A5102026552- Heavy metals in environment
- Meteorological Phenomena and Simulations
- Adversarial Robustness in Machine Learning
- Face and Expression Recognition
- Privacy-Preserving Technologies in Data
- Computer Graphics and Visualization Techniques
- Precipitation Measurement and Analysis
- Traffic Prediction and Management Techniques
- Urban and Freight Transport Logistics
- Imbalanced Data Classification Techniques
- Face recognition and analysis
- Microbial Community Ecology and Physiology
- Advanced Neural Network Applications
- Domain Adaptation and Few-Shot Learning
- Flood Risk Assessment and Management
- Plant Stress Responses and Tolerance
- Data Management and Algorithms
- Medical Image Segmentation Techniques
- Advanced Optical Sensing Technologies
- Advanced MRI Techniques and Applications
- Research Data Management Practices
- Advanced Clustering Algorithms Research
- Metaheuristic Optimization Algorithms Research
- Human Mobility and Location-Based Analysis
- Chemical and Environmental Engineering Research
Changzhou University
2023-2024
Wuhan University
2024
Anhui Normal University
2023-2024
Shanghai Public Security Bureau
2024
Tianjin University
2023
Shandong Jianzhu University
2023
Xidian University
2023
Tianjin Normal University
2022
Fudan University
2019-2021
Chengdu University of Information Technology
2021
The perennial ryegrass Lolium perenne can be used in conjunction with cadmium (Cd)-tolerant bacteria such as Cdq4–2 (Enterococcus spp.) for bioremediation of Cd-contaminated soil. In this study, a theoretical basis was provided to increase the efficiency L. remediation soil using microorganisms maintain stability microbiome. experimental design involved three treatment groups: CK (soil without Cd addition) control, 20 mg·kg–1 soil, and + Cdq4–2, all planted perenne. collected on day 60...
Image ordinal estimation is to predict the label of a given image, which can be categorized as an regression problem. Recent methods formulate problem series binary classification problems. Such cannot ensure that global relationship preserved since relationships among different classifiers are neglected. We propose novel approach, termed Convolutional Ordinal Regression Forest or CORF, for image estimation, integrate and differentiable decision trees with convolutional neural network...
Abstract Lightning matters to human life and natural fires so much that monitoring predicting lightning are highly important. A simple proxy for climatic cloud‐to‐ground (CG) density is evaluated defined as the product of convective available potential energy (CAPE) precipitation rate using data from 2005 2017 in Sichuan Southwest China. CAPE times (CP) relates monthly distribution magnitude negative basin region more closely, while CP describes positive plateau appropriately. Except...
In machine learning and data analysis, dimensionality reduction high-dimensional visualization can be accomplished by manifold using a t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm. We significantly improve this scheme introducing preprocessing strategy for the t-SNE our preprocessing, we exploit Laplacian eigenmaps to reduce first, which aggregate each cluster Kullback–Leibler divergence (KLD) remarkably. Moreover, k-nearest-neighbor (KNN) algorithm is also involved in...
Cadmium (Cd) pollution has been rapidly increasing due to the global rise in industries. Cd not only harms ecological environment but also endangers human health through food chain and drinking water. Therefore, remediation of Cd-polluted soil is an imminent issue. In this work, ryegrass a strain Cd-tolerant bacterium were used investigate impact inoculated bacteria on physiology biochemistry enrichment contaminated with different concentrations (4 20 mg/kg). The results showed that...
The fraudulent website image is a vital information carrier for telecom fraud. efficient and precise recognition of images critical to combating dealing with websites. Current research on websites mainly carried out at the level feature extraction similarity study, which have such disadvantages as difficulty in obtaining data, insufficient analysis, single identification types. This study develops model based entropy method leader decision Inception-v3 transfer learning address these...
The diagnosis of bearing faults is an important guarantee for the healthy operation mechanical equipment. Due to time-varying working conditions equipment, it necessary achieve fault under conditions. However, superposition two-dimensional speed and acceleration brings great difficulties via data-driven models. long short-term memory (LSTM) model based on infinitesimal method effective solve this problem, but its performance still has certain limitations. On basis, article proposes a...
Recorded provenance facilitates reproducible science. Provenance metadata can help determine how data were possibly transformed, processed, and derived from original sources. While is crucial for verification validation, there remains the issue of granularity - detail at which must be provided to a user, especially conducting When are reproduced successfully need detailed minimal an essence recorded suffices. However, when not correctly users want quickly drill down into fine-grained...
With broad applications in various public services like aviation management and urban disaster warning, numerical precipitation prediction plays a crucial role weather forecast. However, constrained by the limitation of observation conventional meteorological models, predictions are often highly biased. To correct this bias, classical correction methods heavily depend on profound experts who have knowledge aerodynamics, thermodynamics meteorology. As can be influenced countless factors,...
In order to reduce the energy consumption of cellular network and build a green communication network, it is very necessary accurately predict changes in traffic load base station. this paper, we propose novel station forecasting model, named LSTCN, which combining Long Short-Term Memory Network (LSTM) Time Convolutional (TCN). We conducted experiments on real data experimental results show that model proposed paper has higher prediction accuracy than previous methods.
Numerical precipitation prediction plays a crucial role in weather forecasting and has broad applications public services including aviation management urban disaster early-warning systems. However, numerical (NWP) models are often constrained by systematic bias due to coarse spatial resolution, lack of parameterizations, limitations observation conventional meteorological models, sample size long-tail distribution. To address these issues, we present data-driven deep learning model, named...
Split Learning (SL) is a distributed learning framework renowned for its privacy-preserving features and minimal computational requirements. Previous research consistently highlights the potential privacy breaches in SL systems by server adversaries reconstructing training data. However, these studies often rely on strong assumptions or compromise system utility to enhance attack performance. This paper introduces new semi-honest Data Reconstruction Attack SL, named Feature-Oriented (FORA)....
Numerical Weather Prediction (NWP) can reduce human suffering by predicting disastrous precipitation in time. A commonly-used NWP the world is European Centre for medium-range weather forecasts (EC). However, it necessary to correct EC forecast through Bias Correcting on Precipitation (BCoP) since we still have not fully understood mechanism of precipitation, making often some biases. The existing BCoPs suffers from limited prior data and fixed Spatio-Temporal (ST) scale. We thus propose an...