Search Results (939)

The chemical gating of gap junction channels is mediated by cytosolic calcium (Ca²⁺_i) at concentrations ([Ca²⁺]_i) ranging from high nanomolar (nM) to low micromolar (µM) range. Since the proteins of gap junctions, connexins/innexins, lack high-affinity Ca²⁺-binding sites, most likely gating is mediated by a Ca²⁺-binding protein, calmodulin (CaM) being the best candidate. Indeed, the role of Ca²⁺-CaM in gating is well supported by studies that have tested CaM blockers, CaM expression inhibition, testing of CaM mutants, co-localization of CaM and connexins, existence of CaM-binding sites in connexins/innexins, and expression of connexins (Cx) mutants, among others. Based on these data, since 2000, we have published a Ca²⁺-CaM-cork gating model. Despite convincing evidence for the Ca²⁺-CaM role in gating, a recent study has proposed an alternative gating model that would involve a direct electrostatic Ca²⁺-connexin interaction. However, this study, which tested the effect of unphysiologically high [Ca²⁺]_i on the structure of isolated junctions, reported that neither changes in the channel’s pore diameter nor connexin conformational changes are present, in spite of exposure of isolated gap junctions to [Ca²⁺]_i as high at the 20 mM. In conclusion, data generated in the past four decades by multiple experimental approaches have clearly demonstrated the direct role of Ca²⁺-CaM in gap junction channel gating. Full article

In order to achieve an effective balance between SAR image simulation fidelity and efficiency, we proposed a ray-tracing-assisted SAR image simulation method under range doppler (RD) imaging geometry. This method utilizes the spatial traversal mode of RD imaging geometry to transmit discrete electromagnetic (EM) waves into the SAR radiation area and follows the Nyquist sampling law to set the density of transmitted EM waves to effectively identify the beam radiation area. The ray-tracing algorithm is used to obtain the backscatter amplitude and real-time slant range of the transmitted EM wave, which can effectively record the multiple backscattering among the components of the distributed target so that the backscattering subfields of each component can be correlated. According to the RD condition equation, the backscattering amplitude is assigned to the corresponding range gate, and the three-dimensional (3D) target is mapped into the two-dimensional (2D) SAR slant-range coordinate system, and the SAR target simulated image is directly obtained. Finally, the simulation images of the proposed method are compared qualitatively and quantitatively with those obtained by commercial simulation software, and the effectiveness of the proposed method is verified. Full article

Named entity recognition (NER) is a crucial step in building knowledge graphs for crop diseases and pests. To enhance NER accuracy, we propose a new NER model—GatedMan—based on the gated fusion unit and Manhattan attention. GatedMan utilizes RoBERTa as a pre-trained model and enhances it using bidirectional long short-term memory (BiLSTM) to extract features from the context. It uses a gated unit to perform weighted fusion between the outputs of RoBERTa and BiLSTM, thereby enriching the information flow. The fused output is then fed into a novel Manhattan attention mechanism to capture the long-range dependencies. The global optimum tagging sequence is obtained using the conditional random fields layer. To enhance the model’s robustness, we incorporate adversarial training using the fast gradient method. This introduces adversarial examples, allowing the model to learn more disturbance-resistant feature representations, thereby improving its performance against unknown inputs. GatedMan achieved F1 scores of 93.73%, 94.13%, 93.98%, and 96.52% on the AgCNER, Peoples_daily, MSRA, and Resume datasets, respectively, thereby outperforming the other models. Experimental results demonstrate that GatedMan accurately identifies entities related to crop diseases and pests and exhibits high generalizability in other domains. Full article

This paper introduces a novel methodology for the real-time mapping of mobility restrictions, utilizing spatial crowdsourcing and Telegram as a traffic event data source. This approach is efficient in regions suffering from limitations in traditional data-capturing devices. The methodology employs ArcGIS Online (AGOL) for data collection, storage, and analysis, and develops a 3W (what, where, when) model for analyzing mined Arabic text from Telegram. Data quality validation methods, including spatial clustering, cross-referencing, and ground-truth methods, support the reliability of this approach. Applied to the Palestinian territory, the proposed methodology ensures the accurate, timely, and comprehensive mapping of traffic events, including checkpoints, road gates, settler violence, and traffic congestion. The validation results indicate that using spatial crowdsourcing to report restrictions yields promising validation rates ranging from 67% to 100%. Additionally, the developed methodology utilizing Telegram achieves a precision value of 73%. These results demonstrate that this methodology constitutes a promising solution, enhancing traffic management and informed decision-making, and providing a scalable model for regions with limited traditional data collection infrastructure. Full article

A picowatt CMOS voltage reference with dual outputs is proposed and simulated in this paper based on a standard 65 nm process. To compensate for the leakage current caused by parasitic reverse-biased PN junctions, an approach employing gate leakage transistors is proposed. Maintaining a maximal temperature coefficient (TC) of 20.40 ppm/°C across an extended temperature range of −10∼155 °C is achieved. Additionally, a voltage divider consisting of diode-connected NMOS transistors is introduced to obtain a lower voltage output without shunting the original branch or utilizing operational amplifiers. Moreover, a novel trimming block is utilized to optimize TC across different process corners. Simulation results demonstrate that a minimum power consumption of only 53.83 pW is achieved and the line sensitivity is 0.077%/V with 0.45 V to 2.5 V supply. The power supply rejection ratio of −76.70 dB at 10 Hz and $V_{DD}$ = 1.8 V is obtained. Full article

Although astaxanthin (AST) has demonstrated a modulatory effect on voltage-gated Ca²⁺ (Cav) channels and excitatory glutamate neuronal transmission in vitro, particularly on the excitability of nociceptive sensory neurons, its action in vivo remains to be determined. This research sought to determine if an acute intravenous administration of AST in rats reduces the excitability of wide-dynamic range (WDR) spinal trigeminal nucleus caudalis (SpVc) neurons in response to nociceptive and non-nociceptive mechanical stimulation in vivo. In anesthetized rats, extracellular single-unit recordings were carried out on SpVc neurons following mechanical stimulation of the orofacial area. The average firing rate of SpVc WDR neurons in response to both gentle and painful mechanical stimuli significantly and dose-dependently decreased after the application of AST (1–5 mM, i.v.), and maximum suppression of discharge frequency for both non-noxious and nociceptive mechanical stimuli occurred within 10 min. These suppressive effects persisted for about 20 min. These results suggest that acute intravenous AST administration suppresses the SpVc nociceptive transmission, possibly by inhibiting Cav channels and excitatory glutamate neuronal transmission, implicating AST as a potential therapeutic agent for the treatment of trigeminal nociceptive pain without side effects. Full article

Semantic segmentation-based Complex Waterway Scene Understanding has shown great promise in the environmental perception of Unmanned Surface Vehicles. Existing methods struggle with estimating the edges of obstacles under conditions of blurred water surfaces. To address this, we propose the Lightweight Dual-branch Mamba Network (LDMNet), which includes a CNN-based Deep Dual-branch Network for extracting image features and a Mamba-based fusion module for aggregating and integrating global information. Specifically, we improve the Deep Dual-branch Network structure by incorporating multiple Atrous branches for local fusion; we design a Convolution-based Recombine Attention Module, which serves as the gate activation condition for Mamba-2 to enhance feature interaction and global information fusion from both spatial and channel dimensions. Moreover, to tackle the directional sensitivity of image serialization and the impact of the State Space Model’s forgetting strategy on non-causal data modeling, we introduce a Hilbert curve scanning mechanism to achieve multi-scale feature serialization. By stacking feature sequences, we alleviate the local bias of Mamba-2 towards image sequence data. LDMNet integrates the Deep Dual-branch Network, Recombine Attention, and Mamba-2 blocks, effectively capturing the long-range dependencies and multi-scale global context information of Complex Waterway Scene images. The experimental results on four benchmarks show that the proposed LDMNet significantly improves obstacle edge segmentation performance and outperforms existing methods across various performance metrics. Full article

The range-Doppler algorithm (RDA), a key technique for generating synthetic aperture radar (SAR) images, offers high-resolution images but requires significant memory resources and involves complex signal processing. Moreover, the multitude of fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) operations in RDA necessitates high bandwidth and lacks data reuse, leading to bottlenecks. This paper introduces a synthetic aperture radar dual in-line memory module (SARDIMM), which executes RDA operations near memory via near-memory processing (NMP), thereby effectively reducing memory accesses, execution time, and energy consumption. The embedded NMP module in SARDIMM optionally supports a combination of FFT, IFFT, and matched filter operations of the RDA for range and azimuth compression. The operator within the NMP module accelerates the FFT by performing two radix-2 single butterfly operations in parallel. The NMP module was implemented and validated on a Xilinx UltraScale+ field-programmable gate array (FPGA) using Verilog-HDL. The acceleration performance of RDA for images of various sizes was evaluated through a simulator modified with gem5 and DRAMSim3 and achieved a 6.34–6.93× speedup and 41.9–48.2% energy savings. Full article

The increasing complexity of modern networks and their evolving needs demand flexible, high-performance packet processing solutions. The P4 language excels in specifying packet processing in software-defined networks (SDNs). Field-programmable gate arrays (FPGAs) are ideal for P4-based packet parsers due to their reconfigurability and ability to handle data transmitted at high speed. This paper introduces three FPGA-based P4-programmable packet parsing architectural designs that translate P4 specifications into adaptable hardware implementations called base, overlay, and pipeline, each optimized for different packet parsing performance. As modern network infrastructures evolve, the need for multi-tenant environments becomes increasingly critical. Multi-tenancy allows multiple independent users or organizations to share the same physical network resources while maintaining isolation and customized configurations. The rise of 5G and cloud computing has accelerated the demand for network slicing and virtualization technologies, enabling efficient resource allocation and management for multiple tenants. By leveraging P4-programmable packet parsers on FPGAs, our framework addresses these challenges by providing flexible and scalable solutions for multi-tenant network environments. The base parser offers a simple design for essential packet parsing, using minimal resources for high-speed processing. The overlay parser extends the base design for parallel processing, supporting various bus sizes and throughputs. The pipeline parser boosts throughput by segmenting parsing into multiple stages. The efficiency of the proposed approaches is evaluated through detailed resource consumption metrics measured on an Alveo U280 board, demonstrating throughputs of 15.2 Gb/s for the base design, 15.2 Gb/s to 64.42 Gb/s for the overlay design, and up to 282 Gb/s for the pipelined design. These results demonstrate a range of high performances across varying throughput requirements. The proposed approach utilizes a system that ensures low latency and high throughput that yields streaming packet parsers directly from P4 programs, supporting parsing graphs with up to seven transitioning nodes and four connections between nodes. The functionality of the parsers was tested on enterprise networks, a firewall, and a 5G Access Gateway Function graph. Full article

Autonomous robots operate on batteries, rendering power efficiency essential. The substantial computational demands of object detection present a significant burden to the low-power cores employed in these robots. Therefore, we propose a grid-based density-based spatial clustering of applications with a noise (DBSCAN) clustering accelerator for light detection and ranging (LiDAR)’s point cloud to accelerate computational speed and alleviate the operational burden on low-power cores. The proposed method for DBSCAN clustering leverages the characteristics of LiDAR. LiDAR has fixed positions where light is emitted, and the number of points measured per frame is also fixed. These characteristics make it possible to impose grid-based DBSCAN on clustering a LiDAR’s point cloud, mapping the positions and indices where light is emitted to a 2D grid. The designed accelerator with the proposed method lowers the time complexity from $O\left(n^2\right)$ to $O\left(n\right)$. The designed accelerator was implemented on a field programmable gate array (FPGA) and verified by comparing clustering results, speeds, and power consumption across various devices. The implemented accelerator speeded up clustering speeds by 9.54 and 51.57 times compared to the i7-12700 and Raspberry Pi 4, respectively, and recorded a 99% reduction in power consumption compared to the Raspberry Pi 4. Comparisons of clustering results also confirmed that the proposed algorithm performed clustering with high visual similarity. Therefore, the proposed accelerator with a low-power core successfully accelerated speed, reduced power consumption, and effectively conducted clustering. Full article

Thermal camouflage is a highly coveted technology aimed at enhancing the survivability of military equipment against infrared (IR) detectors. Recently, two-dimensional (2D) nanomaterials have shown low IR emissivity, widely tunable opto-electronic properties, and compatibility with stealth applications. Among these, graphene and graphene-like materials are the most appealing 2D materials for thermal camouflage applications. In multilayer graphene (MLG), charge density can be effectively tuned through sufficiently intense electric fields or through electrolytic gating. Therefore, MLG’s optical properties, like infrared emissivity and absorbance, can be controlled in a wide range by voltage bias. The large emissivity modulation achievable with this material makes it suitable in the design of thermal dynamic camouflage devices. Generally, the emissivity modulation in the multilayered graphene medium is governed by an intercalation process of non-volatile ionic liquids under a voltage bias. The electrically driven reduction of emissivity lowers the apparent temperature of a surface, aligning it with the background temperature to achieve thermal camouflage. This characteristic is shared by other graphene-based materials. In this review, we focus on recent advancements in the thermal camouflage properties of graphene in composite films and aerogel structures. We provide a summary of the current understanding of how thermal camouflage materials work, their present limitations, and future opportunities for development. Full article

According to the International Electrotechnical Commission, medium voltage ranges from 1 kV to 36 kV. In this voltage range, the field of power electronics has been focusing on developing power converters with high efficiency. Converters for such applications include solid-state transformers, energy storage systems for vehicle charging, electric aircraft, etc. Power ranges could reach tens to hundreds of kilowatts at relatively high frequency (10–50 kHz). Currently, there are no high-frequency power semiconductors capable of switching these voltage levels. Instead of using a single power switch, a string of power switches is used. The upper switches in the string require special attention because they need the highest isolation capabilities and a floating control signal and power supply for the gate driver. Many techniques have been proposed to accomplish this, but they commonly use separate circuits for the control signal and the power supply, increasing the cost, size, and complexity of the gate driver. This paper presents a gate driver for medium voltage with high-voltage isolation capabilities in a single structure for the control signal and the power supply. The proposed gate driver uses a resonant converter that transmits power within the gate driver information. A demodulator separates the gate driver information from the power signal, obtaining the power supply and the control signal for the switch. The paper includes simulation and experimental results that demonstrate the viability of the proposal. The experimental results show the principal features of the gate driver, achieving improvements in complexity, isolation capabilities, and both rise and fall times for large input capacitances of power semiconductor switches. The proposed gate driver presents a rise time of 44 ns and a fall time of 46 ns for the gate input capacitance of currently available SiC MOSFETs. The isolation barrier uses a 25 mm air gap, achieving an isolation capability of approximately 68.2 kV, which exceeds the requirements for MV applications. Full article

Three-dimensional(3D) shape measurement using point clouds has recently gained significant attention. Phase measuring profilometry (PMP) is widely preferred for its robustness against external lighting changes and high-precision results. However, PMP suffers from long computation times due to complex calculations and its high memory usage. It also faces a 2π ambiguity issue, as the measured phase is limited to the 2π range. This is typically resolved using dual-wavelength methods. However, these methods require separate measurements of phase changes at two wavelengths, increasing the data processing volume and computation times. Our study addresses these challenges by implementing a 3D shape measurement system on a System-on-Chip (SoC)-type Field-Programmable Gate Array (FPGA). We developed a PMP algorithm with dual-wavelength methods, accelerating it through high-level synthesis (HLS) on the FPGA. This hardware implementation significantly reduces computation time while maintaining measurement accuracy. The experimental results demonstrate that our system operates correctly on the SoC-type FPGA, achieving computation speeds approximately 11.55 times higher than those of conventional software implementations. Our approach offers a practical solution for real-time 3D shape measurement, potentially benefiting applications in fields such as quality control, robotics, and computer vision. Full article

This work proposes a modular learning framework (MIRA) for rehabilitation robots based on a new deep recurrent neural network (RNN) that achieves adaptive multi-joint motion imitation. The RNN is fed with the fundamental frequencies as well as the ranges of the joint trajectories, in order to predict the future joint trajectories of the robot. The proposed framework also uses a Segment Online Dynamic Time Warping (SODTW) algorithm to quantify the closeness between the robot and patient motion. The SODTW cost decides the amount of modification needed in the inputs to our deep RNN network, which in turn adapts the robot movements. By keeping the prediction mechanism (RNN) and adaptation mechanism (SODTW) separate, the framework achieves modularity, flexibility, and scalability. We tried both Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) RNN architectures within our proposed framework. Experiments involved a group of 15 human subjects performing a range of motion tasks in conjunction with our social robot, Zeno. Comparative analysis of the results demonstrated the superior performance of the LSTM RNN across multiple task variations, highlighting its enhanced capability for adaptive motion imitation. Full article

To improve the reliability and maintainability of the nuclear safety-class digital control system (DCS), this paper conducts a study on the fault prediction of critical components in the output circuit of the nuclear safety-class signal conditioning module. To address the issue of insufficient feature extraction for the minor offset fault feature and the low accuracy of fault prediction, a predictive model based on stacked denoising autoencoder (SDAE) feature extraction and an improved gated recurrent unit (GRU) is proposed. Therefore, fault simulation modeling is performed for critical components of the signal output circuit to obtain fault datasets of critical components, and the SDAE model is used to extract fault features. The fault prediction model based on GRU is established, and the number of hidden layers, the number of hidden layer nodes, and the learning rate of the GRU model are optimized using the adaptive gray wolf optimization algorithm (AGWO). The prediction performance evaluation metrics include the root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and absolute error (EA), which are used for evaluating the prediction results of models such as the AGWO-GRU model, recurrent neural network (RNN) model, and long short-term memory network (LSTM). The results show that the GRU model optimized by AGWO has a better prediction accuracy (errors range within 0.01%) for the faults of the circuit critical components, and, moreover, can accurately and stably predict the fault trend of the circuit. Full article