Industrial Internet of Things (IoT) enables distributed intelligent services varying with the dynamic and realtime industrial environment to achieve Industry 4.0 benefits. In this article, we ...consider a new architecture of digital twin (DT) empowered Industrial IoT, where DTs capture the characteristics of industrial devices to assist federated learning. Noticing that DTs may bring estimation deviations from the actual value of device state, a trusted-based aggregation is proposed in federated learning to alleviate the effects of such deviation. We adaptively adjust the aggregation frequency of federated learning based on Lyapunov dynamic deficit queue and deep reinforcement learning (DRL), to improve the learning performance under the resource constraints. To further adapt to the heterogeneity of industrial IoT, a clustering-based asynchronous federated learning framework is proposed. Numerical results show that the proposed framework is superior to the benchmark in terms of learning accuracy, convergence, and energy saving.
The permanent crosslinked network imparts chemical and mechanical stability to crosslinked elastomers, making their recycling a serious environmental issue. The decomposition products of pentaaryl ...diazoxide in the structure of hydantoin epoxy resin are nitrogen and carbon dioxide, and it is an environmentally friendly resin material. Here, we demonstrate a reversible covalent bonding reaction catalyzed by Zn2+ between the ester bond formed by the hydantoin epoxy resin and the acid anhydride, and construct a reversible crosslinked network structure of the environmentally friendly acid anhydride/hydantoin epoxy resin material. The strength, hardness and toughness of the cured material are greatly improved by optimizing its ratio. The ester exchange bonding can be reversible with temperature, which leads to the shape memory capability of the synthesized cured material. The cured product of HMY1 was recovered with ethylene glycol and completely dissolved at 180°C for 11 h. Ethylene glycol was volatilized at 190°C to obtain decomposed epoxy oligomer (DEO), which was added to the HMY1 resin system (the amount of DEO was 30 wt% of the total mass of the resin), and the network structure was cross‐linked to enable it to be recovered and remanufactured. Thus, this work provides a new avenue for the environmentally friendly development of recyclable, reconfigurable, and thermally adaptable shape‐memory hydantoin epoxy materials.
Highlights
Hydantoin epoxy resin eventually breaks down into non‐toxic CO2 and N2.
Hydantoin epoxy and methyl tetrahydrophthalic anhydride form a reversible dynamic chemical bond catalyzed by Zn2+.
Ethylene glycol can recover and reuse the solidified product.
Diagram of shape memory recovery and green recycling process of HMY cured material.
Ordered mesoporous ceria and ceria-zirconia with high specific surface area were prepared by nanocasting of a mesoporous silica KIT-6 template and used for soot oxidation.The as-synthesized ordered ...mesoporous ceria and ceria-zirconia were characterized by XRD,TEM,Nitrogen adsorption-desorption,Raman spectroscopy,and XRF.The results indicate that mesoporous ceria and ceria-zirconia possess highly ordered mesoporous structure,and exhibited excellent catalytic performance in soot oxidation.T_（50） of mesoporous ceria and ceria-zirconia are 475 and 470 ℃,respectively.The high catalytic activity of mesoporous materials can be attributed to the mesoporous structure and small crystallite size.Moreover,aged mesoporous materials exhibit high catalytic activity.
Industrial Internet of Things (IoT) enables distributed intelligent services varying with the dynamic and realtime industrial devices to achieve Industry 4.0 benefits. In this paper, we consider a ...new architecture of digital twin empowered Industrial IoT where digital twins capture the characteristics of industrial devices to assist federated learning. Noticing that digital twins may bring estimation deviations from the actual value of device state, a trusted based aggregation is proposed in federated learning to alleviate the effects of such deviation. We adaptively adjust the aggregation frequency of federated learning based on Lyapunov dynamic deficit queue and deep reinforcement learning, to improve the learning performance under the resource constraints. To further adapt to the heterogeneity of Industrial IoT, a clustering-based asynchronous federated learning framework is proposed. Numerical results show that the proposed framework is superior to the benchmark in terms of learning accuracy, convergence, and energy saving.
The spark plasma sintering（SPS） method was used to study the mechanism of reaction interface between Zr and Ti3AlC2 with electric current going through it. It was found that electric current greatly ...reduced the bonding temperature of Zr and Ti3AlC2. By the micro-structure analysis of the interface through SEM/EDS, it was found that Al atoms diffused from the Ti3AlC2 substrate into the Zr side and reacted with Zr to form the Zr-Al compounds at the interface, which is the strengthening mechanism of Ti3AlC2-Zr bonding. The thickness of reaction layers（Zr-Al alloy） was from 0.879 to 13.945 mm depending on different sintering condition. Current direction, heating rate, soaking time, pulse patterns all influenced the diffusion of Al atoms which affected the joining quality of Zr and Ti3AlC2.
•A novel unscented Kalman filter without direct feedthrough is proposed for the identification of joint input-system without direct feedthrough.•According to the uncertainty of the identified unknown ...inputs, two algorithms of the proposed method are presented.•The proposed method is verified by linear/nonlinear hysteretic shear structures under external or seismic excitations.
Given the difficulty of accurately measuring external inputs acting on structures, the research on joint input-system identification has gained significant attention in recent years. Among them, several unscented Kalman filters under unknown input (UKF-UI) based methods have been proposed for joint input and system identification. However, the existing UKF-UI methods require unknown inputs to be presented in the observation equations, which is not always available in practical engineering. To solve this problem, an unscented Kalman filter under unknown input without direct feedthrough (UKF-UI-WDF) is proposed in this paper for real-time joint input and system identification of structural systems without direct feedthrough using limited response measurements. The proposed UKF-UI-WDF is derived for recursive identification of the structural states, parameters, and unknown inputs by integrating unknown input estimation into the conventional UKF framework. In each identification step, the unknown inputs are first identified by minimizing posterior residuals of measurements, and then the structural states and parameters are identified by a traditional unscented Kalman filtering using the identified unknown inputs. According to whether the uncertainty of the identified unknown inputs can be quantitatively described or not, two implementation algorithms are provided in this paper. In algorithm 1, only the mean values of unknown inputs can be identified. In algorithm 2, the unknown inputs are identified as a set of sigma points, so not only the mean values but also the variance of the unknown inputs can be estimated. The effectiveness of the proposed method is examined by the joint input and system identification of various case studies of linear and nonlinear hysteretic shear structures subjected to unknown external or base excitations, respectively. The results show that the proposed UKF-UI-WDF method can effectively perform real-time joint input-system identification of structural systems without direct feedthrough using limited response measurements.
Semiconductor photocatalysis, a sustainable and renewable technology, is deemed to be a new path to resolve environmental pollution and energy shortage. The development of effective photocatalysts, ...especially the metal‐free photocatalysts, is a critical determinant of this technique. The recently emerged 2D material of black phosphorus with distinctive properties of tunable direct bandgap, ultrahigh charge mobility, fortified optical absorption, large specific surface area, and anisotropic structure has captured enormous attention since the first exfoliation of bulk black phosphorus into mono‐ or few layered phosphorene in 2014. In this article, the state‐of‐the‐art preparation methods are first summarized for bulk black phosphorus, phosphorene, and black phosphorus quantum dot and then the fundamental structure and electronic and optical properties are analyzed to evaluate its feasibility as a metal‐free photocatalyst. Various modifications on black phosphorus are also summarized to enhance its photocatalytic performance. Furthermore, the multifarious applications such as solar to energy conversion, organic removal, disinfection, nitrogen fixation, and photodynamic therapy are discussed and some of the future challenges and opportunities for black phosphorus research are proposed. This review reveals that the rising star of black phosphorus will be a multifunctional material in the postgraphene era.
The recently emerged 2D material of black phosphorus has captured enormous attention owing to its distinctive intrinsic properties. Recent research on preparation methods and properties of black phosphorus is comprehensively summarized in this review. The modifications and photocatalysis applications are highlighted. Finally, the future challenges and opportunities of black phosphorus for further exploration are proposed.
BACKGROUNDLiver cirrhosis is a significant yet largely preventable and underappreciated cause of global health loss. This study aimed to profile the global and regional burdens of liver cirrhosis ...between 2010 and 2019 and the contributions of various aetiologies. METHODData on the incidence, mortality, and disability-adjusted life years (DALYs) of cirrhosis were obtained from the Global Burden of Disease 2019 study. The burden of cirrhosis was estimated by age, sex, region, aetiology, and socio-demographic index (SDI). The temporal trend was quantified using the annual percentage changes (APC.). RESULTSGlobally, there were 2.05 million new cases and 1.47 million deaths due to cirrhosis in 2019. From 2010 to 2019, the age-standardized incidence rate (ASIR) for cirrhosis increased slightly from 25.19 to 25.35 worldwide, while the age-standardized death rate (ASDR) and age-standardized DALYs (ASDALYs) decreased from 20.37 to 18.00 and 639.86 to 560.43, respectively. Cirrhosis incidence, mortality and DALYs were consistently higher in males than females. Stratification according to the socio-demographic index (SDI) revealed that low SDI countries had the highest ASDR and ASDALYs in 2019, while middle SDI countries had the highest ASIR. Regarding the aetiology of cirrhosis, hepatitis C accounted for the largest proportion of cirrhosis-related incidence (26.9%), death (26.8%) and DALYs (26.3%); however, non-alcoholic fatty liver disease (NAFLD) exhibited a rapidly growing cause of incident cirrhosis (+26.7%), cirrhosis-related death (+25.1%), and DALYs (+21.0%) worldwide during this period. The ASIR for NAFLD also significantly increased with APC 1.080 over the study period. CONCLUSIONSAlbeit the global burden of cirrhosis incidence increased from 2010 to 2019, cirrhosis-associated deaths and DALYs declined significantly. Notably, NAFLD exhibited the most significant increase as a contributor to cirrhosis worldwide.
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