Search Results (319)

The interconnectivity of avionics systems supports the need to incorporate functional safety and information security into airworthiness validation and maintenance protocols, which is critical. This necessity arises from the demanding operational environments and the limitations on defense resource allocation. This study proposes an optimization model for the strategic deployment of defense mechanisms, leveraging the dynamic interplay between attack and defense modeled by non-cooperative game theory and aligning with the maintenance schedules of civil aircraft. By developing an Attack–Defense Tree and conducting a non-cooperative game analysis, this paper outlines strategies from both the attacker’s and defender’s perspectives, assessing the impact of focused defense improvements on the system’s security integrity. The results reveal that the broad expansion of defense measures reduces their effectiveness, whereas targeted deployment significantly enhances protection. Monte Carlo simulations are employed to approximate equilibrium solutions across the strategy space, reducing computational complexity while retaining robustness in capturing equilibrium trends. This approach supports efficient allocation of defense resources, strengthens overall system security, and provides a practical foundation for integrating security analysis into avionics maintenance and certification processes. Full article

Robots play a crucial role in experimental smart cities and are ubiquitous in daily life, especially in complex environments where multiple robots are often needed to solve problems collaboratively. Researchers have found that the swarm intelligence optimization algorithm has a better performance in planning robot paths, but the traditional swarm intelligence algorithm cannot be targeted to solve the robot path planning problem in difficult problem. Therefore, this paper aims to introduce a fuzzy controller, mutation factor, exponential noise, and other strategies on the basis of particle swarm optimization to solve this problem. By judging the moving speed of different particles at different periods of the algorithm, the individual learning factor and social learning factor of the particles are obtained by fuzzy controller, and using the leader particle and random particle, designing a new dynamic balance of mutation factor, with the iterative process of the adaptation value of continuous non-updating counter and continuous updating counter to control the proportion of the elite individuals and random individuals. Finally, using exponential noise to update the matrix of the population every 50 iterations is a way to balance the local search ability and global exploration ability of the algorithm. In order to test the proposed algorithm, the main method of this paper is simulated on simple scenarios, complex scenarios, and random maps consisting of different numbers of static obstacles and dynamic obstacles, and the algorithm proposed in this paper is compared with eight other algorithms. The average path deviation error of the planned paths is smaller; the average distance of untraveled target is shorter; the number of steps of the robot movements is smaller, and the path is shorter, which is superior to the other eight algorithms. This superiority in solving multi-robot cooperative path planning has good practicality in many fields such as logistics and distribution, industrial automation operation, and so on. Full article

Non-cooperative spatial target detection plays a vital role in enabling autonomous on-orbit servicing and maintaining space situational awareness (SSA). However, due to the limited computational resources of onboard embedded systems and the complexity of spaceborne imaging environments, where spacecraft images often contain small targets that are easily obscured by background noise and characterized by low local information entropy, many existing object detection frameworks struggle to achieve high accuracy with low computational cost. To address this challenge, we propose YOLO-GRBI, an enhanced detection network designed to balance accuracy and efficiency. A reparameterized ELAN backbone is adopted to improve feature reuse and facilitate gradient propagation. The BiFormer and C2f-iAFF modules are introduced to enhance attention to salient targets, reducing false positives and false negatives. GSConv and VoV-GSCSP modules are integrated into the neck to reduce convolution operations and computational redundancy while preserving information entropy. YOLO-GRBI employs the focal loss for classification and confidence prediction to address class imbalance. Experiments on a self-constructed spacecraft dataset show that YOLO-GRBI outperforms the baseline YOLOv8n, achieving a 4.9% increase in mAP@0.5 and a 6.0% boost in mAP@0.5:0.95, while further reducing model complexity and inference latency. Full article

This paper introduces a novel four-party evolutionary game model to analyze cooperation dynamics in High-Value Mixed Cropping (HMC) systems integrating non-pesticide cacao, cashew nut, and free-range chicken farming within circular and sharing economy frameworks. The model uniquely examines strategic interactions among local government and three farming family types (cacao, cashew, and chicken), incorporating both regulatory mechanisms and cooperative behaviors. Through rigorous stability analysis and MATLAB simulations based on empirical data from Southeast Vietnam, we identify precise conditions for Evolutionarily Stable Strategies (ESSs) that sustain long-term cooperation. Our results demonstrate that government incentives (subsidies, technical support) and reputational sanctions critically shape farmers’ and consumers’ payoffs, thereby steering the system toward collective action equilibria. In particular, increasing the strength of positive incentives or reputational benefits enlarges the basin of attraction for full-cooperation ESSs, regardless of initial strategy distributions. Conversely, overly punitive sanctions can destabilize collaborative outcomes. These findings underscore the pivotal role of well-balanced policy instruments in fostering resilience, innovation, and resource circulation within rural agroecosystems. Finally, we propose targeted policy recommendations, such as graduated subsidy schemes, participatory monitoring platforms, and cooperative branding initiatives, to reinforce circular economy practices and accelerate progress toward the United Nations Sustainable Development Goals. Full article

Organic agriculture is a production system based on environmentally friendly practices that promote the conservation of natural resources, biodiversity, and the production of high-quality food. Its tenets are linked to the concept of sustainable development, which integrates environmental, social, and economic goals. In the face of global competition and changes in food systems, studying their competitiveness of organic agriculture is essential. It is key to assessing its potential for long-term development and competition with conventional agriculture. The purpose of this study is to identify and assess the spatial differentiation in the competitiveness of organic agriculture in EU countries. This study assessed the level of input and output competitiveness of organic agriculture in selected EU countries using the author’s synthetic taxonomic indicators consisting of several sub-variables. The competitiveness of organic farming in twenty-three countries (Cyprus, Latvia, Portugal, and Finland were not included due to a lack of statistical data) was analysed using one of the linear ordering methods, i.e., a non-pattern method with a system of fixed weights. The research has shown significant spatial differentiation in both the input competitiveness and the outcome competitiveness of organic agriculture in EU countries. In 2023, Estonia had the highest level of input competitiveness, followed by Austria, the Czech Republic, and Sweden. In 2023, Estonia had the highest synthetic indicator of outcome competitiveness, followed by The Netherlands and Denmark. In addition, an assessment was made of changes in EU organic agriculture in 2014–2023 by analysing the direction and dynamics of changes in selected measures of the development potential of organic agriculture in all member states (27 countries). This sector is characterised by high growth dynamics, including both the area under cultivation and the number of producers and processors of organic food. This study identified several important measures to support the development of organic farming (especially in countries where this type of activity is relatively less competitive) through targeted support mechanisms, such as policy and regulatory measures, financing, agricultural training and advisory services, scientific research, encouraging cooperation, and stimulating demand for organic products. Full article

The tethered space net robot offers an effective solution for active space debris removal due to its large capture envelope. However, most existing studies overlook the evasive behavior of non-cooperative targets. To address this, we model an orbital pursuit–evasion game involving a tethered net and propose a game theory-based leader–follower tracking control strategy. In this framework, a virtual leader—defined as the geometric center of four followers—engages in a zero-sum game with the evader. An adaptive dynamic programming method is employed to handle input saturation and compute the Nash Equilibrium strategy. In the follower formation tracking phase, a synchronous distributed model predictive control approach is proposed to update all followers’ control simultaneously, ensuring accurate tracking while meeting safety constraints. The feasibility and stability of the proposed method are theoretically analyzed. Additionally, a body-fixed reference frame is introduced to reduce the capture angle. Simulation results show that the proposed strategy successfully captures the target and outperforms existing methods in both formation keeping and control efficiency. Full article

This paper proposes a novel Guidance and Control architecture for close-range rendezvous and final approach of a chaser spacecraft towards a non-cooperative and tumbling space target. In both phases, reference trajectory generation relies on a Sequential Convex Programming algorithm which iteratively solves a non-linear optimization problem accounting for propellant consumption, relative dynamics, collision avoidance and navigation sensor pointing constraints. At close range, trajectory tracking is entrusted to a translational H-infinity controller, coupled with a quaternion-feed-back regulator for target pointing. In the final approach phase, an attitude-pointing strategy is adopted, requiring a six degree-of-freedom H-infinity controller to follow a reference roto-translational trajectory generated to ensure target-chaser motion synchronization. Performance is evaluated in a high-fidelity simulation environment that includes environmental perturbations, navigation errors, and actuator (i.e., cold gas thrusters and reaction wheels) modelling. In particular, the latter aspects are also addressed by integrating the proposed solution within a complete Guidance, Navigation and Control pipeline including a state-of-the-art LIDAR-based relative navigation filter and a dispatching function for the distribution of commanded control actions to the actuation system. A statistical analysis on 1000 simulations shows the robustness of the proposed approach, achieving centimeter-level position accuracy and sub-degree attitude accuracy near the docking/berthing point. Full article

Polymicrobial biofilms involving fungal and bacterial species are increasingly recognized as contributors to persistent infections, particularly in the oral cavity. Candida albicans and Aggregatibacter actinomycetemcomitans are two commensals that can turn into opportunistic pathogens and are able to form robust biofilms. Objectives: This study aimed to assess the interaction dynamics between these two microorganisms and to evaluate their susceptibility to fluconazole and azithromycin in single- and mixed-species forms. Methods: Biofilm biomass was quantified using crystal violet assays, while biofilm cell viability was assessed through CFU enumeration (biofilm viability assay). To assess the resistance properties of single versus mixed-species coincubations, we applied the antimicrobial susceptibility test (AST) to each drug, and analysed spatial organization with confocal laser scanning microscopy, using PNA-FISH. Results: The results indicated that both species can coexist without significant mutual inhibition. However, a non-reciprocal synergism was also observed, whereby mixed-species biofilm conditions promoted the growth of A. actinomycetemcomitans, while C. albicans growth remained stable. As expected, antimicrobial tolerance was elevated in mixed cultures, likely due to enhanced extracellular matrix production and potential quorum-sensing interactions, contributing to increased resistance against azithromycin and fluconazole. Conclusions: This study provides novel insights into previously rarely explored interactions between C. albicans and A. actinomycetemcomitans. These findings underscore the importance of investigating interspecies interactions within polymicrobial biofilms, as understanding their mechanisms, such as quorum-sensing molecules and metabolic cooperation, can contribute to improved diagnostics and more effective targeted therapeutic strategies against polymicrobial infections. Full article

The space flexible netted pocket capture system provides a flexible and stable solution for capturing non-cooperative space objects. This paper investigates the control problem for the capture of non-cooperative targets undergoing motion. A dynamic model of the capturing net is established based on the absolute nodal coordinate formulation (ANCF) and equivalent plate–shell theory. A contact collision force model is developed using a spring–damper model. Subsequently, a feedforward controller is designed based on the estimated collision force from the dynamic model, aiming to compensate for the collision effects between the target and the net. By incorporating the collision estimation data, an extended state observer is designed, taking into account the collision estimation errors and the flexible uncertainties. A sliding mode feedback controller is then designed using the fast terminal sliding mode control method. Finally, simulation analysis of target capture under different motion states is conducted. The results demonstrate that the spacecraft system’s position and attitude average flutter amplitudes are less than ${10}^{-2}$ m and ${10}^{-2}$ deg. In comparison to standard sliding mode control, the designed controller reduces the attitude jitter amplitude by an order of magnitude, thus demonstrating its effectiveness and superiority. Full article

Fusobacterium nucleatum and activating mutations in the Kirsten rat sarcoma virus oncogene homolog (KRAS) are increasingly recognized as cooperative drivers of colorectal cancer (CRC). F. nucleatum promotes tumorigenesis via adhesion to epithelial cells, modulation of the immune microenvironment, and delivery of virulence factors, while KRAS mutations—present in 60% of CRC cases—amplify proliferative signaling and inflammatory pathways. Here, we review the molecular interplay by which F. nucleatum enhances KRAS-driven oncogenic cascades and, conversely, how KRAS mutations reshape the tumor niche to favor bacterial colonization. We further discuss the use of KRAS as a prognostic biomarker and explore promising non-antibiotic interventions—such as phage therapy, antimicrobial peptides, and targeted small-molecule inhibitors—aimed at selectively disrupting F. nucleatum colonization and virulence. This integrated perspective on microbial–genetic crosstalk offers novel insights for precision prevention and therapy in CRC. Full article

The increasing presence of non-cooperative targets poses significant challenges to the space environment and threatens the sustainability of aerospace operations. Accurate on-orbit perception of such targets, particularly those without cooperative markers, requires advanced algorithms and efficient system architectures. This study presents a hardware–software co-design framework for the pose estimation of non-cooperative targets. Firstly, a two-stage architecture is proposed, comprising object detection and pose estimation. YOLOv5s is modified with a Focus module to enhance feature extraction, and URSONet adopts global average pooling to reduce the computational burden. Optimization techniques, including batch normalization fusion, ReLU integration, and linear quantization, are applied to improve inference efficiency. Secondly, a customized FPGA-based accelerator is developed with an instruction scheduler, memory slicing mechanism, and computation array. Instruction-level control supports model generalization, while a weight concatenation strategy improves resource utilization during convolution. Finally, a heterogeneous DSP–FPGA system is implemented, where the DSP manages data pre-processing and result integration, and the FPGA performs core inference. The system is deployed on a Xilinx X7K325T FPGA operating at 200 MHz. Experimental results show that the optimized model achieves a peak throughput of 399.16 GOP/s with less than 1% accuracy loss. The proposed design reaches 0.461 and 0.447 GOP/s/DSP48E1 for two model variants, achieving a 2× to 3× improvement over comparable designs. Full article

High-resolution ISAR (Inverse Synthetic Aperture Radar) imaging plays a crucial role in dynamic target monitoring for aerospace, maritime, and ground surveillance. Among various remote sensing techniques, ISAR is distinguished by its ability to produce high-resolution images of non-cooperative maneuvering targets. To meet the increasing demands for resolution and robustness, modern ISAR systems are evolving toward wideband and multi-channel architectures. In particular, multi-channel configurations based on large-scale receiving arrays have gained significant attention. In such systems, each receiving element functions as an independent spatial channel, acquiring observations from distinct perspectives. These multi-angle measurements enrich the available echo information and enhance the robustness of target imaging. However, this setup also brings significant challenges, including inter-channel coupling, high-dimensional joint signal modeling, and non-Gaussian, mixed-mode interference, which often degrade image quality and hinder reconstruction performance. To address these issues, this paper proposes a Hybrid Variational Bayesian Multi-Interference (HVB-MI) imaging algorithm based on a hierarchical Bayesian framework. The method jointly models temporal correlations and inter-channel structure, introducing a coupled processing strategy to reduce dimensionality and computational complexity. To handle complex noise environments, a Gaussian mixture model (GMM) is used to represent nonstationary mixed noise. A variational Bayesian inference (VBI) approach is developed for efficient parameter estimation and robust image recovery. Experimental results on both simulated and real-measured data demonstrate that the proposed method achieves significantly improved image resolution and noise robustness compared with existing approaches, particularly under conditions of sparse sampling or strong interference. Quantitative evaluation further shows that under the continuous sparse mode with a 75% sampling rate, the proposed method achieves a significantly higher Laplacian Variance (LV), outperforming PCSBL and CPESBL by 61.7% and 28.9%, respectively and thereby demonstrating its superior ability to preserve fine image details. Full article

T-2 toxin, a type A trichothecene mycotoxin produced by Fusarium species, is widely present in cereals and their processed products, posing a significant contaminant in food safety. To address the food safety challenges caused by this toxin, we established a dual signal enhancement by magnetic separation and split aptamer for ultrasensitive T-2 toxin detection. In this method, the introduction of magnetic graphene oxide (MGO) enhanced signal and increased sensitivity by reducing background interference. The shortened split aptamer reduces non-specific binding to MGO via decreased steric hindrance, thereby facilitating rapid target-induced dissociation and signal generation. A FAM fluorophore-labeled split aptamer probe FAM-SpA1-1 was quenched by MGO. While the fluorescence intensity remained nearly unchanged when the unlabeled split aptamer probe SpA1-2 was introduced alone, a significant fluorescence recovery was observed upon simultaneous addition of SpA1-2 and T-2 toxin. This recovery resulted from the cooperative binding of SpA1-1 and SpA1-2 to T-2 toxin, which distanced the FAM-SpA1-1 probe from MGO. Therefore, the proposed biosensor demonstrated excellent stability, reproducibility, and specificity, with a linear response range of 10–500 pM and a limit of detection (LOD) of 0.83 pM. Satisfactory recovery rates were achieved in spiked wheat (86.0–114.2%) and beer (112.0–129.6%) samples, highlighting the biosensor’s potential for practical applications in real-sample detection. This study establishes the T-2 toxin split aptamer and demonstrates a novel dual-signal enhancement paradigm that pushes the sensitivity frontier of aptamer-based mycotoxin sensors. Full article

Glioblastoma (GBM) is the most aggressive primary brain tumor in adults, characterized by a dismal prognosis and limited therapeutic options. Its highly invasive nature and pronounced intratumoral heterogeneity underscores the urgent need for innovative and targeted therapeutic strategies. One promising approach is synthetic lethality, which exploits cancer-specific genetic vulnerabilities to selectively eliminate tumor cells. A well-characterized example involves the deletion of methylthioadenosine phosphorylase (MTAP), commonly observed in GBM and other malignancies. This review focuses on synthetic lethality targeting protein arginine methyltransferase 5 (PRMT5) in MTAP-deleted GBM. Loss of MTAP leads to the accumulation of methylthioadenosine (MTA), a metabolite that partially inhibits PRMT5, thereby creating a selective vulnerability to PRMT5 inhibition which is used to inhibit the residual function of PRMT5. We critically evaluate preclinical and clinical data on both first- and second-generation PRMT5 inhibitors, with particular emphasis on MTA-cooperative compounds that selectively exploit MTAP deficiency. Despite promising anti-tumor activity in vitro, the clinical efficacy of PRMT5 inhibitors is often limited by the tumor microenvironment, particularly the impact of non-malignant cells that attenuate drug activity. Finally, we explore rational combination strategies that integrate PRMT5 inhibition with existing therapies to enhance clinical outcomes in GBM. Full article

To address the challenge of evaluating a radar cross-section (RCS) for a non-cooperative aircraft with limited aerodynamic shape information, this paper presents a multi-source, data-driven inverse reconstruction method. This approach integrates data fusion techniques to facilitate an initial shape reconstruction, followed by an iterative optimization process that utilizes computational fluid dynamics (CFD) to enhance the shape, accounting for the aerodynamic performance. Additionally, an inverse deduction analysis is effectively employed to ascertain the characteristics of the power system, leading to the design of a double S-curved tail nozzle layout with stealth capabilities. An aerodynamic analysis demonstrates that at Mach 0.6, the lift-to-drag ratio peaks at 27.3 for the attack angle of 4°, after which it declines as the angle increases. At higher angles of attack, complex flow separation occurs and expands with the increasing angle. The electromagnetic simulation results indicate that under vertical polarization, the omnidirectional RCS reaches its peak as the incident angle is deflected downward by 10° and reduces with the growth of the angle, demonstrating angular robustness. Conversely, under horizontal polarization, the RCS is more sensitive to edge-induced rounding. The findings illustrate that this methodology enables accurate shape modeling for non-cooperative targets, thereby providing a fairly solid basis for stealth performance evaluation and the assessment of surprise effectiveness. Full article