Size and temperature effects on the viscosity of water inside carbon nanotubes

The influences of the diameter (size) of single-walled carbon nanotubes (SWCNTs) and the temperature on the viscosity of water confined in SWCNTs are investigated by an "Eyring-MD" (molecular dynamics) method. The results suggest that the relative viscosity of the confined water increases with increasing diameter and temperature, whereas the size-dependent trend of the relative viscosity is almost independent of the temperature. Based on the computational results, a fitting formula is proposed to calculate the size- and temperature- dependent water viscosity, which is useful for the computation on the nanoflow. To demonstrate the rationality of the calculated relative viscosity, the relative amount of the hydrogen bonds of water confined in SWCNTs is also computed. The results of the relative amount of the hydrogen bonds exhibit similar profiles with the curves of the relative viscosity. The present results should be instructive for understanding the coupling effect of the size and the temperature at the nanoscale

A Transformer-Based Substitute Recommendation Model Incorporating Weakly Supervised Customer Behavior Data

The substitute-based recommendation is widely used in E-commerce to provide better alternatives to customers. However, existing research typically uses the customer behavior signals like co-view and view-but-purchase-another to capture the substitute relationship. Despite its intuitive soundness, we find that such an approach might ignore the functionality and characteristics of products. In this paper, we adapt substitute recommendation into language matching problem by taking product title description as model input to consider product functionality. We design a new transformation method to de-noise the signals derived from production data. In addition, we consider multilingual support from the engineering point of view. Our proposed end-to-end transformer-based model achieves both successes from offline and online experiments. The proposed model has been deployed in a large-scale E-commerce website for 11 marketplaces in 6 languages. Our proposed model is demonstrated to increase revenue by 19% based on an online A/B experiment.Comment: 6 pages, 3 figures, 5 tables, accepted in 21st IEEE International Conference on Machine Learning and Application

Phase-Specific Augmented Reality Guidance for Microscopic Cataract Surgery Using Long-Short Spatiotemporal Aggregation Transformer

Phacoemulsification cataract surgery (PCS) is a routine procedure conducted using a surgical microscope, heavily reliant on the skill of the ophthalmologist. While existing PCS guidance systems extract valuable information from surgical microscopic videos to enhance intraoperative proficiency, they suffer from non-phasespecific guidance, leading to redundant visual information. In this study, our major contribution is the development of a novel phase-specific augmented reality (AR) guidance system, which offers tailored AR information corresponding to the recognized surgical phase. Leveraging the inherent quasi-standardized nature of PCS procedures, we propose a two-stage surgical microscopic video recognition network. In the first stage, we implement a multi-task learning structure to segment the surgical limbus region and extract limbus region-focused spatial feature for each frame. In the second stage, we propose the long-short spatiotemporal aggregation transformer (LS-SAT) network to model local fine-grained and global temporal relationships, and combine the extracted spatial features to recognize the current surgical phase. Additionally, we collaborate closely with ophthalmologists to design AR visual cues by utilizing techniques such as limbus ellipse fitting and regional restricted normal cross-correlation rotation computation. We evaluated the network on publicly available and in-house datasets, with comparison results demonstrating its superior performance compared to related works. Ablation results further validated the effectiveness of the limbus region-focused spatial feature extractor and the combination of temporal features. Furthermore, the developed system was evaluated in a clinical setup, with results indicating remarkable accuracy and real-time performance. underscoring its potential for clinical applications

Tilt, Decentration, and Internal Higher-Order Aberrations of Sutured Posterior-Chamber Intraocular Lenses in Patients with Open Globe Injuries

Purpose. To evaluate the tilt, decentration, and internal higher-order aberrations (HOAs) of sutured posterior-chamber intraocular lenses (IOLs) in patients with open globe injuries. Methods. 46 consecutive patients (47 eyes) who underwent transsclerally sutured IOL implantation were enrolled in this prospective cohort study. Nineteen eyes had a history of open globe injury. The tilt and decentration of the IOLs and the visual quality were measured 1 month after surgery. Results. The horizontal tilt and decentration of the IOLs in the open-globe-injury group were significantly higher than those in the control group (both P<0.05). In the open-globe-injury group, the horizontal decentration was significantly greater in the limbus-sclera-involved group (n=11) than in the only-cornea-involved group (n=8, P=0.040). The internal coma, 3rd-order, and total HOA values at pupil sizes of 4 mm (P=0.006) and 6 mm (P=0.013) were significantly higher in the open-globe-injury group than in the controls. Consequently, the optical quality data for the modulation transfer function and the Strehl ratio (all P<0.05) were significantly poorer in the open-globe-injury group. Conclusions. Open globe injuries damage the structural integrity of the eyeball, resulting in more-misaligned sutured IOLs and poorer visual quality

The Role of High Mobility Group Box 1 in Ischemic Stroke

High-mobility group box 1 protein (HMGB1) is a novel, cytokine-like, and ubiquitous, highly conserved, nuclear protein that can be actively secreted by microglia or passively released by necrotic neurons. Ischemic stroke is a leading cause of death and disability worldwide, and the outcome is dependent on the amount of hypoxia-related neuronal death in the cerebral ischemic region. Acting as an endogenous danger-associated molecular pattern (DAMP) protein, HMGB1 mediates cerebral inflammation and brain injury and participates in the pathogenesis of ischemic stroke. It is thought that HMGB1 signals via its presumed receptors, such as toll-like receptors (TLRs), matrix metalloproteinase (MMP) enzymes, and receptor for advanced glycation end products (RAGEs) during ischemic stroke. In addition, the release of HMGB1 from the brain into the bloodstream influences peripheral immune cells. However, the role of HMGB1 in ischemic stroke may be more complex than this and has not yet been clarified. Here, we summarize and review the research into HMGB1 in ischemic stroke

Encapsulating lithium and sodium inside amorphous carbon nanotubes through gold-seeded growth

Abstract(#br)Metallic lithium promises the ultimate anode material for building next-generation Li batteries, though some fundamental hurdles remain unsolved. Li growth induced by hetero particles/atoms has recently emerged as a highly efficient route enabling spatial-control and dendrite-free Li deposition on anode hosts. However, the detailed mechanism of Li nucleation and its interaction with heterogeneous seeds are largely unknown. Herein, we investigate this issue by visualizing Au-seeded Li nucleation processes that guide Li deposition inside the one-dimensional hollow space of individual amorphous carbon nanotubes by in-situ transmission electron microscopy. A reversible two-step conversion process during Au–Li alloying/dealloying reactions is revealed, suggesting that the formation of Li 3 Au plays the actual role in inducing Li nucleation. We propose a front-growth scenario to explain the spatially confined Li growth and stripping kinetic behaviors, which involves the mass addition and removal at the deposition front through ion diffusion along the tubular carbon shell. As a comparison, nanotubes without gold seeds inside exhibit uncontrolled dendrite-like Li growth outside the carbon shell. We further demonstrate that Au-seed growth can be successful in encapsulating sodium metal for the first time. These findings provide mechanistic insights into heterogeneous seeded Li/Na nucleation and space-confined deposition for design of high-performance battery anodes

Subject-independent EEG classification based on a hybrid neural network

A brain-computer interface (BCI) based on the electroencephalograph (EEG) signal is a novel technology that provides a direct pathway between human brain and outside world. For a traditional subject-dependent BCI system, a calibration procedure is required to collect sufficient data to build a subject-specific adaptation model, which can be a huge challenge for stroke patients. In contrast, subject-independent BCI which can shorten or even eliminate the pre-calibration is more time-saving and meets the requirements of new users for quick access to the BCI. In this paper, we design a novel fusion neural network EEG classification framework that uses a specially designed generative adversarial network (GAN), called a filter bank GAN (FBGAN), to acquire high-quality EEG data for augmentation and a proposed discriminative feature network for motor imagery (MI) task recognition. Specifically, multiple sub-bands of MI EEG are first filtered using a filter bank approach, then sparse common spatial pattern (CSP) features are extracted from multiple bands of filtered EEG data, which constrains the GAN to maintain more spatial features of the EEG signal, and finally we design a convolutional recurrent network classification method with discriminative features (CRNN-DF) to recognize MI tasks based on the idea of feature enhancement. The hybrid neural network proposed in this study achieves an average classification accuracy of 72.74 ± 10.44% (mean ± std) in four-class tasks of BCI IV-2a, which is 4.77% higher than the state-of-the-art subject-independent classification method. A promising approach is provided to facilitate the practical application of BCI

A sequential learning model with GNN for EEG-EMG-based stroke rehabilitation BCI

IntroductionBrain-computer interfaces (BCIs) have the potential in providing neurofeedback for stroke patients to improve motor rehabilitation. However, current BCIs often only detect general motor intentions and lack the precise information needed for complex movement execution, mainly due to insufficient movement execution features in EEG signals.MethodsThis paper presents a sequential learning model incorporating a Graph Isomorphic Network (GIN) that processes a sequence of graph-structured data derived from EEG and EMG signals. Movement data are divided into sub-actions and predicted separately by the model, generating a sequential motor encoding that reflects the sequential features of the movements. Through time-based ensemble learning, the proposed method achieves more accurate prediction results and execution quality scores for each movement.ResultsA classification accuracy of 88.89% is achieved on an EEG-EMG synchronized dataset for push and pull movements, significantly outperforming the benchmark method's performance of 73.23%.DiscussionThis approach can be used to develop a hybrid EEG-EMG brain-computer interface to provide patients with more accurate neural feedback to aid their recovery