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Molecular behavior and interactions with microbes during anaerobic degradation of bio-derived DOM in waste leachate

Journal of Environmental Sciences 2023年第4期126卷 174-183页

作者： Junjie Qiu Tianqi Li Fan Lü Yulong Huang Chao Li Hua Zhang Liming Shao Pinjing He institute of Waste Treatment and Reclamation Tongji UniversityShanghai 200092China Shanghai institute of Pollution control and Ecological Security Shanghai 200092China Shanghai engineering Research Center of Multi-source Solid Wastes Co-Processing and Energy Utilization Shanghai 200092China

It is the key to control bio-derived dissolved organic matters (DOM) in order to reduce the effluent concentration of wastewater treatment, especially for waste leachate with high organic contaminants. In the present study, the anaerobic degradation of aerobically stabilized DOM was investigated with DOM substrate isolated through electrodialysis. The degradation of bio-derived DOM was confirmed by reduction of 15%of total organic carbon in 100 *** characterized the molecular behavior of bio-derived DOM by coupling molecular and biological information analysis. Venn based Sankey diagram of mass features showed the transformation of bio-derived DOM mass features. Occurrence frequency analysis divided mass features into six categories so as to distinguish the fates of intermediate metabolites and persistent compounds. Reactivity continuum model and machine learning technologies realized the semi-quantitative determination on the kinetics of DOM mass features in the form of pseudo-first order, and confirmed the reduction of inert mass features. Furthermore, network analysis statistically establish relationship between DOM mass features and microbes to identify the active microbes that are able to utilize bio-derived DOM. This work confirmed the biological technology is still effective in controlling recalcitrant bio-derived DOM during wastewater treatment.

关键词： Soluble microbial products Data mining machine learning Solid waste Orbitrap

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A Novel Hybrid Ensemble Learning Approach for Enhancing Accuracy and Sustainability in Wind Power Forecasting

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Computers, Materials & Continua 2024年第5期79卷 3373-3395页

作者： Farhan Ullah Xuexia Zhang Mansoor Khan Muhammad Abid Abdullah Mohamed School of Electrical engineering Southwest Jiaotong UniversityChengdu611756China College of Intelligent Manufacturing and control engineering Qilu Institute of TechnologyJinan250200China College of Aerospace and Civil engineering Harbin Engineering UniversityHarbin150001China Research Centre Future University in EgyptNew Cairo11835Egypt

Accurate wind power forecasting is critical for system integration and stability as renewable energy reliance *** approaches frequently struggle with complex data and non-linear connections. This article presentsa novel approach for hybrid ensemble learning that is based on rigorous requirements engineering *** approach finds significant parameters influencing forecasting accuracy by evaluating real-time Modern-EraRetrospective Analysis for Research and Applications (MERRA2) data from several European Wind farms usingin-depth stakeholder research and requirements elicitation. Ensemble learning is used to develop a robust model,while a temporal convolutional network handles time-series complexities and data gaps. The ensemble-temporalneural network is enhanced by providing different input parameters including training layers, hidden and dropoutlayers along with activation and loss functions. The proposed framework is further analyzed by comparing stateof-the-art forecasting models in terms of Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE),respectively. The energy efficiency performance indicators showed that the proposed model demonstrates errorreduction percentages of approximately 16.67%, 28.57%, and 81.92% for MAE, and 38.46%, 17.65%, and 90.78%for RMSE for MERRAWind farms 1, 2, and 3, respectively, compared to other existingmethods. These quantitativeresults show the effectiveness of our proposed model with MAE values ranging from 0.0010 to 0.0156 and RMSEvalues ranging from 0.0014 to 0.0174. This work highlights the effectiveness of requirements engineering in windpower forecasting, leading to enhanced forecast accuracy and grid stability, ultimately paving the way for moresustainable energy solutions.

关键词： Ensemble learning machine learning real-time data analysis stakeholder analysis temporal convolutional network wind power forecasting

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Predicting the Bioaccessibility of Soil Cd,Pb,and As with Advanced machine Learning for Continental-Scale Soil Environmental Criteria Determination in China

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Environment & Health 2024年第9期2卷 631-641页

作者： Kunting Xie Jiajun Ou Minghao He Weijie Peng Yong Yuan Guangdong Key Laboratory of Environmental Catalysis and Health Risk control School of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China School of Automation Guangdong University of TechnologyGuangzhou 510006China Health Risk control School of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China

Investigating the bioaccessibility of harmful inorganic elements in soil is crucial for understanding their behavior in the environment and accurately assessing the environmental risks associated with *** batch experimental methods and linear models,however,are time-consuming and often fall short in precisely quantifying *** this study,using 937 data points gathered from 56 journal articles,we developed machine learning models for three harmful inorganic elements,namely,Cd,Pb,and *** thorough analysis,the model optimized through a boosting ensemble strategy demonstrated the best performance,with an average R2 of 0.95 and an RMSE of *** further employed SHAP values in conjunction with quantitative analysis to identify the key features that influence *** utilizing the developed integrated models,we carried out predictions for 3002 data points across China,clarifying the bioaccessibility of cadmium(Cd),lead(Pb),and arsenic(As)in the soils of various sites and constructed a comprehensive spatial distribution map of China using the inverse distance weighting(IDW)interpolation *** on these findings,we further derived the soil environmental standards for metallurgical sites in *** observations from the collected data indicate a reduction in the number of sites exceeding the standard levels for Cd,Pb,and As in mining/smelting sites from 5,58,and 14 to 1,24,and 7,*** research offers a precise and scientific approach for cross-regional risk assessment at the continental scale and lays a solid foundation for soil environmental management.

关键词： machine learning soil environmental criteria potentially harmful elements bioaccessibility sites

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Predicting microbial extracellular electron transfer activity in paddy soils with soil physicochemical properties using machine learning

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Science China(Technological Sciences) 2024年第1期67卷 259-270页

作者： OU JiaJun LUO XiaoShan LIU JunYang HUANG LinYan ZHOU LiHua YUAN Yong School of Automation Guangdong University of TechnologyGuangzhou 510006China Guangdong Key Laboratory of Environmental Catalysis and Health Risk control Guangzhou Key Laboratory Environmental Catalysis and Pollution ControlSchool of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China School of Biomedical and Pharmaceutical Sciences Guangdong University of TechnologyGuangzhou 510006China

Soil extracellular electron transfer(EET)is a pivotal biological process within the realm of ***,EET suffers from a lack of predictive ***,an intricately crafted machine learning model has been developed for the purpose of predicting soil EET by using the physicochemical properties of soil as independent input variables and the EET capabilities in terms of current density(j_(max))and Coulombic charge(C_(out))as dependent output *** autoencoder ensemble stacking(AES)model was developed to address the aforementioned issue by integrating support vector machine,multilayer perceptron,extreme gradient boosting,and light gradient boosting machine algorithms as the stacking *** 10-fold crossvalidation,the AES model exhibited notable improvements in predicting j_(max)and C_(out),with average test R^(2)values of 0.83 and 0.84,respectively,surpassing those of single machine learning(ML)models and the basic ensemble *** utilizing partial correlation plots(PDPs),Shapley Additive explanations(SHAP)values,and SHAP decision plots,we quantitatively explained the impact and contribution of the input molecules on the AES model’s predictions of j_(max)and C_(out).In the context of the SHAP method for the AES model,total carbon(TC)was identified as the most correlated descriptor for j_(max),while total organic carbon(TOC)stood out as the most relevant descriptor for C_(out).In the prediction tasks of j_(max)and C_(out)within the AES model,employing a multitask ML approach allowed the model to benefit from the shared information of input variables,thereby enhancing its overall *** study provides a feasible tool for the prediction of soil EET from soil physiochemical properties and an advanced understanding of the relationship between soil physiochemical properties and EET capability.

关键词： extracellular electron transfer paddy soil machine learning prediction autoencoder ensemble stacking model

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Rational design of chemically complex metallic glasses by hybrid modeling guided machine learning

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npj Computational Materials 2021年第1期7卷 1242-1251页

作者： Z.Q.Zhou Q.F.He X.D.Liu Q.Wang J.H.Luan C.T.Liu Y.Yang Department of Mechanical engineering College of EngineeringCity University of Hong KongKowloonHong KongChina College of Mechatronics and control engineering Shenzhen UniversityShenzhenChina Laboratory for Structures Institute of Materials ScienceShanghai UniversityShanghaiChina Department of Materials Science and engineering College of EngineeringCity University of Hong KongKowloonHong KongChina Department of Advanced Design and System engineering College of EngineeringCity University of Hong KongChina

The compositional design of metallic glasses(MGs)is a long-standing issue in materials science and ***,traditional experimental approaches based on empirical rules are time consuming with a low *** this work,we successfully developed a hybrid machine learning(ML)model to address this fundamental issue based on a database containing~5000 different compositions of metallic glasses(either bulk or ribbon)reported since *** the prior works relying on empirical parameters for featurization of data,we designed modeling guided data descriptors in line with the recent theoretical models on amorphization in chemically complex alloys for the development of the hybrid classification-regression ML *** hybrid ML modeling was validated both numerically and *** importantly,it enabled the discovery of MGs(either bulk or ribbon)through the ML-aided deep search of a multitude of quaternary to scenery alloy *** computational framework herein established is expected to accelerate the design of MG compositions and expand their applications by probing the complex and multi-dimensional compositional space that has never been explored before.

关键词： alloy glasses metallic

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A biologically inspired model for pattern recognition

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2010年第2期11卷 115-126页

作者： Eduardo GONZALEZ Hans LILJENSTROM Yusely RUIZ Guang LI Department of Biomedical engineering Zhejiang University Hangzhou 310027 China Department of Energy and Technology Swedish University of Agricultural Sciences Box 7013 SE-750 Sweden National Laboratory of industrial control Technology Institute of Cyber-Systems and Control Zhejiang University Hangzhou 310027 China

In this paper, a novel bionic model and its performance in pattern recognition are presented and discussed. The model is constructed from a bulb model and a three-layered cortical model, mimicking the main features of the olfactory system. The olfactory bulb and cortex models are connected by feedforward and feedback fibers with distributed delays. The Breast Cancer Wisconsin dataset consisting of data from 683 patients divided into benign and malignant classes is used to demonstrate the capacity of the model to learn and recognize patterns, even when these are deformed versions of the originally learned patterns. The performance of the novel model was compared with three artificial neural networks (ANNs), a back-propagation network, a support vector machine classifier, and a radial basis function classifier. All the ANNs and the olfactory bionic model were tested in a benchmark study of a standard dataset. Experimental results show that the bionic olfactory system model can learn and classify patterns based on a small training set and a few learning trials to reflect biological intelligence to some extent.

关键词： Olfactory system Neural network Bionic model Pattern recognition

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Performance analysis of diffusion-based decode-and-forward relay with depleted molecule shift keying

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Digital Communications and Networks 2021年第3期7卷 399-409页

作者： Jiaxing Wang Mugen Peng Yaqiong Liu State Key Laboratory of Networking and Switching Technology Beijing University of Posts and TelecommunicationsBeijing100876China School of Electronic and control engineering North China Institute of Aerospace Engineering.Langfang Aerospace Testing Technology and Instrument Research and Development CenterLangfang065000China

The distance-decay effect of molecular signals makes communication range a major challenge for diffusion-based Molecular Communication(MC).To solve this problem,the intermediate nano-machine is deployed as a relay between the transmitter and its intended receiver *** this work,we employ the Depleted Molecule Shift Keying(D-MoSK)to model a Decode-and-Forward(DF)relay communication *** closed-form expression of Bit Error Rate(BER)for the concerned DF relay with D-MoSK is ***,the maximum a posteriori probability,minimum error probability,and maximum likelihood schemes are formulated for data *** relationships between BER and other key parameters,including the number of released molecules,receiving radius,and relay position,are investigated in *** results show that the proposed scheme can improve communication reliability ***,the performance gain can be maximized by optimizing the position of the relay and the receiving radius.

关键词： Diffusion Molecular communication Decode-and-Forward relay Bit error rate

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A review of CFD simulation in pressure driven membrane with fouling model and anti-fouling strategy

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Frontiers of Environmental Science & engineering 2024年第8期18卷 1-29页

作者： Shiyong Miao Jiaying Ma Xuefei Zhou Yalei Zhang Huaqiang Chu State Key Laboratory of Pollution control and Resource Reuse Tongji UniversityShanghai 200092China Shanghai institute of Pollution control and Ecological Security Tongji UniversityShanghai 200092China

Pressure-driven membrane filtration systems are widely utilized in wastewater treatment,desalination,and water reclamation and have received extensive attention from *** fluid dynamics(CFD)offers a convenient approach for conducting mechanistic studies of flow and mass transfer characteristics in pressure-driven *** a signature phenomenon in membrane systems,the concentration polarization that accompanies the permeation process is a key factor in membrane performance degradation and membrane fouling *** fouling models(scaling,biofouling and colloidal particle fouling)based on CFD theory have been constructed,and considerable research has been *** representative antifouling strategies with special simulation methods,including patterned membranes,vibration membranes,rotation membranes,and pulsatile flows,have also been *** studies should focus on refining fouling models while considering local hydrodynamic characteristics;experimental observation tools focusing on the internal structure of inhomogeneous fouling layers;techno-economic model of antifouling strategies such as vibrational,rotational and pulsatile flows;and unfavorable hydraulic phenomena induced by rapidly changing flows in simulations.

关键词： Computational fluid dynamics Membrane Fouling model Concentration polarization

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MOF-based materials facilitate efficient anaerobic digestion of organic wastes:integrating substrate bioavailability and microbial syntrophism

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Frontiers of Environmental Science & engineering 2024年第8期18卷 153-157页

作者： Haoyu Liu Ying Xu Xiang Chen Xiankai Wang Hang Wang Xiaohu Dai State Key Laboratory of Pollution control and Resource Reuse School of Environmental Science and EngineeringTongji UniversityShanghai 200092China Shanghai institute of Pollution control and Ecological Security Shanghai 200092China YANGTZE Eco-Environment engineering Research Center China Three Gorges CorporationWuhan 430010China National engineering Research Center of Eco-Environment Protection for Yangtze River Economic Belt Wuhan 430010China

Anaerobic digestion(AD)of organic waste(OW)for methane production is generally *** of AD systems with traditional materials(e.g.,electroconductive materials)is a current focus of research and has been reported to assist methanogenesis by enhancing the productivity of microbial metabolism among syntrophic *** in the AD of organic wastewater,in which microbial cells come into direct contact with the dissolved substrate,in the complex multiphase AD system of OW,low substrate bioavailability is an inevitable obstacle to microbial syntrophism for ***,we propose that improving substrate bioavailability and microbial syntrophism is a powerful strategy for ensuring material-assisted efficient AD of *** on the above considerations,metal-organic frameworks(MOFs),with their exceptionally high porosity,outstanding multi-functionality,and tuneable structures,have excellent potential for application in multiphase anaerobic systems of OW to integrate substrate bioavailability and microbial syntrophism and drive efficient *** addition,MOFs with designable and tuneable natures have great potential for use in identifying suitable materials for anaerobic systems for different types of OW via machine-learning technologies,thereby effectively enhancing methanogenesis from ***,the sustainable application of MOFs in AD and the corresponding environmental risks warrant further investigation.

关键词： Anaerobic digestion Methane production Biodegradability Functional materials Metal-organic frameworks

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Parameters selection in gene selection using Gaussian kernel support vector machines by genetic algorithm

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Journal of Zhejiang University-Science B(Biomedicine & Biotechnology) 2005年第10期6卷 961-973页

作者：毛勇周晓波皮道映孙优贤 WONG Stephen T.C. National Laboratory of Industrial control Technology Institute of Modern Control Engineering Zhejiang University Hangzhou 310027 China Harvard Center for Neurodegeneration and Repair Harvard Medical School and Brigham and Women’s Hospital Harvard Medical School Harvard University Boston MA 02115 USA

In microarray-based cancer classification, gene selection is an important issue owing to the large number of variables and small number of samples as well as its non-linearity. It is difficult to get satisfying results by using conventional linear sta- tistical methods. Recursive feature elimination based on support vector machine (SVM RFE) is an effective algorithm for gene selection and cancer classification, which are integrated into a consistent framework. In this paper, we propose a new method to select parameters of the aforementioned algorithm implemented with Gaussian kernel SVMs as better alternatives to the common practice of selecting the apparently best parameters by using a genetic algorithm to search for a couple of optimal parameter. Fast implementation issues for this method are also discussed for pragmatic reasons. The proposed method was tested on two repre- sentative hereditary breast cancer and acute leukaemia datasets. The experimental results indicate that the proposed method per- forms well in selecting genes and achieves high classification accuracies with these genes.

关键词： Gene selection, Support vector machine (SVM), Recursive feature elimination (RFE), Genetic algorithm (GA),Parameter selection

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