The active control of macro-fiber composite over harmonic vibration of arc-plate structures

This paper offers an actuating equation for MFC arc-plate structures to obtain MFC’s accurate actuating force and actuating bending moment to are-plate structures and increase MFC control effect on vibration. This paper proposes the P1 type MFC arc-plate actuating equation which considers the recombination action of MFC and controlled structure, and arc-plate curvature influence on MFC, obtaining the MFC actuating force and actuating bending moment for arc-plate structures. The vibration control experiment of MFC arc-plate structures is performed, and the deviation between finite element simulation results adopting this equation and the experimental data is less than 8.5 %. The research shows that the P1 type MFC actuating equation deduced in this paper is correct and fully applicative to the MFC vibration control simulation to arc-plate structures

Impact and Challenges of Intelligent IoT in Meteorological Science

The abundant data in meteorological science has facilitated applying big data techniques. The data collection was achieved by researchers using different atmospheric sounding methods in the past few decades. Compared with traditional methods, such as statistical forecast approaches and numerical weather prediction, intelligent Internet of Things (IoT) technologies have attracted extensive attention in meteorological science due to their significant advantages in data processing and analysis. In addition, extreme weather events and meteorological disasters have occurred frequently around the world in recent years. Against this background, this article aims to introduce the application of intelligent IoT technologies in meteorological science and elaborate the encountered open problems as well as the challenges in the future. Along with the introduction of intelligent IoT, a comprehensive review of current studies on meteorological observation, forecast, and services with intelligent IoT is provided. Correspondingly, the impact of intelligent IoT on meteorological businesses is analyzed. Finally, as for the meteorological operations in IoT based on artificial intelligence (AI), some open research issues, countermeasures and future potential research directions are put forward

Deformable Model-Driven Neural Rendering for High-Fidelity 3D Reconstruction of Human Heads Under Low-View Settings

Reconstructing 3D human heads in low-view settings presents technical challenges, mainly due to the pronounced risk of overfitting with limited views and high-frequency signals. To address this, we propose geometry decomposition and adopt a two-stage, coarse-to-fine training strategy, allowing for progressively capturing high-frequency geometric details. We represent 3D human heads using the zero level-set of a combined signed distance field, comprising a smooth template, a non-rigid deformation, and a high-frequency displacement field. The template captures features that are independent of both identity and expression and is co-trained with the deformation network across multiple individuals with sparse and randomly selected views. The displacement field, capturing individual-specific details, undergoes separate training for each person. Our network training does not require 3D supervision or object masks. Experimental results demonstrate the effectiveness and robustness of our geometry decomposition and two-stage training strategy. Our method outperforms existing neural rendering approaches in terms of reconstruction accuracy and novel view synthesis under low-view settings. Moreover, the pre-trained template serves a good initialization for our model when encountering unseen individuals.Comment: Accepted by ICCV2023. Visit our project page at https://github.com/xubaixinxbx/3dhead

Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders

Pre-trained sentence representations are crucial for identifying significant sentences in unsupervised document extractive summarization. However, the traditional two-step paradigm of pre-training and sentence-ranking, creates a gap due to differing optimization objectives. To address this issue, we argue that utilizing pre-trained embeddings derived from a process specifically designed to optimize cohensive and distinctive sentence representations helps rank significant sentences. To do so, we propose a novel graph pre-training auto-encoder to obtain sentence embeddings by explicitly modelling intra-sentential distinctive features and inter-sentential cohesive features through sentence-word bipartite graphs. These pre-trained sentence representations are then utilized in a graph-based ranking algorithm for unsupervised summarization. Our method produces predominant performance for unsupervised summarization frameworks by providing summary-worthy sentence representations. It surpasses heavy BERT- or RoBERTa-based sentence representations in downstream tasks.Comment: Accepted by the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023

Clomazone impact on fungal network complexity and stability

IntroductionSoil fungal network composition and stability are important for soil functions, but there is less understanding of the impact of clomazone on network complexity and stability.MethodsIn this work, two agricultural soils were used to investigate the impact of clomazone on fungal network complexity, composition, and stability. The two soils were treated with clomazone solution (0, 0.8, 8, and 80  mg kg−1) and kept in an incubator.Results and DiscussionUnder the influence of clomazone, the fungal network nodes were decreased by 12–42; however, the average degree was increased by 0.169–1.468 and fungal network density was increased by 0.003–0.054. The keystone nodes were significantly changed after clomazone treatment. Network composition was also impacted. Specifically, compared with control and clomazone treatments in both soils, the shared edges were fewer than 54 in all comparisons, and network dissimilarity was 0.97–0.98. These results suggested that fungal network composition was significantly impacted. The network robustness was increased by 0.0018–0.0209, and vulnerability was decreased by 0.00018–0.00059 in both soils, which indicated that fungal network stability was increased by clomazone. In addition, the functions of network communities were also changed in both soils. These results indicated that clomazone could significantly impact soil fungal networks

Traffic-Aware Multi-Camera Tracking of Vehicles Based on ReID and Camera Link Model

Multi-target multi-camera tracking (MTMCT), i.e., tracking multiple targets across multiple cameras, is a crucial technique for smart city applications. In this paper, we propose an effective and reliable MTMCT framework for vehicles, which consists of a traffic-aware single camera tracking (TSCT) algorithm, a trajectory-based camera link model (CLM) for vehicle re-identification (ReID), and a hierarchical clustering algorithm to obtain the cross camera vehicle trajectories. First, the TSCT, which jointly considers vehicle appearance, geometric features, and some common traffic scenarios, is proposed to track the vehicles in each camera separately. Second, the trajectory-based CLM is adopted to facilitate the relationship between each pair of adjacently connected cameras and add spatio-temporal constraints for the subsequent vehicle ReID with temporal attention. Third, the hierarchical clustering algorithm is used to merge the vehicle trajectories among all the cameras to obtain the final MTMCT results. Our proposed MTMCT is evaluated on the CityFlow dataset and achieves a new state-of-the-art performance with IDF1 of 74.93%.Comment: Accepted by ACM International Conference on Multimedia 202

Selective ion removal by capacitive deionization (CDI)-based technologies

Severe freshwater shortages and global pollution make selective removal of target ions from solutions of great significance for water purification and resource recovery. Capacitive deionization (CDI) removes charged ions and molecules from water by applying a low applied electric field across the electrodes and has received much attention due to its lower energy consumption and sustainability. Its application field has been expanding in the past few years. In this paper, we report an overview of the current status of selective ion removal in CDI. This paper also discusses the prospects of selective CDI, including desalination, water softening, heavy metal removal and recovery, nutrient removal, and other common ion removal techniques. The insights from this review will inform the implementation of CDI technology

Stability of layer-by-layer nanofiltration membranes in highly saline streams

Layer-by-layer (LBL) assembly is an essential method for the preparation of nanofiltration (NF) membranes, offering tunable charge and pore size, high water permeability, and good anti-fouling properties, making them highly suitable for resource recovery, seawater desalination, and other fields. Despite their advantages, LBL NF membranes suffer from salinity instability, limiting their use in highly saline streams. This perspective review provides a summary of the fundamental physical and chemical principles of LBL assembly related to the salinity stability of LBL NF membranes. We critically analyze the driving force of LBL assembly, the binding strength of polyelectrolyte (PE) pairs, and the overcompensation of LBL membranes. We also discuss the factors affecting overcompensation level with respect to two different time scales. Furthermore, we examine the relationship between overcompensation level and salinity stability of LBL membranes, considering physical (osmotic pressure) and chemical (Le Chatelier's principle) aspects. Our analysis demonstrates that the salinity stability of LBL NF membranes in highly saline solutions can be improved by selecting PEs with stronger binding strength, increasing the overcompensation level, and chemical crosslinking. These methods not only enhance the salinity stability of LBL NF membranes but also offer greater potential for their future application in highly saline streams

mirAct: a web tool for evaluating microRNA activity based on gene expression data

MicroRNAs (miRNAs) are critical regulators in the complex cellular networks. The mirAct web server (http://sysbio.ustc.edu.cn/software/mirAct) is a tool designed to investigate miRNA activity based on gene-expression data by using the negative regulation relationship between miRNAs and their target genes. mirAct supports multiple-class data and enables clustering analysis based on computationally determined miRNA activity. Here, we describe the framework of mirAct, demonstrate its performance by comparing with other similar programs and exemplify its applications using case studies