RT. Baseline pain score, vital sign changes were identified as crucial predictors. Implementation of these models in clinical practice could facilitate early risk stratification and personalized pain management. Prospective multicentric studies and external validation are essential for further refinement and generalizability.

Investigation of autosegmentation techniques on T2-weighted MRI for off-line dose reconstruction in MR-linac workflow for head and neck cancers.

BACKGROUND: In order to accurately accumulate delivered dose for head and neck cancer patients treated with the Adapt to Position workflow on the 1.5T magnetic resonance imaging (MRI)-linear accelerator (MR-linac), the low-resolution T2-weighted MRIs used for daily setup must be segmented to enable reconstruction of the delivered dose at each fraction. PURPOSE: In this pilot study, we evaluate various autosegmentation methods for head and neck organs at risk (OARs) on on-board setup MRIs from the MR-linac for off-line reconstruction of delivered dose. METHODS: Seven OARs (parotid glands, submandibular glands, mandible, spinal cord, and brainstem) were contoured on 43 images by seven observers each. Ground truth contours were generated using a simultaneous truth and performance level estimation (STAPLE) algorithm. Twenty total autosegmentation methods were evaluated in ADMIRE: 1-9) atlas-based autosegmentation using a population atlas library (PAL) of 5/10/15 patients with STAPLE, patch fusion (PF), random forest (RF) for label fusion; 10-19) autosegmentation using images from a patient's 1-4 prior fractions (individualized patient prior [IPP]) using STAPLE/PF/RF; 20) deep learning (DL) (3D ResUNet trained on 43 ground truth structure sets plus 45 contoured by one observer). Execution time was measured for each method. Autosegmented structures were compared to ground truth structures using the Dice similarity coefficient, mean surface distance (MSD), Hausdorff distance (HD), and Jaccard index (JI). For each metric and OAR, performance was compared to the inter-observer variability using Dunn's test with control. Methods were compared pairwise using the Steel-Dwass test for each metric pooled across all OARs. Further dosimetric analysis was performed on three high-performing autosegmentation methods (DL, IPP with RF and 4 fractions [IPP_RF_4], IPP with 1 fraction [IPP_1]), and one low-performing (PAL with STAPLE and 5 atlases [PAL_ST_5]). For five patients, delivered doses from clinical plans were recalculated on setup images with ground truth and autosegmented structure sets. Differences in maximum and mean dose to each structure between the ground truth and autosegmented structures were calculated and correlated with geometric metrics. RESULTS: DL and IPP methods performed best overall, all significantly outperforming inter-observer variability and with no significant difference between methods in pairwise comparison. PAL methods performed worst overall; most were not significantly different from the inter-observer variability or from each other. DL was the fastest method (33 s per case) and PAL methods the slowest (3.7-13.8 min per case). Execution time increased with a number of prior fractions/atlases for IPP and PAL. For DL, IPP_1, and IPP_RF_4, the majority (95%) of dose differences were within ± 250 cGy from ground truth, but outlier differences up to 785 cGy occurred. Dose differences were much higher for PAL_ST_5, with outlier differences up to 1920 cGy. Dose differences showed weak but significant correlations with all geometric metrics (R2 between 0.030 and 0.314). CONCLUSIONS: The autosegmentation methods offering the best combination of performance and execution time are DL and IPP_1. Dose reconstruction on on-board T2-weighted MRIs is feasible with autosegmented structures with minimal dosimetric variation from ground truth, but contours should be visually inspected prior to dose reconstruction in an end-to-end dose accumulation workflow.

Associations Between Radiation Oncologist Demographic Factors and Segmentation Similarity Benchmarks: Insights From a Crowd-Sourced Challenge Using Bayesian Estimation.

PURPOSE: The quality of radiotherapy auto-segmentation training data, primarily derived from clinician observers, is of utmost importance. However, the factors influencing the quality of clinician-derived segmentations are poorly understood; our study aims to quantify these factors. METHODS: Organ at risk (OAR) and tumor-related segmentations provided by radiation oncologists from the Contouring Collaborative for Consensus in Radiation Oncology data set were used. Segmentations were derived from five disease sites: breast, sarcoma, head and neck (H&N), gynecologic (GYN), and GI. Segmentation quality was determined on a structure-by-structure basis by comparing the observer segmentations with an expert-derived consensus, which served as a reference standard benchmark. The Dice similarity coefficient (DSC) was primarily used as a metric for the comparisons. DSC was stratified into binary groups on the basis of structure-specific expert-derived interobserver variability (IOV) cutoffs. Generalized linear mixed-effects models using Bayesian estimation were used to investigate the association between demographic variables and the binarized DSC for each disease site. Variables with a highest density interval excluding zero were considered to substantially affect the outcome measure. RESULTS: Five hundred seventy-four, 110, 452, 112, and 48 segmentations were used for the breast, sarcoma, H&N, GYN, and GI cases, respectively. The median percentage of segmentations that crossed the expert DSC IOV cutoff when stratified by structure type was 55% and 31% for OARs and tumors, respectively. Regression analysis revealed that the structure being tumor-related had a substantial negative impact on binarized DSC for the breast, sarcoma, H&N, and GI cases. There were no recurring relationships between segmentation quality and demographic variables across the cases, with most variables demonstrating large standard deviations. CONCLUSION: Our study highlights substantial uncertainty surrounding conventionally presumed factors influencing segmentation quality relative to benchmarks.

Rethinking Training Strategy in Stereo Matching.

In stereo matching, various learning-based approaches have shown impressive performance in solving traditional difficulties on multiple datasets. While most progress is obtained on a specific dataset with a dataset-specific network design, the performance on the single dataset and cross dataset affected by training strategy is often ignored. In this article, we analyze the relationship between different training strategies and performance by retraining some representative state-of-the-art methods (e.g., geometry and context network (GC-Net), pyramid stereo matching network (PSM-Net), and guided aggregation network (GA-Net), etc.). According to our research, it is surprising that the performance of networks on single or cross datasets is significantly improved by pre-training and data augmentation without any particular structure acquirement. Based on this discovery, we improve our previous non-local context attention network (NLCA-Net) to NLCA-Net v2 and train it with the novel strategy and rethink the training strategy of stereo matching concurrently. The quantitative experiments demonstrate that: 1) our model is capable of reaching top performance on both the single dataset and the multiple datasets with the same parameters in this study, which also won the 2nd place in the stereo task of the ECCV Robust vision Challenge 2020 (RVC 2020); and 2) on small datasets (e.g., KITTI, ETH3D, and Middlebury), the model's generalization and robustness are significantly affected by pre-training and data augmentation, even exceeding the network structure's influence in some cases. These observations present a challenge to the conventional wisdom of network architectures in this stage. We expect these discoveries to encourage researchers to rethink the current paradigm of "excessive attention on the performance of a single small dataset" in stereo matching.

Revealing Surfactant Effect of Trifluoromethylbenzene in Medium-Concentrated PC Electrolyte for Advanced Lithium-Ion Batteries.

Despite wide-temperature tolerance and high-voltage compatibility, employing propylene carbonate (PC) as electrolyte in lithium-ion batteries (LIBs) is hampered by solvent co-intercalation and graphite exfoliation due to incompetent solvent-derived solid electrolyte interphase (SEI). Herein, trifluoromethylbenzene (PhCF3 ), featuring both specific adsorption and anion attraction, is utilized to regulate the interfacial behaviors and construct anion-induced SEI at low Li salts' concentration (<1 m). The adsorbed PhCF3 , showing surfactant effect on graphite surface, induces preferential accumulation and facilitated decomposition of bis(fluorosulfonyl)imide anions (FSI- ) based on the adsorption-attraction-reduction mechanism. As a result, PhCF3 successfully ameliorates graphite exfoliation-induced cell failure in PC-based electrolyte and enables the practical operation of NCM613/graphite pouch cell with high reversibility at 4.35 V (96% capacity retention over 300 cycles at 0.5 C). This work constructs stable anion-derived SEI at low concentration of Li salt by regulating anions-co-solvents interaction and electrode/electrolyte interfacial chemistries.

[Progress in evaluation of return to sports after anterior cruciate ligament reconstruction].

Objective: To summarize the evaluation methods of return to sports (RTS) after anterior cruciate ligament reconstruction (ACLR) in recent years, in order to provide reference for clinical practice. Methods: The literature related to the RTS after ACLR was searched from CNKI, Wanfang, PubMed, and Foreign Medical Information Resources Retrieval Platform (FMRS) databases. The retrieval range was from 2010 to 2023, and 66 papers were finally included for review. The relevant literature was summarized and analyzed from the aspects of RTS time, objective evaluation indicators, and psychological evaluation. Results: RTS is the common desire of patients with ACL injury and doctors, as well as the initial intention of selecting surgery. A reasonable and perfect evaluation method of RTS can not only help patients recover to preoperative exercise level, but also protect patients from re-injury. At present, the main criterion for clinical judgement of RTS is time. It is basically agreed that RTS after 9 months can reduce the re-injury. In addition to time, it is also necessary to test the lower limb muscle strength, jumping, balance, and other aspects of the patient, comprehensively assess the degree of functional recovery and determine the different time of RTS according to the type of exercise. Psychological assessment plays an important role in RTS and has a good clinical predictive effect. Conclusion: RTS is one of the research hotspots after ACLR. At present, there are many related evaluation methods, which need to be further optimized by more research to build a comprehensive and standardized evaluation system.

[Three-dimensional kinematic analysis of knee joint after anterior cruciate ligament reconstruction with personalized femoral positioner based on apex of deep cartilage].

Objective: To investigate the changes of knee joint kinematics after anterior cruciate ligament (ACL) reconstruction assisted by personalized femoral positioner based on the apex of deep cartilage (ADC). Methods: Between January 2021 and January 2022, a total of 40 patients with initial ACL rupture who met the selection criteria were randomly divided into the study group (using the personalized femoral positioner based on ADC design to assist ACL reconstruction) and the control group (not using the personalized femoral positioner to assist ACL reconstruction), with 20 patients in each group. Another 20 volunteers with normal knee were collected as a healthy group. There was no significant difference in gender, age, body mass index, and affected side between groups ( P>0.05). Gait analysis was performed at 3, 6, and 12 months after operation using Opti _ Knee three-dimensional knee joint motion measurement and analysis system, and the 6 degrees of freedom (flexion and extension angle, varus and valgus angle, internal and external rotation angle, anteroposterior displacement, superior and inferior displacement, internal and external displacement) and motion cycle (maximum step length, minimum step length, and step frequency) of the knee joint were recorded. The patients' data was compared to the data of healthy group. Results: In the healthy group, the flexion and extension angle was (57.80±3.45)°, the varus and valgus angle was (10.54±1.05)°, the internal and external rotation angle was (13.02±1.66)°, and the anteroposterior displacement was (1.44±0.39) cm, the superior and inferior displacement was (0.86±0.20) cm, and the internal and external displacement was (1.38±0.39) cm. The maximum step length was (51.24±1.29) cm, the minimum step length was (45.69±2.28) cm, and the step frequency was (12.45±0.47) step/minute. Compared with the healthy group, the flexion and extension angles and internal and external rotation angles of the patients in the study group and the control group decreased at 3 months after operation, and the flexion and extension angles of the patients in the control group decreased at 6 months after operation, and the differences were significant ( P<0.05); there was no significant difference in the other time points and other indicators when compared with healthy group ( P>0.05). In the study group, the flexion and extension angles and internal and external rotation angles at 6 and 12 months after operation were significantly greater than those at 3 months after operation ( P<0.05), while there was no significant difference in the other indicators at other time points ( P>0.05). There was a significant difference in flexion and extension angle between the study group and the control group at 6 months after operation ( P<0.05), but there was no significant difference of the indicators between the two groups at other time points ( P>0.05). Conclusion: Compared with conventional surgery, ACL reconstruction assisted by personalized femoral positioner based on ADC design can help patients achieve more satisfactory early postoperative kinematic results, and three-dimensional kinematic analysis can more objectively and dynamically evaluate the postoperative recovery of knee joint.

[Effectiveness of anterior cruciate ligament reconstruction with personalized femoral locator based on apex of deep cartilage].

Objective: To investigate the effectiveness of anterior cruciate ligament (ACL) reconstruction assisted by personalized femoral locator based on the apex of deep cartilage (ADC) combined with patient imaging data. Methods: Between January 2021 and January 2022, a total of 40 patients with primary ACL rupture were selected and randomly divided into study group (ACL reconstruction assisted by personalized femoral locator based on ADC) and control group (ACL reconstruction assisted by intraoperative fluoroscopy and traditional femoral locator), with 20 cases in each group. There was no significant difference in gender, age, body mass index, affected side, cause of injury, and preoperative International Knee Documentation Committee (IKDC) score, Lyshlom score, and Tegner score between the two groups ( P>0.05). IKDC score, Lyshlom score, and Tegner score were used to evaluate the functional recovery of the affected knee before operation and at 3, 6, and 12 months after operation. CT scan and three-dimensional reconstruction were performed before and after operation to measure the horizontal distance from ADC to the anterior cartilage margin (L) and the horizontal distance from ADC to the center of the femoral canal (I), and the anteroposterior position of the bone canal (R) was calculated by I/L; the distance from the center to the distal cartilage margin (D) was measured on the two-dimensional cross section; the R value and D value were compared between the two groups. Results: The operation time of the study group was significantly less than that of the control group [ MD=-6.90 (-8.78, -5.03), P<0.001]. The incisions of the two groups healed by first intention, and no complication such as intra-articular infection, nerve injury, and deep vein thrombosis of lower limbs occurred. There was no significant difference in the R value and D value between the preoperative simulated positioning and the actual intraoperative positioning in the study group [ MD=0.52 (-2.85, 3.88), P=0.758; MD=0.36 (-0.39, 1.11), P=0.351]. There was no significant difference in the actual intraoperative positioning R value and D value between the study group and the control group [ MD=1.01 (-2.57, 4.58), P=0.573; MD=0.24 (-0.34, 0.82), P=0.411]. The patients in both groups were followed up 12-13 months (mean, 12.4 months). The IKDC score, Lysholm score, and Tegner score of the two groups increased gradually with time, and there were significant differences between pre- and post-operation ( P<0.05). There was no significant difference in the scores between the two groups at each time point after operation ( P>0.05). Conclusion: The personalized femoral locator based on ADC can accurately assist the femoral tunnel positioning in ACL reconstruction, which can shorten the operation time when compared with traditional surgical methods, and achieve satisfactory early effectiveness.