Painting with Turbulence

Inspired by a study that identified a strong similarity between Vincent van Gogh\u27s and Jackson Pollock\u27s painting techniques, this thesis explores the interplay between science and art, specifically the unpredictable behaviors in turbulent flows and aesthetic concepts in painting. It utilizes data from a GPU-based air flow simulation, and presents a framework for artists to visualize the chaotic property changes in turbulent flows and create paintings with turbulence data. While the creation of individual brushstrokes is procedural and driven by simulation, artists are able to exercise their aesthetic judgments at various stages during a painting creation. A short animation demonstrates the potential results from this framework

A Continual Deepfake Detection Benchmark: Dataset, Methods, and Essentials

There have been emerging a number of benchmarks and techniques for the detection of deepfakes. However, very few works study the detection of incrementally appearing deepfakes in the real-world scenarios. To simulate the wild scenes, this paper suggests a continual deepfake detection benchmark (CDDB) over a new collection of deepfakes from both known and unknown generative models. The suggested CDDB designs multiple evaluations on the detection over easy, hard, and long sequence of deepfake tasks, with a set of appropriate measures. In addition, we exploit multiple approaches to adapt multiclass incremental learning methods, commonly used in the continual visual recognition, to the continual deepfake detection problem. We evaluate several methods, including the adapted ones, on the proposed CDDB. Within the proposed benchmark, we explore some commonly known essentials of standard continual learning. Our study provides new insights on these essentials in the context of continual deepfake detection. The suggested CDDB is clearly more challenging than the existing benchmarks, which thus offers a suitable evaluation avenue to the future research. Our benchmark dataset and the source code will be made publicly available.Comment: some typos are correcte

Research progress of novel bio-denitrification technology in deep wastewater treatment

Excessive nitrogen emissions are a major contributor to water pollution, posing a threat not only to the environment but also to human health. Therefore, achieving deep denitrification of wastewater is of significant importance. Traditional biological denitrification methods have some drawbacks, including long processing times, substantial land requirements, high energy consumption, and high investment and operational costs. In contrast, the novel bio-denitrification technology reduces the traditional processing time and lowers operational and maintenance costs while improving denitrification efficiency. This technology falls within the category of environmentally friendly, low-energy deep denitrification methods. This paper introduces several innovative bio-denitrification technologies and their combinations, conducts a comparative analysis of their denitrification efficiency across various wastewater types, and concludes by outlining the future prospects for the development of these novel bio-denitrification technologies

A convolutional neural network with equal-resolution enhancement and gradual attention of features for tiny target detection

The detection of tiny targets on the surface with high efficiency and accuracy is significant for the current intelligent manufacturing. Visual inspection methods based on deep learning are widely utilized to detect tiny objects. However, the tiny objects appear less distinct, less wide, and less area occupied in the image. At the same time, there is a lot of object-like noise, which further increases the difficulty of detecting tiny objects. In response to the challenges brought by the complexity of the detection environment, this paper proposes a detection network architecture that combines the enhancement of pixel-level features at equal resolution and the introduction of full-scale features based on attention. The model utilizes the subtle differences between the tiny target and the background and the semantic information of the tiny target outline to enhance the features of the tiny target while significantly reducing its loss in the equal-resolution feature layer. Additionally, a gradual attention mechanism is proposed to guide the network to pay attention to tiny objects features on the full-scale feature layer. The performance of this network architecture is validated on a real dataset. Experiments show that the model exhibits superior performance and outperforms existing resNet50, DenseNet, Racki-Net, and SegDecNet in detecting tiny objects

Deep learning for DNase I hypersensitive sites identification

Abstract Background The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources available on line including experimental datasets and computational tools, the complex language of DHSs remains incompletely understood. Methods Here, we address this challenge using an approach based on a state-of-the-art machine learning method. We present a novel convolutional neural network (CNN) which combined Inception like networks with a gating mechanism for the response of multiple patterns and longterm association in DNA sequences to predict multi-scale DHSs in Arabidopsis, rice and Homo sapiens. Results Our method obtains 0.961 area under curve (AUC) on Arabidopsis, 0.969 AUC on rice and 0.918 AUC on Homo sapiens. Conclusions Our method provides an efficient and accurate way to identify multi-scale DHSs sequences by deep learning

The Effect of Artificial Field Margins on Epigeic Arthropod Functional Groups within Adjacent Arable Land of Northeast China

Providing food security to meet the growing human demand while improving the biodiversity of arable land is a global challenge. Although semi-natural field margins are known to enhance biodiversity in arable land systems globally, the role that abundant artificial field margins play in maintaining epigeic arthropod diversity within arable land remains unclear. Here, we compared epigeic arthropods within adjacent arable land with an artificial field margin (paved and dirt roads) and a semi-natural field margin (ditch, woodland, or grassland), as well as vegetation community characteristics at a field scale for identifying the ecological effects of different field margin types. Our results indicated the following: (i) Compared with semi-natural field margins, there is less epigeic arthropod diversity and less stable ecological networks within adjacent arable land with artificial field margins, with more herbivores within adjacent arable land with artificial field margins and more natural enemies within adjacent arable land with semi-natural field margins. (ii) Arable land adjacent to a dirt road (DR) maintained more resilient ecological networks than that adjacent to a paved road (PR), and there are more flowering plants at DRs, which attracts natural enemies, whereas Orthoptera is more active at PRs with abundant weeds. (iii) The main factors affecting epigeic arthropod functional groups were the tree layer cover (TC), herb layer abundance (HA), and herb layer height (HH) of the artificial and semi-natural field margins. We concluded that increasing the number of flowering plants and removing noxious weeds can eliminate negative effects on epigeic arthropod functional groups within adjacent arable land with artificial field margins. Delineating a certain percentage of vegetation strips to be a buffer zone in artificial field margins or creating a suitable vegetation community in semi-natural field margins can maintain and protect natural enemies and strengthen the ecological network stability between functional groups

Urban Vulnerability Analysis Based on Micro-Geographic Unit with Multi-Source Data—Case Study in Urumqi, Xinjiang, China

This study introduces a novel approach to urban public safety analysis inspired by a streetscape analysis commonly applied in urban criminology, leveraging the concept of micro-geographical units to account for urban spatial heterogeneity. Recognizing the intrinsic uniformity within these smaller, distinct environments of a city, the methodology represents a shift from large-scale regional studies to a more localized and precise exploration of urban vulnerability. The research objectives focus on three key aspects: first, establishing a framework for identifying and dividing cities into micro-geographical units; second, determining the type and nature of data that effectively illustrate the potential vulnerability of these units; and third, developing a robust and reliable evaluation index system for urban vulnerability. We apply this innovative method to Urumqi’s Tianshan District in Xinjiang, China, resulting in the formation of 30 distinct micro-geographical units. Using WorldView-2 remote sensing imagery and the object-oriented classification method, we extract and evaluate features related to vehicles, roads, buildings, and vegetation for each unit. This information feeds into the construction of a comprehensive index, used to assess public security vulnerability at a granular level. The findings from our study reveal a wide spectrum of vulnerability levels across the 30 units. Notably, units X1 (Er Dao Bridge) and X7 (Gold Coin Mountain International Plaza) showed high vulnerability due to factors such as a lack of green spaces, poor urban planning, dense building development, and traffic issues. Our research validates the utility and effectiveness of the micro-geographical unit concept in assessing urban vulnerability, thereby introducing a new paradigm in urban safety studies. This micro-geographical approach, combined with a multi-source data strategy involving high-resolution remote sensing and field survey data, offers a robust and comprehensive tool for urban vulnerability assessment. Moreover, the urban vulnerability evaluation index developed through this study provides a promising model for future urban safety research across different cities

Credit Risk Diffusion in Supply Chain Finance: A Complex Networks Perspective

The diffusion of credit risk in a supply chain finance network can cause serious consequences. Using the “1 + M + N„ complex network model with BA scale-free characteristics, this paper studies the credit risk diffusion in a supply chain finance network, where the credit risk diffusion process is simulated by the SIS epidemic model. We examine the impacts of various key factors, including the general financing ratio, cure time, network structure, and network scale on the credit risk diffusion process. It is found that credit risk diffusion rarely occurs in a network with a low average degree. When the average degree of the network increases, the occurrence of the credit risk diffusion becomes more frequent. Besides, the degree of the initially infected nodes with credit risk does not affect the density of the infected nodes in the steady state, while a higher degree of the cure nodes helps restrain the diffusion of credit risk in the supply chain finance network. Finally, the simulation result based on the supply chain finance network with a core node indicates that the diffusion of the credit risk diffusion in sparse supply chain finance networks with low average degrees is unstable. The results provide better understandings on the credit risk diffusion in supply chain finance networks

Exploring the Potential of BERT-BiLSTM-CRF and the Attention Mechanism in Building a Tourism Knowledge Graph

As an important infrastructure in the era of big data, the knowledge graph can integrate and manage data resources. Therefore, the construction of tourism knowledge graphs with wide coverage and of high quality in terms of information from the perspective of tourists’ needs is an effective solution to the problem of information clutter in the tourism field. This paper first analyzes the current state of domestic and international research on constructing tourism knowledge graphs and highlights the problems associated with constructing knowledge graphs, which are that they are time-consuming, laborious and have a single function. In order to make up for these shortcomings, this paper proposes a set of systematic methods to build a tourism knowledge graph. This method integrates the BiLSTM and BERT models and combines these with the attention mechanism. The steps of this methods are as follows: First, data preprocessing is carried out by word segmentation and removing stop words; second, after extracting the features and vectorization of the words, the cosine similarity method is used to classify the tourism text, with the text classification based on naive Bayes being compared through experiments; third, the popular tourism words are obtained through the popularity analysis model. This paper proposes two models to obtain popular words: One is a multi-dimensional tourism product popularity analysis model based on principal component analysis; the other is a popularity analysis model based on emotion analysis; fourth, this paper uses the BiLSTM-CRF model to identify entities and the cosine similarity method to predict the relationship between entities so as to extract high-quality tourism knowledge triplets. In order to improve the effect of entity recognition, this paper proposes entity recognition based on the BiLSTM-LPT and BiLSTM-Hanlp models. The experimental results show that the model can effectively improve the efficiency of entity recognition; finally, a high-quality tourism knowledge was imported into the Neo4j graphic database to build a tourism knowledge graph

Research of Wafer Level Bonding Process Based on Cu–Sn Eutectic

In 3D-system packaging technologies, eutectic bonding is the key technology of multilayer chip stacking and vertical interconnection. Optimized from the aspects of the thickness of the electroplated metal layer, the pretreatment of the wafer surface removes the oxide layer, the mutual alignment between the wafers, the temperature of the wafer bonding, the uniformity of pressure and the deviation of the bonding process. Under the pretreatment conditions of plasma treatment and citric acid cleaning, no oxide layer was obtained on the metal surface. Cu/Sn bumps bonded under the condition of 0.135 Mpa, temperature of 280 °C, Sn thickness of 3–4 μm and a Cu-thickness of five micrometers. Bonded push crystal strength ≥18 kg/cm2, the average contact resistance of the bonding interface is about 3.35 mΩ, and the bonding yield is 100%. All performance indicators meet and exceed the industry standards