Search Results (4,095)

The cereal cyst nematode, Heterodera avena, is responsible for substantial economic losses in the global production of wheat, barley, and other cereal crops. Extracellular enzymes, particularly those from the glycoside hydrolase 18 (GH18) family, such as chitinases secreted by Trichoderma spp., play a crucial role in nematode control. However, the genome-wide analysis of Trichoderma longibrachiatum T6 (T6) GH18 family genes in controlling of H. avenae remains unexplored. Through phylogenetic analysis and bioinformatics tools, we identified and conducted a detailed analysis of 18 GH18 genes distributed across 13 chromosomes. The analysis encompassed gene structure, evolutionary development, protein characteristics, and gene expression profiles following T6 parasitism on H. avenae, as determined by RT-qPCR. Our results indicate that 18 GH18 members in T6 were clustered into three major groups (A, B, and C), which comprise seven subgroups. Each subgroup exhibits highly conserved catalytic domains, motifs, and gene structures, while the cis-acting elements demonstrate extensive responsiveness to hormones, stress-related signals, and light. These members are significantly enriched in the chitin catabolic process, extracellular region, and chitinase activity (GO functional enrichment), and they are involved in amino sugar and nucleotide sugar metabolism (KEGG pathway enrichment). Additionally, 13 members formed an interaction network, enhancing chitin degradation efficiency through synergistic effects. Interestingly, 18 members of the GH18 family genes were expressed after T6 parasitism on H. avenae cysts. Notably, GH18-3 (Group B) and GH18-16 (Group A) were significantly upregulated, with average increases of 3.21-fold and 3.10-fold, respectively, from 12 to 96 h after parasitism while compared to the control group. Meanwhile, we found that the GH18-3 and GH18-16 proteins exhibit the highest homology with key enzymes responsible for antifungal activity in T. harzianum, demonstrating dual biocontrol potential in both antifungal activity and nematode control. Overall, these results indicate that the GH18 family has undergone functional diversification during evolution, with each member assuming specific biological roles in T6 effect on nematodes. This study provides a theoretical foundation for identifying novel nematicidal genes from T6 and cultivating highly efficient biocontrol strains through transgenic engineering, which holds significant practical implications for advancing the biocontrol of plant-parasitic nematodes (PPNs). Full article

The aim of this work was to systematically evaluate the effects of Lion’s Mane Mushroom powder (LMM, 0–40%) on the physicochemical properties, structural characteristics, and flavor profile of soy protein isolate-based high-moisture meat analogues (HMMAs). Optimal incorporation of 20% LMM significantly enhanced product quality by acting as a secondary phase that inhibited lateral protein aggregation while promoting longitudinal alignment, achieving a peak fibrous degree of 1.54 with dense, ordered fibers confirmed by scanning electron microscopy. Rheological analysis showed that LMM improved viscoelasticity (G′ > G″) through β-glucan; however, excessive addition (≥30%) compromised structural integrity due to insoluble dietary fiber disrupting protein network continuity, concurrently reducing thermal stability as denaturation enthalpy (_ΔH) decreased from 1176.6 to 776.3 J/g. Flavor analysis identified 285 volatile compounds in HMMAs with 20% LMM, including 98 novel compounds, and 101 flavor metabolites were upregulated. The mushroom-characteristic compound 1-octen-3-ol exhibited a marked increase in its Relative Odor Activity Value of 18.04, intensifying mushroom notes. Furthermore, LMM polysaccharides promoted the Maillard reaction, increasing the browning index from 48.77 to 82.07, while β-glucan induced a transition in protein secondary structure from random coil to β-sheet configurations via intramolecular hydrogen bonding. In conclusion, 20% LMM incorporation synergistically improved texture, fibrous structure, and flavor complexity—particularly enhancing mushroom aroma. This research offers valuable insights and a foundation for future research for developing high-quality fungal protein-based meat analogues Full article

This study aimed to investigate the histological features of the intestine of Acipenser dabryanus from 1 to 15 months of age via HE staining, AB-PAS staining, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). The intestine of A. dabryanus comprises the duodenum, spiral valve intestine, and rectum. With age, the duodenal diameter and mucosal/muscular layer thickness increased, the spiral valve intestine’s mucosa thickened and protrusions formed networks, and the rectal diameter enlarged. Abundant mucus cells, predominantly type IV, were found in the duodenum, spiral valve intestine, and rectum of A. dabryanus at different ages by AB-PAS staining. Our study confirmed the presence of ciliated columnar cells (with ‘9 + 2’ cilia structure) with orderly arranged cilia at their apices in the mucosal epithelium of A. dabryanus’s duodenum, spiral valve intestine, and rectum for the first time, as shown by SEM and TEM. The presence of spiral valves and ciliated columnar cells in the intestinal structure of A. dabryanus highlights its unique features and evolutionary significance. These findings highlight A. dabryanus’s unique intestinal features and evolutionary significance, providing a basis for scientific feed formulation and enhancing our understanding of the histological characteristics of the sturgeon intestine. Full article

The selection of an appropriate technological process is essential to achieve optimal results in manufacturing companies. This affects quality, efficiency and competitiveness. In the modern industry, multi-criteria decision-making (MCDM) methods are increasingly used to evaluate, optimize and solve various manufacturing challenges. In this review article, existing methodologies and patents related to optimization and decision making are investigated. The main characteristics and applications of the methods are outlined. The purpose of this article is to provide a systematic review and evaluation of the main MCDM methods used in industrial practice, including through an analysis of relevant methodologies and patents. The methodology involves a structured literature and patent review, focusing on applications of widely used MCDM techniques such as the AHP (analytic hierarchy process), ANP (analytic network process), FUCOM (full consistency method), TOPSIS (technique for order preference by similarity to ideal solution), and VIKOR (višekriterijumsko kompromisno rangiranje). The analysis outlines each method’s strengths, limitations and areas of applicability. Special attention is given to the potential of the FUCOM for process evaluation in manufacturing. The findings are intended to guide researchers and practitioners in selecting appropriate decision-making tools based on specific industrial contexts and objectives. In conclusion, from the comparative analysis made, the methodologies reveal their advantages and disadvantages as well as limitations that arise in their application. Full article

The three-dimensional (3D) structure of clouds is a key factor in atmospheric processes, profoundly influencing solar radiation transfer, weather patterns, and climate dynamics. However, accurately representing this complex structure in radiative transfer models remains a significant challenge. As part of the Deep 3D Scattering of Solar Radiation in the Atmosphere due to Clouds (D3D) project, we conducted a comprehensive study on the role of all-sky imagers (ASIs) in reconstructing observational 3D cloud fields and integrating them into advanced 3D cloud modeling. Since November 2022, a network of four ASIs has been operating across the broader Patras region in Greece, continuously capturing atmospheric measurements over an area of approximately 50 km². Using simultaneously captured images from the ASIs within the network, a 3D cloud reconstruction was performed utilizing advanced image processing techniques, with a primary focus on cumulus cloud scenarios. The Structure from Motion (SfM) technique was employed to reconstruct the 3D structural characteristics of clouds from two-dimensional images. The resulting 3D cloud fields were then integrated into the MYSTIC three-dimensional radiative transfer model to simulate and reconstruct solar irradiance fields. Full article

The subject of this paper is the computational homogenisation and identification of heterogeneous materials in the form of auxetic structures made of materials with nonlinear characteristics. It is assumed that some of the material and topological parameters of the auxetic structures are uncertain and are modelled as interval numbers. Directed interval arithmetic is used to minimise the width of the resulting intervals. The finite element method is employed to solve the boundary value problem, and artificial neural network response surfaces are utilised to reduce the computational effort. In order to solve the identification task, the Pareto approach is adopted, and a multi-objective evolutionary algorithm is used as the global optimisation method. The results obtained from computational homogenisation under uncertainty demonstrate the efficacy of the proposed methodology in capturing material behaviour, thereby underscoring the significance of incorporating uncertainty into material properties. The identification results demonstrate the successful identification of material parameters at the microscopic scale from macroscopic data involving the interval description of the process of deformation of auxetic structures in a nonlinear regime. Full article

As a key technology in multimodal information processing, infrared and visible image fusion holds significant application value in fields such as military reconnaissance, intelligent security, and autonomous driving. To address the limitations of existing methods, this paper proposes the Hierarchical Triple-Feature Interaction Fusion Network (TFI-Fusion). Based on a hierarchical triple-stream feature interaction mechanism, the network achieves high-quality fusion through a two-stage, separate-model processing approach: In the first stage, a single model extracts low-rank components (representing global structural features) and sparse components (representing local detail features) from source images via the Low-Rank Sparse Decomposition (LSRSD) module, while capturing cross-modal shared features using the Shared Feature Extractor (SFE). In the second stage, another model performs fusion and reconstruction: it first enhances the complementarity between low-rank and sparse features through the innovatively introduced Bi-Feature Interaction (BFI) module, realizes multi-level feature fusion via the Triple-Feature Interaction (TFI) module, and finally generates fused images with rich scene representation through feature reconstruction. This separate-model design reduces memory usage and improves operational speed. Additionally, a multi-objective optimization function is designed based on the network’s characteristics. Experiments demonstrate that TFI-Fusion exhibits excellent fusion performance, effectively preserving image details and enhancing feature complementarity, thus providing reliable visual data support for downstream tasks. Full article

The instance segmentation of power distribution equipment in infrared images is a prerequisite for determining its overheating fault, which is crucial for urban power grids. Due to the specific characteristics of power distribution equipment, the objects are generally characterized by small scale and complex structure. Existing methods typically use a backbone network to extract features from infrared images. However, the inherent down-sampling operations lead to information loss of objects. The content regions of small-scale objects are compressed, and the edge regions of complex-structured objects are fragmented. In this paper, (1) the first unmanned aerial vehicle-based infrared dataset PDI for power distribution inspection is constructed with 16,596 images, 126,570 instances, and 7 categories of power equipment. It has the advantages of large data volume, rich geographic scenarios, and diverse object patterns, as well as challenges of distribution imbalance, category imbalance, and scale imbalance of objects. (2) A reconstruction error (RE)-guided instance segmentation framework, coupled with an object reconstruction decoder (ORD) and a difference feature enhancement (DFE) module, is proposed. The former reconstructs the objects, where the reconstruction result indicates the position and degree of information loss of the objects. Therefore, the difference map between the reconstruction result and the input image effectively replays the object features. The latter adaptively compensates for object features by global fusion between the difference features and backbone features, thereby enhancing the spatial representation of objects. Extensive experiments on the constructed and publicly available datasets demonstrate the strong generalization, superiority, and versatility of the proposed framework. Full article

Rocket launches constitute a major anthropogenic source of disturbance in the near-Earth space environment, inducing significant ionospheric perturbations through both chemical and dynamic mechanisms. This study presents a systematic analysis of ionospheric disturbances—specifically, electron density depletion and traveling ionospheric disturbances (TIDs)—triggered by four rocket launches from China’s Jiuquan Satellite Launch Center between 2023 and 2025. Using high-rate, multi-constellation GNSS data from 370 ground stations and BeiDou GEO satellites, we extracted total electron content (TEC) signals and applied advanced detection methods, including the Multi-Rolling-Multi-Image-Tracking (MRMIT) algorithm for depletion identification and a parametric integration framework for quantitative comparison. Our results reveal that all launches produced rapid TEC depletions, evolving along the rocket trajectory and peaking within approximately 30 min. Launch mass was the dominant factor controlling depletion intensity, while propellant chemistry (UDMH-based vs. liquid oxygen/methane) and local time/background TEC levels modulated the recovery rate and spatial extent. Additionally, distinct TIDs exhibiting wave-like and V-shaped structures were observed, propagating outward from the trajectory with latitudinal variations in amplitude and waveform. These findings highlight the critical roles of rocket attributes and ambient ionospheric conditions in shaping disturbance characteristics. The study underscores the value of multi-source GNSS networks and novel methodologies in monitoring anthropogenic space weather effects, with implications for GNSS performance and sustainable space operations. Full article

In recent decades, frequent cold waves and low-temperature events in mid-to-high latitude Eurasia have severely impacted socioeconomic activities in Northeast China. Accurately identifying anticyclones is essential due to their close relation to cold air activity. This study proposes a new anticyclone identification method using the Mask region-based convolutional neural network (Mask R-CNN) model to detect synoptic-scale anticyclones by capturing their two-dimensional structural features and investigating their relationship with snow-ice disasters in Northeast China. It is found that compared with traditional objective identification methods, the new method better captures the overall structural characteristics of anticyclones, significantly improving the description of large-scale, strong anticyclones. Specifically, it incorporates 7.3% of small-scale anticyclones into larger-scale systems. Anticyclones are closely correlated with local cooling and cold air mass changes over Northeast China, with 60% of anticyclones accompanying regional cold air mass accumulation and temperature drops. Two case studies of the rare rain-snow and cold wave events revealed that these events were preceded by the generation and eastward expansion of an upstream anticyclone identified by the new method. This demonstrates that the proposed method can effectively track anticyclones and the evolution of cold high-pressure systems, providing insights into extreme cold events. Full article

The curing of silicone networks from dimethylsilanediol and methylsilanetriol chainbuilder–crosslinker precursor mixtures is investigated from combined quantum/molecular mechanics simulations. Upon screening different crosslinker content from 5 to 15%, we provide a series of atomic-resolution bulk models all featuring 98–99% curing degree, albeit at rather different arrangement of the chains and nodes, respectively. To elucidate the nm scale alignment of the polymer networks, we bridge scales from atomic simulation cells to graph theory and demonstrate the analyses of 3-dimensional percolation of -O-Si-O- bonds, polydimethylsiloxane branching characteristics and the interpenetration of loops. Our findings are discussed in the context of the available experimental data to relate heat of formation, curing degree and elastic properties to the molecular scale structural details—thus promoting the in-depth understanding of silicone resins. Full article

In the task of integrated circuit micrograph acquisition, image super-resolution reconstruction technology can significantly enhance acquisition efficiency. With the advancement of deep learning techniques, the performance of image super-resolution reconstruction networks has improved markedly, but their demand for inference device memory has also increased substantially, greatly limiting their practical application in engineering and deployment on resource-constrained devices. Against this backdrop, we designed image super-resolution reconstruction networks based on feature reuse and structural reparameterization techniques, ensuring that the networks maintain reconstruction performance while being more suitable for deployment in resource-limited environments. Traditional image super-resolution reconstruction networks often redundantly compute similar features through standard convolution operations, leading to significant computational resource wastage. By employing low-cost operations, we replaced some redundant features with those generated from the inherent characteristics of the image and designed a reparameterization layer using structural reparameterization techniques. Building upon local feature fusion and local residual learning, we developed two efficient deep feature extraction modules, and forming the image super-resolution reconstruction networks. Compared to performance-oriented image super-resolution reconstruction networks (e.g., DRCT), our network reduces algorithm parameters by 84.5% and shortens inference time by 49.8%. In comparison with lightweight image reconstruction algorithms, our method improves the mean structural similarity index by 3.24%. Experimental results demonstrate that the image super-resolution reconstruction network based on feature reuse and structural reparameterization achieves an excellent balance between network performance and complexity. Full article

Traffic flow prediction is a prominent research area in intelligent transportation systems, significantly contributing to urban traffic management and control. Existing methods or models for traffic flow prediction predominantly rely on a fixed-graph structure to capture spatial correlations within a road network. However, the fixed-graph structure can restrict the representation of spatial information due to varying conditions such as time and road changes. Drawing inspiration from the attention mechanism, a new prediction model based on the mixed-graph neural network is proposed to dynamically capture the spatial traffic flow correlations. This model uses graph convolution and attention networks to adapt to complex and changeable traffic and other conditions by learning the static and dynamic spatial traffic flow characteristics, respectively. Then, their outputs are fused by the gating mechanism to learn the spatial traffic flow correlations. The Transformer encoder layer is subsequently employed to model the learned spatial characteristics and capture the temporal traffic flow correlations. Evaluated on five real traffic flow datasets, the proposed model outperforms the state-of-the-art models in prediction accuracy. Furthermore, ablation experiments demonstrate the strong performance of the proposed model in long-term traffic flow prediction. Full article

The formation of strategies and tactics of destructive impact (DI) at the stages of complex computer attacks (CCAs) largely depends on the content of intelligence data obtained by the intruder about the attacked elements of distributed information systems (DISs). This study analyzes scientific papers, methodologies and standards in the field of assessing the indicators of awareness of the intruder about the objects of DI and symmetrical indicators of intelligence security of the elements of the DIS. It was revealed that the aspects of changing the quantitative and qualitative characteristics of intelligence data (ID) at the stages of CCA, as well as their impact on the possibilities of using certain types of simple computer attacks (SKAs), are poorly studied and insufficiently systematized. This paper uses technologies for modeling the process of an intruder obtaining ID based on the application of the methodology of black, grey and white boxes and the theory of fuzzy sets. This allowed us to identify the relationship between certain arrays of ID and the possibilities of applying certain types of SCA end-structure arrays of ID according to the levels of identifying objects of DI, and to create a scale of intruder awareness symmetrical to the scale of intelligence protection of the elements of the DIS. Experiments were conducted to verify the practical applicability of the developed models and techniques, showing positive results that make it possible to identify vulnerable objects, tactics and techniques of the intruder in advance. The result of this study is the development of an intruder awareness scale, which includes five levels of his knowledge about the attacked system, estimated by numerical intervals and characterized by linguistic terms. Each awareness level corresponds to one CCA stage: primary ID collection, penetration and legalization, privilege escalation, distribution and DI. Awareness levels have corresponding typical ID lists that can be potentially available after conducting the corresponding type of SCA. Typical ID lists are classified according to the following DI levels: network, hardware, system, application and user level. For each awareness level, the method of obtaining the ID by the intruder is specified. These research results represent a scientific contribution. The practical contribution is the application of the developed scale for information security (IS) incident management. It allows for a proactive assessment of DIS security against CCAs—modeling the real DIS structure and various CCA scenarios. During an incident, upon detection of a certain CCA stage, it allows for identifying data on DIS elements potentially known by the intruder and eliminating further development of the incident. The results of this study can also be used for training IS specialists in network security, risk assessment and IS incident management. Full article

Autism Spectrum Disorder (ASD) is a complex neurodevelopmental condition with diverse behavioral, genetic, and structural characteristics. Due to its heterogeneous nature, early diagnosis of ASD is challenging, and conventional unimodal approaches often fail to capture cross-modal dependencies. To address this, this study introduces an adaptive multimodal fusion framework that integrates behavioral, genetic, and structural MRI (sMRI) data, addressing the limitations of unimodal approaches. Each modality undergoes a dedicated preprocessing and feature optimization phase. For behavioral data, an ensemble of classifiers using a stacking technique and attention mechanism is applied for feature extraction, achieving an accuracy of 95.5%. The genetic data is analyzed using Gradient Boosting, which attained a classification accuracy of 86.6%. For the sMRI data, a Hybrid Convolutional Neural Network–Graph Neural Network (Hybrid-CNN-GNN) architecture is proposed, demonstrating a strong performance with an accuracy of 96.32%, surpassing existing methods. To unify these modalities, fused using an adaptive late fusion strategy implemented with a Multilayer Perceptron (MLP), where adaptive weighting adjusts each modality’s contribution based on validation performance. The integrated framework addresses the limitations of unimodal approaches by creating a unified diagnostic model. The transfer learning framework achieves superior diagnostic accuracy (98.7%) compared to unimodal baselines, demonstrating strong generalization across heterogeneous datasets and offering a promising step toward reliable, multimodal ASD diagnosis. Full article