38 research outputs found
Distribution and Release of Volatile Organic Sulfur Compounds in Yangcheng Lake
No full textVolatile organic sulfur compounds (VOSCs) function as a water–atmosphere link in the global sulfur cycle. It is generally believed that the vast majority of VOSCs are released from the ocean. However, due to the pervasive eutrophication and pollution of inland waters, the VOSC production and emission in rivers, lakes and reservoirs are attracting more attention. In this study, the temporal and spatial distributions of three VOSCs, including methanethiol, Dimethyl sulfide, and dimethyl disulfide in Yangcheng Lake, a eutrophic shallow lake, are investigated monthly and seasonally. Results show that VOSCs are higher in summer and autumn, with the western region as a hotspot. Our results show a positive correlation between VOSC and phytoplankton biomass (p < 0.05). Interestingly, from algal phylum composition, all the phylum, except those with low biomass, played a positive effect on VOSCs’ concentration. We did not find any specific phylum or species of cyanobacteria that contributed solely to the VOSCs. The water-air effluxes of Dimethyl sulfide (DMS) are estimated by a stagnant film model. The DMS effluxes from Yangcheng Lakes were higher than deep lakes and similar to the ocean, indicating that VOSCs, particularly DMS, in those eutrophic shallow lakes were non-negligible
Developing a deep learning-based image analysis model for high-throughput micronucleus assays: Genotoxicity as a sediment quality indicator in East Taihu and Yangcheng Lakes, China
No full textSediment quality has become a growing concern, as sediment-bound anthropogenic pollutants, particularly genotoxic compounds, may serve as secondary pollution sources, posing significant risks to aquatic ecosystems and human health. The in vitro micronucleus (MN) assay, standardized by ISO and OECD guidelines, is widely used for genotoxicity assessment; however, traditional manual MN scoring is labor-intensive, time-consuming, and susceptible to observer bias. Therefore, this study proposed a deep learning-based model for automated identification and quantification of cell nuclei and MN. Among the three trained models, the architecture incorporating hierarchical attention mechanisms, including self-attention and channel-spatial attention, was selected due to its superior segmentation performance. Compared with manual scoring, the model showed 95.63% (nuclei) and 97.38% (MN) agreement in Bland-Altman analysis, while achieving processing speeds approximately 20-fold higher per hour and 60-fold higher per day. Using the model, sediment genotoxicity from East Taihu and Yangcheng Lakes, two major freshwater systems and drinking water resources in China heavily impacted by human activities, was evaluated under both rat-S9 metabolically active and inactive conditions. Significant genotoxicity was observed, with minor discrepancies between manual and model counts, primarily in weakly genotoxic samples. Genotoxicity decreased following S9-activation, likely due to metabolic detoxification or inhibitory effects of co-existing substances. Regardless of metabolic activation, Yangcheng Lake sediments consistently exhibited higher genotoxic effects than those from East Taihu Lake. As a proof-of-concept application of deep learning in environmental genotoxicity assessment, the model architecture has been made publicly available to support high-throughput MN assay applications.9
Effect of S phase characteristics on the formation of recrystallization textures of an Al-Cu-Mg alloy
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P-Texture Effect on the Fatigue Crack Propagation Resistance in an Al-Cu-Mg Alloy Bearing a Small Amount of Silver
No full textP-texture effect on the fatigue crack propagation (FCP) resistance in an Al-Cu-Mg alloy containing a small amount of Ag, is investigated by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM) and electron back scattering diffraction (EBSD). Results shows that the high intensity P-texture sheet has lower σ 0.2 / σ b , lower FCP rate and higher damage tolerance than random texture sheet. Fracture analysis indicates that the striations spacing of high intensity P-texture sheet is much smaller than that of random texture sheet and it has a rougher fatigue fracture surface, which causes a significant roughness induced crack closure (RICC) effect. The calculation results manifest that high intensity P-texture sheet possesses a higher crack closure level reaching 0.73 as compared to random texture sheet (only 0.25). The statistical analysis results reveal the P-grains have large twist angle of 105–170° and tilt angle of 5–60° with neighboring grains, which is similar to Goss-grains. This is the fundamental reason that P-texture sheet has the same FCP resistance and induces fatigue crack deflection as Goss-texture sheet. Additionally, the most {111} slipping planes of P-grains are distributed in the range of 30–50° deviating from transverse direction of the sheet. This results in more {111} slipping planes to participate in cyclic plastic deformation, which is beneficial to reduce fatigue damage accumulation and improve the damage tolerance of Al-Cu-Mg-Ag alloy
Accurate organ segmentation and phenotype extraction of tomato plants based on deep learning and clustering algorithm
No full textIn plant phenotyping research, accurate organ segmentation and phenotype extraction is the key to accelerate the process of big data analysis and intelligent breeding.In this study, we take tomato as an example and propose an improved deep learning combined with clustering algorithm for plant point cloud segmentation. First, a multi-temporal tomato point cloud dataset is constructed by combining multi-view image sequences with neural radiation field (NeRF), and preprocessing and labeling are completed; second, single channel attention (SCA) and global feature aggregation module (GFA) are introduced into the PointNet++ model, respectively, to construct the TomatoSegNet model, which improves the tomato dataset's semantic segmentation performance, while the edge filter was added to the DBSCAN algorithm to improve it and enhance the instance segmentation performance of canopy leaves; finally, a total of six phenotypic parameters were extracted based on the segmented organs. The experimental results show that the TomatoSegNet model has an average precision (mP) of 97.82%, an average recall (mR) of 98.62%, an average F1 score (mF1) of 97.97%, an average intersection and merger ratio (mIoU) of 96.84%, and an overall accuracy (OA) of 94.22% in the tomato dataset, which proves that the use of semantic segmentation algorithms feasibility of stem and leaf segmentation; the improved DBSCAN algorithm achieved an instance segmentation accuracy of 96.03% for leaves, which improved the segmentation accuracy of overlapping leaves; the coefficients of determination between the measured and calculated values of the six phenotypic parameters (plant height, stem thickness, leaf inclination, leaf length, leaf width, and leaf area) were 0.983, 0.903, 0.916, 0.962, 0.951, and 0.978. The method proposed in this study realizes the accurate segmentation and extraction of phenotypic parameters from the 3D point cloud of plants, which provides a valuable reference for automated phenotypic analysis of plants
Stability maintenance at the grassroots: China’s weiwen apparatus as a form of conflict resolution
No full textThis working paper explores the history and potential of “stability maintenance” (weiwen) as a form of conflict resolution in China. Its emphasis on conflict resolution is novel. Previous examinations of the weiwen apparatus have concentrated on its political function, namely to manage resistance within society and maintain the authority of the party-state. This avenue of investigation has proved fruitful as a means of characterising the political motivation and the higher-level strategies involved in stability maintenance. Nonetheless, there remain significant conceptual and empirical gaps relating to how stability maintenance offices and processes actually function, particularly out of larger cities and at local levels. The research described in this paper aims to consider the effectiveness of stability maintenance as a part of the “market” for conflict resolution in local China, and to test the hypothesis that conflict resolution as facilitated by weiwen is the most pragmatic and effective means of actually resolving conflicts in the current Chinese political context, notwithstanding the closeness of the stability maintenance discourse to state authority and its relative distance from rule of law-based methods of dispute resolution..
Study on Mercury Removal Performance of Alkali Adsorbent in Coal-Fired Power Plant
No full textMercury pollution in coal-fired power plants has received widespread attention. At present, being the most mature technology, the activated carbon injection technology has been implemented for mercury removal in coal-fired power plants. However, activated carbon needs to be further transformed which is accompanied with its relatively high application cost. Therefore, the mercury removal with various types of adsorbent has become a hot research topic both domestically and abroad. In this article, based on the alkaline adsorbent removal system of Unit 7 of Datang Yangcheng Power Plant, the mercury removal effect of dry NaHCO3 powder is tested and explored using Ontario Hydro Method (OHM). The results show that at the reaction temperature of SCR, NaHCO3 can convert most of Hg0 and a small amount of Hg2+ into HgP. Furthermore, the HgP conversion rate is close to maximum when the injection volume of NaHCO3 reaches 480 kg/h. This study provides a practical and feasible technical route for mercury removal from coal-fired flue gas
Enhancing biocathode denitrification performance with nano-Fe<sub>3</sub>O<sub>4</sub> under polarity period reversal
No full textThe presence of excessive concentrations of nitrate poses a threat to both the environment and human health, and the bioelectrochemical systems (BESs) are attractive green technologies for nitrate removal. However, the denitrification efficiency in the BESs is still limited by slow biofilm formation and nitrate removal. In this work, we demonstrate the efficacy of novel combination of magnetite nanoparticles (nano-Fe3O4) with the anode-cathode polarity period reversal (PPR-Fe3O4) for improving the performance of BESs. After only two-week cultivation, the highest cathodic current density (7.71 ± 1.01 A m−2) and NO3−-N removal rate (8.19 ± 0.97 g m−2 d−1) reported to date were obtained in the PPR-Fe3O4 process (i.e., polarity period reversal with nano-Fe3O4 added) at applied working voltage of −0.2 and −0.5 V (vs Ag/AgCl) under bioanodic and biocathodic conditions, respectively. Compared with the polarity reversal once only process, the PPR process (i.e., polarity period reversal in the absence of nano-Fe3O4) enhanced bioelectroactivity through increasing biofilm biomass and altering microbial community structure. Nano-Fe3O4 could enhance extracellular electron transfer as a result of promoting the formation of extracellular polymers containing Fe3O4 and reducing charge transfer resistance of bioelectrodes. This work develops a novel biocathode denitrification strategy to achieve efficient nitrate removal after rapid cultivation
SelectQ: Calibration Data Selection for Post-Training Quantization
No full textPost-training quantization (PTQ) can reduce the memory footprint and latency for deep model inference, while still preserving the accuracy of the model, with only a small unlabeled calibration set and without the retraining on full training set. To calibrate a quantized model, current PTQ methods usually randomly select some unlabeled data from the training set as calibration data. However, we prove that the random data selection would result in performance instability and degradation for the activation distribution mismatch. In this paper, we attempt to solve the crucial task on optimal calibration data selection, and propose a novel one-shot calibration data selection method termed SelectQ, which selects specific data for calibration via dynamic clustering. SelectQ uses the statistic information of activation and performs layer-wise clustering to learn an activation distribution on training set. For that purpose, a new metric called Knowledge Distance is proposed to calculate the distances of activation statistics from centroids. Finally, after calibration by the selected data, quantization noise can be alleviated by mitigating the distribution mismatch within activations. Extensive experiments on ImageNet dataset show that our SelectQ increases the Top-1 accuracy of ResNet18 over 15\% in 4-bit quantization, compared to randomly sampled calibration set. It's noteworthy that SelectQ does not involve both the backward propagation and Batch Normalization parameters, which means that it has fewer limitations in practical applications. </p
