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Abstract Deep learning‐based models have become ubiquitous across a wide range of applications, including computer vision, natural language processing, and robotics. Despite their efficacy, one of the significant challenges associated with deep neural network (DNN) models is the potential risk of copyright leakage due to the inherent vulnerability of the entire model architecture and the communication burden of the large models during publishing. So far, it is still challenging for us to safeguard the intellectual property rights of these DNN models while reducing the communication time during model publishing. To this end, this paper introduces a novel approach using knowledge distillation techniques aimed at training a surrogate model to stand in for the original DNN model. To be specific, a knowledge distillation generative adversarial network (KDGAN) model is proposed to train a student model capable of achieving remarkable performance levels while simultaneously safeguarding the copyright integrity of the original large teacher model and improving communication efficiency during model publishing. Herein, comprehensive experiments are conducted to showcase the efficacy of model copyright protection, communication‐efficient model publishing, and the superiority of the proposed KDGAN model over other copyright protection mechanisms.

In recent years, immense developments have occurred in the field of Artificial Intelligence (AI) and the spread of broadband and ubiquitous connectivity technologies. This has led to the development and commercialization of Digital Twin (DT) technology. The widespread adoption of DT has resulted in a new network paradigm called Digital Twin Networks (DTNs), which orchestrate through the networks of ubiquitous DTs and their corresponding physical assets. DTNs create virtual twins of physical objects via DT technology and realize the co-evolution between physical and virtual spaces through data processing, computing, and DT modeling. The high volume of user data and the ubiquitous communication systems in DTNs come with their own set of challenges. The most serious issue here is with respect to user data privacy and security because users of most applications are unaware of the data that they are sharing with these platforms and are naive in understanding the implications of the data breaches. Also, currently, there is not enough literature that focuses on privacy and security issues in DTN applications. In this survey, we first provide a clear idea of the components of DTNs and the common metrics used in literature to assess their performance. Next, we offer a standard network model that applies to most DTN applications to provide a better understanding of DTN’s complex and interleaved communications and the respective components. We then shed light on the common applications where DTNs have been adapted heavily and the privacy and security issues arising from the DTNs. We also provide different privacy and security countermeasures to address the previously mentioned issues in DTNs and list some state-of-the-art tools to mitigate the issues. Finally, we provide some open research issues and problems in the field of DTN privacy and security.

Abstract Precisely locating the voltage sag sources is significant for improving power quality and clarifying the responsibilities of both power supply and consumption. However, the existing location models are complex, computationally intensive, and influenced by the measurement error and fault type, which have low positioning efficiency and large errors. Therefore, this paper proposes a method for locating voltage sag sources based on power polarity and twin characteristics matching. The method firstly uses the polarity of the sequence sag component disturbance reactive power at the monitoring point to determine the sag line. Secondly, the basic realization method of the twin power system is discussed, and the twin characteristics are defined by combining the positive and negative sequence sag component disturbance reactive power and the twin database is established. Thirdly, the spectral norm and Pearson product‐moment correlation coefficient are used to define the data matching degree, and the precise location model of the sag source is established with the objective function of maximizing the data matching between the twin characteristics of the preset and actual sag. Finally, the Improved Whale Optimization Algorithm is used to solve the location model. The correctness and superiority of the proposed method is verified based on IEEE33 node modelling.

In recent years, there has been a surge in interest regarding the intricate physiological interplay between the brain and the heart, particularly during emotional processing. This has led to the development of various signal processing techniques aimed at investigating Brain-Heart Interactions (BHI), reflecting a growing appreciation for their bidirectional communication and influence on each other. Our study contributes to this burgeoning field by adopting a network physiology approach, employing time-delay stability as a quantifiable metric to discern and measure the coupling strength between the brain and the heart, specifically during visual emotional elicitation. We extract and transform features from EEG and ECG signals into a 1 Hz format, facilitating the calculation of BHI coupling strength through stability analysis on their maximal cross-correlation. Notably, our investigation sheds light on the critical role played by low-frequency components in EEG, particularly in the $\delta $ , $\theta $ , and $\alpha $ bands, as essential mediators of information transmission during the complex processing of emotion-related stimuli by the brain. Furthermore, our analysis highlights the pivotal involvement of frontal pole regions, emphasizing the significance of $\delta $ - $\theta $ coupling in mediating emotional responses. Additionally, we observe significant arousal-dependent changes in the $\theta $ frequency band across different emotional states, particularly evident in the prefrontal cortex. By offering novel insights into the synchronized dynamics of cortical and heartbeat activities during emotional elicitation, our research enriches the expanding knowledge base in the field of neurophysiology and emotion research.

This study investigated the behaviour and mechanism of interfacial microstructure evolution during long term high temperature exposure and the effect of interface structure on creep failure in the dissimilar metal welds (DMWs) involving ferritic heat resistant steels with nickel-based filler metal. The welding of nickel-based and ferritic materials led to the formation of interfacial martensite layer in the ferritic steel side of DMW. During long term high temperature exposure, interfacial martensite transformed to ferrite due to C diffusion and migration in the micro-region near the BCC/FCC interface between the martensite layer and the weld metal. The martensite-ferrite transformation was attributed to the large C chemical potential gradient caused by chemical composition gradient. Under creep condition, the failure of DMW was prone to occur along the weld metal/ferritic steel interface, which was related to strain concentration around the interfacial ferrite formed during service. Hence, the relationships between interface structures before and after service and interfacial failure in the DMWs were clarified.

Graph de-anonymization is a technique used to reveal connections between entities in anonymized graphs, which is crucial in detecting malicious activities, network analysis, social network analysis, and more. Despite its paramount importance, conventional methods often grapple with inefficiencies and challenges tied to obtaining accurate query graph data. This paper introduces a neural-based inexact graph de-anonymization, which comprises an embedding phase, a comparison phase, and a matching procedure. The embedding phase uses a graph convolutional network to generate embedding vectors for both the query and anonymized graphs. The comparison phase uses a neural tensor network to ascertain node resemblances. The matching procedure employs a refined greedy algorithm to discern optimal node pairings. Additionally, we comprehensively evaluate its performance via well-conducted experiments on various real datasets. The results demonstrate the effectiveness of our proposed approach in enhancing the efficiency and performance of graph de-anonymization through the use of graph embedding vectors.

This study employed molecular dynamics (MD) simulations combined with X-ray diffraction (XRD) and scratch testing to explore the interface crystallization behavior of amorphous-Co under thermal conditions and the impact of new phase precipitation on the cohesive strength of WC/Co coatings. Microstructural analysis revealed the phase transformation induced by heat treatment, as amorphous-Co gradually transformed into the η-phase, with the transition starting at 873 K and completed at 1173 K. The results of the scratch testing indicated that appropriate heat treatment could enhance the coating's cohesive strength and thereby improve its performance under certain conditions. Nonetheless, at 873 K, the early stage of η-phase nucleation exhibited elastic strain, leading to an abnormal decrease in the coating's cohesive strength. MD simulation results demonstrated that the crystallization of amorphous-Co primarily occurred at the WC/Co interface, and the ideal fracture energy (Wsep) increased with increasing heat-treatment temperature. However, after heat treatment at 873 K, the Wsep between the atomic layers within amorphous-Co slightly decreased and was reflected as a decrease in the coating's cohesive strength at the macroscopic level.

Metaverse describes a new shape of cyberspace and has become a hot-trending word since 2021. There are many explanations about what Meterverse is and attempts to provide a formal standard or definition of Metaverse. However, these definitions could hardly reach universal acceptance. Rather than providing a formal definition of the Metaverse, we list four must-have characteristics of the Metaverse: socialization, immersive interaction, real world-building, and expandability. These characteristics not only carve the Metaverse into a novel and fantastic digital world, but also make it suffer from all security/privacy risks, such as personal information leakage, eavesdropping, unauthorized access, phishing, data injection, broken authentication, insecure design, and more. This paper first introduces the four characteristics, then the current progress and typical applications of the Metaverse are surveyed and categorized into four economic sectors. Based on the four characteristics and the findings of the current progress, the security and privacy issues in the Metaverse are investigated. We then identify and discuss more potential critical security and privacy issues that can be caused by combining the four characteristics. Lastly, the paper also raises some other concerns regarding society and humanity.

Abstract With the rise of edge computing technology and the development of intelligent mobile devices, task offloading in the edge-cloud environment has become a research hotspot. Task offloading is also a key research issue in Mobile CrowdSourcing (MCS), where crowd workers collect sensed data through smart devices they carry and offload to edge-cloud servers or perform computing tasks locally. Current researches mainly focus on reducing resource consumption in edge-cloud servers, but fails to consider the conflict between resource consumption and service quality. Therefore, this paper considers the learning generation offloading strategy among multiple Deep Neural Network(DNN), proposed a Deep Neural Network-based Task Offloading Optimization (DTOO) algorithm to obtain an approximate optimal task offloading strategy in the edge-cloud servers to solve the conflict between resource consumption and service quality. In addition, a stack-based offloading strategy is researched. The resource sorting method allocates computing resources reasonably, thereby reducing the probability of task failure. Compared with the existing algorithms, the DTOO algorithm could balance the conflict between resource consumption and service quality in traditional edge-cloud applications on the premise of ensuring a higher task completion rate.

High-intensity pulsed magnetic field was applied to modify the microstructure and mechanical propertis of quenched 8Cr4Mo4V bearing steel under the cryogenic environment. The microstructual transformation in 8Cr4Mo4V bearing steel was investigated by X-ray diffraction (XRD), Vibrating sample magnetometer (VSM) and Electron Backscatter Diffraction (EBSD) and so on. The results revealed that individual deep cryogenic treatment (DCT) promote the transformation from retained austenite (RA) in quenched steel to martensite. However, this phase transformation will be inhibited by coupling high-intensity magnetic field with deep cryogenic treatment (MDCT). For instance, the relative reduction of RA content in DCT samples was 10 ± 2%, while that in MDCT samples was 3 ± 3%. Furthermore, the modified Williamson-Hall method was utilized to estimate the dislocation characteristics based on XRD profile analysis. It exhibited that the proportion of edge dislocation significantly decrease from 61.8% to 43.7% by DCT, while this type of dislocation remains almost unchanged after MDCT. Moreover, the microhardness of quenched 8Cr4Mo4V steel became more uniform after MDCT than that of DCT. It can be indicated that the martensite transformation is blocked and carbon cluster is dissolved under the condition of coupling pulsed magnetic field treatment.

In recent years, dust storms have occurred frequently, significantly affecting people's daily lives. Therefore, the detection, monitoring, and early warning of dust storms have a great social significance. Previous methods have mainly been based on atmospheric motion to build physical models for weather forecasting. Although there are many meteorological applications based on deep learning, to the best of authors' knowledge, there is no dust storm database with a high spatiotemporal resolution, which is essential for deep learning methods. Since meteorological satellites can observe the Earth's atmosphere from a spatial perspective at a large scale, in this article, a dust storm database is constructed using multichannel and dust label data from the Fengyun-4 A geosynchronous orbiting satellite, namely the large-scale dust storm database based on satellite images and meteorological reanalysis data (LSDSSIMR), with a temporal resolution of 15 min and a spatial resolution of 4 km from March to May of each year during 2020–2022. Meteorological reanalysis data are added to LSDSSIMR for spatiotemporal prediction methods. Each data file is stored in an HDF5 format, and the final LSDSSIMR contains nearly 5400 HDF5 files. Moreover, some traditional dust detection methods based on spectral analysis are executed as a benchmark.

Precipitation nowcasting is very important for the sectors which critically depend on timely and accurate weather information. One of the challenges of precipitation nowcasting is radar echo extrapolation which predicts the radar echo images accurately. Nowadays, the methods of radar echo extrapolation are mostly based on ConvRNNs. Unfortunately, as lead time increases, these methods unavoidably suffer from the problem that high reflectivity values are underestimated. Therefore, we propose a forecast-refinement neural network based on DyConvGRU and U-Net to improve the predicting ability for high reflectivity during radar echo extrapolation. To improve the model’s ability to predict high reflectivities, dynamic convolution, and the forecast-refinement architecture are applied. And to obtain more realistic results, the WGAN’s training strategy is adopted to train the forecast module and the refinement module. Through experiments on a radar dataset from Shanghai, China, the results show that our proposed method obtains higher Probability of Detection (POD), Critical Success Index (CSI), Heidke Skill Score (HSS), and lower False Alarm Rate(FAR).

This study investigates the influence of groove geometry on the high-temperature creep life and fracture behavior of Dissimilar Metal Welds (DMWs) between low-alloy steel 2.25Cr1Mo and austenitic stainless steel 347H using Inconel 82 nickel-based filling metal. This research aims to reveal the effect of groove geometry, especially the stepped groove, on creep crack propagation path and creep life, through a combined approach of finite element simulation considering stress triaxiality and experimental validation. The study reveals that the stepped groove alters the creep crack propagation path, enhancing the endurance life by deflecting cracks away from the weld/heat-affected zone (HAZ) interface and directing them into regions with higher creep resistance. The experimental results verify the simulation findings, revealing that the stepped groove joints exhibited longer creep life with changes in failure location and mechanism compared to the V-groove joints. However, it was found that the stepped groove intensified the stress concentration at the early creep stage. Thus, a good balance should be achieved between the negative (stress concentration at interface) and positive (changing crack paths) effects of the stepped groove to extend the creep life of DMWs.

In the era of the Internet of Things (IoT), the crowdsourcing process is driven by data collected by devices that interact with each other and with the physical world. As a part of the IoT ecosystem, task assignment has become an important goal of the research community. Existing task assignment algorithms can be categorized as offline (performs better with datasets but struggles to achieve good real-life results) or online (works well with real-life input but is difficult to optimize regarding in-depth assignments). This paper proposes a Cross-regional Online Task (CROT) assignment problem based on the online assignment model. Given the CROT problem, an Online Task Assignment across Regions based on Prediction (OTARP) algorithm is proposed. OTARP is a two-stage graphics-driven bilateral assignment strategy that uses edge cloud and graph embedding to complete task assignments. The first stage uses historical data to make offline predictions, with a graph-driven method for offline bipartite graph matching. The second stage uses a bipartite graph to complete the online task assignment process. This paper proposes accelerating the task assignment process through multiple assignment rounds and optimizing the process by combining offline guidance and online assignment strategies. To encourage crowd workers to complete crowd tasks across regions, an incentive strategy is designed to encourage crowd workers’ movement. To avoid the idle problem in the process of crowd worker movement, a drop-by-rider problem is used to help crowd workers accept more crowd tasks, optimize the number of assignments, and increase utility. Finally, through comparison experiments on real datasets, the performance of the proposed algorithm on crowd worker utility value and the matching number is evaluated.

Abstract Background Tuberous root formation and development is a complex process in sweet potato, which is regulated by multiple genes and environmental factors. However, the regulatory mechanism of tuberous root development is unclear. Results In this study, the transcriptome of fibrous roots (R0) and tuberous roots in three developmental stages (Rl, R2, R3) were analyzed in two sweet potato varieties, GJS-8 and XGH. A total of 22,914 and 24,446 differentially expressed genes (DEGs) were identified in GJS-8 and XGH respectively, 15,920 differential genes were shared by GJS-8 and XGH. KEGG pathway enrichment analysis showed that the DEGs shared by GJS-8 and XGH were mainly involved in “plant hormone signal transduction” “starch and sucrose metabolism” and “MAPK signal transduction”. Trihelix transcription factor (Tai6.25300) was found to be closely related to tuberous root enlargement by the comprehensive analysis of these DEGs and weighted gene co-expression network analysis (WGCNA). Conclusion A hypothetical model of genetic regulatory network for tuberous root development of sweet potato is proposed, which emphasizes that some specific signal transduction pathways like “plant hormone signal transduction” “Ca2+signal” “MAPK signal transduction” and metabolic processes including “starch and sucrose metabolism” and “cell cycle and cell wall metabolism” are related to tuberous root development in sweet potato. These results provide new insights into the molecular mechanism of tuberous root development in sweet potato.

Tumor hypoxic environment is an inevitable obstacle for photodynamic therapy (PDT) of melanoma. Herein, a multifunctional oxygen-generating hydrogel loaded with hyaluronic acid-chlorin e6 modified nanoceria and calcium peroxide (Gel-HCeC-CaO2) was developed for the phototherapy of melanoma. The thermo-sensitive hydrogel could act as a sustained drug delivery system to accumulate photosensitizers (chlorin e6, Ce6) around the tumor, followed by cellular uptake mediated by nanocarrier and hyaluronic acid (HA) targeting. The moderate sustained oxygen generation in the hydrogel was produced by the reaction of calcium peroxide (CaO2) with infiltrated H2O in the presence of catalase mimetic nanoceria. The developed Gel-HCeC-CaO2 could efficiently alleviate the hypoxia microenvironment of tumors as indicated by the expression of hypoxia-inducible factor -1α (HIF-1α), meeting the “once injection, repeat irradiation” strategy and enhanced PDT efficacy. The prolonged oxygen-generating phototherapy hydrogel system provided a new strategy for tumor hypoxia alleviation and PDT.

We describe two novel approaches for the determination of glucosamine (GlcN). The first approach is based on the chemical derivatization of GlcN with the non-fluorophor 1,3-diphenyl-1,3-propanedione (DPPD), which results in a condensation product with interesting fluorescent properties. The obtained compound was isolated by silica-gel chromatography and its structure elucidated by NMR and mass spectrometry. The second approach is based on a previously undescribed sensitivity of the enzyme glucosamine-6-phosphate deaminase (GPDA) towards GlcN, which resulted in the precipitation of the enzyme. Using a rational enzyme engineering approach and both liquid-based and plate-based screening methods, mutational GPDA variants with significantly improved precipitation properties were identified and characterized. These novel glucosamine detection methods may be a useful addition to the repertoire of currently available glucosamine detection sensors.

Outsourced computation for neural networks allows users access to state-of-the-art models without investing in specialized hardware and know-how. The problem is that the users lose control over potentially privacy-sensitive data. With homomorphic encryption (HE), a third party can perform computation on encrypted data without revealing its content. In this paper, we reviewed scientific articles and publications in the particular area of Deep Learning Architectures for Privacy-Preserving Machine Learning (PPML) with Fully HE. We analyzed the changes to neural network models and architectures to make them compatible with HE and how these changes impact performance. Next, we find numerous challenges to HE-based privacy-preserving deep learning, such as computational overhead, usability, and limitations posed by the encryption schemes. Furthermore, we discuss potential solutions to the HE PPML challenges. Finally, we propose evaluation metrics that allow for a better and more meaningful comparison of PPML solutions.

Social networks have become one of the most popular platforms for people to communicate and interact with their friends and share personal information and experiences (e.g., Facebook owns over 1.23 billion monthly active users). The increasing popularity of social networks has generated extremely large-scale user data (e.g., Twitter generates 500 million tweets per day and around 200 billion tweets per year). These data can help improve people’s quality of life as well as benefit various interest groups such as advertisers, application developers, and so on. However, privacy may be compromised if learning algorithms are used to infer unpublished privacy information from published data. Hence, user data privacy preservation has become one of the most urgent research issues in social networks.

A public-private-graph (pp-graph) is developed to model social networks with hidden relationships, and it consists of one public graph in which edges are visible to all users, and multiple private graphs in which edges are only visible to its endpoint users. In contrast with conventional graphs where the edges can be visible to all users, it lacks accurate indexes to evaluate the importance of a vertex in a pp-graph. In this paper, we first propose a novel concept, public-private-core (pp-core) number based on the k-core number, which integrally considers both the public graph and private graphs of vertices, to measure how critical a user is. We then give an efficient algorithm for the pp-core number computation, which takes only linear time and space. Considering that the graphs can be always evolving over time, we also present effective algorithms for pp-core maintenance after the graph changes, avoiding redundant re-computation of pp-core number. Extension experiments conducted on real-world social networks show that our algorithms achieve good efficiency and stability. Compared to recalculating the pp-core numbers of all vertices, our maintenance algorithms can reduce the computation time by about 6–8 orders of magnitude.

As a result of climate change and rapid urbanization, urban waterlogging commonly caused by rainstorm, is becoming more frequent and more severe in developing countries. Urban waterlogging sometimes results in significant financial losses as well as human casualties. Accurate waterlogging depth prediction is critical for early warning system and emergency response. However, the existing hydrological models need to obtain more abundant hydrological data, and the model construction is complicated. The waterlogging depth prediction technology based on object detection model are highly dependent on image data. To solve the above problem, we propose a novel approach based on Temporal Convolutional Networks and Long Short-Term Memory networks to predicting urban waterlogging depth with Waterlogging Monitoring Station. The difficulty of data acquisition is small though Waterlogging Monitoring Station and TCN-LSTM model can be used to predict timely waterlogging depth. Waterlogging Monitoring Station is developed which integrates an automatic rain gauge and a water gauge. The rainfall and waterlogging depth can be obtained by periodic sampling at some areas with Waterlogging Monitoring Station. Precise hydrological data such as waterlogging depth and rainfall collected by Waterlogging Monitoring Station are used as training samples. Then training samples are used to train TCN-LSTM model, and finally a model with good prediction effect is obtained. The experimental results show that the difficulty of data acquisition is small, the complexity is low and the proposed TCN-LSTM hybrid model can properly predict the waterlogging depth of the current regional. There is no need for high dependence on image data. Meanwhile, compared with machine learning model and RNN model, TCN-LSTM model has higher prediction accuracy for time series data. Overall, the low-cost method proposed in this study can be used to obtain timely waterlogging warning information, and enhance the possibility of using existing social networks and traffic surveillance video systems to perform opportunistic waterlogging sensing.

Fracture toughness is critical for the fatigue life of the cemented carbide coatings widely used in the aviation fields, and the development of an economical and effective post-treatment method is in demand. In this research, pulsed magnetic treatment was applied to the thermal spraying WC-10Co4Cr coating with a 17.7% improvement in its fracture toughness, and the strengthening mechanism was further investigated. Microstructure characterization indicates that structural relaxation took place in the amorphous Co binder, and the annihilation of some free volume holes mainly contributes to the mechanical property enhancements. Atomic migration related to the structural relaxation was further explained in terms of the magnetic field induced intersystem crossing. Improvement of both the nano-hardness and elastic modulus of the Co binder was also observed as a result of structural relaxation. This work provides a possible non-thermal post-treatment method for not only the different kinds of cemented carbide coatings but also other amorphous functional materials sensitive to crystal defects.

Creep rupture behaviors of dissimilar metal welds (DMWs) between Inconel 617B and 10%Cr martensitic steel with two different filler metals Inconel 617 and 82 were comparatively investigated at 600 °C to study the effect of nickel-based filler metal types on creep properties. Both the crept DMWs ruptured on the steel side prematurely compared to steel base metal (BM). The failure mode of DMW shifts from interfacial failure at weld metal (WM)/steel interface to Type Ⅳ crack in the intercritical heat-affected zone (ICHAZ) of steel with longer rupture life at the same creep condition by replacing Inconel 617 filler metal with Inconel 82. Type Ⅳ crack was related to matrix softening and fine prior austenite grain boundaries (PAGBs) scarcely decorated by precipitates in the ICHAZ. Interfacial failure was controlled by creep voids nucleation, growth and coalescence at the interface, while oxide notch only triggered crack initiation. The failure mode shift is because Inconel 82 filler metal compared to Inconel 617 can achieve slighter creep deformation concentration in the HAZ closely adjacent to interface and avoid interfacial failure of the DMW due to the lower creep strength gradient across the interface it induced. Based on this study, creep strength compatibility should be considered when selecting nickel-based filler metals for similar DMWs to obtain prolonged creep life.

Background: Autonomic nerve system (ANS) plays an important role in regulating cardiovascular function and cerebrovascular function. Traditional heart rate variation (HRV) and emerging skin sympathetic nerve activity (SKNA) analyses from ultra-short-time (UST) data cannot fully reveal neural activity, thereby quantitatively reflect ANS intensity.Methods: Electrocardiogram and SKNA from sixteen patients (seven cerebral hemorrhage (CH) patients and nine control group (CO) patients) were recorded using a portable device. Ten derived HRV (mean, standard deviation and root mean square difference of sinus RR intervals (NNmean, SDNN and RMSSD), ultra-low frequency (

In the present study, the microstructure, toughness and cracking behavior of a batch of X12CrMoWNbVN10-1-1 martensitic heat resistant steels used for manufacturing steam turbine blades in power plant were investigated. The manufacturing route of the X12CrMoWNbVN10-1-1 martensitic steels was casting, forging and heat treatment. These martensitic steels were taken from different positions in the same ingot, but they exhibited different toughness, which were marked as two groups: (i) the matrix located in the core of the ingot, hereinafter referred to sample A; (ii) the matrix located in the edge of the ingot, hereinafter referred to sample B. Impact toughness was characterized by Instrumented Charpy impact test, fracture toughness was determined by single edge notch tension (SENT) test in terms of double-clip gauge method, and crack propagation behavior was observed by in-situ single edge notch bend (SENB) test. Optical microscopy (OM), scanning electron microscopy (SEM) and high-resolution energy dispersive X-ray spectrometry (EDX) were used to examine the microstructure and cracking features. The impact toughness of sample A was much lower than that of sample B, which was proved to be attributed to the large amount of NbC precipitation in sample A. The clustered NbC particles could induce brittle cracking and thus significantly reduced absorbed energy for stable crack propagation, resulting in the poor impact toughness of sample A. It was believed that the large amount of NbC in sample A was precipitated directly from liquid metal during casting process, which might be related to the high niobium content of the core of the ingot due to the low cooling rate. The results revealed that the large number of clustered NbC particles formed in the casting process not only cannot promote precipitation-induced reinforcement, instead might be detrimental to toughness.

Botryosphaeria dothidea is a major pathogen responsible for postharvest kiwifruit soft rot. This study aimed to determine the influence of hydrogen sulfide (H2S) on postharvest resistance to kiwifruit soft rot and the antifungal role of H2S against B. dothidea. The results indicated that H2S (20 μl L−1) restricted the lesion area following inoculation with B. dothidea. H2S enhanced the production of shikimic acid, tyrosine, tryptophan, and phenylalanine while also increasing the total phenols, flavonoids, and lignin. H2S upregulated the expression of AcDHQS, AcSDH, AcSK, AcPAL, AcCAD, and AcCHS. Additionally, sodium hydrosulfide (NaHS)-released H2S inhibited mycelial growth. NaHS concentrations of 20 and 40 mmol L−1 significantly decreased the mycelial weight and malondialdehyde content (MDA) content while increasing cell membrane conductivity and membrane leakage. The results indicate that H2S induces resistance in kiwifruit via a microbicidal role and amino acid metabolism involved in postharvest kiwifruit disease resistance.

Owing to the incremental and diverse applications of cryptocurrencies and the continuous development of distributed system technology, blockchain has been broadly used in fintech, smart homes, public health, and intelligent transportation due to its properties of decentralization, collective maintenance, and immutability. Although the dynamism of blockchain abounds in various fields, concerns in terms of network communication interference and privacy leakage are gradually increasing. Because of the lack of reliable attack analysis systems, fully understanding some attacks on the blockchain, such as mining, network communication, smart contract, and privacy theft attacks, has remained challenging. Therefore, in this study, we examine the security and privacy of the blockchain and analyze possible solutions. We systematical classify the blockchain attack techniques into three categories, then discuss the corresponding attack and defense methods based on these categories. We focus on (1) the attack and defense methods of mining pool attacks for blockchain security issues, such as block withholding, 51%, pool hopping, selfish mining, and fork after withholding attacks, in the attack type of consensus excitation; (2) the attack and defense methods of network communication and smart contracts for blockchain security issues, such as distributed denial-of-service, Sybil, eclipse, and reentrancy attacks, in the attack type of middle protocol; and (3) the attack and defense methods of privacy thefts for blockchain privacy issues, such as identity privacy and transaction information attacks, in the attack type of application service. Finally, we discuss future research directions for blockchain security.

Both the magnetic field treatment and the microwave treatment can affect the concentration of aroma compounds in liquor, but there are few researches on the composite treatment that combining the two. In this study, the effects of separate pulsed magnetic field (PMF), microwave field, and PMF-microwave composite treatment on Chinese soy-sauce flavor liquor were investigated. The concentration changes of various aroma compounds in liquor were analyzed by gas chromatograph, and the change of water-ethanol association structure in liquor was analyzed by proton nuclear magnetic resonance spectroscopy. Sensory evaluation of liquor subjected to different processes was carried out. The results showed that the sensory characteristic of liquor after composite treatment was improved, and the change of aroma compounds is close to that in traditional aging. The PMF treatment can significantly improve the taste of liquor. The results prove that the PMF-microwave composite treatment is a potential artificial aging method.

Social media has more than three billion users sharing events, comments, and feelings throughout the world. It serves as a critical information source with large volumes, high velocity, and a wide variety of data. The previous studies on information spreading, relationship analyzing, and individual modeling, etc., have been heavily conducted to explore the tremendous social and commercial values of social media data. This survey studies the previous literature and the existing applications from a practical perspective. We outline a commonly used pipeline in building social media-based applications and focus on discussing available analysis techniques, such as topic analysis, time series analysis, sentiment analysis, and network analysis. After that, we present the impacts of such applications in three different areas, including disaster management, healthcare, and business. Finally, we list existing challenges and suggest promising future research directions in terms of data privacy, 5G wireless network, and multilingual support.

Community search has been extensively studied in large networks, such as Protein-Protein Interaction (PPI) networks, citation graphs, and collaboration networks. However, in terms of widely existing multi-valued networks, where each node has d (d⩾1) numerical attributes, almost all existing algorithms either completely ignore the attributes of node at all or only consider one attribute. To solve this problem, the concept of skyline community was presented, based on the concepts of k-core and skyline recently. The skyline community is defined as a maximal k-core that satisfies some influence constraints, which is very useful in depicting the communities that are not dominated by other communities in multi-valued networks. However, the algorithms proposed on skyline community search can only work in the special case that the nodes have different values on each attribute, and the computation complexity degrades exponentially as the number of attributes increases. In this work, we turn our attention to the general scenario where multiple nodes may have the same attribute value. Specifically, we first present an algorithm, called MICS, which can find all skyline communities in a multi-valued network. To improve computation efficiency, we then propose a dimension reduction based algorithm, called P-MICS, using the maximum entropy method. Our algorithm can significantly reduce the skyline community searching time, while is still able to find almost all cohesive skyline communities. Extensive experiments on real-world datasets demonstrate the efficiency and effectiveness of our algorithms.

Abstract In this paper, we study the Schrödinger–Poisson system with singularity and critical growth terms. By means of variational methods with an appropriate truncation argument, the existence and multiplicity of positive solutions are obtained.

Paroxysmal atrial fibrillation (PAF) may related to the risk of thromboembolism and is the most common cardiac risk factor of cryptogenic stroke (CS). Due to its paroxysmal characteristics, it is usually diagnosed by continuous long-term ECG. Patients with paroxysmal atrial fibrillation usually have premature beats at the same time which is easy to be confused with the rhythm of atrial fibrillation. Therefore, in this article, we designed a screening algorithm for single premature beat, multi premature beats, bigeminy and trigeminy premature beats, according to their rhythm characteristics to reduce false detection caused by premature beats during the PAF detection process. The proposed elimination method was verified on ECG segments with different types of premature beats, and tested on long-term ECG data of PAF patients. ECG segments of different kinds of premature beats were selected from MIT Atrial Fibrillation database (MIT-AFDB), MIT-BIH Arrhythmia database (MIT-AR) and wearable ECG data from the China Physiological Signal Challenge 2021 (CPSC 2021). The proposed method can effectively eliminate single premature beat segments with 99.5% accuracy, and it also can eliminate more than 95% of ECG segments with other types of premature beats. We designed PAF-score as a new index to evaluate the accuracy of detection, and we also calculate the misjudged and missed segments to comprehensively evaluate the PAF detection algorithm. The proposed method get a PAF-score of 0.912 on MIT-AFDB. The proposed method also has the potential to implant low computing power wearable devices for real-time analysis.

IoT devices’ storage and computation capacities are constantly increasing in recent years, which brings critical challenges in data privacy protection. Federated learning (FL) and blockchain technology are two popular techniques used in IoT data aggregation, where FL enables data training with privacy protection, and blockchain provides a decentralized architecture for data storage and mining. However, very few the state-of-the-art works consider the applicability of the combination of FL and blockchain. In this paper, we adopt the federated averaging algorithm to reduce the communication overhead between the blockchain and end users to achieve higher performance. We also apply the double-mask-then-encrypt approach for end users to submit their local updates in order to protect data privacy. Finally, we propose and implement a non-interactive Public Verifiable Secret Sharing (PVSS) algorithm with Distributed Hash Table (DHT) that solves the user-drop-out problem and improves the communication efficiency between blockchain and end-users. At last, we theoretically analyze the security strengths of the proposed solution and conduct experiments to measure the execution time of PVSS on both the server and clients sides.

Recent advances in Artificial Intelligence (AI) have brought society closer to the long-held dream of creating machines to help with both common and complex tasks and functions. From recommending movies to detecting disease in its earliest stages, AI has become an aspect of daily life many people accept without scrutiny. Despite its functionality and promise, AI has inherent security risks that users should understand and programmers must be trained to address. The ICE (integrity, confidentiality, and equity) cybersecurity labs developed by a team of cybersecurity researchers addresses these vulnerabilities to AI models through a series of hands-on, inquiry-based labs. Through experimenting with and manipulating data models, students can experience firsthand how adversarial samples and bias can degrade the integrity, confidentiality, and equity of deep learning neural networks, as well as implement security measures to mitigate these vulnerabilities. This article addresses the pedagogical approach underpinning the ICE labs, and discusses both sample activities and technological considerations for teachers who want to implement these labs with their students.

Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences. Public summary: • “Can machines think?” The goal of artificial intelligence (AI) is to enable machines to mimic human thoughts and behaviors, including learning, reasoning, predicting, and so on. • “Can AI do fundamental research?” AI coupled with machine learning techniques is impacting a wide range of fundamental sciences, including mathematics, medical science, physics, etc. • “How does AI accelerate fundamental research?” New research and applications are emerging rapidly with the support by AI infrastructure, including data storage, computing power, AI algorithms, and frameworks.

It is of great significance to reduce welding hot cracks and improve the corrosion resistance of nuclear power components by controlling the dilution ratio of the cladding weld bead formed by gas tungsten arc welding (GTAW). This paper presents a method to predict the dilution ratio by using the ratio of the thermal power of forming a unit length weld to the cross-section of the fuse, that is, the linear energy of forming a unit volume of deposited metal, which is characterized by the “Heat Equivalent for Melting Welding Wire” (HEMW). It is found that there is a linear positive correlation between the HEMW and the dilution ratio. With the increase in HEMW, the proportion of deposition heat energy in the whole effective heat input energy decreases, the proportion of base metal melting heat energy increases, and the proportion of non-melting heat energy diffused to the base metal remains basically unchanged. The HEMW is used to predict and control the dilution rate under the condition of pulse welding. By increasing the wire feeding speed under the condition of pulse peak current and reducing the welding speed, a high deposition efficiency, low dilution rate and low heat input is realized, which plays an important guiding role in improving the deposition quality.

This work investigates the role of hydrogen sulfide (H2S) in the browning and regulating the antioxidant defensive system in fresh-cut Chinese water chestnuts. The samples were fumigated with 0, 10, and 15 μl L−1 of H2S and stored at 10°C for 8 days. The results indicated that the H2S treatment significantly inhibited the browning of fresh-cut Chinese water chestnuts, reduced superoxide anion (O2·-) production rate and H2O2 content accumulation, promoted the increase of total phenol content, and enhanced activities of catalase (CAT), superoxide dismutase (SOD), ascorbate peroxidase (APX), and glutathione reductase (GR) (P < 0.05). On the other hand, phenylalanine ammonia lyase (PAL), polyphenol oxidase (PPO), and peroxidase (POD) activities remained at a low level in the H2S treatment (P < 0.05). This result suggested that H2S treatment might be a promising approach to inhibit browning and prolong the shelf life by enhancing oxidation resistance and inhibiting browning enzyme activity of fresh-cut Chinese water chestnuts during storage. Among them, the 15 μl L−1 H2S treatment had the best effect on fresh-cut Chinese water chestnuts.

Stress concentration on a bolt thread, resulting from its own special shape, poses a threat to the fatigue strength of the bolt, which directly affects the safety and reliability of aircraft. In this paper, deep rolling was applied to a bolt thread to improve its fatigue resistance. The properties of the plastic deformation layer, including the surface morphology, microstructure, hardness, and residual stress, as well as the fatigue life of the bolt, were characterized by means of SEM, white light interferometer, EBSD, and fatigue tests. The results showed that the surface roughness of the bottom of the thread was reduced to 0.255 μm, and a plastic deformation layer of about 300 μm in depth was formed after rolling. A more compact streamlined fibrous microstructure, composed of refined grains, with increased dislocation density and hardness and decreased tensile residual stress, was formed in the plastic deformation layer. The fatigue life of the bolts after rolling increased by about 113%, evidencing the comprehensive result of these microstructure modifications.

ABSTRACTEgypt is a hotspot for avian influenza virus (AIV) due to the endemicity of H5N1 and H9N2 viruses. AIVs were isolated from 329 samples collected in 2016–2018; 48% were H9N2, 37.1% were H5N8, 7.6% were H5N1, and 7.3% were co-infections with 2 of the 3 subtypes. The 32 hemagglutinin (HA) sequences of the H5N1 viruses formed a well-defined lineage within clade 2.2.1.2. The 10 HA sequences of the H5N8 viruses belonged to a subclade within 2.3.4.4. The 11 HA of H9N2 isolates showed high sequence homology with other Egyptian G1-like H9N2 viruses. The prevalence of H5N8 viruses in ducks (2.4%) was higher than in chickens (0.94%). Genetic reassortment was detected in H9N2 viruses. Antigenic analysis showed that H9N2 viruses are homogenous, antigenic drift was detected among H5N1 viruses. AI H5N8 showed higher replication rate followed by H9N2 and H5N1, respectively. H5N8 was more common in Southern Egypt, H9N2 in the Nile Delta, and H5N1 in both areas. Ducks and chickens played a significant role in transmission of H5N1 viruses. The endemicity and co-circulation of H5N1, H5N8, and H9N2 AIV coupled with the lack of a clear control strategy continues to provide avenues for further virus evolution in Egypt.

Myocardial infarction (MI) is one of the major causes of death. Thus, understanding the underlying mechanisms of MI and its clinical features, especially its relationship with common electrocardiography measurements, is important. Heart modeling provides a possible method to simulate certain heart conditions. In this paper, a refined MI torso-heart model was proposed to explore the effect of inferior MI on simulated electrocardiograms. This model simulated the electrical activity of a normal heart and an inferior MI heart at three sites (basal, middle, and apical) with three MI sizes (small, moderate, and large), aiming to investigate the effect of inferior MI on the simulated electrocardiograms. Simulated body surface potential maps were compared with clinical body surface potential maps to verify the efficiency of the model. A new measure, namely, the normalized variation coefficient, was proposed for result evaluation. The results showed that the augmented unipolar left lower limb lead was the best choice for inferior MI diagnoses and it showed the most obvious electrocardiography signal differences between normal and inferior MI hearts. The simulation results corresponded well with commonly used clinical diagnostic criteria. Thus, the proposed refined MI torso-heart model provides a finite element simulation method for quantifying the effects of inferior MI on a torso-heart model-based electrocardiogram and has good potential for use in optimizing electrocardiogram detection.

Urban industrial plant areas are highly concentrated, and air pollution is increasingly serious. The quantity of outdoor air quality monitoring sites is insufficient. Aiming at the above questions, related studies propose solutions that use relatively cheap equipment networking to collect pollution data and accurately analyze local monitoring information. In this paper, a new type of outdoor air quality monitoring system is studied and preliminarily practiced and has proven certain feasibility and applicability. The main contributions of this paper are: first, we improve the network layout by employing the Zigbee network, which is combined with factory characteristics, and collected data on carbonic oxide, nitrogen dioxide, sulfur dioxide, ozone, particulate matter, temperature, and humidity. And then, to establish the dilution coefficient and diffusion coefficient of pollution diffusion, we adopt air movement as the energy model and, by utilizing the method of pollution traceability, achieve the complete coverage pollution monitoring of the whole city by local monitoring sites. Finally, we propose an improved long short-term memory (LSTM) method to predict the pollution period of urban air quality. The experimental results show that the improved LSTM prediction model has strong applicability and high accuracy in the period prediction of pollution weather. Meanwhile, by analyzing the specific case in detail, we prove that air pollution in the city is mainly caused by the manufacturing industry. We conclude that it will make a great contribution to the atmospheric environment protection of cities by using weather quality prediction to dynamically adjust the production.

Premature ventricular contraction (PVC) is one of the common ventricular arrhythmias, which may cause stroke or sudden cardiac death. Automatic long-term electrocardiogram (ECG) analysis algorithms could provide diagnosis suggestion and even early warning for physicians. However, they are mutually exclusive in terms of robustness, generalization and low complexity. In this study, a novel PVC recognition algorithm that combines deep learning-based heartbeat template clusterer and expert system-based heartbeat classifier is proposed. A long short-term memory-based auto-encoder (LSTM-AE) network was used to extract features from ECG heartbeats for K-means clustering. Thus, the templates were constructed and determined based on clustering results. Finally, the PVC heartbeats were recognized based on a combination of multiple rules, including template matching and rhythm characteristics. Three quantitative parameters, sensitivity (Se), positive predictive value (P+) and accuracy (ACC), were used to evaluate the performances of the proposed method on the MIT-BIH Arrhythmia database and the St. Petersburg Institute of Cardiological Technics database. Se on the two test databases was 87.51% and 87.92%, respectively; P+ was 92.47% and 93.18%, respectively; and ACC was 98.63% and 97.89%, respectively. The PVC scores on the third China Physiological Signal Challenge 2020 training set and hidden test set were 36,256 and 46,706, respectively, which could rank first in the open-source codes. The results showed that the combination strategy of expert system and deep learning can provide new insights for robust and generalized PVC identification from long-term single-lead ECG recordings.

The microstructure and impact toughness of weld metals (WMs) and heat-affected zones (HAZs) of a low-alloy CrMoV steel gas turbine rotor which had served for 14 years were investigated. The ex-service joints in the turbine part (serving at 500–540 °C) and the compressor part (serving below 300 °C) of the rotor were selected for comparative research. The microstructure of the WMs and HAZs between the turbine part and the compressor part was similar, indicating that there was no significant deterioration in microstructure of the turbine part during service. However, compared with the compressor part WM, the impact energy of the turbine part WM decreased significantly, and FATT50 increased greatly. The degraded toughness of turbine part WM was related to more serious intergranular cracking caused by higher segregation level of phosphorus (P) at prior austenite grain (PAG) boundaries. Welding and post-weld heat treatment led to obvious segregation of P at PAG boundaries in WMs, and the segregation of P in turbine part WM was further aggravated during serving at 500–540 °C. Additionally, the inhomogeneous microstructure of the WMs also aggravated the segregation of P. The toughness of the HAZs in both turbine part and compressor part was high, which was because of fine grains. Furthermore, due to there being more grain boundaries and low P content, the segregation of P in HAZs was slight and its adverse effect on the toughness could be ignored.

The aircraft trajectory clustering analysis in the terminal airspace is conducive to determining the representative route structure of the arrival and departure trajectory and extracting their typical patterns, which is important for air traffic management such as airspace structure optimization, trajectory planning, and trajectory prediction. However, the current clustering methods perform poorly due to the large flight traffic, high density, and complex airspace structure in the terminal airspace. In recent years, the continuous development of Deep Learning has demonstrated its powerful ability to extract internal potential features of large dataset. Therefore, this paper mainly tries a deep trajectory clustering method based on deep autoencoder (DAE). To this end, this paper proposes a trajectory clustering method based on deep autoencoder (DAE) and Gaussian mixture model (GMM) to mine the prevailing traffic flow patterns in the terminal airspace. The DAE is trained to extract feature representations from historical high-dimensional trajectory data. Subsequently, the output of DAE is input into GMM for clustering. This paper takes the terminal airspace of Guangzhou Baiyun International Airport in China as a case to verify the proposed method. Through the direct visualization and dimensionality reduction visualization of the clustering results, it is found that the traffic flow patterns identified by the clustering method in this paper are intuitive and separable.

During the welding of 690 nickel-based alloy, solidification cracking (SFC) and ductility-dip cracking (DDC) easily forms, which has a negative effect on the quality of welded joints and service life. The present study examined the effects of welding heat input and cladding layers on the SFC and DDC, as well as their formation mechanism. The microstructure observation, elemental distribution, and Varestraint test were carried out. The results show that SFC and DDC were formed for the Inconel filler metal 52M, and SFC is more prone to form than DDC. The alloy elements such as Fe, Si, C, and P from base metal can expand the solidification temperature range, such that the SFC sensitivity increases. With the increase of welding heat input, the grain size of cladding metal is increased with a great SFC sensitivity. The increasing welding heat input also makes DDC possible due to the formation of a large angle grain boundary.

Abstract In this paper, we consider the study of a fractional elliptic problem with the Hardy-Sobolev-Maz’ya potential and critical nonlinearities. By means of variational methods and suitable technique, a positive solution to this problem is obtained.

Water-wall tubes are important components in power plants and are used to absorb the heat from the boilers, and they often fail prematurely due to corrosion, erosion, and fatigue during service. To repair the defects, weld overlay repair is often adopted by forming a compressive stress area around the susceptible area to prevent the cracks from propagating. In order to obtain reasonable process parameters of weld overlay repair, a study combining experiments and numerical simulations was performed to investigate the welding residual stress distribution on the repaired area with different welding repair methods. The results reveal that a compressive stress area is generated on the repaired area after the repair, and a center-to-outside welding sequence is better than either a right-to-left welding sequence or an outside-to-center welding sequence when overlaying a one-layer weld; with an increase in the number of weld layers, the compressive stress area of the repaired area is expanded, and the stress level is increased, which results from the upward movement of the compressive stress area of the subsequent-overlaying layer and the superposition of the compression areas of the subsequent-overlaying layer and previous-overlaying layer, respectively. In addition, the number of the weld passes of each layer should be not less than four with the center-to-outside welding sequence.

With the occurrence of Internet of Things (IoT) era, the proliferation of sensors coupled with the increasing usage of wireless spectrums especially the ISM band makes it difficult to deploy real-life IoT. Currently, the cognitive radio technology enables sensors transmit data packets over the licensed spectrum bands as well as the free ISM bands. The dynamic spectrum access technology enables secondary users (SUs) access wireless channel bands that are originally licensed to primary users. Due to the high dynamic of spectrum availability, it is challenging to design an efficient routing approach for SUs in cognitive sensor networks. We estimate the spectrum availability and spectrum quality from the view of both the global statistical spectrum usage and the local instant spectrum status, and then introduce novel routing metrics to consider the estimation. In our novel routing metrics, one retransmission is allowed to restrict the number of rerouting and then increase the routing performance. Then, the related two routing algorithms according to the proposed routing metrics are designed. Finally, our routing algorithms in extensive simulations are implemented to evaluate the routing performance, and we find that the proposed algorithms achieve a significant performance improvement compared with the reference algorithm.