Search Results (1,753)

This research utilizes machine learning (ML)-based malicious node detection techniques to effectively incorporate security and trustworthiness into fifth-generation (5G) and Vehicular Ad hoc Network (VANET) systems, in contrast to traditional methods that do not employ modern techniques. VANET may be vulnerable due to vehicle mobility, network openness, and the conventional network architecture. Therefore, security and trust management using modern methodologies, such as ML approaches, is essential for 5G-enabled VANET integration, which has become a paramount concern. And due to limitations imposed by traditional security methods, which are unable to identify malicious nodes in VANET completely, processing delays are longer. Therefore, this research utilizes the VANET malicious-node dataset designed for real-time malicious node/attack detection in VANET. The proposed ML methodology uses a Random Forest (RF) and an optimized ensemble ML classifier, such as XGBoost and LightGBM, which require a security and trustworthiness solution provided by the RF Trust Extended Authentication (TEA). We simulate vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) mobility, communication behaviors, and trust metrics to assess the accuracy of malicious-vehicular-node features for the identification and detection of attacks, including False Injection, Sybil, blackhole, and Denial-of-Service (DoS). The proposed ML methodology also identifies these attack patterns, providing a realistic dataset for Intelligent Transportation System (ITS) research. In contrast, traditional VANET methods do not. We compared the performance of the proposed ML method with other literature-standard ML and RF methods using metrics such as accuracy, confusion matrices, and precision, Recall, and F1-score to measure effectiveness. In our proposed machine learning (ML) method, we achieve 99% accuracy in classifying MVN and predicting both attack, including False Injection, Sybil, blackhole, and Denial-of-Service (DoS), and benign classes, with precision, recall, and F1-score of 100% each, and establish a trustworthiness score of 100%, Whilst the standard models, such as other VANET methods achieved an accuracy of only 95%, with precision, recall, and F1-score of 98%, without a confusion matrix to confirm the model’s performance. Full article

Spectrum scarcity has become a critical issue due to the rapid deployment of fifth-generation (5G) networks and the explosive growth of future wireless data traffic. Spectrally Efficient Frequency Division Multiplexing (SEFDM) is a promising technique to enhance spectral efficiency by compressing subcarrier spacing and allowing spectral overlap; however, it suffers from severe inter-carrier interference (ICI) caused by the loss of orthogonality. In particular, under Rayleigh fading channels, the combined effects of ICI and multipath fading lead to significant degradation in bit error rate (BER) performance. Conventional SEFDM systems employing a cyclic prefix (CP) encounter an unavoidable error floor due to residual interference stemming from non-orthogonality. On the other hand, while zero-padding (ZP)-based SEFDM offers superior multipath tolerance, further enhancement in communication quality is still desired. This paper proposes a novel receiver architecture utilizing sector antennas to spatially separate multipath components based on the angle of arrival (AoA). Furthermore, we investigate and compare sector selection algorithms specifically tailored for SEFDM systems. Simulation results demonstrate that the proposed method, employing a sector selection scheme based on the maximum channel response power, effectively suppresses inter-symbol interference (ISI) and improves BER performance for both CP-SEFDM and ZP-SEFDM. Furthermore, our quantitative evaluations confirm that the proposed architecture successfully achieves the theoretical maximum spectral efficiency even in higher-order modulation schemes (16QAM), while maintaining a low computational complexity compared to conventional spatial diversity techniques. Full article

The rapid advancement of digital and mobile technologies has reshaped the educational landscape, fostering the adoption of interactive and learner-centered methodologies. Among these, immersive technologies such as Augmented Reality (AR), when coupled with next-generation wireless communication systems, hold the potential to revolutionize knowledge acquisition and student engagement. In this paper, we present the design and development of an AR-based educational tool specifically oriented to teaching concepts of fifth-generation (5G) mobile networks. The tool provides a real-time interactive visualization of 3D network components on mobile devices, enabling learners to explore 5G NSA/SA architectures in an accessible manner with real-world environments through mobile devices and their integrated cameras. The application was developed using Blender for 3D modeling and Unity as the rendering engine, incorporating the Vuforia SDK for marker-based AR tracking, and it was deployed on the Android operating system. Unlike traditional static approaches, the proposed solution enables learners to explore complex network architectures and key functionalities of 5G in an interactive and accessible manner. To assess its perceived effectiveness, quantitative surveys were conducted with both university and high school students, focusing on usability, engagement, and perceived learning outcomes. Results indicate that the tool is user-friendly, enhances motivation, and supports conceptual understanding as perceived by participants of 5G technologies. These findings highlight the potential of AR, supported by advanced wireless networks, as a pedagogical strategy to improve STEM education and foster technological literacy in the era of digital transformation. Full article

Aproceros leucopoda (Hymenoptera: Argidae) is an East-Asian pest of Ulmus spp. that has spread across Europe since the 2000s and, more recently, to North America, causing repeated defoliation of host plants. Laboratory studies have suggested four or five generations per year in Hungary; however, in the field, their number ranges from one to six. In 2012 and 2013, the bionomics of this invasive pest were studied in north-eastern Italy through weekly samplings from April to October, with data related to accumulated degree days (DDs). Although adult captures exhibited five peaks in both years, only in 2012 were eggs of the fifth and last generation of the season. Their apparent absence in 2013 might be due to elm water stress or temperatures much higher than optimal (30 vs. 19.5 °C). From 2013 to today, a progressive decline in A. leucopoda populations has been recorded. The possible reasons for the gradual decline in the sawfly population recorded in the subsequent years are discussed. Several natural enemies were observed. This study can contribute to a better understanding of A. leucopoda population dynamics in newly colonised areas, including the risk to wood production in mixed deciduous plantations if defoliation occurs over many consecutive years. Full article

The study aimed to evaluate the effect of chlorhexidine (CHX), chlorin p6-mediated photodynamic therapy (PDT), magnesium oxide nanoparticles (MgONPs), and Clp6-functionalized MgONPs on smear layer removal and shear bond strength of a two-step etch-and-rinse adhesive to caries-affected dentin. Seventy-five human permanent molars with occlusal carious lesions and ICDAS scores of four and five were included. Twenty-five samples were used to prepare dentin discs 2 mm in thickness. The remaining samples, along with 25 discs, were arbitrarily allocated into five disinfectant groups, with n = 15 per group (10 teeth and 5 discs). Group I: Control, Group II: 2% CHX, Group III: Clp6-mediated PDT, Group IV: MgONPs, and Group V: Clp6-functionalized MgONPs. SL removal assessment, nanoparticle characterization, and EDX were performed using SEM. Fifty CAD were etched, followed by fifth-generation adhesive application and composite build-up. SBS and failure modes were evaluated with a universal testing machine and stereomicroscope, respectively. Group 4 (MgONPs) specimens displayed the maximum cleaning of SL (1.11 ± 0.13) and the highest SBS (10.32 ± 0.18 MPa). However, minimum SL removal (2.87 ± 0.94) and bond strength (7.42 ± 0.25 MPa) were exhibited by Group 1 (No disinfectant) samples. MgONPs possess the potential to be used as a cavity disinfectant, as they efficiently remove SL from CAD and augment the bond integrity outcomes. Full article

The Veracruzan biogeographic province is a central part of the Gulf of Mexico slope and serves as an interface between the Neotropical Region, the Mexican Transition Zone, and the Nearctic Region. We provide an overview of amphibian and reptile diversity in the province, focusing on species richness, endemism, conservation status, and faunal similarity to neighboring biogeographic provinces. In the Veracruzan biogeographic province there are 343 native species of amphibians and reptiles, encompassing nearly one quarter of the Mexican herpetofauna, with over 85% of the families and over 90% of the genera found in Mexico represented. The province therefore possesses exceptional taxonomic richness. It has the fifth highest richness among Mexican biogeographic provinces. The herpetofauna comprises several Neotropical taxa and locally endemic species found among amphibians of montane and cloud forest fauna. Richness of amphibians and reptiles generally increases with province area. Regions of the Mexican Transition Zone exhibit a relatively higher species richness than their Neotropical neighbors. Analyses of faunal similarities between the Veracruzan province and its neighboring provinces and highlight the importance of geographic proximity, environmental continuity, and historical processes for assemblage composition. Amphibians are more threatened than reptiles, with high levels of endemism and vulnerability to habitat loss and emerging diseases, whereas reptiles are more threatened by habitat degradation, exploitation, and invasive species. Our findings show that the Veracruzan biogeographic province is an important reservoir of herpetofaunal diversity and a priority region for conservation in Mexico. Full article

Fifth-generation (5G) mobile networks must simultaneously satisfy stringent latency targets, high user density, and energy-aware operation across heterogeneous services. Cloud Radio Access Networks (C-RAN) provide architectural flexibility through centralized baseband processing, but they also introduce new control challenges related to fronthaul constraints, dynamic traffic variations, and joint radio–compute coordination with Mobile Edge Computing (MEC). This paper proposes a unified AI-driven optimization framework for adaptive 5G C-RAN management, where the controller dynamically tunes key system decisions—including functional split selection, TDD downlink ratio, user–RU association, fronthaul load management, and MEC offloading proportion. To enable fair benchmarking under identical simulation settings, a static baseline policy is compared against five adaptive control strategies: Deep Q-Network (DQN), Proximal Policy Optimization (PPO), Deep Deterministic Policy Gradient (DDPG), Multi-Objective Reinforcement Learning (MORL), and a Deterministic Service-Level Agreement (SLA)-aware controller Penalty-Constrained Hierarchical Action Controller (PCHAC). Performance evaluation across techno-economic and service KPIs shows that intelligent control significantly improves operational profit, tail-latency behavior, and energy efficiency while enhancing SLA compliance compared with non-adaptive operation. The results highlight the practicality of multi-objective and constraint-aware learning for next-generation C-RAN orchestration under scaling traffic demand. Full article

This paper advances user-centric Artificial Intelligence (AI) frameworks for reliability in fifth-generation and beyond (B5G) networks by examining their use in high-demand services such as video streaming. The proposed framework can leverage multi-layer monitoring across the edge–cloud continuum, application-layer metrics, and 5G core performance data to evaluate reliability through Quality of Experience (QoE) optimization. Results demonstrate that improved frame delivery can be achieved via dynamic resource prediction and proactive resource allocation. The study validates the framework’s scalability in dynamic workload conditions, emphasizing its role in mission-critical video services. Full article

Fifth-generation (5G) networks are facing critical security challenges in device authentication for massive Internet of Things deployments while preserving privacy. Traditional federated learning approaches depend on the computationally expensive homomorphic encryption to protect model gradients, resulting in substantial latency and communication overhead, leading to impractical energy consumption for resource-constrained 5G devices. This paper proposes Zero-Knowledge Federated Learning (ZK-FL), eliminating homomorphic encryption by enabling devices to prove model correctness without revealing gradients. Our approach integrates zero-knowledge proofs with FL updates, where each device generates a proof ${\mathrm{Proof}}_i=\mathrm{ZK}({\mathrm{Gradient}}_i,{\mathrm{Hash}}_i)$, demonstrating computational integrity. The experimental results from 10,000 authentication attempts demonstrate ZK-FL achieves 78.4 ms average authentication latency versus 342.5 ms for homomorphic encryption-based FL (77% reduction), proof sizes of 0.128 kB versus 512 kB (99.97% reduction), and energy consumption of 284.5 mJ versus 6525 mJ (95% reduction), while maintaining 99.3% authentication success rate with formal privacy guarantees. These results demonstrate ZK-FL enables practical privacy-preserving authentication for massive-scale 5G deployment. Full article

Fractional differential equations provide a flexible framework for describing evolutionary processes in complex media, where nonlocality and memory effects play central roles, and classical integer-order models are frequently inadequate to capture these behaviors. In this work, we revisit the time-fractional Harry Dym (HD) evolution equation in the Caputo sense and construct high-precision analytical approximations using the recently developed Tantawy technique (TT). The method generates a rapidly convergent fractional-power series in time without resorting to perturbative assumptions, auxiliary decomposition polynomials, linearization procedures, or integral transforms, and it remains computationally economical even at high approximation orders. Closed, compact expressions are derived up to the fifth-order approximation and can be systematically extended, yielding excellent agreement with the known exact solution of the classical/integer HD model and with approximations obtained via the new iterative method. A detailed error analysis is carried out by computing absolute and maximum residual errors over the entire computational domain, demonstrating the accuracy, stability, and robustness of the TT for the HD-type fractional nonlinear evolution equation. From a physical perspective, the proposed framework offers a reliable tool for modeling nonlinear wave structures in dispersive media with significant memory and, more generally, for treating a broad class of fractional nonlinear wave equations arising in physics and engineering. Full article

Declining birth rates are reshaping higher education across East Asia, accelerating the large-scale underutilization and, in some contexts, partial abandonment of university campus assets. Although adaptive reuse has been widely discussed, campus transformation is often framed primarily as a programmatic or policy problem, with limited attention to the inherited spatial logic embedded in campus morphology. This study revisits Le Corbusier’s concept of unlimited growth as a generative framework for campus transformation. Rather than treating it as a museum-specific historical typology, the research reinterprets unlimited growth as a scalable spatial logic defined by modular continuity, circulation hierarchy, and open-ended sequencing. To enhance reproducibility and operational clarity, the study formalizes a typo-morphological decoding protocol—modules, circulation, and growth sequence—and applies it through plan-, section-, and diagram-based analysis. Through comparative examination of three museum precedents—Sanskar Kendra Museum, the National Museum of Western Art (Tokyo), and the Chandigarh Museum and Art Gallery—the study extracts a set of transferable spatial mechanisms: modular increment, circulation-centered ordering, directional displacement, and fifth-façade ecological continuity. These mechanisms are then translated into an operational right-sizing model and tested through a design-operational demonstrator on a single anonymized Taiwanese campus experiencing demographic contraction. The findings indicate that unlimited growth functions not merely as a formal principle but as a spatial governance logic that supports phased consolidation, adaptive recomposition, and system-level coherence under long-term uncertainty. Importantly, this framework contributes to sustainability by reducing land consumption through spatial consolidation, minimizing unnecessary new construction, enabling adaptive reuse of existing campus assets, and improving long-term resource-use efficiency through phased right-sizing and ecological continuity. This study further advances a reproducible, mechanism-based methodological framework for institutional spatial transformation, providing a transferable approach for large-scale campus restructuring under conditions of long-term demographic and environmental uncertainty. Full article

Reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) has emerged as a promising technique to enhance spectral efficiency and coverage in fifth- and sixth-generation wireless networks. However, asymmetric indoor propagation conditions characterized by heterogeneous line-of-sight (LoS) and non-line-of-sight (NLoS) links often degrade user fairness. This paper investigates a downlink RIS-assisted NOMA system under the standardized 3GPP indoor office (InH) channel model to address fairness-oriented design under realistic link-budget constraints. We formulate an optimization problem for max–min fairness that jointly considers discrete RIS element partitioning and NOMA power allocation to achieve a symmetrical allocation of quality of service (QoS). To enable efficient computation, the non-convex problem is transformed into an epigraph form and solved using a low-complexity, bisection-based quasi-convex optimization framework combined with enumeration over RIS partitions. Numerical results demonstrate significant fairness gains; for instance, doubling the RIS array size yields a substantial improvement in the ergodic max–min rate, corresponding to approximately a 66% gain at moderate transmit power levels. Furthermore, by accounting for practical impairments such as imperfect successive interference cancellation (iSIC), imperfect channel state information (iCSI), and RIS implementation losses, the results reveal that fairness-optimal operation consistently prioritizes the far user to overcome severe indoor NLoS attenuation. The proposed framework is also compared with alternating optimization (AO)-based RIS-NOMA, conventional RIS beamforming without partition and RIS-assisted orthogonal multiple access (OMA) schemes. Simulation results confirm that the proposed framework achieves low computational complexity, making it suitable for practical indoor wireless environments. Full article

This paper introduces a compact sub-6 GHz multiple-input multiple-output (MIMO) antenna array developed for 5G smartphone applications. The design employs eight planar inverted-F antenna (PIFA) elements arranged to realize dual-band and dual-polarized operation. The antenna achieves impedance bandwidths of 3.3–3.7 GHz (11.4%) and 5.3–5.8 GHz (10%), covering key sub-6 GHz fifth-generation (5G) bands. To enhance diversity performance, the elements are distributed along the edges of the smartphone mainboard, enabling excitation of orthogonal polarization modes while maintaining an overall board size of 75 mm × 150 mm on an FR4 substrate. Even without the use of dedicated decoupling structures, the closely spaced antenna elements exhibit satisfactory isolation levels, varying between −12 dB and −22 dB across the operating bands. The antenna array achieves wide impedance bandwidths of approximately 400 MHz at 3.5 GHz and more than 500 MHz at 5.5 GHz, supporting high data-rate communication. In addition, the proposed system demonstrates very low correlation and active reflection, with envelope correlation coefficient (ECC) values below 0.002 and total active reflection coefficient (TARC) levels better than −20 dB. User interaction effects are also investigated, and the results confirm acceptable SAR levels and stable radiation behavior in the presence of the human body. Owing to its planar, dual-band/dual-polarization capability and compliance with safety requirements, the proposed antenna represents a promising practical solution for contemporary 5G handheld devices and future multi-band mobile platforms. Full article

Endoscopy refers to the minimally invasive optical visualization and examination of internal structures within the body. Its significance lies in diagnosing intraluminal tissue abnormalities and assisting therapeutics, especially in the gastrointestinal tract. However, conventional optical endoscopes are limited by their insufficient penetration depth. Although endoscopic ultrasound achieves deeper penetration of up to 10 cm, it suffers from compromised spatial resolution. Recent advances have expanded the role of endoscopy from basic mucosal visualization to precision diagnostics, therapeutic assistance, and even intelligent, remote-assisted procedures. An emerging modality, second near-infrared window (NIR-II, 1000–1700 nm) endoscopy, offers deep tissue penetration, reduced scattering, and a high signal-to-noise ratio. This review discusses the clinical requirements of endoscopy across screening, diagnostics and therapeutics. It provides a comparative assessment of current methodologies, and a particular focus is placed on discussing the promising developments in NIR-II endoscopy. Furthermore, we investigate the transformative potential of integrating artificial intelligence and fifth-generation wireless networks into endoscopic practice. The continued evolution and clinical translation of these technologies, particularly NIR-II endoscopy, hold the promise to fundamentally enhance precision medicine in gastroenterology. Full article