Search Results (188)

Chickpea (Cicer arietinum L.) is a key legume crop of global economic and nutritional importance, yet its cultivation in Kazakhstan is constrained by a narrow genetic base and exposure to stress-prone environments. To characterize the diversity available for breeding and conservation, 27 accessions (22 kabuli and 5 desi) were evaluated for phenotypic and molecular diversity to assess its potential for use in breeding programs. Seven agronomic traits were assessed, including plant height, the first pod’s height, the number of main stems per plant, and seed yield components. The collection showed considerable variability across traits, with the plant height ranging from 37 to 75 cm and hundred-seed weight ranging from 21 to 42 g. Strong positive correlations between the number of fertile nodes, number of seeds per plant, and yield per plant (r > 0.83) highlighted their utility as indirect selection criteria. Genotyping with 28 SSR markers revealed 110 alleles (mean 3.9 ± 0.4 per locus) with moderate polymorphism (PIC = 0.493 ± 0.089). Loci CaM00495 and TAI71 were highly informative (PIC > 0.804), while two accessions showed low polymorphism, indicating genetic uniformity. Population structure analysis grouped accessions into four highly admixed clusters. Overall, Kazakh chickpea germplasm exhibits substantial phenotypic and genetic diversity under optimal conditions, providing valuable preliminary data for selecting parental lines for future breeding programs, which should include targeted stress screening to evaluate resilience. Full article

The innovation capability of seed enterprises reflects their core competitiveness and serves as a vital foundation for sustainable agricultural development and modernization. Therefore, evaluating this capability is of great importance. However, existing evaluation methods primarily focus on internal enterprise attributes, overlooking the complex inter-enterprise relationships and lacking sufficient feature fusion capabilities to capture latent information. To address these limitations, this paper proposes a Multi-Channel Graph Convolutional Network (MGCN) model that integrates enterprise attributes with three types of relational graphs. The model adopts a multi-channel architecture for feature extraction and employs a gated attention mechanism for cross-graph feature fusion, jointly considering node features and relation information to improve prediction accuracy. Experimental results demonstrate that MGCN achieves an average accuracy of 83.59% under five-fold cross-validation, outperforming several mainstream models such as Random Forest and traditional GCN. Case studies further reveal that MGCN not only captures key features of individual enterprises but also leverages features and label distribution from neighboring enterprises, facilitating more context-aware classification decisions. In conclusion, the MGCN model provides an effective method for the intelligent evaluation of innovation capability in seed enterprises and supports the formulation of sustainable strategic plans at both the national and enterprise level. Full article

Directed grey-box fuzzers focus on testing specific target code. They have been utilized in various security applications, such as reproducing known crashes and identifying vulnerabilities resulting from incomplete patches. Distance-guided directed fuzzers calculate the distance to the target node for each node in a CFG or CG, which has always been the mainstream in this field. However, the distance can only reflect the relationship between the current node and the target node, and it does not consider the impact of the reaching sequence before the target node. To mitigate this problem, we analyzed the properties of the instrumented function’s call graph after selective instrumentation, and the complexity of reaching the target function sequence was estimated. Assisted by the sequence complexity, we proposed a two-stage function call sequence-based seed-scheduling strategy. The first stage is to select seeds with a higher probability of generating test cases that reach the target function. The second stage is to select seeds that can generate test cases that meet the conditions for triggering the vulnerability as much as possible. We implemented our approach in SEZZ based on SelectFuzz and compare it with related works. We found that SEZZ outperformed AFLGo, Beacon, WindRanger, and SelectFuzz by achieving an average improvement of 13.7×, 1.50×, 9.78×, and 2.04× faster on vulnerability exposure, respectively. Moreover, SEZZ triggered three more vulnerabilities than the other compared tools. Full article

In precision cotton seeding, the toothed-disk precision seeder often experiences issues with missed seeding and multiple seeding. To promptly detect and address these abnormal seeding conditions, this study develops a modular photoelectric sensing monitoring system. Initially, the monitoring time window is divided using the capacitance sensing signal between two seed drop ports. Concurrently, a photoelectric monitoring circuit is designed to convert the time when seeds block the sensor into a level signal. Subsequently, threshold segmentation is performed on the time when seeds block the photoelectric path under different seeding states. The proposed spatiotemporal joint counting algorithm identifies, in real time, the threshold type of the photoelectric sensor’s output signal within the current monitoring time window, enabling the differentiation of seeding states and the recording of data. Additionally, an STM32 micro-controller serves as the core of the signal acquisition circuit, sending collected data to the PC terminal via serial port communication. The graphical display interface, designed with LVGL (Light and Versatile Graphics Library), updates the seeding monitoring information in real time. Compared to photoelectric monitoring algorithms that detect seed pickup at the seed metering disc, the monitoring node in this study is positioned posteriorly within the seed guide chamber. Consequently, the differentiation between single seeding and multiple seeding is achieved with greater accuracy by the spatiotemporal joint counting algorithm, thereby enhancing the monitoring precision of the system. Field test results indicate that the system’s average accuracy for single-seeding monitoring is 97.30%, for missed-seeding monitoring is 96.48%, and for multiple-seeding monitoring is 96.47%. The average probability of system misjudgment is 3.25%. These outcomes suggest that the proposed modular photoelectric sensing monitoring system can meet the monitoring requirements of precision cotton seeding at various seeding speeds. Full article

Traditional seed supply chains face several hidden risks. Certain regulatory departments tend to focus primarily on entity circulation while neglecting the origin and accuracy of data in seed quality supervision, resulting in limited precision and low credibility of traceability information related to quality and safety. Blockchain technology offers a systematic solution to key issues such as data source distortion and insufficient regulatory penetration in the seed supply chain by enabling data rights confirmation, tamper-proof traceability, smart contract execution, and multi-node consensus mechanisms. In this study, we developed a system that integrates blockchain and neural networks to provide seed traceability services. When uploading seed traceability information, the neural network models are employed to verify the authenticity of information provided by humans and save the tags on the blockchain. Various neural network architectures, such as Multilayer Perceptron, Recurrent Neural Network, Fully Convolutional Neural Network, and Long Short-term Memory model architectures, have been tested to determine the authenticity of seed traceability information. Among these, the Long Short-term Memory model architecture demonstrated the highest accuracy, with an accuracy rate of 90.65%. The results demonstrated that neural networks have significant research value and potential to assess the authenticity of information in a blockchain. In the application scenario of seed quality traceability, using blockchain and neural networks to determine the authenticity of seed traceability information provides a new solution for seed traceability. This system empowers farmers by providing trustworthy seed quality information, enabling better purchasing decisions and reducing risks from counterfeit or substandard seeds. Furthermore, this mechanism fosters market circulation of certified high-quality seeds, elevates crop yields, and contributes to the sustainable growth of agricultural systems. Full article

Germination ability and seedling development of grape (Vitis vinifera L.) seeds show significant differences depending on cultivar characteristics and germination conditions, and this situation is known to create significant difficulties in grape breeding programs and vegetative propagation. In this study, we explored the effects of different concentrations of sodium nitroprusside (SNP; 500–3000 ppm) and gibberellic acid (GA₃) on seed germination and seedling growth in several grape cultivars. Our findings show that cultivar, treatment type, and their interaction had significant effects on both germination and growth. The 5 BB rootstock stood out with consistently high germination rates, reaching up to 95% with 1500 ppm SNP. Overall, SNP treatments outperformed both the control and GA₃ applications, although the most effective concentration differed by cultivar. The most beneficial SNP doses ranged between 1000 and 3000 ppm, with 1500 ppm yielding the highest improvement, up to a 21.6% increase compared to the control. Notably, the ‘Çeliksu’ cultivar responded strongly to SNP, while ‘Rizpem’ showed weak germination, regardless of treatment. Seedling growth, as measured by plant height and node number, was also influenced by both treatment and cultivar, with 5 BB again showing the most robust development. Multivariate analyses revealed strong correlations across germination dates and growth traits. Higher SNP concentrations (1500–3000 ppm) consistently promoted better germination and seedling vigor than GA₃ and untreated controls. These results highlight the importance of considering cultivar-specific responses and suggest that well-calibrated SNP applications could be a valuable tool for improving seed-based propagation in grape breeding programs. Full article

Microfluidic devices offer well-defined physical environments that are suitable for effective cell seeding and in vitro three-dimensional (3D) cell culture experiments. These platforms have been employed to model in vivo conditions for studying mechanical forces, cell–extracellular matrix (ECM) interactions, and to elucidate transport mechanisms in 3D tissue-like structures, such as tumor and lymph node organoids. Studies have shown that fluid flow behavior in microfluidic slides (µ-slides) directly influences shear stress, which has emerged as a key factor affecting cell proliferation and differentiation. This study investigates fluid flow in the porous channel of a µ-slide using computational fluid dynamics (CFD) techniques to analyze the impact of perfusion flow rate and porous properties on resulting shear stresses. The model of the µ-slide filled with a permeable biomaterial is considered. Porous media fluid flow in the channel is characterized by adding a momentum loss term to the standard Navier–Stokes equations, with a physiological range of permeability values. Numerical simulations are conducted to obtain data and contour plots of the filtration velocity and flow-induced shear stress distributions within the device channel. The filtration flow is subsequently measured by performing protein perfusions into the slide embedded with native human-derived ECM, while the flow rate is controlled using a syringe pump. The relationships between inlet flow rate and shear stress, as well as filtration flow and ECM permeability, are analyzed. The findings provide insights into the impact of shear stress, informing the optimization of perfusion conditions for studying tissues and cells under fluid flow. Full article

In order to solve the problems of low tracking accuracy of in-orbit satellites by ground stations and slow processing speed of satellite target tracking images, this paper proposes an orbital satellite regional tracking and prediction model based on graph convolutional networks (GCNs). By performing superpixel segmentation on the satellite tracking image information, we constructed an intra-frame superpixel seed graph node network, enabling the conversion of spatial optical image information into artificial-intelligence-based graph feature data. On this basis, we propose and build an in-orbit satellite region of interest prediction model, which effectively enhances the perception of in-orbit satellite feature information and can be used for in-orbit target prediction. This model, for the first time, combines intra-frame and inter-frame graph structures to improve the sensitivity of GCNs to the spatial feature information of in-orbit satellites. Finally, the model is trained and validated using real satellite target tracking image datasets, demonstrating the effectiveness of the proposed model. Full article

In the continuous pursuit of minimally invasive interventions while ensuring a radical excision of lesions, Radio-Guided Surgery (RGS) has been for years the standard for image-guided surgery procedures, such as the Sentinel Lymph Node biopsy (SLN), Radio-guided Seed Localization (RSL), etc. In RGS, the lesion has to be identified precisely, in terms of position and extension. In such a context, going beyond the current one-point probes, introducing portable but high-resolution cameras, handholdable by the surgeon, would be highly beneficial. We developed and tested a novel compact, low-power, handheld gamma camera for radio-guided surgery. This is based on a particular position-sensitive Silicon Photomultiplier (SiPM) technology—the FBK linearly graded SiPM (LG-SiPM). Within the camera, the photodetector is made up of a 3 × 3 array of 10 × 10 mm² SiPM chips having a total area of more than 30 × 30 mm². This is coupled with a pixelated scintillator and a parallel-hole collimator. With the LG-SiPM technology, it is possible to significantly reduce the number of readout channels to just eight, simplifying the complexity and lowering the power consumption of the readout electronics while still preserving a good position resolution. The novel gamma camera is light (weight), and it is made to be a fully stand-alone system, therefore featuring wireless communication, battery power, and wireless recharge capabilities. We designed, simulated (electrically), and tested (functionally) the first prototypes of the novel gamma camera. We characterized the intrinsic position resolution (tested with pulsed light) as being ~200 µm, and the sensitivity and resolution when detecting gamma rays from Tc-99m source measured between 134 and 481 cps/MBq and as good as 1.4–1.9 mm, respectively. Full article

Proactively hunting known attack behaviors within system logs, termed threat hunting, is gaining traction in cybersecurity. Existing methods typically rely on constructing a query graph representing known attack patterns and identifying it as a subgraph within a system-wide provenance graph. However, the large scale and redundancy of provenance data lead to poor matching efficiency and high false-positive rates. To address these issues, this paper introduces SGMNet, a supervised seeded graph-matching network designed for efficient and accurate threat hunting. By selecting indicators of compromise (IOCs) as initial seed nodes, SGMNet extracts compact subgraphs from large-scale provenance graphs, significantly reducing graph size and complexity. It then learns adaptive node-expansion strategies to capture relevant context while suppressing irrelevant noise. Experiments on four real-world system log datasets demonstrate that SGMNet achieves a runtime reduction of over 60% compared to baseline methods, while reducing false positives by 35.2% on average. These results validate that SGMNet not only improves computational efficiency but also enhances detection precision, making it well suited for real-time threat hunting in large-scale environments. Full article

Advanced Persistent Threats (APTs) challenge cybersecurity due to their stealthy, multi-stage nature. For the provenance graph based on fine-grained kernel logs, existing methods have difficulty distinguishing behavior boundaries and handling complex multi-entity dependencies, which exhibit high false positives in dynamic environments. To address this, we propose a Hypergraph Attention Network framework for APT detection. First, we employ anomaly node detection on provenance graphs constructed from kernel logs to select seed nodes, which serve as starting points for discovering overlapping behavioral communities via node aggregation. These communities are then encoded as hyperedges to construct a hypergraph that captures high-order interactions. By integrating hypergraph structural semantics with nodes and hyperedge dual attention mechanisms, our framework achieves robust APT detection by modeling complex behavioral dependencies. Experiments on DARPA and Unicorn show superior performance: 97.73% accuracy, 98.35% F1-score, and a 0.12% FPR. By bridging hypergraph theory and adaptive attention, the framework effectively models complex attack semantics, offering a robust solution for real-time APT detection. Full article

Studying the risk propagation mechanisms in agricultural systems is crucial for maintaining agricultural stability and promoting sustainable development. This research analyzes the risk effects and risk propagation mechanisms in agricultural systems using the DCC-t-Copula-CoVaR model, multi-layer network structures, and the mixed-frequency regression MIDAS model. The study finds that there is significant heterogeneity in risk spillover and absorption in agricultural systems; the risk propagation in agricultural systems is stable, and the stronger the connectivity of industry nodes, the greater the risk. Taking the seed industry as an example, its structural indicator values consistently range between 1.0 and 1.1, with fluctuations closely linked to industry development and policy adjustments. Major risks are caused by risk resonance across multiple industries, not triggered by a single industry alone; the interconnections between industries within the agricultural system can disperse risks, forming a collective risk-sharing mechanism. Understanding these dynamics is essential for developing resilient agricultural practices that support long-term sustainability, ensuring food security, and mitigating environmental impacts. By addressing risk propagation and fostering interconnected risk-sharing mechanisms, agricultural systems can better adapt to challenges such as climate change, resource scarcity, and market volatility, ultimately contributing to a more sustainable and stable global food system. Full article

Purpose: Marker placement in a pathological node improves extirpation rates in breast cancer cases with limited axillary involvement. Our goal was to assess the current indications for seed-marked axillary lymph node dissection (SMALND). Methods: We conducted a descriptive observational study, including 93 patients with cN1 breast cancer treated between January 2019 and December 2023. Seed placement was performed under ultrasound guidance, days before the procedure. Intraoperative detection was achieved using a probe, and resection was confirmed radiologically. Results: The primary indication was post-neoadjuvant therapy (72 patients: 60 for chemotherapy and 12 for hormone therapy), followed by initial surgery (14) and a single axillary recurrence (8). The extirpation rate of the marked axillary lymph node was 100%. In targeted axillary dissection (TAD), the concordance rate between the sentinel node and the marked axillary node was 85%. In the 12 cases of initial surgery, axillary lymphadenectomy was avoided because the marked node matched the sentinel node and was the only one involved. Conclusions: The use of seeds was proven to be highly useful in axillary surgery, both in cases of negativization following neoadjuvant therapy and in those with low axillary involvement or a single axillary recurrence. Full article

Clausena lenis Drake, a valuable medicinal plant in the Rutaceae family, faces threats from wildlife predation, overharvesting, and climate change. In the wild, C. lenis primarily propagates through seeds; however, their rapid loss of viability poses challenges for long-term storage and germplasm conservation. Plant tissue culture offers a practical solution for both its conservation and large-scale production. This study examines seed sterilization, callus induction, shoot multiplication, and root induction protocols for C. lenis. Seeds attained a 100% sterilization rate using 0.2% (w/v) HgCl₂ for 20 min without compromising germination. When cultured on MS medium containing 0.5 mg/L 2,4-D, seed, stem-node, and 1-week-old seedling explants produced abundant callus. A 2.0 mg/L BA treatment achieved 100% shoot induction, with stem-node explants yielding the highest shoot proliferation (3.90 ± 0.31 shoots/explant), followed by 1-week-old seedlings (2.30 ± 0.21 shoots/explant) and seed explants (1.60 ± 0.16 shoots/explant). Rooting was most effective on half-strength MS medium supplemented with 20.0 mg/L IBA, producing an average of 4.30 ± 0.83 roots per shoot in shoot-tip-deprived explants. The rooted plantlets successfully acclimatized, attaining a 100% survival rate in a 1:1:1 mixture of sterile soil, cocopeat, and vermiculite. These findings provide a robust platform for the sustainable propagation and conservation of C. lenis in response to its growing vulnerabilities. Full article

To address the problem of maximizing desired opinions in social networks, we present the Limited Opinion Maximization with Dynamic Propagation Optimization framework, which is grounded in information entropy theory. Innovatively, we introduce the concept of node expression capacity, which quantifies the uncertainty of users’ expression intentions via entropy and effectively identifies the impact of silent nodes on the propagation process. Based on this, in terms of seed node selection, we develop the Limited Opinion Maximization algorithm for multi-stage seed selection, which dynamically optimizes the seed distribution among communities through a multi-stage seeding approach. In terms of node opinion changes, we establish the LODP dynamic opinion propagation model, reconstructing the node opinion update mechanism and explicitly modeling the entropy-increasing effect of silent nodes on the information propagation path. The experimental results on four datasets show that LOMDP outperforms six baseline algorithms. Our research effectively resolves the problem of maximizing desired opinions and offers insights into the dynamics of information propagation in social networks from the perspective of entropy and information theory. Full article