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Exposure to the indoor airborne microbiome is closely related to the air that individuals breathe. However, the floor dust-borne microbiome is commonly used as a proxy for indoor airborne microbiome, and the spatial distribution of indoor airborne microbiome is less well understood. This study aimed to characterize indoor airborne microorganisms at varying heights and compare them with those in floor dust. An assembly of three horizontally and three vertically positioned Petri dishes coated with mineral oil was applied for passive air sampling continuously at three heights without interruption. The airborne microbiomes at the three different heights showed slight stratification and differed significantly from those found in the floor dust. Based on the apportionment results from the fast expectation–maximization algorithm (FEAST), shoe sole dust contributed approximately 4% to indoor airborne bacteria and 14% to airborne fungi, a contribution that is comparable to that from the floor dust-borne microbiome. The results indicated that floor dust may not be a reliable proxy for indoor airborne microbiome. Moreover, the study highlights the need for height-resolved studies of indoor airborne microbiomes among humans in different activity modes and life states. Additionally, shoe sole-dust-associated microorganisms could potentially be a source to “re-wild” the indoor microbiota.

Deep Convolution Neural Networks (CNNs) have become the cornerstone of image classification, but the emergence of adversarial image attacks brings serious security risks to CNN-based applications. As a local perturbation attack, the border attack can achieve high success rates by only modifying the pixels around the border of an image, which is a novel attack perspective. However, existing border attacks have shortcomings in stealthiness and are easily detected. In this article, we propose a novel stealthy border attack method based on deep feature alignment. Specifically, we propose a deep feature alignment algorithm based on style transfer to guarantee the stealthiness of adversarial borders. The algorithm takes the deep feature difference between the adversarial and the original borders as the stealthiness loss and thus ensures good stealthiness of the generated adversarial images. To ensure high attack success rates simultaneously, we apply cross entropy to design the targeted attack loss and use margin loss as well as Leaky ReLU to design the untargeted attack loss. Experiments show that the structural similarity between the generated adversarial images and the original images is 8.8% higher than the state-of-art border attack method, indicating that our proposed adversarial images have better stealthiness. At the same time, the success rate of our attack in the face of defense methods is much higher, which is about four times that of the state-of-art border attack under the adversarial training defense.

Due to the powerful representation ability and superior performance of Deep Neural Networks (DNN), Federated Learning (FL) based on DNN has attracted much attention from both academic and industrial fields. However, its transmitted plaintext data causes privacy disclosure. FL based on Local Differential Privacy (LDP) solutions can provide privacy protection to a certain extent, but these solutions still cannot achieve adaptive perturbation in DNN model. In addition, this kind of schemes cause high communication overheads due to the curse of dimensionality of DNN, and are naturally vulnerable to backdoor attacks due to the inherent distributed characteristic. To solve these issues, we propose an Efficient and Secure Federated Learning scheme (ESFL) against backdoor attacks by using adaptive LDP and compressive sensing. Formal security analysis proves that ESFL satisfies $\epsilon$ε-LDP security. Extensive experiments using three datasets demonstrate that ESFL can solve the problems of traditional LDP-based FL schemes without a loss of model accuracy and efficiently resist the backdoor attacks.

Purpose: In the JUNO, the LS serves as the medium for detecting neutrinos. When purifying the LS using HPN, it is essential to ensure low background levels of radioactive krypton and argon in the HPN Methods: Using the low-temperature physical adsorption properties of activated carbon to adsorb and separate radioactive gases such as radon, krypton, and argon from nitrogen in a liquid nitrogen environment. 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The ${}^{85}$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{85}$$\end{document}Kr concentration in the nitrogen purified by coconut shell activated carbon is 0.46 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu $$\end{document}Bq/m${}^3$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{3}$$\end{document}. Conclusions: After adsorption with activated carbon, the content of ${}^{85}$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{85}$$\end{document}Kr and ${}^{39}$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{39}$$\end{document}Ar in HPN is lower than the 50 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\upmu $$\end{document}Bq/m${}^3$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{3}$$\end{document}required by JUNO. This work validates that the ${}^{85}$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{85}$$\end{document}Kr and ${}^{39}$\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^{39}$$\end{document}Ar concentrations in HPN is fit the JUNO requirement.

Photocatalytic carbon dioxide (CO2) reduction for high-value hydrocarbon fuel production is a promising strategy to tackle global energy demand and climate change. However, this technology faces formidable challenges, primarily stemming from low yield and poor selectivity of C2 products of the desired hydrocarbon fuels. This study reported ZnO/ZnCo2O4Janus hollow nanofibers (ZnO/ZCO JHNFs) prepared by electrospinning and atomic layer deposition. Photocatalytic tests revealed an ethanol yield of 4.99 μmol g–1h–1for ZnO/ZnCo2O4JHNFs, surpassing mixed ZnO/ZnCo2O4nanofibers (ZnO/ZCO NFs) by 4.35 times and pure ZnO by 12.7 times. The selectivity of 58.8% is 2.38 and 4.49 times higher than those of ZnO/ZnCo2O4NFs and ZnO, respectively. These enhancements are attributed to efficient carrier separation facilitated by the ordered internal electric field of the Z-scheme heterojunction interface, validated by the energy band evaluations from experimentation and density functional theory (DFT) simulations and charge separation characterizations of photocurrent, impedance, and photoluminescence spectra. The Janus structure also effectively exposes the surface of ZnCo2O4to CO2molecules, increasing the active site availability, as confirmed by BET nitrogen adsorption/desorption, temperature-programmed desorption tests, and DFT adsorption energy calculations. This study proposes a novel approach for efficient photocatalytic hydrocarbon fuel production, with potential applications in energy and climate crisis mitigation.

The Proactive Recovery (PR) mechanism in Software-Defined Networking (SDN) provides good failure recovery resilience for the Beyond Fifth-Generation/Sixth-Generation (B5G/6G) delay-sensitive applications. However, PR’s fixed single backup path policy for any flow and fine-grained backup forwarding rule configuration poses severe challenges for post-recovery congestion management and limited Ternary Content Addressable Memory (TCAM) space in SDN switches. To this end, we propose K-backup, a load- and TCAM-aware multi-backup fast failure recovery scheme for SDNs. Firstly, K-backup formulates and solves the congestion-aware multi-backup path planning problem for various failure scenarios, exploiting the inherent load diversity of multi-backup paths to minimize the post-recovery maximum link utilization. Secondly, K-backup aggregates flows sharing the same backup-path-weight pair on a link into a cascading table of Fast-Failover and SELECT groups. Meanwhile, each outputted backup path is labeled, and a corresponding label-matching flow table is configured for each intermediate switch to aggregate all flows on that path. Thirdly, K-backup dynamically adjusts the backup path update period based on the network load stabilization to reduce unnecessary controller overhead. Compared with state-of-the-art, K-backup achieves the lowest controller overhead, the best load balancing performance, the near-fewest TCAM space usage, and the near-shortest recovery time.

There is substantial attention to federated learning with its ability to train a powerful global model collaboratively while protecting data privacy. Despite its many advantages, federated learning is vulnerable to backdoor attacks, where an adversary injects malicious weights into the global model, making the global model's targeted predictions incorrect. Existing defenses based on identifying and eliminating malicious weights ignore the similarity variation of the local weights during iterations in the malicious model detection and the presence of benign weights in the malicious model during the malicious local weight elimination, resulting in a poor defense and a degradation of global model accuracy. In this paper, we defend against backdoor attacks from the perspective of local models. First, a malicious model detection method based on interpretability techniques is proposed. The method appends a sampling check after clustering to identify malicious models accurately. We further design a malicious local weight elimination method based on local weight contributions. This method preserves the benign weights in the malicious model to maintain their contributions to the global model. Finally, we analyze the security of the proposed method in terms of model closeness and then verify the effectiveness of the proposed method through experiments. In comparison with existing defenses, the results show that BADFL improves the global model accuracy by 23.14% while reducing the attack success rate to 0.04% in the best case.

Access to healthcare services using public transportation (PT-based healthcare accessibility) is a crucial aspect in achieving healthcare equity as it affects individuals’ ability to receive healthcare. Previous research has focused on the spatial features of healthcare accessibility. However, less attention has been given to its temporal characteristics, which can be influenced by transit schedules, multimodal connectivity, congestion, and other factors. This study proposes a framework to better understand the impacts of temporally varying PT-based healthcare accessibility on healthcare equity. A case study of Shanghai, China is used to illustrate the temporal variation of healthcare accessibility, with a focus on hourly inter- and intra-regional disparities. These disparities are captured using the Gini coefficient and Theil index. Additionally, the study introduces bivariate local Moran’s I to identify healthcare shortage areas and evaluate the spatial autocorrelation between population density and healthcare accessibility. The findings of this study reveal that the accessibility to healthcare services experiences significant fluctuations throughout the day, leading to temporal variations in healthcare equity. Subway service quality contributes more to temporal variations than bus service quality. The lowest point of such equity is reached when PT operates at its full capacity. On a spatial level, individuals residing in newly developed regions, which surround the historical urban core or recently planned city subcenters, tend to experience decreased accessibility to healthcare via public transportation. Consequently, it results in a heightened reliance on motorized transportation in these areas. These findings provide insights that can inform the design of PT accessibility-based strategies, healthcare improvement plans and inclusive housing policies, to address healthcare equity issues in metropolitan areas. By considering both spatial and temporal factors, we can better understand the complex relationships between transportation and healthcare accessibility to promote equitable access to healthcare services and foster social equity.

To motivate users’ assistance for protecting others’ location privacy by distributed K-anonymity in Location-Based Service (LBS), many incentive mechanisms have been proposed, where users obtain monetary compensation for their assistance. However, most existing distributed K-anonymity incentive mechanisms rely on trusted third parties and ignore users’ malicious strategies, which destroys LBS's distributed structure as well as leads to users’ privacy leakage and incentive ineffectiveness. To solve the above problems, we propose a Privacy-Enhanced incentive mechAnism for distributed K-anonymity (PEAK). With determining the monetary transaction relationship and location transmission between users, PEAK enables the anonymous cloaking region construction without the trusted server. Meanwhile, PEAK devises role identification mechanism and accountability mechanism to restrain and punish malicious users, which protects users’ location privacy and implements effective motivation on users’ assistance. Theoretical analysis based on the game theory shows that PEAK constrains users’ malicious strategies while satisfying individual rationality, computational efficiency, and satisfaction ratio. Extensive experiments based on the real-world dataset demonstrate that PEAK improves security and feasibility, especially reaching the success rate of anonymous cloaking region construction to more than 90$\%$% and decreasing the malicious users’ utilities significantly.

The utilization of blockchain technology as a distributed information sharing system has gained widespread adoption across various domains. However, its application to Flying Ad-Hoc Network (FANET), characterized by severe packet loss, poses significant challenges. The high packet loss rates in FANETs can result in decreased consensus success rates and negatively impact information sharing consistency and efficiency. In this paper, we proposed RoUBC, a novel consensus scheme for Flying Ad-Hoc Networks (FANET), which is based on the Raft protocol and is designed to address the challenges posed by the severe packet loss network in FANET. The proposed scheme consists of two phases: leader election and block consensus. In the leader election phase, we integrate multi-criteria decision-making and link prediction algorithms to design an efficient stable-leader election method. In the block consensus phase, we propose a dynamic block verification algorithm based on historical verification information to achieve efficient block consensus. Our theoretical analysis demonstrates that the proposed consensus protocol is safe and live, effectively ensuring the consistency of message sharing in FANET. Experiment results show that our scheme outperforms traditional Raft schemes, with 35% increase in consensus success rate and 25% improvement in consensus efficiency.

With the accelerating adaption of Cloud and Edge computing, cloud-based networked deployment emerges to enable providers to deliver services in a cost-effective and elastic manner. However, security concern remains one of the major obstacles to its wider adaption. Trusted Execution Environment (TEE) has been advocated to protect cloud services in an isolated execution environment. In this paper, we present a new genre of side-channel attack called interface-based side-channel attack and demonstrate its effectiveness on the TEE-assisted networked service system. The root cause of this attack is the input-dependent interface invocation (e.g., interface information and invocation patterns) that can be observed by untrusted software to reveal the control flows inside the enclave. Our evaluation demonstrates that the attack can effectively re-identify encrypted web pages processed in the SGX enclave with an accuracy of 87.6% and a recall of 76.6%, and can reduce the search domain of the 1024 bits RSA private keys to $1.69 \times 10^{-6}$ of the original search domain. As countermeasures, we propose, implement and evaluate a set of static analysis tools to mitigate the newly discovered threats. The key idea is to use inter-procedural dataflow analysis to identify potential leakage via the interface, and then mitigate them during compilation using techniques including branch obfuscation, loop obfuscation, and constant size wrapper.

Mobile crowdsensing (MCS) has been applied in various fields to realize data sharing, where multiple platforms and multiple Mobile Users (MUs) have appeared recently. However, aiming at mutual selection, the existing works ignore making MUs’ utilities with the limited resources and platforms’ utilities while achieving the desired sensing data quality maximum as far as possible. Thus, they cannot motivate both MUs and platforms to participate. To address this problem, standing on both sides of MUs and platforms with conflicting interests, we propose a Progressive two-stage Auction-based Multi-platform Multi-user Mutual Selection scheme (PAM3S). Specifically, in PAM3S, we treat mutual selection as a two-stage auction and devise the auction models for MU and platform using forward and reverse auction ideas, presenting and maximizing the utilities from their respective perspectives. Then, based on the proposed progressive two-stage auction structure, we adopt 0–1 knapsack and Myerson’s price theory to construct the first stage MU-oriented auction and the second stage platform-oriented auction, achieving devised models. Theoretical analysis shows that PAM3S is economically robust. Extensive experiments on the real dataset demonstrate that PAM3S respectively promotes platforms’ and MUs’ utilities by 76.23% and 10.74 times, compared with the existing works.

Nearly half of electric biker (e-biker)-related crashes caused head injuries that could be preventable by wearing a helmet. This study proposes an integrated Theory of Planned Behavior, Health Belief Model, and locus of control framework to investigate the underlying psychological factors influencing helmet use intention using survey data in Shanghai. Structural equation model estimation and multiple group analyses were conducted to understand these heterogeneous impacting factors. Health motivation, perceived behavioral control, attitude, cues to action, and internality were identified as five key factors affecting the intention to use a helmet. Subgroups of e-bikers based on their age, education, income, and driver’s license were more susceptible to targeted helmet use strategies for some psychological factors compared to their counterparts. Study insights shed light on the one-size-fits-all strategy limitations and may assist policymakers in designing behavioral interventions and safety education schemes to promote helmet use among e-bikers in China.

Surface soil moisture plays a pivotal role in various hydrological processes. Precisely assessing soil moisture with high resolution is crucial for effective water resource management, informed agricultural decision-making, and in-depth climate change research. While passive microwave remote sensing is a primary technology for regional soil moisture monitoring, its practical application is hindered by data discontinuity and low resolution. To address these challenges, we propose an integrated learning framework to enhance the continuity and resolution of soil moisture data across China. Leveraging low-resolution passive microwave soil moisture data, moderate-resolution assimilated soil moisture data, and multiple high-resolution ancillary inputs, the network effectively captures the spatiotemporal dynamics of soil moisture through integrating gap-filling, multisource fusion, and spatial downscaling processes. Validation against in situ data demonstrates the significant enhancements achieved by the proposed method, with an average R value of 0.706 and an average unbiased root mean square error of 0.055 m3/m3. Comparative analysis further confirms its superior accuracy and robustness across diverse regions. These findings highlight the potential of this integrated learning framework to advance hydrological applications, enhance agricultural production, and support climate research.

The increasing use of multi-biometric authentication has raised concerns about the security of biometric templates. Many template protection methods based on convolutional neural network have been presented, but most involve a trade-off between authentication accuracy and template security. In this paper, we present a cancelable multi-biometric template protection scheme that combines deep hashing with cancelable distance-preserving encryption (CDPE), which provides high template security without degrading the authentication performance. Specifically, a deep hashing based architecture that minimizes the quantization loss is designed to map face and iris traits to binary codes. Next, CDPE is proposed to generate a protected template given the face binary code and a user-specific key obtained from the iris binary code, which preserves the distance between original templates in the protected domain to ensure authentication performance equivalent to unprotected systems. Digital lockers instead of the key are stored to further enhance the security, which can be unlocked with genuine biometric traits to get the correct key during authentication. Theoretical and experimental results on real face and iris datasets show that our scheme can achieve equal error rate of 0.23% and genuine accept rate of 97.54%, while guaranteeing irreversibility, revocability and unlinkability of protected templates.

Purpose: After robotic-assisted total knee arthroplasty (RA-TKA) surgery, some patients still experience joint discomfort. We aimed to establish an effective machine learning model that integrates radiomic features extracted from computed tomography (CT) scans and relevant clinical information to predict patient satisfaction three months postoperatively following RA-TKA. Materials and methods: After careful selection, data from 142 patients were randomly divided into a training set (n= 99) and a test set (n= 43), approximately in a 7:3 ratio. A total of 1329 radiomic features were extracted from the regions of interest delineated in CT scans. The features were standardized using normalization algorithms, and the least absolute shrinkage and selection operator regression model was employed to select radiomic features with ICC > 0.75 and P< 0.05, generating the Rad-score as feature markers. Univariate and multivariate logistic regression was then used to screen clinical information (age, body mass index, operation time, gender, surgical side, comorbidities, preoperative KSS score, preoperative range of motion (ROM), preoperative and postoperative HKA angle, preoperative and postoperative VAS score) as potential predictive factors. The satisfaction scale ≥ 20 indicates patient satisfaction. Finally, three prediction models were established, focusing on radiomic features, clinical features, and their fusion. Model performance was evaluated using Receiver Operating Characteristic curves and decision curve analysis. Results: In the training set, the area under the curve (AUC) of the clinical model was 0.793 (95% CI 0.681–0.906), the radiomic model was 0.854 (95% CI 0.743–0.964), and the combined radiomic-clinical model was 0.899 (95% CI 0.804–0.995). In the test set, the AUC of the clinical model was 0.908 (95% CI 0.814–1.000), the radiomic model was 0.709 (95% CI 0.541–0.878), and the combined radiomic-clinical model was 0.928 (95% CI 0.842–1.000). The AUC of the radiomic-clinical model was significantly higher than the other two models. The decision curve analysis indicated its clinical application value. Conclusion: We developed a radiomic-based nomogram model using CT imaging to predict the satisfaction of RA-TKA patients at 3 months postoperatively. This model integrated clinical and radiomic features and demonstrated good predictive performance and excellent clinical application potential.

Remote sensing (RS) image change detection (CD) plays a crucial role in monitoring surface dynamics; however, current deep learning (DL)-based CD methods still suffer from pseudo changes and scale variations due to inadequate exploration of temporal differences and under-use of multiscale features. Based on the aforementioned considerations, a spatiotemporal enhancement and interlevel fusion network (SEIFNet) is proposed to improve the ability of feature representation for changing objects. First, the multilevel feature maps are acquired from the Siamese hierarchical backbone. To highlight the disparity in the same location at different times, the spatiotemporal difference enhancement modules (ST-DEMs) are introduced to capture global and local information from bitemporal feature maps at each level. Coordinate attention (CA) and cascaded convolutions are adopted in subtraction and connection branches, respectively. Then, an adaptive context fusion module (ACFM) is designed to integrate interlevel features under the guidance of different semantic information, constituting a progressive decoder. Additionally, a plain refinement module and a concise summation-based prediction head are employed to enhance the boundary details and internal integrity of CD results. The experimental results validate the superiority of our lightweight network over eight state-of-the-art (SOTA) methods on LEVIR-CD, SYSU-CD, and WHU-CD datasets, both in accuracy and efficiency. Also, the effects of different types of backbones and differential enhancement modules are discussed in the ablation experiments in detail. The code will be available at https://github.com/lixinghua5540/SEIFNet.

To motivate data owner (DO) to trade data, the existing data trading allows DO to sell the disturbed data to the data consumer (DC), where the disturbance parameter and the data price are negotiated by them, and DO independently adds the disturbance noise to data (usually continuous type) following the negotiation result. However, DOs may violate the negotiated parameter and add more noise to data while obtaining the negotiated price, which damages DC's disturbed data availability. This deficiency is rooted in the absence of supervision and verifiability on DOs’ independent disturbances. Aiming at the above problem, we devise a privacy-preserved data trading via verifiable data disturbance. Specifically, the honest-but-curious disturbance server (DS) is introduced to generate encrypted verifiable disturbance noises, and secretly distribute noises to DOs referring to the method of private information retrieval. Using homomorphic encryption, DOs finish data disturbance without knowing noises’ specific sizes. Subsequently, DC selects DOs to verify with our proposed anti-forgery verification, where the anti-forgery on both disturbance noise and original data guarantees verification correctness. Theoretical analysis proves that DOs’ original data is preserved in data trading. Extensive experiments using the real-world dataset demonstrate that our scheme can detect more than 80% of malicious DOs and decrease their utilities to punish malicious disturbance compared with existing works.

Federated Learning (FL) ensures collaborative learning among multiple clients while maintaining data locally. However, the traditional synchronous FL solutions have lower accuracy and require more communication time in scenarios where most devices drop out during learning. Therefore, we propose an Asynchronous Federated Learning (AsyFL) scheme using time-weighted and stale model aggregation, which effectively solves the problem of poor model performance due to the heterogeneity of devices. Then, we integrate Symmetric Homomorphic Encryption (SHE) into AsyFL to propose Asynchronous Privacy-Preserving Federated Learning (Asy-PPFL), which protects the privacy of clients and achieves lightweight computing. Privacy analysis shows that Asy-PPFL is indistinguishable under Known Plaintext Attack (KPA) and convergence analysis proves the effectiveness of our schemes. A large number of experiments show that AsyFL and Asy-PPFL can achieve the highest accuracy of 58.40% and 58.26% on Cifar-10 dataset when most clients (i.e., 80%) are offline or delayed, respectively.

The cloud-assisted mobile electronic health (e-health) system facilitates e-health data sharing between healthcare providers and patients, but also raises the security and privacy concerns of e-health data. Although Ciphertext-Policy Attribute-Based Encryption (CP-ABE) has been a promising technique to achieve fine-grained access control over encrypted e-health data, it still incurs high encryption and decryption burdens on mobile users such as smartphones and sensors. In addition, malicious cloud servers may conduct incorrect operations due to various interest incentives (e.g., leaking sensitive information to illegal users, saving computation and storage costs). To solve the above issues, in this paper we first propose an Outsourced CP-ABE (OABE) with verifiable encryption scheme by splitting secret keys corresponding to an attribute set and using the short signature, which not only reduces the encryption and decryption complexities of mobile users but also guarantees that cloud servers correctly perform encryption operations. Then, we extend OABE to construct outsourced CP-ABE with verifiable decryption (OABE+) by utilizing the verifiable tag mechanism, which guarantees that cloud servers correctly conduct the ciphertext transformation. Formal security analysis proves that our schemes are selectively secure against unauthorized accesses and malicious operations. Extensive experiments using various real-world datasets demonstrate that our schemes are efficient and feasible in real applications.

Electronic health (e-health) systems may outsource data such as patient e-health records to mobile cloud servers for efficiency gains (e.g., minimizing local storage and computation costs). However, such a move may result in privacy implications in the presence of semi-honest cloud servers. Searchable Encryption (SE) can potentially facilitate privacy-preserving searches based on keywords for encrypted data stored in the mobile cloud, but most existing SE solutions do not support temporal access control (i.e., a mechanism that grants access permissions to users for specified time ranges). Hence, in this paper we design a time-controllable keyword search scheme by using an attribute-based comparable access control. This allows users to match indexes encrypted at specified time intervals. Then, we improve the basic framework to support efficient user revocation using secret sharing. We then formally prove the security of our proposed frameworks against chosen-keyword attack and key collusion attack, as well as achieving keyword secrecy. We also evaluate the performance of our proposed approach using a real-world dataset to demonstrate their practical utility.

The mobile Internet of Vehicles (IoVs) has great potential for intelligent transportation, and creates spatial data query demands to realize the value of data. Outsourcing spatial data to a cloud server eliminates the need for local computation and storage, but it leads to data security and privacy threats caused by untrusted third-parties. Existing privacy-preserving spatial range query colorredsolutions based on Homomorphic Encryption (HE) have been developed to increase security. However, in the single server model, the private key is held by the query user, which incurs high computation and communication burdens on query users due to multiple rounds of interactions. Moreover, exposing data access patterns to semi-honest servers is highly vulnerable to frequency and statistical attacks. To solve these issues, in this paper we propose a secure spatial location query within arbitrary geometric range while protecting access pattern. Specifically, we apply Paillier algorithm and polynomial fitting technique to achieve secure arbitrary geometric range query, design secure and efficient search protocol to hide data access patterns and alleviate query users from high computation and communication burdens under dual-server model. Formal security analysis shows that our scheme is secure under semi-honest model, and extensive experiments demonstrate that our work can reduce users’ communication costs by more than 90% compared to previous schemes under single server model, which is practice in real-world scenarios.

Unmanned aerial vehicle (UAV)-assisted mobile edge computing (UAV-MEC) has been proposed to offer computing resources for smart devices and user equipment. UAV cluster aided MEC rather than one UAV-aided MEC as edge pool is the newest edge computing architecture. Unfortunately, the data packet exchange during edge computing within the UAV cluster hasn't received enough attention. UAVs need to collaborate for the wide implementation of MEC, relying on the gossip-based broadcast protocol. However, gossip has the problem of long propagation delay, where the forwarding probability and neighbors are two factors that are difficult to balance. The existing works improve gossip from only one factor, which cannot select suitable forwarding probability and avoid redundant messages. Besides, these schemes do not consider the historical packet reception of new neighbors when UAVs fly around, which decreases forwarding efficiency. To solve these problems, we first propose a data structure called Bitgraph that can record the historical packet reception of UAVs. Then, we formulate gossip broadcasting as a partially observable Markov decision process. Based on Bitgraph, we design the reward function. Finally, we design a multi-agent reinforcement learning algorithm, Branching Deep Graph Network (BDGN), which simultaneously makes decisions on forwarding probability and neighbors. Extensive experiments illustrate that our proposal gets more than 29% advantage in terms of the propagation delay and 20% advantage in terms of the redundant messages compared to the existing works.

Vehicular technology has been violently developing recently, and triggers wide discussion. The Internet of Vehicles (IoV), as an important application of vehicular technology, is an emerging research intention. While the decentralization and high efficiency are in line with the needs of IoV, the dependable information transmission asks for a new kind of cryptography urgently. Despite the fact that a slew of related researches are designed, schemes may be vulnerable to privacy leakage, identity tracing, high computation overheads and so on. Therefore, it is urgent to propose a more secure and efficient scheme against various attacks. In this paper, an efficient authenticated key agreement scheme, titled as STCLA, is designed for fog-based IoV, adopting certificateless cryptography. Specifically, STCLA is based on efficient elliptic curve cryptographic technology and adopts pseudonym identity for user anonymity. Then, it is proved secure under random oracle model and is confirmed using Scyther tool. Further, the performance assessment is incorporated at last. Compared with several related schemes, STCLA can satisfy both security and efficiency at the same time.

With the rapid development of mobile communication technologies and network applications, communication models for mobile client and server interaction are becoming increasingly popular. The certificateless public key cryptography is suitable for designing security protocols which are used for mobile devices under the model of client and server. In the last decade, various ID-based protocols have been discussed, but some of them have several flaws. To address the security problems found in the key exchange protocol designed by Hassan et al., we introduce a new protocol named iHEEL protocol, a new key exchange and authentication protocol in client-server environment. Our new protocol is proved to be secure under the random oracle model and computational Diffie-Hellman assumption. Finally, iHEEL protocol is compared with several preceding protocols in terms of security properties and communication cost, which is measured by different data volumes.

With the rapid development of geographic location technology and the explosive growth of data, a large amount of spatial data is outsourced to the cloud server for reducing the local high storage and computing burdens, but at the same time causes security issues. Thus, extensive privacy-preserving spatial data query schemes have been proposed. Most of the existing schemes use Asymmetric Scalar-Product-Preserving Encryption (ASPE) to encrypt data, but ASPE has proven to be insecure against known plaintext attack. And the existing schemes require users to provide more information about query range and thus generate a large amount of ciphertexts, which causes high storage and computational burdens. To solve these issues, based on enhanced ASPE designed in our conference version, we first propose a basic Privacy-preserving Spatial Data Query (PSDQ) scheme by using a new unified index structure, which only requires users to provide less information about query range. Then, we propose an enhanced PSDQ scheme (PSDQ$^+$+) by using Geohash-based $R$R-tree structure (called $GR$GR-tree) and efficient pruning strategy, which greatly reduces the query time. Formal security analysis proves that our schemes achieve Indistinguishability under Chosen Plaintext Attack (IND-CPA), and extensive experiments demonstrate that our schemes are efficient in practice.

We present the first chromosome-level genome assembly of the grasshopper, Locusta migratoria, one of the largest insect genomes. We use coverage differences between females (XX) and males (X0) to identify the X Chromosome gene content, and find that the X Chromosome shows both complete dosage compensation in somatic tissues and an underrepresentation of testis-expressed genes. X-linked gene content from L. migratoriais highly conserved across seven insect orders, namely Orthoptera, Odonata, Phasmatodea, Hemiptera, Neuroptera, Coleoptera, and Diptera, and the 800 Mb grasshopper X Chromosome is homologous to the fly ancestral X Chromosome despite 400 million years of divergence, suggesting either repeated origin of sex chromosomes with highly similar gene content, or long-term conservation of the X Chromosome. We use this broad conservation of the X Chromosome to test for temporal dynamics to Fast-X evolution, and find evidence of a recent burst evolution for new X-linked genes in contrast to slow evolution of X-conserved genes.

When enjoying mobile crowdsensing (MCS), it is vital to evaluate the trustworthiness of mobile users (MUs) without disclosing their sensitive information. However, the existing schemes ignore this requirement in the multiple crowdsourcers (CSs) scenario. The lack of a credible sharing about MUs’ trustworthiness results in an inaccurate trust evaluation, disabling allocating tasks to reliable MUs. To address it, based on the analysis of the desired properties, we propose a scheme enabling dependable data collection with multiple crowdsourcers trust sharing ($D^{2}MTS$D2MTS). Specifically, we design the MU anonymous management. Two kinds of MU generated pseudonym systems without relationships are presented to mark each MU in trust evaluation and task execution, respectively. Through the devised pseudonym changes on these pseudonyms and the common token distribution algorithm, $D^{2}MTS$D2MTS realizes privacy-preserving trust sharing. Moreover, to guarantee credible sharing, based on the hash chain, $D^{2}MTS$D2MTS records MUs’ trustworthiness with the unforgeable signature on the blockchain established by multiple CSs which do not trust each other naturally. Extensive experiments show that compared with the other works, $D^{2}MTS$D2MTS's detection ratio of vicious MUs and the percentage of reliable MUs among the selected ones can increase by 208.61% and 28.27%. Both computational and communication delays are limited.

To solve the data silos issue in distributed machine learning with privacy leakage, privacy-preserving federated learning (PPFL) has been extensively explored in both academic and industrial fields. However, the existing PPFL solutions still suffer from high computation and communication overheads, which result in excessive consumption of communication bandwidth and slow down the training process of FL. To address these issues, we propose a secure and communication-efficient FL scheme using improved compressed sensing and CKKS homomorphic encryption. Specifically, we implement a lossy compression of the model by using discrete cosine transform, then use CKKS homomorphic encryption to encrypt the data transmitted between clients and center server due to its high efficiency and support for batch encryption. Formal security analysis proves that our scheme is secure against indistinguishability under chosen plaintext attack and extensive experiments demonstrate that our scheme achieves a high accuracy at 0.05% compression rate.

With the rapid development of Intelligent Transportation System (ITS), a large number of spatial data are generated in ITS. Although outsourcing spatial data to the cloud server can reduce the high local computation and storage overheads, it will also lead to security and privacy issues. Therefore, it is necessary to have a survey to specifically summarize these advanced privacy-preserving spatial data query schemes. However, the existing surveys considering both location information and keywords of spatial data only summarize the spatial keyword query scheme in plaintext environment, they do not consider the privacy of spatial data. Although there are some surveys on privacy-preserving spatial data query, they only focus on the location information of spatial data without considering descriptive keywords. Therefore, to understand the progress and research trends in the field, we give a comprehensive survey on secure spatial data query in ITS to summarize and analyze the most advanced solutions. Then, we make a comprehensive and detailed comparison of existing solutions in terms of query function, index structure, time complexity, security, etc. Finally, we show some open challenges and potential research directions for privacy-preserving spatial data query.

The development of unmanned aerial vehicle (UAV) technology has been advancing rapidly and is widely applied in various domains. Compared to a single UAV, the multi-UAV system, known as the UAV Ad Hoc Network (UANET), can collaborate to accomplish complex tasks more efficiently. Due to the UAVs communicating through open wireless channels, they are vulnerable to various attacks. Group key agreement protocols are often used to guarantee the security of UANET communication. However, the high dynamics of UAVs and the instability of the wireless links often result in certain UAVs temporarily losing connection with the group, rendering them incapable of participating in the group key agreement process. Regrettably, existing literature on group key agreement does not consider this issue, which makes them not applicable to UANET. To address this issue, we propose a threshold authenticated group key agreement protocol (TAGKA) that can resist the disconnection of group members. Then, considering that the temporarily disconnected UAVs need to obtain the latest group key after the communication links are recovered, we design a group key recovery protocol. The security of the scheme is proved under the random oracle model. Moreover, formal security verification using the Tamarin and rigorous informal security analysis is also performed. Finally, we use NS-3 to simulate the protocol, and the experimental results show that our protocol can be effectively applied to UANET.

Due to the distributed collaboration and privacy protection features, federated learning is a promising technology to perform the model training in virtual twins of Digital Twin for Mobile Networks (DTMN). In order to enhance the reliability of the model, it is always expected that the users involved in federated learning have trustworthy behaviors. Yet, available trust evaluation schemes for federated learning have the problems of considering simplex evaluation factor and using coarse-grained trust calculation method. In this paper, we propose a trust evaluation scheme for federated learning in DTMN, which takes direct trust evidence and recommended trust information into account. A user behavior model is designed based on multiple attributes to depict users’ behavior in a fine-grained manner. Furthermore, the trust calculation methods for local trust value and recommended trust value of a user are proposed using the data of user behavior model as trust evidence. Several experiments were conducted to verify the effectiveness of the proposed scheme. The results show that the proposed method is able to evaluate the trust levels of users with different behavior patterns accurately. Moreover, it performs better in resisting attacks from users that alternately execute good and bad behaviors compared with state-of-the-art scheme. The code for the method proposed in this paper is available at: https://web.xidian.edu.cn/jjguo/en/code.html.

ABSTRACTCurrently, convolutional neural network (CNN) and vision transformer (ViT) are gradually becoming the mainstream for hyperspectral image (HSI) classification. Although CNN and ViT can extract local and global features better respectively, they both have difficulties in expressing the context information of irregular objects. Conversely, the superpixel features can accurately represent the object context information. However, the current algorithms still generally recover superpixel features as pixel features, which ignores the significance of superpixel features as global information. To address these limitations, a bi-branch global+multiscale hybrid network (GMHN) is proposed. In the first branch, the multiscale global features are extracted and then fed into transformer encoder to enhance their relation. In the second branch, Inception-ResNet is used as the backbone to extract the basic local features. The global and local information is integrated by our association module. Finally, a linear layer is used to convert the fused features into sample labels. Experimental results on the recent WHU-Hi-LongKou dataset and one widely-used Salinas dataset show the superiority of GMHN compared to state-of-the-art methods.

As an important means of obtaining information of marine situation, the marine monitoring system relying on UAV has been paid more and more attention by all countries in the world, and the demand for tasks is growing continually. In UAV ad hoc networks, routing protocols with immutable routing policies that lack flexibility are generally incapable of maintaining effective performance due to the complicated and rapidly changing environmental situation and application requirements. In this paper, we propose an intelligent clustering routing approach (ICRA) for UANETs. The ICRA is composed of three components: the clustering module, the clustering strategy adjustment module and the routing module. In the clustering process, each node needs to calculate its utility. In order to maintain high topology stability and long network lifetime in different network states, the reinforcement learning based clustering strategy adjustment module needs continuous learning the benefits brought by adopting different strategies to calculate the nodes utility in a specific network state. With the learned knowledge, clustering strategy adjustment module could determine the optimal clustering strategy according to the current network state. In the routing phase, the proposed scheme can reduce the end-to-end delay and improve the packet delivery rate by introducing inter-cluster forwarding nodes to forward messages among different clusters. Extensive experiments have been conducted to verify ICRA’s robustness and superiority over existing schemes. The results demonstrate that ICRA could achieve better performance than its state-of-the-art counterparts with regard to the clustering efficiency, topology stability, energy efficiency and quality of service.

Location-Based Services (LBSs) are one of the most frequently used mobile applications in the modern society. Geo-Indistinguishability (Geo-Ind) is a promising privacy protection model for LBSs since it can provide formal security guarantees for location privacy. However, Geo-Ind undermines the statistical location distribution of users on the LBS server because of perturbed locations, thereby disabling the server to provide distribution-based services (e.g., traffic congestion maps). To overcome this issue, we give a privacy definition, called DistPreserv, to enable the LBS server to acquire valid location distributions while providing users with strict location protection. Then we propose a privacy-preserving LBS scheme to benefit both users and the server, in which a location perturbation mechanism is designed to achieve the given definition under the guide of the incentive compatibility, and a retrieval area determination method is presented to ensure query accuracy of users by using the dynamic programming on the two-dimensional map plane. Finally, we theoretically prove that the designed mechanism can achieve the definition of DistPreserv and the property of incentive compatibility. Experimental explorations using a real-world dataset indicate that our proposal prominently improves the availability of users’ location distributions by over 90%, while providing high precision and recall of queries.

In this article, a bistatic multiple-input–multiple-output (MIMO) radar that exploits compressive measurements is proposed, where the compressive sampling is involved in each branch of the receive antenna. To fully utilize the bistatic MIMO radar structure, we develop a high-order singular value decomposition (HOSVD)-based algorithm for the direction-of-departure (DOD) and direction-of-arrival (DOA) joint estimation. It is worth noting that HOSVD divides the DOD and DOA into two independent dimensions, thus generating cross items. Consequently, it is difficult to obtain the exact DOD and DOA results. To address this issue, a pairing algorithm is introduced in this article to connect the DOD and DOA parameters, where the relationship between the covariance tensor and covariance matrix is utilized. Furthermore, the Cramér–Rao bound for the proposed compressive bistatic MIMO radar is derived, and computational complexity is also analyzed from the perspective of received signal dimension. Numerical simulations verify that the proposed scheme outperforms the conventional MIMO radar, and the proposed tensor-based algorithm has a better estimation performance than the matrix-based MUSIC.

With increasingly popular GPS-equipped mobile devices (e.g., smartphones, tablets, laptops), massive spatio-textual data has been outsourced to cloud servers for storage and analysis such as spatial keyword search. However, existing privacy-preserving spatial keyword query schemes only support coarse-grained non-temporal access control in single user sharing scenario, which does not scale well in time-related scenes such as message valid period. To solve the above issues, we propose Privacy-preserving Boolean Range Query with Temporal access control in mobile computing (PBRQ-T). Specifically, we first achieve PBRQ with linear search complexity using the adapted Gray code, Bloom filter, and Katz-Sahai-Waters encryption. Then, we provide fine-grained and temporal access control in PBRQ based on the forward/backward derivation function and attribute-based encryption, where PBRQ is executed only when the spatio-textual data is accessible. Finally, an enhanced PBRQ-T (i.e., PBRQ-T$^+$+) with faster-than-linear search complexity is proposed by constructing a Quadtree index structure. Our formal security analysis shows that data privacy and index privacy can be guaranteed during the query process. Our extensive experiments using a real-world dataset demonstrate the efficiency and feasibility of our schemes.

Compared to the 5G network, the future 6G network is a multi-scenario application network containing more unattended terminals. The network slicing technology inherited from 5G enables more service providers to offer the same services with differentiated quality of services (QoS) and costs for their subscribers in 6G networks, in which more complex tasks occur for unattended terminals, such as unmanned factories, unmanned medical care, and unmanned rescues. However, network slice access of unattended terminals faces challenges. Specifically, physical attacks are a significant threat to unattended terminals in an open environment. The constrained resources and seamless services of unattended terminals also have to be considered. Unfortunately, there is currently a lack of an authentication scheme to address the above situation. As new lightweight hardware security primitive, Physical Unclonable Function (PUF) is an ideal solution to promote the security of unattended terminals. This article provides a specific authentication framework for unattended terminals in 6G-enabled network slices. Specifically, based on PUF, we first propose a physically secure slice access authentication protocol. Then we extend it to support fast inter-slice handover authentication, ensuring service continuity. Moreover, we prove the security of the proposed framework and conclude that it satisfies the higher security requirements of the 6G network. Finally, the performance evaluation with three other state-of-the-art authentication schemes demonstrates the superior efficiency of the proposed framework.

This study focuses on the development of a highly sensitive surface-enhanced Raman scattering (SERS) sensor for detecting homocysteine (Hcy) molecules. The Hcy sensor was created by depositing silver nanoparticles (AgNPs) onto the surface of graphene oxide (GO) film to form a dense AgNPs@GO composite film. The AgNPs on the composite film interacted with sulfur atoms (S) of Hcy molecules to form Ag–S bonds, which boosted the chemisorption of Hcy molecules and enabled them to be specifically recognized. The SERS sensor exhibited a maximum enhancement factor of up to 1.1 × 104, with a reliable linear response range from 1 to 60 ng mL−1. The limit of detection (LOD) for Hcy molecules was as low as 1.1 × 10−9M. Moreover, Hcy molecules were successfully distinguished in a mixed solution of γ-aminobutyric acid and Hcy molecules. In this study, a simple preparation process of SERS substrate and a novel detection method for Hcy molecules provided a new pathway for the rapid and effective detection of Hcy molecules in the food and biomedicine fields.

Federated learning is good for building better cooperative intelligent transportation system (C-ITS). Intellectual property protection in C-ITS brings many benefits to all vehicles. Although the protection of model intellectual property by watermark has received much research attention, the existing works only deploy watermark in centralized models. Due to the difference of watermark distribution among vehicles, the global model accuracy of watermark in federated learning is significantly reduced or the local watermark is invalid. To solve these problems, we propose a multi-party entangled watermark algorithm in federated learning. Specifically, in the local training, we propose a watermark enhancement algorithm, which solves the problem of local watermark failure. Then, in the global aggregation, we propose an entanglement aggregation algorithm, which solves the problem of a great loss of global model accuracy. We conduct extensive experiments on public datasets to show the superiority of our proposal. The results show that our scheme can obtain more than 16% and 31% advantages in model accuracy and watermark success rate, respectively, compared with existing watermark schemes in federated learning.

High-resolution nighttime light (HR-NTL) imagery is a reliable indicator of human activity in urban areas at night. However, HR-NTL images on Earth are blurred because of issues such as blooming, noise, and overexposure in the imaging system and transmission path. Toward this end, a nighttime light image deblooming network (NTL-Db-Net) is proposed, which aims to make the Earth clearer at night. The NTL-Db-Net employs a generative adversarial network (GAN) as its deblooming framework. As the lack of ideal clear NTL data for GAN training, our method integrates a blur transfer algorithm to simulate degradation for generating training data. This method was tested using HR-NTL data on Wuhan, Macau, Changchun, Xiamen, Atlanta, and Jerusalem from the HR Jilin-1 satellite constellation. Compared with the results of state-of-the-art self-adjusting model (SEAM), Uformer, and DRBNet, those from NTL-Db-Net demonstrated superior visual features and achieved the highest scores across all selected nonreference evaluation metrics. NTL-Db-Net also exhibited a lower number of network parameters and a smaller time cost. In addition, this study conducted a spatial clustering analysis of nighttime light with road networks and permeable surfaces. The results indicated that nightlights and roads exhibited a distinct spatially aggregated distribution, while permeable surfaces displayed a clear spatially dispersed distribution. The code and data are available at https://github.com/lixinghua5540/NTL-Db-Net.

Spatio-temporal fusion can generate time-series images with high spatial resolution, and it is highly desirable in various applications, especially in monitoring fine dynamic changes of surface features on remote sensing images. Currently, most spatio-temporal fusion methods predict the target fine image by employing the auxiliary fine images on neighboring phases; however, they are generally limited in abrupt land cover changes between the target and the neighboring auxiliary images. In this article, we propose a novel detail injection-based spatio-temporal fusion (DISTF) model to alleviate this problem, by exploring the inherent relationship between the spatio-temporal fusion and spatio-spectral fusion. The proposed DISTF consists of three modules: a three-branch detail injection (TDI) module, a fine detail prediction (FDP) module, and a reconstruction module. The interpretable TDI module is inspired by spatio-spectral fusion, aiming to inject the non-changed detail information extracted from the neighboring fine images into the target coarse image, which can preserve the abrupt change information captured in the target coarse image. The FDP module is designed to further integrate the correlated information from the outputs of TDI and refine the spatial-spectral information to boost the fusion accuracy. Finally, the reconstruction module and the hybrid loss function are designed to more effective reconstruct the high-quality target fine image. The qualitative and quantitative experimental results on two datasets with different types of changes demonstrated that the proposed DISTF method achieves richer spatial detail and more accurate prediction than the eight existing methods.

With the fast development of remote sensing platforms and sensors technology, change detection with heterogeneous remote sensing images (Hete-CD) has become an attractive topic in recent years and plays a vital role in land cover change detection for responding to natural disaster emergencies when homogeneous images are unavailable. Although Hete-CD has been developed for about three decades, and various related methods have been developed and applied successfully in practice, a systematic and comprehensive review of the current achievements regarding Hete-CD remains lacking. Therefore, in this article, we first present an overview of Hete-CD in terms of the related literature. Second, the major techniques of Hete-CD are reviewed in terms of publicly available datasets, the taxonomy of major techniques, results, performance, and quantitative evaluation. Then, some classical methods are selected for comparison and discussion. Finally, based on the discussion and literature review, challenges, opportunities, and future directions for Hete-CD are concluded. The review aims to provide a “one-stop-shop” understanding of the problems with the categories of existing approaches, open opportunities and challenges, and potential future directions for Hete-CD.

Wireless body area networks (WBANs) guarantee timely data processing and secure information preservation within the range of the wireless access network, which is in urgent need of a new type of security technology. However, with the speedy development of hardware, the existing security schemes can no longer meet the new requirements of anonymity and lightweight. New solutions that do not require complex calculations, such as certificateless cryptography, attract great attention from researchers. To resolve these difficulties, Wang et al. designed a new authentication architecture for the WBANs environment, which was claimed to be secure and efficient. However, in this paper, we will show that this scheme is prone to ephemeral key leakage attacks. Further, based on this authentication scheme, an anonymous certificateless scheme is proposed for lightweight devices. Meanwhile, user anonymity is fully protected. The proposed scheme is proved to be secure under a specific security model. In addition, we assess the security attributes our scheme meets through BAN logic and Scyther tool. The comparisons of time consumption and communication cost are given at the end of the paper, to demonstrate that our scheme performs prior to several previous schemes.

Water‐saving, drought‐resistant rice (WDR) (Oryza sativaL.) is a type of new rice cultivars with high drought resistance and low water consumption in the production phase. Ratoon rice cropping is regarded as a resource‐efficient rice production system with low cost and high profit. The development of ratoon rice system with WDR is conducive to the sustainable production of rice and the expansion of rice plantation; however, no such attempt has been made. In this study, a field experiment was conducted for 2 yr to evaluate the feasibility of developing new ratoon rice cropping systems using WDR compared with using common lowland rice (CLR). The productivity, regenerative capacity, and water use efficiency (WUE) were investigated in the water‐saving cultivation system using two WDR cultivars—Jieyou 652 and Jieyou 804—and in the conventional flooding cultivation system using the CLR cultivar Fengliangyouxiang 1. The irrigation WUE for the WDR systems was 89.8–214.8% and 243.4–765.6% higher than for the CLR system in the main and ratoon cropping seasons, respectively. The grain yield was not significantly different in the main cropping season between the WDR and CLR systems; nevertheless, it was lower in the WDR systems because of shorter growth duration and smaller panicles in the ratoon season. Higher accumulations of dry matter (DM), nonstructural carbohydrates (NSCs), and nitrogen in the stubble of main‐season crops is helpful to the promotion of regenerative capacity of WDR in the ratoon season. WDR systems have higher WUE and stronger drought resistance than CLR system.The yield of WDR systems is comparable to that of CLR system in the main cropping season.The yield of WDR systems is lower than that of CLR system in the ratoon cropping season.Short growth duration causes the low productivity in ratoon cropping season of WDR systems.

1T′-phase MoS2possesses excellent electrocatalytic performance, but due to the instability of the thermodynamic metastable phase, its actual electrocatalytic effect is seriously limited. Here, we report a wet-chemical synthesis strategy for constructing rGO/1T′-MoS2/CeO2heterostructures to improve the phase stability of metastable 1T′ phase MoS2monolayers. Importantly, the rGO/1T′-MoS2/CeO2heterostructure exhibits excellent electrocatalytic hydrogen evolution reaction (HER) performance, which is much better than the 1T′-MoS2monolayers. The synergistic effects between CeO2nanoparticles (NPs) and 1T′-MoS2monolayers were systematically investigated. 1T′-MoS2monolayers combined with the cocatalyst of CeO2NPs can produce lattice strain and distortion on 1T′-MoS2monolayers, which can tune the energy band structure, charge transfer, and energy barriers of hydrogen atom adsorption (ΔEH), leading to promotion of the phase activity and stability of 1T′-MoS2monolayers for hydrogen production. Our work offers a feasible method for the preparation of efficient HER electrocatalysts based on the engineering phase stability of metastable materials.