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Bioaerosolization behavior along sewage sludge biostabilization

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Frontiers of Environmental Science & Engineering 2021年第3期15卷 151-164页

作者： Fan Lu tianyu hu Shunyan Wei Liming Shao Pinjing He State Key Laboratory of Pollution Control and Source Reuse Tongji UniversityShanghai 200092China Shanghai Institute of Pollution Control and Ecological Security Shanghai 200092China Institute of Waste Treatment and Reclamation Tongji UniversityShanghai 200092China

Biostabilization is a cost-effective method for the beneficial utilization of sewage ***,during the operation of sludge biostabilization,some microbial species could be released into the atmospheric environment from the solid-phase of sludge easily and present a high risk to human *** study aimed to evaluate the risk of bioaerosol during sludge *** found a total of nine bacterial phyla,one archaeal phylum,and two fungal phyla in the bioaerosol *** them,Proteobacteria,Actinobacteria,Bacteroidetes,and Ascomycota were the dominant *** addition,the bioaerosolization indexes(BI)of prokaryotic phyla and flingal phyla ranged 0-45 and 0-487,*** ilia y Pseudarthrobacter,Pseudomonas,Tremellales spp.,and Fusarium were the preferentially aerosolized microbial genera with maximum bioaerosolization indexes of 19962,10360,1802,3055,and *** bioaerosol concentration during the biostabilization ranged from 160 to 1440 cell/m^(3),and we identified species such as Stenotrophomonas rhizophila and Fusarium graminerum with high bioaerosolization indexes that could be threats to human ***,which belongs to archaeal phyla,had the highest biostabilization index in our *** also found that Pseudarthrobacter was the easiest to aerosolize during the sludge biostabilization process.

关键词： Sludge Composting Bioaerosol Bioaerosolization index High-throughput sequencing 4’,6-diamidino-2-phenylindole(DAPI)

Multi-Scale Fusion Model Based on Gated Recurrent Unit for Enhancing Prediction Accuracy of State-of-Charge in Battery Energy Storage Systems

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 405-414页

作者： Hao Liu Fengwei Liang tianyu hu Jichao Hong huimin Ma School of Computer and Communication Engineering University of Science and Technology BeijingBeijingChina School of Mechanical Engineering University of Science and Technology BeijingBeijingChina IEEE

Accurate prediction of the state-of-charge(SOC)of battery energy storage system(BESS)is critical for its safety and lifespan in electric *** overcome the imbalance of existing methods between multi-scale feature fusion and global feature extraction,this paper introduces a novel multi-scale fusion(MSF)model based on gated recurrent unit(GRU),which is specifically designed for complex multi-step SOC prediction in practical *** correlation analysis is first employed to identify SOC-related *** parameters are then input into a multi-layer GRU for point-wise feature ***,the parameters undergo patching before entering a dual-stage multi-layer GRU,thus enabling the model to capture nuanced information across varying time ***,by means of adaptive weight fusion and a fully connected network,multi-step SOC predictions are *** extensive validation over multiple days,it is illustrated that the proposed model achieves an absolute error of less than 1.5%in real-time SOC prediction.

关键词： Electric vehicle battery energy storage system(BESS) state-of-charge(SOC)prediction gated recurrent unit(GRU) multi-scale fusion(MSF).

Neodymium isotopes distribution and transport in the central North Pacific deep water

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Chinese Science Bulletin 2011年第21期56卷 2243-2250页

作者： EN tianyu LING HongFei hu Rong State Key Laboratory for Mineral Deposits Research School of Earth Sciences and EngineeringNanjing UniversityNanjing 210093China

Radiogenic Neodymium (Nd) isotopes in the central North Pacific deep water have been generally interpreted as reflecting Pacific arcs input according to previous studies,while little is known about the specific path and mode of radiogenic Nd from these arcs to deep *** this study,Nd isotopic data from surface scrapings of 17 Fe-Mn crusts from the central North Pacific deep water (around 20°N) have been *** on these data together with recently published data on dissolved Nd isotopes in seawater for this region,we propose that radiogenic Nd isotopes in North Pacific Intermediate Water (NPIW 300-800 m) from the western Pacific margin is an important source for the central North Pacific deep water,while the surface water of this area has little influence on dominating deep water Nd *** a view emphasizes the role of vertical mixing and advection of the marginal ocean currents in balancing Nd isotopes in central North Pacific deep water,and helps to understand oceanic Nd ***,the values of Nd from surface scrapings of Fe-Mn crusts are relatively homogenous,with an average value of about-3.4,which is higher than that of modern seawater (～-3.9).This difference over short time scales reflects the quick evolution of Nd isotopes in central North Pacific deep water.

关键词：太平洋中部同位素分布深水 Nd同位素钕运输铁锰结壳同位素平衡

颈椎前凸比率可作为多节段脊髓型颈椎病后路术式选择的决策指标

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中国组织工程研究 2025年第9期29卷 1796-1802页

作者：苗嘉航马胜李渠蓬余会林胡天宇高啸冯虎徐州医科大学附属医院江苏省徐州市221000

背景:目前仅有研究表明颈椎前凸比率可作为预测椎板成形术后前凸曲度丢失的重要因素,而颈椎前凸比率这一动力位指标是否可作为颈后路术式选择的决策因素之一尚无定论。目的:探讨颈椎过伸过屈位指标颈椎前凸比率这一动力位指标是否可以... 详细信息

背景:目前仅有研究表明颈椎前凸比率可作为预测椎板成形术后前凸曲度丢失的重要因素,而颈椎前凸比率这一动力位指标是否可作为颈后路术式选择的决策因素之一尚无定论。目的:探讨颈椎过伸过屈位指标颈椎前凸比率这一动力位指标是否可以作为椎板成形和椎板切除融合这两种术式的选择性指标。方法:回顾性分析2015年12月至2020年3月因多节段脊髓型颈椎病接受颈后路手术,且随访时间超过1年的141例患者,其中63例接受椎板切除融合治疗(椎板切除融合组),78例患者接受椎板成形治疗(椎板成形组),对比两组患者的人口统计学资料(性别、年龄、体质量指数、随访时间)、影像学指标(C_(2-7) Cobb角、C_(2-7)运动范围、屈曲位Cobb角、伸展位Cobb角、屈曲活动范围和伸展活动范围)及临床疗效(日本骨科协会评分、目测类比评分)。颈椎曲度变化的评估指标为术前、术后C_(2-7) Cobb角差值(ΔCL)。颈椎前凸比率=屈曲位活动范围/C_(2-7)活动范围×100%。采用受试者工作特征曲线分析颈椎前凸比率预测术后颈椎前凸曲度丢失过多(ΔCL≤-10°)的有效性。依据颈椎前凸比率的临界值(68.5%)将所有患者分为低比率组和高比率组,在不同比率组中再次对比两种术式之间的颈椎曲度指标和临床效果指标。结果与结论:①椎板切除融合和椎板成形术后颈椎前凸曲度均降低(P=0.039,P=0.002),椎板成形组的颈椎前凸曲度损失(ΔCL)大于椎板切除融合组,差异有显著性意义;②基于受试者工作特征曲线分析,颈椎前凸比率对预测颈椎前凸曲度丢失过多(ΔCL≤-10°)具有良好的鉴别能力(曲线下面积=0.792);③在低比率组中,椎板切除融合组和椎板成形组的颈椎前凸曲度变化ΔCL差异无显著性意义(P=0.141);④在高比率组中,椎板成形组的ΔCL大于椎板切除融合组(P=0.001),且椎板成形组发生术后前凸曲度丢失过多(ΔCL≤-10°)的概率更高(43%,29%);⑤提示颈椎前凸比率可作为多节段脊髓型颈椎病后路术式选择的决策指标,比率低的患者可考虑行椎板成形治疗,而对于高比率者,椎板切除融合对前凸的维持效果更佳。

关键词：多节段脊髓型颈椎病椎板成形椎板切除融合颈椎前凸比率动力位影像学指标术后颈椎前凸曲度

Very Short-term Spatial and Temporal Wind Power Forecasting: A Deep Learning Approach

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CSEE Journal of Power and Energy Systems 2020年第2期6卷 434-443页

作者： tianyu hu Wenchuan Wu Qinglai Guo Hongbin Sun Libao Shi Xinwei Shen Tsinghua-Berkeley Shenzhen Institute(TBSI) Shenzhen 518055China TBSI Shenzhen 518055China Department of Electrical Engineering State Key Laboratory of Power SystemsTsinghua UniversityBeijing 100084China Graduate School at Shenzhen Tsinghua UniversityShenzhen 518055China

In power systems that experience high penetration of wind power generation,very short-term wind power forecast is an important prerequisite for look-ahead power *** univariate wind power forecasting methods at presentonly utilize individual wind farm historical ***,studies have shown that forecasting accuracy canbe improved by exploring both spatial and temporal correlations among adjacent wind *** research on spatial-temporal wind power forecasting is based on relatively shallow time series models that,to date,have demonstrated unsatisfactory *** this paper,a convolution operation is used to capture the spatial and temporal correlations among multiple wind farms.A novel convolution-based spatial-temporal wind power predictor(CSTWPP)is *** to CSTWPP’s high nonlinearity and deep architecture,wind power variation features and regularities included in the historical data can be more effectively ***,the online training of CSTWPP enables incremental learning,which makes CSTWPP non-stationary and in conformity with real *** processing units(GPU)is used to speed up the training process,validating the developed CSTWPP for real-time *** studies on 28 adjacent wind farms are conducted to show that the proposed model can achieve superior performance on 5-30 minutes ahead wind power forecasts.

关键词： Convolution neural network deep learning incremental learning short-term wind power forecast spatialtemporal correlation

Weather-Driven Solar Power Forecasting Using D-Informer:Enhancing Predictions with Climate Variables

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Energy Engineering 2024年第5期121卷 1245-1261页

作者： Chenglian Ma Rui Han Zhao An tianyu hu Meizhu Jin School of Electrical Engineering Northeast Electric Power UniversityJilin132013China Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen518055China

Precise forecasting of solar power is crucial for the development of sustainable energy *** forecasting approaches often fail to adequately consider the crucial role of weather factors in photovoltaic(PV)power generation and encounter issues such as gradient explosion or disappearance when dealing with extensive time-series *** overcome these challenges,this research presents a cutting-edge,multi-stage forecasting method called *** method skillfully merges the differential transformation algorithm with the Informer model,leveraging a detailed array of meteorological variables and historical PV power generation *** D-Informer model exhibits remarkable superiority over competing models across multiple performance metrics,achieving on average a 67.64%reduction in mean squared error(MSE),a 49.58%decrease in mean absolute error(MAE),and a 43.43%reduction in root mean square error(RMSE).Moreover,it attained an R2 value as high as 0.9917 during the winter season,highlighting its precision and *** significant advancement can be primarily attributed to the incorporation of a multi-head self-attention mechanism,which greatly enhances the model’s ability to identify complex interactions among diverse input variables,and the inclusion of weather variables,enriching the model’s input data and strengthening its predictive accuracy in time series ***,the experimental results confirm the effectiveness of the proposed approach.

关键词： Power forecasting deep learning weather-driven solar power

The influence of meteorology and phenology on net ecosystem exchange in an eastern Siberian boreal larch forest

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Journal of Plant Ecology 2016年第5期9卷 520-530页

作者： Bao-Lin Xue Qinghua Guo Yongwei Gong tianyu hu Jin Liu Takeshi Ohta State Key Laboratory of Vegetation and Environmental Change Institute of BotanyChinese Academy of SciencesBeijing 100093China School of Engineering Sierra Nevada Research InstituteUniversity of California at MercedCA 95343USA Key Laboratory of Urban Stormwater System and Water Environment Ministry of EducationBeijing University of Civil Engineering and ArchitectureBeijing 100044China Graduate School of Bioagricultural Sciences Nagoya UniversityNagoya 811-2415Japan

Aims Boreal forests play an important role in the global carbon *** with the boreal forests in North America and Europe,relatively few research studies have been conducted in Siberian boreal *** related to the role of Siberian forests in the global carbon balance is thus essential for a full understanding of global carbon *** This study investigated the net ecosystem exchange(NEE)during growing season(May-September)in an eastern Siberian boreal larch forest for a 3-year period in 2004-2006 with contrasting meteorological *** FindingsThe study found that the forest served as a carbon sink during all of the 3 studied years;in addition,the meteorological conditions essentially influenced the specific annual value of the strength of the carbon sinks in each *** 2005 was the warmest year and much wetter than 2004,2005 also featured the greatest amount of ecosystem respiration,which resulted in a minimum value of *** study also found that the phenological changes observed during the three study years had a relatively small effect on annual *** expansion was 26 days earlier in 2005 than in the other 2 years,which resulted in a longer growing season in ***,the NEE in 2005 was counterbalanced by the large rate of ecosystem respiration that was caused by the higher temperatures in the *** study showed that meteorological variables had larger influences on the interannual variations in NEE for a Siberian boreal larch forest,as compared with phenological *** overall results of this study will improve our understanding of the carbon balance of Siberian boreal larch forests and thus can help to forecast the response of these forests to future climate change.

关键词： boreal forest eastern Siberia net ecosystem exchange phenology

Different Regional Patterns in Gray Matter-based Age Prediction

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Neuroscience Bulletin 2023年第6期39卷 984-988页

作者： Nianming Zuo tianyu hu Hao Liu Jing Sui Yong Liu Tianzi Jiang Brainnetome Center Institute of AutomationChinese Academy of SciencesBeijing100190China National Laboratory of Pattern Recognition Institute of AutomationChinese Academy of SciencesBeijing100190China University of Chinese Academy of Sciences Beijing100190China Chinese Institute for Brain Research Beijing102206China CAS Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationChinese Academy of SciencesBeijing100190China Key Laboratory for NeuroInformation of the Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu625014China Queensland Brain Institute University of QueenslandBrisbaneQLD4072Australia

Dear Editor,The brain experiences ongoing changes across different ages to support brain development and functional *** the span of adulthood,although the brain has matured from a neurobiological perspective,it is sti... 详细信息

关键词： shaped continuously Pattern

Allometry-based estimation of forest aboveground biomass combining LiDAR canopy height attributes and optical spectral indexes

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Forest Ecosystems 2022年第5期9卷 617-629页

作者： Qiuli Yang Yanjun Su tianyu hu Shichao Jin Xiaoqiang Liu Chunyue Niu Zhonghua Liu Maggi Kelly Jianxin Wei Qinghua Guo State Key Laboratory of Vegetation and Environmental Change Institute of BotanyChinese Academy of SciencesBeijing100093China University of Chinese Academy of Sciences Beijing100049China Plant Phenomics Research Centre Academy for Advanced Interdisciplinary StudiesCollaborative Innovation Centre for Modern Crop Production co-sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China Department of Environmental Sciences Policy and ManagementUniversity of CaliforniaBerkeleyCA94720-3114USA Division of Agriculture and Natural Resources University of CaliforniaBerkeleyCA94720-3114USA College of Geography and Remote Sensing Sciences Xinjiang UniversityUrumqiXinjiang830017China Xinjiang Lidar Applied Engineering Technology Research Center UrumqiXinjiang830002China Xinjiang Land and Resources Information Center UrumqiXinjiang830002China Institute of Remote Sensing and Geographic Information System School of Earth and Space SciencesPeking UniversityBeijing100871China

Accurate estimates of forest aboveground biomass(AGB)are essential for global carbon cycle studies and have widely relied on approaches using spectral and structural information of forest canopies extracted from various remote sensing ***,combining the advantages of active and passive data sources to improve estimation accuracy remains ***,we proposed a new approach for forest AGB modeling based on allometric relationships and using the form of power-law to integrate structural and spectral *** 60 km^(2) of drone light detection and ranging(LiDAR)data and 1,370 field plot measurements,covering the four major forest types of China(coniferous forest,sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and tropical broadleaf forest),were collected together with Sentinel-2 images to evaluate the proposed *** results show that the most universally useful structural and spectral metrics are the average values of canopy height and spectral index rather than their maximum *** with structural attributes used alone,combining structural and spectral information can improve the estimation accuracy of AGB,increasing R^(2) by about 10%and reducing the root mean square error by about 22%;the accuracy of the proposed approach can yield a R^(2) of 0.7 in different forests *** proposed approach performs the best in coniferous forest,followed by sub-tropical broadleaf forest,coniferous and broadleaf-leaved mixed forest,and then tropical broadleaf ***,the simple linear regression used in the proposed method is less sensitive to sample size and outperforms statistically multivariate machine learning-based regression models such as stepwise multiple regression,artificial neural networks,and Random *** proposed approach may provide an alternative solution to map large-scale forest biomass using space-borne LiDAR and optical images with high accuracy.

关键词： Forest aboveground biomass Drone LiDAR Allometric relationship Power law Tree height Vegetation index