Showing papers by "Beijing University of Technology published in 2020"

Epidemic analysis of COVID-19 in China by dynamical modeling

Abstract: The outbreak of novel coronavirus-caused pneumonia (COVID-19) in Wuhan has attracted worldwide attention. Here, we propose a generalized SEIR model to analyze this epidemic. Based on the public data of National Health Commission of China from Jan. 20th to Feb. 9th, 2020, we reliably estimate key epidemic parameters and make predictions on the inflection point and possible ending time for 5 different regions. According to optimistic estimation, the epidemics in Beijing and Shanghai will end soon within two weeks, while for most part of China, including the majority of cities in Hubei province, the success of anti-epidemic will be no later than the middle of March. The situation in Wuhan is still very severe, at least based on public data until Feb. 15th. We expect it will end up at the beginning of April. Moreover, by inverse inference, we find the outbreak of COVID-19 in Mainland, Hubei province and Wuhan all can be dated back to the end of December 2019, and the doubling time is around two days at the early stage.

Engineering unsymmetrically coordinated Cu-S 1 N 3 single atom sites with enhanced oxygen reduction activity

Abstract: Atomic interface regulation is thought to be an efficient method to adjust the performance of single atom catalysts. Herein, a practical strategy was reported to rationally design single copper atoms coordinated with both sulfur and nitrogen atoms in metal-organic framework derived hierarchically porous carbon (S-Cu-ISA/SNC). The atomic interface configuration of the copper site in S-Cu-ISA/SNC is detected to be an unsymmetrically arranged Cu-S1N3 moiety. The catalyst exhibits excellent oxygen reduction reaction activity with a half-wave potential of 0.918 V vs. RHE. Additionally, through in situ X-ray absorption fine structure tests, we discover that the low-valent Cuprous-S1N3 moiety acts as an active center during the oxygen reduction process. Our discovery provides a universal scheme for the controllable synthesis and performance regulation of single metal atom catalysts toward energy applications. Engineering the coordination environment of single atom catalysts offers to opportunity to optimize electrocatalytic activity. In this work, the authors prepare an unsymmetrical Cu-S1N3 single atom site on porous carbon with high performance in the oxygen reduction reaction.

Iridium single-atom catalyst on nitrogen-doped carbon for formic acid oxidation synthesized using a general host-guest strategy.

Abstract: Single-atom catalysts not only maximize metal atom efficiency, they also display properties that are considerably different to their more conventional nanoparticle equivalents, making them a promising family of materials to investigate. Herein we developed a general host–guest strategy to fabricate various metal single-atom catalysts on nitrogen-doped carbon (M1/CN, M = Pt, Ir, Pd, Ru, Mo, Ga, Cu, Ni, Mn). The iridium variant Ir1/CN electrocatalyses the formic acid oxidation reaction with a mass activity of 12.9 $${{{\rm{A}}\,{\rm{mg}}^{-1}_{{\rm{Ir}}}}}$$ whereas an Ir/C nanoparticle catalyst is almost inert (~4.8 × 10−3 $${{{\rm{A}}\,{\rm{mg}}^{-1}_{{\rm{Ir}}}}}$$). The activity of Ir1/CN is also 16 and 19 times greater than those of Pd/C and Pt/C, respectively. Furthermore, Ir1/CN displays high tolerance to CO poisoning. First-principle density functional theory reveals that the properties of Ir1/CN stem from the spatial isolation of iridium sites and from the modified electronic structure of iridium with respect to a conventional nanoparticle catalyst. Single-atom catalysts maximize metal atom efficiency and exhibit properties that can be considerably different to their nanoparticle equivalent. Now a general host–guest strategy to make various single-atom catalysts on nitrogen-doped carbon has been developed; the iridium variant electrocatalyses the formic acid oxidation reaction with high mass activity and displays high tolerance to CO poisoning.

A Hybrid BlockChain-Based Identity Authentication Scheme for Multi-WSN

Abstract: Internet of Things (IoT) equipment is usually in a harsh environment, and its security has always been a widely concerned issue. Node identity authentication is an important means to ensure its security. Traditional IoT identity authentication protocols usually rely on trusted third parties. However, many IoT environments do not allow such conditions, and are prone to single point failure. Blockchain technology with decentralization features provides a new solution for distributed IoT system. In this paper, a blockchain based multi-WSN authentication scheme for IoT is proposed. The nodes of IoT are divided into base stations, cluster head nodes and ordinary nodes according to their capability differences, which are formed to a hierarchical network. A blockchain network is constructed among different types of nodes to form a hybrid blockchain model, including local chain and public chain. In this hybrid model, nodes identity mutual authentication in various communication scenarios is realized, ordinary node identity authentication operation is accomplished by local blockchain, and cluster head node identity authentication are realized in public blockchain. The analysis of security and performance shows that the scheme has comprehensive security and better performance.

Engineering the Atomic Interface with Single Platinum Atoms for Enhanced Photocatalytic Hydrogen Production.

Abstract: It is highly desirable but challenging to optimize the structure of photocatalysts at the atomic scale to facilitate the separation of electron-hole pairs for enhanced performance. Now, a highly efficient photocatalyst is formed by assembling single Pt atoms on a defective TiO2 support (Pt1 /def-TiO2 ). Apart from being proton reduction sites, single Pt atoms promote the neighboring TiO2 units to generate surface oxygen vacancies and form a Pt-O-Ti3+ atomic interface. Experimental results and density functional theory calculations demonstrate that the Pt-O-Ti3+ atomic interface effectively facilitates photogenerated electrons to transfer from Ti3+ defective sites to single Pt atoms, thereby enhancing the separation of electron-hole pairs. This unique structure makes Pt1 /def-TiO2 exhibit a record-level photocatalytic hydrogen production performance with an unexpectedly high turnover frequency of 51423 h-1 , exceeding the Pt nanoparticle supported TiO2 catalyst by a factor of 591.

Single-atom site catalysts for environmental catalysis

Abstract: In recent decades, the environmental protection and long-term sustainability have become the focus of attention due to the increasing pollution generated by the intense industrialization. To overcome these issues, environmental catalysis has increasingly been used to solve the negative impact of pollutants emission on the global environment and human health. Supported platinum-metal-group (PGM) materials are commonly utilized as the state-of-the-art catalysts to eliminate gaseous pollutants but large quantities of PGMs are required. By comparison, single-atom site catalysts (SACs) have attracted much attention in catalysis owing to their 100% atom efficiency and unique catalytic performances towards various reactions. Over the past decade, we have witnessed burgeoning interests of SACs in heterogeneous catalysis. However, to the best of our knowledge, the systematic summary and analysis of SACs in catalytic elimination of environmental pollutants has not yet been reported. In this paper, we summarize and discuss the environmental catalysis applications of SACs. Particular focus was paid to automotive and stationary emission control, including model reaction (CO oxidation, NO reduction and hydrocarbon oxidation), overall reaction (three-way catalytic and diesel oxidation reaction), elimination of volatile organic compounds (formaldehyde, benzene, and toluene), and removal/decomposition of other pollutants (Hg0 and SO3). Perspectives related to further challenges, directions and design strategies of single-atom site catalysts in environmental catalysis were also provided.

O-coordinated W-Mo dual-atom catalyst for pH-universal electrocatalytic hydrogen evolution.

Abstract: Single-atom catalysts (SACs) maximize the utility efficiency of metal atoms and offer great potential for hydrogen evolution reaction (HER). Bimetal atom catalysts are an appealing strategy in virtue of the synergistic interaction of neighboring metal atoms, which can further improve the intrinsic HER activity beyond SACs. However, the rational design of these systems remains conceptually challenging and requires in-depth research both experimentally and theoretically. Here, we develop a dual-atom catalyst (DAC) consisting of O-coordinated W-Mo heterodimer embedded in N-doped graphene (W1Mo1-NG), which is synthesized by controllable self-assembly and nitridation processes. In W1Mo1-NG, the O-bridged W-Mo atoms are anchored in NG vacancies through oxygen atoms with W─O─Mo─O─C configuration, resulting in stable and finely distribution. The W1Mo1-NG DAC enables Pt-like activity and ultrahigh stability for HER in pH-universal electrolyte. The electron delocalization of W─O─Mo─O─C configuration provides optimal adsorption strength of H and boosts the HER kinetics, thereby notably promoting the intrinsic activity.

Multi-UAV-Enabled Load-Balance Mobile-Edge Computing for IoT Networks

Abstract: Unmanned aerial vehicles (UAVs) have been widely used to provide enhanced information coverage as well as relay services for ground Internet-of-Things (IoT) networks. Considering the substantially limited processing capability, the IoT devices may not be able to tackle with heavy computing tasks. In this article, a multi-UAV-aided mobile-edge computing (MEC) system is constructed, where multiple UAVs act as MEC nodes in order to provide computing offloading services for ground IoT nodes which have limited local computing capabilities. For the sake of balancing the load for UAVs, the differential evolution (DE)-based multi-UAV deployment mechanism is proposed, where we model the access problem as a generalized assignment problem (GAP), which is then solved by a near-optimal solution algorithm. Based on this, we are capable of achieving the load balance of these drones while guaranteeing the coverage constraint and satisfying the quality of service (QoS) of IoT nodes. Furthermore, a deep reinforcement learning (DRL) algorithm is conceived for the task scheduling in a certain UAV, which improves the efficiency of the task execution in each UAV. Finally, sufficient simulation results show the feasibility and superiority of our proposed load-balance-oriented UAV deployment scheme as well as the task scheduling algorithm.

Dual heterogeneous structures lead to ultrahigh strength and uniform ductility in a Co-Cr-Ni medium-entropy alloy.

Abstract: Alloys with ultra-high strength and sufficient ductility are highly desired for modern engineering applications but difficult to develop. Here we report that, by a careful controlling alloy composition, thermomechanical process, and microstructural feature, a Co-Cr-Ni-based medium-entropy alloy (MEA) with a dual heterogeneous structure of both matrix and precipitates can be designed to provide an ultra-high tensile strength of 2.2 GPa and uniform elongation of 13% at ambient temperature, properties that are much improved over their counterparts without the heterogeneous structure. Electron microscopy characterizations reveal that the dual heterogeneous structures are composed of a heterogeneous matrix with both coarse grains (10∼30 μm) and ultra-fine grains (0.5∼2 μm), together with heterogeneous L12-structured nanoprecipitates ranging from several to hundreds of nanometers. The heterogeneous L12 nanoprecipitates are fully coherent with the matrix, minimizing the elastic misfit strain of interfaces, relieving the stress concentration during deformation, and playing an active role in enhanced ductility. Improving both strength and ductility simultaneously in structural metals and alloys remains a challenge. Here, the authors design a heterogeneous structure in a Co-Cr-Ni alloy that results in ultrahigh strength and significant uniform elongation.

Self-Learning Optimal Regulation for Discrete-Time Nonlinear Systems Under Event-Driven Formulation

Abstract: The self-learning optimal regulation for discrete-time nonlinear systems under event-driven formulation is investigated. An event-based adaptive critic algorithm is developed with convergence discussion of the iterative process. The input-to-state stability (ISS) analysis for the present nonlinear plant is established. Then, a suitable triggering condition is proved to ensure the ISS of the controlled system. An iterative dual heuristic dynamic programming (DHP) strategy is adopted to implement the event-driven framework. Simulation examples are carried out to demonstrate the applicability of the constructed method. Compared with the traditional DHP algorithm, the even-based algorithm is able to substantially reduce the updating times of the control input, while still maintaining an impressive performance.

The formation mechanism and fluorophores of carbon dots synthesized via a bottom-up route

Abstract: Carbon dots (CDs) with incomparable optical properties have attracted extensive attention. However, some unclear issues remain, which has impeded the basic understanding and practical application of CDs. The formation process and chemical structure of CDs are critical factors for understanding their optical properties. In this review, recent progress in the formation and fluorophores of CDs is summarized and discussed, which draws a clear picture of related research and indicates a promising future for further studies.

Flexible Nitrite Supply Alternative for Mainstream Anammox: Advances in Enhancing Process Stability.

Abstract: Anaerobic ammonium oxidation (anammox) has attracted extensive attention as a potentially sustainable and economical municipal wastewater treatment process. However, its large-scale application is limited by unstable nitrite (NO2--N) production and associated excessive nitrate (NO3--N) residue. Thus, our study sought to evaluate an efficient alternative to the current nitritation-based anammox process substituting NO2--N supply by partial-denitrification (PD; NO3--N → NO2--N) under mainstream conditions. Ammonia (NH4+-N) was partly oxidized to NO3--N and removed via a PD coupled anammox (PD/A) process by mixing the nitrifying effluents with raw wastewater (NH4+-N of 57.87 mg L-1, COD of 176.02 mg L-1). Excellent effluent quality was obtained with< 5 mg L-1 of total nitrogen (TN) despite frequent temperature fluctuations (25.7-16.3 °C). The genus Thauera (responsible for PD) was the dominant denitrifiers (36.4%-37.4%) and coexisted with Candidatus Brocadia (anammox bacteria; 0.33%-0.46%). The efficient PD/A allowed up to 50% reduction in aeration energy consumption, 80% decrease in organic resource demand, and lower nitrous oxide (N2O) production compared to conventional nitrification/denitrification process. Our study demonstrates that coupling anammox with flexible NO2--N supply has great potential as a stable and efficient mainstream wastewater treatment.

Structure of the global plastic waste trade network and the impact of China’s import Ban

Abstract: Millions of tonnes (teragrams) of plastic waste are traded around the world every year, which plays an important role in partially substituting virgin plastics as a source of raw materials in plastic product manufacturing. In this paper, global plastic waste trade networks (GPWTNs) from 1988 to 2017 are established using the UN-Comtrade database. The spatiotemporal evolution of the GPWTNs is analyzed. Attention is given to the country ranks, inter- and intra-continental trade flows, and geo-visual communities in the GPWTNs. We also evaluate the direct and indirect impacts of China’s plastic waste import ban on the GPWTNs. The results show that the GPWTNs have small-world and scale-free properties and a core-periphery structure. The geography of the plastic waste trade is structured by Asia as the dominant importer and North America and Europe as the largest sources of plastic waste. China is the unrivaled colossus in the global plastic waste trade. After China’s import ban, the plastic waste trade flows have been largely redirected to Southeast Asian countries. Compared with import countries, export countries are more important for the robustness of GPWTNs. Clearly, developed countries will not announce bans on plastic waste exports; these countries have strong motivation to continue to shift plastic waste to poorer countries. However, the import bans from developing countries will compel developed countries to build new disposal facilities and deal with their plastic waste domestically.

Research on Remaining Useful Life Prediction of Rolling Element Bearings Based on Time-Varying Kalman Filter

Abstract: Rolling bearings are the key components of rotating machinery. Thus, the prediction of remaining useful life (RUL) is vital in condition-based maintenance (CBM). This paper proposes a new method for RUL prediction of bearings based on time-varying Kalman filter, which can automatically match different degradation stages of bearings and effectively realize the prediction of RUL. The evolution of monitoring data in normal and slow degradation stages is a linear trend, and the evolution in accelerated degradation stage is nonlinear. Therefore, Kalman filter models based on linear and quadratic functions are established. Meanwhile, a sliding window relative error is constructed to adaptively judge the bearing degradation stages. It can automatically switch filter models to process monitoring data at different stages. Then, the RUL can be predicted effectively. Two groups of bearing run-to-failure data sets are utilized to demonstrate the feasibility and validity of the proposed method.

FlowGuard: An Intelligent Edge Defense Mechanism Against IoT DDoS Attacks

Abstract: Internet-of-Things (IoT) devices are getting more and more popular in recent years and IoT networks play an important role in the industry as well as people’s activities. On the one hand, they bring convenience to every aspect of our daily life; on the other hand, they are vulnerable to various attacks that in turn cancels out their benefits to a certain degree. In this article, we target the defense techniques against IoT Distributed Denial-of-Service (DDoS) attacks and propose an edge-centric IoT defense scheme termed FlowGuard for the detection, identification, classification, and mitigation of IoT DDoS attacks. We present a new DDoS attack detection algorithm based on traffic variations and design two machine learning models for DDoS identification and classification. To demonstrate the effectiveness of the two machine learning models, we generate a large data set by DDoS simulators BoNeSi and SlowHTTPTest, and combine it with the CICDDoS2019 data set, to test the identification and classification accuracy as well as the model efficiency. Our results indicate that the identification accuracy of the proposed long short-term memory is as high as 98.9%, which significantly outperforms the other four well-known learning models mentioned in the most related work. The classification accuracy of the proposed convolutional neural network is up to 99.9%. Besides, our models satisfactorily meet the delay requirements of IoT when deployed in edge servers with computational powers higher than a personal computer.

Winding-Locked Carbon Nanotubes/Polymer Nanofibers Helical Yarn for Ultrastretchable Conductor and Strain Sensor.

Abstract: ：Wearable and stretchable electronics including various conductors and sensors are featured with their light weight, high flexibility and easy integration into functional devices or textile...

Phosphonium Modification Leads to Ultrapermeable Antibacterial Polyamide Composite Membranes with Unreduced Thickness

Abstract: Water transport rate in network membranes is inversely correlated to thickness, thus superior permeance is achievable with ultrathin membranes prepared by complicated methods circumventing nanofilm weakness and defects. Conferring ultrahigh permeance to easily prepared thicker membranes remains challenging. Here, a tetrakis(hydroxymethyl) phosphonium chloride (THPC) monomer is discovered that enables straightforward modification of polyamide composite membranes. Water permeance of the modified membrane is ≈6 times improved, give rising to permeability (permeance × thickness) one magnitude higher than state-of-the-art polymer nanofiltration membranes. Meanwhile, the membrane exhibits good rejection (RNa2SO4 = 98%) and antibacterial properties under crossflow conditions. THPC modification not only improves membrane hydrophilicity, but also creates additional angstrom-scale channels in polyamide membranes for unimpeded transport of water. This unique mechanism provides a paradigm shift in facile preparation of ultrapermeable membranes with unreduced thickness for clean water and desalination.

Engineering grain boundaries at the 2D limit for the hydrogen evolution reaction.

Abstract: Atom-thin transition metal dichalcogenides (TMDs) have emerged as fascinating materials and key structures for electrocatalysis. So far, their edges, dopant heteroatoms and defects have been intensively explored as active sites for the hydrogen evolution reaction (HER) to split water. However, grain boundaries (GBs), a key type of defects in TMDs, have been overlooked due to their low density and large structural variations. Here, we demonstrate the synthesis of wafer-size atom-thin TMD films with an ultra-high-density of GBs, up to ~1012 cm−2. We propose a climb and drive 0D/2D interaction to explain the underlying growth mechanism. The electrocatalytic activity of the nanograin film is comprehensively examined by micro-electrochemical measurements, showing an excellent hydrogen-evolution performance (onset potential: −25 mV and Tafel slope: 54 mV dec−1), thus indicating an intrinsically high activation of the TMD GBs. Transition metal dichalcogenides demonstrate fascinating capabilities for electrocatalytic H2 evolution, although the activities vary widely depending on nanomaterial sites available. Here, authors show the grain boundaries of atomically thin MoS2 to be especially active sites for H2 evolution.

A MnO2-based catalyst with H2O resistance for NH3-SCR: Study of catalytic activity and reactants-H2O competitive adsorption

Abstract: MnO2 with interlayer Ce3+ cation (Ce-MnO2) was synthesized by ion exchange approach. The Ce-MnO2 exhibited better low-temperature NH3-SCR catalytic activity as compared to Ce0.38Mn0.62O2-CP prepared by co-precipitation. The good performance was due to easy supply of labile oxygen species generated by migration of Ce species in Ce-MnO2 catalyst, which could promote the oxidation of reactants. The special structure of Ce-MnO2 promoted better properties in terms of low-temperature reduction properties, lower element species, adsorption capacity of reactants and surface acid sites, which was beneficial to NH3-SCR at low-temperature. It also showed superior H2O resistance with NOx conversion above 90 % below 150 °C. The competitive adsorptions between H2O and reactants over both of Ce-containing catalysts were investigated by in-situ DRIFT and TPD. The results indicated NOx with strong competitive adsorption on Ce-MnO2 surface than H2O. No competitive adsorption between NH3 and H2O over both of Ce-containing catalysts was also observed, different from previous reports.

Microporous Hydrogen-Bonded Organic Framework for Highly Efficient Turn-Up Fluorescent Sensing of Aniline.

Abstract: A microporous three-dimensional (3D) hydrogen-bonded organic framework (HOF-20) has been constructed from an aromatic-rich tetratopic carboxylic acid, 5-(2,6-bis(4-carboxyphenyl)pyridin-4-yl)isophthalic acid (H4BCPIA) The activated HOF-20a has a moderately high Brunauer-Emmett-Teller (BET) surface area of 1323 m2 g-1 and excellent stability in water and HCl aqueous solution HOF-20 exhibits highly efficient turn-up fluorescent sensing of aniline in water with a detection limit of 224 μM and is selective toward aniline in the presence of aromatic interferents, owing to the hydrogen bonding and edge-to-face π-π stacking interactions between the HOF-20 host and the guest aniline molecules, as demonstrated in the single-crystal X-ray structure of HOF-20⊃aniline Density functional theory (DFT) calculations further demonstrate that the recognition of aniline molecules by HOF-20 could restrict the rotation of the aromatic rings in H4BCPIA linkers, reducing the nonradiative decay pathways upon photoexcitation and subsequently enhancing the fluorescence intensity

An Explainable Machine Learning Framework for Intrusion Detection Systems

Abstract: In recent years, machine learning-based intrusion detection systems (IDSs) have proven to be effective; especially, deep neural networks improve the detection rates of intrusion detection models. However, as models become more and more complex, people can hardly get the explanations behind their decisions. At the same time, most of the works about model interpretation focuses on other fields like computer vision, natural language processing, and biology. This leads to the fact that in practical use, cybersecurity experts can hardly optimize their decisions according to the judgments of the model. To solve these issues, a framework is proposed in this paper to give an explanation for IDSs. This framework uses SHapley Additive exPlanations (SHAP), and combines local and global explanations to improve the interpretation of IDSs. The local explanations give the reasons why the model makes certain decisions on the specific input. The global explanations give the important features extracted from IDSs, present the relationships between the feature values and different types of attacks. At the same time, the interpretations between two different classifiers, one-vs-all classifier and multiclass classifier, are compared. NSL-KDD dataset is used to test the feasibility of the framework. The framework proposed in this paper leads to improve the transparency of any IDS, and helps the cybersecurity staff have a better understanding of IDSs' judgments. Furthermore, the different interpretations between different kinds of classifiers can also help security experts better design the structures of the IDSs. More importantly, this work is unique in the intrusion detection field, presenting the first use of the SHAP method to give explanations for IDSs.

Deep Dual-Channel Neural Network for Image-Based Smoke Detection

Abstract: Smoke detection plays an important role in industrial safety warning systems and fire prevention. Due to the complicated changes in the shape, texture, and color of smoke, identifying the smoke from a given image still remains a substantial challenge, and this has accordingly aroused a considerable amount of research attention recently. To address the problem, we devise a new deep dual-channel neural network (DCNN) for smoke detection. In contrast to popular deep convolutional networks (e.g., Alex-Net, VGG-Net, Res-Net, and Dense-Net and the DNCNN that is specifically devoted to detecting smoke), our proposed end-to-end network is mainly composed of dual channels of deep subnetworks. In the first subnetwork, we sequentially connect multiple convolutional layers and max-pooling layers. Then, we selectively append the batch normalization layer to each convolutional layer for overfitting reduction and training acceleration. The first subnetwork is shown to be good at extracting the detailed information of smoke, such as texture. In the second subnetwork, in addition to the convolutional, batch normalization, and max-pooling layers, we further introduce two important components. One is the skip connection for avoiding the vanishing gradient and improving the feature propagation. The other is the global average pooling for reducing the number of parameters and mitigating the overfitting issue. The second subnetwork can capture the base information of smoke, such as contours. We finally deploy a concatenation operation to combine the aforementioned two deep subnetworks to complement each other. Based on the augmented data obtained by rotating the training images, our proposed DCNN can promptly and stably converge to the perfect performance. Experimental results conducted on the publicly available smoke detection database verify that the proposed DCNN has attained a very high detection rate that exceeds 99.5% on average, superior to state-of-the-art relevant competitors. Furthermore, our DCNN only employs approximately one-third of the parameters needed by the comparatively tested deep neural networks. The source code of DCNN will be released at https://kegu.netlify.com/ .