Search Results (12,100)

This paper deals with the topic of reducing drag force acting on aircraft wings by incorporating novel winglet designs, such as multi-tip, bird-type, and twisted. The high-speed NASA common research model (CRM) was selected as the baseline model, and winglet designs were retrofitted while keeping the projected wingspan constant. Computational analysis was performed using RANS coupled with the Spalart–Allmaras turbulence model to determine aerodynamic coefficients, such as C_L and C_D. It was observed that the multi-tip and bird-type designs performed exceptionally well at a low angle of attack (0°). A parametric study was conducted on multi-tip winglets by tweaking the parameters such as sweep angle (Λ), tip twist (Є), taper ratio (λ), and cant angle (Φ). The best combination of parameters for optimal aerodynamic performance while maintaining the wing root bending moment was determined using both the Taguchi method and Taguchi-based grey relational analysis (T-GRA) coupled with principal component analysis (PCA). Also, the percentage contribution of each parameter was determined by using the analysis of variance (ANOVA) method. At the design point, the optimized winglet design outperformed the baseline design by 18.29% in the Taguchi method and by 20.77% in the T-GRA coupled with the PCA method based on aerodynamic efficiency and wing root bending moment. Full article

The multi-microwave sources reactor can significantly reduce energy consumption and processing time with broad application prospects in industrial processing. In order to optimize the temperature field of materials, this paper proposes the heating strategy of multi-microwave sources based on collaborative switching, particularly in distributed combined heat source networks with poor communication conditions. Firstly, simplifying system control variables to enhance the design of the microwave intelligent agent system, and optimizing the time-frequency characteristics of combined power output from multi-microwave sources to emphasize the process of energy partition. Meanwhile, an event-triggered strategy reduces communication frequency and energy consumption between agents. Secondly, a fixed positive lower limit ${\tau }_{min}$ is used in event-triggered to avoid Zeno behavior caused by DoS attacks. Finally, The finite element method was used with the time domain for thermal analysis. The simulation results of SiC show that the energy utilization efficiency of microwave heating equipment is increased by 4.3∼10.7%, temperature uniformity is improved by 25.6∼43.6%, and the results of the potato experiment simulation showed that the multi-microwave source collaborative switching heating strategy can effectively optimize the temperature field distribution of the material. Full article

Cyberattacks have become more frequent and more violent in recent years. To date, defensive infrastructure has been relatively static, and security functions are usually placed in a common order that does not depend on the current situation. We propose the concept of attack-aware Security Service Function Chain reordering. The idea is to change the order of security functions depending on the malicious traffic observed. We present the basic idea, evaluate the impact of the function chain order, and introduce a framework for function chain reordering. Our evaluation shows that the order often has a significant impact on the performance of the security function chain and that there is no single order that outperforms all other orders in every situation. The proposed proof-of-concept framework successfully validates the feasibility of attack-aware security function chain reordering, and we propose additional extensions to eliminate the remaining deficiencies. Full article

The water lily aphid (Rhopalosiphum nymphaeae) is a highly polyphagous herbivore that causes severe damage to many terrestrial and aquatic plants, especially lotus. Due to environmental concerns about water pollution and other issues caused by chemical control methods, there is an urgent need to develop effective and sustainable control methods. The harlequin ladybird (Harmonia axyridis) is a well-known aphid predator and may pose a potential threat to R. nymphaeae. To study the predation ability of H. axyridis at different developmental stages on R. nymphaeae, we assessed the functional response, attack rate, and search effect of H. axyridis larvae and adults preying on R. nymphaeae. The numerical response of this process was also evaluated under a constant ladybird-to-aphid ratio and constant aphid density conditions, respectively. Our results showed that all predator stages exhibited type II functional responses. The predation rate of individual H. axyridis on R. nymphaeae nymphs significantly increased as prey density increased. In contrast, the search effect of H. axyridis gradually decreased with an increase in prey density. Meanwhile, H. axyridis at different developmental stages possess varying predation abilities; fourth instar and adult H. axyridis were found to be highly efficient predators of R. nymphaeae. H. axyridis adults exhibited the highest predation ability and predation rate, while both the adult and fourth-instar larvae exhibited the highest attack rate. Moreover, fourth-instar larvae exhibited the highest search effect value at initially lower prey densities, although adults surpassed them at higher prey densities. Our results also indicated that H. axyridis exhibited varying degrees of intraspecific interference and self-interference influence as predator density increases. These results strongly support H. axyridis as an effective biocontrol agent for R. nymphaeae. Full article

Rice–Xanthomonas oryzae pv. oryzicola (Xoc) is one of the commonly used rice models of host–pathogen interactions. Xoc causes bacterial leaf streak (BLS) and has quarantine status. As a Gram-negative pathogen, Xoc usually employs type III secretion effectors (T3SEs), including transcription activator-like effectors (TALEs) and non-TALEs, to interfere with the innate immunity of rice. However, few major resistance genes corresponding to Xoc are found in rice cultivations; only Rxo1-AvrRxo1 and Xo1-TALEs interactions have been discovered in rice–Xoc. In this review, we focus on the role of the T3S system (T3SS) in Xoc virulence and consider the reported non-TALEs, including AvrRxo1, AvrBs2, XopN, XopC2, XopAP, and XopAK, as well as TALEs including Tal2g/Tal5d, Tal2h, Tal2a, Tal7, Tal10a, TalI, Tal2b, and Tal2c. Interestingly, AvrRxo1, XopC2, and XopAP disturb stomatal opening to promote infection through targeting diverse signaling pathways in rice. Otherwise, Tal2b and Tal2c, respectively, activate two rice salicylic acid (SA) hydroxylation genes to redundantly suppress the SA-mediated basal defense, and TalI, which has unknown targets, suppresses the SA signaling pathway in rice. In addition, other Xoc virulence factors are discussed. In conclusion, several T3SEs from Xoc interfere with similar defense pathways in rice to achieve invasion, providing an outlook for the control of this disease through manipulating the conserved pathways. Full article

Smart security devices, such as smart locks, smart cameras, and smart intruder alarms are increasingly popular with users due to the enhanced convenience and new features that they offer. A significant part of this convenience is provided by the device’s companion smartphone app. Information on whether secure and ethical development practices have been used in the creation of these applications is unavailable to the end user. As this work shows, this means that users are impacted both by potential third-party attackers that aim to compromise their device, and more subtle threats introduced by developers, who may track their use of their devices and illegally collect data that violate users’ privacy. Our results suggest that users of every application tested are susceptible to at least one potential commonly found vulnerability regardless of whether their device is offered by a known brand name or a lesser-known manufacturer. We present an overview of the most common vulnerabilities found in the scanned code and discuss the shortcomings of state-of-the-art automated scanners when looking at less structured programming languages such as C and C++. Finally, we also discuss potential methods for mitigation, and provide recommendations for developers to follow with respect to secure coding practices. Full article

A self-driving vehicle can navigate autonomously in smart cities without the need for human intervention. The emergence of Autonomous Connected Vehicles (ACVs) poses a substantial threat to public and passenger safety due to the possibility of cyber-attacks, which encompass remote hacking, manipulation of sensor data, and probable disablement or accidents. The sensors collect data to facilitate the network’s recognition of local landmarks, such as trees, curbs, pedestrians, signs, and traffic lights. ACVs gather vast amounts of data, encompassing the exact geographical coordinates of the vehicle, captured images, and signals received from various sensors. To create a fully autonomous system, it is imperative to intelligently integrate several technologies, such as sensors, communication, computation, machine learning (ML), data analytics, and other technologies. The primary issues in ACVs involve data privacy and security when instantaneously exchanging substantial volumes of data. This study investigates related data security and privacy research in ACVs using the Blockchain-enabled Federated Reinforcement Learning (BFRL) framework. This paper provides a literature review examining data security and privacy in ACVs and the BFRL framework that can be used to protect ACVs. This study presents the integration of FRL and Blockchain (BC) in the context of smart cities. Furthermore, the challenges and opportunities for future research on ACVs utilising BFRL frameworks are discussed. Full article

Network risk assessment should include the impact of the relationship between vulnerabilities, in order to conduct a more in-depth and comprehensive assessment of vulnerabilities and network-related risks. However, the impact of extracting the relationship between vulnerabilities mainly relies on manual processes, which are subjective and inefficient. To address these issues, this paper proposes a dual-layer knowledge representation model that combines various attributes and structural information of entities. This article first constructs a vulnerability knowledge graph and proposes a two-layer knowledge representation learning model based on it. Secondly, in order to more accurately assess the actual risk of vulnerabilities in specific networks, this paper proposes a vulnerability risk calculation model based on impact relationships, which realizes the risk assessment and ranking of vulnerabilities in specific network scenarios. Finally, based on the research on automatic prediction of the impact relationship between vulnerabilities, this paper proposes a new Bayesian attack graph network risk assessment model for inferring the possibility of device intrusion in the network. The experimental results show that the model proposed in this study outperforms traditional evaluation methods in relationship prediction tasks, demonstrating its efficiency and accuracy in complex network environments. This model achieves efficient resource utilization by simplifying training parameters and reducing the demand for computing resources. In addition, this method can quantitatively evaluate the success probability of attacking specific devices in the network topology, providing risk assessment and defense strategy support for network security managers. Full article

Due to the superiority of quantum computing, traditional cryptography is facing a severe threat. This makes the security evaluation of cryptographic systems in quantum attack models both significant and urgent. For symmetric ciphers, the security analysis heavily relies on cryptanalysis tools. Thus, exploring the use of quantum algorithms in traditional cryptanalysis tools has garnered considerable attention. In this study, we utilize quantum algorithms to improve impossible differential attacks and design two quantum automated tools to search for impossible differentials. The proposed quantum algorithms exploit the idea of miss-in-the-middle and the properties of truncated differentials. We rigorously prove their validity and calculate the quantum resources required for their implementation. Compared to the existing classical automated cryptanalysis, the proposed quantum tools have the advantage of accurately characterizing S-boxes while only requiring polynomial complexity, and can take into consideration the impact of the key schedules in a single-key model. Full article

In autonomous driving, Frequency-Modulated Continuous-Wave (FMCW) radar has gained widespread acceptance for target detection due to its resilience and dependability under diverse weather and illumination circumstances. Although deep learning radar target identification models have seen fast improvement, there is a lack of research on their susceptibility to adversarial attacks. Various spoofing attack techniques have been suggested to target radar sensors by deliberately sending certain signals through specialized devices. In this paper, we proposed a new adversarial deep learning network for spoofing attacks in radar target detection (RTD). Multi-level adversarial attack prevention using deep learning is designed for the coherence pulse deep feature map from DAALnet and Range-Doppler (RD) map from TDDLnet. After the discrimination of the attack, optimization of hybrid deep learning (OHDL) integrated with enhanced PSO is used to predict the range and velocity of the target. Simulations are performed to evaluate the sensitivity of AOHDL for different radar environment configurations. RMSE of AOHDL is almost the same as OHDL without attack conditions and it outperforms the earlier RTD implementations. Full article

Detecting phishing webpages is a critical task in the field of cybersecurity, with significant implications for online safety and data protection. Traditional methods have primarily relied on analyzing URL features, which can be limited in capturing the full context of phishing attacks. In this study, we propose an innovative approach that integrates HTML DOM graph modeling with URL feature analysis using advanced deep learning techniques. The proposed method leverages Graph Convolutional Networks (GCNs) to model the structure of HTML DOM graphs, combined with Convolutional Neural Networks (CNNs) and Transformer Networks to capture the character and word sequence features of URLs, respectively. These multi-modal features are then integrated using a Transformer network, which is adept at selectively capturing the interdependencies and complementary relationships between different feature sets. We evaluated our approach on a real-world dataset comprising URL and HTML DOM graph data collected from 2012 to 2024. This dataset includes over 80 million nodes and edges, providing a robust foundation for testing. Our method demonstrated a significant improvement in performance, achieving a 7.03 percentage point increase in classification accuracy compared to state-of-the-art techniques. Additionally, we conducted ablation tests to further validate the effectiveness of individual features in our model. The results validate the efficacy of integrating HTML DOM structure and URL features using deep learning. Our framework significantly enhances phishing detection capabilities, providing a more accurate and comprehensive solution to identifying malicious webpages. Full article

In complex environments, fish often suffer from reduced physiological functioning due to starvation, which may have a significant effect on their behavioral adaptive strategies to predator attacks. We selected qingbo (Spinibarbus sinensis, which prefers flowing water habitats) and demasone cichlid (Chindongo demasoni, which prefers still water habitats), to investigate the differences in group distribution and dynamics between the two species when faced with a simulated predation attack under different trophic states (fasted for 2 weeks or fed). We chose to conduct our experiments in a six-arm maze that included a central area and six arms of equal length and width and to obtain evidence of how the fish used the various areas of the maze to respond to simulated predation attacks. We found that the two fish species differed in their responses to simulated predation attacks under different trophic states. The group structure of the two species was relatively stable, and the effect of fasting on the qingbo group was not significant, whereas the demasone cichlid group was more susceptible to the effects of fasting, shelter and a simulated predation attack. In an environment with shelter, both species had the same anti-predator strategy and tended to enter the shelter arm to hide after encountering a simulated predation attack. However, differences in the anti-predator strategies of the two species emerged in the no-shelter environment, with the qingbo tending to enter the arm to hide, whereas the demasone cichlid group chose to enter the central area to congregate, and this phenomenon was more pronounced in the fasted group. In conclusion, our research shows that even group-stable fish may shift their anti-predation strategies (i.e., entering a shelter to hide shifts to aggregating in situ into a shoal) when starved and that the worse the swimming ability of the fish, the more affected they are by starvation. Full article

In recent years, there has been a tremendous increase in the use of connected devices as part of the so-called Internet of Things (IoT), both in private spaces and the industry. Integrated distributed systems have shown many benefits compared to isolated devices. However, exposing industrial infrastructure to the global Internet also generates security challenges that need to be addressed to benefit from tighter systems integration and reduced reaction times. Machine learning algorithms have demonstrated their capacity to detect sophisticated cyber attack patterns. However, they often consume significant amounts of memory, computing resources, and scarce energy. Furthermore, their training relies on the availability of datasets that accurately represent real-world data traffic subject to cyber attacks. Network attacks are relatively rare events, as is reflected in the distribution of typical training datasets. Such imbalanced datasets can bias the training of a neural network and prevent it from successfully detecting underrepresented attack samples, generally known as the problem of imbalanced learning. This paper presents a shallow neural network comprising only 110 ReLU-activated artificial neurons capable of detecting representative attacks observed on a communication network. To enable the training of such small neural networks, we propose an improved attack-sharing loss function to cope with imbalanced learning. We demonstrate that our proposed solution can detect network attacks with an F1 score above 99% for various attacks found in current intrusion detection system datasets, focusing on IoT device communication. We further show that our solution can reduce the false negative detection rate of our proposed shallow network and thus further improve network security while enabling processing at line rate in low-complexity network intrusion systems. Full article

Chlorine dioxide (ClO₂) is widely used for the quality preservation of postharvest horticultural plants. However, the molecular mechanism of how ClO₂ works is not clear. The purpose of this study was to understand ethylene-related molecular signaling in ClO₂-treated fresh-cut cauliflower florets. Transcriptome analysis was used to investigate ethylene-related gene regulation. A total of 182.83 Gb clean data were acquired, and the reads of each sample to the unique mapped position of the reference genome could reach more than 85.51%. A sum of 2875, 3500, 4582 and 1906 differential expressed genes (DEGs) were identified at 0 d, 4 d, 8 d and 16 d between the control group and ClO₂-treated group, respectively. DEGs were enriched in functions such as ‘response to oxygen-containing compounds’ and ‘phosphorylation’, as well as MAPK signaling pathway, plant hormone transduction pathway and so on. Genes, including OXI1, MPK3, WRKY22 and ERF1, which are located at the junction of wounding, pathogen attack, pathogen infection or ethylene signal transduction pathways, were up-regulated in response to stress. ETR and CTR1 (both up-regulated), as well as three down-regulated genes, including BolC5t34953H (a probable NAC), BolC1t05767H (a probable NAC) and BolC2t06548H (a probable ERF13), might work as negative regulators for ethylene signal transduction. In conclusion, ethylene-related genes and pathways are involved in ClO₂ treatment, which might enhance stress resistance and have a negative feedback mechanism. Full article

Cybercrime is currently rapidly developing, requiring an increased demand for information security knowledge. Attackers are becoming more sophisticated and complex in their assault tactics. Employees are a focal point since humans remain the ‘weakest link’ and are vital to prevention. This research investigates what cognitive and internal factors influence information security awareness (ISA) among employees, through quantitative empirical research using a survey conducted at a Dutch financial insurance firm. The research question of “How and to what extent do cognitive and internal factors contribute to information security awareness (ISA)?” has been answered, using the theory of situation awareness as the theoretical lens. The constructs of Security Complexity, Information Security Goals (InfoSec Goals), and SETA Programs (security education, training, and awareness) significantly contribute to ISA. The most important research recommendations are to seek novel explaining variables for ISA, further investigate the roots of Security Complexity and what influences InfoSec Goals, and venture into qualitative and experimental research methodologies to seek more depth. The practical recommendations are to minimize the complexity of (1) information security topics (e.g., by contextualizing it more for specific employee groups) and (2) integrate these simplifications in various SETA methods (e.g., gamification and online training). Full article