Search Results (740)

Status epilepticus occurs when a seizure lasts more than five minutes or when multiple seizures occur with incomplete return to baseline. SE induces a myriad of pathological changes involving synaptic and extra-synaptic factors. The transition from a self-limiting seizure to a self-sustaining one is established by maladaptive receptor trafficking, whereby GABA_A receptors are progressively endocytosed while glutamatergic receptors (NMDA and AMPA) are transported to the synaptic membrane, causing excitotoxicity and alteration in glutamate-dependent downstream signaling. The subsequent influx of Ca²⁺ exposes neurons to increased levels of [Ca²⁺]i, which overwhelms mitochondrial buffering, resulting in irreversible mitochondrial membrane depolarization and mitochondrial injury. Oxidative stress resulting from mitochondrial leakage and increased production of reactive oxygen species activates the inflammasome and induces a damage-associated molecular pattern. Neuroinflammation perpetuates oxidative stress and exacerbates mitochondrial injury, thereby jeopardizing mitochondrial energy supply in a state of accelerated ATP consumption. Additionally, Ca²⁺ overload can directly damage neurons by activating enzymes involved in the breakdown of proteins, phospholipids, and nucleic acids. The cumulative effect of these effector pathways is neuronal injury and neuronal death. Surviving neurons undergo long-term alterations that serve as a substrate for epileptogenesis. This review highlights the multifaceted mechanisms underlying SE self-sustainability, pharmacoresistance, and subsequent epileptogenesis. Full article

An Analog-to-Digital Converter (ADC) is an indispensable part of image sensor systems. This paper presents a silicon-based 13-bit 100 kS/s two-step single-slope analog-to-digital converter (TS-SS ADC) for infrared image sensors with a frame rate of 100 Hz. For the charge leakage and offset voltage issues inherent in conventional TS-SS ADC, a four-terminal comparator was employed to resolve the fine ramp voltage offset caused by charge redistribution in storage and parasitic capacitors. In addition, a current-steering digital-to-analog converter (DAC) was adopted to calibrate the voltage reference of the dynamic comparator and mitigate differential nonlinearity (DNL)/integral nonlinearity (INL). To eliminate quantization dead zones, a 1-bit redundancy was incorporated into the fine quantization circuit. Finally, the quantization scheme consisted of 7-bit coarse quantization followed by 7-bit fine quantization. The ADC was implemented using an SMIC 55 nm processSemiconductor Manufacturing International Corporation, Shanghai, China. The post-simulation results show that when the power supply is 3.3 V, the ADC achieves a quantization range of 1.3 V–3 V. Operating at a 100 kS/s sampling rate, the proposed ADC exhibits an effective number of bits (ENOBs) of 11.86, a spurious-free dynamic range (SFDR) of 97.45 dB, and a signal-to-noise-and-distortion ratio (SNDR) of 73.13 dB. The power consumption of the ADC was 22.18 mW. Full article

The magnetic flux leakage (MFL) method is widely acknowledged as a highly effective non-destructive evaluation (NDE) technique for detecting local damage in ferromagnetic structures such as steel wire ropes. In this study, a multi-channel MFL sensor module was developed, incorporating a purpose-designed Hall sensor array and magnetic yokes specifically shaped for steel cables. To validate the proposed damage detection method, artificial damages of varying degrees were inflicted on wire rope specimens through experimental testing. The MFL sensor module facilitated the scanning of the damaged specimens and measurement of the corresponding MFL signals. In order to improve the signal-to-noise ratio, a comprehensive set of signal processing steps, including channel equalization and normalization, was implemented. Subsequently, the detected MFL distribution surrounding wire rope defects was transformed into MFL images. These images were then analyzed and processed utilizing an object detection method, specifically employing the YOLOv9 network, which enables accurate identification and localization of defects. Furthermore, a quantitative defect detection method based on image size was introduced, which is effective for quantifying defects using the dimensions of the anchor frame. The experimental results demonstrated the effectiveness of the proposed approach in detecting and quantifying defects in steel cables, which combines deep learning-based analysis of MFL images with the non-destructive inspection of steel cables. Full article

Low-temperature stress severely limits plant growth and reduces agricultural productivity. Calmodulin-like (CML) proteins are crucial calcium sensors in plant cold responses. Transcriptome analysis of cold-stressed Saussurea involucrata identified seven differentially expressed CML genes. qRT-PCR confirmed that SiCML6 was strongly induced at 4 °C and −2 °C. Bioinformatics analysis showed that SiCML6 encodes a transmembrane protein containing an EF-hand domain. This protein carries a signal peptide and shows the closest phylogenetic relationship to Helianthus annuus CML3. Its promoter contains ABA, methyl jasmonate (MeJA), and cold-response elements. Arabidopsis plants overexpressing SiCML6 showed significantly higher survival rates at −2 °C than wild-type plants. Under freezing stress, SiCML6-overexpressing lines exhibited reduced malondialdehyde content, relative electrolyte leakage, and ROS accumulation (H₂O₂ and O₂⁻), along with increased proline, soluble sugars, soluble proteins, and total antioxidant capacity (T-AOC). SiCML6 elevated the expression of cold-responsive genes CBF3 and COR15a under normal conditions and further upregulated CBF1/2/3 and COR15a at 4 °C. Thus, low temperatures induced SiCML6 expression, which was potentially regulated by ABA/MeJA. SiCML6 enhances freezing tolerance by mitigating oxidative damage through boosted T-AOC and osmoprotectant accumulation while activating the CBF-COR signaling pathway. This gene is a novel target for improving crop cold resistance. Full article

Subsea pipelines, operating in high-pressure and high-salinity conditions, face ongoing risks of leakage. Pipeline leaks can pollute the marine environment and, in severe cases, cause safety incidents, endangering human lives and property. Regular integrity inspections of subsea pipelines are critical to prevent corrosion-related leaks. This study develops a magnetic flux leakage (MFL)-based pig for detecting corrosion in subsea pipelines. Using a three-dimensional finite element model, this study analyzes the effects of defect geometry, lift-off distance, and operating speed on MFL signals. It proposes a defect estimation method based on axial peak-to-valley values and radial peak spacing, with inversion accuracy validated against simulation results. This study establishes a theoretical and practical framework for subsea pipeline integrity management, providing an effective solution for corrosion monitoring. Full article

As the core device for protecting the safety of the pressure-bearing system, the spring full-open safety valve is prone to various forms of valve seat sealing surface damage after long-term opening and closing impact, corrosion, and medium erosion, which may lead to internal leakage. In view of the problems that the high-frequency acoustic emission signal of the internal leakage of the safety valve has, namely, a large number of energy-overlapping areas in the frequency domain, the overall signal presents broadband characteristics, large noise content, and no obvious time–frequency characteristics. A composite denoising method, IWTD, improved wavelet threshold function with dual adjustable factors, and the improved VMD algorithm is proposed. In view of the problem that the optimal values of the dual adjustment factors a and b of the function are difficult to determine manually, an improved dung beetle optimization algorithm is proposed, with the maximum Pearson coefficient as the optimization target; the optimization is performed within the value range of the dual adjustable factors a and b, so as to obtain the optimal value. In view of the problem that the key parameters K and α in VMD decomposition are difficult to determine manually, the maximum Pearson coefficient is taken as the optimization target, and the improved dung beetle algorithm is used to optimize within the value range of K and α, so as to obtain the IVMD algorithm. Based on the IVMD algorithm, the characteristic decomposition of the internal leakage acoustic emission signal occurs after the denoising of the IWTD function is performed to further improve the denoising effect. The results show that the Pearson coefficients of all types of internal leakage acoustic emission signals after IWTD-IVMD composite noise reduction are greater than 0.9, which is much higher than traditional noise reduction methods such as soft and hard threshold functions. Therefore, the IWTD-IVMD composite noise reduction method can extract more main features out of the measured spring full-open safety valve internal leakage acoustic emission signals, and has a good noise reduction effect. Feature recognition after noise reduction can provide a good evaluation for the safe operation of the safety valve. Full article

As transistor dimensions continue to scale below 10 nm, traditional MOSFET architectures face increasing limitations from short-channel effects, gate leakage, and variability. FinFETs, especially junctionless FinFETs on silicon-on-insulator (SOI) substrates, offer improved electrostatic control and simplified fabrication, making them attractive for deeply scaled nodes. In this work, we propose a novel Stepped-Spacer Structured FinFET (S³-FinFET) that incorporates a three-layer HfO₂/Si₃N₄/HfO₂ spacer configuration designed to enhance electrostatics and suppress parasitic effects. Using 2D TCAD simulations, the S³-FinFET is evaluated in terms of key performance metrics, including transfer/output characteristics, ON/OFF current ratio, subthreshold swing (SS), drain-induced barrier lowering (DIBL), gate capacitance, and cut-off frequency. The results show significant improvements in leakage control and high-frequency behavior. These enhancements make the S³-FinFET particularly well-suited for Field-Programmable Gate Arrays (FPGAs), where power efficiency, speed, and signal integrity are critical to performance in reconfigurable logic environments. Full article

The estimation of leakage faults in evaporation tubes of supercharged boilers is crucial for ensuring the safe and stable operation of the central steam system. However, leakage faults of evaporation tubes feature high time dependency, strong coupling among monitoring parameters, and interference from noise. Additionally, the large number of monitoring parameters (approximately 140) poses a challenge for spatiotemporal feature extraction, feature decoupling, and establishing a mapping relationship between high-dimensional monitoring parameters and leakage, rendering the precise quantitative estimation of evaporation tube leakage extremely difficult. To address these issues, this study proposes a novel deep learning framework (LSTM-CNN–attention), combining a Long Short-Term Memory (LSTM) network with a dual-pathway spatial feature extraction structure (ACNN) that includes an attention mechanism(attention) and a 1D convolutional neural network (1D-CNN) parallel pathway. This framework processes temporal embeddings (LSTM-generated) via a dual-branch ACNN—where the 1D-CNN captures local spatial features and the attention models’ global significance—yielding decoupled representations that prevent cross-modal interference. This architecture is implemented in a simulated supercharged boiler, validated with datasets encompassing three operational conditions and 15 statuses in the supercharged boiler. The framework achieves an average diagnostic accuracy (ADA) of over 99%, an average estimation accuracy (AEA) exceeding 90%, and a maximum relative estimation error (MREE) of less than 20%. Even with a signal-to-noise ratio (SNR) of −4 dB, the ADA remains above 90%, while the AEA stays over 80%. This framework establishes a strong correlation between leakage and multifaceted characteristic parameters, moving beyond traditional threshold-based diagnostics to enable the early quantitative assessment of evaporator tube leakage. Full article

This study aimed to investigate the tyrosine kinase inhibitor Nintedanib and its potential role in reversing epithelial–mesenchymal transition (EMT) induced by transforming growth factor beta 2 (TGF-β2) in retinal pigment epithelial (RPE) cells, along with its therapeutic potential using a mouse model of subretinal fibrosis. We hypothesized that the blockade of angiogenesis promoting and fibrosis inducing signaling using the receptor tyrosine kinase inhibitor Nintedanib (OfevTM) can prevent or reverse EMT both in vitro and in our in vivo model of subretinal fibrosis. Primary human retinal pigment epithelial cells (phRPE) and adult retinal pigment epithelial cell line (ARPE-19) cells were treated with TGF-β210 ng/mL for two days followed by four days of Nintedanib (1 µM) incubation. Epithelial and mesenchymal phenotypes were assessed by morphological examination, quantitative real-time polymerase chain reaction(qPCR) (ZO-1, Acta2, FN, and Vim), and immunocytochemistry (ZO-1, vimentin, fibronectin, and αSMA). Metabolites were measured using luciferase-based assays. Extracellular acidification and oxygen consumption rates were measured using the Seahorse XF system. Metabolic-related genes (GLUT1, HK2, PFKFB3, CS, LDHA, LDHB) were evaluated by qPCR. A model of subretinal fibrosis using the two-stage laser-induced method in C57BL/6J mice assessed Nintedanib’s therapeutic potential. Fibro-vascular lesions were examined 10 days later via fluorescence angiography and immunohistochemistry. Both primary and ARPE-19 RPE stimulated with TGF-β2 upregulated expression of fibronectin, αSMA, and vimentin, and downregulation of ZO-1, consistent with morphological changes (i.e., elongation). Glucose consumption, lactate production, and glycolytic reserve were significantly increased in TGF-β2-treated cells, with upregulation of glycolysis-related genes (GLUT1, HK2, PFKFB3, CS). Nintedanib treatment reversed TGF-β2-induced EMT signatures, down-regulated glycolytic-related genes, and normalized glycolysis. Nintedanib intravitreal injection significantly reduced collagen-1+ fibrotic lesion size and Isolectin B4+ neovascularization and reduced vascular leakage in the two-stage laser-induced model of subretinal fibrosis. Nintedanib can induce Mesenchymal-to-Epithelial Transition (MET) in RPE cells and reduce subretinal fibrosis through metabolic reprogramming. Nintedanib can therefore potentially be repurposed to treat retinal fibrosis. Full article

Internal leakage within the valve body constitutes a severe potential safety hazard in industrial fluid control systems, attributable to its high concealment and the resultant difficulty in detection via conventional methodologies. Acoustic emission (AE) technology, functioning as an efficient non-destructive testing approach, is capable of capturing the transient stress waves induced by leakage, thereby furnishing an effective means for the real-time monitoring and quantitative assessment of internal leakage within the valve body. This paper conducts a systematic review of the theoretical foundations, signal-processing methodologies, and the latest research advancements related to the technology for detecting internal leakage in the valve body based on acoustic emission. Firstly, grounded in Lechlier’s acoustic analogy theory, the generation mechanism of acoustic emission signals arising from valve body leakage is elucidated. Secondly, a detailed analysis is conducted on diverse signal processing techniques and their corresponding optimization strategies, encompassing parameter analysis, time–frequency analysis, nonlinear dynamics methods, and intelligent algorithms. Moreover, this paper recapitulates the current challenges encountered by this technology and delineates future research orientations, such as the fusion of multi-modal sensors, the deployment of lightweight deep learning models, and integration with the Internet of Things. This study provides a systematic reference for the engineering application and theoretical development of the acoustic emission-based technology for detecting internal leakage in valves. Full article

Underground cast-in-place pipes (CIPP, Diameter of 2’–5’) are used to transport water for the Phoenix, AZ area. These pipes have developed leaks due to their age and changes in the environment, resulting in a significant waste of water. Currently, leaks can only be identified when water pools above ground occur and are then manually confirmed through the inside of the pipe, requiring the shutdown of the water system. However, many leaks may not develop a puddle of water, making them even harder to identify. The primary objective of this research was to develop an inspection method utilizing drone-based infrared imagery to remotely and non-invasively sense thermal signatures of abnormal soil moisture underneath urban surface treatments caused by the leakage of water pipelines during the regular operation of water transportation. During the field tests, five known leak sites were evaluated using an intensive experimental procedure that involved conducting multiple flights at each test site and a stringent filtration process for the measured temperature data. A detectable thermal signal was observed at four of the five known leak sites, and these abnormal thermal signals directly overlapped with the location of the known leaks provided by the utility company. A strong correlation between ground temperature and shading before sunset was observed in the temperature data collected at night. Thus, a shadow and solar energy model was implemented to estimate the position of shadows and energy flux at given times based on the elevation of the surrounding structures. Data fusion between the metrics of shadow time, solar energy, and the temperature profile was utilized to filter the existing points of interest further. When shadows and solar energy were considered, the final detection rate of drone-based infrared imaging was determined to be 60%. Full article

Under complex operating conditions, vibration signals from rotating machinery often exhibit non-stationary characteristics with non-proportional and closely spaced instantaneous frequency (IF) components. Traditional time–frequency analysis (TFA) methods struggle to accurately extract such features due to energy leakage and component mixing. In response to these issues, an enhanced time–frequency analysis approach, termed Local Entropy Selection Scaling–Reassigning Chirplet Transform (LESSRCT), has been developed to improve the representation accuracy for complex non-stationary signals. This approach constructs multi-channel time–frequency representations (TFRs) by introducing multiple scales of chirp rates (CRs) and utilizes a Rényi entropy-based criterion to adaptively select multiple optimal CRs at the same time center, enabling accurate characterization of multiple fundamental components. In addition, a frequency reassignment mechanism is incorporated to enhance energy concentration and suppress spectral diffusion. Extensive validation was conducted on a representative synthetic signal and three categories of real-world data—bat echolocation, inner race bearing faults, and wind turbine gearbox vibrations. In each case, the proposed LESSRCT method was compared against SBCT, GLCT, CWT, SET, EMCT, and STFT. On the synthetic signal, LESSRCT achieved the lowest Rényi entropy of 13.53, which was 19.5% lower than that of SET (16.87) and 35% lower than GLCT (18.36). In the bat signal analysis, LESSRCT reached an entropy of 11.53, substantially outperforming CWT (19.91) and SBCT (15.64). For bearing fault diagnosis signals, LESSRCT consistently achieved lower entropy across varying SNR levels compared to all baseline methods, demonstrating strong noise resilience and robustness. The final case on wind turbine signals demonstrated its robustness and computational efficiency, with a runtime of 1.31 s and excellent resolution. These results confirm that LESSRCT delivers robust, high-resolution TFRs with strong noise resilience and broad applicability. It holds strong potential for precise fault detection and condition monitoring in domains such as aerospace and renewable energy systems. Full article

To improve the accuracy of insulation state online monitoring for capacitive dry-type current transformers (CTs) and to address the limitations of traditional methods relying on potential transformer (PT) voltage signals and reference devices, a virtual voltage-based online monitoring method is proposed. First, the leakage current is collected through a core-type current transformer on the end-screen grounding line. Combined with group measurement data from surge arresters and dry-type CTs on the same busbar, phase constraints are established, and a least mean square (LMS) algorithm is utilized to iteratively train the virtual voltage reference phase. Subsequently, the resistive current and dielectric loss factor (tan δ) are calculated based on the virtual voltage reference phase to achieve online insulation state monitoring. Simulation results demonstrate that the proposed method can accurately obtain the virtual voltage reference phase and effectively identify the degradation trend of dry-type CTs. Field applications validate the feasibility of the method, with monitoring data fluctuations (±20 μA for the resistive current and ±0.002 for the dielectric loss) meeting engineering requirements. This method eliminates the need for PT signals and reference devices, thus providing a novel approach for the online insulation monitoring of dry-type CTs. Full article

Neural networks are increasingly attractive for digital predistortion applications due to their demonstrated superior performance. This is mainly attributed to their ability to capture the intrinsic traits of nonlinear systems. This paper presents a novel hybrid predistorter labeled as the look-up table assisted bidirectional long-short term memory (BiLSTM) neural network (LUT-A-BiNN) that combines a neural network cascaded with a look-up table in a manner that both sub-models complement each other. The main motivation in using this two-box arrangement is to eliminate the highly nonlinear static distortions of the PA with the look-up table, allowing the neural network to focus on the compensation of the dynamic distortions. The proposed predistorter is experimentally validated using 5G test signals. The results demonstrate the ability of the proposed predistorter to achieve a 5 dB enhancement in the adjacent channel leakage ratio when compared to its single-box counterpart (BiLSTM neural network predistorter) while maintaining the signal-agnostic performance of the BiLSTM predistorter. Full article

This paper proposes a dual-mode CMOS analog front-end (AFE) circuit for short-wave infrared (SWIR) image sensors, which integrates a hybrid readout circuit (ROIC) and a 12-bit two-step single-slope analog-to-digital converter (TS-SS ADC). The ROIC dynamically switches between buffered-direct-injection (BDI) and direct-injection (DI) modes, thus balancing injection efficiency against power consumption. While the DI structure offers simplicity and low power, it suffers from unstable biasing and reduced injection efficiency under high background currents. Conversely, the BDI structure enhances injection efficiency and bias stability via an input buffer but incurs higher power consumption. To address this trade-off, a dual-mode injection architecture with mode-switching transistors is implemented. Mode selection is executed in-pixel via a low-leakage transmission gate and coordinated by the column timing controller, enabling low-current pixels to operate in low-noise BDI mode, whereas high-current pixels revert to the low-power DI mode. The TS-SS ADC employs a four-terminal comparator and dynamic reference voltage compensation to mitigate charge leakage and offset, which improves signal-to-noise ratio (SNR) and linearity. The prototype occupies 2.1 mm × 2.88 mm in a 0.18 µm CMOS process and serves a 64 × 64 array. The AFE achieves a dynamic range of 75.58 dB, noise of 249.42 μV, and 81.04 mW power consumption. Full article