Search Results (176)

Offshore wind energy is an attractive renewable energy source due to its advantages. However, the chaotic marine environment makes the analysis of offshore wind energy extremely difficult. Furthermore, studying the behavior of wind turbines under rare and hazardous natural events such as rogue waves is crucial for the safety and operation of wind turbines and the platforms mounted on them. Therefore, this study numerically investigates the aerodynamic, hydrodynamic, and structural properties of the National Renewable Energy Laboratory (NREL) 5 MW wind turbines under the effect of the Draupner wave, the first marine rogue wave ever recorded. To this end, the geometric and structural information of the NREL 5 MW wind turbines mounted on monopile and jacket platforms is explained. The characteristics of the Draupner wave and the variations in its wave height time series are investigated. The recorded wave height time series values are imported into the QBlade program, and the dynamics of NREL 5MW monopile and jacket wind turbines are simulated. Based on the simulation data, the aerodynamic, hydrodynamic, and structural properties of these structures are examined and analyzed. The results demonstrate that Draupner waves have a significant effect on the aerodynamic, hydrodynamic, and structural parameters of the wind turbines. These parameters are observed to reach their highest values, particularly between the 250th and 280th seconds, when the Draupner wave height reaches its peak. Our findings indicate that the jacket structure experienced higher total forces due to its larger wetted surface area and geometric complexity, while the monopile foundation showed higher inertial loading in the X-direction because of its larger added mass. Additionally, we observed that total aerodynamic power generation is significantly affected by the passage of the Draupner rogue wave. We discuss our findings and their limitations. This numerical study is intended to be a milestone for researchers working on the structural health of offshore wind turbines and platforms under the effect of rogue waves. Full article

This paper focuses on a high-order Korteweg–de Vries wave equation. The extended F-expansion method, a modified form of Kudryashov’s auxiliary equation approach, is employed to construct Jacobi elliptic function solutions for this equation. Three distinct families of solutions are obtained, including solitary waves, breathers, dark/bright solitons, bright–dark interaction solitons, and rogue-like solutions. To better illustrate the complex nonlinear dynamics of the high-order Korteweg–de Vries wave equation, representative solutions are selected, and their moduli are visualized using Maple software through three-dimensional, two-dimensional, and contour plots. Full article

Rogue waves are sudden, extreme events that pose a threat to offshore structures’ safety. Accurately replicating nonlinear rogue waves in laboratory settings is challenging but crucial for evaluating extreme loads. Recently, the time-reversal (TR) method based on the time-reversal feature of nonlinear water wave equations, such as the cubic Schrödinger equation, has shown breakthroughs in experimental rogue wave generation. However, when generating rogue waves of large steepness and strong nonlinearity (especially high-order rogue waves), this method encounters issues such as significantly insufficient wave height and weakened nonlinear characteristics. In this article, a modified time-reversal (MTR) method is proposed based on the dynamic transfer function between the rogue wave surface history and the motion of the wave-generator paddle. MTR adopts a two-round (just like TR) but seven-step procedure for high-order rogue wave generation. Using MTR, high-order rogue waves with respect to 1st–5th-order Peregrine breathers are successfully generated in a physical wave flume. Analysis of shape indices and the energy spectrum shows that MTR greatly improves the quality of high-order rogue wave generation over the TR method. It does this by increasing the focused wave height, improving wave profile accuracy, and better preserving the highly nonlinear features of rogue waves. Using the proposed MTR method, a fifth-order rogue wave was generated with a maximum steepness of 0.03. This exceeds previous studies, where the maximum wave steepness was typically around 0.01. Consequently, this work nearly triples the wave steepness compared to earlier results, yielding the steepest fifth-order rogue wave observed in water wave research. Full article

The (2+1)-dimensional Caudrey–Dodd–Gibbon (CDG) equation, which can frequently be used to be describe the propagations of shallow-water waves and plasma physics, is solved by various methods in this paper, thereby revealing its nonlinear dynamical behavior. First, through the linear superposition principle in conjunction with symmetric Hirota bilinear method, we obtain a bilinear form of the CDG equation, which possesses several symmetry properties, and construct the resonant solutions to exponential waves. Then, the one-rogue wave solutions to the CDG equation are constructed via the ansatz method. Finally, we show three-dimensional diagrams and density graphs of the yielded solutions to better show the dynamic characteristics. Full article

Background/Objectives: The rapid development of safe and efficacious vaccines is often hindered by extensive, mandated non-clinical safety evaluations in animals. With the aim to provide scientific evidence supporting a “vaccine platform approach”, here we present the complete non-clinical studies for two investigational vaccines, GRAd-COV2 and GRAdHIVNE1, based on GRAd, a gorilla-derived group C adenoviral vector. Methods: The biodistribution of GRAd genomes following the intramuscular administration of the vaccines was assessed in rats by a sensitive qPCR method. Local tolerance and systemic toxic effects were evaluated in single- and repeated-dose toxicity studies in rabbits. Results: GRAd-COV2 and GRAdHIVNE1 were well-tolerated. Distribution was highly confined to the injection site and draining lymph nodes, and toxicity profile consisted of transient, non-adverse inflammatory responses, while the expected immune responses to the encoded antigens were successfully induced. Notably, both vaccines demonstrated a consistent safety profile despite transgene and backbone differences, comparable to other replication-defective adenoviral vectors. Conclusions: The established non-clinical safety profile of the GRAd platform provides a robust foundation for a more efficient and streamlined regulatory pathway. By leveraging this prior knowledge, future GRAd-based vaccines can achieve accelerated clinical development while fully adhering to the ethical principles of replacement, reduction, and refinement of animal use in research. Full article

In recent years, the convergence of advanced technologies has enabled real-time data access and sharing across diverse devices and networks, significantly amplifying cybersecurity risks. For organizations with digital infrastructures, network security is crucial for mitigating potential cyber-attacks. They establish security policies to protect systems and data, but employees may intentionally or unintentionally bypass these policies, rendering the network vulnerable to internal and external threats. Detecting these policy violations is challenging, requiring frequent manual system checks for compliance. This paper addresses key challenges in safeguarding digital assets against evolving threats, including rogue access points, man-in-the-middle attacks, denial-of-service (DoS) incidents, unpatched vulnerabilities, and AI-driven automated exploits. We propose a Blockchain-Enhanced Network Scanning and Monitoring (BENSAM) Framework, a multi-layered system that integrates advanced network scanning with a structured database for asset management, policy-driven vulnerability detection, and remediation planning. Key enhancements include device profiling, user activity monitoring, network forensics, intrusion detection capabilities, and multi-format report generation. By incorporating blockchain technology, and leveraging immutable ledgers and smart contracts, the framework ensures tamper-proof audit trails, decentralized verification of policy compliance, and automated real-time responses to violations such as alerts; actual device isolation is performed by external controllers like SDN or NAC systems. The research provides a detailed literature review on blockchain applications in domains like IoT, healthcare, and vehicular networks. A working prototype of the proposed BENSAM framework was developed that demonstrates end-to-end network scanning, device profiling, traffic monitoring, policy enforcement, and blockchain-based immutable logging. This implementation is publicly released and is available on GitHub. It analyzes common network vulnerabilities (e.g., open ports, remote access, and disabled firewalls), attacks (including spoofing, flooding, and DDoS), and outlines policy enforcement methods. Moreover, the framework anticipates emerging challenges from AI-driven attacks such as adversarial evasion, data poisoning, and transformer-based threats, positioning the system for the future integration of adaptive mechanisms to counter these advanced intrusions. This blockchain-enhanced approach streamlines security analysis, extends the framework for AI threat detection with improved accuracy, and reduces administrative overhead by integrating multiple security tools into a cohesive, trustworthy, reliable solution. Full article

In this work, we study a generalised high-order nonlinear Schrödinger equation with time-dependent coefficients, embracing a wide range of physical influences. By employing the Darboux transformation, we construct explicit breather and rogue wave solutions, illustrating how the spectral parameter governs waveform transitions. In these dynamics, dispersion determines stability and symmetry, nonlinearity influences the peak amplitude and width, and third-order dispersion introduces asymmetry and drift in the wave profile. We have demonstrated that stabilization, destabilization and shifting of the centre of the localization, or drifting towards the soliton in space or even temporal directions, can be possible by manoeuvring the spectral parameter relating dispersion and nonlinearity in optical fibre. Manoeuvring the spectral parameter relates the dispersion $a_1\left(t\right)$ and nonlinearity from 100 t to 0.1 t leads to the stabilization of the soliton by a notable decrease in the amplitude for two hundred folds. The results reveal that the inclusion of higher-order term functions as a control mechanism for managing instability and localisation in nonlinear optical fibre systems, offering promising prospects for future developments in nonlinear optics. Full article

In this paper, we propose a cooperative security method for unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs) based on the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm to address the scenario of unauthorized rogue drones (RDs) intruding into an airport’s restricted airspace. The proposed method integrates artificial intelligence techniques with engineering solutions to enhance the autonomy and effectiveness of air–ground cooperation in airport security. Specifically, the MADDPG algorithm enables the Security Interception UAVs (SI-UAVs) to autonomously detect and counteract RDs by optimizing their decision-making processes in a multi-agent environment. Additionally, Particle Swarm Optimization (PSO) is employed for distance-based target assignment, allowing each SI-UAV to autonomously select intruder targets based on proximity. To address the challenge of limited SI-UAV flight range, a power replenishment mechanism is introduced, where each SI-UAV automatically returns to the nearest UGV for recharging after reaching a predetermined distance. Meanwhile, UGVs perform ground patrols across different airport critical zones (e.g., runways and terminal perimeters) according to pre-designed patrol paths. The simulation results demonstrate the feasibility and effectiveness of the proposed security strategy, showing improvements in the reward function and the number of successful interceptions. This approach effectively solves the problems of target allocation and limited SI-UAV range in multi-SI-UAV-to-multi-RD scenarios, further enhancing the autonomy and efficiency of air–ground cooperation in ensuring airport security. Full article

To address the challenges of poor adaptability to spatial heterogeneity, easy breakage of amplitude–phase coupling relationships, and insufficient physical consistency in complex optical wavefield reconstruction, this paper proposes the DdONN-PINNs hybrid framework. Focused on preserving the intrinsic symmetries of wave physics, the framework achieves deep integration of optical neural networks and physics-informed information. Centered on an architecture of “SIREN shared encoding–domain-specific output”, it utilizes the periodic activation property of SIREN encoders to maintain the spatial symmetry of wavefield distribution, incorporates learnable Fourier diffraction layers to model physical propagation processes, and adopts native complex-domain modeling to avoid splitting the real and imaginary parts of complex amplitudes—effectively adapting to spatial heterogeneity while fully preserving amplitude-phase coupling in wavefields. Validated on rogue wavefields governed by the Nonlinear Schrödinger Equation (NLSE), experimental results demonstrate that DdONN-PINNs achieve an amplitude Mean Squared Error (MSE) of $2.94\times {10}^{-3}$ and a phase MSE of $5.86\times {10}^{-4}$, outperforming non-domain-decomposed models and ReLU-activated variants significantly. Robustness analysis shows stable reconstruction performance even at a noise level of $\sigma =0.1$. This framework provides a balanced solution for wavefield reconstruction that integrates precision, physical interpretability, and robustness, with potential applications in fiber-optic communication and ocean optics. Full article

Groundwater represents a critical global resource, increasingly threatened by overexploitation and pollution from contaminants such as arsenic (As), fluoride (F), nitrates (NO₃⁻), and heavy metals in arid to semi-arid regions like Mexico. Traditional Water Quality Indices ($WQIs$), while useful, suffer from subjectivity in assigning weights, which can lead to misinterpretations. This study addresses these limitations by developing a novel, objective Groundwater Quality Index ($GWQI$) through the seamless integration of Machine Learning (ML) models. Utilizing a database of 775 wells from the Mexican National Water Commission (CONAGUA), Principal Component Analysis (PCA) was applied to achieve significant dimensionality reduction. We successfully reduced the required monitoring parameters from 13 to only three key indicators: total dissolved solids (TDSs), chromium (Cr), and manganese (Mn). This reduction allows for an 87% decrease in the number of indicators, maximizing efficiency and generating potential savings in monitoring resources without compromising water quality prediction accuracy. Six $WQI$ methods and six ML models were evaluated for quality prediction. The Unified Water Quality Index (${WQI}_u$) demonstrated the best performance among the $WQIs$ evaluated and exhibited the highest correlation (R² = 0.85) with the traditional $WQI$ based on WHO criteria. Furthermore, the ML Support Vector Machine with polynomial kernel (svmPoly) model achieved the maximum predictive accuracy for ${WQI}_u$ (R² = 0.822). This robust $GWQI$-ML approach establishes an accurate, objective, and efficient tool for large-scale groundwater quality monitoring across Mexico, facilitating informed decision-making for sustainable water management and enhanced public health protection. Full article

Three-wave resonant interaction equations can model nonlinear dynamics in many fields, e.g., fluids, optics, and plasma. Rogue waves, i.e., modes algebraically localized in both space and time, are obtained analytically. The aim of this paper is to study degenerate three-wave resonant interaction equations, where two out of the three interacting wave packets have identical group velocities. Physically, degenerate resonance typically occurs for dispersion relation, possessing many branches, e.g., internal waves in a continuously stratified fluid. Here, the Nth-order rogue wave solutions for this dynamical model are presented. Based on these solutions, we examine the effects of the group velocity on the width and structural profiles of the rogue waves. The width of the rogue waves exhibit a linear increase as the group velocity increases, a feature well-correlated with the prediction made using modulation instability. In terms of structural profiles, first-order rogue waves display ‘four-petal’ and ‘eye-shaped’ patterns. Second-order rogue waves can reveal intriguing configurations, e.g., ‘butterfly’ patterns and triplets. To ascertain the robustness of these modes, numerical simulations with random initial conditions were performed. Sequences of localized modes resembling these analytical rogue waves were observed. A spectral jump was observed, with the jump broadening in the case of rogue wave triplets. Furthermore, we predict new rogue waves based on information from two existing ones obtained using the deep learning technique in the context of rogue wave triplets. This predictive model holds potential applications in ocean engineering. Full article

Modern AI systems can strategically misreport information when incentives diverge from truthfulness, posing risks for oversight and deployment. Prior studies often examine this behavior within a single paradigm; systematic, cross-architecture evidence under a unified protocol has been limited. We introduce the Strategy Elicitation Battery (SEB), a standardized probe suite for measuring deceptive reporting across large language models (LLMs), reinforcement-learning agents, vision-only classifiers, multimodal encoders, state-space models, and diffusion models. SEB uses Bayesian inference tasks with persona-controlled instructions, schema-constrained outputs, deterministic decoding where supported, and a probe mix (near-threshold, repeats, neutralized, cross-checks). Estimates use clustered bootstrap intervals, and significance is assessed with a logistic regression by architecture; a mixed-effects analysis is planned once the per-round agent/episode traces are exported. On the latest pre-correction runs, SEB shows a consistent cross-architecture pattern in deception rates: ViT $80.0\%$, CLIP $15.0\%$, Mamba $10.0\%$, RL agents $10.0\%$, Stable Diffusion $10.0\%$, and LLMs $5.0\%$ (20 scenarios/architecture). A logistic regression on per-scenario flags finds a significant overall architecture effect (likelihood-ratio test vs. intercept-only: ${\chi }^2\left(5\right)=41.56$, $p=7.22\times {10}^{-8}$). Holm-adjusted contrasts indicate ViT is significantly higher than all other architectures in this snapshot; the remaining pairs are not significant. Post-correction acceptance decisions are evaluated separately using residual deception and override rates under SEB-Correct. Latency varies by architecture (sub-second to minutes), enabling pre-deployment screening broadly and real-time auditing for low-latency classes. Results indicate that SEB-Detect deception flags are not confined to any one paradigm, that distinct architectures can converge to similar levels under a common interface, and that reporting interfaces and incentive framing are central levers for mitigation. We operationalize “deception” as reward-sensitive misreport flags, and we separate detection from intervention via a correction wrapper (SEB-Correct), supporting principled acceptance decisions for deployment. Full article

In 2024, the conservative media outlet Daily Wire produced a documentary film entitled Am I Racist? Created by political commentator and author Matt Walsh and director Justin Folk, the film became one of the highest-grossing documentaries of the last decade. Unlike traditional documentaries, Walsh employs a rhetoric of irony against anti-racist adherents to obstruct their influence and inoculate mostly conservative viewers. His method, however, is unusual and even questionable in conservative Christian circles. The film is analyzed using a Bakhtinian analysis of dialogic opposition wherein Walsh embodies three ironic characters—Rogue, Fool, and Clown—in order to expose the monologue of anti-racism. The analysis demonstrates the dialogization of the anti-racist monologue through rhetorical enactments of anacrisis and syncrisis. Through juxtapositions of anti-racist ideologists and their everyday racist opponents, Walsh obstructs the future effectiveness of the ideology. Even more, by becoming a DEI expert himself, he performatively distorts the monologue to victimize opponents and entertain viewers through the public spectacle. Ultimately, Am I Racist? demonstrates a unique modern turn and strategy in conservative and, more importantly, Christian rhetorical strategies that needs more attention in the future. Full article

Viral infections represent a critical threat to cultivated plant species. In papaya cultivation, two viral diseases—papaya mosaic (caused by papaya ringspot virus type P—PRSV-P) and papaya sticky disease (caused by a virus complex of papaya meleira virus—PMeV, and papaya meleira virus—PMeV2)—are prevalent and capable of devastating entire plantations, incurring substantial economic losses. Current diagnostic practices rely on visual identification of symptoms and elimination of infected plants (roguing). Monitoring photosynthetic efficiency in orchards prone to PRSV-P and PMeV2 coinfection may allow early intervention, mitigating productivity losses and reducing fruit quality. This study aimed to evaluate chlorophyll a fluorescence as a biomarker for photosynthetic impairment and symptom severity in papaya infected with PRSV-P and/or PMeV2 and to explore the feasibility of early detection of the infection by these dual pathogens, as an exploratory study under field conditions. Chlorophyll a fluorescence revealed details about the physiology of plants coinfected with the complex of PMeV2 and PRSV-P: the electron motive force within PSII decreases in infected plants and in those without visual symptoms of infection, being proportional to the age and developmental stage of the plants. A slowdown in the multiple electron transfer turnover of PSII and a decrease in the efficiency of the redox reactions of photosystem I were observed in plants with or without visual detection of infection. The evidence generated suggests that the chlorophyll a fluorescence technique can be used to monitor the pathophysiological state of plants under biotic stress. Full article

In this study, the extended (3 + 1)-dimensional Jimbo–Miwa equation, which has not been previously studied using Lie symmetry techniques, is the focus. We derive new symmetry reductions and exact invariant solutions, including lump and rogue wave structures. Additionally, precise solitary wave solutions of the extended (3 + 1)-dimensional Jimbo–Miwa equation using the multivariate generalized exponential rational integral function technique (MGERIF) are studied. The extended (3 + 1)-dimensional Jimbo–Miwa equation is crucial for studying nonlinear processes in optical communication, fluid dynamics, materials science, geophysics, and quantum mechanics. The multivariate generalized exponential rational integral function approach offers advantages in addressing challenges involving exponential, hyperbolic, and trigonometric functions formulated based on the generalized exponential rational function method. The solutions provided by MGERIF have numerous applications in various fields, including mathematical physics, condensed matter physics, nonlinear optics, plasma physics, and other nonlinear physical equations. The graphical features of the generated solutions are examined using 3D surface graphs and contour plots, with theoretical derivations. This visual technique enhances our understanding of the identified answers and facilitates a more profound discussion of their practical applications in real-world scenarios. We employ the MGERIF approach to develop a technique for addressing integrable systems, providing a valuable framework for examining nonlinear phenomena across various physical contexts. This study’s outcomes enhance both nonlinear dynamical processes and solitary wave theory. Full article