Search Results (6,218)

Tomato brown rugose fruit virus (ToBRFV) has been causing severe agricultural damage worldwide since its recent discovery. While related to tobacco mosaic virus, its properties and infection mechanisms are poorly understood. To uncover their structure and nanomechanics, we carried out atomic force microscopy (AFM) measurements on individual ToBRFV particles. The virions are rod-shaped with a height and width of 9 and 30 nm, respectively. Length is widely distributed (5–1000 nm), with a mode at 30 nm. ToBRFV rods displayed a 22.4 nm axial periodicity related to structural units. Force spectroscopy revealed a Young’s modulus of 8.7 MPa, a spring constant of 0.25 N/m, and a rupture force of 1.7 nN. In the force curves a step was seen at a height of 3.3 nm, which is related to virion wall thickness. Wall thickness was also estimated by predicting coat protein structure with AlphaFold, yielding a protein with a length of 7.3 nm. Accordingly, the structural element of ToBRFv is a right circular cylinder with an equal height and diameter of ~22 nm and a wall thickness between 3.3 and 7.3 nm. Thus, at least four to nine serially linked units are required to encapsidate a single, helically organized RNA genome. Fragmentation of ToBRFV into these cylindrical structural units may result in a facilitated release of the genome and thus efficient infection. Full article

Surface modification of carbon fibers (CFs) is a critical step in preparing carbon fiber-reinforced composites. This study developed a continuous experimental process that integrates electrochemical anodic oxidation and chemical vapor deposition to fabricate carbon nanotubes/carbon fiber (CNTs/CF) reinforcements. The effects of temperature and hydrogen flow rate during CNT growth on the resulting reinforcements were systematically investigated. The surface morphology and mechanical properties of the modified materials were characterized using scanning electron microscopy, Raman spectroscopy, and single-fiber tensile testing. Employing an Fe_0.5Ni_0.5 bimetallic catalyst under optimized conditions (550 °C, H₂ flow rate: 0.45 mol/min, C₂H₂ flow rate: 0.30 mol/min), the resulting reinforcement exhibited an 8.7% increase in tensile strength compared to as-received CF. Full article

Germplasm resources embody the genetic diversity of plants and form the foundation for breeding and the ongoing improvement of elite cultivars. The establishment of germplasm banks, along with their systematic evaluation, constitutes a critical step toward the conservation, sustainable use, and innovative utilization of these resources. Liriodendron, a rare and endangered tree genus with species distributed in both East Asia and North America, holds considerable ecological, ornamental, and economic significance. However, a standardized evaluation system for Liriodendron germplasm remains unavailable. In this study, 297 Liriodendron germplasm accessions were comprehensively evaluated using 34 phenotypic traits and whole-genome resequencing data. Substantial variation was observed in most phenotypic traits, with significant correlations identified among several characteristics. Cluster analysis based on phenotypic data grouped the accessions into three distinct clusters, each exhibiting unique distribution patterns. This classification was further supported by principal component analysis (PCA), which effectively captured the underlying variation among accessions. These phenotypic groupings demonstrated high consistency with subsequent population structure analysis based on SNP markers (K = 3). Notably, several key traits exhibited significant divergence (p < 0.05) among distinct genetic clusters, thereby validating the coordinated association between phenotypic variation and molecular markers. Genetic diversity and population structure were assessed using 4204 high-quality single-nucleotide polymorphism (SNP) markers obtained through stringent filtering. The results indicated that the Liriodendron sino-americanum displayed the highest genetic diversity, with an expected heterozygosity (He) of 0.18 and a polymorphic information content (PIC) of 0.14. In addition, both hierarchical clustering and PCA revealed clear population differentiation among the accessions. Association analysis between three phenotypic traits (DBH, annual height increment, and branch number) and SNPs identified 25 highly significant SNP loci (p < 0.01). Of particular interest, the branch number-associated locus SNP_17_69375264 (p = 1.03 × 10⁻⁵) demonstrated the strongest association, highlighting distinct genetic regulation patterns among different growth traits. A minimal set of 13 core SNP markers was subsequently used to construct unique DNA fingerprints for all 297 accessions. In conclusion, this study systematically characterized phenotypic traits in Liriodendron, identified high-quality and core SNPs, and established correlations between key phenotypic and molecular markers. These achievements enabled differential analysis and genetic diversity assessment of Liriodendron germplasm, along with the construction of DNA fingerprint profiles. The results provide crucial theoretical basis and technical support for germplasm conservation, accurate identification, and utilization of Liriodendron resources, while offering significant practical value for variety selection, reproduction and commercial applications of this species. Full article

We analyzed the accuracy of digitized adiabatic quantum computing to form the entangled three-qubit Greenberger–Horne–Zeilinger (GHZ) state on two IBM quantum computers and four quantum simulators by comparison with direct calculations using a Python code (version 3.12). We initialized three-qubit systems in the ground state of the Hamiltonian for noninteracting spins in an applied magnetic field in the x direction. We then gradually varied the Hamiltonian to an Ising model form with nearest-neighbor zz spin coupling with an eight-step discretization. The von Neumann entropy provides an indicator of the accuracy of the discretized adiabatic evolution. The von Neumann entropy of the density matrix from the Python code (version 3.12) remains very close to zero, while the von Neumann entropy of the density matrices on the quantum computers increases almost linearly with the step number in the process. The GHZ witness operator indicates that the quantum simulators incorporate a GHZ component in part. The states on the two quantum computers acquire partial GHZ character, even though the trace of the product of the GHZ witness operator with the density matrix not only remains positive but also rises monotonically from Step 5 to Step 8. Each of the qubits becomes entangled during the adiabatic evolution in all of the calculations, as shown by the single-qubit reduced density matrices. Full article

Currently, there is a lack of methods for detecting the mechanism of gas explosion propagation within flameproof enclosures and the dynamic behavior of flameproof enclosures under explosion impact. Therefore, this paper studies a method for detecting the vibration characteristics of coal mine explosion-proof equipment under internal gas explosions using laser Doppler. First, a model of gas explosion propagation and explosion transmission response in flameproof enclosures is established to reveal the mechanism of gas explosion transmission inside coal mine flameproof enclosures. Second, a laser Doppler measurement method for coal mine flameproof enclosures is proposed, along with a step-by-step progressive vibration characteristic analysis method. This begins with a single-frequency dimension analysis using the Fourier transform (FFT), extends to time–frequency joint analysis using the short-time Fourier transform (STFT) to incorporate a time scale, and then advances to a three-dimensional linkage of scale, time, and frequency using the wavelet transform (DWT) to solve the limitation of the fixed window length of the STFT, thereby achieving a dynamic characterization of the detonation response characteristics. Finally, a non-symmetric Gaussian impact load inversion model is constructed to validate the overall scheme. The experimental results show that the FFT analysis identified a 2000 Hz main frequency, along with the global frequency components of the flameproof enclosure vibration signal, the STFT analysis revealed the dynamic evolution of the 2000 Hz main frequency and global frequency over time, and the wavelet transform achieved higher accuracy positioning of the frequency amplitude in the time domain, with better time resolution. Finally, the experimental platform showed an error of less than 5% compared with the actual measured impact load, and the error between the inverted impact load and the actual load was less than 15%. The experimental platform is feasible, and the inversion model has good accuracy. The laser Doppler measurement method has significant advantages over traditional coal mine flameproof equipment measurement and analysis methods and can provide further failure analysis and prevention, design optimization, and safety performance evaluation of flameproof enclosures in the future. Full article

Although genomic selection can accelerate livestock breeding, its application in many countries is hindered due to the limited size of reference populations. To address this issue, researchers have explored methods of combining multiple breeds to create reference populations, aiming to enhance the accuracy of genomic prediction. The main objective of this study was to evaluate the impact of the construction of mixed reference populations at different genetic distance levels on the accuracy of multi-breed genome prediction in multi-breed beef cattle populations using three evaluation methods: GBLUP, ssGBLUP, and wGBLUP. In order to study the effect of genetic correlation on multiple populations and to resolve the optimal mixing ratio, we considered six scenarios, including (1) population A as the main body, where the nearest 10% of individuals in populations B and C were added; (2) population A was the main body, where the 15% of individuals with the closest genetic distance in groups B and C were added; and (3) population A as the main body, where the 20% of individuals in populations B and C with the closest genetic distance were added. Our results suggest that the wGBLUP model can be enhanced when the mixing ratio is 15%, and the wGBLUP model shows higher accuracy in predicting populations with different LD decay patterns. Among them, whether combined with PopB or PopC, the wGBLUP model shows better prediction ability than the GBLUP and ssGBLUP models. However, when the mixing ratio is 10% or 20%, the accuracy of the three models is less than 15%, and the wGBLUP and ssGBLUP models show high and stable accuracy. Our results highlight the importance of considering the proportion of mixing between different populations when using genetic assessment models to predict accuracy, especially for endemic beef cattle breeds with different genetic structures and LD patterns and limited resources. However, this study also has some limitations. First, the determination of the optimal mixing ratio still needs further exploration, especially for populations with different genetic structures and LD patterns. Second, future studies can introduce more advanced models to further improve prediction accuracy. Full article

Background: Circulating miRNAs have been identified as potential biomarkers for the early diagnosis and monitoring of cancers. However, limitations of polymerase chain reaction (PCR)-based methods are currently delaying the transition of miRNA research into clinical practice. These include labour-intensive workflows, exposure to errors and difficulties in detecting and quantifying low-abundance miRNAs. Objectives: This review emphasizes the need to develop amplification-free (“PCR-free”) technologies to improve the reliability, scalability and practicality of miRNA diagnostics in clinical settings. Methods: This review explores recent advances in PCR-free technologies developed over the past five years. It focuses on innovative methods, such as bead-based assays and sensor detection platforms, which serve as valuable alternatives to conventional PCR-based approaches. These emerging technologies have the potential to overcome the key limitations of PCR by offering streamlined workflows, reduced error rates and enhanced compatibility with a variety of clinical sample types. Crucially, they enable absolute quantification without the need for pre-nucleic acid extraction, reverse transcription or amplification, as well as the simultaneous detection of multiple miRNAs within a single assay. These provide cost-effective and scalable solutions for comprehensive biomarker profiling. The transition from PCR-based to PCR-free technologies is a significant step forward in miRNA diagnostics, overcoming long-standing technical barriers and paving the way for broader adoption of miRNA analysis in routine clinical settings. This shift supports the advancement of precision medicine and holds promises for improving early cancer detection. Full article

Monocular depth estimation (MDE) is a cornerstone of computer vision and is applied to diverse practical areas such as autonomous vehicles, robotics, etc., yet even the latest methods suffer substantial errors in high-dynamic-range (HDR) scenes where over- or under-exposure erases critical texture. To address this challenge in real-world autonomous driving scenarios, we propose the Exposure-Aware Single-Step Diffusion Framework for Monocular Depth Estimation (EASD). EASD leverages a pre-trained Stable Diffusion variational auto-encoder, freezing its encoder to extract exposure-robust latent RGB and depth representations. A single-step diffusion process then predicts the clean depth latent vector, eliminating iterative error accumulation and enabling real-time inference suitable for autonomous vehicle perception pipelines. To further enhance robustness under extreme lighting conditions, EASD introduces an Exposure-Aware Feature Fusion (EAF) module—an attention-based pyramid that dynamically modulates multi-scale features according to global brightness statistics. This mechanism suppresses bias in saturated regions while restoring detail in under-exposed areas. Furthermore, an Exposure-Balanced Loss (EBL) jointly optimises global depth accuracy, local gradient coherence and reliability in exposure-extreme regions—key metrics for safety-critical perception tasks such as obstacle detection and path planning. Experimental results on NYU-v2, KITTI, and related benchmarks demonstrate that EASD reduces absolute relative error by an average of 20% under extreme illumination, using only 60,000 labelled images. The framework achieves real-time performance (<50 ms per frame) and strikes a superior balance between accuracy, computational efficiency, and data efficiency, offering a promising solution for robust monocular depth estimation in challenging automotive lighting conditions such as tunnel transitions, night driving and sun glare. Full article

Interstitial lung diseases (ILD) significantly impact health and mortality, affecting millions of individuals worldwide. During the COVID-19 pandemic, lung ultrasonography (LUS) became an indispensable diagnostic and management tool for lung disorders. However, utilising LUS to diagnose ILD requires significant expertise. This research aims to develop an automated and efficient approach for diagnosing ILD from LUS videos using AI to support clinicians in their diagnostic procedures. We developed a binary classifier based on a state-of-the-art CSwin Transformer to discriminate between LUS videos from healthy and non-healthy patients. We used a multi-centric dataset from the Royal Melbourne Hospital (Australia) and the ULTRa Lab at the University of Trento (Italy), comprising 60 LUS videos. Each video corresponds to a single patient, comprising 30 healthy individuals and 30 patients with ILD, with frame counts ranging from 96 to 300 per video. Each video is annotated using the corresponding medical report as ground truth. The datasets used for training the model underwent selective frame filtering, including reduction in frame numbers to eliminate potentially misleading frames in non-healthy videos. This step was crucial because some ILD videos included segments of normal frames, which could be mixed with the pathological features and mislead the model. To address this, we eliminated frames with a healthy appearance, such as frames without B-lines, thereby ensuring that training focused on diagnostically relevant features. The trained model was assessed on an unseen, separate dataset of 12 videos (3 healthy and 9 ILD) with frame counts ranging from 96 to 300 per video. The model achieved an average classification accuracy of 91%, calculated as the mean of three testing methods: Random Sampling (92%), Key Featuring (92%), and Chunk Averaging (89%). In RS, 32 frames were randomly selected from each of the 12 videos, resulting in a classification with 92% accuracy, with specificity, precision, recall, and F1-score of 100%, 100%, 90%, and 95%, respectively. Similarly, KF, which involved manually selecting 32 key frames based on representative frames from each of the 12 videos, achieved 92% accuracy with a specificity, precision, recall, and F1-score of 100%, 100%, 90%, and 95%, respectively. In contrast, the CA method, where the 12 videos were divided into video segments (chunks) of 32 consecutive frames, with 82 video segments, achieved an 89% classification accuracy (73 out of 82 video segments). Among the 9 misclassified segments in the CA method, 6 were false positives and 3 were false negatives, corresponding to an 11% misclassification rate. The accuracy differences observed between the three training scenarios were confirmed to be statistically significant via inferential analysis. A one-way ANOVA conducted on the 10-fold cross-validation accuracies yielded a large F-statistic of 2135.67 and a small p-value of 6.7 × 10⁻²⁶, indicating highly significant differences in model performance. The proposed approach is a valid solution for fully automating LUS disease detection, aligning with clinical diagnostic practices that integrate dynamic LUS videos. In conclusion, introducing the selective frame filtering technique to refine the dataset training reduced the effort required for labelling. Full article

Industrial pump systems require real-time fault diagnosis for predictive maintenance, but conventional Chain-of-Thought (COT) reasoning faces computational bottlenecks when processing high-frequency vibration data. This paper proposes Vibration-Aware COT (VA-COT), a novel framework that integrates multi-domain feature fusion (time, frequency, time–frequency) with adaptive reasoning depth control. Key innovations involve expert prior-guided dynamic feature selection to optimize edge-device inputs, complexity-aware reasoning chains reducing computational steps by 40–65% through confidence-based early termination, and lightweight deployment on industrial ARM-based single-board computers (SBCs). Evaluated on a 12-class pump fault dataset (5400 samples from centrifugal/gear pumps), VA-COT achieves 93.2% accuracy surpassing standard COT (89.3%) and CNN–LSTM (Convolutional Neural Network-Long Short-Term Memory network) (91.2%), while cutting latency to <1.1 s and memory usage by 65%. Six-month validation at pump manufacturing facilities demonstrated 35% maintenance cost reduction and 98% faster diagnostics versus manual methods, proving its viability for IIoT (Industrial Internet of Things) deployment. Full article

This paper presents a literature review of low-power hybrid wind–photovoltaic (PV) systems and introduces a 3 m diameter prototype rotor featuring twelve PV-coated pivoting blades stiffened by a peripheral rim. Existing solutions—foldable umbrella concepts, Darrieus rotors with PV-integrated blades, and morphing blades—are surveyed, and current gaps in simultaneous wind + PV co-generation on a single moving structure are highlighted. Key performance indicators such as power coefficient (C_p), DC ripple, cell temperature difference (ΔT), and levelised cost of energy (LCOE) are defined, and an integrated assessment methodology is proposed based on blade element momentum (BEM) and computational fluid dynamics (CFD) modelling, dynamic current–voltage (I–V) testing, and failure modes and effects analysis (FMEA) to evaluate system performance and reliability. Preliminary results point to moderate aerodynamic penalties (ΔC_p ≈ 5–8%), PV output during rotation equal to 15–25% of the nominal PV power (P_PV), and an estimated 70–75% reduction in blade–root bending moment when the peripheral ring converts each blade from a cantilever to a simply supported member, resulting in increased blade stiffness. Major challenges include the collective pitch mechanism, dynamic shading, and wear of rotating components (slip rings); however, the suggested technical measures—maximum power point tracking (MPPT), string segmentation, and redundant braking—keep performance within acceptable limits. This study concludes that the concept shows promise for distributed microgeneration, provided extensive experimental validation and IEC 61400-2-compliant standardisation are pursued. This paper has a dual scope: (i) a concise literature review relevant to low-Re flat-blade aerodynamics and ring-stiffened rotor structures and (ii) a multi-fidelity aero-structural study that culminates in a 3 m prototype proposal. We present the first evaluation of a hybrid wind–PV rotor employing untwisted flat-plate blades stiffened by a peripheral ring. Using low-Re BEM for preliminary loading, steady-state RANS-CFD (k-ω SST) for validation, and elastic FEM for sizing, we assemble a coherent load/performance dataset. After upsizing the hub pins (Ø 30 mm), ring (50 × 50 mm), and spokes (Ø 40 mm), von Mises stresses remain < 25% of the 6061-T6 yield limit and tip deflection ≤ 0.5%·R acrosscut-in (3 m s⁻¹), nominal (5 m s⁻¹), and extreme (25 m s⁻¹) cases. CFD confirms a broad efficiency plateau at λ = 2.4–2.8 for β ≈ 10° and near-zero shaft torque at β = 90°, supporting a three-step pitch schedule (20° start-up → 10° nominal → 90° storm). Cross-model deviations for C_p, torque, and pressure/force distributions remain within ± 10%. This study addresses only the rotor; off-the-shelf generator, brake, screw-pitch, and azimuth/tilt drives are intended for later integration. The results provide a low-cost manufacturable architecture and a validated baseline for full-scale testing and future transient CFD/FEM iterations. Full article

Smoothing and interpolation of zero-field (Z) and infield (I) heating steps in Thellier-type paleointensity determinations have been tested. Paleomagnetic samples of different materials were artificially magnetized with an applied field of 50 µT. Six samples were measured following the standard double-heating Coe-variation experimental protocol, and the obtained results were used to test several mathematical functions to smooth the experimental data. The best smoothed results were obtained using a Five Parameters Logistic (5PL) function that resulted in field estimates of good quality, although not better than those obtained experimentally. Therefore, the smoothing of de- and remagnetization data appears unnecessary. In addition to smoothing, the tested functions can be used to interpolate additional Z and, indirectly, also I steps. Interpolation using cubic Hermite splines (without any smoothing) displays a better performance than interpolation (and smoothing) using the 5PL function. A new single-step heating method is presented, combining experimental and interpolated de- and remagnetization steps. The new method would not be applicable for retrieving reliable ancient field intensities on its own, but it could save measuring time under some circumstances. Full article

While autonomous driving systems and intelligent transportation infrastructures become increasingly software-defined and network-connected, ensuring their cybersecurity has become a critical component of traffic safety. Large language models (LLMs) have recently shown promise in automating aspects of penetration testing, yet most existing approaches remain limited to simple, single-step exploits. They struggle to handle complex, multi-stage vulnerabilities that demand precise coordination, contextual reasoning, and knowledge reuse. This is particularly problematic in safety-critical domains, such as autonomous vehicles, where subtle software flaws can cascade across interdependent subsystems. In this work, we present CurriculumPT, a novel LLM-based penetration testing framework specifically designed for the security of intelligent systems. CurriculumPT combines curriculum learning and a multi-agent system to enable LLM agents to progressively acquire and apply exploitation skills across common vulnerabilities and exposures-based tasks. Through a structured progression from simple to complex vulnerabilities, agents build and refine an experience knowledge base that supports generalization to new attack surfaces without requiring model fine-tuning. We evaluate CurriculumPT on 15 real-world vulnerabilities scenarios and demonstrate that it outperforms three state-of-the-art baselines by up to 18 percentage points in exploit success rate, while achieving superior efficiency in execution time and resource usage. Our results confirm that CurriculumPT is capable of autonomous, scalable penetration testing and knowledge transfer, laying the groundwork for intelligent security auditing of modern autonomous driving systems and other cyberphysical transportation platforms. Full article

Single remote sensing image dehazing aims to eliminate atmospheric scattering effects without auxiliary information. It serves as a crucial preprocessing step for enhancing the performance of downstream tasks in remote sensing images. Conventional approaches often struggle to balance haze removal and detail restoration under non-uniform haze distributions. To address this issue, we propose a Dual-domain Feature Fusion Network (DFFNet) for remote sensing image dehazing. DFFNet consists of two specialized units: the Frequency Restore Unit (FRU) and the Context Extract Unit (CEU). As haze primarily manifests as low-frequency energy in the frequency domain, the FRU effectively suppresses haze across the entire image by adaptively modulating low-frequency amplitudes. Meanwhile, to reconstruct details attenuated due to dense haze occlusion, we introduce the CEU. This unit extracts multi-scale spatial features to capture contextual information, providing structural guidance for detail reconstruction. Furthermore, we introduce the Dual-Domain Feature Fusion Module (DDFFM) to establish dependencies between features from FRU and CEU via a designed attention mechanism. This leverages spatial contextual information to guide detail reconstruction during frequency domain haze removal. Experiments on the StateHaze1k, RICE and RRSHID datasets demonstrate that DFFNet achieves competitive performance in both visual quality and quantitative metrics. Full article

The internal transcribed spacer (ITS) is one of the most extensively utilized in the taxonomy of the genus Morus due to its generally concerted evolution. Although non-concerted evolution of nuclear ribosomal DNA (nrDNA) has been reported in some species, genome-wide nrDNA characteristics in the genus Morus remain poorly understood. In this study, 158 single-nucleotide polymorphisms (SNPs) and 15 insertions and deletions (InDels) were identified within the nrDNA regions of 542 mulberry accessions representing sixteen Morus species. These wide occurrences of heterogeneous SNPs and InDels revealed the intra-individual polymorphism within the nrDNA region of Morus, indicating the incomplete concerted evolution of nrDNA. Notably, 66 out of 158 SNPs and 13 out of 15 InDels were localized within the ITS regions (ITS1-5.8S-ITS2), indicating a high degree of polymorphism in the ITS, which was further validated through classical cloning and Sanger sequencing methodologies. The 13/16 bp InDel located in the ITS1 region was utilized to develop a rapid and reliable cleaved amplified polymorphic sequence (CAPS) marker-based method for distinguishing M. alba and M. notabilis from other Morus species, eliminating the need for a clone-based sequencing step or comparative phenotypic analysis. Phylogenetic analysis based on nrDNA SNPs from 542 mulberry accessions revealed six distinct clades, corresponding to the six Morus species. These findings offer novel new insights into the taxonomy, conservation, and breeding improvement of Morus species. Full article