Experimental study of mechanical property for prestressed rubber bearing

To overcome the shortages of existing Rubber Bearings (RBs), an innovative type of isolator, named as Prestressed Rubber Bearing (PRB), is presented in this paper. Base on conventional laminated Rubber Bearing (RB), PRB is developed by increasing the thickness of rubber layers, setting vertical ducts and installing prestress tendons. Through the vertical and horizontal monotonic loading test, the vertical and horizontal stiffness of PRBs are investigated. The empirical formulas for stiffness are proposed. Moreover, the hysteresis behavior and the energy dissipation capacity of PRBs are studied by reversed cyclic loading test. The results show that PRBs not only have the horizontal isolating capacity as conventional RBs, but also have the capacity of horizontal displacement-limitation and improved capacity of energy dissipation

AspectMMKG: A Multi-modal Knowledge Graph with Aspect-aware Entities

Multi-modal knowledge graphs (MMKGs) combine different modal data (e.g., text and image) for a comprehensive understanding of entities. Despite the recent progress of large-scale MMKGs, existing MMKGs neglect the multi-aspect nature of entities, limiting the ability to comprehend entities from various perspectives. In this paper, we construct AspectMMKG, the first MMKG with aspect-related images by matching images to different entity aspects. Specifically, we collect aspect-related images from a knowledge base, and further extract aspect-related sentences from the knowledge base as queries to retrieve a large number of aspect-related images via an online image search engine. Finally, AspectMMKG contains 2,380 entities, 18,139 entity aspects, and 645,383 aspect-related images. We demonstrate the usability of AspectMMKG in entity aspect linking (EAL) downstream task and show that previous EAL models achieve a new state-of-the-art performance with the help of AspectMMKG. To facilitate the research on aspect-related MMKG, we further propose an aspect-related image retrieval (AIR) model, that aims to correct and expand aspect-related images in AspectMMKG. We train an AIR model to learn the relationship between entity image and entity aspect-related images by incorporating entity image, aspect, and aspect image information. Experimental results indicate that the AIR model could retrieve suitable images for a given entity w.r.t different aspects.Comment: Accepted by CIKM 202

Understanding Translationese in Cross-Lingual Summarization

Given a document in a source language, cross-lingual summarization (CLS) aims at generating a concise summary in a different target language. Unlike monolingual summarization (MS), naturally occurring source-language documents paired with target-language summaries are rare. To collect large-scale CLS data, existing datasets typically involve translation in their creation. However, the translated text is distinguished from the text originally written in that language, i.e., translationese. In this paper, we first confirm that different approaches of constructing CLS datasets will lead to different degrees of translationese. Then we systematically investigate how translationese affects CLS model evaluation and performance when it appears in source documents or target summaries. In detail, we find that (1) the translationese in documents or summaries of test sets might lead to the discrepancy between human judgment and automatic evaluation; (2) the translationese in training sets would harm model performance in real-world applications; (3) though machine-translated documents involve translationese, they are very useful for building CLS systems on low-resource languages under specific training strategies. Lastly, we give suggestions for future CLS research including dataset and model developments. We hope that our work could let researchers notice the phenomenon of translationese in CLS and take it into account in the future.Comment: Accepted to the Findings of EMNLP 202

Analytical and experimental investigation on eigenfrequency-based damage diagnosis of cantilever beam

This paper presents two eigenfrequency-based damage diagnosis methods in a cantilever beam. The analytical relationship has been established between the eigenfrequency and damage parameters, including relative damage location and severity. On the premise that pre-damaged eigenfrequencies are known, a diagnosis algorithm without requirement of material properties is proposed based on change ratios of the first three eigenfrequencies. If pre-damaged eigenfrequencies are unfeasible to be acquired, a three-contour method based on only post-damaged eigenfrequencies is introduced to estimate damage parameters. The uniqueness of solution is discussed. Both the numerical simulation by the finite element method and the experiment on real beams are conducted and result in a good agreement between actual damage parameters and calculated values by using the proposed methods

Rethinking Normalization Methods in Federated Learning

Federated learning (FL) is a popular distributed learning framework that can reduce privacy risks by not explicitly sharing private data. In this work, we explicitly uncover external covariate shift problem in FL, which is caused by the independent local training processes on different devices. We demonstrate that external covariate shifts will lead to the obliteration of some devices' contributions to the global model. Further, we show that normalization layers are indispensable in FL since their inherited properties can alleviate the problem of obliterating some devices' contributions. However, recent works have shown that batch normalization, which is one of the standard components in many deep neural networks, will incur accuracy drop of the global model in FL. The essential reason for the failure of batch normalization in FL is poorly studied. We unveil that external covariate shift is the key reason why batch normalization is ineffective in FL. We also show that layer normalization is a better choice in FL which can mitigate the external covariate shift and improve the performance of the global model. We conduct experiments on CIFAR10 under non-IID settings. The results demonstrate that models with layer normalization converge fastest and achieve the best or comparable accuracy for three different model architectures.Comment: Submitted to DistributedML'22 worksho

Learning 6-DoF Fine-grained Grasp Detection Based on Part Affordance Grounding

Robotic grasping is a fundamental ability for a robot to interact with the environment. Current methods focus on how to obtain a stable and reliable grasping pose in object level, while little work has been studied on part (shape)-wise grasping which is related to fine-grained grasping and robotic affordance. Parts can be seen as atomic elements to compose an object, which contains rich semantic knowledge and a strong correlation with affordance. However, lacking a large part-wise 3D robotic dataset limits the development of part representation learning and downstream applications. In this paper, we propose a new large Language-guided SHape grAsPing datasEt (named LangSHAPE) to promote 3D part-level affordance and grasping ability learning. From the perspective of robotic cognition, we design a two-stage fine-grained robotic grasping framework (named LangPartGPD), including a novel 3D part language grounding model and a part-aware grasp pose detection model, in which explicit language input from human or large language models (LLMs) could guide a robot to generate part-level 6-DoF grasping pose with textual explanation. Our method combines the advantages of human-robot collaboration and LLMs' planning ability using explicit language as a symbolic intermediate. To evaluate the effectiveness of our proposed method, we perform 3D part grounding and fine-grained grasp detection experiments on both simulation and physical robot settings, following language instructions across different degrees of textual complexity. Results show our method achieves competitive performance in 3D geometry fine-grained grounding, object affordance inference, and 3D part-aware grasping tasks. Our dataset and code are available on our project website https://sites.google.com/view/lang-shapeComment: 14 pages, 7 figures, 6 table

Comprehensive toxicological, metabolomic, and transcriptomic analysis of the biodegradation and adaptation mechanism by Achromobacter xylosoxidans SL-6 to diuron

Biodegradation was considered a promising and environmentally friendly method for treating environmental pollution caused by diuron. However, the mechanisms of biodegradation of diuron required further research. In this study, the degradation process of diuron by Achromobacter xylosoxidans SL-6 was systematically investigated. The results suggested that the antioxidant system of strain SL-6 was activated by adding diuron, thereby alleviating their oxidative stress response. In addition, degradation product analysis showed that diuron in strain SL-6 was mainly degraded by urea bridge cleavage, dehalogenation, deamination, and ring opening, and finally cis, cis-muconic acid was generated. The combined analysis of metabolomics and transcriptomics revealed the biodegradation and adaptation mechanism of strain SL-6 to diuron. Metabolomics analysis showed that after the strain SL-6 was exposed to diuron, metabolic pathways such as tricarboxylic acid cycle (cis, cis-muconic acid), glutathione metabolism (oxidized glutathione), and urea cycle (arginine) were reprogrammed in the cells. Furthermore, diuron could induce the production of membrane transport proteins in strain SL-6 cells and overexpress antioxidant enzyme genes, finally ultimately promoting the up-regulation of genes encoding amide hydrolases and dioxygenases, which was revealed by transcriptomics studies. This work enriched the biodegradation mechanism of phenylurea herbicides and provided guidance for the removal of diuron residues in the environment and promoting agriculture sustainable development

Comparison of joint status using ultrasound assessments and Haemophilia Joint Health Score 2.1 in children with haemophilia

IntroductionUltrasound (US) has gained popularity in the evaluation of haemophilic joint diseases because it enables the imaging of soft-tissue lesions in the joints and bone-cartilage lesions. We aimed to determine the correlation between US evaluations and clinical assessments performed using HJHS 2.1 and to evaluate their respective characteristics in assessing early haemophilic arthropathy.MethodsA total of 178 joints (32 knees, 85 elbows, and 61 ankles) in 45 haemophilia A patients (median age, 10 years; range, 6–15) were assessed using US and HJHS 2.1. Ultrasonographic scoring was performed in consensus assessments by one imager by using the US scores.ResultsThe total HJHS 2.1 and US scores showed a strong correlation (rS=0.651, P=0.000, CI: 0.553–0.763), with an excellent correlation for the elbows (rS=0.867, P=0.000, CI: 0.709–0.941) and a substantial correlation for the knees (rS=0.681, P=0.000, CI: 0.527–0.797). The correlation for the ankles was relatively moderate (rS=0.518, P=0.000, CI: 0.308–0.705). Nine subjects (15.5%) without abnormalities, as indicated by HJHS 2.1, showed haemophilic arthropathy in US scoring. All nine joints showed moderate (1/9) to severe (8/9) synovial thickening in the ankle (5/9) and elbow joints (4/9). In contrast, 50 joints (50.5%) showed normal US scores and abnormal changes as indicated by HJHS 2.1. S scores correlated well with HJHS 2.1 for overall and individual joints.DiscussionUS could identify some early pathological changes in joints showing normal clinical findings, but still cannot replace the HJHS; however, it can serve as an imaging examination complementing HJHS 2

Integrated assessment of trace elements in a marine ranching area based on multi-species and multi-level biomarkers: a case study in China’s national-level marine ranching demonstration area

The goal of this study was to evaluate the trace element pollution in a marine ranching area in China based on molecular responses (expression of metallothionein and antioxidant enzyme genes), and biochemical biomarkers (metallothionein content, antioxidant enzyme activities, and malonaldehyde level) in four indicator species. We collected samples of two fish (Lateolabrax maculatus and Sebastes schlegelii), one crustacean (Charybdis japonica), and one gastropod (Rapana venosa) from the western Furong Island marine ranching area and from an adjacent area in March 2022 and measured the trace element content in these indicator species as well as in the seawater and sediment. We found that the bioaccumulation characteristics of trace elements and the response patterns of biomarkers were species specific. Moreover, not every biomarker was significantly correlated with environmental trace element content. We then established two biomarker combinations indicative of trace element pollution in seawater and sediment, respectively, based on the correlation between biomarkers and trace element contents. The selected biomarkers were integrated using integrated biomarker response version 2 (IBRv2). IBRv2 values in the studied marine ranching area were lower than those in the adjacent area. Additionally, these values were consistent with the bioaccumulation of trace elements in the indicator species, the integrated trace element pollution index for seawater, and the potential risk index for sediment. These results show that this multi-biomarker and multi-species IBRv2 approach provided a comprehensive diagnosis of trace element pollution in the marine ranching area. Therefore, its application may be beneficial for marine environmental monitoring and management in view of the ecotoxicological impact of pollutants on organisms