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Edge-assisted visual SLAM plays a crucial role in enabling innovative mobile applications, such as autonomous swarm inspection, search-and-rescue, and smart logistics. Constrained by the computational capacities of lightweight mobile devices, current approaches delegate lightweight, time-sensitive tracking tasks to the mobile end while offloading resource-intensive, latency-tolerant map optimization tasks to the edge. However, our pilot study reveals several limitations of the tracking-optimization decoupled paradigm, stemming from the disruption of inter-dependencies between the two tasks. In this paper, we design and implement edgeSLAM2, an innovative system that reshapes the edge-assisted visual SLAM paradigm by tightly integrating tracking and partial-yet-crucial optimization on mobile. edgeSLAM2 harnesses the heterogeneous computing units offered by the commercial systems-on-chip (SoCs) to enhance the computational capacity of mobile devices, which in turn, allows edgeSLAM2 to design a suit of novel algorithms for map sync, optimization, and tracking that accommodate such architectural upgrade. By capitalizing on the full potential of on-chip intelligence, edgeSLAM2 supports both solitary and collaborative SLAM with accuracy and immediacy, underpinned by a cohesive software-hardware co-design. We deploy edgeSLAM2 on drones for industrial inspection. Comprehensive experiments in one of the world’s largest oil fields over three months demonstrate its superior performance.

This paper considers reconfigurable intelligent surface (RIS)-enabled federated learning (FL) system, where the FL users communicate with the access point (AP) via RIS. To reveal the impact of RIS and learning rate on FL aggregation, the theoretical result of minimum global communication rounds and local iteration rounds are derived. Based on the obtained convergence results of FL, we formulate an optimization problem to minimize the energy consumption of the proposed RIS-assisted FL system by jointly optimizing the passive beamforming of RIS, the CPU computing frequency, the bandwidth, and the transmit power of users. To solve the non-convex problem, we propose a block coordinate descent (BCD) optimization algorithm based on successive convex approximation (SCA) to decompose the original problem into four sub-problems. Specifically, the closed-form solutions are derived for the CPU frequency, RIS reflection matrix, and communication bandwidth. For the transmit power sub-problem, we propose a linear approximation algorithm based on the first-order Taylor expansion to ensure solution accuracy. Finally, simulation results show that: 1) the energy consumption of the proposed RIS-assisted FL system can be greatly reduced compared to that without optimizing the passive beamforming of RIS and the transmit power; 2) The learning performance of the proposed RIS-enabled FL system is closed to the FL without wireless communication interference; and 3) The proposed algorithm can not only significantly reduce energy consumption, but also fast convergence in terms of the FL model training and testing.

The magnetic composite gel bead (Fe3O4–C@SA GB) adsorbent was prepared by sodium alginate (SA) crosslinking with pitaya peel-derived porous carbons (PPDPCs) and magnetic iron oxide nanoparticles (Fe3O4NPs). The adsorption effects of Fe3O4–C@SA GBs on heavy metal ions (HMIs) and 17 β-estradiol (E2) in water are evaluated by classical kinetic models and isotherm models. The pseudo-second-order kinetic model shows that Fe3O4–C@SA GBs have maximum adsorption capacities of 9.62, 7.50, and 13.61 mg/g for Cu (II), Cd (II), and Pb (II), respectively. Meanwhile, the highest adsorption performance of the synthesized gel beads to E2 is of ca. 276.3 mg/g. In addition, the Fe3O4–C@SA GBs can still maintain a high level of adsorption efficiency after five adsorption cycles, displaying economic efficiency and reusability. Hence, this work provides useful insights into the efficient adsorption elimination of pollutants in sewage and the corresponding adsorption mechanisms.

Reconfigurable intelligent surface (RIS) represent a novel form of electromagnetic metamaterial that have been extensively studied for user equipment (UE) positioning by exploiting the multipath propagation of signals. A novel RIS-assisted localization prediction (RLP) method based on Bayesian optimization and long short-term memory (BO-LSTM) has been proposed in this paper. This method capitalizes on the predictive advantages of LSTM for data sequence and RIS's flexible and controllable multidimensional feature parameters, establishing a mobile UE localization model in an RIS-assisted wireless communications system based on the interplay between time slot transmission power and user location information. In order to provide a more stable communication environment for data collection during the localization process, a power allocation optimization (PAO) method is proposed for maximizing time slot channel capacity in the RLP system based on the number of RIS reflection elements. The study conducts a thorough comparison of simulation results of BO-LSTM, convolutional neural networks (CNN)-LSTM and improved bidirectional LSTM (BiLSTM) combined with Adaptive boost, employing adaptive moment estimation (Adam) and stochastic gradient descent with momentum (SGDM) optimizers. Experimental results demonstrate that the BO-LSTM-based RLP method exhibits improved prediction accuracy. These findings suggest the effectiveness of the proposed method and highlight its potential for further enhancement.

•A novel biaxial thermo‑mechanical grain‑based model (BTM-GBM) is developed.•Cooling causes more damage to the rock than heating during thermal shock.•The impact of initial stress on thermally induced cracks is relatively minor.•The weakening of peak stress by thermal shock is pronounced at high initial stress.•The variations in initial stress and temperature alter the failure mode of granite.

2D layered metal halide perovskites (MHPs) are a potential material for fabricating self-powered photodetectors (PDs). Nevertheless, 2D MHPs produced via solution techniques frequently exhibit multiple quantum wells, leading to notable degradation in the device performance. Besides, the wide band gap in 2D perovskites limits their potential for broad-band photodetection. Integrating narrow-band gap materials with perovskite matrices is a viable strategy for broad-band PDs. In this study, the use of methylamine acetate (MAAc) as an additive in 2D perovskite precursors can effectively control the width of the quantum wells (QWs). The amount of MAAc greatly affects the phase purity. Subsequently, PbSe QDs were embedded into the 2D perovskite matrix with a broadened absorption spectrum and no negative effects on ferroelectric properties. PM6:Y6 was combined with the hybrid ferroelectric perovskite films to create a self-powered and broad-band PD with enhanced performance due to a ferro-pyro-phototronic effect, reaching a peak responsivity of 2.4 A W–1at 940 nm.

Automatic modulation classification (AMC) is a crucial technique for the design of intelligent transceivers and has received considerable research attention. Conventional feature-based (FB) methods have the advantage of low computational complexity. However, these methods are highly sensitive to the distribution shifts of the received signal caused by the variation of channel effects and have rarely been studied in orthogonal frequency division multiplexing (OFDM) systems under unseen synthetic channels with multipath fading effects, carrier frequency offset (CFO), phase offset (PO) and additive noise. To solve this problem, this paper proposes a novel FB method using the error vector magnitude (EVM) features for AMC tasks (termed as EVM-AMC), which can achieve reliable classification performance for the communication scenarios considering unseen synthetic channels in OFDM systems. Specifically, we first propose the axisymmetric mapping-based self-circulant differential division (AM-SCDD) algorithm to convert the received signal into the non-negative spectral quotient (NNSQ) sequence, deeply suppressing the synthetic channel effects. Subsequently, we derive the EVM features by analyzing the matched error vectors between the generated NNSQ sequence and the predefined NNSQ constellation symbol (NNSQCS) masks. During this process, a percentile-based filter is utilized to remove the outliers in each matched error vector. Finally, the feature samples collected from various channel conditions are sent to the multi-class support vector machine (SVM) classifiers for training and testing. Two candidate modulation type sets are employed to evaluate the performance of the proposed EVM-AMC method under both the constant and changing channel conditions. Our numerical results demonstrate that 1) the proposed method exhibits impressive robustness and generalization when dealing with unseen synthetic channels, 2) the proposed method yields the best classification performance when compared to the conventional FB AMC methods in the presence of channel effects.

Recent years have witnessed an increasing demand for human fall detection systems. Among all existing methods, Wi-Fi-based fall detection has become one of the most promising solutions due to its pervasiveness. However, when applied to a new domain, existing Wi-Fi-based solutions suffer from severe performance degradation caused by low generalizability. In this paper, we propose XFall, a domain-adaptive fall detection system based on Wi-Fi. XFall overcomes the generalization problem from three aspects. To advance cross-environment sensing, XFall exploits an environment-independent feature called speed distribution profile, which is irrelevant to indoor layout and device deployment. To ensure sensitivity across all fall types, an attention-based encoder is designed to extract the general fall representation by associating both the spatial and temporal dimensions of the input. To train a large model with limited amounts of Wi-Fi data, we design a cross-modal learning framework, adopting a pre-trained visual model for supervision during the training process. We implement and evaluate XFall on one of the latest commercial wireless products through a year-long deployment in real-world settings. The result shows XFall achieves an overall accuracy of 96.8%, with a miss alarm rate of 3.1% and a false alarm rate of 3.3%, outperforming the state-of-the-art solutions in both in-domain and cross-domain evaluation.

In this paper, we propose a novel 1200V SiC MOSFET featuring the embedded junction-controlled-diode (JCD-MOSFET) and demonstrate its static and dynamic characteristics through TCAD simulations. Without sacrificing blocking and conduction performance, the adoption of JCD can effectively reduce knee voltage to 1.7V based on unipolar carrier conduction mode. Due to the reduced peak reverse recovery current and reverse recovery charge, the JCD-MOSFET achieves 30.8% lower turn-on losses than conventional MOSFET. Meanwhile, the fabrication process for the JCD-MOSFET is the same as conventional MOSFET without an extra mask. This proposed JCD-MOSFET prototype shows great potential in target applications in the near future.

Recent years have seen a surge of machine learning (ML) in chemistry for predicting chemical properties, but a low-cost, general-purpose, and high-performance model, desirable to be accessible on central processing unit (CPU) devices, remains not available. For this purpose, here we introduce an atomic attention mechanism into many-body function corrected neural network (MBNN), namely, MBNN-att ML model, to predict both the extensive and intensive properties of molecules and materials. The MBNN-att uses explicit function descriptors as the inputs for the atom-based feed-forward neural network (NN). The output of the NN is designed to be a vector to implement the multihead self-attention mechanism. This vector is split into two parts: the atomic attention weight part and the many-body-function part. The final property is obtained by summing the products of each atomic attention weight and the corresponding many-body function. We show that MBNN-att performs well on all QM9 properties, i.e., errors on all properties, below chemical accuracy, and, in particular, achieves the top performance for the energy-related extensive properties. By systematically comparing with other explicit-function-type descriptor ML models and the graph representation ML models, we demonstrate that the many-body-function framework and atomic attention mechanism are key ingredients for the high performance and the good transferability of MBNN-att in molecular property prediction.

The effects of aluminum (Al) additions (0.04, 0.4, and 0.8 wt%) on the microstructural evolution, tensile properties, and sheared-edge ductility of dual phase (DP) steels were investigated. The DP steels were processed with distinct coiling temperatures after finish rolling, cold rolled, and subjected to two continuous galvanizing line (CGL) simulations: standard galvanizing (GI) and supercooling process (SC). Al expanded both the ɑ-ferrite and (ɑ+γ) phase fields at the expense of the γ-austenite region. Continuous cooling transformation (CCT) diagrams showed that Al increased ferrite start temperatures, broadened the ferrite transformation zone, and reduced austenite stability. Austenite growth kinetics during intercritical annealing, quantified using a modified Johnson-Mehl-Avrami-Kolmogorov (JMAK) model, revealed Avrami exponents ranging from 0.72 to 0.81 and apparent activation energies increasing from 422.9 to 496.7 kJ mol−1with Al additions. Microstructural characterization revealed larger ferrite grain sizes and higher ferrite volume fractions with higher Al levels. Ultimate tensile strength (UTS) was influenced by Al contents, coiling temperatures, and CGL simulation methods. Global ductility (total elongation, TE) was highest for the 0.8Al Steel with a high coiling temperature and GI annealing, while local ductility (hole expansion ratio, HER) was maximized for the 0.04 Al Steel with a low coiling temperature and SC annealing. The majority of the CGL simulated DP steels exhibited good strength-ductility combinations, with the 0.8Al Steels demonstrating superior properties compared to current automotive advanced high-strength steel (AHSS) grades.

Purpose: Accurate preoperative diagnosis of lymph node metastasis (LNM) in papillary thyroid carcinoma (PTC) remains an unsolved problem. This study aimed to construct a nomogram and scoring system for predicting LNM based on the clinical characteristics of patients with PTC. Methods: 1400 patients with PTC who underwent thyroidectomy and lymph node dissection at the First Affiliated Hospital of Sun Yat-sen University were retrospectively enrolled and randomly divided into training and internal testing sets. Furthermore, 692 patients with PTC from three other medical centers were collected as external testing sets. Least absolute shrinkage and selection operator (LASSO) was used to screen the predictors, and a nomogram was constructed. In addition, a scoring system was constructed using 10-fold cross-validation. The performances of the two models were verified among datasets and compared with preoperative ultrasound (US). Results: Six independent predictors were included in the multivariate logistic model: age, sex, US diagnosis of LNM, tumor diameter, location, and thyroid peroxidase antibody level. The areas under the receiver operating characteristic curve (AUROC) (95% confidence interval) of this nomogram in the training, internal testing, and three external testing sets were 0.816 (0.791–0.840), 0.782 (0.727–0.837), 0.759 (0.699–0.819), 0.749 (0.667–0.831), and 0.777 (0.726–0.828), respectively. The AUROC of the scoring system were 0.810 (0.785–0.835), 0.772 (0.718–0.826), 0.736 (0.675–0.798), 0.717 (0.635–0.799) and 0.756 (0.704–0.808), respectively. The prediction performances were both significantly superior to those of preoperative US (P< 0.001). Conclusion: The nomogram and scoring system performed well in different datasets and significantly improved the preoperative prediction of LNM than US alone.

Tensile strength is one of the important mechanical parameters for controlling the development of cracks in the Loess Plateau. However, the tensile strength of soil is difficult to be precisely measured due to the lack of satisfying laboratory techniques. Unconfined penetration test (UPT) is commonly used as an indicator to predict soil tensile strength characteristics, while there has been less research study about test parameter values range of UPT and micromechanical characteristics at failure. This study finished 50 groups of UPTs of remolded loess with different disk diameters (12.36–27.81 mm) at various sample heights (37–87 mm) based on Q2loess in Lintong areas, and the crack development and failure essence in the different failure modes process were investigated by the discrete element modeling. The results showed that disk diameters and sample height were closely correlated with the failure mode. The failure of remolded loess in the UPT is not only tensile failure, which also can occur compression failure and tensile–compression coupling failure. Among them, when the ratio of disk diameters to sample height is about in the range of 0.189–0.309, the sample appears tensile failure. In addition, the ratio of compressive and tensile crack numbers in the sample is 0.16–0.22 in the tensile failure mode. The numerical simulation method provides a way for the observation of failure process and crack development, thereby improving the understanding of failure essence in UPT. The findings will provide a reference way to improve test theory and method of loess tensile strength.

In the current internet era, the detection of hidden topics in social media and the discovery of key leaders of public opinion can monitor and guide the dissemination of public opinion on hot events, which is conducive to maintaining social stability. However, the topics learned by the existing topic discovery methods are sometimes ambiguous and need complicated follow-up processing. Therefore, this paper provides some solutions to these problems by introducing community network structure and analysing the connectivity between users. Based on the community discovery algorithm and topic model, a system for discovering and analysing the influence of opinion communities in social networks is developed. The software system carries out topic detection on the basis of community structure division. Combined with the text emotion analysis model, the system also analyses the social emotion trend of each community to hot events. The experimental results on microblog data verify the superiority of the algorithm and the system in topic detection and the effectiveness of mining social emotions.

The edge-based multi-agent visual SLAM is crucial for emerging mobile applications like search-and-rescue, inventory automation, and industrial inspection. It uses a central node to manage the global map and schedule tasks for agents. However, as the number of agents increases, the system faces scalability challenges due to operational overhead, such as data redundancy, bandwidth consumption, and localization errors. In this paper, we introduce SwarmMap, a framework designed to enhance the scalability of collaborative visual SLAM service in edge offloading settings. SwarmMap consists of three system modules: a change log-based server-client synchronization mechanism, a priority-aware task scheduler, and a lean global map representation. These modules work together to address the challenges of data explosion problems. SwarmMap is open-source and compatible with the robotic operating system (ROS). Existing visual SLAM applications could incorporate SwarmMap through SwarmAPI, a set of well-packaged APIs, to compose SwarmMap’s function modules to enhance their performance and capacity in multi-agent scenarios. Comprehensive evaluations and a three-month case study at one of the world’s largest oilfields demonstrate that SwarmMap can serve $2\times $ more agents (> 20 agents) than the state-of-the-arts with the same resource overhead, meanwhile maintaining an average trajectory error of $38cm$ , outperforming existing works by > 55%.

In this paper, we investigate the fairness of integrated sensing and communication (ISAC) systems assisted by simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) and non-orthogonal multiple access (NOMA) for eliminating the interference of the sensing signal before decoding the signals of communication users. We formulate the problem of maximizing the fairness between communication users and the sensing target by jointly designing the transmit beamforming vectors of the base station (BS) and the coefficient matrices of the STAR-RIS. For tackling the challenging optimization problem, a low-complexity algorithm based on successive convex approximation (SCA) and semidefinite programming (SDP) techniques is proposed for obtaining the transmit beamforming vectors and the STAR-RIS coefficient matrices. For the ISAC system with a single user, we further derive the closed-form expression of the BS transmit beamforming vector for reducing the complexity of the algorithm. Then, the non-convex optimization problem of the STAR-RIS coefficient matrices can be solved efficiently by transforming it into a convex problem. Simulation results show that the fairness of the proposed STAR-RIS-NOMA assisted ISAC system outperforms the conventional RIS-NOMA assisted ISAC system and the conventional RIS and orthogonal multiple access (RIS-OMA) assisted ISAC system.

The complexity of traffic scenarios, the spatial-temporal feature correlations pose higher challenges for traffic prediction research. Traffic spatial-temporal model is an essential method in this research field, primarily focusing on capturing the spatial-temporal features among nodes and their neighboring nodes. However, existing methods lack comprehensive consideration of directional and hierarchical features among traffic nodes. They are mostly applicable to scenarios with random uniform distribution of nodes, but not suitable for more complex small-scale aggregation distribution scenarios. Therefore, this study proposes the Tree Convolutional Network (TreeCN), a tree-based structure. The data design and model design of TreeCN focus on capturing the directional and hierarchical features among nodes. The directional and hierarchical relationships among nodes are represented by the plane tree matrix and constructed as the spatial tree matrix. The TreeCN, with a full convolution network, performs a bottom-up convolution structure on the tree matrix to complete the task of node feature capturing. In this study, TreeCN is thoroughly compared with statistical, machine learning, and deep learning methods in traffic time series prediction. The experimental results show that TreeCN not only performs well in scenarios with random uniform distribution but also exhibits outstanding effect in more complex small-scale aggregation distribution. Moreover, TreeCN adheres to the design principles of Graph Convolutional Networks (GCN) in capturing the spatial features of traffic nodes and can further capture directional and hierarchical features among them. This is expected to make TreeCN a new method to handle complex traffic scenarios and improve prediction accuracy.

At ACSAC 2019, we introduced a new cryptographic primitive called proof of aliveness (PoA), allowing us to remotely and automatically track the running status (aliveness) of devices in the fields in cyber-physical systems. We proposed to use a one-way function (OWF) chain structure to build an efficient proof of aliveness, such that the prover sends every node on the OWF chain in a reverse order periodically. However, the finite nodes in OWF chains limited its practicality. We enhance our first PoA construction by linking multiple OWF chains together using a pseudo-random generator chain in our second PoA scheme. This enhancement allows us to integrate one-time signature (OTS) schemes into the structure of the second construction to realize the auto-replenishment of the aliveness proofs for continuous use without interruption for reinitialization. In this work, our primary motivation is to further improve our secondary PoA and auto-replenishment schemes. Instead of storing the tail nodes of multiple OWF chains on the verifier side, we use a Bloom Filter to compress them, reducing the storage cost by $ 4.7$4.7 times. Moreover, the OTS-based auto-replenishment solution cannot be applied to our first scheme, and it is not so efficient despite its standard model security. To overcome these limitations, we design a new auto-replenishment scheme from a hash-based commitment under the random oracle model in this work, which is much faster and can be used by both PoA schemes. Considering the implementation on a storage/memory-constrained device, we particularly study the strategies for efficiently generating proofs.

Objective: Bladder outlet obstruction (BOO) results in significant fibrosis in the chronic stage and elevated bladder pressure. Piezo1 is a type of mechanosensitive (MS) channel that directly responds to mechanical stimuli. To identify new targets for intervention in the treatment of BOO-induced fibrosis, this study investigated the impact of high hydrostatic pressure (HHP) on Piezo1 activity and the progression of bladder fibrosis. Methods: Immunofluorescence staining was conducted to assess the protein abundance of Piezo1 in fibroblasts from obstructed rat bladders. Bladder fibroblasts were cultured under normal atmospheric conditions (0 cmH2O) or exposed to HHP (50 cmH2O or 100 cmH2O). Agonists or inhibitors of Piezo1, YAP1, and ROCK1 were used to determine the underlying mechanism. Results: The Piezo1 protein levels in fibroblasts from the obstructed bladder exhibited an elevation compared to the control group. HHP significantly promoted the expression of various pro-fibrotic factors and induced proliferation of fibroblasts. Additionally, the protein expression levels of Piezo1, YAP1, ROCK1 were elevated, and calcium influx was increased as the pressure increased. These effects were attenuated by the Piezo1 inhibitor Dooku1. The Piezo1 activator Yoda1 induced the expression of pro-fibrotic factors and the proliferation of fibroblasts, and elevated the protein levels of YAP1 and ROCK1 under normal atmospheric conditions in vitro.However, these effects could be partially inhibited by YAP1 or ROCK inhibitors. Conclusion: The study suggests that HHP may exacerbate bladder fibrosis through activating Piezo1.

Location awareness is becoming increasingly crucial in unmanned aerial vehicles (UAVs). However, in the absence of global positioning system (GPS) signals in indoor scenes, how to obtain accurate positioning results has become a frontier issue that has attracted much attention. This article proposes a resilient evolutionary multisensor fusion localization framework named “REFLoc” to achieve accurate indoor positioning of UAVs. Motivated by the ease of deployment and the wide feasibility of precise indoor localization, we introduce ultrawideband (UWB) as global localization. The global localization provides the absolute position information of the UAV indoors, but it may be affected by signal blocking, occlusion, or interference. To address these noise effects, we introduce a hybrid threshold wavelet denoising (HTWD) algorithm to effectively preprocess UWB data. We also integrate visual-inertial odometry (VIO) as the support of local tracking to provide continuous and high-frequency motion estimation. We dynamically adjust the weight parameters and noise covariance matrices by using the resilient evolutionary fusion method. Then, we further seamlessly integrate the global localization and local tracking results in the optimized EKF framework. This improves the robustness of the system to environmental changes and outliers. We evaluate the performance of REFLoc in public datasets and the real world, and the experimental results show that the proposed framework can effectively improve the robustness and accuracy of the UAVs’ localization.

Bidirectional service function chain (BSFC) consists of multiple virtual network functions (VNFs). Through VNF deployment and link mapping, BSFCs can be embedded into resource-constrained mobile edge networks to provide low-latency network function services to users participating in interactive applications such as multi-player online games. Data from these users are routed through BSFCs to the edge node where the application is located for interaction and then returned to the users through the BSFCs, thus enabling synchronization among multiple users. However, the edge nodes or links have limited computing or bandwidth resources to serve only a fraction of users simultaneously. Therefore, the embedding decisions among different users can affect each other. In this paper, we propose a novel BSFC embedding strategy for interactive applications with the goal of minimizing computing and bandwidth resources while satisfying users’ latency requirements. We first model the BSFC embedding problem as an integer nonlinear programming problem. Then, by closely examining the complexity of the problem, we propose a distributed algorithm based on game theory. We theoretically analyze the properties of the proposed algorithm and show that it can obtain a solution with a worst-case performance bound. Finally, extensive experiments show that the proposed algorithm outperforms several existing algorithms.

Radio frequency fingerprint identification (RFFI) is a physical layer security methodology to recognize individual devices by leveraging hardware imperfections inevitably induced in the manufacturing process. However, the performance degradation caused by the time-varying channel impacts and interferences has severely restricted the development of RFFI. To this end, we present a channel-agnostic RFFI system, which consists of three modules, i.e., signal preprocessing module, feature extraction module, and classification module. In the signal preprocessing module, we first propose a novel approach, referred to as limiter-based spectral circular shift bidirectional division (LB-SCSBD), to generate two parallel spectral quotient (SQ) sequences. Then, we define the spectral quotient constellation (SQC) symbols according to different modulation formats, and thereby transform the SQ sequences into four magnitude-based sequences in terms of two channel-robust signal representations, i.e., the SQ magnitude (SQM) and SQC error vector magnitude (SQC-EVM). In the feature extraction module, we present a moment-based statistical feature extractor (MB-SFE) to extract the device-specific information from the above four sequences. In the classification module, the extracted statistics are fed into the multi-class support vector machine (SVM) for training and testing. We take WiFi as a case study and evaluate the performance of the proposed RFFI system by classifying eight simulated device models and six universal software radio peripheral (USRP) transmitter radios. Experimental results show that (i) the proposed method achieves the accuracies of 99.84% and 98.26% with eight devices in QPSK and 16QAM cases, as well as the accuracy of 92.42% with six USRP devices (ii) the proposed method exhibits superior classification performance in comparison to some existing RFFI methods, leading to a significant accuracy improvement of at least 38.33%.

In this study, the low cycle fatigue properties of a new nickel–iron-based superalloy HT700P at different strain amplitudes at 650℃ and 700℃ were investigated. The results show that at 650℃, with strain amplitudes of 0.3% and 0.7%, the fatigue life of the samples was 17,007 cycles (14 h) and 580 cycles (1.13 h), respectively. At 700℃, the fatigue life was 13,966 cycles (11.65 h) and 469 cycles (0.91 h) for the same strain amplitudes.When the total strain amplitude is 0.3%, the alloy exhibits the behaviour of cyclic softening first, then cyclic hardening and finally cyclic softening. When the total strain amplitude is 0.7%, the alloy exhibits cyclic softening behaviour as a whole. Cyclic softening is related to the continuous cutting of the γ′ phase by dislocations and the coarsening of the γ′ phase, whereas cyclic hardening is caused by the interaction between dislocations and precipitated phases. By analyzing the fatigue fracture morphology, it can be seen that the fatigue cracks sprouted from the surface of the specimen; when the temperature was 650℃ and the total strain amplitude was 0.3% and 0.7%, the fracture morphology of the samples was mainly characterized by the fatigue glow and extended inwardly in the mode of transgranular mode. The microscopic mechanism of low-week fatigue in HT700P high-temperature alloy is plane slip.

Environment depth estimation by fusing camera and radar enables a broad spectrum of applications such as autonomous driving, environmental perception, context-aware localization and navigation. Various pioneering approaches have been proposed to achieve accurate and dense depth estimation by integrating vision and LiDAR through deep learning. However, due to the challenges of sparse sampling of in-vehicle LiDARs, high ground-truth annotation overhead, and severe dynamics in real environments, existing solutions have not yet achieved widespread deployment on commercial autonomous vehicles. In this paper, we propose LeoVR, a motion-inspired self-supervised visual-LiDAR fusion approach that enables accurate environment depth estimation. Leveraging the vehicle motion information, LeoVR employs two effective system frameworks to (i) optimize the depth estimation results, and (ii) provide supervision signals for DNN training. We fully implemented LeoVR on both a robotic testbed and a commercial vehicle and conducted extensive experiments over an 8-month period. The results demonstrate that LeoVR achieves remarkable performance with an average depth estimation error of 0.17 m, outperforming existing state-of-the-art solutions by $> $> 45.9%. Besides, even cold-start in real environments by self-supervised training, LeoVR still achieves an average error of 0.2 m, outperforming the related works by $> $> 47.8% and comparable to supervised training methods.

In this article, a cooperative non-orthogonal multiple access (NOMA) mobile edge computing (MEC) network aided by reconfigurable intelligent surfaces (RIS) and simultaneous wireless information and power transfer (SWIPT) is considered, where the transmitters offload parts of their computing tasks to the MEC server via a relay and an RIS for enhancing the transmission environment. The goal is to minimize the task delay and energy consumption for the considered networks by jointly designing the RIS's reflection beamforming vector, the power splitting (PS) ratio, the power allocation factors of the relay, as well as the offloading transmit power and the offloading task ratio. To solve the proposed problems, an iterative optimization algorithm based on the successive convex approximation (SCA) method and the penalty method is proposed to decompose the formulated problems into two sub-problems. In particular, we jointly optimize the PS ratio, the power allocation coefficients and the edge computing resource based on the SCA technology, and then the penalty method is used to optimize the RIS's reflecting beamforming vector. Simulation results illustrate that the proposed NOMA-MEC networks aided by RIS and SWIPT are superior to the conventional orthogonal multiple access MEC networks with SWIPT aided by RIS in terms of task delay and energy consumption.

Graph partitioning problem is a classical NP-hard problem. The improvement of graph partitioning results by vertex migration is an important class of methods for graph partitioning. The goal of graph partitioning is getting a partition with the least number of cut edges, while also satisfying the capacity limit of the partition. In this paper, an optimization model for vertex migration is proposed, considering the influence between neighboring vertices, so that the objective function value of the model is exactly equal to the amount of cut edge variation. The model is converted into a mixed 0–1 linear programming by introducing variables. Then, a heuristic iterative algorithm is designed, in which the mixed 0–1 linear programming model is transformed into a series of small-scale models that contain less integer variables. In the experiment, the method in this paper is simulated and compared with balanced label propagation methods and their related methods. The improvement effect of these methods based on three different initialization methods is analyzed. Extensive numerical experiments on five commonly used datasets validate the effectiveness and efficiency of the proposed method.

This is a study of the sequences of Betti numbers of finitely generated modules over a complete intersection local ring, R. The subsequences (βiR(M))with even, respectively, odd iare known to be eventually given by polynomials in iwith equal leading terms. We show that these polynomials coincide if I□, the ideal generated by the quadratic relations of the associated graded ring of R, satisfies heightI□≥codimR-1, and that the converse holds if Ris homogeneous or codimR≤4. Subsequently Avramov, Packauskas, and Walker proved that the terms of degree j>codimR-heightI□of the even and odd Betti polynomials are equal. We give a new proof of that result, based on an intrinsic characterization of residue rings of c.i. local rings of minimal multiplicity obtained in this paper. We also show that that bound is optimal.

The intelligent transportation system (ITS) was supported by the sixth generation (6G) wireless networks, since it has great potential to realize intelligent transportation with the benefit for the society and economy. In order to overcome the practical problem of spectrum scarcity, ultra-low latency, large-scale connectivity in ITS, we propose a cooperative multiple-input single output non-orthogonal multiple access (MISO-NOMA) for ITS with intelligent reflecting surface (IRS) and simultaneous wireless information and power transfer (SWIPT). An user fairness optimization problem is formulated to maximize the fairness rate of the vehicles, subject to the quality of service requirements of the vehicles and the successive interference cancellation. The optimization problem involves the transmit beamformers design, the IRS reflection matrix design, and the power splitting ratio of the SWIPT, which lead to the problem is difficult to solve. For solving the challenging problem, an iterative successive convex approximation and semi-definite relaxation based algorithm is proposed. Explicitly, we firstly adopt the method of reconstructing epigraph for simplification due to the objective function is non-convex, and then the original problem is decomposed into two sub-problems that are easy to solve. Finally, Experimental results illustrate that the user fairness of the proposed cooperative MISO-NOMA for ITS with IRS and SWIPT is better than that of both the IRS-NOMA for ITS without SWIPT and the IRS-OMA for ITS.

Reconfigurable intelligent surfaces (RISs) are programmable metasurfaces capable of optimizing signal strength and reducing interference, serving as key components in maintaining the integrity of semantic information during transmission. This letter explores the establishment of additional semantic transmission and reflection pathways by deploying RISs in different cells. An optimization strategy, maximizing mutual information (MI) for quality of experience (QoE)-aware modeling of the R-SC system (QR-SC), is proposed to enhance both semantic and communication performance. Additionally, a QoE-aware model is utilized for users to gauge semantic transmission performance. Experimental results indicate that QR-SC can elevate the performance of semantic communication while ensuring reliable transmission, highlighting the substantial potential of RIS in digital and energy simultaneous transmission.

In this article, the power spectrum information geometry (PSIG) detector, which inherits the performance advantages of matrix information geometry (MIG) detectors in heterogeneous clutter backgrounds, is proposed. The PSIG detector can address two urgent problems in applications of MIG detectors, which are the expensive computation expense and unavailable acquisition ability of target velocity. Specifically, the PSIG detector utilizes power spectra (PSs) instead of high-dimensional covariance matrices to characterize sample data and employs subband filter bank to extend the detection from range cells to range-Doppler cells; thus, it requires less computation expense and can obtain the target velocity information according to the Doppler cell. Experiments based on the real data show the advantages of the proposed PSIG detectors in comparison with competitive methods. Especially, in the experiments with the real-recorded airborne radar data, the proposed method can effectively suppress the heterogeneous main-lobe clutter without any prior knowledge and provides detection probability improvement of more than 30% to the competitive methods with low false-alarm ratios.

ABSTRACTThis study examines Chinese civil servants’ attitudes toward genetically modified organisms by reviewing a national survey of 3,018 Chinese civil servants. The findings show that Chinese civil servants hold a more positive attitude to GMOs than the wider Chinese “general public”, with a similar level of genetic scientific literacy and belief in GMOs conspiracy theories and their influence mechanisms. While the Chinese civil servants’ occupational literacy plays an important role in their GMOs attitude. This study provides a new mind-set for studying some specific groups’ attitudes toward GMOs and related food policies.

Localization and navigation play a key role in many location-based services and have attracted numerous research efforts. In recent years, visual SLAM has been prevailing for autonomous driving. However, the ever-growing computation resources demanded by SLAM impede its applications to resource-constrained mobile devices. In this paper, we present the design, implementation, and evaluation of edgeSLAM, an edge-assisted real-time semantic visual SLAM service running on mobile devices. edgeSLAM leverages the state-of-the-art semantic segmentation algorithm to enhance localization and mapping accuracy, and speeds up the computation-intensive SLAM and semantic segmentation algorithms by computation offloading. The key innovations of edgeSLAM include an efficient computation offloading strategy, an opportunistic data sharing method, an adaptive task scheduling algorithm, and a multi-user support mechanism. We fully implement edgeSLAM and plan to open-source it. Extensive experiments are conducted under 3 datasets. The results show that edgeSLAM can run on mobile devices at 35fps and achieve 5cm localization accuracy from real-world experiments, outperforming existing solutions by more than 15%. We also demonstrate the usability of edgeSLAM through 2 case studies of pedestrian localization and robot navigation. To the best of our knowledge, edgeSLAM is the first edge-assisted real-time semantic visual SLAM for mobile devices.