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Online soft measurement for wastewater treatment system based on hybrid deep learning

Frontiers of Environmental Science & Engineering 2024年第2期18卷 77-89页

作者： Wenjie Mai zhenguo chen Xiaoyong Li Xiaohui Yi Yingzhong Zhao Xinzhong He Xiang Xu Mingzhi Huang SCNU Environmental Research Institute Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety&MOE Key Laboratory of Theoretical Chemistry of EnvironmentSchool of EnvironmentSouth China Normal UniversityGuangzhou 510006China Fujian Environmental Protection Design Institute Co.Ltd. Fuzhou 350000China Huashi(Fujian)Environment Technology Co.Ltd. Quanzhou362001China SCNU Qingyuan Institute of Science and Technology Innovation Co.Ltd. Qingyuan 511517China Econ Technology Co.Ltd. Yantai 265503China

The existing automated wastewater treatment control systems encounter challenges such as the utilization of specialized testing instruments, equipment repair complications, high operational costs, substantial operational errors, and low detection accuracy. An effective soft measure model offers a viable approach for real-time monitoring and the development of automated control in the wastewater treatment process. Consequently, a novel hybrid deep learning CNN-BNLSTM-Attention (CBNLSMA) model, which incorporates convolutional neural networks (CNN), bidirectional nested long and short-term memory neural networks (BNLSTM), attention mechanisms (AM), and Tree-structure Parzen Estimators (TPE), has been developed for monitoring effluent water quality during the wastewater treatment process. The CBNLSMA model is divided into four stages: the CNN module for feature extraction and data filtering to expedite operations;the BNLSTM module for temporal data’s temporal information extraction;the AM module for model weight reassignment;and the TPE optimization algorithm for the CBNLSMA model’s hyperparameter search optimization. In comparison with other models (TPE-CNN-BNLSTM, TPE-BNLSTM-AM, TPE-CNN-AM, PSO-CBNLSTMA), the CBNLSMA model reduced the RMSE for effluent COD prediction by 25.4%, decreased the MAPE by 32.9%, and enhanced the R2 by 14.9%. For the effluent SS prediction, the CBNLSMA model reduced the RMSE by 26.4%, the MAPE by 21.0%, and improved the R2 by 35.7% compared to other models. The simulation results demonstrate that the proposed CBNLSMA model holds significant potential for real-time effluent quality monitoring, indicating its high potential for automated control in wastewater treatment processes.

关键词： Prediction model Soft measurement CNN-BNLSTM-AM model TPE optimization algorithm

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Plant Chemical Defenses against Insect Herbivores—Using theWild Tobacco as a Model

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Phyton-International Journal of Experimental Botany 2024年第4期93卷 641-659页

作者： Guangwei Sun Xuanhao Zhang Yi Liu Liguang Chai Daisong Liu zhenguo chen Shiyou Lü Hubei Provincial Tobacco Research Institute Wuhan430030China Shiyan Branch of Hubei Tobacco Company Shiyan442000China State Key Laboratory of Biocatalysis and Enzyme Engineering School of Life SciencesHubei UniversityWuhan430062China

The Nicotiana genus, commonly known as tobacco, holds significant importance as a crucial economic crop. Confrontedwith an abundance of herbivorous insects that pose a substantial threat to yield, tobacco has developed adiverse and sophisticated array of mechanisms, establishing itself as a model of plant ecological defense. Thisreview provides a concise overview of the current understanding of tobacco’s defense strategies against herbivores.Direct defenses, exemplified by its well-known tactic of secreting the alkaloid nicotine, serve as a potent toxinagainst a broad spectrum of herbivorous pests. Moreover, in response to herbivore attacks, tobacco enhancesthe discharge of volatile compounds, harnessing an indirect strategy that attracts the predators of the herbivores.The delicate balance between defense and growth leads to the initiation of most defense strategies only after aherbivore attack. Among plant hormones, notably jasmonic acid (JA), play central roles in coordinating thesedefense processes. JA signaling interacts with other plant hormone signaling pathways to facilitate the extensivetranscriptional and metabolic adjustments in plants following herbivore assault. By shedding light on these ecologicaldefense strategies, this review emphasizes not only tobacco’s remarkable adaptability in its natural habitatbut also offers insights beneficial for enhancing the resilience of current crops.

关键词： Tobacco herbivore chemical defense jasmonic acid regulation

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Efficient NO_x abatement by passive adsorption over a Pd-SAPO-34 catalyst prepared by solid-state ion exchange

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Chinese Chemical Letters 2024年第4期35卷 210-213页

作者： Linhui Liu Wuwan Xiong Mingli Fu Junliang Wu zhenguo Li Daiqi Ye Peirong chen National Engineering Laboratory for VOCs Pollution Control Technology and Equipment Guangdong Provincial Key Laboratory of Atmospheric Environment and Pollution ControlSchool of Environment and EnergySouth China University of TechnologyGuangzhou 510006China National Engineering Laboratory for Mobile Source Emission Control Technology China Automotive Technology&Research Center Co.Ltd.Tianjin 300300China

Palladium-exchanged chabazite(Pd-CHA) zeolites as passive NO_x adsorbers(PNAs) enable efficient purification of nitrogen oxides(NO_x) in cold-start diesel exhausts. Their commercial application, however,is limited by the lack of facile preparation method. Here, high-performance CHA-type Pd-SAPO-34 zeolite was synthesized by a modified solid-state ion exchange(SSIE) method using PdO as Pd precursor,and demonstrated superior PNA performance as compared to Pd-SAPO-34 prepared by conventional wetchemistry strategies. Structural characterization using Raman spectroscopy and X-ray diffraction revealed that the SSIE method avoided water-induced damage to the zeolite framework during Pd loading. Mechanistic investigations on the SSIE process by in situ infrared spectroscopy and X-ray photoelectron spectroscopy disclosed that, while PdO precursor was mainly converted to Pd^(2+) cations coordinated to the zeolite framework by consuming the-OH groups of the zeolite, a portion of PdO could also undergo thermal decomposition to form highly dispersed Pd~0 clusters in the pore channels. This simplified and scalable SSIE method paves a new way for the cost-effective synthesis of defect-free high-performance Pd-SAPO-34 zeolites as PNA catalysts.

关键词： Pd-SAPO-34 Passive NO_x adsorber Pd loading Solid-state ion exchange Pd transformation

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A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score,revised Geneva score,and Years algorithm

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Chinese Medical Journal 2024年第6期137卷 676-682页

作者： Linfeng Xi Han Kang Mei Deng Wenqing Xu Feiya Xu Qian Gao Wanmu Xie Rongguo Zhang Min Liu zhenguo Zhai chen Wang Capital Medical University Beijing 100069China National Center for Respiratory Medicine State Key Laboratory of Respiratory Health and MultimorbidityNational Clinical Research Center for Respiratory DiseasesInstitute of Respiratory MedicineChinese Academy of Medical SciencesDepartment of Pulmonary and Critical Care MedicineCenter of Respiratory MedicineChina-Japan Friendship HospitalBeijing 100029China Institute of Advanced Research Infervision Medical Technology Co.Ltd.Beijing 100025China Department of Radiology China-Japan Friendship HospitalBeijing 100029China Chinese Academy of Medical Sciences and Peking Union Medical College Beijing 100730China Department of Radiology Peking University China-Japan Friendship School of Clinical MedicineBeijing 100191China

Background:Acute pulmonary embolism(APE)is a fatal cardiovascular disease,yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs.A simple,objective technique will help clinicians make a quick and precise diagnosis.In population studies,machine learning(ML)plays a critical role in characterizing cardiovascular risks,predicting outcomes,and identifying biomarkers.This work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment models.Methods:This is a single-center retrospective study.Patients with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets.A total of 8 ML models,including random forest(RF),Naïve Bayes,decision tree,K-nearest neighbors,logistic regression,multi-layer perceptron,support vector machine,and gradient boosting decision tree were developed based on the training set to diagnose APE.Thereafter,the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies,including the Wells score,revised Geneva score,and Years algorithm.Eventually,the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic(ROC)analysis.Results:The ML models were constructed using eight clinical features,including D-dimer,cardiac troponin T(cTNT),arterial oxygen saturation,heart rate,chest pain,lower limb pain,hemoptysis,and chronic heart failure.Among eight ML models,the RF model achieved the best performance with the highest area under the curve(AUC)(AUC=0.774).Compared to the current clinical assessment strategies,the RF model outperformed the Wells score(P=0.030)and was not inferior to any other clinical probability assessment strategy.The AUC of the RF model for diagnosing APE onset in internal validation set was 0.726.Conclusions:Based on RF algorithm,a novel prediction model was finally constructed for APE diagnosis.When compared to the current clinica

关键词： Acute pulmonary embolism Machine learning Wells score Revised Geneva score Years algorithm

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Insights into a novel nitrogen removal process based on simultaneous anammox and denitrification(SAD) following nitritation with in-situ NOB elimination

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Journal of Environmental Sciences 2023年第3期125卷 160-170页

作者： Jing chen Jia Zeng Yiran He Shiquan Sun Haipeng Wu Yaoyu Zhou zhenguo chen Jianhui Wang Hong chen School of Hydraulic and Environmental Engineering Changsha University of Science&TechnologyChangsha 410114China Engineering and Technical Center of Hunan Provincial Environmental Protection for River-Lake Dredging Pollution Control Changsha 410114China College of Resources and Environment Hunan Agricultural UniversityChangsha 410128China School of Environment and Energy South China University of TechnologyGuangzhou 510006China School of Food science and Bioengineering Changsha University of Science&TechnologyChangsha 410114China

Simultaneous anammox and denitrification(SAD) is an efficient approach to treat wastewater having a low C/N ratio;however, few studies have investigated a combination of SAD and partial nitritation(PN). In this study, a lab-scale up-flow blanket filter(UBF) and zeolite sequence batch reactor(ZSBR) were continuously operated to implement SAD and PN advantages, respectively. The UBF achieved a high total nitrogen(TN) removal efficiency of over 70% during the start-up stage(days 1–50), and reached a TN removal efficiency of 96%in the following 90 days(days 51–140) at COD/NH_(4)^(+)-N ratio of 2.5. The absolute abundance of anammox bateria increased to the highest value of 1.58 × 107copies/μL DNA;Comamonadaceae was predominant in the UBF at the optimal ratio. Meanwhile, ZSBR was initiated on day 115 as fast nitritation process to satisfy the influent requirement for the UBF. The combined process was started on day 140 and then lasted for 30 days, during the combined process, between the two reactors, the UBF was the main contributor for TN(66.5% ± 4.5%)and COD(71.8% ± 4.9%) removal. These results demonstrated that strong SAD occurred in the UBF when following a ZSBR with in-situ NOB elimination. This research presents insights into a novel biological nitrogen removal process for low C/N ratio wastewater treatment.

关键词： Simultaneous anammox and denitrification Up-flow blanket filter Zeolite sequence batch reactor Biological nitrogen removal Wastewater treatment

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细胞分裂周期相关蛋白5(CDCA5)在人类肿瘤中致癌作用的泛癌分析

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西南大学学报（自然科学版） 2024年第6期46卷 98-108页

作者：陈铭丁勇高振国广州中医药大学第一附属医院重庆医院(重庆市北碚区中医院)耳鼻咽喉科重庆400700 川北医学院附属南充市中心医院四川南充637100

CDCA5高表达已被发现在多种肿瘤中具有致癌作用.运用生物信息学方法探讨CDCA5在泛癌中的表达特征,通过TCGA和GTEX数据库获得CDCA5在肿瘤样本和癌旁样本中的表达情况,从基因表达、生存分析、基因变异和免疫浸润等方面探讨了CDCA5在不同... 详细信息

CDCA5高表达已被发现在多种肿瘤中具有致癌作用.运用生物信息学方法探讨CDCA5在泛癌中的表达特征,通过TCGA和GTEX数据库获得CDCA5在肿瘤样本和癌旁样本中的表达情况,从基因表达、生存分析、基因变异和免疫浸润等方面探讨了CDCA5在不同肿瘤发病机制中的作用;通过GSCA(Gene Set Cancer Analysis)分析CDCA5表达与药物IC_(50)(半数抑制浓度)的关系并用免疫组化验证了该基因;通过STRING和GEPIA2数据库获取与CDCA5相关的基因并进行KEGG(Kyoto Encyclopedia of Genes and Genomes)富集分析.结果表明:CDCA5在大多数肿瘤中高表达,且与部分肿瘤患者的预后存在明显的相关性.CDCA5表达与肿瘤浸润免疫细胞(TIICs)显著相关并且TIICs标记物表现出不同的免疫浸润模式.拷贝数变异(CNV)与CDCA5的表达呈正相关,甲基化与CDCA5的表达呈负相关.IC_(50)数据显示多种药物可显著抑制CDCA5的表达.免疫组化实验证实了CDCA5在肝恶性肿瘤和结肠恶性肿瘤中具有促癌作用,可作为肿瘤治疗的生物标志物,为肿瘤治疗提供了一种新的研究方向.

关键词：细胞分裂周期相关蛋白5 泛癌分析免疫浸润肿瘤

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傅里叶变换下的粗细双路径图像修复算法

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计算机工程与应用 2024年第1期60卷 217-226页

作者：陈刚盛况杨振国刘文印广东工业大学计算机学院广州510006 广东开放大学人工智能学院广州510091 鹏城实验室网络空间安全研究中心广东深圳518005

针对传统的粗细双路径图像修复算法在修复图像时提取全局特征能力弱和所修复图像与原图像存在频域差,导致修复的图像全局结构差和存在伪影的问题,提出了傅里叶变换下的粗细双路径图像修复算法。为了改善编码器特征提取能力,设计了具有... 详细信息

针对传统的粗细双路径图像修复算法在修复图像时提取全局特征能力弱和所修复图像与原图像存在频域差,导致修复的图像全局结构差和存在伪影的问题,提出了傅里叶变换下的粗细双路径图像修复算法。为了改善编码器特征提取能力,设计了具有压缩奖惩机制的编码器来提升网络采集全局信息的能力;在编码器训练时首次引入焦频损失来监督图像的修复,缩小了修复图像与原图像的频域差,提升了算法修复高频成分的能力,改善了修复图像伪影和模糊性。将该算法应用于CelebA数据集,所提的算法修复的图像比基线算法所修复的图像的峰值信噪比(PNSR)、结构相似性(SSIM)等性能分别提高了1.18%~6.14%,0.11%~2.24%,而距离得分(FID)降低了34.58%~38.79%。实验结果表明,所提算法以微小的时间成本获取了较好的性能提升,增强了修复图像的全局结构性和清晰度。

关键词：压缩奖惩块傅里叶变换焦频损失图像修复

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计及网络动态特性的电-热互联系统仿射优化法

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中国电机工程学报 2024年第12期44卷 4715-4731,I0010页

作者：陈飞雄蔡明杰郭奕鑫邵振国福建省电器智能化工程技术研究中心(福州大学电气工程与自动化学院) 福建省福州市350108

热网动态特性带来能量传输延时的同时,使得空间上的源荷不确定性对系统造成的影响存在时序上的先后差异,导致电-热互联系统日前调度转变为复杂的时空耦合问题。为此,该文提出一种计及网络动态特性的仿射优化法。首先,利用流量分段法与... 详细信息

热网动态特性带来能量传输延时的同时,使得空间上的源荷不确定性对系统造成的影响存在时序上的先后差异,导致电-热互联系统日前调度转变为复杂的时空耦合问题。为此,该文提出一种计及网络动态特性的仿射优化法。首先,利用流量分段法与节点网络模型,构建计及能量传输延时且适用于环网的热网动态模型。在此基础上,利用仿射算法与牛顿-拉夫逊能流计算原理,通过线性化建模方式与模型凸化方法,构建仿射凸优化模型。其次,利用仿射优化法解决电-热互联系统不确定性优化调度问题,实现对源荷不确定性传播轨迹的跟踪及其影响的量化分析。最后,33节点电-热互联系统仿真算例证明,所提方法能够揭示网络动态特性下源荷不确定性影响机理,并获得具有低保守性、高经济性的调度方案。

关键词：电-热互联系统热网动态特性不确定源跟踪仿射算法日前调度

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基于特征集重构与多标签分类模型的谐波源定位方法

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电力自动化设备 2024年第2期44卷 147-154页

作者：邵振国林潇张嫣陈飞雄林洪洲福州大学电气工程与自动化学院福建福州350108 福建省电器智能化工程技术研究中心福建福州350108

传统基于谐波状态估计的谐波源定位方法需要专门的同步相量量测装置,工程应用受到限制。为此,基于电能质量监测装置所采集的非同步量测数据,提出了基于特征集重构与多标签分类模型的谐波源定位方法。利用监测数据的充分统计量来挖掘量... 详细信息

传统基于谐波状态估计的谐波源定位方法需要专门的同步相量量测装置,工程应用受到限制。为此,基于电能质量监测装置所采集的非同步量测数据,提出了基于特征集重构与多标签分类模型的谐波源定位方法。利用监测数据的充分统计量来挖掘量测时段的谐波信息,同时利用标签特定特征学习算法重构特征集,从而消除冗余特征以及无关特征对于谐波源定位精度的影响;提出基于邻接矩阵以及灵敏度分析的测点配置方法,结合电路网络拓扑信息实现测点的优化配置;提出基于改进极限学习机的谐波源定位方法,该方法以重构特征集为输入,建立多标签分类模型,实现谐波源定位。通过仿真与算例分析,验证了所提方法的可行性及有效性。

关键词：电能质量谐波源定位非同步谐波监测数据极限学习机标签特定特征学习算法

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博物馆数据库建设标准化尝试——以中国人民抗日战争纪念馆图片、音视频数据库为例

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中国博物馆 2024年第2期 97-105页

作者：陈洋郝振国郭亚茜中国人民抗日战争纪念馆北京100165 同方知网数字出版技术股份有限公司北京100192

博物馆数据库建设在我国已有一定的积累,并有提高标准水平的现实需求。建设数据库所需要的元数据规范,国内外许多成果可资借鉴。建立包含有形无形文化遗产的全资源博物馆数据库是数字博物馆、智慧博物馆的基础,建立一套数据库元数据编... 详细信息

博物馆数据库建设在我国已有一定的积累,并有提高标准水平的现实需求。建设数据库所需要的元数据规范,国内外许多成果可资借鉴。建立包含有形无形文化遗产的全资源博物馆数据库是数字博物馆、智慧博物馆的基础,建立一套数据库元数据编目规则则是博物馆数据库建设的基础。中国人民抗日战争纪念馆图片、音视频数据库管理系统的框架结构、功能作用及其背后的元数据著录规范,可为博物馆数据库建设提供实践经验。

关键词：博物馆博物馆数据库元数据元数据规范

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