Upcycling of waste polyolefin plastics still meets with economic and technological challenges in practice. In this work, the catalytic hydrogenolysis‐isomerization of nondegradable polyolefin plastic ...waste to high‐value gasoline, diesel, and light lubricants with highly branched chain is achieved over a bifunctional Rh/Nb2O5 catalyst under relatively mild conditions. Owing to the high efficiency of metallic Rh active sites, the dehydrogenation/hydrogenation of long carbon chains of polyolefins is enhanced. With the assistance of strong Brønsted acidity of Nb2O5, the cleavage of C−C bonds, skeletal rearrangements, as well as the β‐scission of alkylcarbenium ions occurs, which boosts the one‐step solvent‐free catalytic hydrogenolysis and isomerization of polyolefins. In addition, the preliminary economic analysis shows that this technology is economical, feasible, and has great potential in accelerating the transition to a circular plastics economy for sustainable development.
Break it down: Catalytic hydrogenolysis‐isomerization of nondegradable polyolefin plastics to high‐value gasoline, diesel, and light lubricants with highly branched chains is achieved over Rh/Nb2O5 bifunctional catalyst under relatively mild conditions. Preliminary economic analysis shows that this technology is economically feasible and has great potential in accelerating the transition to a circular plastics economy.
Abstract
Background
Humans are widely exposed to perfluoroalkyl substances (PFAS), which have been found to be associated with various adverse birth outcomes. As blood pressure (BP) is an important ...parameter reflecting cardiovascular health in early life, it is necessary to investigate the association of PFAS exposure during early lifetime and BP in childhood. Therefore, we investigated the potential association between PFAS levels in umbilical cord blood and BP of the offspring at 4 years of age in a prospective cohort study.
Methods
PFAS in umbilical cord blood samples after birth were measured with high-performance liquid chromatography/tandem mass spectrometry in the Shanghai Birth Cohort. BP was measured at 4 years of age in the offspring. Multiple linear regression model was used to investigate the association between individual PFAS level and BP of the offspring. Bayesian kernel machine regression (BKMR) was used to analyze the relationship between the PFAS mixture and BP of the offspring, while weighted quantile sum (WQS) regression was utilized for sensitivity analysis.
Results
A total of 129 mother-child pairs were included in our analysis. In multiple linear regressions, we observed that long-chain PFAS, mainly including perfluorooctane sulfonate (PFOS), perfluorodecanoic acid (PFDA) and perfluoroundecanoic acid (PFUA), was negatively associated with systolic blood pressure (SBP), diastolic blood pressure (DBP) and mean arterial blood pressure (MAP). BKMR showed that an increase in umbilical cord blood PFAS mixture levels was significantly associated with a decrease in SBP, DBP and MAP Estimated differences (SD): -0.433 (0.161); -0.437 (0.176); -0.382 (0.179), respectively. The most important component in the association with SBP, DBP, and MAP was PFUA. PFDoA was found to be positively associated with SBP, DBP and MAP in both models. Sensitivity analysis with WQS regression showed consistent results.
Conclusion
Our findings suggested that umbilical blood PFAS exposure was negatively associated with BP in offspring at 4 years of age, including SBP, DBP, and MAP.
Instance matching is a key task in knowledge graph fusion, and it is critical to improving the efficiency of instance matching, given the increasing scale of knowledge graphs. Blocking algorithms ...selecting candidate instance pairs for comparison is one of the effective methods to achieve the goal. In this paper, we propose a novel blocking algorithm named MultiObJ, which constructs indexes for instances based on the Ordered Joint of Multiple Objects' features to limit the number of candidate instance pairs. Based on MultiObJ, we further propose a distributed framework named Follow-the-Regular-Leader Instance Matching (FTRLIM), which matches instances between large-scale knowledge graphs with approximately linear time complexity. FTRLIM has participated in OAEI 2019 and achieved the best matching quality with significantly efficiency. In this research, we construct three data collections based on a real-world large-scale knowledge graph. Experiment results on the constructed data collections and two real-world datasets indicate that MultiObJ and FTRLIM outperform other state-of-the-art methods.
Nowadays, our lives have benefited from various vision-based applications, such as video surveillance, human identification and aided driving. Unauthorized access to the vision-related data greatly ...threatens users’ privacy, and many encryption schemes have been proposed to secure images and videos in those conventional scenarios. Neuromorphic vision sensor (NVS) is a brand new kind of bio-inspired sensor that can generate a stream of impulse-like events rather than synchronized image frames, which reduces the sensor’s latency and broadens the applications in surveillance and identification. However, the privacy issue related to NVS remains a significant challenge. For example, some image reconstruction and human identification approaches may expose privacy-related information from NVS events. This work is the first to investigate the privacy of NVS. We firstly analyze the possible security attacks to NVS, including grayscale image reconstruction and privacy-related classification. We then propose a dedicated encryption framework for NVS, which incorporates a 2D chaotic mapping to scramble the positions of events and flip their polarities. In addition, an updating score has been designed for controlling the frequency of execution, which supports efficient encryption on different platforms. Finally, extensive experiments have demonstrated that the proposed encryption framework can effectively protect NVS events against grayscale image reconstruction and human identification, and meanwhile, achieve high efficiency on various platforms including resource-constrained devices.
Predicting the mechanical behaviors of structure and perceiving the anomalies in advance are essential to ensuring the safe operation of infrastructures in the long run. In addition to the incomplete ...consideration of influencing factors, the prediction time scale of existing studies is rough. Therefore, this study focuses on the development of a real-time prediction model by coupling the spatio-temporal correlation with external load through autoencoder network (ATENet) based on structural health monitoring (SHM) data. An autoencoder mechanism is performed to acquire the high-level representation of raw monitoring data at different spatial positions, and the recurrent neural network is applied to understanding the temporal correlation from the time series. Then, the obtained temporal-spatial information is coupled with dynamic loads through a fully connected layer to predict structural performance in next 12 h. As a case study, the proposed model is formulated on the SHM data collected from a representative underwater shield tunnel. The robustness study is carried out to verify the reliability and the prediction capability of the proposed model. Finally, the ATENet model is compared with some typical models, and the results indicate that it has the best performance. ATENet model is of great value to predict the real-time evolution trend of tunnel structure.
•A more credible prediction model is presented to solve practical engineering problem.•Timely prediction for the subsequent state and precise perception for anomaly in advance.•Interdisciplinary research of machine learning in the analysis of structural mechanical behaviors.
Slab track structures become deformed under the effects of differential subgrade settlement. According to the properties of the China Railway Track System (CRTS) II slab track on a subgrade, a ...three-dimensional (3D) coupled model based on both the discrete element method (DEM) and finite difference method (FDM) was developed. The slab track and subgrade were simulated using the FDM and DEM, respectively. The coupled model was verified. The deformation of the slab track and contact forces of gravel grains in the surface layer of the subgrade were studied under differential subgrade settlement. The effects of settlement wavelength, settlement amplitude, and other types of settlements were also discussed. The results demonstrate that the settlement amplitude and settlement wavelength of the subgrade have significant effects on track deformation. The deformation amplitude of the slab track increases nonlinearly with an increasing settlement amplitude of the subgrade. Increases in the settlement wavelength and amplitude of the subgrade significantly increase the maximum value of the contact force of the gravel grains in the subgrade. The maximum contact force of gravel grains near the boundaries of the settlement section can reach two to three times that of the unsettled condition, which makes it easy to accelerate the plastic settlement of the subgrade.
Machine learning models have recently demonstrated the ability to mine structural health monitoring data. While existing machine learning models can provide better performance than the previous ...univariate time-series model, it is still an open challenge of fully mining the spatial and temporal characteristics of the structural response to increase their accuracy. In addition, the heterogeneous correlation is always missed in traditional models. This article proposes a heterogeneous structural response prediction (HSRP) framework based on the deep learning model to improve the performance. The HSRP framework cannot only make full use of spatial and temporal correlations but also mine the correlation between heterogeneous responses. Motivated by recent studies in machine learning, an attention module is introduced to learn the correlation between different responses and initial the weights of sensors and past response. The convolutional neural network is also implemented to extract the spatial features and the long short-term memory network is used to extract the weekly, daily, and hourly patterns of structural response. A real-world data set collected from a bridge is used to evaluate the performance of the proposed model on single-step prediction and multistep prediction. The experimental results show that the proposed model outperforms several widely used benchmark models. Furthermore, additional experiments and evaluations are implemented to investigate the sensitivity and robustness of the proposed model.
In a Fizeau interferometer, off-axis illumination will lead to fringe optimization. Primarily due to the unique structure of our interferometer, we first analyze the influence of the optical ...properties of the parallel plate as a part of the interferometer on the optimal incident angle. Generally, the incident angle determination is mainly based on the graphing method proposed by Langenbeck and the estimation formula proposed by Kajava. However, Langenbeck’s method is cumbersome, and the error of Kajava’s estimation formula is large. Based on the predecessors, this paper proposes a modified method of determining the optimal angle of incidence and further derives more accurate optimal angle expressions than Kajava’s. By simply substituting the wedge angle of the wedge cavity and the reflectivity of the cavity, the optimum incidence angle can be obtained immediately. Thus, it eliminates the tedious and complex process of finding the optimum incident angle by graphing method and makes the formula method the simplest method to find the optimum incident angle. Finally, the comparison of the interference intensity at the optimum incidence angle calculated by the improved method and normal incidence is given. It is found that the beam has a good suppression effect on the sub-peak when it is incident at the optimum incident angle calculated by the method in this paper.
Lead halide perovskites are intriguing semiconductors for lasers due to high quantum yield, tunable bandgaps, and facile solution‐process ability. However, limited by the weak optical confinement, ...continuous‐wave (CW) pumped lasing, as one prerequisite for the electrically pumped lasing, is still challenging in bare lead halide perovskites without high‐quality factor (Q) artificial optical cavity. Herein, the lasing emission in methylammonium lead tribromide (MAPbBr3) incorporated with a vertical microcavity under continuous pumping at 80 K is reported. The single‐crystalline MAPbBr3 perovskite nanoplates are fabricated by the two‐step solution method. The MAPbBr3‐based vertical cavity surface‐emitting laser (VCSEL) presents a low threshold of 55.2 W cm−2 and a high Q‐factor of 1140 at low temperature. The low threshold lasing emission can be attributed to strong optical confinement in the high‐Q cavity and great photoluminescence enhancement at 80 K, which is induced by a transition from tetragonal to orthorhombic phase, demonstrated by in situ temperature Raman spectroscopy. These findings envisage the prospective applications of single‐crystalline metal halide perovskites in practicable laser devices.
In this work, high‐quality MAPbBr3 nanoplates are successfully prepared by a modified solution‐growth method in ambient atmosphere, and the temperature‐dependent phase transition is observed by Raman scattering technique. More excitingly, by incorporating these nanoplates into a vertical microcavity, continuous‐wave lasing of single‐crystalline perovskites at low‐temperature is achieved.
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