Search Results (556)

Unidirectional multilevel back-to-back (BTB) converters are widely employed in renewable energy generation systems and in motor drives for coal mining operations. However, the current zero-crossing distortion (CZCD) on the grid side and the neutral-point potential (NPP) imbalance on the common DC bus all restrict its applicability, such as in grids with stringent low harmonic requirements and in medium to high power situations. This paper proposes a coordinated control strategy to simultaneously address these issues theoretically. The study focuses on topology comprising a Vienna rectifier structure on the grid side and a three-level NPC inverter structure on the load side. In the proposed strategy, the current distortion angle, the manifestation of CZCD, is first eliminated by reactive current compensation on the Vienna rectifier side. Furthermore, the coupling between CZCD and NPP imbalance is resolved by reconstructing the neutral-point current target function. Ultimately, the optimal zero-sequence voltage (ZSV) is obtained using an interpolation function and then injected into the three-phase reference voltages of the inverter side to balance the NPP on the DC bus. The strategy transforms the influence of the rectifier on the NPP from an unknown coupling factor into a known disturbance and enables the inverter to actively compensate for variations in the overall converter system. An experimental platform was independently developed to verify the effectiveness of the proposed control strategy. Full article

Passive radio frequency identification (RFID) sensing systems integrate wireless energy transfer with information identification. However, conventional passive RFID systems still face three key challenges in practical applications: low RF energy harvesting efficiency, high power consumption of sensor loads, and high complexity of tag anti-collision algorithms. To address these issues, this paper proposes a hardware–software co-optimized RFID sensor system. For hardware, low threshold RF Schottky diodes are selected, and an input inductor is introduced into the voltage multiplier rectifier to boost the signal amplitude, thereby enhancing the radio frequency to direct current (RF-DC) energy conversion efficiency. In terms of loading, a low-power management strategy is implemented for the power supply and control logic of the sensor node to minimize the overall system energy consumption. For algorithmic implementation, a Dual-Threshold Stepped Dynamic Frame Slotted ALOHA (DTS-DFSA) anti-collision algorithm is proposed, which adaptively adjusts the frame length based on the observed collision ratio, eliminating the need for complex tag population estimation. The algorithm features low computational complexity and is well suited for resource constrained embedded platforms. Through simulation validation, we compare the conversion efficiency of the RF energy harvesting circuit before and after improvement, the current of the sensor load in active and idle states, and the performance of the proposed algorithm against the low-complexity DFSA (LC-DFSA). The results show that the maximum conversion efficiency of the improved RF energy harvesting circuit has increased from 60.56% to 68.69%; specifically, the sensor load current drastically drops from approximately 2.0 $\mathrm{m}\mathrm{A}$ in the active state to around 74 $\mathsf{\mu }\mathrm{A}$ in the idle state, validating the efficacy of the proposed power gating strategy, and the proposed DTS-DFSA algorithm outperforms existing low-complexity schemes in both identification efficiency and computational complexity. Full article

Endothelial ion signaling is crucial for the proper function of the arterial microcirculation, regulating local blood flow to meet metabolic demands and contributing to the regulation of systemic arterial pressure. The role of endothelial ion channels in the precise control of vascular resistance has been primarily investigated in animal models, where the microvasculature is more readily accessible. This review aims to discuss current knowledge on the role of endothelial ion signaling in vasomotor regulation in the human microcirculation, focusing on potassium (K⁺) channels (K_IR2.1, K_ATP, SK_Ca/IK_Ca), Transient Receptor Potential (TRP) channels, particularly TRP Vanilloid 1 (TRPV1) and TRPV4, and Piezo1 channels. The analysis examines the organization of the endothelial ionic signaling machinery in the most extensively studied human microvascular beds, such as the skin, skeletal muscle, and brain, while also discussing vascular reactivity in vessels isolated ex vivo. Accumulating evidence indicates that a distinct repertoire of endothelial ion channels engages diverse endothelium-dependent vasorelaxant pathways across different vascular beds. Understanding how endothelial channels regulate the microvascular unit is predicted to foster the search for alternative therapeutic strategies for treating cardiovascular and neurodegenerative disorders associated with endothelial dysfunction. Full article

This paper addresses the core conflict between long-range communication and ultra-low power requirements in sensing nodes for Wireless Sensor Networks (WSNs) by proposing a wake-up receiver (WuRx) design featuring nanowatt-level power consumption and high sensitivity. Conventional architectures are plagued by low energy efficiency, poor demodulation reliability, and insufficient clock synchronization accuracy, which hinders their practical application in real-world scenarios like WSNs. The proposed design employs an event-triggered mechanism, where a continuously operating, low-power WuRx monitors the channel and activates the main system only after validating a legitimate command, thereby significantly reducing standby power. At the system design level, a key innovation is direct conjugate matching between the antenna and a multi-stage rectifier, replacing the traditional 50 Ohm interface, which substantially improves energy transmission efficiency. Furthermore, a mean-detection demodulation circuit is introduced to dynamically generate an adaptive reference level, effectively overcoming the challenge of discriminating shallow modulation caused by signal saturation in the near-field region. At the baseband processing level, a configurable fault-tolerant correlator logic and a data-edge-triggered clock synchronization circuit are designed, combined with oversampling techniques to suppress clock drift and enhance the reliability of long data packet reception. Fabricated in a TSMC 0.18 µm CMOS process, the receiver features an ultra-low power consumption of 305 nW at 0.5 V and a high sensitivity of −47 dBm, enabling a communication range of up to 400 m in the 920–925 MHz band. Through synergistic innovation at both the circuit and system levels, this research provides a high-efficiency, high-reliability wake-up solution for long-range WSN nodes, effectively promoting the large-scale application of WSN technology in practical deployments. Full article

The patch-clamp technique is widely regarded as the gold standard in cellular electrophysiology and can be applied in several configurations. In the cell-attached (C-A) mode, it enables the recording of single-channel currents, whereas the whole-cell (W-C) mode allows for the measurement of macroscopic currents, representing the collective activity of many channels. When the recording configuration was switched from C-A to W-C on the same cell, the current amplitude increased dramatically, while action currents (ACs) were completely abolished, indicating a profound alteration in the cell’s electrophysiological response under the new setup. In excitable cells, the occurrence of ACs, representing propagated action potentials, can interfere with C-A single-channel recordings. To address this, a high-K⁺ solution is typically applied to the bath to suppress the ACs. The inwardly rectifying K⁺ (Kir), ATP-sensitive K⁺ (K_ATP) and large-conductance Ca²⁺-activated K⁺ (BK_Ca) channels are crucial members of the K⁺ channel family that facilitate the efflux of K⁺ ions, driven by the K⁺ electrochemical gradient. These channels are primarily distinguished by their rectification properties and gating kinetics. For instance, K_ATP channels exhibit a bursting kinetic pattern with inward rectifying property, while BK_Ca channels display strong outward rectification. Mitoxantrone, which belongs to a class of drugs called anthracenediones, can suppress the activity of Kir channels in differentiated RAW 264.7 cells, with no change in single-channel conductance. The respiratory stimulator GAL-021 acts as a BK_Ca channel inhibitor, and it suppresses channel activity and shifts the activation curve to the right, suggesting a voltage-dependent blockade that stabilizes the channel in a closed state. GAL-021 does not change the single-channel conductance, indicating it is a gating modifier rather than an open-pore blocker. The functional roles of ion channels are fundamentally important. Correspondingly, the field is transitioning to artificial intelligence for automated single-cell patch-clamp experiments, though brain slice recordings still require manual techniques. Full article

This study focuses on classifying electromyographic (EMG) signals to identify seven specific hand movements, including complete hand closure, individual finger closures, and a pincer grip. Accurately distinguishing these movements is challenging due to overlapping muscle activation patterns. To address this, a methodology structured in five stages was developed: placement of electrodes on specific forearm muscles to capture electrical activity during movements; acquisition of EMG signals from twelve participants performing the seven types of movements; preprocessing of the signals through filtering and rectification to enhance quality, followed by the extraction of features from three distinct types of preprocessed signals—filtered, rectified, and envelope signals—to facilitate analysis in the temporal, frequency, and time–frequency domains; extraction of relevant features such as amplitude, shape, symmetry, and frequency variance; and classification of the signals using eight machine learning algorithms: support vector machine (SVM), multiclass logistic regression, k-nearest neighbors (k-NN), Bayesian classifier, artificial neural network (ANN), random forest, XGBoost, and LightGBM. The performance of each algorithm was evaluated using different sets of features derived from the preprocessed signals to identify the most effective approach for classifying hand movements. Additionally, the impact of various signal representations on classification accuracy was examined. Experimental results indicated that some algorithms, especially when an expanded set of features was utilized, achieved improved accuracy in classifying hand movements. These findings contribute to the development of more efficient control systems for myoelectric prostheses and offer insights for future research in EMG signal processing and pattern recognition. Full article

The green-making stage is crucial for forming the distinctive aroma and flavor of Tieguanyin tea. Current green-making stage recognition relies on tea makers’ sensory experience, which is labor-intensive and time-consuming. To address these issues, this paper proposes a lightweight automatic recognition model named T-GSR for the accurate and objective identification of Tieguanyin tea green-making stages. First, an extensive set of Tieguanyin tea images at different green-making stages was collected. Subsequently, preprocessing techniques, i.e., multi-color-space fusion and morphological filtering, were applied to enhance the representation of target tea features. Furthermore, three targeted improvements were implemented based on the MobileNet V3 backbone network: (1) an adaptive residual branch was introduced to strengthen feature propagation; (2) the Rectified Linear Unit (ReLU) activation function was replaced with the Gaussian Error Linear Unit (GELU) to improve gradient propagation efficiency; and (3) an Improved Coordinate Attention (ICA) mechanism was adopted to replace the original Squeeze-and-Excitation (SE) module, enabling more accurate capture of complex tea features. Experimental results demonstrate that the T-GSR model outperforms the original MobileNet V3 in both classification performance and model complexity, achieving a recognition accuracy of 93.38%, an F1-score of 93.33%, with only 3.025 M parameters and 0.242 G FLOPs. The proposed model offers an effective solution for the intelligent recognition of Tieguanyin tea green-making stages, facilitating online monitoring and supporting automated tea production. Full article

This study evaluated the protective effects of naringin (NG) against intestinal injury in 7-day-old piglets infected with porcine epidemic diarrhea virus (PEDV). Eighteen piglets (Duroc × Landrace × Large, body weight = 2.58 ± 0.05 kg) were divided into three treatment groups based on similar body weights and equal numbers of males and females: the blank control group (CON group), the PEDV infection group (PEDV group), and the NG intervention + PEDV infection group (NG + PEDV group) (n = 6 per group). The experiment lasted for 11 days, comprising a pre-feeding period from days 0 to 3 and a formal experimental period from days 4 to 10. On days 4–10 of the experiment, piglets in the NG + PEDV group were orally administered NG (10 mg/kg). On Day 8 of the experiment, piglets in the PEDV and NG + PEDV groups were inoculated with PEDV (3 mL, 10⁶ 50% tissue culture infective dose (TCID₅₀) per milliliter). On day 11 of the experiment, piglets were euthanized for sample collection. PEDV infection caused significant intestinal damage, including a decreased (p < 0.05) villus height in the duodenum and ileum and an increased (p < 0.05) crypt depth in all intestinal segments. This intestinal damage was accompanied by an impaired absorptive function, as indicated by reduced (p < 0.05) serum D-xylose. Further results showed that PEDV compromised the intestinal antioxidant capacity by decreasing (p < 0.05) glutathione peroxidase and catalase activities, and it stimulated the intestinal inflammatory response by upregulating (p < 0.05) the expression of key inflammatory genes, including regenerating family member 3 gamma (REG3G; duodenum, jejunum, colon), S100 calcium binding protein A9 (S100A9; ileum, colon), interleukin 1 beta (IL-1β; ileum, colon), and S100 calcium binding protein A8 (S100A8; colon). PEDV also suppressed the intestinal lipid metabolism pathway by downregulating (p < 0.05) the ileal expression of Solute Carrier Family 27 Member 4 (SLC27A4), Microsomal Triglyceride Transfer Protein (MTTP), Apolipoprotein A4 (APOA4), Apolipoprotein C3 (APOC3), Diacylglycerol O-Acyltransferase 1 (DGAT1), and Cytochrome P450 Family 2 Subfamily J Member 34 (CYP2J34). Moreover, PEDV suppressed the intestinal antiviral ability by downregulating (p < 0.05) interferon (IFN) signaling pathway genes, including MX dynamin like GTPase 1 (MX1) and ISG15 ubiquitin like modifier (ISG15) in the duodenum; weakened intestinal water and ion transport by downregulating (p < 0.05) aquaporin 10 (AQP10) and potassium inwardly rectifying channel subfamily J member 13 (KCNJ13) in the duodenum, aquaporin 7 (AQP7) and transient receptor potential cation channel subfamily V member 6 (TRPV6) in the ileum, and TRPV6 and transient receptor potential cation channel subfamily M member 6 (TRPM6) in the colon; and inhibited intestinal digestive and absorptive function by downregulating (p < 0.05) phosphoenolpyruvate carboxykinase 1 (PCK1) in the duodenum and sucrase-isomaltase (SI) in the ileum. Notably, NG effectively counteracted these detrimental effects. Moreover, NG activated the IFN signaling pathway in the jejunum and suppressed PEDV replication in the colon. In conclusion, NG alleviates PEDV-induced intestinal injury by enhancing the antioxidant capacity, suppressing inflammation, normalizing the expression of metabolic and transport genes, and improving the antiviral ability. Full article

Among various activation functions, the Rectified Linear Unit (ReLU) has become the most widely adopted due to its computational simplicity and effectiveness in mitigating the vanishing-gradient problem. In this work, we investigate the advantages of employing ReLU as the activation function and establish its theoretical significance. Our analysis demonstrates that ReLU-based neural networks possess the universal approximation property. In addition, we provide a theoretical explanation for the phenomenon of neuron death in ReLU-based neural networks. We further validate the effectiveness of this explanation through empirical experiments. Full article

To address the issue of low efficiency in recovering low-frequency vibration energy during vehicle operation, this paper proposes a piezoelectric energy capture harvester based on wheel vibration. The device employs a parallel configuration of dual cantilever beam piezoelectric transducers in its mechanical structure, with additional mass blocks to optimize its resonant characteristics in the low-frequency range. A synchronous switch energy harvesting circuit was designed. By actively synchronizing the switch with the peak output voltage of the piezoelectric element, it effectively circumvents the turn-on voltage threshold limitations of diodes in bridge rectifier circuits, thereby enhancing energy conversion efficiency. A dynamic model of this device was established, and multiphysics simulation analysis was conducted using COMSOL-Multiphysics to investigate the modal characteristics, stress distribution, and output performance of the energy harvester. This revealed the influence of the piezoelectric vibrator’s thickness ratio and the mass block’s weight on its power generation capabilities. Experimental results indicate that under 20 Hz, 12 V sinusoidal excitation, the system achieves an average output power of 3.019 mW with an average open-circuit voltage reaching 16.70 V. Under simulated road test conditions at 70 km/h, the output voltage remained stable at 6.86 V, validating its feasibility in real-world applications. This study presents an efficient and reliable solution for self-powering in-vehicle wireless sensors and low-power electronic devices through mechatronic co-design. Full article

Magnesium ions regulate synaptic and nonsynaptic neuronal excitability from intracellular (Mg²⁺_i) and extracellular (Mg²⁺_o) domains, modulating voltage- and ligand-gated ion channels. K+ inward rectifier (Kir) channel inward rectification arises from Mg²⁺_i blocking the pore and outward K⁺ current, while Mg²⁺_o targets external sites. Mg²⁺_i causes voltage-dependent Ca²⁺ voltage-gated (Ca_V) and Na⁺ voltage-gated (Na_V) channel block while phosphorylation modulates channel activity. Mg²⁺_o elicits direct voltage-dependent Ca_V channel block, and screens surface charge, and in Na_V channels reduces conduction and may cause depolarization by quantum tunneling across closed channels. Mg²⁺_i is an allosteric large conductance Ca²⁺-activated K⁺ (BK) channel activator, binding to low-affinity sites to alter Ca²⁺ and voltage sensitivity but reduces small conductance Ca²⁺-activated K⁺ (SK) channels’ outward K⁺ current and induces inward rectification. N-Methyl-D-aspartate receptor (NMDAR) channels are inhibited by Mg²⁺_i binding within the pore, while Mg²⁺_o stabilizes excitability through voltage-dependent block, Mg²⁺_o forms Mg-ATP complex modifying purinergic P2X receptor (P2XR) channel affinity and gating and directly blocks the pore. Mg²⁺_o reduces gamma-aminobutyric acid type A receptor (GABA_AR) channel Cl⁻ current amplitude and augments susceptibility to blockers. Mg²⁺_o and Mg²⁺_i block nicotinic acetylcholine receptor (nAChR) channels through voltage-dependent pore binding and surface charge screening, impeding current flow and altering gating. Full article

The absence of a unifying pathogenetic mechanism in metabolic dysfunction-associated steatotic liver disease (MASLD), formerly known as non-alcoholic fatty liver disease (NAFLD), has significantly hindered therapeutic progress. Appreciation that the delivery of excessive amounts of calories to the liver via the portal circulation might be a key parallel between MASLD and the twin steatotic liver disease, alcohol-related liver disease (ALD), establishes a consolidated framework that could guide rational drug design and precise therapeutic approaches. This review contends that, in both ALD and MASLD, the unique dual blood supply to the liver, from both portal vein and hepatic artery as well as the distinctive blood flow control physiology, prevents hepatic arterial oxygen delivery from adequately compensating for the increased metabolic demands induced by excess caloric intake—alcohol in ALD and food in MASLD—resulting in hepatocellular injury. Over four decades ago, Lautt postulated that this ‘oxygen-nutrient mismatch’ could play a role in ALD. We have extended this paradigm to MASLD, theorizing that analogous mechanisms may be involved in both conditions. Evidence that comorbidities, which are associated with recurrent episodes of hypoxemia, such as obstructive sleep apnea (OSA), exacerbate MASLD progression, supports this. ALD is less strongly linked to metabolic syndrome than MASLD. This may be due to inherent differences in hepatic substrate processing. Carbohydrates, lipids, and proteins undergo diverse and flexible cytosolic metabolic pathways, especially under metabolic stress. In contrast, hepatic ethanol metabolism is predominantly linear and obligately oxidative, providing limited metabolic adaptability. Future perspectives could focus on rectifying the imbalance between hepatic oxygen delivery and nutrient availability. This might be accomplished by attenuating hepatic caloric excess using emerging pharmacotherapies for weight reduction, augmenting hepatic oxygenation through hyperbaric oxygen therapy, or increasing hepatic arterial blood flow with agents such as obeticholic acid. Furthermore, enhancement of hepatic basal metabolic activity with thyroid hormone receptor-β agonists, like resmiritom may confer similar therapeutic effects. Full article

Active-control coils on Keda Torus eXperiment (KTX) are used to suppress error fields and mitigate MHD instabilities, thereby extending discharge duration and improving plasma confinement quality. Achieving effective active MHD control imposes stringent requirements on the coil power supplies: wide-bandwidth and high-precision current regulation, deterministic low-latency response, and tightly synchronized operation across 136 independently driven coils. Specifically, the supplies must deliver up to ±200 A with fast slew rates and bandwidths up to several kilohertz, while ensuring sub-100 μs control latency, programmable waveforms, and inter-channel synchronization for real-time feedback. These demands make the power supply architecture a key enabling technology and motivate this work. This paper presents the design and simulation of the KTX active-control coil power supply. The system adopts a modular AC–DC–AC topology with energy storage: grid-fed rectifiers charge DC-link capacitor banks, each H-bridge IGBT converter (20 kHz) independently drives one coil, and an EMC filter shapes the output current. Matlab/Simulink R2025b simulations under DC, sinusoidal, and arbitrary current references demonstrate rapid tracking up to the target bandwidth with ±0.5 A ripple at 200 A and limited DC-link voltage droop (≤10%) from an 800 V, 50 mF storage bank. The results verify the feasibility of the proposed scheme and provide a solid basis for real-time multi-coil active MHD control on KTX while reducing instantaneous grid loading through energy storage. Full article

Human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) represent a robust platform for modelling inherited cardiac disorders. Comparative analysis of ion channel activity in patient-specific and isogenic control lines provides critical insights into the molecular mechanisms underlying channelopathies and arrhythmias. Atrial-specific hiPSC-CMs (hiPSC-aCMs) exhibit distinct electrophysiological properties governed by unique ion channel expression profiles, underscoring the need for optimized methodologies to record atrial ionic currents accurately. Here, we characterized the electrophysiological features of hiPSC-aCMs using the Nanion Patchliner automated patch-clamp system. An optimized cell dissociation protocol was developed to enhance cell integrity and seal formation, while tailored intra- and extracellular solutions were employed to isolate specific ionic currents. Using this approach, we reliably recorded major atrial currents, including the sodium current (I_Na), L-type calcium current (I_CaL), transient outward potassium current (I_to), ultrarapid component of the delayed rectifier current (I_Kur), small-conductance calcium-activated potassium current (I_SK), and pacemaker funny current (I_f). The resulting current profiles were reproducible and consistent with those observed in native atrial cardiomyocytes. These findings establish the feasibility of the automated electrophysiological characterization of ion channels in hiPSC-aCMs. This platform enables more efficient investigation of pathogenic variants and facilitates the development of targeted therapeutics for atrial arrhythmias and related channelopathies. Full article

Activation functions are fundamental to the representational capacity and optimization dynamics of deep neural networks. Although numerous nonlinearities have been proposed, ranging from classical sigmoid and tanh to modern smooth and trainable functions, no single activation is universally optimal, as each involves trade-offs among gradient flow, stability, computational cost, and expressiveness. This study introduces TRex, a novel activation function that combines the efficiency and linear growth of rectified units with the smooth gradient propagation of saturating functions. TRex features a non-zero, smoothed negative region inspired by tanh while maintaining near-linear behavior for positive inputs, preserving gradients and reducing neuron inactivation. We evaluate TRex against five widely used activation functions (ReLU, ELU, Swish, Mish, and GELU) across eight convolutional architectures (AlexNet, DenseNet-121, EfficientNet-B0, GoogLeNet, LeNet, MobileNet-V2, ResNet-18, and VGGNet) on two benchmark datasets (MNIST and Fashion-MNIST) and a real-world medical imaging dataset (SkinCancer). The results show that TRex achieves competitive accuracy, AUC, and convergence stability across most deep, connectivity-rich architectures while maintaining computational efficiency comparable to those of other smooth activations. These findings highlight TRex as a contextually efficient activation function that enhances gradient flow, generalization, and training stability, particularly in deeper or densely connected architectures, while offering comparable performance in lightweight and mobile-optimized models. Full article