Search Results (161)

The increasing demand for noninvasive inspection (NII) of complex civil infrastructures requires overcoming the limitations of traditional ground-penetrating radar (GPR) systems in addressing diverse and large-scale applications. The solution proposed in this study focuses on an initial design that integrates a low-SWaP (Size, Weight, and Power) ultra-wideband (UWB) impulse radar with realistic electromagnetic modeling for deployment on unmanned aerial vehicles (UAVs). The system incorporates ultra-realistic antenna and propagation models, utilizing Finite Difference Time Domain (FDTD) solvers and multilayered media, to replicate realistic airborne sensing geometries. Verification and calibration are performed by comparing simulation outputs with laboratory measurements using varied material samples and target models. Custom signal processing algorithms are developed to extract meaningful features from complex electromagnetic environments and support anomaly detection. Additionally, machine learning (ML) techniques are trained on synthetic data to automate the identification of structural characteristics. The results demonstrate accurate agreement between simulations and measurements, as well as the potential for deploying this design in flight tests within realistic environments featuring complex electromagnetic interference. Full article

Tribological processes in extreme environments pose serious material challenges, requiring coatings that resist both wear and corrosion. This review summarizes recent advances in protective coatings engineered for extreme environments such as high temperatures, chemically aggressive media, and high-pressure and abrasive domains, as well as cryogenic and space applications. A comprehensive overview of promising coating materials is provided, including ceramic-based coatings, metallic and alloy coatings, and polymer and composite systems, as well as nanostructured and multilayered architectures. These materials are deployed using advanced coating technologies such as thermal spraying (plasma spray, high-velocity oxygen fuel (HVOF), and cold spray), chemical and physical vapor deposition (CVD and PVD), electrochemical methods (electrodeposition), additive manufacturing, and in situ coating approaches. Key degradation mechanisms such as adhesive and abrasive wear, oxidation, hot corrosion, stress corrosion cracking, and tribocorrosion are examined with coating performance. The review also explores application-specific needs in aerospace, marine, energy, biomedical, and mining sectors operating in aggressive physiological environments. Emerging trends in the field are highlighted, including self-healing and smart coatings, environmentally friendly coating technologies, functionally graded and nanostructured coatings, and the integration of machine learning in coating design and optimization. Finally, the review addresses broader considerations such as scalability, cost-effectiveness, long-term durability, maintenance requirements, and environmental regulations. This comprehensive analysis aims to synthesize current knowledge while identifying future directions for innovation in protective coatings for extreme environments. Full article

This paper presents the results of a comprehensive study of the structure, phase composition, thermal corrosion, and tribological properties of multilayer gradient coatings based on YSZ/NiCrAlY obtained using detonation spraying. X-ray phase analysis showed that the coatings consist entirely of metastable tetragonal zirconium dioxide (t’-ZrO₂) phase stabilized by high temperature and rapid cooling during spraying. SEM analysis confirmed the multilayer gradient phase distribution and high density of the structure. Wear resistance, optical profilometry, wear quantification, and coefficient of friction measurements were used to evaluate the operational stability. The results confirm that the structural parameters of the coating, such as porosity and phase gradient, play a key role in improving its resistance to thermal corrosion and CMAS melt, which makes such coatings promising for use in high-temperature applications. It is shown that a dense and thick coating effectively prevents the penetration of aggressive media, providing a high barrier effect and minimal structural damage. Tribological tests in the temperature range from 21 °C to 650 °C revealed that the best characteristics are observed at 550 °C: minimum coefficient of friction (0.63) and high stability in the stage of stable wear. At room temperature and at 650 °C, there is an increase in wear due to the absence or destabilization of the protective layer. Full article

In this paper, a solution to the problem of electromagnetic field scattering on a periodic, constrained, planar antenna structure placed on the boundary of two dielectric media was formulated. The scattering matrix of such a structure was derived, and its generalization for the case of an antenna with a multilayer dielectric substrate was defined. By applying the Galerkin spectral method, the problem was reduced to a system of algebraic equations for the coefficients of current distribution on metal elements of the antenna grid, considering the distribution of the electromagnetic field on Floquet harmonics. The finite transverse dimension of the antenna was considered by introducing, to the solution of the case of an unconstrained antenna, a window function on the antenna aperture. The presented formalism allows modeling the operation of periodic, dielectric, composite antenna arrays. Full article

Modern industrial ecosystems, particularly those embracing Industry 4.0, increasingly depend on coherent optical networks operating at 400 Gbps and beyond. These high-capacity infrastructures, coupled with advanced digital signal processing and phase-sensitive detection, enable real-time data exchange for automated manufacturing, robotics, and interconnected factory systems. However, they introduce multilayer security challenges—ranging from hardware synchronization gaps to protocol overhead manipulation. Moreover, the rise of large-scale quantum computing intensifies these threats by potentially breaking classical key exchange protocols and enabling the future decryption of stored ciphertext. In this paper, we present a systematic vulnerability analysis of coherent optical networks that use OTU4 framing, Media Access Control Security (MACsec), and 400G ZR+ transceivers. Guided by established risk assessment methodologies, we uncover critical weaknesses affecting management plane interfaces (e.g., MDIO and I²C) and overhead fields (e.g., Trail Trace Identifier, Bit Interleaved Parity). To mitigate these risks while preserving the robust data throughput and low-latency demands of industrial automation, we propose a post-quantum security framework that merges spectral phase masking with multi-homodyne coherent detection, strengthened by quantum key distribution for key management. This layered approach maintains backward compatibility with existing infrastructure and ensures forward secrecy against quantum-enabled adversaries. The evaluation results show a substantial reduction in exposure to timing-based exploits, overhead field abuses, and cryptographic compromise. By integrating quantum-safe measures at the optical layer, our solution provides a future-proof roadmap for network operators, hardware vendors, and Industry 4.0 stakeholders tasked with safeguarding next-generation manufacturing and engineering processes. Full article

Nanostructured multilayer anticorrosion coatings offer an effective strategy to mitigate the poor corrosion resistance of aluminum alloys and extend their service life. In this study, four types of Ti/Cr multilayer coatings with varied modulation periods along the growth direction were deposited on 7050 aluminum alloy substrates using direct current magnetron sputtering. The cross-sectional microstructure of the coatings was characterized by scanning electron microscopy (SEM), while their mechanical and corrosion properties were systematically evaluated through nanoindentation and electrochemical measurements. The influence of modulation period distribution on the corrosion resistance of Ti/Cr multilayers was thoroughly investigated. The results show that the average thickness of the Ti/Cr multilayer coatings is 680 nm, the structure is dense, and the coarse columnar crystals are not seen. All Ti/Cr multilayer coatings significantly reduced the corrosion current density of 7050 aluminum alloy by about 10 times compared with that of the substrate, showing good protective effect. Modulation period along the coating growth direction decreases the Ti/Cr multilayer coating surface heterogeneous interface density increases, inhibits the formation of corrosion channels, hindering the penetration of corrosive media, and the other three coatings and aluminum alloy compared to its corrosion surface did not see obvious pore corrosion, showing the most excellent corrosion resistance. Full article

The study of dynamical processes on complex networks constitutes a foundational domain bridging applied mathematics, statistical physics, systems theory, and data science. Temporal evolution, not static topology, determines the controllability, stability, and inference limits of real-world systems, from epidemics and neural circuits to power grids and social media. However, the methodological landscape remains fragmented, with distinct communities advancing separate formalisms for spreading, control, inference, and design. This review presents a unifying six-pillar framework for the analysis of network dynamics: (i) spectral and structural foundations; (ii) deterministic mean-field reductions; (iii) control and observability theory; (iv) adaptive and temporal networks; (v) probabilistic inference and belief propagation; (vi) multilayer and interdependent systems. Within each pillar, we delineate conceptual motivations, canonical models, analytical methodologies, and open challenges. Our corpus, selected via a PRISMA-guided screening of 134 mathematically substantive works (1997–2024), is organized to emphasize internal logic and cross-pillar connectivity. By mapping the field onto a coherent methodological spine, this survey aims to equip theorists and practitioners with a transferable toolkit for interpreting, designing, and controlling dynamic behavior on networks. Full article

The geological containment of high-level radioactive waste has become widely accepted among international organizations, and it has been adopted by many countries as part of their national nuclear waste disposal plan. The multi-barrier system, including the compacted bentonite blocks or pellets serving as human-made containment or buffer media, is the key component of high-level radioactive waste disposal, which contains a waste canister that isolates the nuclear waste from a human being geosphere for one million years. The bentonite clay surrounding the nuclear waste capsule is subjected to prolonged exposure to elevated temperatures because of the continuous decay of radioactivity. Long-term heating at high temperatures could change the buffers’ microstructural characteristics and physicochemical and hydromechanical properties, which can influence their self-sealing ability. This paper offers a comprehensive overview of the current understanding of thermal effects on bentonite-based buffer systems. The thermal impact on the microstructure, Atterberg limits, and swelling pressure of bentonite clay are intensely reviewed, and the findings are summarized. This review paper highlights new insights into the design of multi-layered containment approaches for high-level radioactive waste isolation. Full article

With frequent interactions between social media platforms, the dissemination of information and the interaction of opinions on the internet have become increasingly complex and diverse. This increase in information complexity not only affects the formation of public opinion but may also exacerbate the spread of diseases. Based on multilayer complex networks and combined with the Deffuant-I model, this paper explores the dual impact of information complexity and individual characteristics on both information and disease propagation. Through systematic simulation experiments, this paper analyzes the mechanisms of information complexity, individual compromise, and cognitive ability in the evolution of propagation. This study shows that the interactive effects of individual characteristics and information complexity have a significant impact on disease spread. This research not only provides a new theoretical perspective for understanding complex information dissemination but also offers valuable insights for public policymakers in promoting social harmony and addressing public health emergencies. Full article

Communication barriers between deaf and hearing individuals present significant challenges to social inclusion, highlighting the need for effective technological aids. This study aimed to bridge this gap by developing a mobile system for the real-time translation of Portuguese Sign Language (LGP) alphabet gestures into text, addressing a specific technological void for LGP. The core of the solution is a mobile application integrating two distinct machine learning approaches trained on a custom LGP dataset: firstly, a Convolutional Neural Network (CNN) optimized with TensorFlow Lite for efficient, on-device image classification, enabling offline use; secondly, a method utilizing MediaPipe for hand landmark extraction from the camera feed, with classification performed by a server-side Multilayer Perceptron (MLP). Evaluation tests confirmed that both approaches could recognize LGP alphabet gestures with good accuracy (F1-scores of approximately 76% for the CNN and 77% for the MediaPipe+MLP) and processing speed (1 to 2 s per gesture on high-end devices using the CNN and 3 to 5 s under typical network conditions using MediaPipe+MLP), facilitating efficient real-time translation, though performance trade-offs regarding speed versus accuracy under different conditions were observed. The implementation of this dual-method system provides crucial flexibility, adapting to varying network conditions and device capabilities, and offers a scalable foundation for future expansion to include more complex gestures. This work delivers a practical tool that may contribute to improve communication accessibility and the societal integration of the deaf community in Portugal. Full article

This paper explores the impact of the Israel–Palestine conflict on the stock performance of U.S. companies and their public positions on the conflict. In an era where corporate positions on geopolitical issues are increasingly scrutinized, understanding the market implications of such statements is critical. This research aims to capture the complex, non-linear relationships between corporate actions, media coverage, and financial outcomes by integrating traditional statistical techniques with advanced machine learning models. To achieve this, we constructed a novel dataset combining public corporate announcements, media sentiment (including headline and article body tone), and philanthropic activities. Using both classification and regression models, we predicted whether companies had affiliations with Israel and then analyzed how these affiliations, combined with other features, affected their stock returns over a 30-day period. Among the models tested, ensemble learning methods such as stacking and boosting achieved the highest classification accuracy, while a Multi-Layer Perceptron (MLP) model proved most effective in forecasting abnormal stock returns. Our findings highlight the growing relevance of machine learning in financial forecasting, particularly in contexts shaped by geopolitical dynamics and public discourse. By demonstrating how sentiment and corporate stance influence investor behavior, this research offers valuable insights for investors, analysts, and corporate decision-makers navigating sensitive political landscapes. Full article

This study explores the development and optimization of quartz-based filtration media for industrial oil–water separation, focusing on enhancing surface wettability, minimizing fouling, and improving oil rejection efficiency. High-purity quartz particles (SiO₂: 98%, Fe₂O₃: 0.18%, particle size: 0.8–1.8 mm) were evaluated in three configurations: raw, acid-washed, and surface-coated with hydrophilic nanoparticles (Al₂O₃ and P₂O₅). The filtration medium was constructed as a packed-bed of quartz particles rather than a continuous sintered membrane, providing a cost-effective and modular structure for separation processes. Comprehensive material characterization was performed using X-ray diffraction (XRD), Scanning Electron Microscopy (SEM), and Energy Dispersive Spectroscopy (EDS). XRD confirmed the crystalline stability of quartz across all treatments, while SEM and EDS revealed enhanced surface morphology and elemental distribution—especially phosphorus and aluminum—in coated samples. Performance testing with synthetic oily wastewater (initial oil concentration: 183,754.8 mg/L) demonstrated that the coated quartz medium achieved superior separation, reducing residual oil concentration to 29.3 mg/L, compared to 1583.7 mg/L and 1859.8 mg/L for washed and raw quartz, respectively. Contact angle analysis confirmed improved hydrophilicity in coated media, which also exhibited lower fouling propensity. Taguchi optimization (conducted via Minitab 21.3) and regression modeling identified surface coating and operational pressure (optimal at 2.5 bar) as the most significant parameters influencing oil rejection. Post-filtration SEM and XRD confirmed structural integrity and coating durability. Additionally, flux recovery above 90% after backwashing indicated strong regeneration capability. These findings validate surface-modified quartz packed beds as robust, scalable, and economically viable alternatives to conventional membranes in oily wastewater treatment. Future research will explore multilayer coatings, long term performance under aggressive conditions, and AI-based prediction models. Full article

Multimodal interaction technology has become a key aspect of remote education by enriching student engagement and learning results as it utilizes the speech, gesture, and visual feedback as various sensory channels. This publication reflects on the latest breakthroughs in multimodal interaction and its usage in remote learning environments, including a multi-layered discussion that addresses various levels of learning and understanding. It showcases the main technologies, such as speech recognition, computer vision, and haptic feedback, that enable the visitors and learning portals to exchange data fluidly. In addition, we investigate the function of multimodal learning analytics in order to measure the cognitive and emotional states of students, targeting personalized feedback and refining instructional strategies. Though multimodal communication may bring a historical improvement to the mode of online education, the platform still faces many issues, such as media synchronization, higher computational demand, physical adaptability, and privacy concerns. These problems demand further research in the fields of algorithm optimization, access to technology guidance, and the ethical use of big data. This paper presents a systematic review of the application of multimodal interaction in remote education. Through the analysis of 25 selected research papers, this review explores key technologies, applications, and challenges in the field. By synthesizing existing findings, this study highlights the role of multimodal learning analytics, speech recognition, gesture-based interaction, and haptic feedback in enhancing remote learning. Full article

The integration of Finite Element Method (FEM) simulations with laser-based techniques has significantly advanced acoustic research by enhancing wave measurement, analysis, and prediction in complex solid media. This review examines the role of the FEM in laser-based acoustics for wave propagation, defect detection, biomedical diagnostics, and engineering applications. FEM models simulate ultrasonic wave generation and propagation in single-layer and multilayered structures, while laser-based experimental techniques provide high-resolution validation, improving modeling accuracy. The synergy between laser-generated ultrasonic waves and FEM simulations enhances defect detection and material integrity assessment, making them invaluable for non-destructive evaluation. In biomedical applications, the FEM aids in tissue characterization and disease detection, while in engineering, its integration with laser-based methods contributes to noise reduction and vibration control. Furthermore, this review provides a comprehensive synthesis of FEM simulations and experimental validation while also highlighting the emerging role of artificial intelligence and machine learning in optimizing FEM models and improving computational efficiency, which has not been addressed in previous studies. Key advancements, challenges, and future research directions in laser-based acoustic applications are discussed. Full article

The display methods for traditional cultural heritage lack personalization and emotional interaction, making it difficult to stimulate the public’s deep cultural awareness. This is especially true in commercialized historical districts, where cultural value is easily overlooked. Balancing cultural value and commercial value in information display has become one of the challenges that needs to be addressed. To solve the above problems, this article focuses on the identification of deep cultural values and the optimization of the information display in Beijing’s Qianmen Street, proposing a framework for cultural information mining and display based on affective computing and large language models. The pre-trained models QwenLM and RoBERTa were employed to analyze text and image data from user-generated content on social media, identifying users’ emotional tendencies toward various cultural value dimensions and quantifying their multilayered understanding of architectural heritage. This study further constructed a multimodal information presentation model driven by emotional feedback, mapping it into virtual reality environments to enable personalized, multilayered cultural information visualization. The framework’s effectiveness was validated through an eye-tracking experiment that assessed how different presentation styles impacted users’ emotional engagement and cognitive outcomes. The results show that the affective computing and multimodal data fusion approach to cultural heritage presentation accurately captures users’ emotions, enhancing their interest and emotional involvement. Personalized presentations of information significantly improve users’ engagement, historical understanding, and cultural experience, thereby fostering a deeper comprehension of historical contexts and architectural details. Full article