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As a high-performance polymer with exceptional mechanical, thermal, and insulating properties, polyimide (PI) has been widely used as flexible circuit substrates for microelectronics, portable electronics, and wearable devices. Due to the growing demand for further thinning and lightweighting of electronic products, PI films need to have further enhanced mechanical properties. Traditional nanofiller-reinforced PI films often exhibit reduced ductility and limited improvements in strength. Therefore, it remains a challenge to simultaneously improve the strength and toughness of PI films while preserving their ductility. In this study, we report an exceptionally strong and ductile PI doubly reinforced by one-dimensional rigid-rod para-aramid, poly(p-aminophenylene aminoterephthalamide ((NH2)2-PPTA), and two-dimensional graphene oxide (GO) nanosheets. The amino side groups of (NH2)2-PPTA react with the anhydride end groups of PI, forming covalent bonds. At a (NH2)2-PPTA content of only 0.4 wt %, the (NH2)2-PPTA/PI film displays significantly enhanced mechanical properties. When 0.4 wt % of GO is added together with (NH2)2-PPTA, the tensile strength, tensile toughness, and strain at break reach 284.8 ± 5.3 MPa, 277.9 ± 7.6 MJ/m3, and 132.6 ± 3.8%, which are ∼178, ∼312, and ∼51% higher, respectively, than those of pure PI. Moreover, the doubly reinforced PI film also exhibits a 206% increase in tear strength and significantly enhanced folding resistance. The dual reinforcement of PI with (NH2)2-PPTA and GO improves the mechanical properties more efficiently than any single reinforcing agents previously reported and overcomes the disadvantage of most inorganic nanofillers that reduce ductility.

Although 5G and beyond communication technology empower a large number of edge heterogeneous devices and applications, the stringent security remains a major concern when dealing with the millions of edge computing tasks in the highly dynamic heterogeneous networks (HDHNs). Blockchains contribute significantly to addressing security challenges by guaranteeing the reliability of data and information. Since the node’s mobility, there are risks of exiting the network and leaving the remaining tasks noncomputed. Therefore, we model the cost function of offloaded computing tasks as a dynamic stochastic game. To reduce the computational complexity, the Time-Variant Mean-Field term (TVMF) is adopted to solve the cost-optimized problem. What’s more, we design an Adaptivity-Aware Practical byzantine fault tolerance consensus Protocol (AAPP) to dynamically formulate domains, execute leader node selection with regard to task completion and quickly verify computational results. In addition, a Dynamic Multi-domain Fractional Repetition uncoded repair storage (DMFR) scheme with variant redundancy is proposed to reduce the storage pressure and repair overhead. The simulation is implemented to demonstrate our scheme outperforms the benchmarks in terms of cost and time overhead.

In this letter, we introduce a wide impedance bandwidth microstrip comb-line array antenna in the millimeter-wave band. To achieve wide impedance bandwidth performance, a series-fed array antenna consisting of three types of radiating structures is designed on a substrate utilizing printed circuit board technology. Different from the traditional comb-line array, parasitic components are introduced in the vicinity of the radiating elements to obtain low sidelobe levels (SLLs). The antenna is constructed and measured to confirm its viability. The measured impedance bandwidth (voltage standing-wave ratio (VSWR) $\leq$ 2) is 30.02% (20.1 GHz to 27.2 GHz) with satisfactory impedance matching. Meanwhile, the SLL of the comb antenna is about −22 dB at 26.6 GHz. Therefore, the proposed antenna boasts advantages including a simple manufacturing process with low fabrication cost, ease of integration, wide impedance bandwidth, and low sidelobes.

Osteocytes are the main stress-sensing cells in bone. The substances secreted by osteocytes under mechanical loading play a crucial role in maintaining body homeostasis. Osteocytes have recently been found to release exosomes into the circulation, but whether they are affected by mechanical loading or participate in the regulation of systemic homeostasis remains unclear.

It is known that in seamless inertial navigation system (INS)-based integrated drone localization, once data outage occurs in auxiliary measurement technologies, such as global navigation satellite system and ultrawideband (UWB), traditional integrated navigation data fusion filters may give large errors. To overcome this issue, we develop a dual free-size least squares support vector machines (fLS-SVM) assisted maximum correntropy Kalman filter (KF) (mcKF) for fusing INS and UWB data. In the fLS-SVM structure, the interactive multiple models method is used to improve the adaptability of the training set of LS-SVM. Then, the fLS-SVM method is adopted to estimate position error from INS when the UWB data is outage and then used again to provide the estimation error of the previous LS-SVM algorithm. The mcKF is derived on the basis of the maximum correntropy criterion. One mcKF is to realize data fusion from INS and UWB, and another one is for estimating the INS's position error for the fLS-SVM's output. Experimental testing is done in INS/UWB-based indoor mobile drone localization environments. It is shown that the dual fLS-SVM assisted mcKF algorithm outperforms the KF, general KF with colored measurement noise, mcKF, KF+LS-SVM, and dual KF+LS-SVM it reduces the position errors by an average of about 93.64%, 93.63%, 93.63%, 39.99%, and 94.10%, respectively, in two tests.

On account of the introduction of the rotor cage, the inherent torque ripple is a challenging problem in the design of line-start permanent magnet motors (LSPMSMs). In order to reveal the influence of the rotor cage on the torque ripple, we studied the torque ripple of the LSPMSM using a torque separation method based on Maxwell's stress tensor. A 280 kW, four-pole LSPMSM is used as the research object. The contribution of rotor slotting and bar currents to the torque ripple is evaluated. The relationship between the torque ripple and the harmonic magnetic field induced by rotor slotting and bar currents is obtained. Furthermore, the effect of rotor slotting and bar currents on the torque ripple of LSPMSMs is determined.

To enable the permanent magnet motor to have self- starting characteristics, the bar is added to the rotor, causing the gap position between the rotor slot bottom and the magnetic isolation bridge (SB & MIB) to become the most vulnerable position for line-start permanent magnet synchronous motor (LSPMSM). To ensure the performance of the motor, this article takes both electromagnetic and mechanical performance as constraints to find the optimal distance for SB & MIB. An 18.5 kW LSPMSM is used as the research object, analyze the influence trend of SB & MIB on the electromagnetic performance of motor torque ripple, efficiency, radial electromagnetic force (REF), spatial harmonic distribution, as well as mechanical performance such as centrifugal force and thermal stress. Taking into account the above objectives, obtain the optimal values for SB & MIB. And verify the relationship between motor torque and load conditions through experiments.

Developing antibacterial and biocompatible coatings remains a challenge. A promising new thought is addressed to control the ion release behaviors through the design of the phases, structures, and morphologies of the products. In this study, TiO2-CuxO/Ag coatings were prepared via co-sputtering combined with annealing, the changes brought by different annealing temperatures were characterized, the physical, electrochemical, and antibacterial properties were concerned, and the relationship between biological performance and ions-releasing behavior was investigated. Results showed that the TiO2-CuxO/Ag coatings performed the morphologies of larger Ag particles seeded like islands on the dense CuO, such features improved the hydrophilicity and corrosion resistance of the Ti substrate. Rising the annealing temperature further enhanced the surface roughness, hydrophilicity, Young's modulus, hardness, and dynamic coefficient of friction of the coatings. Within the initial 3 h of soaking, both metal ions contributed to playing the antibacterial roles and produced a synergistic enhancement effect to satisfy the reduction of Staphylococcus aureusto 99.9%. In the next 3–120 h soaking, Ag releasing was suppressed and Cu releasing maintained its slow and stable trend, which made sure that the MC3T3-E1 cells could proliferate and adhere on the negatively charged coating surface, and copper oxide nanoparticles were phagocytized by cells. Among all TiO2-CuxO/Ag coatings, the M500sample displayed superior performances on the surface roughness, contact angle, mechanical Young's modulus, and surface hardness, also realized the aim of killing pathogenic bacteria first and then activating cell survival, which enabled it to be potentially applied in clinical treatment.

The multitude of airborne point clouds limits the point cloud processing efficiency. Superpoints are grouped based on similar points, which can effectively alleviate the demand for computing resources and improve processing efficiency. However, existing superpoint segmentation methods focus only on local geometric structures, resulting in inconsistent spectral features of points within a superpoint. Such feature inconsistencies degrade the performance of subsequent tasks. Thus, this study proposes a novel Superpoint Segmentation method that jointly utilizes spatial Geometric and Spectral Information for multispectral point cloud superpoint segmentation (GSI-SS). Specifically, a similarity metric that combines spatial geometry and spectral information is proposed to facilitate the consistency of geometric structures and object attributes within segmented superpoints. Following the formation of the primary superpoints, an intersuperpoint pointexchange mechanism that maximizes feature consistency within the final superpoints is proposed. Experiments are conducted on two real multispectral point cloud datasets, and the proposed method achieved higher recall, precision, Fscore, and lower global consistency and feature classification errors. The experimental results demonstrate the superiority of the proposed GSI-SS over several state-of-the-art methods.

Background Several studies have demonstrated the anti-inflammatory properties of gastrodin. However, its specific role and mechanism in the context of osteoarthritis (OA) remain unclear.Objectives The objective of this study was to explore the function and mechanism of gastrodin in both in vivo and in vitro OA models.Materials and Methods In this study, the targets of gastrodin against osteoarthritis were analyzed using bioinformatics methods. The effect of gastrodin on neutrophil infiltration and inflammatory cytokines in OA synovial tissue was evaluated using H&E staining. The levels of inflammatory cytokines were determined using ELISA and Q-PCR. The extracellular matrix degradation-related proteins and PI3K/AKT/FoxO1 signaling pathway were tested using western blotting.Results Bioinformatics analysis predicted that the target of gastrodin against OA might act on the FoxO pathway. The use of gastrodin resulted in a significant reduction in the infiltration of neutrophils in the synovial tissue of rats, as observed through H&E staining. Additionally, gastrodin was found to attenuate IL-1β-induced inhibition of chondrocyte viability, pro-inflammatory cytokine production, and extracellular matrix degradation. This effect can be attributed to the suppression of PI3K/AKT/FoxO1 by gastrodin.Conclusion These findings indicate that gastrodin is a treatment for OA.

Aerial tracking has recently shown significant potential in vision-based measurement. Despite significant improvements, accurate occlusion tracking remains a difficult challenge. Existing training enhancement or global search struggles to handle occlusion in aerial views due to similar distractors and background interferences. To handle occlusion in aerial tracking, we propose a query-guided redetection tracker (QRDT) based on a Siamese neural network, improving the redetection discrimination from three stages. First, the query update (QU) branch is introduced to keep the target appearance dynamically updated via interframe information transfer. Next, we propose the cross-fusion layer (CFL), which models the semantic correlation between the search feature and the updated query feature, to draw attention to the occluded target. Finally, to address tracking failure and distraction from similar targets, the trajectory during full occlusion (FO) is reliably predicted by the Kalman filter. Our tracker achieves leading tracking performance on several benchmarks, with an average speed of 48.9 frames/s. The code and models are available at https://github.com/xyl-507/QRDT.

Contemporary research has highlighted that the design possibilities for Orthogonal Artificial Noise (AN) are circumscribed by the inherent limitations in channel degrees of freedom, yielding a scant assortment of design alternatives. In this letter, Non-Orthogonal Artificial Noise (NORAN) emerges as a viable substitute. Its applications have been recognized in relay communication systems such as those involving Unmanned Aerial Vehicles (UAVs) and Intelligent Reflecting Surfaces (IRS). Despite this, the mathematical exploration of the Secrecy Capacity (SC) function within wireless communication systems incorporating NORAN has been scant. This letter pioneers the examination of the concave-convex nature of the SC function in such systems. The findings reveal that, subject to certain transmit power conditions, the SC function for NORAN is inherently convex, implying a decrement in SC upon NORAN’s deployment, which negates its intended effect. In response to this conundrum, this discourse introduces Pilot-based Codebook Non-Orthogonal Artificial Noise (PCAN), wherein the codebook capitalizes on pilot signals exchanged between the receiver and sender as an encryption key. The receiver, with the aid of the pilot signals, retrieves the forthcoming PCAN from the codebook, thereby neutralizing the PCAN’s influence during the reception of information. This innovation equates the SC functions of PCAN and AN within the same channel framework. Additionally, this letter delves into the optimization conundrum of PCAN, considering scenarios of both perfect and imperfect Channel State Information (CSI). Through theoretical analysis, it is established that with an ideal CSI and an signal-to-noise ratio (SNR) surpassing zero, the quintessential power allocation for PCAN equates to half of the total transmission power. Contrariwise, under imperfect CSI parameters, PCAN fails to deliver efficacy.

Multiple extended objects (EOs) tracking has attracted a lot of attention due to the fast development of high-resolution sensors. A remarkable feature of EOs, compared to the traditional point target, is that an EO normally produces more than one measurement, resulting in challenges in finding the associations among objects and measurements. In this article, we are interested in tracking an unknown number of EOs with multiple sensors by resorting to the message-passing (MP) method. The existence probability and belief of each EO are explicitly estimated, where the belief is modeled by a mixture of gamma Gaussian inverse Wishart (GGIW) distribution, to jointly estimate the measurement rate (MR), centroid state, and extension. The marginal posterior of EOs is approximately by belief, which is obtained by running the loopy sum-product algorithm (LSPA) on a suitably devised factor graph. As a result, the computational load of the proposed algorithm increases linearly with respect to the number of targets, thus admitting the scalability. Simulation experiments are carried out to verify the performance of the proposed method.

Remote sensing cross-scene classification has always been an important research field, especially in the field of 3-D classification, which is of great significance. Considering the diversity of collection conditions, seasons, and regional styles, deep learning networks well-trained on one source domain dataset tend to suffer from severe performance degradation when applied to other target domain datasets. To tackle the issue, in this article, we propose a new cross-scene classification method, which combines pre-alignment and Shannon entropy constraint to accomplish unsupervised domain adaptive classification (PS-UDA). On the one hand, the pre-alignment employs $L_{2}$ -paradigm constraint and Laplace matrix to pre-align the features. With the $L_{2}$ -paradigm constraint, the originally distant features of the source and target domain are constrained to the same sphere surface, and it is easier to make the distribution alignment on the sphere surface. Further, the Laplace matrix is used to map the source and target domain. In this way, similar features of the source and target domain are further aligned, and dissimilar features become discrete from each other. On the other hand, this article employs the Shannon entropy constraint to motivate the network to obtain more high-confidence target domain pseudo-labels. In addition, to fully utilize the unlabeled target domain information, the target domain features are augmented using the adjacency matrix. Experimental results of two cross-scene multispectral point cloud classifications demonstrate that the proposed PS-UDA can effectively mitigate the spectral shift issue in cross-scene multispectral point clouds, achieving state-of-the-art performance.

The development of Internet of Things (IoT) technology leads to the continuous growth of IoT devices. Due to the characteristics of resource constraints and interdependencies, IoT devices are vulnerable to many security issues. Remote attestation can verify the integrity of remote devices. However, there are some problems that exist, such as only verifying the integrity of static and runtime data, attestation evidence growing proportionally to the number of services, resulting in significant increase in runtime overhead. Therefore, this article proposes a blockchain-assisted integrity attestation scheme for distributed dependency IoT services. The scheme builds a distributed service integrity model capable of measuring the integrity of both static data and runtime data of distributed IoT services. In addition, we propose a secure and trusted blockchain query mechanism, which can effectively resist tampering or storage errors in the database data of a certain node in the blockchain. Furthermore, a two-way authentication distributed service integrity attestation protocol is designed to protect the communication security between participants without excessive performance overhead. We demonstrate the security of the attestation protocol with automated validation of Internet security protocols and applications. The feasibility and performance of the proposed scheme are verified by simulating the Wismote sensor using the Cooja simulator.

The emerging metaverses can be regarded as the current wave of the digital civilization revolution due to it build a new connection between the physical and virtual space, and such connections make it possible for virtual space to interact with real word. However, some concerns, such as privacy data leakage incidents, still hinder the widespread deployment of the metaverse. The blockchained federated learning (FL) architecture can train a federated global model towards multiple stakeholders without the need for centralized management servers and the risk of privacy data leaking as a new paradigm to address the metaverse dilemma. Although many studies have been proposed for the great development potential of blockchain and federated learning for metaverse, few studies, if not none, have explored security threat against the blockchained FL architecture. This work report a prominent phenomenon that the combination of FL and blockchain may pose a more serious security threat, and first to propose Collusion-based Poisoning Attacks (CPA) in the blockchained FL architecture. To be specific, the malicious participants manipulate the model training process so that poisoned global models are aggregated with the most significant probability during the FL training phase. Then submits to the blockchain system and enables the poisoned models to be affected for all participants due to the strong consistency of blockchain. Experimental analysis demonstrates that the proposed CPA can achieve superior performance on multiple aspects and has better performances than the state-of-the-art attack method on stealth and robustness. This research can shed light on the hidden vulnerabilities of the blockchained FL architecture and lay the foundation for build a more open and secure metaverse.