Singlet oxygen (1O2) is an excellent active species for eliminating organic pollutants in single-atom catalyst (SAC) catalyzed peroxymonosulfate (PMS)-based Fenton-like systems. However, it is difficult to achieve hig...
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Singlet oxygen (1O2) is an excellent active species for eliminating organic pollutants in single-atom catalyst (SAC) catalyzed peroxymonosulfate (PMS)-based Fenton-like systems. However, it is difficult to achieve high efficiency and selectivity in 1O2 generation as a result of the lack of effective SACs. In this work, a Co SAC (Co-N-C) containing highly uniform Co-N4 active sites with an ultralow-level Co loading of 0.34 wt% is fabricated, delivering boosted performance in tetracycline degradation with an extremely rapid rate of 0.573 min, which outperforms most advanced catalysts in reported studies. A vibrating-sample magnetometer is used to preliminarily determine the atomically dispersed Co sites in Co-N-C according to the obvious paramagnetic characteristics of Co-N-C, and this is in good agreement with the test results obtained by using an aberration-corrected high-angle annular dark-field scanning transmission electron microscope and synchrotron radiation facility. Electron spin resonance and free radical quenching experiments prove that Co-N-C facilitates PMS activation with the 1O2 generation of almost 100%. Mechanism analyses suggest that the Co-N4 site strengthens the adsorption of the terminal O of PMS, realizing enhanced electron transfer from Co-N-C to PMS, promoting the simultaneous and spontaneous cleavage of O-O and O-H bonds to produce O*, followed by a reaction with another O* to generate 1O2.
Large-span roofs can be damaged by uneven snow loads caused by snow drifting. To predict wind-induced snow loads more accurately, this paper proposes a numerical simulation method. The method considers the interaction...
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Large-span roofs can be damaged by uneven snow loads caused by snow drifting. To predict wind-induced snow loads more accurately, this paper proposes a numerical simulation method. The method considers the interaction of wind and snow, the changing characteristics of snow phase boundaries over time, and corrects for snow deposition and erosion flux. The effectiveness of the method was confirmed by simulating wind-snow flow on a three-centered cylindrical shell and comparing the results with wind tunnel measurements. The simulation considered different snowfall conditions and time-varying snow phase boundaries on a large-span shell-shaped roof. The study investigated the influence of snowfall conditions and dynamic changes in snow phase boundaries on snow cover distribution by comparing simulation results.
The trifluoromethoxy group（OCF3） has garnered significant attention and exhibited unique characteristics in the realms of life sciences, pharmaceuticals, and agrochemicals. The development of efficient methods for s...
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The trifluoromethoxy group（OCF3） has garnered significant attention and exhibited unique characteristics in the realms of life sciences, pharmaceuticals, and agrochemicals. The development of efficient methods for synthesizing diverse trifluoromethoxy molecules is therefore of paramount importance. However, due to the weak nucleophilicity and instability of the OCF3group,many catalysts have shown limited applicability in OCF3compound synthesis. Encouragingly, silver catalysts have emerged as notable contenders and have effectively facilitated the synthesis of a variety of alkyl, aromatic and alkenyl OCF3products through direct trifluoromethoxylation and indirect methods. This review aims to provide a comprehensive overview of silvermediated/catalyzed reactions for the synthesis of trifluoromethoxy molecules, focusing particularly on the roles of silver and the reaction mechanisms.
With the development of aerospace technology, the increasing population of space debris has posed a great threat to the safety of spacecraft. Because of the small volume and long distance, space debris tends to have l...
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With the development of aerospace technology, the increasing population of space debris has posed a great threat to the safety of spacecraft. Because of the small volume and long distance, space debris tends to have low signal-to-noise ratio (SNR), and while taking the limitations of ground observation methods into account, it is necessary to enhance the spacecraft's capacity for space situational awareness (SSA). Besides, the active search and long exposure time of the surveillance system will extend the star spot to be a streak-like target, making image enhancement and target extraction more difficult. Considering that traditional methods have some defects in low-SNR streak detection, such as low effectiveness and large time consumption, this article proposes a method for low-SNR streak extraction based on local contrast and maximum likelihood estimation (MLE), which can detect spatial objects with SNR 2.0 efficiently. In the proposed algorithm, local contrast will be applied for crude classifications, which will return connected components as preliminary results, then MLE will be performed to reconstruct the connected components of targets via orientated growth and the precision can be further improved. The algorithm has been verified with both simulated streaks and real star tracker images, and the average centroid error of the proposed algorithm is close to the state-of-the-art method like the optimal directional connected component (ODCC). At the same time, the algorithm in this article has significant advantages in efficiency compared with the ODCC. In conclusion, the algorithm in this article is of high speed and precision, which guarantees its promising applications in the extraction of high dynamic targets.
In recent years, phishing email-mediated attacks are proliferating. When victims are enterprise employees, internal security of the enterprise systems will also be threatened. Currently, blockchain technology can effe...
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In recent years, phishing email-mediated attacks are proliferating. When victims are enterprise employees, internal security of the enterprise systems will also be threatened. Currently, blockchain technology can effectively improve the security and privacy of traditional email, but attacks initiated from within are still fatal. Therefore, we propose a double-layer detection framework in this paper. Firstly, from the perspective of individual security, Long Short-Term Memory (LSTM) and extreme gradient boosting tree (XGBoost) are used to build a phishing email detection model. The model generalization ability and precision rate are improved by adding a custom loss function in the training process. Then, from the perspective of group security, Bidirectional LSTM and Attention mechanism are used to build an insider threat detection model. Our model has better results for multi-domain time series and anomaly detection in comparison to different models and existing insider threat detection models. We test the effectiveness of the proposed framework through real phishing email cases and insider threat attack events on our simulation verification platform. The experimental results demonstrate that our proposed framework can protect enterprise systems from phishing attacks and insider threats. We also point out that this framework can be applied to mitigate the increasingly serious blockchain security threats.
Device fingerprinting can be used by Internet Service Providers (ISPs) to identify vulnerable IoT devices for early prevention of threats. However, due to the wide deployment of middleboxes in ISP networks, some impor...
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Device fingerprinting can be used by Internet Service Providers (ISPs) to identify vulnerable IoT devices for early prevention of threats. However, due to the wide deployment of middleboxes in ISP networks, some important data, e.g., 5-tuples and flow statistics, are often obscured, rendering many existing approaches invalid. It is further challenged by the high-speed traffic of hundreds of terabytes per day in ISP networks. This paper proposes DeviceRadar, an online IoT device fingerprinting framework that achieves accurate, real-time processing in ISPs using programmable switches. We innovatively exploit "key packets" as a basis of fingerprints only using packet sizes and directions, which appear periodically while exhibiting differences across different IoT devices. To utilize them, we propose a packet size embedding model to discover the spatial relationships between packets. Meanwhile, we design an algorithm to extract the "key packets" of each device, and propose an approach that jointly considers the spatial relationships and the key packets to produce a neighboring key packet distribution, which can serve as a feature vector for machine learning models for inference. Last, we design a model transformation method and a feature extraction process to deploy the model on a programmable data plane within its constrained arithmetic operations and memory to achieve line-speed processing. Our experiments show that DeviceRadar can achieve state-of-the-art accuracy across 77 IoT devices with 40 Gbps throughput, and requires only 1.3% of the processing time compared to GPU-accelerated approaches.
The integration of renewable energy sources (RESs) into active distribution networks (ADNs) is essential for reducing carbon emissions but often results in voltage fluctuations and violations. This paper proposes a hi...
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The integration of renewable energy sources (RESs) into active distribution networks (ADNs) is essential for reducing carbon emissions but often results in voltage fluctuations and violations. This paper proposes a hierarchical voltage control framework that effectively coordinates diverse controllable devices with various response times in an ADN. The framework comprises three stages: day-ahead scheduling of on-load tap changer (OLTC), intra-day optimization for droop slopes and references for droop controllers in Soft Open Points (SOPs) and distributed generators (DGs), and real-time local voltage regulation. Unlike existing approaches, the proposed approach analytically establishes voltage stability constraints and incorporates them into droop slope optimization for local controllers, mitigating voltage oscillation risks. Additionally, a novel deviation-aware optimization method is developed to calculate optimal voltage references. This method treats the deviations between fixed-point voltages and their references as uncertainties and accounts for their impacts on voltage security through chance-constrained programming. Simulation results demonstrate the effectiveness of the proposed framework in improving voltage regulation performance with guaranteed stability.
Steroid estrogens (SEs) play a significant role as endocrine-disrupting substances, and one of their major sources is animal manure. However, there is limited information available regarding the loss of SEs in farmlan...
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Steroid estrogens (SEs) play a significant role as endocrine-disrupting substances, and one of their major sources is animal manure. However, there is limited information available regarding the loss of SEs in farmland soil after the application of commercial composted animal manure or fertilizers. To address this gap, our study aimed to simulate rainfall and flood irrigation scenarios and investigate the loss characteristics of SEs, as well as Chemical Oxygen Demand (COD), Total Nitrogen (TN), and Total Phosphorus (TP) in runoff from soil-manure mixtures. The results demonstrated that the loss concentrations of SEs (73.1 ng/L of the mean E2 beta active equivalent factor) presented a potential environmental risk. Additionally, substituting composted manure with commercial organic fertilizers lead to a significant reduction in TP (maximum 56%) and TN (maximum 24%) loss. Consequently, the application of commercial organic fertilizers offers considerable advantages in maintaining nitrogen and phosphorus fertilization efficiency while controlling SEs loss. Furthermore, our study explored the synergistic pollution mechanism among these pollutants and observed significant correlations between SEs and TN, TP, and COD loss concentrations, indicating the simultaneous occurrence and migration of these pollutants in agricultural non-point source pollution. These results provide valuable insights into the environmental risk associated with SEs from agricultural non-point sources.
Neuromorphic computing is an emerging research field that aims to develop new intelligent systems by integrating theories and technologies from multiple disciplines, such as neuroscience, deep learning and microelectr...
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Neuromorphic computing is an emerging research field that aims to develop new intelligent systems by integrating theories and technologies from multiple disciplines, such as neuroscience, deep learning and microelectronics. Various software frameworks have been developed for related fields, but an efficient framework dedicated to spike-based computing models and algorithms is lacking. In this work, we present a Python-based spiking neural network (SNN) simulation and training framework, named SPAIC, that aims to support brain-inspired model and algorithm research integrated with features from both deep learning and neuroscience. To integrate different methodologies from multiple disciplines and balance flexibility and efficiency, SPAIC is designed with a neuroscience-style frontend and a deep learning-based backend. Various types of examples are provided to demonstrate the wide usability of the framework, including neural circuit simulation, deep SNN learning and neuromorphic applications. As a user-friendly, flexible, and high-performance software tool, it will help accelerate the rapid growth and wide applicability of neuromorphic computing methodologies.
Simultaneous lightwave information and power transfer (SLIPT) is a potential way to meet the demands of sustainable power supply and high-rate data transfer in next-generation networks. Although resonant beam-based SL...
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Simultaneous lightwave information and power transfer (SLIPT) is a potential way to meet the demands of sustainable power supply and high-rate data transfer in next-generation networks. Although resonant beam-based SLIPT (RB-SLIPT) can realize high-power energy transfer, high-rate data transfer, human safety, and self-alignment simultaneously, mobile transmission channel (MTC) analysis under non-line-of-sight (NLOS) propagation has not been investigated. In this paper, we propose analytical models and simulation tools for reflector-assisted NLOS transmission of RB-SLIPT, where transmission loss and accurate beam field profile of NLOS MTC can be obtained with a receiver at arbitrary positions and attitude angles. We establish analytical models relying on full diffraction theory for beam propagation between tilted or off-axis planes. Then, we provide three numerical methods (i.e., NUFFT-based, cubic interpolation-based, and linear interpolation-based methods) in simulations. Moreover, to deal with the contradiction between limited computing memory and high sampling requirements for long-range transmission analysis, we propose a multi-hop sliding window approach, which can reduce the sampling number by a factor of thousands. Finally, numerical results demonstrate that RB-SLIPT can achieve 3W charging power and 10bit/s/Hz data rate over a 2m distance in NLOS scenarios.
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