Water quality anomaly detection refers to the identification of abnormal changes in water parameters, which is crucial for ensuring environmental safety and preventing contamination events. With the growing volume of water environment sensing data and increasing demand for intelligent, transparent water quality management systems, achieving accurate, rapid, and interpretable anomaly detection has become a critical challenge in early warning systems. To tackle this challenge, this work proposes an anomaly detection model named Shapelet Temporal Evolution Graph Network (STEG), which constructs time-aware Shapelets and adopts graph attention networks to build Shapelets evolution graphs, learning multidimensional dynamic relationships within and between time segments. By incorporating both local and global temporal evolution factors, the approach ensures the interpretability of both the detection process and its resulting outputs. Experiments on two real-world datasets show that STEG outperforms state-of-the-art methods in terms of anomaly detection accuracy and generalization. Moreover, it provides clear and transparent reasoning for water quality anomaly detection.

In many fields, spatiotemporal prediction is gaining more and more attention,e.g., air pollution, weather forecasting, and traffic forecasting. Water quality prediction is a spatiotemporal prediction task. However, there are several challenges in water quality prediction: 1) Water quality time series has a complex nonlinear relationship, making it difficult to predict; 2) Water quality sensors are distributed on the river networks and have a strong spatial dependence on water quality prediction; and 3) Poor long-term forecast accuracy. To solve these problems, this work proposes a spatiotemporal prediction model called a Fusion Spatio-temporal Graph Convolution Neural network (FSGCN). First, This work uses a temporal attention mechanism to solve the nonlinear problem of water quality time series. Second, It adopts a graph convolution to extract spatial dependencies of river networks, and the fusion of spatiotemporal can more easily capture spatiotemporal features. Third, it adopts a temporal convolution residual mechanism, improving long-term series prediction accuracy. This work adopts two real-world datasets to evaluate the proposed FSGCN, and experiments demonstrate that FSGCN outperforms several state-of-the-art methods in terms of prediction accuracy.Note to Practitioners—This work considers the critical problem of spatiotemporal water quality prediction. Accurate water quality prediction can effectively prevent environmental pollution. Traditional water quality prediction only focuses on time series features without considering spatial features. In this work, a novel spatiotemporal prediction approach is proposed that combines spatial-temporal graph fusion construction networks for water quality time series prediction in a real-time manner. This work shows that this approach can achieve longer forecasting sequences and more accurate results than traditional forecasting methods. As a practical consequence of this research, spatiotemporal graph fusion convolution networks for water quality prediction can effectively integrate multi-dimensional data and improve the accuracy of long-term water quality prediction. This approach can also be applied to other fields, including intelligent transportation, smart manufacturing, finance, the Internet of Things, and urban computing.

Sulfur dioxide (SO2) emissions are the main problems causing the air pollution and respiratory disease, which makes desulfurization important in the coal combustion and chemical industry processes. The wet-flue-gas desulfurization (WFGD) is currently an effective method to deal with this issue. However, the WFGD system is actually a complex process with multi-variable coupling, high nonlinearity and large time-delay, which brings great challenges for the traditional mechanism-based modeling and control. In this paper, a neurodynamics-driven model predictive control (NDMPC) with soft-measurement is proposed to predict and control the dynamics of SO2to improve the operational performance of the WFGD system. First, we design a self-organizing fuzzy neural network (SOFNN) as the soft-measuring model, which is driven by the data and neurodynamics to adaptively predict SO2in the WFGD system. Second, the designed SOFNN is considered as a predictive model that can give the predicted values for the future moments. Third, we also design a loss function where the one-step output of the predictive model and the control law are the independent variables. The resulting rolling optimization can give the optimal control law sequence by minimizing the loss function. Finally, simulation experiments on the practical data from the WFDG system show that the NDMPC outperforms the other methods in terms of the soft-measurement and control performance, especially generally reducing the SO2emission and economic cost by 60.25% and 22.27%, respectively.

For nonlinear coupled ODE-PDE systems with stochastic actuator failures, this article introduces an intermittent sampled-data fuzzy (ISDF) control under spatially local averaged measurements (LAMs). To precisely characterize the nonlinear coupled ODE-PDE system, a Takagi-Sugeno fuzzy model is firstly introduced. Second, considering the stochastic actuator failures caused by Markov jump, an ISDF control strategy is developed for the nonlinear coupled ODE-PDE system under spatially LAMs, which only demands a minimal number of sensors and actuators. Then, through constructing a time-dependent switched Lyapunov functional, the stochastically exponential stability conditions are given in terms of linear matrix inequalities (LMIs) and ISDF control gains are obtained by solving these LMIs. Finally, the effectiveness of the developed approach is validated through its application to hypersonic rocket car (HRC) control.

The safety performance function (SPF) is an extensively employed tool in road safety assessment. However, traditional modeling methods often fall short of effectively capturing the intricate interdependencies among diverse traffic variables. To address this limitation, a feature importance analyzable resilient deep neural network (RDNN) is proposed as an alternative approach. This model begins with an explainable autoencoder that delineates the relationship between observed collisions and road characteristics. Subsequently, it introducesa prioriunsupervised feature importance analysis process that enriches the original input data. The enhanced input is then processed by a novel RDNN, featuring an automated Gaussian transfer function and resilient supervised learning, both meticulously designed for precise modeling. Ultimately, the efficacy of the proposed framework is demonstrated through several case studies on real-world applications, utilizing data collected from highways in Canada and the U.S.

Water quality prediction is an indispensable task in water environment and source management. The existing predictive models are mainly designed by data-driven artificial neural networks (ANNs), especially deep learning models for large-scale water quality prediction. However, the state of water environment is a dynamic process where the stationarity of water quality data suffers from time variation and human activities, which leads to a poor prediction accuracy because ANNs receive whole water quality data passively, including abnormal conditions. We consider such a tough problem in this article and propose an event-triggered deep fuzzy neural network (ET-DFNN) to pursue the better performance of water quality prediction in the complex water environment. First, a deep pretraining model is constructed to extract the effective features from raw water quality data. Second, we construct a DFNN model where the extracted effective features are considered as the input variables. Third, some events are defined to characterize the abnormal conditions of state evolution in water quality. The DFNN is trained and updated using different learning strategies only when the corresponding events are triggered, otherwise it ignores the current data sample and directly goes to the next data sample. The practical data-based experimental results show that the ET-DFNN achieves better prediction performance in accuracy and efficiency than its peers. Especially, the training efficiency of ET-DFNN is improved by 57.94% on total phosphorus prediction and 48.31% on biochemical oxygen demand prediction, respectively.

To construct an accurate crash prediction model, the road safety performance function (SPF), which provides a safety guide for the management department, is often used. In traditional parametric SPFs, the importance of traffic features is calculated using analytic expression, but the model is inaccurate and low in generalization. This article proposes a machine learning-based method to replace parametric SPFs, this framework is built based on integrated visual feature importance, global model training, and a structure self-organizing scheme. From the analysis, this model can not only predict multiregional car crashes accurately but can also provide a feature importance and selection guide for the management department to better understand it. At last, experiments using real-world data collected from Highway 401 Ontario Canada and several highways in the U.S. show that the proposed framework outperformed other State-of-the-Art models in terms of interpretability, accuracy, generalizability, and model conciseness.

Deep belief network (DBN) is an effective deep learning model, which can learn the complex data by extracting features hierarchically. However, the successful application of DBN depends on the suitable size of the structure (the number of hidden neurons), which is still an open problem. Currently, the network structure size is basically determined by experience with a time-consuming process. In this article, a complexity-based structural optimization (CBSO) algorithm, based on multiobjective ordinal optimization (MOO), is developed for designing the DBN structure. First, the problem formulation of structural optimization of DBN is given, where the multiple objectives are to minimize the fitting error and complexity. Second, the lower bound for alignment probability in optimizing DBN structure is developed according to MOO. Finally, an effective method to maximize the probability of correct select is given to pursue the good tradeoff between the complexity and the performance. The performance of proposed CBSO algorithm is demonstrated via predicting and controlling water quality of wastewater treatment process (WWTP) using the CBSO-DBN-based model predictive control (MPC) strategy. The simulation results show that the resulting CBSO-DBN can find the better structure design by using CBSO algorithm with smaller fitting error and limited computational complexity, and thereby achieve the better performance in WWTP than its peers. Especially, the CBSO-DBN-MPC improves the control accuracy by 76.16% and computational complexity by 50.45%, respectively.

Owing to high complexity and time-variant operation, as well as increasingly requirements for water quality, stability, and reliability, the wastewater treatment process (WWTP) is regarded as an adaptive control problem. In this study, a data-driven adaptive control with deep learning (DRAC-DL) is developed to improve the operational performance of the WWTP. First, a feedback controller is designed to construct the closed-loop control scheme. Second, an adaptive deep belief network (ADBN), based on the data-driven self-incremental learning strategy, is proposed to approximate the ideal control law. Third, the stability of the DRAC-DL scheme is analyzed in detail. The main advantage of DRAC-DL lies in its improved robustness and efficiency, which benefit from the Lyapunov-based closed-loop strategy and the efficient ADBN controller. Finally, the feasibility and applicability of DRAC-DL are verified by two parts: 1) simulation on the nonlinear system and 2) application to the WWTP on the benchmark simulation model No.1. The experimental results show the applicability and effectiveness, among which DRAC-DL reduces the output fluctuation (variance) by no less than 82% and realizes the better stability and robustness.

Single-cell ribonucleic acid (RNA) sequencing (scRNA-seq) has been a powerful technology for transcriptome analysis. However, the systematic validation of diverse computational tools used in scRNA-seq analysis remains challenging. Here, we propose a novel simulation tool, termed as Simulation of Cellular Heterogeneity (SimCH), for the flexible and comprehensive assessment of scRNA-seq computational methods. The Gaussian Copula framework is recruited to retain gene coexpression of experimental data shown to be associated with cellular heterogeneity. The synthetic count matrices generated by suitable SimCH modes closely match experimental data originating from either homogeneous or heterogeneous cell populations and either unique molecular identifier (UMI)-based or non-UMI-based techniques. We demonstrate how SimCH can benchmark several types of computational methods, including cell clustering, discovery of differentially expressed genes, trajectory inference, batch correction and imputation. Moreover, we show how SimCH can be used to conduct power evaluation of cell clustering methods. Given these merits, we believe that SimCH can accelerate single-cell research.

Wastewater treatment processes (WWTPs) have been considered as complex control problems, because effluent water standard, stability and multioperational conditions need to be taken into account. In this article, an event-driven model predictive control with deep learning (EMPC-DL) is proposed for the control problems to improve the running performance of WWTPs. First, several events are defined based on different operational conditions reflected by operational data. Then, an event-driven deep belief network (EDBN) is developed based on deep learning to approximate the nonlinear characteristics of the WWTPs. Second, a quadratic optimization is designed to solve the control law of MPC based on the predictive output of the EDBN. The major advantage of quadratic optimization is its efficiency, which is achieved by an efficient strategy that only needs one-step prediction of EDBN during one-time rolling optimization. Third, this article gives convergence and stability analysis of EMPC-DL. Finally, the feasibility and applicability of EMPC-DL are demonstrated on the benchmark simulation model No. 1 (BSM1). The experimental results show that EMPC-DL achieves the more satisfactory performance in modeling, controlling, and tracking water quality parameters than its peers.

BACKGROUND: Clustered regularly interspaced short palindromic repeats (CRISPR) and their spacers are important components of prokaryotic CRISPR-Cas systems. In order to analyze the CRISPR loci of multiple genomes more intuitively and comparatively, here we propose a visualization analysis tool named CrisprVi. RESULTS: CrisprVi is a Python package consisting of a graphic user interface (GUI) for visualization, a module for commands parsing and data transmission, local SQLite and BLAST databases for data storage and a functions layer for data processing. CrisprVi can not only visually present information of CRISPR direct repeats (DRs) and spacers, such as their orders on the genome, IDs, start and end coordinates, but also provide interactive operation for users to display, label and align the CRISPR sequences, which help researchers investigate the locations, orders and components of the CRISPR sequences in a global view. In comparison to other CRISPR visualization tools such as CRISPRviz and CRISPRStudio, CrisprVi not only improves the interactivity and effects of the visualization, but also provides basic statistics of the CRISPR sequences, and the consensus sequences of DRs/spacers across the input strains can be inspected from a clustering heatmap based on the BLAST results of the CRISPR sequences hitting against the genomes. CONCLUSIONS: CrisprVi is a convenient tool for visualizing and analyzing the CRISPR sequences and it would be helpful for users to inspect novel CRISPR-Cas systems of prokaryotes.

Deep belief network (DBN) is an effective learning model based on deep learning. It can hierarchically transform the input data via stacked feature detectors. As a predictive model, DBN has shown a promising prospect in model predictive control (MPC). However, its successful application relies seriously on the suitable structure size (the numbers of hidden layers and neurons), which is challenging to determine. In this work, we present a theoretical bound on its minimum structure size in order to accurately approximate a desired control law of MPC, called DBN-MPC. First, according to the Markov assumption, a controlled system is equivalent to a quadratic program, which only depends on the current system state. Second, a universal theorem is proposed to give a bound on the minimum structure size of DBN from the perspective of piecewise affine function analysis. Third, a partial least square regression is used to fine-tune DBN to overcome the problems of local-minimum and time-consuming training process. Finally, we demonstrate the effectiveness of the proposed method through two classical experiments: 1) tracking control of a benchmark dynamical system and 2) temperature control of a practical second-order continuous stirred tank reactor (CSTR) system. The experimental results generally give an answer to the question that how deep is deep enough for DBN to approximate an MPC law.Note to Practitioners—The proposed deep belief network (DBN)-model predictive control (MPC) scheme presents an answer to the question that how deep is deep enough for DBN, from the perspective of approximating an MPC law. Given an industrial system, the proposed DBN-MPC scheme can give the best tracking control performance with minimal computational complexity through an effective self-growing DBN and optimal approximation theorem of MPC law. In practical applications, it is better for practitioners to obtain accurate historical input and output data. During the operation process, DBN-MPC will be more practical if more accurate initial structure size of DBN is provided in advance because the initial structure size of DBN is the starting point of the dynamic optimization.

A fuzzy neural network (FNN) is an effective learning system that combines neural network and fuzzy logic, which has achieved great success in nonlinear system modeling. However, when the input is practical complex data with external disturbance, the existing FNN cannot extract effective input features sufficiently, leading to unsatisfactory performances in learning speed and accuracy. It also fails to achieve a better generalization capability because of its fixed structure size (the number of rule neurons). In this article, an efficient self-organizing FNN (SOFNN) with incremental deep pretraining (IDPT), called IDPT-SOFNN, is developed to overcome these shortcomings. First, IDPT is designed to extract effective features and consider them as the input of the SOFNN. Different from the existing pretraining, the self-growing structure of IDPT improves pretraining efficiency with a more compact structure. Second, the SOFNN can dynamically add and delete neurons according to the current error and error-reduction rate. In this case, it can obtain better modeling performance with a more compact structure as well. Third, as a novel hybrid model with the cascade dual-self-organizing algorithm, the IDPT-SOFNN combines the advantage of IDPT and SOFNN. Moreover, the convergence and stability are analyzed. Finally, simulation studies and comparisons demonstrate that the proposed IDPT-SOFNN has better performances than its peers in learning speed, accuracy, and generalization capability.

A continuous stirred-tank reactor (CSTR) system is widely applied in wastewater treatment processes. Its control is a challenging industrial-process-control problem due to great difficulty to achieve accurate system identification. This work proposes a deep learning-based model predictive control (DeepMPC) to model and control the CSTR system. The proposed DeepMPC consists of a growing deep belief network (GDBN) and an optimal controller. First, GDBN can automatically determine its size with transfer learning to achieve high performance in system identification, and it serves just as a predictive model of a controlled system. The model can accurately approximate the dynamics of the controlled system with a uniformly ultimately bounded error. Second, quadratic optimization is conducted to obtain an optimal controller. This work analyzes the convergence and stability of DeepMPC. Finally, the DeepMPC is used to model and control a second-order CSTR system. In the experiments, DeepMPC shows a better performance in modeling, tracking, and antidisturbance than the other state-of-the-art methods.

Deep belief network (DBN) is an efficient learning model for unknown data representation, especially nonlinear systems. However, it is extremely hard to design a satisfactory DBN with a robust structure because of traditional dense representation. In addition, backpropagation algorithm-based fine-tuning tends to yield poor performance since its ease of being trapped into local optima. In this article, we propose a novel DBN model based on adaptive sparse restricted Boltzmann machines (AS-RBM) and partial least square (PLS) regression fine-tuning, abbreviated as ARP-DBN, to obtain a more robust and accurate model than the existing ones. First, the adaptive learning step size is designed to accelerate an RBM training process, and two regularization terms are introduced into such a process to realize sparse representation. Second, initial weight derived from AS-RBM is further optimized via layer-by-layer PLS modeling starting from the output layer to input one. Third, we present the convergence and stability analysis of the proposed method. Finally, our approach is tested on Mackey-Glass time-series prediction, 2-D function approximation, and unknown system identification. Simulation results demonstrate that it has higher learning accuracy and faster learning speed. It can be used to build a more robust model than the existing ones.

A deep belief network (DBN) is one of the most effective ways to realize a deep learning technique, and has been attracting more and more attentions in nonlinear system modeling. However, it can not provide satisfactory results in learning speed and modeling accuracy, which is mainly caused by gradient diffusion. To address these problems and promote its development in cross-models, we propose an efficient DBN with a fuzzy neural network (DBFNN) for nonlinear system modeling. In this novel framework, DBN is considered as a pre-training technique to realize fast weight-initialization and to obtain a feature-representation vector. An FNN-based learning framework is developed for supervised modeling so as to eliminate the gradient diffusion issue, where its input happens to be the feature-representation vector. As a novel cross-model, DBFNN combines the advantages of both pre-training technique of DBN and an FNN model to improve nonlinear system modeling capability. A classical benchmark problem is used to demonstrate its superiority over existing single-models in learning speed and modeling accuracy.

A deep belief network (DBN) is effective to create a powerful generative model by using training data. However, it is difficult to fast determine its optimal structure given specific applications. In this paper, a growing DBN with transfer learning (TL-GDBN) is proposed to automatically decide its structure size, which can accelerate its learning process and improve model accuracy. First, a basic DBN structure with single hidden layer is initialized and then pretrained, and the learned weight parameters are frozen. Second, TL-GDBN uses TL to transfer the knowledge from the learned weight parameters to newly added neurons and hidden layers, which can achieve a growing structure until the stopping criterion for pretraining is satisfied. Third, the weight parameters derived from pretraining of TL-GDBN are further fine-tuned by using layer-by-layer partial least square regression from top to bottom, which can avoid many problems of traditional backpropagation algorithm-based fine-tuning. Moreover, the convergence analysis of the TL-GDBN is presented. Finally, TL-GDBN is tested on two benchmark data sets and a practical wastewater treatment system. The simulation results show that it has better modeling performance, faster learning speed, and more robust structure than existing models. Note to Practitioners-Transfer learning (TL) aims to improve training effectiveness by transferring knowledge from a source domain to target domain. This paper presents a growing deep belief network (DBN) with TL to improve the training effectiveness and determine the optimal model size. Facing a complex process and real-world workflow, DBN tends to require long time for its successful training. The proposed growing DBN with TL (TL-GDBN) accelerates the learning process by instantaneously transferring the knowledge from a source domain to each new deeper or wider substructure. The experimental results show that the proposed TL-GDBN model has a great potential to deal with complex system, especially the systems with high nonlinearity. As a result, it can be readily applicable to some industrial nonlinear systems.

A simple yet common scheduling problem is identified, as a special case of the R||Cmaxproblem. We name it Linear Makespan Minimization on Unrelated Parallel Machines (LMMUPM). A novel algorithm, MOBSA (Multi-Objective Based Scheduling Algorithm), is presented to solve it. Two auxiliary problems are introduced as the basis of our algorithm. The first one can be reduced to a Multi-Objective Integer Program, while the second is constructed based on the solution of the first one. Results on random datasets revealed that MOBSA produced smaller and more stable makespans than other scheduling algorithms. Additionally, the makespan produced by MOBSA was within 1% of the optimum for every case. Presently, MOBSA has been applied to parallelize EMAN, one of the most popular software packages for cryo-electron microscopy single particle reconstruction. High speedups and ideal load balancing have been obtained. It is expected that MOBSA is also applicable to other similar applications.

MOTIVATION: EMAN is one of the most popular software packages for single particle reconstruction. But the particle clusters produced during its model refining stage are of low qualities. We attempt to refine the particle clusters by more accurately determining orientations of particles, and thereby achieving higher resolutions of consequent 3D structures. RESULTS: A particle reclustering framework (PRF) is introduced, which consists of three components. Each of them is responsible for one of the basic tasks of PRF: normalization, threshold determination and reclustering. Our implementation is also described and proved to meet the constraints proposed by PRF. Experiments revealed that our implementation improved resolutions of consequent structures for most cases, but only a little extra execution time was incurred. Therefore, it is practical to incorporate PRF in EMAN to improve qualities of generated 3D structures. AVAILABILITY AND IMPLEMENTATION: Implementation of our algorithm is available upon request from the authors.