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22 pages, 2470 KiB  
Article
Do Regional Differences Matter? Spatiotemporal Evolution and Convergence of Household Carbon Emissions in China
by Zihao Xu, Yue Xu and Jingning Shi
Sustainability 2025, 17(9), 4064; https://doi.org/10.3390/su17094064 - 30 Apr 2025
Viewed by 403
Abstract
Understanding how household carbon emissions vary across time and regions is essential for promoting low-carbon lifestyles and advancing sustainability, yet this dimension remains underexplored—especially in large, diverse economies like China. This study addresses that gap by analyzing household carbon emissions across 29 Chinese [...] Read more.
Understanding how household carbon emissions vary across time and regions is essential for promoting low-carbon lifestyles and advancing sustainability, yet this dimension remains underexplored—especially in large, diverse economies like China. This study addresses that gap by analyzing household carbon emissions across 29 Chinese provinces from 2000 to 2022, focusing on regional differences and convergence patterns. Using spatial and convergence models, we find persistent clustering—where provinces with high or low emissions group together—though these patterns shift gradually. Emissions have generally risen nationwide, with convergence trends emerging in the east, central, south, and north, while the west and northeast show inconsistent dynamics. Notably, emissions in one province are influenced by those in neighboring provinces, particularly in central China, due to close economic and energy ties. Industrial structure slows convergence at the national level, whereas stronger economic development, better education, and higher industrialization contribute to narrowing regional disparities—especially in southern China. These findings offer new insights for designing region-specific strategies that align household emissions management with China’s broader climate and sustainability goals. Full article
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25 pages, 1799 KiB  
Article
Promoting Rural Revitalization via Natural Resource Value Realization in National Parks: A Case Study of Baishanzu National Park
by Hongyu Luo, Guangning Sun, Weilong Zhou, Jihe Lian, Yanfei Sun and Yingen Hu
Land 2025, 14(2), 298; https://doi.org/10.3390/land14020298 - 31 Jan 2025
Cited by 1 | Viewed by 1093
Abstract
The realization of natural resource value serves as a critical entry point for advancing rural revitalization within the framework of ecological civilization construction, representing an essential approach to balancing ecological conservation and economic development in national parks. Based on clarifying the logical relationship [...] Read more.
The realization of natural resource value serves as a critical entry point for advancing rural revitalization within the framework of ecological civilization construction, representing an essential approach to balancing ecological conservation and economic development in national parks. Based on clarifying the logical relationship and the driving mechanisms between the realization of natural resource value and rural revitalization, this paper employs field observation and in-depth interviews using Baishanzu National Park as a case study to analyze how general control zones in national parks can promote rural revitalization under ecological constraints through the realization of natural resource value. The results indicate the following: (1) By constructing a framework of “realistic background—pathway selection—model condensation—effectiveness analysis”, the mechanism of how natural resource value realization promotes rural revitalization can be analyzed, with a focus on its pathways and models. (2) The pathways for realizing natural resource value to promote rural revitalization include resource integration, investment development, capital production and operation, and the circulation and exchange of ecological products and services. These pathways contribute to various dimensions of rural revitalization at different stages: assetization, capitalization, productization, and monetization. (3) Within different functional zones of the general control area in national parks, including ecological restoration zones, traditional utilization zones, and recreation and exhibition zones, the value of natural resources can promote rural revitalization through three realization modes: preservation, transformation, and value-added enhancement, reflecting diverse approaches and differentiated outcomes of value realization. To comprehensively promote rural revitalization in national parks through the realization of natural resource value, it is first necessary to fully identify regional resource endowments, conservation objectives, and developmental constraints. Second, regional resources should be integrated to pursue synergistic innovation. Finally, attention must be paid to achieving comprehensive benefits for sustainable development. The research findings provide valuable references for the high-quality development of national parks and rural revitalization. Full article
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14 pages, 12089 KiB  
Article
Changes and Trade-Offs of Ecological Service Functions of Public Welfare Forests (2000–2019) in Southwest Zhejiang Province, China
by Ziqiang Liu, Deguo Han, Limin Ye, Yuanke Xu and Yong Zhang
Forests 2024, 15(12), 2197; https://doi.org/10.3390/f15122197 - 13 Dec 2024
Viewed by 906
Abstract
Studying the factors influencing ecosystem regulation services in southwestern Zhejiang is of great significance for formulating reasonable pricing strategies for forest ecosystem regulation services and optimizing ecological security. This study constructed a theoretical framework for analyzing forest ecosystem regulation services and assessed the [...] Read more.
Studying the factors influencing ecosystem regulation services in southwestern Zhejiang is of great significance for formulating reasonable pricing strategies for forest ecosystem regulation services and optimizing ecological security. This study constructed a theoretical framework for analyzing forest ecosystem regulation services and assessed the spatiotemporal evolution and influencing factors of forest ecosystem regulation services using InVEST model calculations and spatial autocorrelation analysis. The results showed that all ecosystem services of forests in the study improved from 2000 to 2019, with the exception of soil conservation. The water conservation function increased significantly from 2000 to 2019, with an overall increase of 3.53%. The biodiversity conservation function in 2019 also increased significantly, with an average increase of 2.16% compared with 2000. The synergies mainly occurred between water source regulation and soil conservation, soil conservation and biodiversity, and forest recreation and carbon storage. Forest Reserve was precipitation, canopy closure, elevation, and soil texture, and their driving forces differed at different time scales. The trade-offs mainly occurred between soil conservation and forest recreation, forest recreation and biodiversity, and carbon storage and biodiversity. The research results provide a reference for achieving ecological protection and high-quality development in the southwestern region of Zhejiang. Full article
(This article belongs to the Special Issue Advances in Forest Carbon, Water Use and Growth Under Climate Change)
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17 pages, 1870 KiB  
Article
Semantically-Enhanced Feature Extraction with CLIP and Transformer Networks for Driver Fatigue Detection
by Zhen Gao, Xiaowen Chen, Jingning Xu, Rongjie Yu, Heng Zhang and Jinqiu Yang
Sensors 2024, 24(24), 7948; https://doi.org/10.3390/s24247948 - 12 Dec 2024
Cited by 2 | Viewed by 1546
Abstract
Drowsy driving is a leading cause of commercial vehicle traffic crashes. The trend is to train fatigue detection models using deep neural networks on driver video data, but challenges remain in coarse and incomplete high-level feature extraction and network architecture optimization. This paper [...] Read more.
Drowsy driving is a leading cause of commercial vehicle traffic crashes. The trend is to train fatigue detection models using deep neural networks on driver video data, but challenges remain in coarse and incomplete high-level feature extraction and network architecture optimization. This paper pioneers the use of the CLIP (Contrastive Language-Image Pre-training) model for fatigue detection. And by harnessing the power of a Transformer architecture, sophisticated and long-term temporal features are adeptly extracted from video sequences, paving the way for more nuanced and accurate fatigue analysis. The proposed CT-Net (CLIP-Transformer Network) achieves an AUC (Area Under the Curve) of 0.892, a 36% accuracy improvement over the prevalent CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) end-to-end model, reaching state-of-the-art performance. Experiments show that the CLIP pre-trained model more accurately extracts facial and behavioral features from driver video frames, improving the model’s AUC by 7% over the ImageNet-based pre-trained model. Moreover, compared with LSTM, the Transformer more flexibly captures long-term dependencies among temporal features, further enhancing the model’s AUC by 4%. Full article
(This article belongs to the Section Intelligent Sensors)
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16 pages, 4745 KiB  
Article
Quantum Circuit Architecture Search on a Superconducting Processor
by Kehuan Linghu, Yang Qian, Ruixia Wang, Meng-Jun Hu, Zhiyuan Li, Xuegang Li, Huikai Xu, Jingning Zhang, Teng Ma, Peng Zhao, Dong E. Liu, Min-Hsiu Hsieh, Xingyao Wu, Yuxuan Du, Dacheng Tao, Yirong Jin and Haifeng Yu
Entropy 2024, 26(12), 1025; https://doi.org/10.3390/e26121025 - 26 Nov 2024
Cited by 9 | Viewed by 1203
Abstract
Variational quantum algorithms (VQAs) have shown strong evidence to gain provable computational advantages in diverse fields such as finance, machine learning, and chemistry. However, the heuristic ansatz exploited in modern VQAs is incapable of balancing the trade-off between expressivity and trainability, which may [...] Read more.
Variational quantum algorithms (VQAs) have shown strong evidence to gain provable computational advantages in diverse fields such as finance, machine learning, and chemistry. However, the heuristic ansatz exploited in modern VQAs is incapable of balancing the trade-off between expressivity and trainability, which may lead to degraded performance when executed on noisy intermediate-scale quantum (NISQ) machines. To address this issue, here, we demonstrate the first proof-of-principle experiment of applying an efficient automatic ansatz design technique, i.e., quantum architecture search (QAS), to enhance VQAs on an 8-qubit superconducting quantum processor. In particular, we apply QAS to tailor the hardware-efficient ansatz toward classification tasks. Compared with heuristic ansätze, the ansatz designed by QAS improves the test accuracy from 31% to 98%. We further explain this superior performance by visualizing the loss landscape and analyzing effective parameters of all ansätze. Our work provides concrete guidance for developing variable ansätze to tackle various large-scale quantum learning problems with advantages. Full article
(This article belongs to the Special Issue Quantum Information: Working Towards Applications)
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13 pages, 2995 KiB  
Article
Transformer Fault Diagnosis Utilizing Feature Extraction and Ensemble Learning Model
by Gonglin Xu, Mei Zhang, Wanli Chen and Zhihui Wang
Information 2024, 15(9), 561; https://doi.org/10.3390/info15090561 - 11 Sep 2024
Cited by 2 | Viewed by 1525
Abstract
This paper proposes a novel method for diagnosing faults in oil-immersed transformers, leveraging feature extraction and an ensemble learning algorithm to enhance diagnostic accuracy. Initially, Dissolved Gas Analysis (DGA) data from transformers undergo a cleaning process to ensure data quality and reliability. Subsequently, [...] Read more.
This paper proposes a novel method for diagnosing faults in oil-immersed transformers, leveraging feature extraction and an ensemble learning algorithm to enhance diagnostic accuracy. Initially, Dissolved Gas Analysis (DGA) data from transformers undergo a cleaning process to ensure data quality and reliability. Subsequently, an interactive ratio method is employed to augment features and project DGA data into a high-dimensional space. To refine the feature set, a combined Filter and Wrapper algorithm is utilized, effectively eliminating irrelevant and redundant features. The final step involves optimizing the Light Gradient Boosting Machine (LightGBM) model using IAOS algorithm for transformer fault classification; this model is an ensemble learning model. Experimental results demonstrate that the proposed feature extraction method enhances LightGBM model’s accuracy to 86.84%, representing a 6.58% improvement over the baseline model. Furthermore, optimization with IAOS algorithm increases the diagnostic accuracy of LightGBM model to 93.42%, an additional gain of 6.58%. Full article
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16 pages, 3535 KiB  
Article
Direct Epoxidation of Hexafluoropropene Using Molecular Oxygen over Cu-Impregnated HZSM-5 Zeolites
by Jie-Ming Huang, Jingning Guo, Chengmiao Xu, An Su, Ke-Jun Wu and Chao-Hong He
Processes 2024, 12(7), 1520; https://doi.org/10.3390/pr12071520 - 19 Jul 2024
Viewed by 1108
Abstract
This study explores a novel method of directly epoxidizing hexafluoropropene with molecular oxygen under gaseous conditions using a Cu/HZSM-5 catalytic system (Cu/HZ). An in-depth investigation was conducted on the catalytic performance of Cu-based catalysts on various supports and Cu/HZ catalysts prepared under different [...] Read more.
This study explores a novel method of directly epoxidizing hexafluoropropene with molecular oxygen under gaseous conditions using a Cu/HZSM-5 catalytic system (Cu/HZ). An in-depth investigation was conducted on the catalytic performance of Cu-based catalysts on various supports and Cu/HZ catalysts prepared under different conditions. Cu/HZ catalysts exhibited better catalytic performance than other porous medium-supported Cu catalysts for the epoxidation of hexafluoropropene by molecular oxygen. The highest propylene oxide yield of 35.6% was achieved over the Cu/HZ catalyst prepared under conditions of 350 °C with a Cu loading of 1 wt%. By applying characterization techniques including XRD, BET, NH3-TPD, and XPS to different catalyst samples, the relationship between the interaction of Cu2+ and HZSM-5 and the reactivity of the catalyst was studied, thereby elucidating the influence of calcination temperature and loading on the reactivity. Finally, we further proposed the possible mechanism of how isolated Cu2+ and acid sites improve catalytic performance. Full article
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17 pages, 3190 KiB  
Article
Influence of Spring Dust Storm on Atmospheric Particulate-Bound Mercury in a Typical Inland City of Northern China: Characteristics, Sources, and Risk Assessment
by Xiaofei Li, Rui Zhang, Lekhendra Tripathee, Jingning Guo, Wen Yang and Junming Guo
Sustainability 2024, 16(10), 4096; https://doi.org/10.3390/su16104096 - 14 May 2024
Cited by 3 | Viewed by 1937
Abstract
Particulate-bound mercury (PBM) has a large dry-deposition rate and removal coefficient, both of which import mercury into terrestrial and marine ecosystems, causing global environmental problems. In order to illustrate the concentration characteristics, main sources, and health risk of PBM in the atmospheric environment [...] Read more.
Particulate-bound mercury (PBM) has a large dry-deposition rate and removal coefficient, both of which import mercury into terrestrial and marine ecosystems, causing global environmental problems. In order to illustrate the concentration characteristics, main sources, and health risk of PBM in the atmospheric environment during the spring dust storm period in Xi’an in 2022, PM2.5 samples were collected in Xi’an in March 2022. The concentration of PBM and the PM2.5 composition, including water-soluble ions and elements, were analyzed. The input of dust caused a significant increase in the concentration of PBM, Ca2+, Na+, Mg2+, SO42−, and metal elements in the aerosol. The research results revealed that the dust had a strong enrichment influence on the atmospheric PBM in Xi’an. Anthropogenic mercury emissions and long-distance migration in the sand source area promote the rise in PBM concentration and should be included in the mercury inventory. The values of the risk index for a certain metal (Eri) (572.78–1653.33) and the geo-accumulation index (Igeo) (2.47–4.78) are calculated during this study, showing that atmospheric PBM has a strong pollution level with respect to the ecological environment and that Hg mainly comes from anthropogenic mercury emissions. The non-carcinogenic health risk of atmospheric PBM in children (8.48 × 10−2) is greater than that in adults (1.01 × 10−2). The results show that we need to pay more attention to children’s health in the process of atmospheric mercury pollution control. This study discusses the distribution characteristics of PBM during spring sandstorms and the effects of atmospheric mercury on residents’ health, providing a basis for studying the sustainable development of environmental health and formulating effective strategies for mercury emission control. Full article
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17 pages, 7005 KiB  
Article
Construction of Aerosol Model and Atmospheric Correction in the Coastal Area of Shandong Peninsula
by Kunyang Shan, Chaofei Ma, Jingning Lv, Dan Zhao and Qingjun Song
Remote Sens. 2024, 16(7), 1309; https://doi.org/10.3390/rs16071309 - 8 Apr 2024
Cited by 2 | Viewed by 1773
Abstract
Applying standard aerosol models for atmospheric correction in nearshore coastal waters introduces significant uncertainties due to their inability to accurately represent aerosol characteristics in these regions. To improve the accuracy of remote sensing reflectance (Rrs) products in the nearshore [...] Read more.
Applying standard aerosol models for atmospheric correction in nearshore coastal waters introduces significant uncertainties due to their inability to accurately represent aerosol characteristics in these regions. To improve the accuracy of remote sensing reflectance (Rrs) products in the nearshore waters of the Shandong Peninsula, this study develops an aerosol model based on aerosol data collected from the Mu Ping site in the coastal area of the Shandong Peninsula, enabling tailored atmospheric correction for this specific region. Given the pronounced seasonal variations in aerosol optical properties, monthly aerosol models were developed. The monthly aerosol model is derived using the average values of aerosol microphysical properties. Compared to the standard aerosol model, this model is more effective in characterizing the absorption and scattering characteristics of aerosols in the study area. Corresponding lookup tables for the aerosol model were created and integrated into the NIR-SWIR atmospheric correction algorithm. According to the accuracy evaluation indexes of RMSD, MAE, and UPD, it can be found that the atmospheric correction results of the aerosol model established in this paper are better than those of the standard aerosol model, especially in the 547 nm band. It demonstrates that the new aerosol model outperforms the standard model in atmospheric correction performance. With the increasing availability of aerosol observational data, the aerosol model is expected to become more accurate and applicable to other satellite missions. Full article
(This article belongs to the Special Issue Aerosol and Atmospheric Correction)
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18 pages, 7202 KiB  
Article
PM2.5 and O3 in an Enclosed Basin, the Guanzhong Basin of Northern China: Insights into Distributions, Appointment Sources, and Transport Pathways
by Xiaofei Li, Jingning Guo, Xuequan Wan, Zhen Yang, Lekhendra Tripathee, Feng Yu, Rui Zhang, Wen Yang and Qiyuan Wang
Sustainability 2024, 16(7), 3074; https://doi.org/10.3390/su16073074 - 7 Apr 2024
Cited by 1 | Viewed by 1884
Abstract
Aerosol samples (PM2.5) were collected in Xi’an (XN) from 11 August to 11 September 2021 and in Qinling (QL) from 14 July to 24 August 2021, respectively. In addition, ozone (O3) data were collected in order to investigate the [...] Read more.
Aerosol samples (PM2.5) were collected in Xi’an (XN) from 11 August to 11 September 2021 and in Qinling (QL) from 14 July to 24 August 2021, respectively. In addition, ozone (O3) data were collected in order to investigate the characteristics and source areas of PM2.5 and O3 in the Guanzhong Basin (GB). The concentrations of PM2.5, organic carbon (OC), and elemental carbon (EC) in XN (53.40 ± 17.42, 4.61 ± 2.41, and 0.78 ± 0.60 μg m−3, respectively) were higher than those in QL (27.57 ± 8.27, 4.23 ± 1.37, and 0.67 ± 0.53 μg m−3, respectively) in summer. Total water-soluble ions (TWSIIs) accounted for 19.40% and 39.37% of the PM2.5 concentrations in XN and QL, respectively. O3 concentrations in summer were 102.44 ± 35.08 μg m−3 and 47.95 ± 21.63 μg m−3 in XN and QL, respectively, and they showed a significant correlation with Ox. The positive matrix factorization (PMF) model identified three main sources in XN and QL, including coal combustion source (COB), secondary aerosol (SA), and dust sources (DUSs). The potential source contribution function (PSCF) and a concentration weight trajectory (CWT) model with back-trajectory analysis showed that Inner Mongolia, the interior of Shaanxi, and nearby areas to the southwest were the sources and source areas of carbonaceous matter in XN and QL. The results of this study can contribute to the development of prevention and control policies and guidelines for PM2.5 and O3 in the GB. Furthermore, long-term and sustainable measuring and monitoring of PM2.5 and O3 are necessary, which is of great significance for studying climate change and the sustainable development of the environment. Full article
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15 pages, 6631 KiB  
Article
Identification the Pathogen Cause a New Apple Leaf Blight in China and Determination the Controlling Efficacy for Five Botanical Fungicides
by Enchen Li, Jia Liu, Shuwu Zhang and Bingliang Xu
J. Fungi 2024, 10(4), 255; https://doi.org/10.3390/jof10040255 - 27 Mar 2024
Cited by 4 | Viewed by 2263
Abstract
Alternaria leaf blight has recently been described as an emerging fungal disease of apple trees which is causing the significant damage in the apple-growing areas of Tianshui and Jingning, Gansu, China. In the present study, the pathogen species involved in apple leaf blight [...] Read more.
Alternaria leaf blight has recently been described as an emerging fungal disease of apple trees which is causing the significant damage in the apple-growing areas of Tianshui and Jingning, Gansu, China. In the present study, the pathogen species involved in apple leaf blight and its biological characteristics were identified, and the inhibitory activity of different botanical fungicides against the pathogen was evaluated in vitro. Four strains were isolated from the symptomatic areas of necrotic apple leaves, and initially healthy leaves showed similar symptoms to those observed in orchards after inoculation with the ABL2 isolate. The ABL2 isolate was identified as Alternaria tenuissima based on the morphological characteristics of its colonies, conidiophores, and conidia, and this was also confirmed by multi-gene sequence (ITS, OPA10-2, Alta-1, and endoPG) analysis and phylogenic analysis. The optimum temperature, pH, carbon source, and nitrogen source for the growth of A. tenuissima mycelia were 28 °C, 6–7, soluble starch, and soy flour, respectively. In addition, the botanical fungicide eugenol exhibited the highest inhibitory effect on the mycelial growth and conidia germination of A. tenuissima, and the median effective concentration (EC50) values were 0.826 and 0.755 μg/mL, respectively. The protective and curative efficacy of eugenol were 86.85% and 76.94% after inoculation in detached apple leaves at a concentration of 4 μg/mL. Our research provides new insights into the control of apple leaf blight disease by applying botanical fungicides. Full article
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15 pages, 2654 KiB  
Article
Quantifying the Threshold Effects and Factors Impacting Physiological Health Benefits of Forest Exposure
by Bo Yang, Weishuai Ta, Wen Dong, Danping Ma, Jihan Duan, Huajun Lin, Dubin Dong, Jian Chen, Songwei Zeng, Yan Shi, Jianyun Pan and Yuan Ren
Forests 2024, 15(3), 555; https://doi.org/10.3390/f15030555 - 19 Mar 2024
Cited by 3 | Viewed by 2367
Abstract
The growing awareness of the health advantages offered by forests has underscored the significance of forest exposure as an upstream preventive measure against disease. While numerous studies have confirmed the physical and mental health benefits associated with forests, there is still a lack [...] Read more.
The growing awareness of the health advantages offered by forests has underscored the significance of forest exposure as an upstream preventive measure against disease. While numerous studies have confirmed the physical and mental health benefits associated with forests, there is still a lack of quantitative understanding regarding the relationship between forest exposure and physiological health benefits (PHB). Particularly, there is insufficient knowledge about the threshold effects derived from short-term forest exposure. In this study, we propose a PHB threshold model for assessing forest exposure that introduces the concepts of efficiency threshold and benefits threshold. A pilot study was conducted in three typical natural forest sites to validate the proposed model. Electroencephalogram (EEG) was continuously measured as the physiological indicator, while meteorological, environmental, and demographic factors were simultaneously collected. The results show that: (1) the proposed PHB threshold model is applicable in a natural forest environment; (2) despite the longer time required to reach the PHB thresholds, forest exposure yielded more significant and prolonged health benefits compared to urban green spaces; (3) meteorological factors, such as temperature and relative humidity, play a crucial role in impacting the PHB threshold model; and (4) exposure to forests is better for deep thinking and relaxation than urban green spaces. These findings emphasize the potential of forests to offer a respite from the stresses of modern life and promote holistic well-being. Full article
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20 pages, 924 KiB  
Review
Deciphering the Role of the Gut Microbiota in Exposure to Emerging Contaminants and Diabetes: A Review
by Xueqing Li, Huixia Niu, Zhengliang Huang, Man Zhang, Mingluan Xing, Zhijian Chen, Lizhi Wu and Peiwei Xu
Metabolites 2024, 14(2), 108; https://doi.org/10.3390/metabo14020108 - 6 Feb 2024
Cited by 1 | Viewed by 2672
Abstract
Emerging pollutants, a category of compounds currently not regulated or inadequately regulated by law, have recently become a focal point of research due to their potential toxic effects on human health. The gut microbiota plays a pivotal role in human health; it is [...] Read more.
Emerging pollutants, a category of compounds currently not regulated or inadequately regulated by law, have recently become a focal point of research due to their potential toxic effects on human health. The gut microbiota plays a pivotal role in human health; it is particularly susceptible to disruption and alteration upon exposure to a range of toxic environmental chemicals, including emerging contaminants. The disturbance of the gut microbiome caused by environmental pollutants may represent a mechanism through which environmental chemicals exert their toxic effects, a mechanism that is garnering increasing attention. However, the discussion on the toxic link between emerging pollutants and glucose metabolism remains insufficiently explored. This review aims to establish a connection between emerging pollutants and glucose metabolism through the gut microbiota, delving into the toxic impacts of these pollutants on glucose metabolism and the potential role played by the gut microbiota. Full article
(This article belongs to the Special Issue Effects of Environmental Exposure on Host and Microbial Metabolism)
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17 pages, 7852 KiB  
Article
Considering Image Information and Self-Similarity: A Compositional Denoising Network
by Jiahong Zhang, Yonggui Zhu, Wenshu Yu and Jingning Ma
Sensors 2023, 23(13), 5915; https://doi.org/10.3390/s23135915 - 26 Jun 2023
Cited by 3 | Viewed by 1626
Abstract
Recently, convolutional neural networks (CNNs) have been widely used in image denoising, and their performance has been enhanced through residual learning. However, previous research mostly focused on optimizing the network architecture of CNNs, ignoring the limitations of the commonly used residual learning. This [...] Read more.
Recently, convolutional neural networks (CNNs) have been widely used in image denoising, and their performance has been enhanced through residual learning. However, previous research mostly focused on optimizing the network architecture of CNNs, ignoring the limitations of the commonly used residual learning. This paper identifies two of its limitations, which are the neglect of image information and the lack of effective consideration of image self-similarity. To solve these limitations, this paper proposes a compositional denoising network (CDN), which contains two sub-paths, the image information path (IIP) and the noise estimation path (NEP), respectively. IIP is trained via an image-to-image method to extract image information. For NEP, it utilizes image self-similarity from the perspective of training. This similarity-based training method constrains NEP to output similar estimated noise distributions for different image patches with a specific kind of noise. Finally, image information and noise distribution information are comprehensively considered for image denoising. Experimental results indicate that CDN outperforms other CNN-based methods in both synthetic and real-world image denoising, achieving state-of-the-art performance. Full article
(This article belongs to the Section Sensing and Imaging)
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14 pages, 2381 KiB  
Article
Source Apportionment and Model Applicability of Heavy Metal Pollution in Farmland Soil Based on Three Receptor Models
by Jiawei Ma, Kaining Lanwang, Shiyan Liao, Bin Zhong, Zhenhua Chen, Zhengqian Ye and Dan Liu
Toxics 2023, 11(3), 265; https://doi.org/10.3390/toxics11030265 - 13 Mar 2023
Cited by 8 | Viewed by 2277
Abstract
The identification of the source of heavy metal pollution and its quantification are the prerequisite of soil pollution control. The APCS-MLR, UNMIX and PMF models were employed to apportion pollution sources of Cu, Zn, Pb, Cd, Cr and Ni of the farmland soil [...] Read more.
The identification of the source of heavy metal pollution and its quantification are the prerequisite of soil pollution control. The APCS-MLR, UNMIX and PMF models were employed to apportion pollution sources of Cu, Zn, Pb, Cd, Cr and Ni of the farmland soil in the vicinity of an abandoned iron and steel plant. The sources, contribution rates and applicability of the models were evaluated. The potential ecological risk index revealed greatest ecological risk from Cd. The results of source apportionment illustrated that the APCS-MLR and UNMIX models could verify each other for accurate allocation of pollution sources. The industrial sources were the main sources of pollution (32.41~38.42%), followed by agricultural sources (29.35~31.65%) and traffic emission sources (21.03~21.51%); and the smallest proportion was from natural sources of pollution (11.2~14.42%). The PMF model was easily affected by outliers and its fitting degree was not ideal, leading to be unable to get more accurate results of source analysis. The combination of multiple models could effectively improve the accuracy of pollution source analysis of soil heavy metals. These results provide some scientific basis for further remediation of heavy metal pollution in farmland soil. Full article
(This article belongs to the Section Toxicity Reduction and Environmental Remediation)
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