Search Results (147)

Multi-hop inter-domain wireless networks play a vital role in future heterogeneous communication systems by improving data transmission efficiency and security assurance. Despite the advances in secure routing techniques in areas such as node authentication and encryption, they still suffer from the shortcomings of frequent key updates, high computational overhead, and poor adaptability to large-scale dynamic topologies. To address these limitations, we propose a new routing method—the Secure Cross-Layer Route—designed for multi-hop inter-domain wireless networks to achieve unified optimization of security, delay, and throughput. First, we construct a multi-objective optimization model that integrates authentication delay, link load, and resource states, enabling balanced trade-offs between security and transmission performance in dynamic conditions. Second, we introduce a cross-layer information fusion mechanism that allows nodes to adapt routing costs in real time under heterogeneous network conditions, thereby improving path reliability and load balancing. Furthermore, a risk-aware dynamic key update strategy is developed to handle behavioral uncertainty among nodes, reducing authentication overhead and enhancing attack resilience. Experimental evaluations conducted on four datasets with varying network scales demonstrate the superior performance of the proposed method. Experimental results demonstrated that the proposed method achieves at least 28% improvement in effective throughput, reduces average authentication delay by approximately 30%, and increases the secure link ratio by at least 10%, outperforming mainstream routing algorithms under multi-constraint conditions. Full article

In this review work, the different routes and methods for preparing hybrid materials based on nanostructured block copolymers (BCPs) and magnetic nanoparticles (MNPs) are analyzed, as they can be potentially employed in different sectors like biomedicine, electronic or optoelectronic devices, data storing devices, etc. The first procedure for their preparation consists of the nanostructuring of BCPs in the presence of previously synthesized NPs by modifying their surface for increasing compatibility with the matrix or employing magnetic fields for NP orientation, which can also promote the orientation of nanodomains. Surface modification with surfactants led to the selective confinement of NPs depending on the interaction (mainly hydrogen bonding) degree and their intensity. Surface modification with brushes can be performed by three methods, including grafting from, grafting to, or grafting through. Those methods are compared in terms of success for the positioning and confinement of NPs in the desired domains, showing the crucial importance of brush length and grafting density, as well as of NP amount and modification degree in the self-assembled morphology. Regarding the use of external magnetic fields, the importance of relative amounts of MNPs and BCPs employed and that of the magnetic field intensity for the orientation of the NPs and the nearby BCP domains is shown. The second procedure, consisting of the in situ synthesis of NPs inside the nanodomains by a reduction in the respective metallic ions or employing metal-containing BCPs for the generation of MNP patterns or arrays, is also shown. In all cases, the transference of magnetic properties to the nanocomposite was successful. Finally, a brief summary of some aspects about the use of BCPs for the synthesis, encapsulation, and release of MNPs is shown, as they present potential biomedical applications such as cancer treatment, among others. Full article

The most important criterion in the design of unmanned air vehicles is to successfully complete the given task and consume minimum energy in the meantime. This paper presents a comparison of the performances of metaheuristic methods such as Particle Swarm Optimization (PSO) and Grey Wolf Optimization (GWO) to design controllers and DC/DC buck converters for optimizing the energy consumption and path following error of a PEM fuel cell-powered quadrotor system. Hence, the system consists of two PSO- and GWO-based optimizers. Optimizer I is used for determining the parameters of the PD controller, which is used for minimizing the route-tracking error. On the other hand, the I controller parameters and the values of the DC/DC buck converters’ components are determined by Optimizer II to minimize the voltage-tracking errors of the converters. Both optimizers work together in the system and try to minimize tracking errors while also minimizing power consumption by using suitable objective functions. Simulation results demonstrate the effectiveness of the PSO- and GWO-based design of the controllers and converters in enhancing energy efficiency and improving the quadrotor’s flight stability. For step inputs, the GWO-based optimized system shows better performance according to power consumption and the time domain criteria such as rise time and settling time. However, the PSO-based optimized system shows 24.707% better performance for overshoot. On the other hand, 10.8866% less power consumption is observed for the GWO-based optimized system. This power efficient performance of the GWO-based system increases to 18% for the complex route involving ramp and step inputs. Then, a 39 s route test was performed and the total power consumptions for the GWO-based optimized and PSO-based optimized systems were observed to be 168.0015 W/s and 179.9070 W/s, respectively. This means that GWO-based optimizers provide more energy-efficient performance for complex routes. On the other hand, it was determined that the tracking errors in the performance of the desired and actual values of both translational and rotational movement parameters and the forces and torques required for the quadrotor to follow this route were obtained at a maximum of 4% for systems optimized with both techniques. This shows that the full systems optimized with both GWO and PSO algorithms significantly increase their energy efficiency and provide maximum route-following performance. Full article

Current Satellite-Based Augmentation Systems (SBASs) improve the positioning accuracy and integrity of GPS satellites and provide safe civil aviation navigation services for procedures from en-route to LPV-200 precision approach over specific regions. SBAS systems, such as WAAS, EGNOS, GAGAN, and MSAS, already operate. The development of operational SBAS systems is in transition due to the extension of L1 SBAS services to new regions and the improvements expected by the introduction of dual frequency multi-constellation (DFMC) services, which allow the use of more core constellations such as Galileo and the use of ionosphere-free L1/L5 signal combination. The UKSBAS Testbed is a demonstration and feasibility project in the framework of ESA’s Navigation Innovation Support Programme (NAVISP), which is sponsored by the UK’s HMG with the participation of the Department for Transport and the UK Space Agency. UKSBAS Testbed’s main objective is to deliver a new L1 SBAS signal in space (SIS) from May 2022 in the UK region using Viasat’s Inmarsat-3F5 geostationary (GEO) satellite and Goonhilly Earth Station as signal uplink over PRN 158, as well as L1 SBAS and DFMC SBAS services through the Internet. SBAS messages are generated by GMV’s magicSBAS software and fed with data from the Ordnance Survey’s station network. This paper provides an assessment of the performance achieved by the UKSBAS Testbed during the last two years of operations at the SIS and user level, including a number of experimentation campaigns performed in the aviation and maritime domains, comprising ground tests at airports, flight tests on aircraft and sea trials on a vessel. This assessment includes, among others, service availability (e.g., APV-I, LPV-200), protection levels (PL), and position errors (PE) statistics over the service area and in a network of receivers. Full article

Ubiquitous connectivity is envisioned through the integration of terrestrial (TNs) and non-terrestrial networks (NTNs). However, NTNs face multiple routing and Quality of Service (QoS) provisioning challenges due to the mobility of network nodes. Distributed Software-Defined Networking (SDN) combined with Multi-Agent Deep Reinforcement Learning (MADRL) is widely used to introduce programmability and intelligent Traffic Engineering (TE) in TNs, yet applying DRL to NTNs is hindered by frequently changing state sizes, model scalability, and coordination issues. This paper introduces 3DQR, a novel TE framework that combines hierarchical multi-controller SDN, hierarchical MADRL based on Graph Neural Networks (GNNs), and network topology predictions for QoS path provisioning, effective load distribution, and flow rejection minimisation in future 3D networks. To enhance SDN scalability, introduced are metrics and path operations abstractions to facilitate domain agents coordination by the global agent. To the best of the authors’ knowledge, 3DQR is the first routing scheme to integrate MADRL and GNNs for optimising centralised routing and path allocation in SDN-based 3D mobile networks. The evaluations show up to a 14% reduction in flow rejection rate, a 50% improvement in traffic distribution, and effective QoS class prioritisation compared to baseline techniques. 3DQR also exhibits strong transfer capabilities, giving consistent performance gains in previously unseen environments. Full article

Advancements in intelligent vehicular networks and computing systems have created new possibilities for innovative approaches that enhance traffic safety, comfort, and transportation performance. Machine Learning (ML) has become widely employed for boosting conventional data-driven methodologies in various scientific study domains. The integration of a Vehicle-to-Everything (V2X) system with ML enables the acquisition of knowledge from multiple places, enhances the operator’s awareness, and predicts future crashes to prevent them. The information serves multiple functions, such as determining the most efficient route, increasing the driver’s knowledge, forecasting movement strategy to avoid risky circumstances, and eventually improving user convenience, security, and overall highway experiences. This article thoroughly examines Artificial Intelligence (AI) and ML methods that are now investigated through different study endeavors in vehicular ad hoc networks (VANETs). Furthermore, it examines the benefits and drawbacks accompanying such intelligent methods in the context of the VANETs system and simulation tools. Ultimately, this study pinpoints prospective domains for vehicular network development that can utilize the capabilities of AI and ML. Full article

Miniaturized pixel sizes in near-eye digital displays lead to pixel emission patterns with large divergence angles, necessitating efficient beam collimation solutions to improve the light coupling efficiency. Traditional beam collimation optics, such as lenses and cavities, are wavelength-sensitive and cannot simultaneously collimate red (R), green (G), and blue (B) light. In this work, we employed inverse design optimization and finite-difference time-domain (FDTD) simulation techniques to design a collimator comprised of nano-sized photonic structures. To alleviate the challenges of the spatial incoherence nature of micro-LED emission light, we developed a strategy called dual-task optimization. Specifically, the method models light collimation as a dual task of color routing. By optimizing a color router, which routes incident light within a small angular range to different locations based on its spectrum, we simultaneously obtained a beam collimator, which can restrict the output of the light emitted from the routing destination with a small divergence angle. We further evaluated the collimation performance for spatially incoherent RGB micro-LED light in an FDTD using a multiple-dipole simulation method, and the simulation results demonstrate that our designed collimator can increase the light coupling efficiency from approximately 30% to 60% within a divergence angle of ±20° for all R/G/B light under the spatially incoherent emission. Full article

Intelligent unmanned clusters have played a crucial role in military reconnaissance, disaster rescue, border patrol, and other domains. Nevertheless, due to factors such as multipath propagation, electromagnetic interference, and frequency band congestion in high dynamic scenarios, unmanned cluster networks experience frequent topology changes and severe spectrum limitations, which hinder the provision of connected, elastic and autonomous network support for data interaction among unmanned aerial vehicle (UAV) nodes. To address the conflict between the demand for reliable data transmission and the limited network resources, this paper proposes an AODV routing protocol based on node energy consumption and mobility optimization (AODV-EM) from the perspective of network routing protocols. This protocol introduces two routing metrics: node energy based on node degree balancing and relative node mobility, to comprehensively account for both the balance of network node load and the stability of network links. The experimental results demonstrate that the AODV-EM protocol exhibits better performance compared to traditional AODV protocol in unmanned cluster networks with dense node distribution and high mobility, which not only improves the efficiency of data transmission, but also ensures the reliability and stability of data transmission. Full article

In this work, an effective route for achieving high-performance all-polymer materials through the proper manipulation of the material microstructure and starting from a waste material is proposed. In particular, recycled polyethylene terephthalate (rPET) fibers from discarded safety belts were used as reinforcing phase in melt-compounded high-density polyethylene (HDPE)-based systems. The formulated composites were subjected to hot- and cold-stretching for obtaining filaments at different draw ratios. The performed characterizations pointed out that the material morphology can be profitably modified through the application of the elongational flow, which was proven able to promote significant microstructural evolutions of the rPET dispersed domains, eventually leading to the obtainment of micro-fibrillated all-polymer composites. Furthermore, tensile tests demonstrated that hot-stretched and, especially, cold-stretched materials show significantly enhanced tensile modulus and strength as compared to the unfilled HDPE filaments, likely due to the formation of a highly oriented and anisotropic microstructure. Full article

Large language models (LLMs) are quickly implemented to answer question and support systems to automate customer experience across all domains, including medical use cases. Models in such environments should solve multiple problems like general knowledge questions, queries to external sources, function calling and many others. Some cases might not even require a full-on text generation. They possibly need different prompts or even different models. All of it can be managed by a routing step. This paper focuses on interpretable few-shot approaches for conversation routing like latent embeddings retrieval. The work here presents a benchmark, a sorrow analysis, and a set of visualizations of the way latent embeddings routing works for long-context conversations in a multilingual, domain-specific environment. The results presented here show that the latent embeddings router is able to achieve performance on the same level as LLM-based routers with additional interpretability and higher level of control over model decision-making. Full article

The Computing Network is an emerging paradigm that integrates network and computing resources. One of its goals is to satisfy the requirements of delay-sensitive services through network scheduling capabilities. However, traditional TCP/IP networks are deficient in accurately being aware of requirements and performing flexible routing based on service levels. Information-Centric Networking (ICN) addresses these issues through its flexible protocol design and content-based routing mechanism. Additionally, the integration of Software-Defined Networking (SDN) technology further enhances its routing flexibility. Therefore, this paper proposes an ICN-based delay-sensitive service scheduling architecture with an SDN stateful programmable data plane. The network nodes are first layered based on the type of computing clusters they are linked with, and then within each layer, they are divided into several domains according to delay constraints. Then, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm, combined with the Best-Worst Method (BWM) weighting method, is adopted to evaluate the candidate clusters, and the corresponding scheduling strategy is executed in the stateful programmable data plane. The simulation results show that compared with other scheduling architectures and traditional TOPSIS with the Entropy Weight Method (EWM), the proposed architecture and algorithm show significant advantages in reducing the overall delay of service requests and improving the scheduling success ratio, as well as the load balance of the computing clusters. Full article

Background: This study began with a search in three databases, totaling six libraries (ChemBridge-DIVERSet, ChemBridge-DIVERSet-EXP, Zinc_Drug Database, Zinc_Natural_Stock, Zinc_FDA_BindingDB, Maybridge) with approximately 2.5 million compounds with the aim of selecting potential inhibitors with antiproliferative activity on the chimeric tyrosine kinase encoded by the BCR-ABL gene. Methods: Through hierarchical biochemoinformatics, ADME/Tox analyses, biological activity prediction, molecular docking simulations, synthetic accessibility and theoretical synthetic routes of promising compounds and their lipophilicity and water solubility were realized. Results: Predictions of toxicological and pharmacokinetic properties (ADME/Tox) using the top100/base (600 structures), in comparison with the commercial drug imatinib, showed that only nine exhibited the desired properties. In the prediction of biological activity, the results of the nine selected structures ranged from 13.7% < Pa < 65.8%, showing them to be potential protein kinase inhibitors. In the molecular docking simulations, the promising molecules LMQC01 and LMQC04 showed significant values in molecular targeting (PDB 1IEP—resolution 2.10 Å). LMQC04 presented better binding affinity (∆G = −12.2 kcal mol⁻¹ with a variation of ±3.6 kcal mol⁻¹) in relation to LMQC01. The LMQC01 and LMQC04 molecules were advanced for molecular dynamics (MD) simulation followed by Molecular Mechanics with generalized Born and Surface Area solvation (MM-GBSA); the comparable, low and stable RMSD and ΔE values for the protein and ligand in each complex suggest that the selected compounds form a stable complex with the Abl kinase domain. This stability is a positive indicator that LMQC01 and LMQC04 can potentially inhibit enzyme function. Synthetic accessibility (SA) analysis performed on the AMBIT and SwissADME webservers showed that LMQC01 and LMQC04 can be considered easy to synthesize. Our in silico results show that these molecules could be potent protein kinase inhibitors with potential antiproliferative activity on tyrosine kinase encoded by the BCR-ABL gene. Conclusions: In conclusion, the results suggest that these ligands, particularly LMQC04, may bind strongly to the studied target and may have appropriate ADME/Tox properties in experimental studies. Considering future in vitro or in vivo assays, we elaborated the theoretical synthetic routes of the promising compounds identified in the present study. Based on our in silico findings, the selected ligands show promise for future studies in developing chronic myeloid leukemia treatments. Full article

The poor timeliness of routing has always been an urgent problem in practical operator networks, especially in situations with large-scale networks and multiple network domains. In this article, a pruning idea of routing integrated with Dijkstra’s shortest path searching is utilized to accelerate the process of routing in large-scale multi-domain elastic optical networks (EONs). The layered-graph approach is adopted in the spectrum allocation stage. To this end, an efficient heuristic algorithm is proposed, called “Branch-and-Bound based Routing and Layered Graph based Spectrum Allocation algorithm (BBR-LGSA)”, which is an integrated RSA algorithm. Notably, the significant reduction in algorithm time complexity is not only reflected in the pruning method used in the routing stage but also in the construction of auxiliary graphs during the spectrum allocation stage utilizing the Branch-and-Bound method. Simulation results show that the proposed BBR-LGSA significantly reduces the average running time by nearly 78% with higher spectrum utilization in large-scale multi-domain EONs, compared with benchmark algorithms. In addition, the impact of key parameters on performance comparisons of different algorithms is evaluated. Full article

Glioblastoma multiforme (GBM) represents one of the most critical oncological diseases in neurological practice, being considered highly aggressive with a dismal prognosis. At a worldwide level, new therapeutic methods are continuously being researched. Magnetic hyperthermia (MHT) has been investigated for more than 30 years as a solution used as a single therapy or combined with others for glioma tumor assessment in preclinical and clinical studies. It is based on magnetic nanoparticles (MNPs) that are injected into the tumor, and, under the effect of an external alternating magnetic field, they produce heat with temperatures higher than 42 °C, which determines cancer cell death. It is well known that iron oxide nanoparticles have received FDA approval for anemia treatment and to be used as contrast substances in the medical imagining domain. Today, energetic, efficient MNPs are developed that are especially dedicated to MHT treatments. In this review, the subject’s importance will be emphasized by specifying the number of patients with cancer worldwide, presenting the main features of GBM, and detailing the physical theory accompanying the MHT treatment. Then, synthesis routes for thermally efficient MNP manufacturing, strategies adopted in practice for increasing MHT heat performance, and significant in vitro and in vivo studies are presented. This review paper also includes combined cancer therapies, the main reasons for using these approaches with MHT, and important clinical studies on human subjects found in the literature. This review ends by describing the most critical challenges associated with MHT and future perspectives. It is concluded that MHT can be successfully and regularly applied as a treatment for GBM if specific improvements are made. Full article

As the main inter-domain routing protocol in today’s internet, the Border Gateway Protocol (BGP) faces serious security risks during actual usage. Research on BGP malicious attack methods requires a realistic network environment, and evaluation methods based on physical networks often suffer from high costs and insufficient flexibility. Thus, we propose an efficient BGP simulated network deployment system based on a virtualization technology called the SOD–BGP. This system, combining cloud computing and virtualization technologies, creates a scalable, highly flexible basic network environment that allows for the automated simulation and evaluation of actual BGP prefix hijacking attack scenarios. A Resource Public Key Infrastructure (RPKI) simulation suite is introduced into the system, emulating a certificate issuance system, certificate storage, and a certificate synchronization verification mechanism, thus aligning the simulation environment with real-world usage scenarios. Finally, we propose a data collection and performance evaluation technique to evaluate BGP networks deploying RPKI under different attack scenarios and to explore the effectiveness of RPKI defense mechanisms at various deployment rates. A comparative analysis with other simulation techniques demonstrates that our approach achieves a balanced performance in terms of deployment speed, complexity, and RPKI integrity, providing a solid simulation technology foundation for large-scale BGP security defense strategies. Full article