We experimentally demonstrated the feasibility of an underwater continuous-variable quantum key distribution (CV-QKD) system based on four-state protocol, which is promising to guarantee the ...unconditionally secure underwater wireless optical communication. CV-QKD parameter estimation is performed after transmitting quantum coherent signal from Alice to Bob through a water tank. The secure key rate under collective attack of the demonstrated CV-QKD system is estimated as 22.9 kbits/s at a channel loss of 12.4 dB. In addition, the performance is also investigated with various water types and the maximum underwater transmission distance of the demonstrated CV-QKD system is estimated as 148.7 m in the pure sea water.
•Serum sodium-glucose cotransporter-2: potential biomarker for acute ischemic stroke.•Sodium-glucose cotransporter-2 unrelated to stroke severity or functional prognosis.•Stroke patients have higher ...serum sodium-glucose cotransporter-2 levels.•Sodium-glucose cotransporter-2 linked to the onset time of acute ischemic stroke.•Sodium-glucose cotransporter-2 and post-stroke inflammation, oxidative stress.
Recently acquired data suggests that sodium-glucose cotransporter-2 (SGLT2) may be a therapeutic target for cerebral ischemia. The specific impact of SGLT2 in acute ischemic stroke (AIS) remains unknown. We aimed to explore the levels of SGLT2 in AIS patients and its association with functional prognosis.
In this study, 132 AIS patients and 44 healthy controls were recruited prospectively to determine serum SGLT2 levels. Logistic regression analysis was employed to assess the association between serum SGLT2 level and stroke risk as well as 3-month outcome. Receiver operating characteristic (ROC) curves were utilized to evaluate predictive values for blood biomarkers.
Serum SGLT2 levels were significantly higher (P =.000) in AIS patients (47.1 (interquartile range IQR: 42.4–50.9) ng/mL) than healthy controls (35.7 (IQR: 28.6–39.5) ng/mL). The optimal SGLT2 cutoff point for diagnosing AIS was 39.55 ng/mL, with a sensitivity of 90.2 % and specificity of 77.3 %. Serum levels of SGLT2 were negatively correlated with the onset time of AIS (linear fit R2 = 0.056, P =.006), but were not associated with National Institutes of Health Stroke Scale (NIHSS) scores (r = 0.007, P >.05) and lesion volume (r = -0.151, P >.05). SGLT2 was not remarkably different between patients with unfavorable and favorable outcomes (46.7 (IQR: 41.9–49.6) ng/mL vs 47.6 (IQR: 42.5–51.9) ng/mL; P =.321).
The serum SGLT2 concentration may be a potential biomarker for the diagnosis of AIS. However, it does not exhibit any association with disease severity or functional prognosis.
Background subtraction is a popular technique used in accurate foreground extraction with a stationary background. Since most outdoor surveillance videos are taken in complex environments, their ..."stationary" backgrounds change in some unknown patterns, which make the perfect foreground extraction very difficult. Based on visual background extractor (ViBe) scheme, in this paper we propose a new background subtraction algorithm which includes two innovative mechanisms and several other improved technique tricks. The paper inherits and develops background modeling based on pixel sample values, and use dynamic noise sampling and complementary learning to overcome the pixel-wise background model's intrinsic shortcomings. Besides, the algorithm works on the quantitative analysis without any estimation of the probability density function (pdf). Hence, it takes relatively low computational cost. Extensive experiments on a popular public dataset show that the proposed method has much better precision than ViBe, and could get the best precision and the highest average ranking compared with 27 state-of-the-art algorithms presented on the change detection website.
Vehicle-to-vehicle (V2V) communications is an important technology in vehicular ad hoc network (VANET) to support autonomous data exchange among vehicles. Multiple V2V communications modes have been ...investigated for VANET, including dedicated short-range communications (DSRC) based on IEEE 802.11p and LTE-V2X, which are suitable for different packet transmission cases. In order to fully exploit the strengths of various modes, hybrid V2V communications strategies are designed in this paper, where each vehicle is allowed to choose different modes for different kinds of transmissions in separate frequency bands for transmission throughput improvement. Furthermore, since it is usually impractical to decide all the vehicles' communications strategies globally due to high computational complexity and heavy overhead to broadcast the decisions, we model the selection of hybrid V2V communications strategies for vehicles as an evolutionary game. A strategy selection algorithm is then proposed, where each vehicle can select its hybrid V2V communications strategy locally based on its evaluation of the payoffs for different strategies and limited signalling from the base station. Simulation results demonstrate that the proposed algorithm can converge to an asymptotic stable state, which can improve the transmission throughput of vehicles, and is robust to the slight evaluation errors of payoffs and the strategy mutations of a few vehicles.
The long-term storage of corrosive chemical media in oil tanks makes them susceptible to corrosion. Therefore, it is crucial to conduct regular safety evaluations of the tank corrosion using ...non-destructive testing techniques such as acoustic emission. First, to address the limitations of traditional AE sensors, this study combines optical fiber acoustic emission systems with deep learning technology to effectively collect data on tank bottom plate corrosion and interference signals. Additionally, to address the issues of low identification accuracy and vulnerability to human interference in the recognition of AE signals resulting from corrosion on tank bottom plates, this paper proposes a transfer learning strategy. It integrates the PReLU activation function and the channel attention mechanism CBAM to build the backbone network model, which enables intelligent identification of optical fiber AE signals. Moreover, the Grad-CAM++ interpretability algorithm is utilized to visualize the internal logical processing of the model, achieving initial localization of various AE signal characteristics on tank bottom plates and offering crucial insights for the on-site identification of diverse signal features. The experimental results demonstrate that the refined model significantly enhances recognition performance, achieving an average accuracy rate of 95.1%. Specifically, for corrosion-related signals, including metal dissolution and hydrogen evolution, the identification rates surpass 98%.
Pilot length is a crucial parameter in cell-free multiple-input multiple-output (MIMO) networks, which could significantly affect the downlink throughput. In this letter, we jointly optimize the user ...scheduling, power allocation and pilot length for user-centric (UC) cell-free MIMO networks. We consider the frequency band is uniformly split into several sub-channels. As the number of users scheduled on one sub-channel is commonly small, orthogonal pilot sequences are adopted among users on each sub-channel to avoid pilot contamination. To achieve user fairness, we aim to maximize the minimum ergodic user rate in the downlink transmission, yielding a challenging mix-integer non-convex optimization. Inspired by the weighted <inline-formula> <tex-math notation="LaTeX">l_{1} </tex-math></inline-formula>-norm approximation in compressive sensing together with successive convex approximation (SCA), an iterative scheme is then proposed to solve the problem. Simulation results demonstrate that the proposed scheme outperforms its state-of-the-art counterpart, which is a three-step algorithm including user grouping, sub-channel allocation, and power optimization.
Traffic forecasting is essential in the development of intelligent transportation systems, as it enables the formulation of effective traffic dispatching strategies and contributes to the reduction ...of traffic congestion. The abundance of research focused on modeling complex spatiotemporal correlations for accurate traffic prediction, however many of these prior works perform feature extraction based solely on prior graph structures, thereby overlooking the latent graph connectivity inherent in the data and degrading a decline in prediction accuracy. In this study, we present a novel Attention-based Multiple Graph Convolutional Recurrent Network (AMGCRN) to capture dynamic and latent spatiotemporal correlations in traffic data. The proposed model comprises two spatial feature extraction modules. Firstly, a dot product attention mechanism is utilized to construct an adaptive graph to extract the similarity of road structure. Secondly, the graph attention mechanism is leveraged to enhance the extraction of local traffic flow features. The outputs of these two spatial feature extraction modules are integrated through a gating mechanism and fed into a Gated Recurrent Unit (GRU) to make spatiotemporal interaction predictions. Experimental results on two real-world traffic datasets demonstrate the superiority of the proposed AMGCRN over state-of-the-art baselines. The results suggest that the proposed model is effective in capturing complex spatiotemporal correlations and achieving about 1% improvements in traffic forecasting.
This paper describes a seamless three-dimensional （3-D） localization and navigation system for smartphones. The smartphone includes an atmospheric pressure sensor to measure the user＇s altitude that ...is combined with the outdoor Global Positioning System （GPS） and indoor WiFi-APs localization systems in a seamless 3-D localization system. The smartphone software also provides seamless navigation services by updating map information for both indoor and outdoor locations through the mobile Internet. The indoor floor information calculated from the altitude information is used to project localization anchor nodes, e.g., WiFi-AP, on different floors onto the user＇s floor with an indoor 3-D localization algorithm using projection distances based on a Received Signal Strength （RSS） algorithm. Tests show that the 3-D method reduces systematic errors and achieves much higher accuracy than the traditional two-dimensional localization method.
Forced periodic operation is a technique that periodically changes the manipulating variable of a chemical reaction system in order to exploit nonlinear dynamics to improve reactant conversion rate. ...However, the analysis and design of a periodically operated chemical process is a significant challenge. To resolve this problem, recently, Nonlinear Frequency Response (NFR) based methods have been proposed. However, because of the need to derive the NFR from a first principle model, existing NFR methods can only perform qualitative analysis to simple processes and are often difficult to be applied in engineering practice. The present study proposes a novel data driven approach to the analysis and optimal design of forced periodic operation of chemical reactions. From the data generated numerically using the first principle model or experimentally from experimental tests, the approach produces a data-driven NFR model that can readily be used for both quantitative study and optimal design of forced periodic operation of any complexities. This can fundamentally address the challenges faced by the existing NFR methods, and provides an effective approach that can potentially be applied in engineering practice. Simulation studies and experimental works are carried out on the application of the new method to an isothermal CSTR system and a laboratory-scale carbon dioxide absorption process, respectively. The results verify the effectiveness and advantage of the newly proposed data driven approach and demonstrate the potential of the new approach in engineering applications.
This study examined high amylose maize starch (HAMS) treated with different ratios of a water/ionic liquid (IL) mixture and mixed with lauric acid (LA) and heated to form an amylose-lipid complex. IL ...can destroy the granule structure of starch, which releases more linear starch chains during the subsequent heating process and promotes the complexation reaction between HAMS and LA. Following the IL treatment of starch, HAMS's diffraction intensities, thermal stability and short-range order structure all decreased. The diffraction intensity of HAMS-LA complexes showed the following order: water:IL-4:1 > water:IL-8:1 > water:IL-2:1 > water:IL-11:1 > water:IL-0.5:1 > untreated sample. The creation of the V-type complex, according to a thermogravimetric study, improved the thermal stability of IL-treated HAMS. Results from Fourier transform infrared and Raman spectroscopy indicate that when IL-treated HAMS interacted with LA, the degree of short-range order structure of complexes increased.
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•The formation of HAMS-lipid complex was affected by the ratio of water/AMIMCL.•The moderate concentration of AMIMCL would conductive to inclusion complex production.•AMIMCL pretreatment could improve the complexation ability of HAMS.
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