Object Detection in Foggy Scenes by Embedding Depth and Reconstruction into Domain Adaptation

Most existing domain adaptation (DA) methods align the features based on the domain feature distributions and ignore aspects related to fog, background and target objects, rendering suboptimal performance. In our DA framework, we retain the depth and background information during the domain feature alignment. A consistency loss between the generated depth and fog transmission map is introduced to strengthen the retention of the depth information in the aligned features. To address false object features potentially generated during the DA process, we propose an encoder-decoder framework to reconstruct the fog-free background image. This reconstruction loss also reinforces the encoder, i.e., our DA backbone, to minimize false object features.Moreover, we involve our target data in training both our DA module and our detection module in a semi-supervised manner, so that our detection module is also exposed to the unlabeled target data, the type of data used in the testing stage. Using these ideas, our method significantly outperforms the state-of-the-art method (47.6 mAP against the 44.3 mAP on the Foggy Cityscapes dataset), and obtains the best performance on multiple real-image public datasets. Code is available at: https://github.com/VIML-CVDL/Object-Detection-in-Foggy-ScenesComment: Accepted by ACC

Chinese industrial air pollution emissions based on the continuous emission monitoring systems network

As the world's largest industrial producer, China has generated large amount of industrial atmospheric pollution, particularly for particulate matter (PM), SO2 and NOx emissions. A nationwide, time-varying, and up-to-date air pollutant emission inventory by industrial sources has great significance to understanding industrial emission characteristics. Here, we present a nationwide database of industrial emissions named Chinese Industrial Emissions Database (CIED), using the real smokestack concentrations from China's continuous emission monitoring systems (CEMS) network during 2015-2018 to enhance the estimation accuracy. This hourly, source-level CEMS data enables us to directly estimate industrial emission factors and absolute emissions, avoiding the use of many assumptions and indirect parameters that are common in existing research. The uncertainty analysis of CIED database shows that the uncertainty ranges are quite small, within ±7.2% for emission factors and ±4.0% for emissions, indicating the reliability of our estimates. This dataset provides specific information on smokestack concentrations, emissions factors, activity data and absolute emissions for China's industrial emission sources, which can offer insights into associated scientific studies and future policymaking

Finite element analysis of rapid canine retraction through reducing resistance and distraction

Objective: The aims of this study were to compare different surgical approaches to rapid canine retraction by designing and selecting the most effective method of reducing resistance by a three-dimensional finite element analysis. Material and Methods: Three-dimensional finite element models of different approaches to rapid canine retraction by reducing resistance and distraction were established, including maxillary teeth, periodontal ligament, and alveolar. The models were designed to dissect the periodontal ligament, root, and alveolar separately. A 1.5 N force vector was loaded bilaterally to the center of the crown between first molar and canine, to retract the canine distally. The value of total deformation was used to assess the initial displacement of the canine and molar at the beginning of force loading. Stress intensity and force distribution were analyzed and evaluated by Ansys 13.0 through comparison of equivalent (von Mises) stress and maximum shear stress. Results: The maximum value of total deformation with the three kinds of models occurred in the distal part of the canine crown and gradually reduced from the crown to the apex of the canine; compared with the canines in model 3 and model 1, the canine in model 2 had the maximum value of displacement, up to 1.9812 mm. The lowest equivalent (von Mises) stress and the lowest maximum shear stress were concentrated mainly on the distal side of the canine root in model 2. The distribution of equivalent (von Mises) stress and maximum shear stress on the PDL of the canine in the three models was highly concentrated on the distal edge of the canine cervix. . Conclusions: Removal of the bone in the pathway of canine retraction results in low stress intensity for canine movement. Periodontal distraction aided by surgical undermining of the interseptal bone would reduce resistance and effectively accelerate the speed of canine retraction

Cotton Fusarium wilt diagnosis based on generative adversarial networks in small samples

This study aimed to explore the feasibility of applying Generative Adversarial Networks (GANs) for the diagnosis of Verticillium wilt disease in cotton and compared it with traditional data augmentation methods and transfer learning. By designing a model based on small-sample learning, we proposed an innovative cotton Verticillium wilt disease diagnosis system. The system uses Convolutional Neural Networks (CNNs) as feature extractors and applies trained GAN models for sample augmentation to improve classification accuracy. This study collected and processed a dataset of cotton Verticillium wilt disease images, including samples from normal and infected plants. Data augmentation techniques were used to expand the dataset and train the CNNs. Transfer learning using InceptionV3 was applied to train the CNNs on the dataset. The dataset was augmented using GAN algorithms and used to train CNNs. The performances of the data augmentation, transfer learning, and GANs were compared and analyzed. The results have demonstrated that augmenting the cotton Verticillium wilt disease image dataset using GAN algorithms enhanced the diagnostic accuracy and recall rate of the CNNs. Compared to traditional data augmentation methods, GANs exhibit better performance and generated more representative and diverse samples. Unlike transfer learning, GANs ensured an adequate sample size. By visualizing the images generated, GANs were found to generate realistic cotton images of Verticillium wilt disease, highlighting their potential applications in agricultural disease diagnosis. This study has demonstrated the potential of GANs in the diagnosis of cotton Verticillium wilt disease diagnosis, offering an effective approach for agricultural disease detection and providing insights into disease detection in other crops

Augmenting intrinsic fenton-like activities of MOF-derived catalysts via N-molecule-assisted self-catalyzed carbonization

To overcome the ever-growing organic pollutions in the water system, abundant efforts have been dedicated to fabricating efficient Fenton-like carbon catalysts. However, the rational design of carbon catalysts with high intrinsic activity remains a long-term goal. Herein, we report a new N-molecule-assisted self-catalytic carbonization process in augmenting the intrinsic Fenton-like activity of metal–organic-framework-derived carbon hybrids. During carbonization, the N-molecules provide alkane/ammonia gases and the formed iron nanocrystals act as the in situ catalysts, which result in the elaborated formation of carbon nanotubes (in situ chemical vapor deposition from alkane/iron catalysts) and micro-/meso-porous structures (ammonia gas etching). The obtained catalysts exhibited with abundant Fe/Fe–Nx/pyridinic-N active species, micro-/meso-porous structures, and conductive carbon nanotubes. Consequently, the catalysts exhibit high efficiency toward the degradation of different organic pollutions, such as bisphenol A, methylene blue, and tetracycline. This study not only creates a new pathway for achieving highly active Fenton-like carbon catalysts but also takes a step toward the customized production of advanced carbon hybrids for diverse energy and environmental applications

Gene Expression Analysis Reveals Novel Gene Signatures Between Young and Old Adults in Human Prefrontal Cortex

Human neurons function over an entire lifetime, yet the molecular mechanisms which perform their functions and protecting against neurodegenerative disease during aging are still elusive. Here, we conducted a systematic study on the human brain aging by using the weighted gene correlation network analysis (WGCNA) method to identify meaningful modules or representative biomarkers for human brain aging. Significantly, 19 distinct gene modules were detected based on the dataset GSE53890; among them, six modules related to the feature of brain aging were highly preserved in diverse independent datasets. Interestingly, network feature analysis confirmed that the blue modules demonstrated a remarkably correlation with human brain aging progress. Besides, the top hub genes including PPP3CB, CAMSAP1, ACTR3B, and GNG3 were identified and characterized by high connectivity, module membership, or gene significance in the blue module. Furthermore, these genes were validated in mice of different ages. Mechanically, the potential regulators of blue module were investigated. These findings highlight an important role of the blue module and its affiliated genes in the control of normal brain aging, which may lead to potential therapeutic interventions for brain aging by targeting the hub genes