The increasing use of the Unmanned Aerial Vehicle (UAV) swarms in real-time and mission-critical operations requires such communication infrastructure not only to meet security and adaptation demands, but also to be transparent, interpretable. This article gives an Explainable Multi-Agent Reinforcement Learning (EMARL) framework of an intelligent and safe Flying Ad Hoc Networks (FANETs) communication model. The offered system combines a decentralized learning system by Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm, a trust-based security system, and an Explainable AI (XAI) package of SHAP, LIME, and attention visualization techniques. The EMARL system allows every UAV agent to autonomously base their decision on the routing policy that is both interpretable and the result of a combination of local observations, learned policies, as well as trust estimates of the adjacent agents. Network modeling and mobility dynamics of UAV is simulated through NS-3, UAV mobility dynamics through AirSim, and a Python-based MARL engine in order to train policies and make decisions that are coordinated. The evaluation of the performance reveals that EMARL has always seen improved packet delivery ratio (PDR), improved accuracy, reduced delay, improved energy efficiency and false positive rate over the traditional protocols like, Ad hoc On-Demand Distance Vector Routing (AODV), Trust based, Q-Routing and Standard, MARL even under jamming and Sybil attack conditions. Exploitability-based measurements also validate the framework as an entity that provides clarity and accountability of decisions, thus enhancing human interpretability and credence. At the ablation studies, the presence of the XAI and trust modules is deemed to be essential to ensure the robustness of the system. Comprehensively, the EMARL framework is the vital step on the path aiming at safe, interpretable, and scalable UAV swarm communications in dynamic and hostile scenes.

Abstract Reaction–diffusion infectious disease models are widely used to describe the spatial distribution of infected individuals. In this study, we construct network-based reaction–diffusion models that incorporate both higher-order interactions and advection mechanisms, formulated on a triangular lattice torus network. This idealized structure is adopted to facilitate explicit derivation and linear stability analysis of the theoretical conditions for Turing instability—analyses that would be considerably more challenging in complex heterogeneous geometries. To address the computational challenge of generating higher-order node Laplacian matrices in large-scale networks, we develop a dimensionality reduction strategy using graph-structured edge Laplacians. The theoretical analysis reveals how higher-order interactions and advection jointly influence the onset of Turing patterns. Furthermore, model fitting to real epidemic data—using a mobility network constructed from 2020 inter-prefectural commuting flows across Japan’s 47 prefectures—shows that incorporating higher-order interactions results in better fitting performance compared to conventional models.

Effect of Sm3+ ions on structure and reactivity of S53P4 and 13-93 bioactive glasses.

This study explores the multi-step mobility of digital nomads and its implications for global tourist flows and destination connectivity. Unlike traditional tourists who typically travel to a single location and return, digital nomads move through multiple destinations in a continuous and dynamic pattern. Using network theory as the conceptual framework, the study integrates Geographic Information Systems (GIS), network analysis, and netnographic methods within a mixed methods design to examine spatial mobility and inter-destination relationships. Data derived from the Nomad List platform were analysed to map tourist flows and identify key nodes within the global mobility network. Centrality measures, such as degree, betweenness, and closeness, were used to determine which cities act as hubs and which remain peripheral. The findings indicate that cities such as Bangkok, Lisbon, New York, and Barcelona serve as central hubs, while others occupy marginal positions within the network. The study shows that digital nomads influence not only individual destination dynamics but also broader spatial structures. Their mobility is shaped by factors beyond tourism appeal, including digital infrastructure, affordability, and ease of access. This study contributes to the literature by reconceptualising digital nomad mobility as aggregate multi-step configurations and by modelling inter-destination connectivity through a network-based approach, rather than focusing on single-destination choice or individual travel itineraries. Additionally the study advances the literature by reconceptualizing digital nomad mobility as aggregate multi-step configurations and framing digital nomads as structural mobility actors within global tourism networks.

ABSTRACT Based on cross-national data on scientific talent mobility from 2006 to 2021, this study constructs a global talent mobility network using complex network analysis. It examines how integration into this network affects technological catch-up in latecomer countries and the mechanisms underlying this process. This study finds that integration into the global talent mobility network significantly facilitates technological catch-up in latecomer countries, with multinational cooperation in knowledge production serving as a key channel through which this effect occurs. Moreover, when latecomer countries have a greater technological gap than frontier countries, a larger economic scale, or a higher internet coverage rate, integration into the global talent mobility network exerts a stronger promoting effect on technological catch-up.

Past research on neighborhood ascent—socioeconomic increases among residents and housing—focuses on residential environments, overlooking how connections beyond their boundaries influence neighborhood change. Using geocoded tweets from over 375,000 Twitter users in the 50 most populous US cities, we explore how a city’s mobility network relates to neighborhood socioeconomic (SES) ascent from 2010 to 2019—whether ascent is more likely in cities more or less structurally connected via residents’ routine travels. We find that neighborhoods located in cities with greater racially segregated mobility, particularly initially lower-SES neighborhoods, are more likely to ascend compared to those in cities where residents more frequently visit neighborhoods of different racial compositions than their own. While ascent patterns are similar across White and Hispanic neighborhoods, regardless of city type, neighborhoods with no racial majority are more likely to ascend in cities with greater segregated mobility. Black neighborhoods are least likely to ascend, underscoring how deeply entrenched racial hierarchies continue to shape neighborhood trajectories. Our results highlight broader urban dynamics structuring neighborhood ascent, revealing how stratification processes extend well beyond where people live.

Energy efficient and delay sensitive routing with predictive sink mobility in mobile wireless sensor network using triple parallel convolutional neural network optimized by cleaner fish algorithm

This research addresses key optimization challenges in next-generation wireless communication networks, such as 5G, 6G, and beyond, using artificial intelligence (AI) techniques. The main goal is to explore how AI methods can improve resource allocation, power control, interference management, traffic prediction, and mobility management in diverse and changing wireless environments. The study seeks to provide a clear understanding of how different AI approaches work and how they fit into developing network structures. The methodology includes a thorough review and analysis of AI techniques like supervised and unsupervised machine learning, deep learning, reinforcement learning, evolutionary algorithms, and hybrid models. This research examines these methods based on their principles, strengths, and real-world applications for optimizing wireless networks. It combines findings from various case studies and experimental results to show AI's role in improving network adaptability and efficiency. Key findings show that AI-driven optimization significantly surpasses traditional heuristic and static methods because it enables real-time, data-driven decision-making. This leads to better network throughput, lower latency, improved energy efficiency, and more effective interference management. AI techniques, especially reinforcement learning and deep learning, support adaptive resource management, predictive traffic handling, and smooth mobility in ultra-dense and multi-tier networks. The impact of this research shows how AI can transform wireless networks into intelligent, scalable, and sustainable systems that can support new applications like autonomous systems, smart cities, and immersive multimedia. The study concludes that ongoing progress in explainable AI, federated learning, and AI-native network design will be crucial for tackling issues related to scalability, privacy, and understanding, thus shaping the future of wireless communications.

Mobility networks are vital for economic activity, social interaction, and urban development, yet they remain highly vulnerable to external shocks. The COVID-19 pandemic profoundly disrupted human mobility, but most studies have focused on short-term responses or macroscopic patterns, leaving long-term structural transformations underexplored. Here, we analyze Mexico’s intermunicipal mobility network from 2020 to 2021 using a mesoscopic decomposition framework that distinguishes local (short-distance) components from global (long-distance) connections. This multiscale approach moves beyond static or node-level metrics to reveal how connectivity itself was reshaped. Clustering analysis and change point detection further uncover temporal shifts in mobility dynamics. Our results show three clear phases. Before the pandemic, the network was dense and highly connected. During the pandemic, mobility fragmented into smaller, locally cohesive clusters, reflecting sharp declines in long-distance travel. After restrictions eased, mobility partially recovered but never fully returned to its pre-pandemic structure, indicating lasting behavioral and structural shifts. Regional disparities were pronounced: western and northwestern regions showed greater resilience, while southeastern regions remained fragmented longer. Broader lifestyle changes—including remote work, digitalization, and e-commerce—reinforced local clustering and weakened interregional ties, pointing to a durable reconfiguration of mobility networks. By integrating a temporal, multiscale perspective, this study reveals how crises reshape both local cohesion and interregional connectivity. Beyond documenting disruption, it shows that mobility systems do not simply “bounce back.” Instead, they reorganize, often unevenly, underscoring the urgency for adaptive transport policies, resilient urban planning, and digital infrastructure capable of supporting mobility in a permanently altered landscape. These insights provide a data-driven foundation for future mobility resilience strategies.

Abstract Tectonic processes profoundly influenced the dispersal, evolution, and archaeological record of our Paleolithic ancestors. However, in-depth reconstructions of human resilience against seismic events come mostly from contexts dating to the last 13,000 years. Here, we present geophysical, geological, geochronological, and archaeological data from the open-air site of Vale Boy in southwestern Iberia, revealing how foragers mitigated earthquake impacts between ~ 30,000 and 24,000 years ago. At Vale Boi, faulting formed sedimentary traps that were recurrently exploited by hunter-gatherers and periodically buried by rockfalls, likely triggered by ≥ 5.7 Mw earthquakes. Despite seismic destruction, hunter-gatherers repeatedly returned to the site, drawn by its strategic access to key resources. They mitigated seismic risks by increasing their mobility and even abandoning Vale Boi, as seen during the Gravettian and at the early/late Proto-Solutrean transition. When seismic and climatic stressors co-occurred (Heinrich Event 2), they did not abandon the site. Instead, they adopted strategies to limit their exposure to rockfall hazard while securing access to increasingly vital coastal and estuarine resources. Until the early Proto-Solutrean, tightly knit social networks supported the survival of Vale Boi foragers during periods of high stress, such as the aftermath of seismic rockfalls. During the late Proto-Solutrean, an expansion of super-regional connections might have functioned as a proactive buffer against future tectonic shocks. Our findings demonstrate that forager resilience to seismic events relied on flexible adjustments in mobility and social connectivity. Despite limitations deriving from its single-site focus, this study underscores the value of deep archaeological sequences for disentangling human responses to intertwined geological and ecological pressures.

Abstract With increasing awareness of the privacy risks posed by mobile phone location data, researchers need ways to use mobility data while offering stronger privacy guarantees to the individuals included in this data. A promising approach to this challenge is the creation of privacy-preserving mobility insights from decentralized location data using Local Differential Privacy (LDP). However, mobility data generated with LDP, based on the introduction of noise by individual mobile devices, is limited by the volume of noise required to achieve individual privacy. In this paper, we provide a fully reproducible model of the accuracy of mobility networks generated with LDP compared to mobility network data generated with more traditional privacy mechanisms: Central Differential Privacy (CDP) and K-anonymity. Using a simulated mobile phone mobility dataset informed by real-world travel patterns in the USA, we explore the trade-off between privacy and data utility provided by different parameters in a federated system with LDP. We also explore the impact of spatial and temporal aggregation on data accuracy, showing that long-standing considerations regarding the appropriate units of analysis for geographic data play a key role in determining the utility of federated mobility data with LDP. Our paper facilitates an in-depth understanding of the trade-offs between privacy and data utility entailed by the future adoption of a federated approach which uses LDP to generate insights from decentralized mobility data.

Water under nanoscale confinement is central to biological function, catalysis, and soft materials, yet how geometry dictates its structure and dynamics remains unresolved. Here, we establish a direct link between interfacial curvature and confined water behavior using an archaeal-inspired phytantriol-water lipidic mesophase platform. By systematically tuning curvature across lamellar, double-gyroid cubic, and reverse micellar phases, and integrating structural, thermodynamic, and ultrafast spectroscopies, we show that geometry controls the dimensionality and mobility of the hydrogen-bond network. Planar interfaces enforce 2D networks that slow down interfacial water through spatial constrain, whereas curved bicontinuous and micellar topologies promote 3D networks with accelerated reorientation. These findings reveal a geometric principle for governing water dynamics in soft nanoconfinement, providing molecular level design rules for confined transport and reactivity in membranes and functional materials.

High performance requirements in today's data-rich environments, such as stores with many sensors and systems that make quick decisions, cannot be fulfilled by old designs that treat connectivity as just a basic infrastructure element focused on being available and cheap. Smart and adaptable in-store technologies, such as Internet of Things devices that monitor the physical store environment, are required to support the digital nervous system. AI processes the raw telemetry, creating predictive models and deriving control actions. Fifth-generation wireless network technology offers deterministic performance, smooth mobility, and logical network isolation through what is called network slicing. These technologies need to converge into integrated federated architectures. Observability is fragmented, control models are centralized, and latency is inconsistent, leading to operational challenges, especially at scale. This article describes foundational attributes of architectures that embed intelligence, essential governance, and resilience into the underlying network infrastructure. Layers organize these attributes, which include physical sensing, differentiated transport, edge intelligence, distributed control, and governance. Further metrics include effective autonomous responses, predictability of latency across the network, and the reduction of operational load. A further aim is to form slf-controlled networks in retail with governance and ethics in mind.

Vehicular Ad Hoc Networks (VANETs) play a crucial role in enabling intelligent transportation systems by supporting real-time vehicle-to-vehicle and vehicle-to-infrastructure communications. However, high node mobility, frequent topology changes, and intermittent connectivity significantly affect communication reliability and network performance. To address these challenges, this paper proposes an intelligent relay vehicle optimization approach aimed at enhancing mobility management in VANET environments. The proposed scheme dynamically selects optimal relay vehicles based on key parameters such as vehicle mobility patterns, relative speed, link stability, and network connectivity conditions. By intelligently adapting to rapidly changing vehicular scenarios, the approach improves data forwarding efficiency, reduces packet loss, and enhances overall communication reliability. Simulation-based performance evaluation demonstrates that the proposed method outperforms conventional relay selection techniques in terms of packet delivery ratio, end-to-end delay, and network throughput. The results indicate that intelligent relay vehicle optimization is an effective solution for robust and efficient mobility management in VANETs, particularly in high-speed and dense traffic conditions.

Social relationships are central to shaping international migration patterns, yet the link between widescale network structure and mobility decisions remains poorly understood. Here, we investigate two key mechanisms by which social networks influence migration behavior: transmission of information and resources, and comparison of social status. These mechanisms suggest distinct sets of alters that an ego may emulate with respect to their migration behaviors, resulting in divergent mobility trajectories within and across communities. Leveraging longitudinal data from 73 Honduran villages ([Formula: see text] individuals) over six years, we use a Linear Network Autocorrelation Modeling framework to disentangle the effects of kinship, friendship, and economic ties on international migration decisions. Our findings reveal that incorporating social network factors as predictors significantly improves model fit. While indicators for resource-sharing processes substantially contribute to model performance, the inclusion of structural comparison mechanisms does not provide additional explanatory power. These results underscore the critical role of information and resource transmission within social networks in facilitating migration behaviors.

The explosive nature of the mobile learning services demands very high expectation of the next-generation wireless communication systems in regard to reliability, latency and scalability. Traditional wireless systems (Traditional wireless architecture, 2010) are mostly made with best-effort data delivery, and in a dynamic network environment and mobility of users, they tend to increasingly fail to maintain successful learning sessions. The paper is a next generation wireless architecture that is distinctly tailored to support effective mobile learning services by successful network and resource management stratum. This architecture is proposed to combine dynamic spectrum allocation, learning-aware traffic scheduling, and mobility-resilient sessions control to maintain a consistent level of service quality of various learning workloads. The overall performance assessment is carried out based on the essential measurements of a wireless network corresponding to such indexes as end-to-end latency, the ratio of the delivered packets, throughput, handover failure rate, and fairness. Experimental findings show that the proposed scheme greatly minimises the communication latency, increases the reliability of delivering packets and increases service stability as opposed to traditional resource management schemes, especially with high user density and dynamic mobility conditions. The results bring to light the applicability of network-centric architectural design in facilitating viable, scalable and responsive mobile learning services in next-generation wireless networks.

Vehicular ad hoc networks (VANET) are a traditional approach to providing minimal reliance on existing infrastructure, though they can experience high communication overhead and network disruptions. Vehicular micro clouds (VMCs) provide a promising solution to overcome the challenges of VANET by reducing communication latency through collaborative data allocation and data offloading. This paper offers a comparative performance analysis of VANET communications versus stationary and dynamic VMCs. Specifically, it studies incident management through speed and lane-changing advisories and freeway platooning. To further enhance the analysis, the performance of both communication architectures is evaluated using DSRC communication protocols versus cellular technologies (C-V2X, 4G LTE, and 5G NR). The system-level features, such as driving safety and vehicular mobility are measured to evaluate the efficacy of the communication systems under free-flow and incident-induced traffic conditions. The stationary cloud's latency and packet loss ratio are found to be 6.2% and 4.8% higher than those of the dynamic clouds, respectively. In addition, the stationary and dynamic cloud systems show advantages in reducing travel time delay, even at high penetration rates of the connected vehicles. The results suggest a shift towards more reliance on connected vehicular clouds to minimise the risks of message interference and system overload, whilst fostering intelligent freeway traffic management systems.

Frontal ring-opening metathesis polymerization (FROMP) enables rapid, energy-efficient access to high-performance thermosets and thermoplastics, but the range of accessible properties remains constrained by the rigidity of norbornene-type backbones. Here we introduce a side-chain plasticization strategy for FROMP, wherein norbornene esters bearing n-alkyl groups of varying length (n = 8, 12, 16) are copolymerized with dicyclopentadiene (DCPD) or hydrogenated DCPD (DCPD-H2). Systematic incorporation of these pendants tunes free volume, resulting in predictable reductions in glass transition temperature (Tg), decreased moduli, and a transition from rigidthermosets to elastomersexceeding 800% elongation at break. Free-volume analysis via dynamic mechanical analysis and solvent swelling ratios confirms pendant length and distribution as key parameters governing network porosity and mobility. Moreover, high-alkyl-content formulations exhibit nonlinear front propagation (spin modes) and strain-induced whitening-features that highlight opportunities for spatial patterning and cooperative molecular alignment under load. Collectively, these results establish side-chain engineering as a versatile design principle for expanding FROMP into elastomeric regimes, providing a scalable pathway to soft, tunable, and structurally programmable materials.

Abstract Efficient monitoring of the condition of road infrastructure is essential for the provision of a reliable and sustainable mobility and transportation network. The assessment of the structural condition of the asphalt base layer is particularly important in this respect. This paper presents a data-driven approach for an innovative road monitoring system for the non-destructive and continuous determination of the degree of degradation of asphalt roads. The innovation of the project lies in the application of Artificial Intelligence methods to derive the degradation state of the asphalt base layer on the basis of sensor measurements obtained by means of a novel hybrid sensor fabric integrated directly into the asphalt base layer. The proposed Machine Learning-based diagnosis relies heavily on the quality of sensor data. Therefore, we introduce a new method to evaluate the significance of sensor measurements using time series analysis techniques. The feasibility and functionality of the approach is demonstrated through extensive experiments by embedding the sensor material in real asphalt specimens, which are subject to controlled load tests.

Energy-efficient routing and predictive sink mobility in mobile wireless sensor networks using reflection equivariant quantum neural network and star fish optimization algorithms