Voronoi treemaps are used to depict nodes and their hierarchical relationships simultaneously. However, in addition to the hierarchical structure, data attributes, such as co-occurring features or similarities, frequently exist. Examples include geographical attributes like shared borders between countries or contextualized semantic information such as embedding vectors derived from large language models. In this work, we introduce a Voronoi treemap algorithm that leverages data similarity to generate neighborhood-preserving treemaps. First, we extend the treemap layout pipeline to consider similarity during data preprocessing. We then use a Kuhn-Munkres matching of similarities to centroidal Voronoi tessellation (CVT) cells to create initial Voronoi diagrams with equal cell sizes for each level. Greedy swapping is used to improve the neighborhoods of cells to match the data's similarity further. During optimization, cell areas are iteratively adjusted to their respective sizes while preserving the existing neighborhoods. We demonstrate the practicality of our approach through multiple real-world examples drawn from infographics and linguistics. To quantitatively assess the resulting treemaps, we employ treemap metrics and measure neighborhood preservation.

Energy acquisition from visible light communication (VLC) signals has recently received research focus in hybrid radio frequency (RF)/VLC systems. In this article, we study the effect of energy acquisition on the achievable sum-rate (ASR) of these systems. The joint uplink-downlink ASR maximization in a time-switching energy acquisitionbased hybrid RF/VLC system is investigated. We obtain fundamental results on the optimal time for performing energy acquisition. Furthermore, strict bounds on ASR pertaining to the energy acquisition conditions have also been obtained. Through simulations, it is observed that the optimal time coefficient obtained in this work outperforms the existing energy acquisition schemes by 40 Mbps in the ASR.

Rapid localization of unknown gas leak sources in urban areas is critical for effective emergency response and impact mitigation. While deploying autonomous robots to assess and localize emission sources has proven effective, current approaches are inadequate in a large-scale, constrained area. To address this, we propose a Goal-oriented multi-Robot collAborative Source Search (GRASS) framework for large-scale constrained environments. This framework employs a three-step coupled strategy-goal determination, allocation, and execution-leveraging the fusion of posterior probabilities and hybrid sensed information to achieve efficient and reliable gas source localization. Specifically, goal determination module defines two distinct goals to dynamically balance exploration (reducing estimation uncertainty through systematic coverage) and exploitation (directing robots toward estimated source locations via Gaussian Mixture Models). Moreover, goal allocation module adapts to source estimation reliability and local context, enabling robots to prioritize exploration during sparse data periods and shift to exploitation as estimations improve. In terms of goal execution, it resolves conflicts between goal pursuit and collision avoidance through designing adaptive path planning mechanisms that integrates a modified A* algorithm with a contour-tracing method. Finally, extensive simulations demonstrate that GRASS significantly outperforms two baseline methods, achieving higher success rates (increasing at least 9%) and requiring less search time (reducing at least 215.95 s) in various settings. These advantages are also confirmed by a real-world case study. Our work advances information fusion-driven environmental monitoring for resilient cities by providing an autonomous solution for source localization in complex urban environments.

The ERC4907 standard enables rentable Non-Fungible Tokens (NFTs) but is limited to single-user, single-time-slot authorization, which severely limits its applicability and efficiency in decentralized multi-slot scheduling scenarios. To address this limitation, this paper proposes Multi-slot ERC4907 (M-ERC4907) extension method. The M-ERC4907 method introduces novel functionalities to support the batch configuration of multiple time slots and simultaneous authorization of multiple users, thereby effectively eliminating the rigid sequential authorization constraint of ERC4907. The experiment was conducted on the Remix development platform. Experimental results show that the M-ERC4907 method significantly reduces on-chain transactions and overall Gas consumption, leading to enhanced scalability and resource allocation efficiency.

Distributed systems in which concurrent proposals are mutually exclusive face a fundamental stability constraint under network delay. In open systems where global state progression is event-driven rather than round-driven, propagation delay creates a conflict window within which overlapping proposals may generate competing branches. This paper derives a density-delay law for such exclusive state progression processes. Under independent proposal arrivals and bounded propagation delay, overlap is approximated by a Poisson model and fork depth is represented by a birth-death process. The analysis shows that maintaining bounded fork depth as the number of participants grows requires the density-delay product λΔ to remain O(1), implying that aggregate proposal intensity must stay bounded and yielding an inverse-scaling law g(N)=O(1/N) at the unit level. Simulation experiments across varying network sizes and propagation delays align with a common density-delay curve, supporting the predicted scaling behavior. The result provides a compact law for stable event-driven state progression in open distributed systems and offers a scaling-based interpretation of Bitcoin-style difficulty adjustment as a decentralized way to regulate effective event density.

Beyond diagonal reconfigurable intelligent surface(BD-RIS) offers promising potentials for improving secrecy performance in wireless environments, due to its additional degree of freedom and higher flexibility in manipulating wireless channels compared with its conventional counterpart. However, a fully connected BD-RIS requires a considerable amount of hardware components and incurs a significant increase in circuit complexity. In this paper we investigate Physical Layer Security(PLS) performance in a multi-input-single-output(MISO) system with the aid of a tree-connected BD-RIS, whose tunable admittance components are connected to form a tree topology. The ensuing joint optimization of the transmit covariance matrix and RIS’s scattering matrix is nonconvex. Additionally, the tree topology maps the scattering matrix to a symmetrical and tridiagonal susceptance matrix. To solve this problem we decompose it into two subproblems, and then we employ the Dinkelbach method and convex approximation to handle nonconvexities in the problem formulation, and propose a Riemannian conjugate gradient algorithm to ensure that symmetry and tridiagonality are maintained throughout the iterative process. Numerical results demonstrate that our proposed scheme achieves secrecy performance comparable to the fully connected BD-RIS while significantly reducing circuit complexity.

Dynamic Spectrum Sharing can enhance spectrum resource utilization by promoting the dynamic distribution of spectrum resources. However, to effectively implement dynamic spectrum resource allocation, certain mechanisms are needed to incentivize primary users to proactively share their spectrum resources. This paper, based on the ERC404 standard and integrating Non-Fungible Token and Fungible Token technologies, proposes a spectrum securitization model to incentivize spectrum resource sharing and implements it on the Ethereum test net.

Traditional centralized scholarship evaluation processes typically require students to submit detailed academic records and qualification information, which exposes them to risks of data leakage and misuse, making it difficult to simultaneously ensure privacy protection and transparent auditability. To address these challenges, this paper proposes a scholarship evaluation system based on Decentralized Identity (DID) and Zero-Knowledge Proofs (ZKP). The system aggregates multidimensional ZKPs off-chain, and smart contracts verify compliance with evaluation criteria without revealing raw scores or computational details. Experimental results demonstrate that the proposed solution not only automates the evaluation efficiently but also maximally preserves student privacy and data integrity, offering a practical and trustworthy technical paradigm for higher education scholarship programs.

Embedded phase-change random-access memory (ePCRAM) applications demand superior data retention in amorphous phase-change materials (PCMs). Traditional PCM design strategies have focused on enhancing the thermal stability of the amorphous phase, often at the expense of the crystallization speed. While this approach supports reliable microchip operations, it compromises the ability to achieve rapid responses. To address this limitation, we modified ultrafast-crystallizing Sb thin films by incorporating Sc dopants, achieving the highest 10-year retention temperature (∼175°C) among binary antimonide PCMs while maintaining a sub-10-ns SET operation speed. This reconciliation of two seemingly contradictory properties arises from the unique kinetic features of the 5-nm-thick Sc12Sb88 films, which exhibit an enlarged fragile-to-strong crossover in viscosity at medium supercooled temperature zones and an incompatible sublattice ordering behavior between the Sc and Sb atoms. By tailoring the crystallization kinetics of PCMs through strategic doping and nanoscale confinement, we provide new opportunities for developing robust yet swift ePCRAMs.

With the growing need for extensive data storage, enhancing the storage density of nonvolatile memory technologies presents a significant challenge for commercial applications. This study explores the use of monatomic antimony (Sb) in multi-level phase-change storage, leveraging its thickness-dependent crystallization behavior. We optimized nanoscale Sb films capped with a 4-nm SiO2 layer, which exhibit excellent amorphous thermal stability. The crystallization temperature ranges from 165 to 245 degrees C as the film thickness decreases from 5 to 3 nm. These optimized films were then assembled into a multilayer structure to achieve multi-level phase-change storage. A typical multilayer film consisting of three Sb layers was fabricated as phase-change random access memory (PCRAM), demonstrating four distinct resistance states with a large on/off ratio (similar to 10(2)) and significant variation in operation voltage (similar to 0.5 V). This rapid, reversible, and low-energy multi-level storage was achieved using an electrical pulse as short as 20 ns at low voltages of 1.0, 2.1, 3.0, and 3.6 V for the first, second, and third SET operation, and RESET operation, respectively. The multi-level storage capability, enabled by segregation-free Sb with enhanced thermal stability through nano-confinement effects, offers a promising pathway toward high-density PCRAM suitable for large-scale neuromorphic computing.

This study investigates a physical layer security-based Unmanned Aerial Vehicle(UAV) communication system aimed at protecting confidential signals from being intercepted by eavesdroppers. The system utilizes dual UAVs, where one UAV serves as an aerial base station for communication with ground users, and the other UAV acts as an interference device to deceive eavesdroppers. In the presence of multiple eavesdroppers and legitimate users, a deep reinforcement learning (DRL) approach called Deep Q-Networks(DQN) is proposed. It jointly optimizes the trajectory of the UAV, the transmission power of the UAV transmitter, and user scheduling to maximize the confidentiality performance in the presence of multiple eavesdroppers. For high-dimensional and continuous state space in the training process, DQN effectively improves its convergence performance in offline learning by implementing experiential replay and target network technology. Simulation results demonstrate that compared to the other two baseline methods, this approach achieves faster convergence speed and better secrecy rate performance.

A campus public resources (CPRs) sharing model is proposed by applying Decentralized Identifiers (DIDs) and Non-Fungible Tokens (NFTs) in smart campus. The integration of DIDs empowers users with enhanced autonomy and control over their digital identities, providing improved security and convenience while extending resource accessibility to off-campus individuals. Furthermore, the model employs the NFT standard of ERC4907 to achieve a clear separation between resource ownership and usage rights. Leveraging the inherent distributed and transparent nature of blockchain technology, the CPRs sharing model effectively eliminates data barriers between departments, fostering a collaborative environment. A high-performance platform based on the model is developed to meet the requirements of sharing CPRs within a university setting.

The rapid integration of Unmanned Aerial Vehicle(UAV) with existing network infrastructure brings enormous benefits to users, however it also introduces vulnerabilities to information security. In this paper we investigate a UAV-aided secure communication system, where a UAV is deployed to transmit confidential information to a ground user. Specifically, a mobile eavesdropper is moving in the vicinity of the ground user, attempting to intercept legitimate data transmissions. Its unpredictable trajectory poses as an extra security risk to UAV data transmission. Driven by this security challenge, we aim to optimize the UAV trajectory to maximize user average secrecy rate. Due to the fast changing environment caused by unpredictable movement of the eavesdropper, this nonconvex optimization problem is difficult to solve. Instead, we propose an online algorithm leveraging the Q-learning framework to deliver online decisions on UAV trajectory. With the help of carefully designed reward signals, the agent is able to learn an effective policy with desirable learning outcomes. Numerical results validate the effectiveness of the proposed algorithm, and shed light on learning outcomes with a variety of learning parameters.

The precise characterization and modeling of Cyber-Physical-Social Systems (CPSS) requires more comprehensive and accurate data, which imposes heightened demands on intelligent sensing capabilities. To address this issue, Crowdsensing Intelligence (CSI) has been proposed to collect data from CPSS by harnessing the collective intelligence of a diverse workforce. Our first and second Distributed/Decentralized Hybrid Workshop on Crowdsensing Intelligence (DHW-CSI) have focused on principles and high-level processes of organizing and operating CSI, as well as the participants, methods, and stages involved in CSI. This perspective reports the outcomes of the latest DHW-CSI, focusing on Autonomous Crowdsensing (ACS) enabled by foundation intelligence and its associated technologies such as decentralized autonomous organizations and operations, large language models, and human-oriented operating systems. Specifically, we explain what ACS is and explore its distinctive features in comparison to traditional crowdsensing. Moreover, we present the “6A-goal” of ACS and propose potential avenues for future research.

Crystallization determines the programming speed of phase-change memory devices; while, the nucleation phenomenon of many phase-change materials (PCMs) is not entirely understood, especially concerning the atomic structures and dynamic behaviors of the subcritical nuclei. This is undoubtedly an insurmountable challenge for scandium antimony telluride (ScxSb2Te3) PCM as its subnanosecond-crystallization nature impedes the real-time observation of the transient nucleation process. To solve the puzzle, atomic probe tomography and transmission electron microscopy are employed to circumvent the technical difficulties; for the first time, the atomistic information of the heterogeneous nuclei in ScxSb2Te3 is unveiled, such as enriched Sc approximate to 25 at% in core composition, approximate to 1.0 nm in geometric size, and approximate to 1023-1024 m-3 in spatial density. The unique nanoscale chemical inhomogeneity ensures the unusual stabilities and dynamics of the early-stage nuclei, reinforcing them to survive the melt-quenching action and greatly suppressing the nucleating randomness, thereby facilitating simultaneous and prompt crystal growth throughout the amorphous phase to achieve ultrafast crystallization. The present study offers a new insight into the nonclassical pathways, which will improve understanding and promote better regulation of the nucleation phenomenon in functional materials. Nanometer-size Sc-rich domains with the approximate Sc1Sb1Te2 composition act as intrinsic and robust heterogeneous nuclei in the amorphous ScxSb2Te3 phase, greatly suppressing nucleation randomness; and thereby, facilitating subnanosecond ultrafast crystallization.image

Given the growing complexity of decision-making scenarios, the singular and unalterable electronic voting approach depending on centralized authorities is no longer satisfying the requirements of practicality. To address these limitations and accommodate evolving decision-making scenarios, this work proposes an anonymous weighted voting system. The proposed system employs distributed Elgamal encryption to encrypt the voting weights. This encryption method possesses a homomorphic property that allows for vote counting in a ciphertext state, thereby ensuring computational privacy. Additionally, the system utilizes blind and ring signatures to enhance the protection of voter identity, simultaneously ensuring the authenticity and legitimacy of the votes. The integration of blockchain smart contract technology automates the vote counting process and promotes transparency, eliminating the need for centralized vote counting agencies. Through analysis and experimentation, the proposed voting system is proved to be both feasible and secure.

Integrating unmanned aerial vehicles(UAVs) with wireless systems is widely anticipated to bring enormous benefits to network users, and is considered as a key enabling technology for next generation wireless networks. However deployment of UAVs raises security concerns, as they are prone to eavesdropping attacks. In this paper we investigate secure data transmissions in a UAV-aided communication system from the physical layer security(PLS) perspective. The UAV is employed as a flying base station and transmit confidential information to a ground user. Specifically, an eavesdropper is moving in the vicinity of the ground user, attempting to intercept legitimate data transmission. We aim to optimize UAV trajectory to maximize secrecy throughput and due to the fast changing environment with unpredictable movements of the eave, this nonconvex problem is difficult to solve. Instead, we propose a deep reinforcement learning framework by reformulating this problem as a Markov Decision Process(MDP). By carefully designing state spaces,state-dependent action sets and time-dependant reward signals, we aim to help the agent learn an optimized trajectory and complete the flight task within time limit. We adapt the deep Q network(DQN) algorithm and extend it with prioritized experience replay(PER) and importance sampling method to ensure a stable and effective learning outcome. Simulation results validate the effectiveness of the learning algorithm and confirm that the agent is able to learn an effective policy with desirable and robust learning outcomes. Compared with benchmark schemes, our proposed scheme is shown to achieve considerable performance gain in secrecy throughput.

The scandium doped antimony tellurides (ScxSb2Te3), as promising phase-change memory materials, possess the merits of ultrafast crystallization speed and ultralow resistance drift, of the amorphous phases, ensuring the development of cache-type universal memory and high-accuracy computing chip. There is keenness to further explore the annealing effect in the crystalline ScxSb2Te3 phases to seek a potential metal–insulator transition (MIT) in electrical conduction, by which more intermediate resistance states of superior stability can be generated to enhance the programming contrast and accuracy. In this work, we have identified the metastable rock salt ScxSb2Te3 as an Anderson-type insulator and verified that the MIT occurs in its stable rhombohedral grains when lattice vacancies are highly ordered into the van der Waals-like gaps. The Sc dopant can exert profound influence on retarding the vacancy-ordering procedure, even completely prohibiting the MIT for the Sc-rich compounds. Our work suggests that tuning Sc content in ScxSb2Te3 alloys provides a simple route to engineer the material microstructures and electrical properties for the desired memory and computing performances.

A hybrid radio frequency (RF) and light fidelity (LiFi) network combines the strengths of RF and LiFi technologies. RF offers broad coverage, while LiFi provides high data rates. As these technologies operate on non-interfering spectra, they can co-exist without interfering with each other. This setup not only boosts data rate but also makes the network more reliable, especially when physical obstacles might block signals. However, resource management in hybrid RF/LiFi networks is challenging because of the dynamic environment and the different characteristics of the two technologies. Efficient resource allocation maximizes the data rate in these networks. In this paper, we introduce a model-free deep reinforcement learning (DRL) approach to solve the resource allocation problem in hybrid RF/LiFi networks. Our DRL model is designed to handle real-world conditions, considering factors like blockages and user mobility. Unlike traditional methods that need extensive modeling and assumptions, our approach learns directly from interacting with the environment, making it highly adaptable and robust. Through simulations, it is observed that our method improves resource utilization and overall network performance, achieving a 62.8% increase in sum rate and a 42.8% improvement in optimal transmit power compared to conventional methods.