Search Results (511)

Lectin receptor-like kinases (LecRLKs) are a large subfamily of receptor-like kinases (RLKs), and their N-terminal lectin domain is predicted to reversibly bind to carbohydrates. Within this family, G-type LecRLKs represent a distinct subclass defined by an extracellular S-locus glycoprotein (SLG) domain, which was originally identified for its role in governing self-incompatibility in Brassica species. Emerging evidence suggests that G-type LecRLKs are involved in plant immunity; however, only a small fraction have been functionally characterized, leaving the roles of most family members largely unknown. In this study, we identified RK3 (Receptor Kinase 3) as the most strongly induced gene within the G-type LecRLK clade VI upon infection with Pseudomonas syringae pv. tomato DC3000 (Pst DC3000). Through both gain- and loss-of-function analyses, we demonstrated that RK3 positively regulates flg22-induced immune signaling events, including oxidative burst and mitogen-activated protein kinase (MAPK) activation, as well as downstream responses such as defense gene expression and ethylene production. Remarkably, the immune-enhancing activity of RK3 does not require its kinase domain. Critically, both full-length RK3 and a kinase-deleted variant (RK3-ΔK) constitutively interact with FLS2 (Flagellin-Sensing 2) and BAK1 (BRASSINOSTEROID INSENSITIVE 1-associated receptor kinase 1). This provides direct evidence that RK3 functions primarily as a co-regulatory component within the PRR complex, independent of its kinase activity. Moreover, ectopic expression of RK3 in tomato enhanced resistance to Pst DC3000, highlighting its potential utility in engineering disease resistance in crops. Thus, RK3 reveals a non-canonical, kinase-independent mechanism by which a G-type LecRLK potentiates plant immunity, expanding our understanding of RLK signaling complexity. Full article

Objectives: The dependence of non-small cell lung cancer (NSCLC) on glutamine has made targeting glutamine metabolism an attractive therapeutic approach. Dietary interventions are increasingly considered as adjuvant cancer therapies. This study aims to explore the relationship between glutamine starvation and ferroptosis in NSCLC and to elucidate the underlying molecular mechanisms. Methods: The effects of glutamine starvation were evaluated both in A549 and H460 NSCLC cell lines and in vivo using xenograft models in SCID mice. Assessments included cell viability, migration, clonogenic capacity, and the expression of key proteins. To gain mechanistic insight, AMPK was either overexpressed or inhibited, and key markers of ferritinophagy (including ULK1, BECN1, NCOA4, and LC3-II/I) and ferroptosis (such as ACSL4, GPX4, and xCT) were analyzed. Results: Glutamine starvation markedly suppressed tumor growth in both in vitro and in vivo settings, while also reducing cell migration and clonogenicity in cultured cells. This intervention activated AMPK, as indicated by increases in both total and phosphorylated forms, and upregulated PDZD8 expression. Mechanistically, AMPK activation played a critical role in driving ferritinophagy and ferroptosis—manipulation of AMPK consistently altered key markers of these processes. Furthermore, AMPK levels influenced PDZD8 protein expression. Notably, overexpressing PDZD8 alone was sufficient in promoting both ferritinophagy and ferroptosis, indicating that PDZD8 acts as a critical downstream mediator of AMPK in this pathway. Conclusions: Our findings reveal that glutamine starvation triggers ferroptosis in NSCLC via activation of ferritinophagy, mediated by the AMPK/PDZD8 signaling pathway. These results support the potential of dietary glutamine restriction as a novel therapeutic approach for NSCLC. Full article

Wearable inertial sensors produce multi-axis motion signals with rich spatial and temporal structure. Existing deep-learning pipelines for human activity recognition (HAR) rarely tackle three issues jointly: explicit modeling of the body-part grouping of multi-location inertial channels, bidirectional temporal modeling at linear-time cost, and dynamic, time-varying attention for non-stationary motion. We aim to close these three gaps within a single architecture. To this end we propose Dynamic-Attentive Selective Mamba (DASM), which combines three components: Group-Aware Convolutions (GroupConv) for body-part-aware local features, a Bidirectional Mamba (BiMamba) module for linear-time forward and backward temporal context, and a Dynamic CBAM (DCBAM) that produces per-timestep channel and spatial attention for non-stationary windows. On the UCI Daily and Sports Activities dataset (19 classes, 8 subjects), under stratified segment-level 5-fold cross-validation (3 seeds, 15 runs/model), DASM reaches 99.89% accuracy and F1, a 0.11% gain over CNN-BiGRU-CBAM and 0.50% over Multi-STMT; under leave-one-subject-out (LOSO), it reaches 89.34%, 1.69% above the strongest baseline. The 10.55% drop under LOSO shows that segment-level results overestimate cross-subject generalization. Ablations show small but statistically detectable gains (Cohen’s $d\in [0.4,0.7]$ per module, $d\ge 1.5$ full-vs-baseline). We therefore position the contribution as a structured architecture within a near-saturated benchmark; broader deployment claims require multi-dataset subject-independent validation. Full article

This paper develops a dynamic signaling framework to analyze how educational investment evolves under imperfect information and how the informational value of credentials changes over time. It addresses a central question: under what conditions do signaling equilibria become fragile, and how does this fragility generate educational overinvestment and credential inflation in equilibrium? The model features heterogeneous productivity groups and endogenous educational choices, in which education plays both a signaling and a productive role. Informational frictions and wage-setting mechanisms jointly determine equilibrium configurations, allowing for separation, pooling, and mixed equilibria. The analysis shows that separating equilibria are inherently fragile: when signaling costs decline or when the share of lower-productivity workers becomes sufficiently small, incentives for imitation intensify, progressively eroding informational differentiation. This fragility gives rise to a cascade mechanism of overinvestment, whereby individuals increase educational attainment beyond efficient levels to preserve relative positioning. As a result, signaling distortions propagate across educational levels, generating persistent credential inflation and weakening the informational content of degrees. The framework also identifies conditions under which mixed equilibria may dominate separating equilibria in terms of aggregate welfare, particularly when the proportion of low-productivity workers is limited. By incorporating a productive dimension of education, the model distinguishes between pure signaling rents and genuine productivity gains, providing a unified interpretation of overeducation, declining returns to credentials, and persistent wage dispersion. Finally, the analysis characterizes an optimal taxation scheme that eliminates inefficient signaling rents while preserving incentives for productivity-enhancing investment. Taken together, the results highlight how equilibrium fragility, informational distortions, and strategic educational measures provide a unified explanation for diploma inflation, equilibrium segmentation, and persistent deviations from socially optimal investment levels. Full article

Explanation-guided learning improves the transparency of aspect-based sentiment analysis by coupling predictions with supporting evidence. However, the explanatory signals used for supervision are often unstable under small input perturbations and semantically incomplete at the token level, which limits their effectiveness for training guidance. To address this problem, we propose IEG, an internal–external explanation-guided framework that integrates three complementary components. First, IEG performs importance-guided data augmentation by editing low-importance tokens to create semantics-preserving variants. Second, it applies explanation consistency regularization through sufficiency and comprehensiveness constraints to stabilize internal rationales. Third, it aligns gradient-based evidence with phrase-level rationales generated by an LLM, thereby introducing semantically richer external supervision. Mathematically, IEG is formulated as a composite regularized empirical-risk objective that couples classification loss with sufficiency, comprehensiveness, and external alignment penalties over aspect-conditioned inputs. Experiments on three benchmark datasets show that IEG consistently outperforms strong explanation-guided baselines, with gains of up to 1.77 accuracy points and 2.69 macro-F1 points, while also improving rationale-oriented evaluation as higher comprehensiveness and lower sufficiency. Full article

The output voltage harmonics of electric vehicle (EV) charging converters directly affect grid power quality. This paper proposes a harmonic analysis method based on geometric algebra (GA), which employs a multivector representation of signals and least squares estimation to accurately extract fundamental, integer-order, and inter-harmonics. A coupling coefficient is defined to quantify the phase correlation between frequency components. Based on measured data, harmonic characteristics under four typical operating conditions are analyzed, and an adaptive PID controller is designed to dynamically adjust the virtual resistance for harmonic suppression. The results show that the GA method significantly reduces spectral leakage under non-integer-period sampling conditions, with amplitude estimation errors below ±2%. The total harmonic distortion (THD) decreases with increasing active power and increases with reactive power injection. The droop coefficient exhibits a non-monotonic effect, while reducing the proportional gain raises the THD. Adaptive control reduces the average THD by 14.0–28.5% with a total response time of less than 0.05 s. The coupling coefficient $C_{13}$ is strongly positively correlated with THD and negatively correlated with the maximum Lyapunov exponent computed using the Rosenstein small-data method (correlation coefficient −0.85), confirming the intrinsic relationship between coupling and stability. Compared with fast Fourier transform (FFT) and other methods, GA achieves higher accuracy under short data records and non-integer-period sampling. This paper provides a complete theoretical framework and engineering solution for harmonic suppression in charging converters. Full article

This work presents a high-efficiency and linear Ka-band power amplifier (PA) designed in a $0.13$ $\mathsf{\mu }$m depletion-mode GaAs pHEMT process, targeting 5G phased-array systems. To minimize passive losses, the output matching network employs an all-transmission-line architecture. Phase mismatches among output branches are compensated directly within the interstage and output matching networks via tailored distributed and capacitive components. Device-level reliability is proactively addressed by maintaining adequate voltage headroom under worst-case load mismatch, based on voltage standing wave ratio (VSWR) analysis. The amplifier achieves a peak small-signal gain of $15.8$ dB at 27 GHz. Under continuous-wave excitation at 27 GHz, it delivers $32.9$ dBm output power at the 1-dB compression point with $32.8$% power-added efficiency (PAE), reaching a peak saturated output of $33.2$ dBm and $35.9$% PAE. When driven by a 64-QAM signal with a 250 MHz symbol rate, the PA maintains an average output power of $26.3$ dBm and an average PAE of $12.2$%, with an rms EVM of $3.4$% and an SNR of $25.5$ dB. Full article

In this paper, an adaptive biasing technique for an upper-mid band GaAs power amplifier is proposed. The proposed technique applies an adaptive bias circuit (ABC) to the driver stage (DS). In multistage power amplifier architectures, only the minimal current required to drive the power stage (PS) is typically consumed by the DS. Consequently, the overall current consumption of the amplifier is primarily governed by the substantially larger current consumed by the PS. Therefore, for an equivalent improvement in amplitude-to-amplitude (AM-AM) distortion, a higher power-added efficiency (PAE) is achieved when the ABC is applied to the DS than when it is applied to the PS. The proposed power amplifier is operated over the 7 to 16 GHz frequency range, achieving a small-signal gain of 14 to 16 dB, a PAE of 18 to 28% at the 1 dB compression point, and an output power of 21.5 to 24 dBm. Full article

A novel hand gesture recognition framework based on surface electromyography (sEMG) is proposed for soldier operational scenarios under small-sample conditions. The framework integrates Empirical Mode Decomposition (EMD) for signal reconstruction, histogram-based features, and the Deep Forest (DF) classifier. Evaluations are conducted under two protocols: subject-wise evaluation and mixed-subject nested 8-fold cross-validation. Under subject-wise evaluation, the proposed EMD-HIST-DF method achieves 99.94% accuracy with 0.00027 ms per sample. Under mixed-subject nested 8-fold cross-validation, 98.41% accuracy is maintained with 0.00053 ms per sample. Ablation studies confirm the significant contribution of EMD-based signal enhancement in the mixed-subject setting (approximately 10.6 percentage points, p < 0.001). Parameter sensitivity analysis guides optimal parameter selection, and statistical tests confirm significant performance gains over baseline methods. Confusion matrices illustrate high per-class accuracy with minimal inter-class confusion. The framework shows potential as a promising solution for accurate, efficient, and sample-sparing gesture recognition in resource-constrained environments such as supernumerary robotic limb control. Full article

This research focuses on the design and simulation of a V-band single-chip transmit-and-receive front-end integrating an LNA, PA and switching functions for ISL terminals. Two technologies are compared: a 60 nm GaN/Si HEMT from MESC and a 100 nm GaAs HEMT from UMS. In Tx mode, the proposed design targets a saturated output power of at least 20 dBm and a power-added efficiency of no less than 5%. In Rx mode, the goal is 4 dB noise figure. In both cases, the small signal gain must exceed 20 dB across the 59–71 GHz band. Full article

This paper presents a 13 W Ku-band GaN HEMT MMIC power amplifier employing a coupled-line interstage stabilization technique for radar sensor front-end applications. High-efficiency and stable power amplification in the Ku-band is essential for radar sensing systems, where low-frequency instability and process sensitivity often limit multistage GaN amplifier performance. To address these challenges, a coupled-line interstage network is introduced instead of conventional series capacitors and parallel RC stabilization circuits. The proposed structure effectively suppresses low-frequency gain while maintaining RF performance and improving robustness against process variations due to its planar transmission-line implementation. The two-stage power amplifier was fabricated using a 0.25 μm commercial GaN HEMT MMIC process. For compact implementation, the coupled-line structure was realized in a meandered layout and verified through full electromagnetic simulations. Measured small-signal results show a gain (S21) of 18.6–21.6 dB, with input and output return losses (S11 and S22) of −3.3 to −10.2 dB and −4.4 to −7.2 dB, respectively, over 13.5–16 GHz. Large-signal measurements demonstrate a saturated output power of 40.7–41.5 dBm and a power-added efficiency of 21.3–28.1% across the same frequency range. The fabricated MMIC achieved stable operation without oscillation, validating the effectiveness of the proposed coupled-line stabilization approach for Ku-band radar sensor systems. Full article

Background/Objectives: Steady-State Visual Evoked Potential-based Brain–Computer Interfaces face a critical trade-off between system accuracy and user visual fatigue. To address this challenge, the objective of this study was to determine how the spatial manipulation of stimulus size modulates the full spectral dynamics of the Electroencephalogram, encompassing both the periodic oscillatory response and the aperiodic (1/f) background noise. Methods: Twenty-two healthy subjects completed a sustained visual attention task using a competitive stimulus paradigm (20 Hz and 30 Hz) presented in three spatial dimensions (Small, Medium, and Big). Parieto-occipital brain signals were decomposed using the spectral parameterization algorithm (SpecParam) to extract frequency-specific visually evoked response power and the aperiodic slope, while visual fixation was continuously monitored via eyetracking. Results: Increasing stimulus size induced a statistically significant gain in the power of the attended signal (Target) without increasing the response of the peripheral distractor. Simultaneously, larger stimuli produced a significant increase in the aperiodic slope during 20 Hz attention and visual rest, suggesting increased cortical inhibition and a reduction in broadband neural activity. This aperiodic modulation was not observed at 30 Hz. Conclusions: The improvement in Signal-to-Noise Ratio with increasing stimulus size arises from a dual neurophysiological mechanism: enhancement of the periodic evoked response together with a reduction in background neural noise. Full article

Islanded photovoltaic microgrids with limited inertial support can undergo steep frequency excursions after sudden generation loss or abrupt load changes. This paper develops and evaluates a synthetic inertia strategy supported by a supercapacitor energy storage unit for fast frequency containment in this type of system. The proposed approach commands rapid active-power injection or absorption from the measured rate of change of frequency, thereby emulating the immediate inertial contribution usually associated with rotating machines while preserving a simple and physically interpretable control structure. The supercapacitor is represented through a resistance–capacitance model that includes equivalent series resistance and is interfaced through a bidirectional buck–boost power converter subject to practical current, voltage, and power limits. Rather than claiming a fundamentally new storage-support concept, the contribution of this paper lies in providing a transparent and constraint-consistent benchmark that integrates measured operating profiles, explicit supercapacitor limits, hybrid frequency–RoCoF support, and stress-aware comparative assessment under a common set of plant assumptions. The methodology is assessed in time-domain simulations under representative benchmark disturbances, including an approximately ten percent photovoltaic generation loss, a ten percent load increase, and a combined event. Performance is evaluated through the peak rate of change of frequency, frequency nadir, integral error indices, time outside the admissible band, and supercapacitor stress indicators such as current peaks, voltage depletion, and energy throughput. An additional non-ideal assessment is also included to examine the behavior of the RoCoF-based support law under bounded frequency-measurement perturbations and delayed control action. A complementary variability-driven case based on a highly fluctuating measured irradiance window is also used to examine the behavior of the adaptive energy-management mechanism under repeated photovoltaic-power variations. A local small-signal analysis is also included to show that the selected gain region is dynamically plausible in the unsaturated regime. The results show that the proposed adaptive hybrid strategy improves the overall frequency response while maintaining admissible supercapacitor operation, thus providing a stronger methodological basis for rapid frequency support in islanded photovoltaic microgrids. Full article

Point cloud semantic segmentation is a core component in indoor scene understanding and autonomous driving. Under weak point-level supervision, only a small subset of points is annotated, making effective use of unlabeled points critical yet non-trivial. Many existing approaches rely on prediction confidence to filter pseudo labels or enforce consistency, which can bias training toward easy points and amplify early mistakes. Consequently, confidently wrong predictions may be reinforced, while uncertain points around class boundaries or in geometrically complex regions are less utilized, limiting further gains. An evidential uncertainty decomposition framework is introduced for weakly supervised point cloud semantic segmentation. Network outputs are interpreted as evidential distributions, and uncertainty is decomposed to separate lack-of-knowledge uncertainty from boundary-related ambiguity, providing a more informative reliability signal for unlabeled points. Based on this signal, different constraints are applied to different subsets: reliable points are trained with pseudo labels together with prototype-based regularization to encourage intra-class compactness; boundary-ambiguous points are guided by evidential consistency to improve boundary learning; and points with high epistemic uncertainty are excluded from pseudo-label-based supervision to mitigate error reinforcement. In addition, an uncertainty calibration term on sparsely labeled points helps stabilize training. Experiments on S3DIS, ScanNet-V2, and SemanticKITTI yield 67.7%, 59.7%, and 53.3% mIoU, respectively, with only 0.1% labeled points, comparing favorably with prior weakly supervised point cloud segmentation methods. Full article

With the widespread adoption of various types of Unmanned Aerial Vehicles (UAVs), their non-compliant operations pose a severe challenge to public safety, necessitating the urgent identification and detection of UAV targets. However, in complex backgrounds, UAV targets exhibit small-scale dimensions and low contrast, coupled with extremely low signal-to-noise ratios. This forces conventional target detection methods to confront issues such as feature convergence, missed detections, and false alarms. To address these challenges, we propose a Multi-View Feature Fusion Network (MVFF) that achieves precise identification of small, low-contrast UAV targets by leveraging complementary multi-view information. First, we design a collaborative view alignment fusion module. This module employs a cross-map feature fusion attention mechanism to establish pixel-level mapping relationships and perform deep fusion, effectively resolving geometric distortion and semantic overlap caused by imaging angle differences. Furthermore, we introduce a view feature smoothing module that employs displacement operators to construct a lightweight long-range modeling mechanism. This overcomes the limitations of traditional convolutional local receptive fields, effectively eliminating ghosting artifacts and response discontinuities arising from multi-view fusion. Additionally, we developed a small object binary cross-entropy loss function. By incorporating scale-adaptive gain factors and confidence-aware weights, this function enhances the learning capability of edge features in small objects, significantly reducing prediction uncertainty caused by background noise. Comparative experiments conducted on a multi-perspective UAV dataset demonstrate that our approach consistently outperforms existing state-of-the-art methods across multiple performance metrics. Specifically, it achieves a Structure-measure of 91.50% and an F-measure of 85.14%, validating the effectiveness and superiority of the proposed method. Full article