Search

Abstract Studies involving acoustic telemetry typically use stationary acoustic receivers arranged in an array or grid. Unmanned surface vehicle (USV)-based mobile receivers offer advantages over the latter approach: the USV can be programmed to autonomously carry a receiver to and from target locations, more readily adapting to a survey’s spatial scope and scale. This work examines the acoustic detection performance of a low-cost USV developed as a flexible sensing platform. The USV was fitted with an acoustic receiver and operated over multiple waypoints set at increasing distances from the transmitter in two modes: drifting and station-keeping. While drifting, the USV was allowed to drift from the waypoint; while station-keeping, the USV used its thruster to hold position. Detection performance of the USV was similar to that of stationary receivers while drifting, but significantly worse while station-keeping. Noise from the USV thruster was hypothesized as a potential cause of poor detection performance during station-keeping. Detection performance varied with the depth of the tethered receiver such that detection range was greater during the deepest (4.6 m) trials than during shallower (1.1 and 2.9 m) trials. These results provide insight and guidance on how a USV can be best used for acoustic telemetry, namely, navigating to a planned waypoint, drifting and lowering the receiver to a desired depth for listening, and then navigating to the next waypoint.

Abstract Finding a real thermoelectric (TE) material that excels in various aspects of TE performance, mechanical properties, TE power generation, and cooling is challenging for its commercialization. Herein, we report a novel multifunctional Ge0.78Cd0.06Pb0.1Sb0.06Te material with excellent TE performance and mechanical strength, which is utilized to construct candidate TE power generation and cooling devices near room temperature. Specifically, the effectiveness of band convergence, combined with optimized carrier concentration and electronic quality factor, distinctly boosts the Seebeck coefficient, thus greatly improving the power factor. Advanced electron microscopy observation indicates that complex multi‐scale hierarchical structures and strain field distributions lead to ultra‐low lattice thermal conductivity, and also effectively enhance mechanical properties. High ZT ~ 0.6 at 303 K, average ZTave ~ 1.18 from 303 to 553 K, and Vickers hardness of ~200 Hv in Ge0.78Cd0.06Pb0.1Sb0.06Te are obtained synchronously. Particularly, a 7‐pair TE cooling device with a maximum ΔT of ~45.9 K at Th = 328 K, and a conversion efficiency of ~5.2% at Th = 553 K is achieved in a single‐leg device. The present findings demonstrate a unique approach to developing superior multifunctional GeTe‐based alloys, opening up a promising avenue for commercial applications.

In the rapidly evolving field of cybersecurity, the integration of multi-source, heterogeneous, and fragmented data into a coherent knowledge graph has garnered considerable attention. Such a graph elucidates semantic interconnections, thereby facilitating sophisticated analytical decision support. Central to the construction of a cybersecurity knowledge graph is Named Entity Recognition (NER), a critical technology that converts unstructured text into structured data. The efficacy of NER is pivotal, as it directly influences the integrity of the knowledge graph. The task of NER in cybersecurity, particularly within the Chinese linguistic context, presents distinct challenges. Chinese text lacks explicit space delimiters and features complex contextual dependencies, exacerbating the difficulty in discerning and categorizing named entities. These linguistic characteristics contribute to errors in word segmentation and semantic ambiguities, impeding NER accuracy. This paper introduces a novel NER methodology tailored for the Chinese cybersecurity corpus, termed CSBERT-IDCNN-BiLSTM-CRF. This approach harnesses Iterative Dilated Convolutional Neural Networks (IDCNN) for extracting local features, and Bi-directional Long Short-Term Memory networks (BiLSTM) for contextual understanding. It incorporates CSBERT, a pre-trained model adept at processing few-shot data, to derive input feature representations. The process culminates with Conditional Random Fields (CRF) for precise sequence labeling. To compensate for the scarcity of publicly accessible Chinese cybersecurity datasets, this paper synthesizes a bespoke dataset, authenticated by data from the China National Vulnerability Database, processed via the YEDDA annotation tool. Empirical analysis affirms that the proposed CSBERT-IDCNN-BiLSTM-CRF model surpasses existing Chinese NER frameworks, with an F1-score of 87.30% and a precision rate of 85.89%. This marks a significant advancement in the accurate identification of cybersecurity entities in Chinese text, reflecting the model’s robust capability to address the unique challenges presented by the language’s structural intricacies.

Cross-site scripting vulnerability (XSS) is one of the most frequently exploited and harmful vulnerabilities among web vulnerabilities. In recent years, many researchers have used different machine learning methods to detect network attacks, but these methods have not achieved high accuracy and recall rates and cannot effectively combat XSS attacks. Designing a model that can achieve high accuracy and truly proactive defense against reflected XSS vulnerabilities has become a top priority for maintaining user network security at this stage. In this paper, we propose a detection model for reflected XSS vulnerabilities based on the paths-attention method (PATS model). Firstly, the model converts vulnerability data into an intermediate representation of abstract syntax trees, then traverses the abstract syntax tree to generate multiple sets of syntactic paths, and then converts them into vector representations through word embedding matrices. The model extracts semantic features using attention mechanisms to improve training effectiveness by assigning appropriate weights to different sets of syntactic paths as it learns with neural networks, which realizes the transformation from passive defense to active defense. Additionally, in the dataset processing section, we point out the shortcomings of current research datasets and construct a reliable dataset composed of 1000 malicious samples from NIST and 10,000 benign samples from GitHub for experimentation purposes. Experimental results show that compared with other machine learning models, the paths-attention method can achieve an accuracy rate of 90.25% and F1-score of 81.62%, while reducing the training time by half to 30 h.

With the focus on network security and the goal of meeting the requirements of fast speed and high accuracy of abnormal traffic detection of industrial Internet of Things, this paper proposes a hierarchical abnormal traffic detection method for the industrial Internet of Things. This method includes two abnormal detection methods: the first (crude) one detects traffic frequency based on statistical analysis; the second (sophisticated) one detects traffic attributes based on a clustering algorithm. The hierarchical detection method first detects the abnormal frequency of the network traffic. Property exceptions are then detected for suspected traffic. The paper first calculates the difference in the value of traffic frequency and smoothes it. Then, the exponential weighted moving average model is used to make the data conform to the statistical law, and the deviation correction of the model is proposed to reduce the error caused by the initial value. Focusing on the fact that a fixed threshold is not suitable for a dynamic network environment, a two-layer threshold interval method is proposed to reduce the rate of false alarms. For traffic attribute detection, this paper designs a clustering optimization anomaly detection algorithm for complex attribute feature data. The algorithm classifies the weighted distance and safety coefficient of the data according to the priority of the traffic attribute features, selects the data with the high safety coefficient as the clustering center, clusters the multi-feature data around the center, and applies it to the attribute anomaly detection. The simulation results show that the traffic frequency detection algorithm based on statistical analysis proposed in this paper can quickly detect the traffic frequency anomalies in the network. Moreover, the clustering optimization anomaly detection algorithm based on complex attribute features proposed in this paper can effectively detect the malicious attributes contained in network traffic and achieve a high detection rate and a low false detection rate to ensure the safety and reliability of the industrial Internet of Things.

Abstract Background Autonomous underwater vehicles (AUVs) and animal telemetry have become important tools for understanding the relationships between aquatic organisms and their environment, but more information is needed to guide the development and use of AUVs as effective animal tracking platforms. A forward-facing acoustic telemetry receiver (VR2Tx 69 kHz; VEMCO, Bedford, Nova Scotia) attached to a novel AUV (gliding robotic fish) was tested in a freshwater lake to (1) compare its detection efficiency (i.e., the probability of detecting an acoustic signal emitted by a tag) of acoustic tags (VEMCO model V8-4H 69 kHz) to stationary receivers and (2) determine if detection efficiency was related to distance between tag and receiver, direction of movement (toward or away from transmitter), depth, or pitch. Results Detection efficiency for mobile (robot-mounted) and stationary receivers were similar at ranges less than 300 m, on average across all tests, but detection efficiency for the mobile receiver decreased faster than for stationary receivers at distances greater than 300 m. Detection efficiency was higher when the robot was moving toward the transmitter than when moving away from the transmitter. Detection efficiency decreased with depth (surface to 4 m) when the robot was moving away from the transmitter, but depth had no significant effect on detection efficiency when the robot was moving toward the transmitter. Detection efficiency was higher when the robot was descending (pitched downward) than ascending (pitched upward) when moving toward the transmitter, but pitch had no significant effect when moving away from the transmitter. Conclusion Results suggested that much of the observed variation in detection efficiency is related to shielding of the acoustic signal by the robot body depending on the positions and orientation of the hydrophone relative to the transmitter. Results are expected to inform hardware, software, and operational changes to gliding robotic fish that will improve detection efficiency. Regardless, data on the size and shape of detection efficiency curves for gliding robotic fish will be useful for planning future missions and should be relevant to other AUVs for telemetry. With refinements, gliding robotic fish could be a useful platform for active tracking of acoustic tags in certain environments.

It is critical to detect malicious code for the security of the Internet of Things (IoT). Therefore, this work proposes a malicious code detection algorithm based on the novel feature fusion–malware image convolutional neural network (FF-MICNN). This method combines a feature fusion algorithm with deep learning. First, the malicious code is transformed into grayscale image features by image technology, after which the opcode sequence features of the malicious code are extracted by the n-gram technique, and the global and local features are fused by feature fusion technology. The fused features are input into FF-MICNN for training, and an appropriate classifier is selected for detection. The results of experiments show that the proposed algorithm exhibits improvements in its detection speed, the comprehensiveness of features, and accuracy as compared with other algorithms. The accuracy rate of the proposed algorithm is also 0.2% better than that of a detection algorithm based on a single feature.

Today, a wide variety of applications with different requirements are rapidly developed in industrial wireless sensor networks, and providing the Quality of Service (QoS) for this kind of communication network is inevitable. It is difficult to solve the problems of poor adaptability and difficulty in the implementation of the QoS-based network configuration and management in traditional network architecture. We present a hierarchical software-defined network architecture for wireless sensor networks, which makes the complex network management possible and the system more adaptable. Furthermore, we propose a QoS-based routing protocol, called QSDN-WISE, which consists of a clustering algorithm, a routing algorithm, and local network maintenance. A double-cluster head-based uneven clustering algorithm, called DCHUC, avoids the energy hole phenomenon and reduces the workload of a single cluster head. The centralized QSDN-WISE routing algorithm constructs two heterogeneous forwarding paths for nodes, which meets the requirements for different data levels. Local network maintenance reduces the number of control messages in the network. The simulation results indicate that the QSDN-WISE can provide the QoS support for data with different requirements, balance the network energy consumption, and prolong the network's lifetime.

Channel estimation is a virtual component to ensure the performance in the orthogonal frequency division multiplexing (OFDM). Smoothing filter is often used to suppress noise to improve the estimation performance. However, the conventional smoothing filtering methods have paid little attention on the virtual subcarriers, which is required in practical system but will introduce interference. Therefore, in this paper, we proposed a universal channel estimation algorithm in which the discrete Fourier transform (DFT) smoothing filtering is used when the virtual subcarriers is considered. What is more, the proposed method is applicable to the OFDM system with either block-type or comb-type pilot, and it can be easily extended to multiple input multiple output (MIMO) system. The proposed method employs windowing, extraction and adaptive filter modules one or more based on conventional DFT-based channel estimation for different pilot patterns. The simulation results show that the proposed method has better performance than the conventional DFT-based channel estimation under the same complexity.

Species-specific monitoring activities represent fundamental tools for natural resource management and conservation but require techniques that target species-specific traits or markers. Sea lamprey, a destructive invasive species in the Laurentian Great Lakes and conservation target in North America and Europe, is among very few fishes that possess and use oral suction, yet suction has not been exploited for sea lamprey control or conservation. Knowledge of specific characteristics of sea lamprey suction (e.g., amplitude, duration, and pattern of suction events; hereafter 'suction dynamics') may be useful to develop devices that detect, record, and respond to the presence of sea lamprey at a given place and time. Previous observations were limited to adult sea lampreys in static water. In this study, pressure sensing panels were constructed and used to measure oral suction pressures and describe suction dynamics of juvenile and adult sea lampreys at multiple locations within the mouth and in static and flowing water. Suction dynamics were largely consistent with previous descriptions, but more variation was observed. For adult sea lampreys, suction pressures ranged from -0.6 kPa to -26 kPa with 20 s to 200 s between pumps at rest, and increased to -8 kPa to -70 kPa when lampreys were manually disengaged. An array of sensors indicated that suction pressure distribution was largely uniform across the mouths of both juvenile and adult lampreys; but some apparent variation was attributed to obstruction of sensing portal holes by teeth. Suction pressure did not differ between static and flowing water when water velocity was lower than 0.45 m/s. Such information may inform design of new systems to monitor behavior, distribution and abundance of lampreys.

Abstract Background Mitochondrial dysfunction has been implicated in the pathogenesis of ischemic heart disease, exacerbating cardiomyocytes injury in myocardial infarction (MI). Peroxisome proliferator-activated receptor gamma co-activator (PGC-1α) has been recognized as the key regulator of mitochondrial biogenesis and energy metabolism. Yiqihuoxue decoction (YQHX), a Traditional Chinese Medicine (TCM) prescription, can prevent and treat ischemic heart disease. However, the mechanisms of YQHX on PGC-1α expression in the ischemic heart have remained unclear. Methods Myocardial ischemia rat model and ischemia/hypoxia injury model in the cardiomyocytes were used to minic human cardiovascular disease. Rats were randomly assigned into 4 groups: Sham, Model, YQHX (8.2 g/kg) and Trimetazidine (10 mg/kg) group. 28 days after MI, cardiac functions and morphology were detected by echocardiography and HE staining, respectively. In vitro, the effects of YQHX on H9c2 cell viability, LDH and ROS were detected, respectively. PGC-1α relevant proteins were evaluated by Western blotting. Results In vivo, echocardiography and HE staining results showed that YQHX improved cardiac functions and modified pathological changes. YQHX enhanced PGC-1α expression and improved the mitochondrial ultrastructure and functions in rats MI model for 4 weeks. Further, we explored its potential mechanisms in cardiomyocytes. In vitro, YQHX significantly enhanced cell viability and reduced LDH release and ROS production induced by hypoxia in cardiomyocytes. Interestingly, exposure of cardiomyocytes to hypoxic conditions for 12 h induced the downregulation of PGC-1α expression, but the expression levels nearly returned to the normal state after hypoxia for 24 h. YQHX significantly enhanced PGC-1α expression between 12 h and 24 h induced by hypoxia through a mechanism associated with the activation of AMPK phosphorylation in H9c2 cells. In addition, YQHX upregulated the expression of Tfam and NRF-1, while NRF-1 expression was completely blocked by an AMPK inhibitor. YQHX largely restored the mitochondrial morphology and increased mitochondrial membrane potential in hypoxia-induced injury. Furthermore, the UHPLC-LTQ-Orbitrap-MSn analysis found that there were 87 chemical constituents in YQHX. Conclusions These results suggest that the protective effect of YQHX on cardiomyocytes against hypoxia-induced injury may be attributed to activation of PGC-1α and maintenance of mitochondrial functions through a mechanism involving the activation of AMPK phosphorylation.

Objective: Yiqi Huoxue Decoction (YQHX) has been widely used for clinical treatment of ischemic heart disease. While oxidative stress plays a key role in the pathogenesis of ischemic heart disease, the function and molecular mechanism underlying antioxidative protective effects of YQHX on H9c2 cardiomyocytes against ischemia/hypoxia (I/H) have yet to be well clarified. Methods: H9c2 cells were subjected to 12 h of hypoxia with serum-free conditions and then treated with or without YQHX (100–400 μg/mL). Cell viability was examined using a CCK-8 assay. Maleic dialdehyde (MDA) and superoxide dismutase (SOD) activity were detected using commercial kits. Intracellular reactive oxygen species (ROS) levels and mitochondrial membrane potential were measured using fluorescence microscopy and confocal laser-scanning microscopy, respectively. Ultrastructural details of mitochondria in H9c2 cells were observed using transmission electron microscopy. The antioxidative protective pathway was assessed by measuring mRNA and protein expression of Nrf2 and HO-1, as well as AMPK activation. Results: I/H injury gradually induced oxidative stress. Treatment with YQHX significantly increased cell viability and reversed I/H-induced oxidative stress, including reducing the production of oxidative stress products (ROS and MDA), increasing SOD levels, improving mitochondrial morphology, and increasing mitochondrial membrane potential. YQHX was also observed to increase I/H-induced expression of Nrf2 and HO-1, and the activation effects of YQHX were blocked by an AMPK inhibitor. In addition, HPLC analysis showed that YQHX contained two active antioxidative constituents (calycosin and ferulic acid). Conclusion: The results suggest that anti-oxidative effects exerted by YQHX in H9c2 cardiomyocytes may be linked to upregulation of the AMPK-mediated Nrf2/HO-1 pathway. Keywords: Hypoxia, Yiqi Huoxue Decoction, Chinese medicine, Cardiomyocytes, Oxidative stress

In wireless sensor networks, cross-layer optimization is a hot topic. In order to balance energy consumption a clustering based cross-layer optimization model is established in this paper. In this model, the network layer could get the related information from physical layer and the datalink layer to effectively route packets. Based on the cross-layer optimization model, an energy-efficient ring cross-layer optimization algorithm is proposed and a new routing algorithm called Leach-Cross-Layer Optimization (CLO) is also proposed for a ring monitoring domain. This Leach-CLO routing algorithm is based on the typical Leach algorithm. The experiment results show that this routing algorithm could effectively and efficiently balance the energy consumption in wireless sensor networks compared with related algorithms.

Fault detection is a method of crucial importance to guarantee Quality of Service (QoS) in industrial wireless sensor network (IWSN). Timely and accurate detection for fault nodes can increase the robustness of IWSN. Therefore, in order to reduce the network detection load and improve the system scalability, a cluster-based fault detection (CFD) algorithm is proposed. In this algorithm, the IWSN is clustered based on Low Energy Adaptive Clustering Hierarchy (LEACH) protocol at the beginning, and then the reliability of all cluster head nodes is verified. In each cluster, the cluster head is responsible for fault detection in it. In addition, the introduction of node credibility mechanism enables CFD algorithm to tolerate transient faults. Simulation results demonstrate that CFD algorithm has better performances in terms of network energy consumption and detection accuracy.

Given the multiple applications for micro-electro-mechanical system (MEMS) mirror devices, most of the research efforts are focused on improving device performance in terms of tilting angles, speed, and their integration into larger arrays or systems. The modeling of these devices is crucial for enabling a platform, in particular, by allowing for the future control of such devices. In this paper, we present the modeling of a MEMS mirror structure with four actuators driven by the phase-change of a thin film. The complexity of the device structure and the nonlinear behavior of the actuation mechanism allow for a comprehensive study that encompasses simpler electrothermal designs, thus presenting a general approach that can be adapted to most MEMS mirror designs based on this operation principle. The MEMS mirrors presented in this work are actuated by Joule heating and tested using optical techniques. Mechanical and thermal models including both pitch and roll displacements are developed by combining theoretical analysis (using both numerical and analytical tools) with experimental data and subsequently verifying with quasi-static and dynamic experiments.