•Sludge enhanced dewatering technologies are comprehensively reviewed.•Mechanisms of different sludge enhanced dewatering technologies are discussed.•Process adaptability of different sludge ...conditioning methods are analysed.•Sludge electro-dewatering and its coupled processes are discussed.•Sludge enhanced dewatering technology and its coupled processes are prospected.
Sludge is an inevitable by product of sewage treatment, and it includes pathogens, heavy metals, organic pollutants and other toxic substances. The components of sludge are complex and variable with extracellular polymeric substances (EPS) being one. EPS are highly hydrophilic and compressible, and make sludge dewatering difficult. Therefore, the development of efficient sludge-dewatering technology is an important means of mitigating rapid sludge growth. At present, the main methods used for sludge deep-dewatering technology are chemical preconditioning with high-pressure filtration and electrical mechanical dewatering. The selection of chemical preconditioning directly determines the final efficiency of the sludge-dewatering process. In this paper, we conduct a comprehensive review of the problems related to sludge dewatering and systematically summarise the impact of different chemical conditioning technologies on the efficiency of sludge dewatering. Furthermore, the characteristics of different enhanced dewatering technologies are evaluated and analysed for their adaptability and final disposal methods. We believe that this review can clarify the chemical conditioner mechanism to improve sludge dewatering, provide reference debugging information for the sludge-dewatering process and promote the development of efficient and environmentally friendly sludge-dewatering technology.
Fog is an emergent architecture for computing, storage, control, and networking that distributes these services closer to end users along the cloud-to-things continuum. It covers both mobile and ...wireline scenarios, traverses across hardware and software, resides on network edge but also over access networks and among end users, and includes both data plane and control plane. As an architecture, it supports a growing variety of applications, including those in the Internet of Things (IoT), fifth-generation (5G) wireless systems, and embedded artificial intelligence (AI). This survey paper summarizes the opportunities and challenges of fog, focusing primarily in the networking context of IoT.
MOFs have a highly ordered self‐assembled nanostructure, high surface area, nanoporosity with tunable size and shape, reliable host–guest interactions, and responsiveness to physical and chemical ...stimuli which can be exploited to address critical issues in sensor applications. On the one hand, the nanoscale pore size of MOFs ranging from less than 1 nm to ≈ 10 nm not only allows the diffusion of small molecules into the pores or through the MOF layer, but also excludes other larger molecules depending on the size, shape, and conformation of MOFs. On the other hand, MOFs with flexible structure exhibit a dynamic response to external stimuli, including guest molecules, temperature, pressure, pH, and light. Due to the unsaturated coordination metal sites and active functional groups, the interaction between certain analytes and active sites results in high selectivity. In this review, we summarize the latest studies on MOF‐based electronic sensors in terms of the function of MOFs, discuss challenges, and suggest perspectives.
Metal–organic frameworks (MOFs) that respond to physical and chemical stimuli are promising materials for electronic sensors owing to their outstanding sensing performance. In this Review, the functionality of MOFs as a mass‐loaded layer, filtration layer, electronic function layer, and optically sensitive layer is discussed.
Presently, various high end systems have been utilized to check the instantaneous change in the climate through deep monitoring methods with advanced mathematical modelling, whereas the major problem ...in the present research is the data management and precision in detecting various disaster conditions of the smart environment. Though The present instrument for disaster prediction has come up with various satellites and radar, which suffers instrumentation and data management issues that has been resolved in this research. The prominent improvement in the IoT technology shows better fine-grain structure, more flexibility and accuracy. The Proposed technique uses Advanced Adaptive Wavelet Sampling Algorithm (AAWSA)that has been designed and developed in this paper helps to improve the precision range of the instrument during disaster prediction in the urban region. The developed instrument utilizes advanced mathematical modelling with the linear data analytics approach which shows emerging outcomes than the present system which are used in practice.
Cyber-physical systems embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber-physical systems have ...proved resistant to modeling due to their intrinsic complexity arising from the combination of physical and cyber components and the interaction between them. This study proposes a general framework for discovering cyber-physical systems directly from data. The framework involves the identification of physical systems as well as the inference of transition logics. It has been applied successfully to a number of real-world examples. The novel framework seeks to understand the underlying mechanism of cyber-physical systems as well as make predictions concerning their state trajectories based on the discovered models. Such information has been proven essential for the assessment of the performance of cyber-physical systems; it can potentially help debug in the implementation procedure and guide the redesign to achieve the required performance.
Privacy-preserving distributed machine learning becomes increasingly important due to the recent rapid growth of data. This paper focuses on a class of regularized empirical risk minimization machine ...learning problems, and develops two methods to provide differential privacy to distributed learning algorithms over a network. We first decentralize the learning algorithm using the alternating direction method of multipliers, and propose the methods of dual variable perturbation and primal variable perturbation to provide dynamic differential privacy. The two mechanisms lead to algorithms that can provide privacy guarantees under mild conditions of the convexity and differentiability of the loss function and the regularizer. We study the performance of the algorithms, and show that the dual variable perturbation outperforms its primal counterpart. To design an optimal privacy mechanism, we analyze the fundamental tradeoff between privacy and accuracy, and provide guidelines to choose privacy parameters. Numerical experiments using customer information database are performed to corroborate the results on privacy and utility tradeoffs and design.
This paper is concerned of the loop closure detection problem for visual simultaneous localization and mapping systems. We propose a novel approach based on the stacked denoising auto-encoder (SDA), ...a multi-layer neural network that autonomously learns an compressed representation from the raw input data in an unsupervised way. Different with the traditional bag-of-words based methods, the deep network has the ability to learn the complex inner structures in image data, while no longer needs to manually design the visual features. Our approach employs the characteristics of the SDA to solve the loop detection problem. The workflow of training the network, utilizing the features and computing the similarity score is presented. The performance of SDA is evaluated by a comparison study with Fab-map 2.0 using data from open datasets and physical robots. The results show that SDA is feasible for detecting loops at a satisfactory precision and can therefore provide an alternative way for visual SLAM systems.
Decomposition-based algorithms have become increasingly popular for evolutionary multiobjective optimization. However, the effect of scalarizing methods used in these algorithms is still far from ...being well understood. This paper analyzes a family of frequently used scalarizing methods, the L p methods, and shows that the p value is crucial to balance the selective pressure toward the Pareto optimal and the algorithm robustness to Pareto optimal front (PF) geometries. It demonstrates that an L p method that can maximize the search ability of a decomposition-based algorithm exists and guarantees that, given some weight, any solution along the PF can be found. Moreover, a simple yet effective method called Pareto adaptive scalarizing (PaS) approximation is proposed to approximate the optimal p value. In order to demonstrate the effectiveness of PaS, we incorporate PaS into a state-of-the-art decomposition-based algorithm, i.e., multiobjective evolutionary algorithm based on decomposition (MOEA/D), and compare the resultant MOEA/D-PaS with some other MOEA/D variants on a set of problems with different PF geometries and up to seven conflicting objectives. Experimental results demonstrate that the PaS is effective.