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Metal and alloy nanoparticles synthesized by chemical reduction have attracted increasing attention due to their superior physical, chemical, and biological properties. However, most chemical synthesis processes rely on the use of harsh reducing agents and complicated chemical ingredients. Herein, we report a novel reduction-agent-free and surfactant (stabilizer)-free strategy to synthesize Cu, Ag, Au, Cu-Pt, Cu-Au, Cu-Au-Pt-Pd, and Au-Pt-Pd-Cu nanoparticles by ultrasound-assisted dealloying of Mg-based metallic glasses. The formation mechanism of the metal and alloy nanoparticles is revealed by a detailed investigation of sequential intermediate products. We demonstrate that the glass-liquid phase transition of the initially dealloying metallic glasses, together with the synergistic effect of dealloying and ultrasound-driven ligament-breakage of small enough nanoporous intermediates, play key roles in preparing the uniformly dispersed metal and alloy nanoparticles. This approach greatly simplifies the up-scaling synthesis of monometallic and bimetallic nanoparticles, and also provides a general strategy for synthesizing unprecedented multimetallic nanoparticles.

Impurities in industrial silicon strongly affect the performance and use value of silicon products, so that raw industrial silicon must be refined to reduce the impurity content and improve its quality. In this work, the effect of the conditions of the refining process on the removal of impurities was studied. The slag agent and industrial silicon powder are uniformly mixed, and a mixed gas of Ar-H 2 O-O 2 is blown into the melt for refining experiments when the set refining temperature is reached. 3% mass fraction high-purity boron powder is pre-melted into industrial silicon raw materials to prepare Si-3%B alloy raw materials, and then the Si-3%B alloy raw materials are refined and pickled with CaO-SiO 2 -CaCl 2 . The composition of the slag agent, the slag-agent-to-silicon mass ratio, the refining time, and the refining temperature conditions were explored, and the best efficiency of boron removal was found to be 96.77%, with the boron content in silicon decreased from 22 ppmw to the lowest value of 0.6 ppmw. The boron content in refined silicon shows a clear decrease, demonstrating that slag-making gas blowing refining is effective for the removal of impurities in industrial silicon.

Cascading failure simulation data contain many fault chains (FCs), which can present cascading failure propagation paths. In cascading failure process, some component outages play dominating roles in propagation, indicating the common characteristics among different FCs. The commonness is embodied as combinations of components which are vulnerable to trip and cause serious blackout consequences. In addition, a component outage will increase the outage probability of relevant components and induce dependent outage in subsequent stage. Such relevance between two components can be called component outage causality. A combination of sequential component outages with outage causalities, which exists in different FCs and leads to system load loss, can be regarded as a cascading failure pattern (CFP). Statistical characteristics of FCs indicate that CFPs are variously distributed in different FCs, can present propagation paths and cause different impacts on system blackouts. This paper proposes a cascading failure pattern (CFP) identification method based on sequential pattern mining approach. The proposed method focuses on mining CFPs from massive FCs, quantifies the influence of CFP on system blackout and identifies the critical ones. The proposed method is verified with FCs data on IEEE 39-bus and 118-bus test systems.

This article presents a novel target detection method, which is named as circular target feature detection framework based on a deep convolutional neural network (DCNN). The central proposition of this method uses the optimized DCNN architecture to detect the target and locate the position of the circle accurately in the image field of view. In this article, a Hough transform based on threshold processing (HTP) is embedded into the optimized DCNN architecture, which calculates the center positions and radius of all circles by training the circular samples for each detected rectangular frame. It can efficiently identify small circular target materials in the industry and screen out unqualified particles. The experimental results show that the boundary information of the circles is obtained clearly from the complex noise background images, thereby accurately determining the location of the circle. It has some advantages over only using a specific circular recognition algorithm. We proposed the new study on HTP-DCNN, which has extremely high accuracy in the field of machine vision positioning with circles for industrial applications.

This paper focuses on the time-varying formation tracking issue for nonlinear multiagent systems (MASs). Based on the explicit characterizations of frequency, duration, and magnitude properties for deception attacks, a hybrid framework is proposed for time-varying formation tracking of nonlinear MASs. To realize the desired formation tracking performance under deception attacks, the distributed edge-based event-triggered communication strategies are proposed with Zeno-freeness. The designed strategies are resilient to deception attacks under some appropriate assumptions, to realize a predefined formation and simultaneously track the convex combination of leaders’ states. The designed control strategies render that we do not need to detect when the deception attack happens. Furthermore, the obtained results can be deduced to deal with consensus/synchronization problems, target enclosing problems for MASs with one/multiple leaders, where the communication is attacked by malicious attackers. An example of time-varying formation tracking of unmanned aerial vehicles is provided to show the effectiveness of the obtained results.

Advanced model-based control methods have been widely used in industrial process control, but excellent performance requires regular maintenance of its model. Reinforcement learning can online upda...

The economic dispatch problem (EDP) is one of the fundamental and important problems in power systems. The objective of EDP is to determine the output generation of generators to minimize the total generation cost under various constraints. In this article, a finite-time consensus-based distributed optimization algorithm is proposed to solve EDP. It is only required that each device in the communication network has access to its own local generation cost function, designed virtual local demand and its neighbors’ local optimization variables. The proposed finite-time algorithm can solve EDP, if the gain parameters in the algorithm satisfy some conditions under undirected and connected time-varying graphs. Moreover, the bounded or linear increasing assumption on the gradient and subgradient of objecive functions is relaxed in this algorithm. Examples under several cases are provided to verify the effectiveness of the proposed distributed optimization algorithm.

Nanoporous metals prepared by dealloying have attracted increasing attention due to their interesting size-dependent physical, chemical, and biological properties. However, facile fabrication of metallic ultrathin freestanding nanoporous films (UF-NPFs) by dealloying is still challenging. Herein, we report a novel strategy of facile preparation of flexible Cu, Cu3Ag, and CuAg UF-NPFs by dealloying thick Mg-Cu(Ag)-Gd metallic glass ribbons. During dealloying, the local reaction latent heat-induced glass transition of the precursor ribbons leads to the formation of a solid/liquid interface between the initially dealloyed nanoporous layer and the underlying supercooled liquid layer. Due to the bulging effect of in situ generated H2 on the solid/liquid interface, Cu, Cu3Ag, and CuAg UF-NPFs with thicknesses of ∼200 nm can self-peel off from the outer surface of the dealloying ribbons. Moreover, it was found that the surface-enhanced Raman scattering (SERS) detection limit of Rhodamine 6G (R6G) on the Cu and CuAg UF-NPF substrates are 10−6 M and 10−11 M, respectively, which are lower than most of the Cu and Cu-Ag substrates prepared by other methods. This work presents a reliable simple strategy to synthesize a variety of cost effective and flexible metallic UF-NPFs for functional applications.

The various optical components have wide applications in aerospace industries. In the manufacturing process of optical components that are made of optical glass materials, the main challenges still are how to ensure the high requirements in dimensional accuracy and surface quality. In this paper, the CNC polishing method with inner channel liquid supply (ICLS) is proposed to polish glass materials based on the chemical mechanical polishing mechanism. The comparative experiment with both inner and external liquid supply was designed, and it is found that the successive and steady material removal rate (MRR) can be achieved by using the proposed polishing method. Then, the effect of polishing pressure, tool rotation speed, and polishing time on polishing quality was analyzed with single-factor experiments. Finally, the optimal polishing parameters were obtained with the orthogonal experiment. The results show that the average surface roughness is reduced from 238.16 to 1.34 nm, and the nanoscale super-smooth surface is achieved, which proves the feasibility of the CNC polishing method with ICLS.

Although the confined behaviors of liquids, especially water, in the nanoscale pores of silica have been investigated using both experimental and theoretical methods, the reports about the structur...

The aromatics complex is essential in the production of p-xylene and contains several distillation towers and reactors, making this process highly energy intensive. To save energy for the aromatics...

The economic dispatch problem (EDP) plays a fundamental and significant role in smart grids. Its purpose is to decide the output power of every generator in smart grids for achieving the minimal generation cost. With advantages in flexibility, robustness, and scalability, it is desirable to apply distributed optimization methods to solve EDPs. In most existing distributed optimization approaches, all generators explicitly exchange their states with neighbors to obtain the optimal solution, which may result in disclosing the privacy information of generators. This problem becomes worse if there are some adversaries aimed at inferring privacy information from the communication network for nefarious purposes. For privacy preservation, a privacy preserving distributed optimization algorithm over time-varying directed communication networks is proposed in this article by adding conditional noises to the exchanged states. It is proved that this proposed algorithm is able to solve the EDP. Moreover, the convergence rate and privacy analysis of the proposed algorithm are also shown in this article. An example is provided to confirm the effectiveness of this proposed algorithm.

The optimization of cyclic scheduling for an ethylene cracking furnace system (ECFS) is beneficial for ethylene plants. The scheduling problem involves multiple feeds, tanks, furnaces, and differen...

Hybrid modeling, aiming to integrate the advantages of both first-principles models and data-driven models, is an important technology for refinery process simulation and optimization. The commonly...

Land mobile satellite communication and physical layer security are considered as the promising paradigms in beyond 5G networks. In this paper, we establish the multi-relay hybrid satellite-terrestrial systems (HSTSs) with artificial noise (AN) to investigate the reliability and security, in which the satellite sends signals to the terrestrial destination through multiple decode-and-forward relays and there exists an eavesdropper trying to overhear the useful information. In order to improve the overall system performance, two relay selection (RS) schemes: i) optimal relay-receiver pair selection (ORRPS) and ii) suboptimal relay selection (SRS) are considered. ORRPS selects an optimal relay-receiver pair and an AN signal to maximize the corresponding signal-to-interference-plus-noise ratios of ${R_{k}} \to D\left ({{R_{k} \to E} }\right)$ and the SRS refers to selecting a relay that optimize the instantaneous channel gain ${\left |{ {{h_{R_{k}D}}} }\right |^{2}}\left ({{{{\left |{ {{h_{R_{k}E}}} }\right |}^{2}}} }\right)$ among $K$ relays, with the traditional round-robin selection scheme as a benchmark for comparison. Furthermore, the exact closed-form analytical expressions of outage probability (OP) and intercept probability (IP) under different RS schemes are derived for AN and non-AN conditions. To obtain more insights, the asymptotic analysis for high signal-to-noise-ratio regime is carried out. Based on the asymptotic analysis of OP, the diversity orders of the HSTSs are obtained. The numerical results and theoretical analysis shown that: i) The reliability of TRRS scheme is the worst among the three RS schemes, and the reliability of ORRPS scheme is the best; ii) The AN technology can significantly improve the security of the system, although it has a little adverse affect on the reliability; iii) Infrequent light shadowing has the best reliability, frequent heavy shadowing has the worst outage performance, and the change of security is opposite; iv) The OP of SRS and ORRP schemes decreased with the increase of $K$ , while the IP changed on the contrary, and the reliability and security of TRRS scheme did not change obviously.

This article focuses on the formation control problem of nonlinear multiagent systems under denial-of-service attacks. The formation control can be preserved by the distributed hybrid event-triggering strategies (HETSs). As a balance between periodic and continuous event-triggering strategies, HETS arranges a tradeoff between the resource utilization and the communication frequency among agents. Theoretical results are verified using a benchmark problem of six miniature quadrotor prototypes.

The 3-D seismic data reconstruction can be understood as an underdetermined inverse problem, and thus, some additional constraints need to be provided to achieve reasonable results. A prevalent scheme in 3-D seismic data reconstruction is to compute the best low-rank approximation of a formulated Hankel matrix by rank-reduction methods with a rank constraint. However, the predefined Hankel structure is easily damaged by the low-rank approximation, which leads to harming its recovery performance. In this article, we present a structured tensor completion (STC) framework to simultaneously exploit both the Hankel structure and the low-tubal-rank constraint to further enhance the performance. Unfortunately, under the assumption of elementwise sampling used by existing methods, STC is intractable to be solved since Hankel constraints cannot be expressed as linear tensor equations. Instead, tubal sampling is proposed to describe the missing trace behavior more accurately and further build a bridge between tensor and matrix completion (MC) to overcome the solving issue in two aspects: through the bridge from tensor to MC, STC can be solved efficiently using MC from random samplings of each frontal slice in the Fourier domain. Through the bridge from matrix to tensor completion, various tensor models within the framework can be developed from noise-specific MC to meet the need for data reconstruction in changeable noise environments. Moreover, alternating-minimization and alternating-direction methods of multipliers are developed to solve the proposed STC. The superior performance of STC is demonstrated in both synthetic and field seismic data.

In chemical industry, most processes face the challenge of high energy consumption. The approach presented in this study can reduce the energy footprint and increase efficiency. The energy system of a separation process in ethylene manufacturing is used to demonstrate the effectiveness of the approach. The chilling train system of the separation process in a typical ethylene plant consumes most cooling and provides appropriate feed for distillation columns. The steady state simulation of system was presented and the simulation results were proved accurate. The conventional exergy analysis identifies that Dephlegmator No.1 (a heat exchange and mass transfer device) has the highest exergy destruction (1401.28 kW). Based on advanced exergy analysis, Dephlegmator No.1 has the highest rate of avoidable exergy destruction (89.04 %). Finally, a multi-objective optimisation aiming to maximise system exergy efficiency and to minimise operational cost was performed and the Pareto frontier was obtained. The multi-objective optimized exergy efficiency is 79.53 % (improved by 0.61 %) and the operational cost is 0.02031 yuan/kg (saved by 11.19 %). This study will guide future research to reduce energy consumption in process manufacturing.

Urea line heating system is critical for Selective Catalytic Reduction (SCR) aftertreatment system under low temperature environment. Traditionally, the Electronic Control Unit (ECU) motivates urea lines heating command and diagnostics directly. This paper illustrates a Controller Area Network (CAN)-based line heater architecture development for the SCR aftertreatment system, as well as the diagnostics generation. The CAN-based line heater diagnostics is validated on vehicle experimental with four different given cases. It can be seen from the validation results that the diagnostics logic and algorithm are reasonable, and the line heater failure modes could be detected correctly including open circuit, short circuit and inducement. In addition, the failure modes are able to set or clear for each of the urea lines according to the diagnostics logic. ECU and ECU1 could communicate well between each other by CAN. This study introduces a new line heater architecture for SCR aftertreatment system which is meaningful for the whole vehicle assembly, which could be considered in the real application in future.

The integration of carbon capture and CO2 utilization could be a promising solution to the crisis of global warming. By integrating calcium-looping (CaL) and the reverse-water-gas-shift (RWGS) reaction, a high-temperature CO2 capture and in situ conversion technology is successfully realized in one fixed-bed column at the same operating temperature of 650 °C. Inspired by the heterojunction photocatalytic mechanism, the heterojunction-redox catalysis strategy is proposed for the first time by doping the bimetallic Fe3+/Fe2+ and Co3+/Co2+ redox couples into a hierarchical porous CaO/MgO composite. The presence of different valence states of doped Fe and Co oxides not only provides extra oxygen vacancies to facilitate CO2 adsorption, and hence adsorption enhanced conversion (AEC), but also significantly lowers the electric potential difference of Fe3+/Fe2+ through the newly formed Fermi level in Fe5Co5Mg10CaO, which makes electron spillover easier to improve the catalytic activity in the RWGS reaction for CO2 conversion. More importantly, with the high-temperature refractory MgO and the highly disperse Fe and Co oxides in Fe5Co5Mg10CaO, the problem of CaO sintering is successfully solved. An excellent and stable high-temperature CO2 capture capacity of 9.0–9.2 mmol g−1, an in situ CO2 conversion effeciency near 90% and a CO selectivity close to 100% are achieved in the integrated CaL/RWGS process. In addition, experimental and simulation scale-up studies further demonstrate its pratical scalability. Economic evaluation reveals that the integrated CaL/RWGS technology is much more cost-effective than the individual CaL and RWGS processes. Therefore, the heterojunction-redox strategy provides a unique way to design bifunctional adsorbent/catalyst materials. The integrated CaL/RWGS process could be a promising technology for CO2 capture and utilization.

Establishing quantitative structure-property relationships for the rational design of small molecule drugs at the early discovery stage is highly desirable. Using natural language processing (NLP), we proposed a machine learning model to process the line notation of small organic molecules, allowing the prediction of their melting points. The model prediction accuracy benefits from training upon different canonicalized SMILES forms of the same molecules and does not decrease with increasing size, complexity, and structural flexibility. When a combination of two different canonicalized SMILES forms is used to train the model, the prediction accuracy improves. Largely distinguished from the previous fragment-based or descriptor-based models, the prediction accuracy of this NLP-based model does not decrease with increasing size, complexity, and structural flexibility of molecules. By representing the chemical structure as a natural language, this NLP-based model offers a potential tool for quantitative structure-property prediction for drug discovery and development.

In the context of failure control of servo system driven by twin motors, there are still no available results to compensate unknown actuator failures which seem inevitable in practice by adaptive backstepping technique. Therefore, to rise the reliability of the system, we aim at addressing such a problem by proposing an adaptive robust actuator failure compensation control scheme based on backstepping technique for servo system driven by twin motors. Unlike the traditional backstepping, the estimation of unknown coefficient of intermediate state variable is introduced in coordinate changes. In addition, matching and non-matching uncertainties have been fully considered in the controller design. Simulation results show that the designed controller can ensure the boundedness of all the signals no matter actuator failures occur or not.

Tensor sparse coding (TSC) is a method used to excavate 3D volume structures extended by sparse coding (SC), which is increasingly applied in data noise attenuation. Existing TSC approaches control the intensity of noise attenuation by using a predetermined soft or hard threshold that relies on the noise variance. However, the noise variance in seismic data is unknown and varies with time and space, leading to the conventional TSC method not being able to track this change. To address this issue, we proposed a tensor sparse coding model with spatially adaptive coherent constraint (TSC-SAC) to find an optimal adjustable threshold without the demand for prior knowledge of the noise variance. The threshold is determined by the coherence of the residual with respect to the dictionary. Moreover, a tensor spatial coherence orthogonal matching pursuit algorithm (TSC-OMP) is developed for solving sparse representation. Unlike the existing threshold strategy in traditional TSC methods, TSC-OMP utilizes an ideal spatially adaptive coherence threshold to regulate the sparsity, which can effectively preserve the valuable information in processing for noise suppression. By comparing with four state-of-the-art denoising algorithms, we then demonstrated the superior performance of TSC-SAC on both a synthetic and two field data sets.

Adenosine triphosphate (ATP) is amphiphilic in nature and has the characteristics of a hydrotrope because of the charged triphosphate moiety and the large aromatic ring located on each end of its structure. Previous studies revealed that ATP can effectively maintain the solubility and prevent liquid-liquid phase separation (LLPS) of some biological proteins. In this study, we assessed the impact of ATP on the stability of a model therapeutic IgG1 antibody (MA1) to evaluate its potential application in protein formulation design. In our system, ATP promotes rapid LLPS of MA1 and we demonstrate that the ATP-MA1 static interaction drives phase separation of MA1. The attractive protein-protein interaction increased exclusively in the presence of ATP but not in the presence of other ATP analogues, such as adenosine diphosphate, adenosine monophosphate, and adenine. Through an intrinsic fluorescence quenching study, we revealed that ATP bound to MA1 electrostatically and formed static interactions; furthermore, such static ATP-MA1 interactions significantly altered the surface property of the protein and the protein-protein interactions and subsequently induced LLPS of MA1. This ATP-induced LLPS could be effectively eliminated by Mg2+, which chelated with ATP and thus negated ATP-MA1 static interaction. Our results revealed the unique molecular mechanism of ATP-induced rapid LLPS of MA1.

High-speed rail (HSR) systems potentially provide a more efficient way of door-to-door transportation than airplane. However, they also pose unprecedented challenges in delivering seamless Internet service for on-board passengers. In this paper, we conduct the first large-scale disconnection-centric measurement study of TCP performance over LTE on HSR. Our measurement targets the main HSR route in China operating at 300/350 km/h. We performed extensive data collection obtaining 378.3 GB data collected over 56639 km of trips. Leveraging such a unique dataset, we measure important performance metrics such as TCP goodput, latency and loss rate across different congestion control algorithm, mobile carrier, and different train speed. We further develop the LTE disconnection taxonomy, and conduct a in-depth correlation study between TCP stall and LTE disconnection. Our findings reveal the networking performance on today’s HSR environment “in the wild”, as well as identify several root causes of performance inefficiencies, which together highlight the need to develop dedicated protocol mechanisms that are friendly to extreme mobility.

This paper presents a novel approach to mitigating the effect of temperature variations on the bridges’ dynamic modal properties for more reliably detecting scour damage around bridge piles based on the vibration-based measurements. The novelty of the presented approach lies in its ability to reasonably remove the impacts on the modal properties of bridges, particularly caused by changes in material properties and structural boundary conditions due to temperature variations without explicitly modeling these complex effects. The main idea is to adopt the probabilistic-based machine learning method, Gaussian Process Model, to learn the correlation between the changes of modal properties of a monitored bridge and the corresponding temperature variations from in situ sensor measurements, and probabilistically infer the bridge scour based on the modified vibration measurements, which have mitigated the identified impacts of temperature variations, by applying Bayesian inference through the Transitional Markov Chain Monte Carlo simulation. The proposed approach and its applicability are presented and validated through the numerical simulation of a prototype bridge, demonstrating its potential for practical application for mitigating effects of temperature variations or other environmental impacts for vibration-based Structural Health Monitoring. The limitation of the presented study and future research needs are also discussed.

This article presents a secure communication scheme based on the quantized synchronization of master-slave neural networks under an event-triggered strategy. First, a dynamic event-triggered strategy is proposed based on a quantized output feedback, for which a quantized output feedback controller is formed. Second, theoretical criteria are derived to ensure the bounded synchronization of master-slave neural networks. With these criteria, an explicit upper bound is given for the synchronization error. Sufficient conditions are also provided on the existence of quantized output feedback controllers. A Chua's circuit is chosen to illustrate the effectiveness of our theoretical results. Third, a secure communication scheme is presented based on the synchronization of master-slave neural networks by combining the basic principle of cryptology. Then, a secure image communication is studied to verify the feasibility and security performance of the proposed secure communication scheme. The impact of the quantization level and the event-triggered control (ETC) on image decryption is investigated through experiments.

This article is concerned with the stability problem for a class of Lipschitz-type nonlinear systems in networked environments, which are suffered from random and impulsive deception attacks. The attack is modeled as a randomly destabilizing impulsive sequence, whose impulsive instants and impulsive gains are both random with only the expectations available. Almost sure stability is ensured based on Doob's Martingale Convergence Theorem. Sufficient conditions are derived for the solution of the nonlinear system to be almost surely stable. An example is given to verify the effectiveness of the theoretical results. It is shown that the random attack will be able to destroy the stability, therefore, a large feedback gain may be necessary.

Albumin-conjugated drugs attain KRAS mutant cancer targeting through KRAS-enhanced macropinocytosis and intensified lysosomal degradation due to reduced neonatal Fc receptor (FcRn) expression. The cytosolic delivery of active payloads relies on endocytosis and subsequent intracellular processing of albumin delivery vehicles, wherein complex regulatory mechanisms and molecular machineries are closely involved. Despite the obvious merit of KRAS targeting, could such an endocytic process involving extra molecular regulators also bring about extra vulnerabilities to albumin-conjugated drugs, particularly, unexpected drug resistance? To assess such risks, here we performed an unbiased drug resistance mechanism comparison in pancreatic cancer, between free triptolide (TP, a potent cytotoxin) and albumin-conjugated TP, using genome-wide CRISPR-Cas9 loss-of-function screens. GTF2H5, a subunit of GTF2H transcription factor complex, was the only hit identified regardless of forms of TP treatment. With drug efficacy tests on GTF2H5 knockout clones, we further concluded that GTF2H5 deficiency conferred drug resistance primarily due to the pharmacological mechanism of action (MoA) of TP. In addition, molecules previously considered to be able to affect endocytosis and intracellular processing were not enriched during the screening with albumin-conjugated TP. With the aid of genome-wide CRISPR-Cas9 loss-of-function screens, we conclude that the pharmacological resistance of the active payload, rather than any potential loss-of-function mutations in endocytic molecular machineries, is the solely crucial drug resistance mechanism of albumin-conjugated drugs.

Recent years have witnessed the rapid growth of mobile virtual network operators (MVNOs), which operate on top of existing cellular infrastructures of base carriers, while offering cheaper or more flexible data plans compared to those of the base carriers. In this paper, we present a two-year measurement study towards understanding various fundamental aspects of today’s MVNO ecosystem, including its architecture, customers, performance, economics, and the complex interplay with the base carrier. Our study focuses on a large commercial MVNO with one million customers, operating atop a nation-wide base carrier. Our measurements clarify several key concerns raised by MVNO customers, such as inaccurate billing and potential performance discrimination with the base carrier. We also leverage big data analytics, statistical modeling, and machine learning to address the MVNO’s key concerns with regard to data usage prediction, data plan reselling, customer churn mitigation, and billing delay reduction. Our proposed techniques can help achieve higher revenues and improved services for commercial MVNOs.

The microstructure evolution during aging at high temperatures is usually used to thermodynamically simulate those cases of aging at low temperatures but for a very long time for P92 steel, because high temperature can accelerate the microstructure process. Therefore, in the present research, in order to comprehensively understand the microstructure evolution mechanisms during aging at especially high temperatures, the as-tempered P92 steel was exposed at 790 °C. Optical microscopy (OM), scanning electron microscopy (SEM) and transmission electron microscopy (TEM) were employed to characterize the microstructures. The dominant mechanisms at the four stages in microstructure evolution process during thermal exposure are (I) dislocation annihilation, (II) lath broadening and equiaxed subgrain nucleation, (III) equiaxed subgrain growth, (IV) recrystallization nucleation and growth. The martensitic lath broadening is dominated by both the motion of “Y”-type lath boundary and the combination of parallel lath boundary. The subgrain growth is by virtue of both the combination of the equiaxed subgrain and the bowing out of subgrain boundary.

The main focus of this work is to propose a data-driven residual generation based performance monitoring method for a industrial nonlinear p-xylene (PX) oxidation reaction process. In the proposed ...

Halide perovskite semiconductors exhibit ultralow thermal conductivities, making them potentially suitable for thermoelectric applications. Nevertheless, the thermoelectric properties of the protot...

Piezoelectric stack transducers in d33 mode have a much higher mechanical-to-electric energy conversion efficiency compared with d31 mode piezoelectric harvesters. However, multilayered piezoelectric stacks usually operate in off-resonance due to the higher stiffness and thereby have a lower power output under low-frequency excitations. This paper proposes to apply the dynamic magnetic pre-loading to a piezoelectric stack transducer to significantly increase the power output. The energy harvesting system consists of a multilayered piezoelectric stack with a compliant force amplification frame, a proof mass, and two magnets configured in attraction. The static force–displacement relationship of the magnets is identified from experiments and extended to a dynamic model capable of characterizing the dynamic magnetic interaction. An electromechanical model is developed based on the theoretical derivation and the experimentally identified parameters to predict the voltage outputs under different resistive loads. Approximate analytical solutions are derived by using the harmonic balance method and show good agreements with the numerical and experimental results. The performance of the system is examined and compared with that of the harvester without magnetic pre-loading. The influences of the distance between the two magnets and the electrical resistive loads on the power output are investigated. Results indicate the energy harvesting system with magnetic pre-loading can produce over thousand times more power than the system without magnetic pre-loading at the base excitation of 3 Hz and 0.5 m/s2, far below the resonance at 243 Hz

Sulfamethazine (SMZ) is an important sulfonamide antibiotic. Although the concentration in the environment is small, it is harmful. The drug residues can be transferred, transformed or accumulated, affecting the growth of animals and plants. In this study, the integrated fixed-film activated sludge membrane bioreactor (IFAS-MBR) were constructed to investigate the performance and degradation mechanism of SMZ. The addition of SMZ had a significant impact on the removal of the chemical oxygen demand (COD) and ammonia nitrogen (NH4+-N). The optimal operating conditions were hydraulic retention time (HRT) at 10 h and solid retention time (SRT) at 80 d, respectively. On this basis, the effects of different SMZ concentrations on nutrient removal, degradation, and sludge characteristics were compared. The removal efficiency of SMZ increased with the increase of SMZ concentration. The maximum removal rate was as high as 87%. The SMZ dosage also had an obvious effect on sludge characteristics. As the SMZ concentration increased, the extracellular polymer substances (EPS) concentration and the membrane resistance both decreased, which were beneficial for the reduction of membrane fouling. Finally, seven kinds of SMZ biodegradation intermediates were identified, and the possible degradation pathways were speculated. The microbial community results showed that the microbial diversity and richness in the reactor decreased after adding SMZ to the influent. The relative abundance of Bacteroidetes, Actinobacteria, Saccharibacteria and Nitrospirae increased at the phylum level. Sphingobacteria and Betaproteobacteria became dominant species at the class level. The relative abundance of norank-p-Saccharibacteria and Nitrospirae increased significantly, and norank-p-Saccharibacteria may be the dominant bacteria for SMZ degradation.

Due to their on-body and ubiquitous nature, wearables can generate a wide range of unique sensor data creating countless opportunities for deep learning tasks. We propose DeepWear, a deep learning (DL) framework for wearable devices to improve the performance and reduce the energy footprint. DeepWear strategically offloads DL tasks from a wearable device to its paired handheld device through local network connectivity such as Bluetooth. Compared to the remote-cloud-based offloading, DeepWear requires no Internet connectivity, consumes less energy, and is robust to privacy breach. DeepWear provides various novel techniques such as context-aware offloading, strategic model partition, and pipelining support to efficiently utilize the processing capacity from nearby paired handhelds. Deployed as a user-space library, DeepWear offers developer-friendly APIs that are as simple as those in traditional DL libraries such as TensorFlow. We have implemented DeepWear on the Android OS and evaluated it on COTS smartphones and smartwatches with real DL models. DeepWear brings up to 5.08X and 23.0X execution speedup, as well as 53.5 and 85.5 percent energy saving compared to wearable-only and handheld-only strategies, respectively.

In the refinery process, a vast amount of data is generated in daily production. How to make full use of these data to improve the simulation’s accuracy is crucial to enhancing the refinery operating level. In this paper, a novel deep learning framework integrating the self-organizing map (SOM) and the convolutional neural network (CNN) is developed for modeling the industrial hydrocracking process. The SOM is used to map input variables into two-dimensional maps to extract process features. Then, these maps are fed into the CNN to predict the outputs of the hydrocracking process. The SOM adopted is free of training, which reduces the computational complexity, simplifies the application, and improves the prediction accuracy. Practical guidance on the application of the proposed framework is provided by comparing and analyzing different structures and parameters. Finally, an online modeling scheme is developed and applied in an actual hydrocracking process. Experimental results demonstrate that the proposed framework has great performance in modeling the hydrocracking process and provides a good reference for process optimization.

In this paper, an adaptive backstepping control scheme is proposed for a class of nonlinear systems with unknown time delay. The unknown time delay in the system depends on all system states, which will inevitably destroy the triangular structure of the control system. Therefore, traditional backstepping can not be used to design the controller. In order to overcome the problem caused by “non-triangular structure”, we construct a linear projection between the state vector and its transform in controller design. Using inequality and adaptive backstepping control, we compensate the uncertainties caused by unknown time delays. Finally, the simulation results show that the proposed algorithm is effective.

In this paper, a duplexing antenna and two multi-band filtering antennas using resonator loaded slot are presented. The loaded slot can be flexibly designed as a dual-band or a tri-band antenna by changing the number of the loaded resonator. The operating frequencies of the slot antenna can be easily controlled through tuning the lengths and positions of the loaded elements. By integrating the multi-band antenna and the multi-mode resonator, duplexing and filtering antennas can be realized. The duplexing antenna consists of two single-mode resonators, a dual-mode stub-loaded resonator as the junction resonator and a dual-band slot antenna. The dual-band and tri-band filtering antennas each contains a dual-mode resonator or a tri-mode resonator, radiating through resonator loaded slot antenna. Since the slot is the only radiation element, all the operating bands of the three antennas show good bi-directional radiation patterns. For validation, all of the antennas are designed, manufactured and measured.

Cyber attacks pose severe threats on synchronization of multi-agent systems. Deception attack, as a typical type of cyber attack, can bypass the surveillance of the attack detection mechanism silently, resulting in a heavy loss. Therefore, the problem of mean-square bounded synchronization in multi-agent systems subject to deception attacks is investigated in this paper. The control signals can be replaced with false data from controller-to-actuator channels or the controller. The success of the attack is measured through a stochastic variable. A distributed impulsive controller using a pinning strategy is redesigned, which ensures that mean-square bounded synchronization is achieved in the presence of deception attacks. Some sufficient conditions are derived, in which upper bounds of the synchronization error are given. Finally, two numerical simulations with symmetric and asymmetric network topologies are given to illustrate the theoretical results.

In this paper, we design an event-triggering based hybrid control strategy for the time-varying attitude tracking control of the aircraft system, where the networked communication is confined with denial-of-service (DoS) attacks. The designed control strategy introduces a binary-logic-variable-based hybrid control law to overcome the topological constraint on $\text{SO}(3)$ , together with an event-triggering sampling mechanism to determine when to update the designed controller. Combined with the two kinds of flows/jumps and a general explicit characterization of frequency/duration properties for DoS attacks, we establish a general hybrid formalism for the attitude tracking control. The designed event-based hybrid control scheme renders the globally asymptotic tracking control resilient to DoS attacks. Simulation results are presented to show the effectiveness of the established hybrid control strategy.

Smart manufacturing is critical in improving the quality of the process industry. In smart manufacturing, there is a trend to incorporate different kinds of new-generation information technologies into process-safety analysis. At present, green manufacturing is facing major obstacles related to safety management, due to the usage of large amounts of hazardous chemicals, resulting in spatial inhomogeneity of chemical industrial processes and increasingly stringent safety and environmental regulations. Emerging information technologies such as artificial intelligence (AI) are quite promising as a means of overcoming these difficulties. Based on state-of-the-art AI methods and the complex safety relations in the process industry, we identify and discuss several technical challenges associated with process safety: ① knowledge acquisition with scarce labels for process safety; ② knowledge-based reasoning for process safety; ③ accurate fusion of heterogeneous data from various sources; and ④ effective learning for dynamic risk assessment and aided decision-making. Current and future works are also discussed in this context. Keywords: Process industry, Smart manufacturing, Green manufacturing, Artificial intelligence

Chemical spills on complex geometry are difficult to model due to the uneven concentration distribution caused by air flow over ground obstacles. Computational fluid dynamics (CFD) is one of the powerful tools to estimate the building-resolving wind flow as well as pollutant dispersion. However, it takes too much time and requires enormous computational power in emergency situations. As a time demanding task, the estimation of the chemical spill consequence for emergency response requires abundant wind field information. In this paper, a comprehensive wind field reconstruction framework is proposed, providing the ability of parameter tuning for best reconstruction accuracy. The core of the framework is a data regression model built on principal component analysis (PCA) and extreme learning machine (ELM). To improve the accuracy, the wind field estimation from the regression model is further revised from local wind observations. The optimal placement of anemometers is provided based on the maximum projection on minimum eigenspace (MPME) algorithm. The fire dynamic simulator (FDS) generates high-resolution data of wind flow over complex geometries for the framework to be implemented. The reconstructed wind field is evaluated against simulation data and an overall reconstruction error of 9% is achieved. When used in real case, the error increases to around 12% since no convergence check is available. With parameter tuning abilities, the proposed framework provides an efficient way of reconstructing the wind flow in congested areas.

Yttria, yttria-ceria and yttria-ceria-lanthana stabilized zirconia powders were prepared by coprecipitation. Their tetragonal-to-monoclinic phase (t→m) transformation was investigated by calcining the powders in a temperature range of 400–1400 °C for 2 h. The results show that after doping with 0.1 to 0.3 mol.% La2O3 and calcining at 1400 °C in air, unusual redox behaviours of cerium were detected in the 1.5 mol.% Y2O3+5.5 mol.% CeO2 co-stabilized zirconia. Grain refinement and a sharp reduction in oxygen vacancy concentration were observed simultaneously. The t→m transition was not found in the 0.1 mol.% La2O3 doped zirconia but appeared in the cases with a higher dopant content. The changes are discussed with regards to the grain size, valence change of cerium, presence of oxygen vacancies, and segregation of the dopants at grain boundaries.

Raw material blending process is an essential part of the cement production process. The main purpose of the process is to guarantee a certain oxide composition for the raw meal at the outlet of the mill by regulating the four raw materials. But the chemical compositions of raw materials vary from time to time, resulting in difficulties to control the oxide compositions to a predefined value. Therefore, a novel algorithm to estimate the chemical compositions of the raw materials is developed. The paper mainly consists of two parts. In model construction part, a novel constrained least square model is proposed to overcome the deviation introduced by long-term drift of the material components, and the model parameters are estimated with an online strategy. And in validation part, the approach is implemented to two examples including datasets from simulation model and the actual industrial process. The final results show the effectiveness of the proposed method.

In this communication, an approach for designing multi-band microstrip slot antennas is proposed. Multiple resonators are loaded on a conventional microstrip slot antenna to introduce multiple resonances. The number of the operational bands of the antenna can be easily controlled by varying the number of the loading resonators. The locations of each resonance can be adjusted by varying the positions and lengths of the loading resonators together with the geometry of the slot if necessary. For validation, prototypes with dual-, tri-, and quad-band operations are designed, fabricated, and measured. Since no modified slot is used in this design method, all the operating bands show good bidirectional radiation patterns. The measured and simulated results agree well. The proposed antenna features low profile, small radiating slot, and easy fabrication, showing promising application prospect for modern communication system.

Bi2S3, with earth-abundant compositions and a low thermal conductivity, is regarded as a candidate thermoelectric material. In this work, Bi2S3 samples that were doped with x % mol InCl3 were successfully fabricated via a mechanical alloying and spark plasma sintering process. InCl3, as an n-type donor dopant, was added to the Bi2S3 system to improve its electrical transport properties and optimize its thermal conductivity. Upon doping, the electrical conductivity of Bi2S3 doped with 1 mol% InCl3 reaches up to 62 Scm−1, and the Seebeck coefficient maintains a relatively large value of −244 μV K−1 at 673 K, which results in a maximum power factor of 363 μW m−1 K−2. Furthermore, due to a simultaneously reduced thermal conductivity at high temperature, a ZT peak of 0.57 is obtained at 673 K along the parallel to the press direction for the sample doped with 1.0 mol% InCl3, which is almost four times higher than that of pristine Bi2S3 (0.14 at 673 K). The elastic properties and Debye temperature of Bi2S3 are also calculated to analyze the origin of the intrinsically low thermal conductivity and are compared to those of other thermoelectric materials with a low thermal conductivity.

Vertical roller mill (VRM) is an increasingly popular comminution equipment in cement plants. Raw meal fineness at the outlet of VRM is one of the most important indicators to measure product quality. A soft sensing model developed for powder fineness in real-time could assist operators to monitor the comminution process online. However, due to frequent fluctuation of raw material properties, intrinsic nonlinearity of the process and changeable operation conditions, the data present a multimodal characteristic. Therefore, this paper proposes an indicator to measure the similarity between variables, that is, the shortest distance between nodes in the constructed weighted network. By combining the Girvan Newman (GN) algorithm, the nodes in the variable network are divided into multiple groups, and based on this, the distributed PCA (DPCA) similarity is adopted for time series segmentation (TSS). Compared with traditional similarities between samples (distance, density, etc.), the similarity between time series focuses more on the dynamic characteristics of variables. And the implementation of DPCA similarity is equivalent to increasing the sparse characteristics of principal components, which is beneficial for enhancing the generalization of the model. Support vector regression (SVR) models along with a support vector machine (SVM) based classifier are built to obtain final predictions of the powder fineness. Effectiveness of the proposed method is verified by actual industrial data. It has a root mean square error (RMSE) index of 0.3451 on the test set, which is much smaller than that of the multi-modal soft sensor based on either clustering approaches (0.6524) or PCA similarity based TSS method (0.4282).

Carrying over 75% of the last-mile mobile Internet traffic, WiFi has inevitably become an enticing target for various security threats. In this work, we characterize a wide variety of real-world WiFi threats at an unprecedented scale, involving 19 million WiFi access points (APs) mostly located in China, by deploying a crowdsourced security checking system on 14 million mobile devices in the wild. Leveraging the collected data, we reveal the landscape of nationwide WiFi threats for the first time. We find that the prevalence, riskiness, and breakdown of WiFi threats deviate significantly from common understandings and prior studies. In particular, we detect attacks at around 4% of all WiFi APs, uncover that most WiFi attacks are driven by an underground economy, and provide strong evidence of web analytics platforms being the bottleneck of its monetization chain. Further, we provide insightful guidance for defending against WiFi attacks at scale, and some of our efforts have already yielded real-world impact---effectively disrupted the WiFi attack ecosystem.

Object detection algorithms play an important role in the field of violation detection. However, small target detection is full of challenges in scenes related to human. The relationship between objects is usually not considered in object detection algorithms, which will make the model over relay on the high-order features and not make full use of local features. To address this issue, a novel framework named PointDet is proposed to learn local features which optimizes the detection effect of small targets in chemical plants. Since most of the targets to be detected in our dataset are highly correlated with human, human local features are used when designing the framework. First, we use a trained pose estimation model to extract local key point features. However, if local features are used directly, the relationship between them cannot be fully considered. Based on this situation, we have designed the one-vs-others module and the adaptive-graph-convolution module to reconstruct local features. In addition, for the output layer, the most challenging problem is how to better detect small targets. In our task, various small objects such as gloves, goggles, etc. have an obvious positional relationship with the local features of human body. In the output layer, we have designed a head attention module to make full use of this situation to optimize the small target detection problem. Specifically, our framework significantly outperforms state-of-the-art by 7.8 AP scores on field work dataset in chemical plants.