Progress in Ionic Liquids as Reaction Media, Monomers and Additives in High-Performance Polymers

In this chapter, we will review the progress in ionic liquids (ILs) widely used as reaction media, monomers and additives in the synthesis, chemical modification and physical processing of high-performance polymers (HPPs). Using ILs as reaction media in the syntheses of HPPs, the high-molecular-weight polymers were obtained in good yields and the shortened dehydration time compared to the conventional methods, the separation efficiency of products was improved. It is particularly noteworthy that the number of novel copolymers of HPPs with polymerisable ILs has steadily increased in recent years. In addition, ILs have been used as various types of additives such as the components of polymer materials, plasticizers and porogenic agents in the physical processing of HPPs, and the materials prepared include membranes, microcapsules, nanocomposites (NCs), electrolytes and grease

Plasmonic Thin Film Solar Cells

Thin film solar cell technology represents an alternative way to effectively solve the world’s increasing energy shortage problem. Light trapping is of critical importance. Surface plasmons (SPs), including both localized surface plasmons (LSPs) excited in the metallic nanoparticles and surface plasmon polaritons (SPPs) propagating at the metal/semiconductor interfaces, have been so far extensively investigated with great interests in designing thin film solar cells. In this chapter, plasmonic structures to improve the performance of thin film solar cell are reviewed according to their positions of the nanostructures, which can be divided into at least three ways: directly on top of thin film solar cell, embedded at the bottom or middle of the optical absorber layer, and hybrid of metallic nanostructures with nanowire of optical absorber layer

Enhanced Methanol Oxidation and CO Tolerance Using CeO\u3csub\u3e2\u3c/sub\u3e-Added Eggshell Membrane-Templated Pd Network Electrocatalyst

Macroporous Pd and CeO2-added Pd network catalysts have been synthesized using eggshell membrane (ESM) as a template for enhanced methanol oxidation and CO tolerance. The microstructural characterization revealed a hierarchically ordered macroporous network of Pd reproducing the fibrous structure of ESM for a Pd-only catalyst, and a flower-like CeO2-decorated Pd morphological architecture for the CeO2-added Pd catalyst synthesized by a precipitation method. XRD patterns indicated Pd and CeO2 phases with good crystallinity. The cyclic voltammetry studies showed an enhanced electrocatalytic activity for methanol oxidation in acidic aqueous medium. Because of the preferential formation of Ce–CO bonds over Pd–CO bonds, the incorporation of CeO2 into Pd-based catalysts results in an increased CO tolerance, making it a robust catalyst for methanol oxidation in direct methanol fuel cells

Physically Plausible Animation of Human Upper Body from a Single Image

We present a new method for generating controllable, dynamically responsive, and photorealistic human animations. Given an image of a person, our system allows the user to generate Physically plausible Upper Body Animation (PUBA) using interaction in the image space, such as dragging their hand to various locations. We formulate a reinforcement learning problem to train a dynamic model that predicts the person's next 2D state (i.e., keypoints on the image) conditioned on a 3D action (i.e., joint torque), and a policy that outputs optimal actions to control the person to achieve desired goals. The dynamic model leverages the expressiveness of 3D simulation and the visual realism of 2D videos. PUBA generates 2D keypoint sequences that achieve task goals while being responsive to forceful perturbation. The sequences of keypoints are then translated by a pose-to-image generator to produce the final photorealistic video.Comment: WACV 202

Metamorphic Testing Integer Overflow Faults of Mission Critical Program: A Case Study

For mission critical programs, integer overflow is one of the most dangerous faults. Different testing methods provide several effective ways to detect the defect. However, it is hard to validate the testing outputs, because the oracle of testing is not always available or too expensive to get, unless the program throws an exception obviously. In the present study, the authors conduct a case study, where the authors apply a metamorphic testing (MT) method to detect the integer overflow defect and alleviate the oracle problem in testing critical program of Traffic Collision Avoidance System (TCAS). Experimental results show that, in revealing typical integer mutations, compared with traditional safety property testing method, MT with a novel symbolic metamorphic relation is more effective than the traditional method in some cases.</jats:p

A targeted proteomics screen reveals serum and synovial fluid proteomic signature in patients with gout

ObjectiveTo characterize the inflammatory proteome in both serum and synovial fluid (SF) of patients with gout, in comparison to healthy controls and individuals with osteoarthritis (OA), by utilizing a high-quality, high-throughput proteomic analysis technique.MethodsUsing the Olink Target 48 Inflammation panel, we measured serum concentrations of 45 inflammatory proteins in gout, OA, and healthy controls. We analyzed protein levels in SF samples from gout and OA, performed ROC curve analyses to identify diagnostic biomarkers, evaluate efficacy, and set cut-off values. Additionally, A protein-protein interaction (PPI) network was used to study protein relationships and significance.ResultsWe have delineated the proteomic landscape of gout and identified 20 highly differentially expressed proteins (DEPs) in the serum of gout patients in comparison to that of healthy controls, which included VEGF-A, MMP-1, TGF-α, and OSM with corresponding area under the curve (AUC) values of 0.95, 0.95, 0.92, and 0.91 respectively. For the analysis of synovial fluid, 6 proteins were found to be elevated in gout in contrast to osteoarthritis (OA), among which IP-10, VEGF-A, IL-8, and MIP-3β had corresponding AUC values of 0.78, 0.78, 0.76, and 0.75 respectively. The protein-protein interaction (PPI) network analysis identified significantly prominent pathways in gout.ConclusionThis research marks a significant advancement in elucidating the inflammatory profile present in the serum and synovial fluid of individuals suffering from gout. Our discoveries have identified several novel proteins in both serum and synovial fluid that are potential biomarkers for diagnostic purposes and are believed to have critical roles as pathogenic factors in the pathophysiology of gout

SM3^3: Self-Supervised Multi-task Modeling with Multi-view 2D Images for Articulated Objects

Reconstructing real-world objects and estimating their movable joint structures are pivotal technologies within the field of robotics. Previous research has predominantly focused on supervised approaches, relying on extensively annotated datasets to model articulated objects within limited categories. However, this approach falls short of effectively addressing the diversity present in the real world. To tackle this issue, we propose a self-supervised interaction perception method, referred to as SM$^3$, which leverages multi-view RGB images captured before and after interaction to model articulated objects, identify the movable parts, and infer the parameters of their rotating joints. By constructing 3D geometries and textures from the captured 2D images, SM$^3$ achieves integrated optimization of movable part and joint parameters during the reconstruction process, obviating the need for annotations. Furthermore, we introduce the MMArt dataset, an extension of PartNet-Mobility, encompassing multi-view and multi-modal data of articulated objects spanning diverse categories. Evaluations demonstrate that SM$^3$ surpasses existing benchmarks across various categories and objects, while its adaptability in real-world scenarios has been thoroughly validated

DTF-Net: Category-Level Pose Estimation and Shape Reconstruction via Deformable Template Field

Estimating 6D poses and reconstructing 3D shapes of objects in open-world scenes from RGB-depth image pairs is challenging. Many existing methods rely on learning geometric features that correspond to specific templates while disregarding shape variations and pose differences among objects in the same category. As a result, these methods underperform when handling unseen object instances in complex environments. In contrast, other approaches aim to achieve category-level estimation and reconstruction by leveraging normalized geometric structure priors, but the static prior-based reconstruction struggles with substantial intra-class variations. To solve these problems, we propose the DTF-Net, a novel framework for pose estimation and shape reconstruction based on implicit neural fields of object categories. In DTF-Net, we design a deformable template field to represent the general category-wise shape latent features and intra-category geometric deformation features. The field establishes continuous shape correspondences, deforming the category template into arbitrary observed instances to accomplish shape reconstruction. We introduce a pose regression module that shares the deformation features and template codes from the fields to estimate the accurate 6D pose of each object in the scene. We integrate a multi-modal representation extraction module to extract object features and semantic masks, enabling end-to-end inference. Moreover, during training, we implement a shape-invariant training strategy and a viewpoint sampling method to further enhance the model's capability to extract object pose features. Extensive experiments on the REAL275 and CAMERA25 datasets demonstrate the superiority of DTF-Net in both synthetic and real scenes. Furthermore, we show that DTF-Net effectively supports grasping tasks with a real robot arm.Comment: The first two authors are with equal contributions. Paper accepted by ACM MM 202

An exploration of distinguishing subjective cognitive decline and mild cognitive impairment based on resting-state prefrontal functional connectivity assessed by functional near-infrared spectroscopy

PurposeFunctional near-infrared spectroscopy (fNIRS) has shown feasibility in evaluating cognitive function and brain functional connectivity (FC). Therefore, this fNIRS study aimed to develop a screening method for subjective cognitive decline (SCD) and mild cognitive impairment (MCI) based on resting-state prefrontal FC and neuropsychological tests via machine learning.MethodsFunctional connectivity data measured by fNIRS were collected from 55 normal controls (NCs), 80 SCD individuals, and 111 MCI individuals. Differences in FC were analyzed among the groups. FC strength and neuropsychological test scores were extracted as features to build classification and predictive models through machine learning. Model performance was assessed based on accuracy, specificity, sensitivity, and area under the curve (AUC) with 95% confidence interval (CI) values.ResultsStatistical analysis revealed a trend toward compensatory enhanced prefrontal FC in SCD and MCI individuals. The models showed a satisfactory ability to differentiate among the three groups, especially those employing linear discriminant analysis, logistic regression, and support vector machine. Accuracies of 94.9% for MCI vs. NC, 79.4% for MCI vs. SCD, and 77.0% for SCD vs. NC were achieved, and the highest AUC values were 97.5% (95% CI: 95.0%–100.0%) for MCI vs. NC, 83.7% (95% CI: 77.5%–89.8%) for MCI vs. SCD, and 80.6% (95% CI: 72.7%–88.4%) for SCD vs. NC.ConclusionThe developed screening method based on resting-state prefrontal FC measured by fNIRS and machine learning may help predict early-stage cognitive impairment