A secure cross-domain interaction scheme for blockchain-based intelligent transportation systems

Si, H., Li, W., Wang, Q., Cao, H., Bação, F., & Sun, C. (2023). A secure cross-domain interaction scheme for blockchain-based intelligent transportation systems. PeerJ Computer Science, (November 2023), 1-36. https://doi.org/10.7717/peerj-cs.1678, https://doi.org/10.7717/peerj-cs.1678/supp-1, https://doi.org/10.7717/peerj-cs.1678/supp-2---This work was supported by the Henan Province Key Science-technology Research Project under Grant No. 232102520006 and 232102210122, the Key Research Project of Henan Provincial Higher Education Institution under Grant No. 23A520005, and the Henan Province Major Public Welfare Projects under Grant No. 201300210300. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.In the intelligent transportation system (ITS), secure and efficient data communication among vehicles, road testing equipment, computing nodes, and transportation agencies is important for building a smart city-integrated transportation system. However, the traditional centralized processing approach may face threats in terms of data leakage and trust. The use of distributed, tamper-proof blockchain technology can improve the decentralized storage and security of data in the ITS network. However, the cross-trust domain devices, terminals, and transportation agencies in the heterogeneous blockchain network of the ITS still face great challenges in trusted data communication and interoperability. In this article, we propose a heterogeneous cross-chain interaction mechanism based on relay nodes and identity encryption to solve the problem of data cross-domain interaction between devices and agencies in the ITS. First, we propose the ITS cross-chain communication framework and improve the cross-chain interaction model. The relay nodes are interconnected through libP2P to form a relay node chain, which is used for cross-chain information verification and transmission. Secondly, we propose a relay node secure access scheme based on identity-based encryption to provide reliable identity authentication for relay nodes. Finally, we build a standard cross-chain communication protocol and cross-chain transaction lifecycle for this mechanism. We use Hyperledger Fabric and FISCO BCOS blockchain to design and implement this solution, and verify the feasibility of this cross-chain interaction mechanism. The experimental results show that the mechanism can achieve a stable data cross-chain read throughput of 2,000 transactions per second, which can meet the requirements of secure and efficient cross-chain communication and interaction among heterogeneous blockchains in the ITS, and has high application value.publishersversionpublishe

Potential of Core-Collapse Supernova Neutrino Detection at JUNO

JUNO is an underground neutrino observatory under construction in Jiangmen, China. It uses 20kton liquid scintillator as target, which enables it to detect supernova burst neutrinos of a large statistics for the next galactic core-collapse supernova (CCSN) and also pre-supernova neutrinos from the nearby CCSN progenitors. All flavors of supernova burst neutrinos can be detected by JUNO via several interaction channels, including inverse beta decay, elastic scattering on electron and proton, interactions on C12 nuclei, etc. This retains the possibility for JUNO to reconstruct the energy spectra of supernova burst neutrinos of all flavors. The real time monitoring systems based on FPGA and DAQ are under development in JUNO, which allow prompt alert and trigger-less data acquisition of CCSN events. The alert performances of both monitoring systems have been thoroughly studied using simulations. Moreover, once a CCSN is tagged, the system can give fast characterizations, such as directionality and light curve

Detection of the Diffuse Supernova Neutrino Background with JUNO

As an underground multi-purpose neutrino detector with 20 kton liquid scintillator, Jiangmen Underground Neutrino Observatory (JUNO) is competitive with and complementary to the water-Cherenkov detectors on the search for the diffuse supernova neutrino background (DSNB). Typical supernova models predict 2-4 events per year within the optimal observation window in the JUNO detector. The dominant background is from the neutral-current (NC) interaction of atmospheric neutrinos with 12C nuclei, which surpasses the DSNB by more than one order of magnitude. We evaluated the systematic uncertainty of NC background from the spread of a variety of data-driven models and further developed a method to determine NC background within 15\% with {\it{in}} {\it{situ}} measurements after ten years of running. Besides, the NC-like backgrounds can be effectively suppressed by the intrinsic pulse-shape discrimination (PSD) capabilities of liquid scintillators. In this talk, I will present in detail the improvements on NC background uncertainty evaluation, PSD discriminator development, and finally, the potential of DSNB sensitivity in JUNO

Real-time Monitoring for the Next Core-Collapse Supernova in JUNO

Core-collapse supernova (CCSN) is one of the most energetic astrophysical events in the Universe. The early and prompt detection of neutrinos before (pre-SN) and during the SN burst is a unique opportunity to realize the multi-messenger observation of the CCSN events. In this work, we describe the monitoring concept and present the sensitivity of the system to the pre-SN and SN neutrinos at the Jiangmen Underground Neutrino Observatory (JUNO), which is a 20 kton liquid scintillator detector under construction in South China. The real-time monitoring system is designed with both the prompt monitors on the electronic board and online monitors at the data acquisition stage, in order to ensure both the alert speed and alert coverage of progenitor stars. By assuming a false alert rate of 1 per year, this monitoring system can be sensitive to the pre-SN neutrinos up to the distance of about 1.6 (0.9) kpc and SN neutrinos up to about 370 (360) kpc for a progenitor mass of 30$M_{\odot}$ for the case of normal (inverted) mass ordering. The pointing ability of the CCSN is evaluated by using the accumulated event anisotropy of the inverse beta decay interactions from pre-SN or SN neutrinos, which, along with the early alert, can play important roles for the followup multi-messenger observations of the next Galactic or nearby extragalactic CCSN.Comment: 24 pages, 9 figure

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

A Novel Approach to Grade Cotton Aphid Damage Severity with Hyperspectral Index Reconstruction

As a kind of important insect pest of cotton crops, aphids cause serious damage in cotton yields and quality worldwide, posing a significant risk to economic losses. Automatic detection of the pest damage level plays an important role in cotton field management. However, it is usually regarded as a classification problem in machine learning, where the disease severity levels are taken as independent categories and the inter-level relationship has not fully been considered. To utilize the inherited relations among different severity levels caused by cotton aphids, a novel approach based on the spectral index reconstruction was proposed in this study. First, six types of initial spectral indices were reconstructed based on healthy samples in the training set. Then, the severity sequences corresponding to the reconstructed initial spectral indices (RISIs) were sorted and compared with the ideal sequence. After attaining sequences most consistent with the ideal one, the ratio between the inter- and intra- levels was calculated to select the sensitive RISI. Moreover, the range of each severity level was established by the thresholds between adjacent grades of the selected sensitive RISI, which was finally used to determine the disease severity level caused by cotton aphids. Results of the cotton aphids showed that the proposed approach achieved a grading performance with OA = 0.944, AA = 0.900, and Kappa coefficient = 0.928. Hence, the proposed approach based on hyperspectral index reconstruction is effective and has potential application in grading the aphid infestation severity of cotton

A Novel Approach to Grade Cotton Aphid Damage Severity with Hyperspectral Index Reconstruction

As a kind of important insect pest of cotton crops, aphids cause serious damage in cotton yields and quality worldwide, posing a significant risk to economic losses. Automatic detection of the pest damage level plays an important role in cotton field management. However, it is usually regarded as a classification problem in machine learning, where the disease severity levels are taken as independent categories and the inter-level relationship has not fully been considered. To utilize the inherited relations among different severity levels caused by cotton aphids, a novel approach based on the spectral index reconstruction was proposed in this study. First, six types of initial spectral indices were reconstructed based on healthy samples in the training set. Then, the severity sequences corresponding to the reconstructed initial spectral indices (RISIs) were sorted and compared with the ideal sequence. After attaining sequences most consistent with the ideal one, the ratio between the inter- and intra- levels was calculated to select the sensitive RISI. Moreover, the range of each severity level was established by the thresholds between adjacent grades of the selected sensitive RISI, which was finally used to determine the disease severity level caused by cotton aphids. Results of the cotton aphids showed that the proposed approach achieved a grading performance with OA = 0.944, AA = 0.900, and Kappa coefficient = 0.928. Hence, the proposed approach based on hyperspectral index reconstruction is effective and has potential application in grading the aphid infestation severity of cotton

Apple Surface Defect Detection Method Based on Weight Comparison Transfer Learning with MobileNetV3

Apples are ranked third, after bananas and oranges, in global fruit production. Fresh apples are more likely to be appreciated by consumers during the marketing process. However, apples inevitably suffer mechanical damage during transport, which can affect their economic performance. Therefore, the timely detection of apples with surface defects can effectively reduce economic losses. In this paper, we propose an apple surface defect detection method based on weight contrast transfer and the MobileNetV3 model. By means of an acquisition device, a thermal, infrared, and visible apple surface defect dataset is constructed. In addition, a model training strategy for weight contrast transfer is proposed in this paper. The MobileNetV3 model with weight comparison transfer (Weight Compare-MobileNetV3, WC-MobileNetV3) showed a 16% improvement in accuracy, 14.68% improvement in precision, 14.4% improvement in recall, and 15.39% improvement in F1-score. WC-MobileNetV3 compared to MobileNetV3 with fine-tuning improved accuracy by 2.4%, precision by 2.67%, recall by 2.42% and F1-score by 2.56% compared to the classical neural networks AlexNet, ResNet50, DenseNet169, and EfficientNetV2. The experimental results show that the WC-MobileNetV3 model adequately balances accuracy and detection time and achieves better performance. In summary, the proposed method achieves high accuracy for apple surface defect detection and can meet the demand of online apple grading

Simple Approach to Improving the Amplified Spontaneous Emission Properties of Perovskite Films

Organo-lead halide perovskite has emerged as a promising optical gain media. However, continuous efforts are needed to improve the amplified spontaneous emission (ASE) even lasing properties to evade the poor photostability and thermal instability of the perovskites. Herein, we report that simply through the coating of polymer layer, the CH₃NH₃PbBr₃ polycrystalline films prepared by a modified sequential deposition process show remarkably enhanced photoluminescence and prolonged decay lifetime. As a result, under nanosecond pulse pumping, the ASE threshold of the perovskite films is significantly reduced from 303 to 140 μJ/cm². Furthermore, the light exposure stability is improved greatly after the polymer coating. We confirmed that the polymer layer plays the roles of both surface passivation and symmetric waveguides. Our results may shed light upon the stable and sustained output of laser from perovskite materials