Search Results (268)

Large-scale computer simulations were employed to investigate the conformational response of the spike protein components S1 and S2 using a coarse-grained model. Temperature was systematically varied to assess the balance between stabilizing residue–residue interactions and thermal fluctuations. The resulting contact profiles reveal distinct segmental reorganization and self-assembly behaviors between S1 and S2. At lower, thermoresponsive temperatures, pronounced segmental globularization occurs in the N-terminal domain (NTD; M153–K202) and receptor-binding domain (RBD; E406–E471) of S1, whereas S2 exhibits alternating regions of high and low contact density. Increasing temperature reduces this segmental globularization, leaving only minor persistence at elevated temperatures. The temperature dependence of the radius of gyration (R_g) further demonstrates the contrasting thermal behaviors of S1 and S2. For S1, R_g increases continuously and monotonically with temperature, reaching a steady-state value approximately 50% higher than that at low temperature. In contrast, S2 displays a non-monotonic response: R_g initially rises to a maximum nearly sevenfold higher than its low-temperature value, then decreases with further temperature increase. Scaling analysis of the structure factor reveals that the globularity of S1 diminishes significantly upon heating, while S2 becomes modestly more compact yet retains its predominantly fibrous character. Full article

Experimental studies published to date on the effects of silver nanoparticles (AgNPs) on plants have yielded highly contradictory results: reported outcomes range from growth inhibition to stimulation. The objective of this research was to test the hypothesis that the ontogenetic stage at the time of exposure to AgNPs is a key determinant of both the qualitative profile and quantitative magnitude of plant responses. For this purpose, laboratory seed priming and small-plot field experiments with wheat plants (Triticum aestivum L.) treated with stabilized dispersions of AgNPs at 1–100 mg∙L⁻¹ were conducted. It was shown that seed priming with low concentrations of AgNPs (1–5 mg∙L⁻¹) did not affect wheat seedling growth, whereas dispersions at ≥25 mg∙L⁻¹ suppressed development. In agreement, antioxidant enzyme activities (POD, CAT, PPO) increased at 1–5 mg·L⁻¹ and decreased at 100 mg·L⁻¹. By contrast, foliar treatments of field-grown wheat increased plant population density, plant height, spike structure metrics, and grain yield. The optimal regimen—three foliar applications at 5 mg·L⁻¹—increased grain yield by 12.1% from 5.89 t·ha⁻¹ to 6.60 t·ha⁻¹. At low doses of AgNPs, activities of peroxidase, catalase, and polyphenol oxidase in seedlings tissues increased, indicating activation of nonspecific defense mechanisms; at higher concentrations, activities of these enzymes decreased, indicating antioxidant system exhaustion and dysfunction. The findings demonstrate dose- and stage-dependent effects and corroborate the central role of the developmental stage of wheat in determining responses to AgNPs, indicating opportunities to optimize stage-aware, low-dose application regimes to enhance productivity while minimizing phytotoxic risk. Full article

We model the short-period cataclysmic variable EZ Lyn with MESA binary evolution and infer its present-day parameters through a staged statistical search. First, we compute a coarse grid of tracks in $(M_{1,0},P_0)$ at fixed $M_{2,0}$ and rank snapshots by a profile likelihood. We then resample the neighbourhood of the minimum to build a refined $\Delta {\chi }^2$ surface. Finally, we sample this surface with an affine-invariant MCMC to obtain posteriors, using a likelihood that treats the one-sided constraint on the donor temperature and the ambiguity of component roles in the binary output. The best-fit snapshot reproduces the observables and identifies EZ Lyn as a period bouncer with a substellar donor. We infer $M_{\mathrm{WD}}=0.850\pm 0.019{\mathrm{M}}_{\odot }$, $M_2=0.0483\pm 0.0137{\mathrm{M}}_{\odot }$, $R_{\mathrm{WD}}=0.0092\pm 0.0001{\mathrm{R}}_{\odot }$, $R_2=0.099\pm 0.005{\mathrm{R}}_{\odot }$, $T_{\mathrm{WD}}=11,500\pm 20\mathrm{K}$, and $T_2=1600\pm 50\mathrm{K}$. The instantaneous mass-transfer rate at the best-fit snapshot is $\dot{M}=3.66\times {10}^{-11}{\mathrm{M}}_{\odot }{\mathrm{yr}}^{-1}$, consistent with the secular range implied by the white-dwarf temperature. Independent checks from the Roche mean-density relation, surface gravities, and the semi-empirical donor sequence support the solution. In population context, EZ Lyn lies in the period-minimum spike and on the low-mass tail of the donor mass–period plane. The classification is robust to modest displacements along the shallow $\Delta {\chi }^2$ valley. We release inlists, tracks, and analysis scripts for reproducibility. Full article

In particle image velocimetry (PIV), overexposure is particularly common in regions with high illumination. In particular, strong scattering or background reflection at the liquid–gas interface will make the overexposure phenomenon more obvious, resulting in local pixel saturation, which will significantly reduce the particle image quality, and thus reduce the particle recognition rate and the accuracy of velocity field estimation. This study addresses the overexposure challenges in particle image velocimetry applications, mainly to address the challenge that the velocity field cannot be measured due to the difficulty in effectively detecting particles in the exposed area. In order to address the challenge of overexposure, this paper does not use traditional frame-based high-speed cameras, but instead proposes a particle image velocimetry algorithm based on adaptive integral spike camera data using a neuromorphic vision sensor (NVS). Specifically, by performing target-background segmentation on high-frequency digital spike signals, the method suppresses high illumination background regions and thus effectively mitigates overexposure. Then the spike data are further adaptively integrated based on both regional background illumination characteristics and the spike frequency features of particles with varying velocities, resulting in high signal-to-noise ratio (SNR) reconstructed particle images. Flow field computation is subsequently conducted using the reconstructed particle images, with validation through both simulation and experiment. In simulation, in the overexposed area, the average flow velocity estimation error of frame-based cameras is 8.594 times that of spike-based cameras. In the experiments, the spike camera successfully captured continuous high-density particle trajectories, yielding measurable and continuous velocity fields. Experimental results demonstrate that the proposed particle image velocimetry algorithm based on the adaptive integration of the spike camera effectively addresses overexposure challenges caused by high illumination of the liquid–gas interface in flow field measurements. Full article

This study develops an innovative unmanned aerial vehicle (UAV)-based intelligent system for winter wheat yield prediction, addressing the inefficiencies of traditional manual counting methods (with approximately 15% error rate) and enabling quantitative analysis of water–fertilizer interactions. By integrating an enhanced Faster Region-Based Convolutional Neural Network (Faster R-CNN) architecture with multi-source data fusion and machine learning, the system significantly improves both spike detection accuracy and yield forecasting performance. Field experiments during the 2022–2023 growing season captured high-resolution multispectral imagery for varied irrigation regimes and fertilization treatments. The optimized detection model incorporates ResNet-50 as the backbone feature extraction network, with residual connections and channel attention mechanisms, achieving a mean average precision (mAP) of 91.2% (calculated at IoU threshold 0.5) and 88.72% recall while reducing computational complexity. The model outperformed YOLOv8 by a statistically significant 2.1% margin (p < 0.05). Using model-generated spike counts as input, the random forest (RF) model regressor demonstrated superior yield prediction performance (R² = 0.82, RMSE = 324.42 kg·ha⁻¹), exceeding the Partial Least Squares Regression (PLSR) (R² +46%, RMSE-44.3%), Least Squares Support Vector Machine (LSSVM) (R² + 32.3%, RMSE-32.4%), Support Vector Regression (SVR) (R² + 30.2%, RMSE-29.6%), and Backpropagation (BP) Neural Network (R²+22.4%, RMSE-24.4%) models. Analysis of different water–fertilizer treatments revealed that while organic fertilizer under full irrigation (750 m³ ha⁻¹) conditions achieved maximum yield benefit (13,679.26 CNY·ha⁻¹), it showed relatively low water productivity (WP = 7.43 kg·m⁻³). Conversely, under deficit irrigation (450 m³ ha⁻¹) conditions, the 3:7 organic/inorganic fertilizer treatment achieved optimal WP (11.65 kg m⁻³) and WUE (20.16 kg∙ha⁻¹∙mm⁻¹) while increasing yield benefit by 25.46% compared to organic fertilizer alone. This research establishes an integrated technical framework for high-throughput spike monitoring and yield estimation, providing actionable insights for synergistic water–fertilizer management strategies in sustainable precision agriculture. Full article

We present a unified framework for fuzzy statistical convergence of Grünwald–Letnikov (GL) fractional differences in Bag–Samanta fuzzy normed linear spaces, addressing memory effects and nonlocality inherent to fractional-order models. Theoretically, we establish the uniqueness, linearity, and invariance of fuzzy statistical limits and prove a Cauchy characterization: fuzzy statistical convergence implies fuzzy statistical Cauchyness, while the converse holds in fuzzy-complete spaces (and in the completion, otherwise). We further develop an inclusion theory linking fuzzy strong Cesàro summability—including weighted means—to fuzzy statistical convergence. Via the discrete Q-operator, all statements transfer verbatim between nabla-left and delta-right GL forms, clarifying the binomial GL↔discrete Riemann–Liouville correspondence. Beyond structure, we propose density-based residual diagnostics for GL discretizations of fractional initial-value problems: when GL residuals are fuzzy statistically negligible, trajectories exhibit Ulam–Hyers-type robustness in the fuzzy topology. We also formulate a fuzzy Korovkin-type approximation principle under GL smoothing: Cesàro control on the test set $\{1,x,x^2\}$ propagates to arbitrary targets, yielding fuzzy statistical convergence for positive-operator sequences. Worked examples and an engineering-style case study (thermal balance with memory and bursty disturbances) illustrate how the diagnostics certify robustness of GL numerical schemes under sparse spikes and imprecise data. Full article

This paper introduces a new quadruped spider robot platform specializing in environmental reconnaissance and mapping. The robot measures 180 mm × 180 mm × 95 mm and weighs 385 g, including the battery, providing a compact yet capable platform for reconnaissance missions. The robot consists of an ESP32 microcontroller and eight servos that are disposed in a biomimetic layout to achieve the biological gait of an arachnid. One of the major design revolutions is in the power distribution network (PDN) of the robot, in which two DC-DC buck converters (LM2596M) are used to isolate the power domains of the computation and the mechanical subsystems, thereby enhancing reliability and the lifespan of the robot. The theoretical analysis demonstrates that this dual-domain architecture reduces computational-domain voltage fluctuations by 85.9% compared to single-converter designs, with a measured voltage stability improving from 0.87 V to 0.12 V under servo load spikes. Its proprietary Bluetooth protocol allows for both the sending and receiving of controls and environmental data with fewer than 120 ms of latency at up to 12 m of distance. The robot’s mapping system employs a novel motion-compensated probabilistic algorithm that integrates ultrasonic sensor data with IMU-based motion estimation using recursive Bayesian updates. The occupancy grid uses 5 cm × 5 cm cells with confidence tracking, where each cell’s probability is updated using recursive Bayesian inference with confidence weighting to guide data fusion. Experimental verification in different environments indicates that the mapping accuracy (92.7% to ground-truth measurements) and stable pattern of the sensor reading remain, even when measuring the complex gait transition. Long-range field tests conducted over 100 m traversals in challenging outdoor environments with slopes of up to 15° and obstacle densities of 0.3 objects/m² demonstrate sustained performance, with 89.2% mapping accuracy. The energy saving of the robot was an 86.4% operating-time improvement over the single-regulator designs. This work contributes to the championing of low-cost, high-performance robotic platforms for reconnaissance tasks, especially in search and rescue, the exploration of hazardous environments, and educational robotics. Full article

Recent advances in Radio Frequency (RF)-based device classification have shown promise in enabling secure and efficient wireless communications. However, the energy efficiency and low-latency processing capabilities of neuromorphic computing have yet to be fully leveraged in this domain. This paper is a first step toward enabling an end-to-end neuromorphic system for RF device classification, specifically supporting development of a neuromorphic classifier that enforces temporal causality without requiring non-neuromorphic classifier pre-training. This Spiking Neural Network (SNN) classifier streamlines the development of an end-to-end neuromorphic device classification system, further expanding the energy efficiency gains of neuromorphic processing to the realm of RF fingerprinting. Using experimentally collected WirelessHART transmissions, the TI-SNN achieves classification accuracy above 90% while reducing fingerprint density by nearly seven-fold and spike activity by over an order of magnitude compared to a baseline Rate-Encoded SNN (RE-SNN). These reductions translate to significant potential energy savings while maintaining competitive accuracy relative to Random Forest and CNN baselines. The results position the TI-SNN as a step toward a fully neuromorphic “RF Event Radio” capable of low-latency, energy-efficient device discrimination at the edge. Full article

Microplastics (MPs) pollution has become a global environmental issue. Soil, as a key environmental medium, serves as an important sink and carrier of MPs. Accurate and efficient extraction of MPs from soil matrices is essential for understanding their distribution, composition, and environmental behavior. This study presents a refined extraction method that combines two-step density separation with sodium chloride (NaCl, 1.20 g/cm³), hydrogen peroxide (H₂O₂) digestion for organic matter removal and a Fractionated Filtration Method (FFM) to capture MPs across multiple particle size ranges. Polymer identification and size characterization were performed using the high-throughput Agilent 8700 Laser Direct Infrared (LDIR) imaging system. Method validation demonstrated a recovery rate of 85% based on 100 μm MPs standards spiked into soil and minimal background contamination of 5–8 particles in blank controls, confirming the reliability of the workflow. Applying this method to agricultural soils from the Hetao Irrigation District revealed widespread MP contamination, with concentrations ranging from 5778 to 31,489 particles/kg and an average of 16,461 ± 8097 particles/kg. More than 99% of MPs were smaller than 500 μm, with the 10–30 μm fraction dominating the distribution. Polypropylene (PP), polyamide (PA), and polyethylene (PE) accounted for over 90% of detected MPs. This refined method enables reproducible extraction and accurate characterization of fine MPs in complex soil environments and provides a practical foundation for advancing standardized soil MP monitoring protocols. Full article

Modern neuromorphic processors exhibit neuron densities that are orders of magnitude lower than those of the biological cortex, hindering the deployment of large-scale spiking neural networks (SNNs) on single chips. To bridge this gap, we propose HDNIP, a 40 nm high-density neuromorphic inference platform with a density-first architecture. By eliminating area-intensive on-chip SRAM and using 1280 compact cores with a time-division multiplexing factor of up to 8192, HDNIP integrates 10 million neurons and 80 billion synapses within a 44.39 ${\mathrm{mm}}^2$ synthesized area. This achieves an unprecedented neuron density of 225 k neurons/${\mathrm{mm}}^2$, over 100 times greater than prior art. The resulting bandwidth challenges are mitigated by a ReRAM-based near-memory computation strategy combined with input reuse, reducing off-chip data transfer by approximately 95%. Furthermore, adaptive TDM and dynamic core fusion ensure high hardware utilization across diverse network topologies. Emulator-based validation using large SNNs, demonstrates a throughput of 13 GSOP/s at a low power consumption of 146 mW. HDNIP establishes a scalable pathway towards single-chip, low-SWaP neuromorphic systems for complex edge intelligence applications. Full article

Background: When an antigen molecule is exposed to serum, many different kinds of antibodies bind to it. The complexity of these binding events is only poorly characterized by assays that generate a single variable, generally reflecting the fractional saturation of the antigen, as the readout. Methods: We have previously devised an assay that delivers the essential biochemical variables to determine fractional saturation as the output: an equilibrium dissociation constant for affinity, the ratio of antibody concentration to the equilibrium constant and the concentration of bound antibodies under reference conditions. Here we propose a visualization method for the practical and informative display of these variables. Results: Using total antigen concentration and free and bound antibody concentration as coordinates in a three-dimensional space, a surface plot can depict the behavior of serum antibodies in the measurement range and identify the values of the key variables of binding activity. This surface display (antibody binding in 3-concentration display, Ab3cD) was used for the characterization of antibody binding to the SARS-CoV-2 spike protein in seronegative and seropositive sera. We demonstrate that this visualization scheme is suitable for presenting both individual and group differences and that epitope density changes, not commonly measured by immunoassays, are also revealed by the method. Conclusions: We recommend the use of 3D visualization whenever detailed, informative and characteristic differences in serum antibody reactivity are studied. Full article

This study investigates age-related differences in motor unit (MU) properties and neuromuscular control during isometric elbow flexion across the human lifespan. High-density surface electromyography (sEMG) was recorded from the biceps brachii of 44 participants, divided into three groups: Child (8–14 years), Adult (20–40 years), and Elder (65–80 years). MU spike trains were extracted noninvasively by sEMG decomposition. Then the discharge rate, MU action potential (MUAP) morphology, recruitment threshold, and common neural drive were quantified and compared across age groups. This study provides novel insights into force tracking performance, revealing that both children and elders exhibit higher errors compared to young adults, likely due to immature or declining motor control systems. Significant differences in MU discharge patterns were observed across force levels and age groups. Children and elders displayed lower MU discharge rates at low force levels, which increased at higher forces. In contrast, adults demonstrated higher MU action potential peak-to-peak amplitudes (PPV) and recruitment thresholds (RTs), along with steeper PPV-RT slopes, suggesting a narrower RT range in children and older adults. Principal component analysis revealed a strong correlation between common neural drive and force across all groups, with neural drive being weaker in elders. Overall, young adults exhibited the most efficient and synchronized MU control, while children and older adults showed distinct deviations in discharge intensity, recruitment strategies, and neural synergy. These findings comprehensively characterize MU adaptations across the lifespan, offering implications for developmental neurophysiology and age-specific neuromuscular diagnostics and interventions. Full article

This study presents an innovative unmanned aerial vehicle (UAV)-based intelligent detection method utilizing an improved Faster Region-based Convolutional Neural Network (Faster R-CNN) architecture to address the inefficiency and inaccuracy inherent in manual wheat spike counting. We systematically collected a high-resolution image dataset (2000 images, 4096 × 3072 pixels) covering key growth stages (heading, grain filling, and maturity) of winter wheat (Triticum aestivum L.) during 2022–2023 using a DJI M300 RTK equipped with multispectral sensors. The dataset encompasses diverse field scenarios under five fertilization treatments (organic-only, organic–inorganic 7:3 and 3:7 ratios, inorganic-only, and no fertilizer) and two irrigation regimes (full and deficit irrigation), ensuring representativeness and generalizability. For model development, we replaced conventional VGG16 with ResNet-50 as the backbone network, incorporating residual connections and channel attention mechanisms to achieve 92.1% mean average precision (mAP) while reducing parameters from 135 M to 77 M (43% decrease). The GFLOPS of the improved model has been reduced from 1.9 to 1.7, an decrease of 10.53%, and the computational efficiency of the model has been improved. Performance tests demonstrated a 15% reduction in missed detection rate compared to YOLOv8 in dense canopies, with spike count regression analysis yielding R² = 0.88 (p < 0.05) against manual measurements and yield prediction errors below 10% for optimal treatments. To validate robustness, we established a dedicated 500-image test set (25% of total data) spanning density gradients (30–80 spikes/m²) and varying illumination conditions, maintaining >85% accuracy even under cloudy weather. Furthermore, by integrating spike recognition with agronomic parameters (e.g., grain weight), we developed a comprehensive yield estimation model achieving 93.5% accuracy under optimal water–fertilizer management (70% ETc irrigation with 3:7 organic–inorganic ratio). This work systematically addresses key technical challenges in automated spike detection through standardized data acquisition, lightweight model design, and field validation, offering significant practical value for smart agriculture development. Full article

Durum wheat, the world’s second most cultivated wheat species, is a staple crop, critical for global food security, including in Ethiopia where it serves as a center of diversity. However, climate change and genetic erosion threaten its genetic resources, necessitating genomic studies to support conservation and breeding efforts. This study characterized genome-wide diversity, population structure (STRUCTURE, principal coordinate analysis (PCoA), neighbor-joining trees, analysis of molecular variance (AMOVA)), and selection signatures (F_ST, Hardy–Weinberg deviations) in Ethiopian durum wheat by analyzing 376 genotypes (148 accessions) using an Illumina Infinium 25K single nucleotide polymorphism (SNP) array. A set of 7842 high-quality SNPs enabled the assessments, comparing landraces with cultivars and breeding populations. Results revealed moderate genetic diversity (mean polymorphism information content (PIC) = 0.17; gene diversity = 0.20) and identified 26 loci under selection, associated with key traits like grain yield, stress tolerance, and disease resistance. AMOVA revealed 80.1% variation among accessions, with no significant differentiation by altitude, region, or spike density. Landraces formed distinct clusters, harboring unique alleles, while admixture suggested gene flow via informal seed exchange. The findings highlight Ethiopia’s rich durum wheat diversity, emphasizing landraces as reservoirs of adaptive alleles for breeding. This study provides genomic insights to guide conservation and the development of climate-resilient cultivars, supporting sustainable wheat production globally. Full article

The synthesis and structural characterization of three new triple-stranded helical complexes ([Dy₂(L²)₃]2Cl∙15H₂O (C1), [Y₂(L²)₃]3(NO₃)Cl∙14H₂O∙DMSO (C2), and [Eu₂(L⁴)₃]∙12H₂O (C3), where L² and L⁴ are ligands derived from pyridoxal hydrochloride and succinic or adipic acid dihydrazides, respectively, were described. The X-ray data, combined with spectroscopic measurements, indicated that L² and L⁴ act as bis-tridentate ligands, presenting two tridentate chelating cavities O,N,O to obtain the dinuclear complexes C1–C3. Their antiviral profile was predicted via in silico calculations in terms of interaction with the structural severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike glycoprotein in the down- and up-states and complexed with the cellular receptor angiotensin-converting enzyme 2 (ACE2). The best affinity energy values (−9.506, −9.348, and −9.170 kJ/mol for C1, C2, and C3, respectively) were obtained for the inorganic complexes docked in the model spike-ACE2, with C1 being suggested as the most promising candidate for a future in vitro validation. The obtained in silico antiviral trend was supported by the prediction of the electronic and physical–chemical properties of the inorganic complexes via the density functional theory (DFT) approach, representing an original and relevant contribution to the bioinorganic and medicinal chemistry fields. Full article