Search Results (2,378)

This work presents a low-power photoplethysmography (PPG) readout integrated circuit (IC) that achieves a wide dynamic range (DR) through the direct integration of a voltage-controlled oscillator (VCO)-based quantizer into the photodiode driver. Conventional PPG readout circuits rely on either transimpedance amplifier (TIA) or light-to-digital converter (LDC) topologies, both of which require auxiliary DC suppression loops. These additional loops not only raise power consumption but also limit the achievable DR. The proposed design eliminates the need for such circuits by embedding a linear regulator with a mirroring scale calibrator and a time-domain quantizer. The quantizer provides first-order noise shaping, enabling accurate extraction of the AC PPG signal while the regulator directly handles the large DC current component. Post-layout simulations show that the proposed readout achieves a signal-to-noise-and-distortion ratio (SNDR) of 40.0 dB at 10 µA DC current while consuming only 0.80 µW from a 2.5 V supply. The circuit demonstrates excellent stability across process–voltage–temperature (PVT) corners and maintains high accuracy over a wide DC current range. These features, combined with a compact silicon area of 0.725 mm² using TSMC 250 nm bipolar–CMOS–DMOS (BCD) process, make the proposed IC an attractive candidate for next-generation wearable and biomedical sensing platforms. Full article

Computational fluid dynamics and machine learning (ML) models are employed to investigate the relationships between scaffold topology and key flow parameters, including permeability (k), average wall shear stress ($WS{S}_a$), and the 25th and 75th percentiles of WSS. Statistical analysis showed that $WS{S}_a$ values are consistent with those found in common scaffold architectures, while percentile-based WSS properties provided insight into shear environments relevant for bone and cartilage differentiation. No significant effect of pore shape was observed on k and $WS{S}_a$. Correlation analysis revealed that k was positively associated with topological features of the scaffold, whereas $WSS$ metrics were negatively correlated with these properties. ML models trained on six topological and flow inputs achieved a performance of $R^2$ above 0.9 for predicting k and $WS{S}_a$, demonstrating strong predictive capability based on the topology. Their performance decreased for $WS{S}_{25\%}$ and $WS{S}_{75\%}$, reflecting the difficulty in capturing more specific shear events. These findings highlight the potential of ML to guide scaffold design by linking topology to flow conditions critical for osteogenesis. Full article

The increasing integration of artificial intelligence agents (AIAs) based on large language models (LLMs) is transforming many spheres of society. These agents act as human assistants, forming Distributed Intelligent Systems (DISs) and engaging in opinion formation, consensus-building, and collective decision-making. However, complex DIS network topologies introduce significant uncertainty into these processes. We propose a quantum-inspired graph signal processing framework to model collective behavior in a DIS interacting with an external environment represented by an influence matrix (IM). System topology is captured using scale-free and Watts–Strogatz graphs. Two contrasting interaction regimes are considered. In the first case, the internal structure fully aligns with the external influence, as expressed by the commutativity between the adjacency matrix and the IM. Here, a renormalization-group-based scaling approach reveals minimal reservoir influence, characterized by full phase synchronization and coherent dynamics. In the second case, the IM includes heterogeneous negative (antagonistic) couplings that do not commute with the network, producing partial or complete spectral disorder. This disrupts phase coherence and may fragment opinions, except for the dominant collective (Perron) mode, which remains robust. Spectral entropy quantifies disorder and external influence. The proposed framework offers insights into designing LLM-participated DISs that can maintain coherence under environmental perturbations. Full article

The resilience and sustainable development of modern power distribution systems faces escalating challenges due to increasing renewable integration and extreme events. Traditional single-system approaches often overlook the spatiotemporal coordination of cross-domain restoration resources. In this paper, we propose a multi-stage resilience enhancement method that employs transportation and hydrogen energy systems. This approach coordinates the pre-event preventive allocation and multi-stage collaborative scheduling of diverse restoration resources, including remote-controlled switches (RCSs), mobile hydrogen emergency resources (MHERs), and hydrogen production and refueling stations (HPRSs). The proposed framework supports cross-stage dynamic optimization scheduling, enabling the development of adaptive resource dispatch strategies tailored to the characteristics of different stages, including prevention, fault isolation, and service restoration. The model is applicable to complex scenarios involving dynamically changing network topologies and is formulated as a mixed-integer linear programming (MILP) problem. Case studies based on the IEEE 33-bus system show that the proposed method can restore a distribution system’s resilience to approximately 87% of its normal level following extreme events. Full article

Wireless sensor networks (WSNs) are key enablers of efficient communication in the Internet of Things (IoT) ecosystem. These networks comprise numerous sensor nodes that collaboratively collect and transmit data, requiring adaptive and energy-efficient management. However, high node density and resource limitations introduce challenges such as control overhead, packet collisions, interference, and energy inefficiency. To mitigate these issues, this paper adopts the Hybrid Wireless Mesh Protocol (HWMP), standardized under IEEE 802.11s for wireless mesh networks (WMNs), as the routing protocol in WSNs. HWMP’s hybrid design combining reactive and proactive routing is well-suited for dynamic and mobile environments, making it applicable to WSNs operating under similar conditions. Building on this foundation, we propose a community-aware topology control mechanism that constructs a Connected Dominating Set (CDS) to serve as the network’s energy-efficient backbone. Node selection is guided by centrality metrics and detected community structures to enhance routing efficiency and network longevity. The mechanism is evaluated across six mobility scenarios characterized by realistic movement patterns. Comparative results show that incorporating community structure significantly improves routing performance and reduces energy consumption, validating the approach’s effectiveness in real-world WSN deployments. Full article

Real-time distribution system state estimation is hampered by limited observability, frequent topology changes, and measurement errors. Neural networks can capture the nonlinear characteristics of power-grid operation through a data-driven approach that possesses important theoretical value and is promising for engineering applications. In that context, we develop a deep learning framework that leverages General Attributed Multiplex Heterogeneous Network Embedding to explicitly encode the multiplex, heterogeneous structure of distribution networks and to support inductive learning that adapts to dynamic topology. A cross-modal attention mechanism further models fine-grained interactions between input measurements and node/edge attributes, enabling the capture of nonlinear correlations essential for accurate state estimation. To ensure physical feasibility, soft power-flow residuals are incorporated into training as a physics-constrained regularization, guiding predictions toward consistency with grid operation. Extensive studies on IEEE/CIGRE 14-, 70-, and 179-bus systems show that the proposed method surpasses conventional weighted least squares and representative neural baselines in accuracy, convergence speed, and computational efficiency while exhibiting strong robustness to measurement noise and topological uncertainty. Full article

This study provides an in-depth analysis of the learning dynamics of multichannel optical neurons based on spatial solitons generated in lithium niobate crystals. Single-node and multi-node configurations with different topological complexities (3 × 3, 4 × 4, and 5 × 5) were compared, assessing how the number of channels, geometry, and optical parameters affect the speed and efficiency of learning. The simulations indicate that single-node neurons achieve the desired imbalance more rapidly and with lower energy expenditure, whereas multi-node structures require higher intensities and longer timescales, yet yield a greater variety of responses, more accurately reproducing the functional diversity of biological neural tissues. The results highlight how the plasticity of these devices can be entirely modulated through optical parameters, paving the way for fully optical photonic neuromorphic networks in which memory and computation are co-localized, with potential applications in on-chip learning, adaptive routing, and distributed decision-making. Full article

Identifying and ranking influential nodes is central to tasks such as targeted immunization, misinformation containment, and resilient design. Structural entropy (SE) offers a principled, community-aware scoring rule, yet the one-shot (static) use of SE may become suboptimal after each intervention, as the residual topology and its modular structure change. We introduce iterative structural entropy (ISE), a simple yet powerful modification that recomputes SE on the residual graph before every removal, thus turning node targeting into a sequential, feedback-driven policy. We evaluate SE and ISE on seven benchmark networks using (i) cumulative structural entropy (CSE), (ii) cumulative sum of largest connected component sizes (LCCs), and (iii) dynamic panels that track average shortest-path length and diameter within the residual LCC together with a near-threshold percolation proxy (expected outbreak size). Across datasets, ISE consistently fragments earlier and more decisively than SE; on the Netscience network, ISE reduces the cumulative LCC size by $43\%$ (RLCCs $=0.567$). In parallel, ISE achieves perfect discriminability (monotonicity $M=1.0$) among positively scored nodes on all benchmarks, while SE and degree-based baselines display method-dependent ties. These results support ISE as a practical, adaptive alternative to static SE when sequential decisions matter, delivering sharper rankings and faster structural degradation under identical measurement protocols. Full article

Current cloud–edge collaboration collaboration architectures face challenges in security resource scheduling due to their mostly static nature, which cannot keep up with real-time attack patterns and dynamic security needs. To address this, this paper proposes a dynamic scheduling method using Deep Reinforcement Learning (DQN) and SRv6 technology. The method establishes a multi-dimensional feature space by collecting network threat indicators and security resource states; constructs a dynamic decision-making model with DQN to optimize scheduling strategies online by encoding security requirements, resource constraints, and network topology into a Markov Decision Process; and enables flexible security service chaining through SRv6 for precise policy implementation. Experimental results demonstrate that this approach significantly reduces security service deployment delays (by up to 56.8%), enhances resource utilization, and effectively balances the security load between edge and cloud. Full article

This review summarizes recent advances in multifunctional metamaterials (MF-MMs) for electromagnetic (EM) wave absorption. MF-MMs overcome the key limitations of conventional absorbers—such as narrow bandwidth, limited functionality, and poor environmental adaptability—offering enhanced protection against EM security threats in radar, aerospace, and defense applications. This review focuses on an integrated structure-material-function co-design strategy, highlighting advances in three-dimensional (3D) lattice architectures, composite laminates, conformal geometries, bio-inspired topologies, and metasurfaces. When synergized with multicomponent composites, these structural innovations enable the co-regulation of impedance matching and EM loss mechanisms (dielectric, magnetic, and resistive dissipation), thereby achieving broadband absorption and enhanced multifunctionality. Key findings demonstrate that 3D lattice structures enhance mechanical load-bearing capacity by up to 935% while enabling low-frequency broadband absorption. Composite laminates achieve breakthroughs in ultra-broadband coverage (1.26–40 GHz), subwavelength thickness (<5 mm), and high flexural strength (>23 MPa). Bio-inspired topologies provide wide-incident-angle absorption with bandwidths up to 31.64 GHz. Metasurfaces facilitate multiphysics functional integration. Despite the significant potential of MF-MMs in resolving broadband stealth and multifunctional synergy challenges via EM wave absorption, their practical application is constrained by several limitations: limited dynamic tunability, incomplete multiphysics coupling mechanisms, insufficient adaptability to extreme environments, and difficulties in scalable manufacturing and reliability assurance. Future research should prioritize intelligent dynamic response, deeper integration of multiphysics functionalities, and performance optimization under extreme conditions. Full article

This paper presents dual-layer frequency selective surfaces (FSSs) with frequency division control function through an integrated tunable transmission window at a lower frequency and an absorption performance at a higher frequency. The bottom frequency selective surface (FSS) layer, configured as a bandpass structure, incorporates a gradient gap square-ring element loaded with varactor diodes. This configuration enables dynamic tuning of the L-band transmission window from 1.26 GHz to 1.9 GHz via varactor capacitance modulation. Simultaneously, the top FSS layer utilizes a square-ring-cross-slot topology. Leveraging the strong reflection characteristic of the bottom FSS at higher frequencies in conjunction with dielectric loss mechanisms, the structure achieves absorption performance within the 5.56 GHz to 5.72 GHz band. Measurement results indicate insertion loss at operational frequencies within the transmission window remains below 1.41 dB, while the absorption peak reaches approximately −30 dB. Close agreement between simulated and measured results validates the proposed design. Full article

Wireless sensor networks (WSNs) are deployed in terrestrial environments. However, on the sea surface, sensor nodes can drift due to ocean currents and wind; thus, network topologies continuously evolve, and the communication between nodes is frequently disrupted. These unstable connections significantly degrade data transmission stability and overall network performance. These problems are particularly significant in maritime regions where the sea state changes rapidly, thus imposing stringent technical requirements on the design of long-range, reliable, low-latency, and persistent sensing systems. This study proposes a wireless sensor network architecture for sea surface drifting nodes, which is termed Drift-Aware Routing and Clustering with Recovery (DARCR). The proposed system consists of three major components: (1) an enhanced dynamic drift model that more accurately predicts node movement for realistic ocean conditions; (2) a cluster-based framework that prevents disconnection and minimizes delay, which improves cluster stability and adaptability to dynamic environments through refined clustering and route setup mechanisms; and (3) a self-recovery routing strategy for re-establishing communication after disconnection. The proposed method is evaluated using ocean current data from the Copernicus Ocean Data Center simulating a 60-h drifting scenario around the central Taiwan Strait. The experimental results show that the average hourly disconnection rate is maintained at 6.2%, with a variance of 0.31%, and the transmission of newly sensed data is completed within 3 to 5 s, with a maximum delay of approximately 10 s. These findings demonstrate the feasibility of maintaining communication stability and low-latency data transmission for sea surface WSNs that operate in highly dynamic marine conditions. Full article

In the post-disaster recovery of power distribution networks (PDNs), electric vehicles (EVs) possess a great potential as mobile energy storage units. When supported by vehicle-to-grid (V2G)-enabled charging stations, EVs can provide effective supplementary power for disaster-stricken areas. However, most existing stations only support unidirectional charging, limiting the resilience-enhancing potential of V2G. To address this gap, this paper proposes a resilience-oriented restoration optimization model that jointly considers the siting of V2G-enabled charging stations and PDN topology reconfiguration. A novel metric—Resilience Entropy—is introduced to dynamically characterize the recovery process. The model explicitly describes fault propagation and circuit breaker operations, while incorporating power flow and radial topology constraints to ensure secure operation. EV behavioral uncertainty is also considered to enhance model adaptability under real-world post-disaster conditions. The optimal siting scheme is obtained by solving the proposed model. Case studies demonstrate the model’s effectiveness in improving post-disaster supply and recovery efficiency, and analyze the impact of user participation willingness on V2G-based restoration. Full article

With the maturation of reusable launch vehicle technology and satellite mass-production capabilities, global mega-constellation projects have entered a phase of rapid expansion. Inter-satellite networking is a key approach for enhancing constellation performance, as it crucially impacts overall constellation effectiveness. However, existing studies mostly focus on the network layer protocol optimization, with insufficient attention to topological structure design, and fail to fully consider the engineering challenges associated with inter-orbit Inter-Satellite Links (ISLs). To address these issues, this paper proposes a heterogeneous ISL topology architecture for mega-constellations, centered on “stable high-speed laser backbone connection within intra-orbit planes + dynamic and flexible radio network between inter-orbit planes”. First, we clarify the optimization objectives for mega-constellation topological design under this architecture and theoretically prove that the optimization problem is NP-hard. Building on this, we introduce Topological Structure Units (TSUs) and employ a unit reuse strategy to simplify topological design. Furthermore, we propose a TSU-based heterogeneous ISL topological design algorithm. Considering the uneven satellite distribution across latitude zones within the constellation, we further propose a regional TSU-based topological design algorithm. Finally, through simulation experiments in Starlink and GW constellation scenarios, we conduct multi-dimensional verification to demonstrate the effectiveness of the proposed algorithms in reducing end-to-end delay and decreasing ISL hops. Full article

Urban morphology has long been studied through typologies, spatial configurations, and historical change, yet cities are not static artifacts but dynamic environments continually reshaped by people, infrastructures, and politics. This article brings Actor–Network Theory (ANT) into dialogue with Aldo Rossi’s notion of the locus to rethink urban design as both enduring form and relational process. Building on Manuel Lima’s taxonomy, the study develops a methodological workflow that translates street networks into visualizations, pairing embeddings with topographic maps to highlight structural patterns. Applied to a comparative set of cities, the analysis distinguishes three broad morphological tendencies—archetypal, geometrical, and relational—each reflecting different logics of urban organization. The results show how scale and connectivity condition the interpretability of embeddings, revealing both alignments and divergences between cartographic and topological representations. Beyond empirical findings, the article frames network literacy as a meeting ground for design theory, science and technology studies, and information visualization. It concludes by proposing that advancing urban morphology today requires not only new computational tools but also sustained interdisciplinary collaboration across design, urban studies, and data science. Full article