Search Results (411)

In permanent magnet synchronous motor (PMSM) drive systems, the nonlinearity of the inverter and non-sinusoidal nature of back EMF generate harmonics in the stator current, resulting in torque ripple and reduced motor efficiency. Although the proportional resonant (PR) controller is widely employed for harmonic suppression, the standard resonant controller is constrained by its narrow bandwidth and can only suppress a single harmonic order. To address these issues, an adaptive harmonic suppression strategy using a proportional multi-resonant (PMR) controller based on the generalized frequency selector (GFS) is proposed. Firstly, the sources and characteristics of the stator current harmonics were analyzed based on the mathematical model of PMSM. Subsequently, a proportional resonance controller was designed according to the tracking filtering characteristics of the GFS, and a proportional multi-resonance controller targeting multi-order harmonics was constructed. The stability of the current closed-loop system under the algorithm was analyzed. Finally, simulation and experimental results demonstrated that the proposed algorithm effectively suppressed current harmonics and significantly improved the current waveform. Full article

Traditional front-wheel-steering (FWS) unmanned vehicles frequently encounter maneuverability bottlenecks in confined spaces or during rapid formation changes due to inherent kinematic limitations. To mitigate these constraints, this study proposes an adaptive multi-mode (AMM) cooperative formation control framework tailored for four-wheel independent drive and steering (4WIDS) platforms. The methodology constructs a unified planner based on the virtual structure concept, integrated with an autonomous steering-mode selector. By synthesizing real-time mission requirements with longitudinal and lateral tracking errors, the system dynamically switches between crab steering, four-wheel counter-steering (4WCS), and conventional FWS modes to optimize spatial utilization. Validated within a seven-vehicle MATLAB/Simulink environment, simulation results demonstrate that the crab-steering mode significantly reduces relocation time for small lateral adjustments by eliminating redundant heading changes, whereas FWS and 4WCS modes are preferentially selected to ensure stability during high-speed or large-span maneuvers. These findings confirm that the proposed AMM strategy effectively reconciles the trade-off between agility and stability, providing a robust solution for complex cooperative maneuvering tasks. Full article

Few-Shot Text Classification (FSTC) aims to classify text accurately into predefined categories using minimal training samples. Recently, prompt-tuning-based methods have achieved promising results by constructing verbalizers that map input data to the label space, thereby maximizing the utilization of pre-trained model features. However, existing verbalizer construction methods often rely on external knowledge bases, which require complex noise filtering and manual refinement, making the process time-consuming and labor-intensive, while approaches based on pre-trained language models (PLMs) frequently overlook inherent prediction biases. Furthermore, conventional data augmentation methods focus on modifying input instances while overlooking the integral role of label semantics in prompt tuning. This disconnection often leads to a trade-off where increased sample diversity comes at the cost of semantic consistency, resulting in marginal improvements. To address these limitations, this paper first proposes a novel Bayesian Mutual Information-based method that optimizes label mapping to retain general PLM features while reducing reliance on irrelevant or unfair attributes to mitigate latent biases. Based on this method, we propose two synergistic generators that synthesize semantically consistent samples by integrating label word information from the verbalizer to effectively enrich data distribution and alleviate sparsity. To guarantee the reliability of the augmented set, we propose a Low-Entropy Selector that serves as a semantic filter, retaining only high-confidence samples to safeguard the model against ambiguous supervision signals. Furthermore, we propose a Difficulty-Aware Adversarial Training framework that fosters generalized feature learning, enabling the model to withstand subtle input perturbations. Extensive experiments demonstrate that our approach outperforms state-of-the-art methods on most few-shot and full-data splits, with F1 score improvements of up to +2.8% on the standard AG’s News benchmark and +1.0% on the challenging DBPedia benchmark. Full article

Sneak current refers to leakage currents in RRAM crossbar arrays without selector devices, disrupting the accuracy of weighted sum operations in neuromorphic systems, leading to performance degradation and increased power consumption. This study presents a bilayer RRAM structure with a selector layer designed to suppress sneak current in neuromorphic synapse arrays. By utilizing a TiO₂/HfO₂ bilayer structure, it is demonstrated that increasing the thickness of TiO₂ and the work function of the top electrode effectively suppresses current under reverse bias compared to single-layer devices. The bilayer structure achieves rectification levels of 10 to 30 times higher than the single-layer configuration, while increasing the work function of the top electrode yields rectification improvements ranging from 10 to 40 times. This approach enhances the accuracy of synaptic weighted sum operations. Full article

Chirality remains the most neglected axis of pesticide residue science. Many active ingredients are sold as racemates although their enantiomers differ in potency, persistence, transport, and toxicology; as a result, total concentration is a poor surrogate for risk. This review synthesizes green and enantioselective strategies spanning the full analytical–remediation continuum. We survey solvent-minimized sample preparation approaches (SPME/TF-SPME, FPSE, µSPE, DLLME with DES/NADES), MS-compatible chiral separations (immobilized polysaccharide CSPs in LC and SFC, cyclodextrin-based selectors in GC, CE/CEC), and HRMS-enabled confirmation and suspect screening. Complex matrices (e.g., fermented beverages such as wine and high-sugar products) are critically discussed, together with practical matrix-tolerant workflows and the complementary role of chiral GC for hydrophobic residues. We then examine emerging enantioselective materials—MIPs, MOFs/COFs, and cyclodextrin-based sorbents—for extraction and preconcentration and evaluate stereoselective removal via adsorption, biodegradation, and chiral photocatalysis. Finally, we propose toxicity-weighted enantiomeric fraction (EF) metrics for decision-making, outline EF-aware green treatment strategies, and identify metrological and regulatory priorities (CRMs, ring trial protocols, FAIR data). Our thesis is simple: to reduce hazards efficiently and sustainably, laboratories and practitioners must measure—and manage—pesticide residues in the chiral dimension. Full article

The absence of molecular tools for manipulation of gene expression in the pathogenic free-living amoeba Naegleria fowleri has historically limited our understanding of gene function in the organism and has coincidently impacted the identification of potential druggable pathways and proteins. Here, we describe the development of approaches for the generation of transgenic amoebae using polyethyleneimine nanoparticles to deliver plasmids designed to confer antibiotic resistance and fluorescence to the cells. Through a series of optimization steps, we found that transfection of plasmids encoding the fluorescent protein mCherry fused by a T2A self-cleaving peptide to a codon-optimized puromycin acetyltransferase selectable marker yielded fluorescent cells that were resistant up to 100 µg/mL puromycin. Transfected trophozoites harbored between 45 and 65 copies of the transgene per cell and both fluorescence and resistance were persistent in the presence of selector through continued passages. The development of these approaches is anticipated to enable application of an array of genetic manipulation techniques including forward and reverse genetics to the study of this important pathogen. Full article

Background: Standard single-cell RNA sequencing (scRNA-seq) analysis workflows face significant limitations, particularly the rigidity of clustering-dependent methods that can obscure subtle cellular heterogeneity and the potential loss of biologically meaningful cells during stringent quality control (QC) filtering. This study aims to develop scSelector (v1.0), an interactive software toolkit designed to empower researchers to flexibly select and analyze cell populations directly from low-dimensional embeddings, guided by their expert biological knowledge. Methods: scSelector was developed using Python, relying on core dependencies such as Scanpy (v1.9.0), Matplotlib (v3.4.0), and NumPy (v1.20.0). It integrates an intuitive lasso selection tool with backend analytical modules for differential expression and functional enrichment analysis. Furthermore, it incorporates Large Language Model (LLM) assistance via API integration (DeepSeek/Gemini) to provide automated, contextually informed cell-type and state prediction reports. Results: Validation across multiple public datasets demonstrated that scSelector effectively resolves functional heterogeneity within broader cell types, such as identifying distinct alpha-cell subpopulations with unique remodeling capabilities in pancreatic tissue. It successfully characterized rare populations, including platelets in PBMCs and extremely low-abundance endothelial cells in liver tissue (as few as 53 cells). Additionally, scSelector revealed that cells discarded by standard QC can represent biologically functional subpopulations, and it accurately dissected the states of outlier cells, such as proliferative NK cells. Conclusions: scSelector provides a flexible, researcher-centric platform that moves beyond the constraints of automated pipelines. By combining interactive selection with AI-assisted interpretation, it enhances the precision of scRNA-seq analysis and facilitates the discovery of novel cell types and complex cellular behaviors. Full article

Cancer cell plasticity drives metastasis and therapy resistance through dynamic transitions between epithelial, mesenchymal, and neural crest stem-like (NCSC) states; however, a unifying mechanism that stabilizes these transitions remains undefined. To address this gap, we introduce a N-cadherin (CDH2)-centered redox–adhesion–exosome (RAX) hub that links oxidative signaling, adhesion dynamics, and exosome-mediated immune communication into a closed-loop framework. Within this network, reactive oxygen species (ROS) pulses license epithelial–mesenchymal transition (EMT), AXL–FAK/Src signaling consolidates mesenchymal adhesion, and selective exosomal cargoes—including miR-21, miR-200, miR-210, and PD-L1—propagate plasticity and immune evasion. Lipid peroxidation acts as a central checkpoint connecting ROS metabolism to PUFA membrane remodeling and ferroptosis vulnerability, buffered by NRF2–GPX4 and FSP1/DHODH axes, thereby converting transient oxidative pulses into persistent malignant states. Mechanistically, the RAX hub synthesizes findings from EMT/CSC biology, ferroptosis defenses, and exosome research into a self-reinforcing system that sustains tumor heterogeneity and stress resilience. Evidence from single-cell and spatial transcriptomics, intravital ROS imaging, and exosome cargo-selector studies supports the feasibility of this model. We further outline validation strategies employing HyPer–EMT–CDH2 tri-reporters, CRISPR perturbation of YBX1/ALIX cargo selectors, and spatial multi-omics in EMT-high tumors. Clinically, tumors enriched in EMT/NCSC programs—such as melanoma, neuroblastoma, small-cell lung cancer, pancreatic ductal adenocarcinoma, and triple-negative breast cancer (TNBC)—represent RAX-dependent contexts. These insights highlight biomarker-guided opportunities to target adhesion switches, ferroptosis defenses, and exosome biogenesis through lipid peroxidation-centered strategies using liquid-biopsy panels (exosomal CDH2, miR-200, miR-210) combined with organoid and xenograft models. By linking lipid peroxidation to ferroptosis defense and oxidative stress adaptation, the RAX hub aligns with the thematic focus of lipid metabolism and redox control in cancer progression. Collectively, the RAX framework may provide a conceptual basis for precision oncology by reframing metastasis and therapy resistance as emergent network properties. Full article

Deep learning-based image compression achieves remarkable average rate-distortion performance but is prone to failure on noisy, high-frequency, or high-entropy inputs. This work systematically investigates these failure cases and proposes a noise-aware hybrid compression framework to address them. A High-Frequency Vulnerability Index (HFVI) is proposed, integrating frequency energy, encoder Jacobian sensitivity, and texture entropy into a unified measure of degradation susceptibility. Guided by HFVI, the system incorporates a selective zero-shot denoising module (P2PA) and a lightweight hybrid codec selector that determines, for each image, whether P2PA is necessary and selecting the more reliable codec (a learning-based model or JPEG2000) accordingly, without retraining any compression backbones. Experiments span a 200,000-image cross-domain benchmark incorporating general datasets, synthetic noise (eight levels), and real-noise datasets demonstrate that the proposed pipeline improves PSNR by up to 1.28 dB, raises SSIM by 0.02, reduces LPIPS by roughly 0.05, and decreases the failure-case rate by 6.7% over the best baseline (Joint-IC). Additional intensity-profile and cross-validation analyses further validate the robustness and deployment readiness of the method, showing that the hybrid selector provides a practical path toward reliable, noise-adaptive deep image compression. Full article

Plant growth-promoting bacteria (PGPB) can act as biostimulants, improving the growth of plants in sustainable agriculture systems that seek to reduce synthetic agrochemical input. Bacteria present in seeds are closely associated with vertical transmission and thus represent a potential trove of biostimulants. Capsicum species are notable for producing capsaicin, a compound with antimicrobial activity that may influence microbial communities associated with pepper fruits and seeds. Using Luria–Bertani (LB) media infused with capsaicin, we isolated bacteria from bell peppers, jalapeno peppers, and habanero peppers, which we verified to have different levels of capsaicin through high-performance liquid chromatography with ultraviolet detection (HPLC-UV). Minimum inhibitory concentration (MIC) assays indicated that the capsaicin resistance of isolated bacteria did not correlate with the pungency level of the host pepper variety. Of the total isolated bacteria, four showed promise as plant growth promoters; two belong to the genera Pseudomonas, one Agrobacterium, and one Bacillus. Our isolates tested positively for potassium and phosphate solubilization, urease production, and indole-3-acetic acid (IAA) phytohormone production. Inoculation of these bacteria into surface-sterilized red clover (Trifolium pratense) and Kentucky bluegrass (Poa pratensis) showed significant improvements in germination rate, seedling root length, and seedling shoot height. These results show that the pungency of peppers does not influence the capsaicin resistance of isolated bacteria. Additionally, seedborne PGPB have the potential for plant growth improvement through various mechanisms, reducing the need for synthetic chemicals. Full article

In practical engineering geophysics, anomalous bodies are typically three-dimensional (3-D) structures, making it inaccurate to represent the subsurface geoelectric model using a two-dimensional (2-D) assumption. Furthermore, the underlying mechanism of the telluric electrical field frequency selection method (TEFSM) remains insufficiently understood. To address these limitations, this study presents a 3-D forward modeling algorithm based on the edge-based finite element method to solve the TEFSM forward problem. This paper also investigates the application of TEFSM in mineral water exploration, striving to minimize the influence of strong electromagnetic interference sources such as high-voltage power lines. Specifically, the paper presents the forward theory of TEFSM and analyzes the causes of galvanic distortion, particularly static shift. Numerical simulations examine the response characteristics of anomalous bodies and the influence of galvanic distortion. The results indicate that galvanic distortion enhances shallow local anomalies in the modulus of the electric field while masking deeper targets. In contrast, the phase of the electric field effectively reflects deeper anomalous bodies and is minimally affected by galvanic distortion. Future improvements in frequency selectors may enable reliable phase measurements, thereby enhancing data interpretability. Subsequently, the TEFSM was applied to field data collected during mineral water exploration. The field test results confirm the effectiveness of TEFSM and demonstrate that it is a portable, simple, low-cost, and highly efficient method for groundwater detection. Full article

Bearings are among the most failure-prone components in rotating systems, making early fault detection crucial in industrial applications. While recent publications have focused on this issue, challenges remain, particularly in dealing with heavy-tailed or non-cyclic impulsive noise in recorded signals. Such noise poses significant challenges for classical fault selectors like kurtosis-based methods. Moreover, many deep-learning approaches struggle in these environments, as they often assume Gaussian or stationary noise and rely on large labeled datasets that are rarely available in practice. To address this, we propose a robust sparse non-negative matrix factorization (NMF) method based on the maximum-correntropy criterion, which is known for its robustness in the presence of heavy-tailed noise. This methodology is applied to identify fault frequency bands in the spectrogram of the signal. The effectiveness of the approach is validated using simulated fault signals under both Gaussian and heavy-tailed noise conditions through Monte Carlo simulations. A statistical efficiency analysis confirms robustness to random perturbations. Additionally, three real datasets are used to evaluate the performance of the proposed method. Results from both simulations and real-world data demonstrate the effectiveness of the proposed approach. Full article

The conversion of agricultural residues into high-value organic amendments is fundamental to sustainable farming systems. Corn cobs represent a widely available lignocellulosic resource; however, their rigid structural properties often hinder efficient biodegradation during composting. This study evaluated whether optimizing corn cob particle size could improve aerobic composting performance by enhancing humification and compost quality. Corn cobs were ground into three particle sizes (6-mesh, 10-mesh, and 20-mesh) and composted with a commercial microbial inoculant for up to 51 days. Physicochemical properties, humic substance fractions (HSC, HAC, FAC), microbial community dynamics (16S rRNA and ITS sequencing), and maturity indicators were monitored. The 10-mesh treatment (M10) exhibited the most favorable composting outcomes, achieving the greatest degree of humification (HA/FA = 2.85; HAC = 48.30 g/kg) and the most pronounced aromatic condensation in humic acids. M10 also supported a more diverse and metabolically specialized microbial consortium, with notable enrichment of lignocellulose-degrading and humus-forming genera (e.g., Streptomyces, Thermobifida). Consequently, M10 produced the most mature compost, reflected by the highest germination index (93.63%) and the lowest heavy-metal accumulation, meeting agricultural safety standards. Structural equation modeling revealed that particle size influenced humification primarily by modulating microbial community structure (path coefficient = 0.86), highlighting particle size as a key environmental selector in composting systems. Overall, 10-mesh particle size created an optimal aeration–moisture balance that stimulated microbial metabolism, accelerated organic matter degradation, and enhanced stable organic matter formation. These findings demonstrate that corn cob particle size significantly governs composting efficiency and final product quality. Selecting a 10-mesh size presents a practical pretreatment strategy to accelerate biomass turnover and produce safe, nutrient-rich compost, providing an effective approach for sustainable bioconversion of agricultural residues. Full article

Stress, workload, and uncertainty characterize occupational tasks across sports, healthcare, military, and transportation domains. Emerging theory and empirical research suggest that coordinated whole-body movements may reflect these transient mental states. Wearable sensors and optical motion capture offer opportunities to quantify such movement dynamics and classify mental states that influence occupational performance and human–machine interaction. We tested this possibility in a small pilot study (N = 10) designed to test feasibility and identify preliminary movement features linked to mental states. Participants performed a perceptual decision-making task involving facial emotion recognition (i.e., deciding whether depicted faces were happy versus angry) with variable levels of stress (via a risk of electric shock), workload (via time pressure), and uncertainty (via visual degradation of task stimuli). The time series of movement trajectories was analyzed both holistically (full trajectory) and by phase: lowered (early), raising (middle), aiming (late), and face-to-face (sequential). For each epoch, up to 3844 linear and non-linear features were extracted across temporal, spectral, probability, divergence, and fractal domains. Features were entered into a repeated 10-fold cross-validation procedure using 80/20 train/test splits. Feature selection was conducted with the T-Rex Selector, and selected features were used to train a scikit-learn pipeline with a Robust Scaler and a Logistic Regression classifier. Models achieved mean ROC AUC scores as high as 0.76 for stress classification, with the highest sensitivity during the full movement trajectory and middle (raise) phases. Classification of workload and uncertainty states was less successful. These findings demonstrate the potential of movement-based sensing to infer stress states in applied settings and inform future human–machine interface development. Full article

The presented work is a logical continuation of the study of the chemical composition of the Lovozero district waters (the Kola Peninsula, Russia), an area inhabited by indigenous populations. The problem was posed due to the discovery of rare earth elements in drinking water in the Lovozero district (the Krasnoshchelye village). For monitoring, inductively coupled plasma was used, and the “water–rock” interaction was studied using “Selector” software. The results showed the Western Keivy Massif influence on the chemical composition of natural waters, which are used for drinking purposes for humans and animals. The interaction of water with magmatic rocks such as gabbro and subalkaline granites also leads to the formation of some major cations, anions, and heavy metals. Li, Sr, Y, La, and Ce concentrations are higher than in the Central’niy water intake located within the Khibiny Massif. The results of the modeling demonstrate the high migration capabilities of rare earth elements. The presence of rare elements and REEs in drinking surface and groundwaters, if consumed on a regular basis, can cause diseases of the nervous system and other organs. Full article