Search Results (10,746)

The large-scale integration of renewable energy causes grid strength to vary over a wide range, exposing grid-following (GFL) inverters to both mid- and high-frequency resonance and subsynchronous oscillation (SSO). Conventional fixed-parameter designs cannot simultaneously maintain stability and dynamic performance because the phase-locked loop (PLL) and grid-voltage feedforward (GVF) dominate different frequency bands. This paper therefore proposes a backpropagation-neural-network (BPNN)-based adaptive parameter control strategy with frequency-band-coordinated regulation. First, a q-axis small-signal output-admittance model incorporating the current loop, digital delay, PLL, and GVF is established. The model reveals that the GVF coefficient primarily shapes mid- and high-frequency admittance under strong and moderately weak grids, whereas the PLL bandwidth becomes the dominant factor in low-frequency and subsynchronous stability under ultra-weak grids. Based on this mechanism, a BPNN is constructed with the grid short-circuit ratio (SCR) as the input and the GVF coefficient and PLL bandwidth as the outputs. Training targets are generated offline using parameter sweeps and performance screening based on current total harmonic distortion, Point of Common Coupling (PCC) voltage error, and settling time. During operation, the GVF coefficient is adjusted first, and the PLL bandwidth is reduced only when the grid becomes ultra-weak. Simulation results over $\mathrm{SCR}=1.25$–10 demonstrate that the proposed strategy preserves stable operation while providing better transient and harmonic performance than fixed-parameter and single-parameter tuning schemes in the cases studied. Full article

Secure transmission and storage of color images remain challenging tasks due to strong inter-pixel correlations and high data volume. This work proposes a one-dimensional sine-tent-logistic-exponential map (STLEM) equipped with numerical boundary correction rules to mitigate finite-precision numerical degradation so as to enhance the unpredictability of chaos-driven cryptosystems. We benchmark STLEM against classic logistic, tent, and sine maps via Lyapunov exponents, autocorrelation, approximate entropy, permutation entropy, Lempel-Ziv complexity, and Kolmogorov–Sinai entropy. Bifurcation diagrams, the 0–1 test, and NIST statistical tests are further adopted to characterize its chaotic dynamics and randomness. Comparative results verify that STLEM achieves improved dynamical complexity and randomness performance. Built upon the proposed STLEM, this paper constructs a color-image encryption scheme that employs a 256-bit master key and two groups of chaotic parameters to produce key-related chaotic sequences. The cryptosystem integrates dynamic edge expansion, chaotic permutation, position-dependent adaptive DNA encoding, DNA-domain chained diffusion, and two successive row-column permutation phases. HMAC-SHA-256 is utilized to generate plaintext-aware initial conditions and perform ciphertext authentication prior to decryption. Experimental validations demonstrate complete plaintext recovery under valid secret inputs, while authentication rejects invalid keys and tampered ciphertexts. Ciphered images exhibit high information entropy, negligible adjacent-pixel correlations, and satisfactory number of pixel change rate (NPCR) and unified average changing intensity (UACI) metrics. Benefiting from a sufficiently large key space and $O\left(MN\log \right(MN\left)\right)$ computational complexity, the proposed scheme is resilient against brute-force attacks and well suited for secure color-image communication scenarios, rather than acting as a general-purpose replacement for standard block ciphers. Full article

This paper studies a coupled system of viscoelastic wave equations with frictional damping, cross-tempered fractional damping, viscoelastic memory, and logarithmic source nonlinearities. The fractional damping acts across the two components, so that the fractional feedback in each equation is generated by the velocity of the other component. To handle the memory and fractional terms, we introduce suitable history and diffusive variables and reformulate the problem as an evolution system in an extended energy space. Under appropriate assumptions on the relaxation kernels, fractional parameters, and nonlinear exponent, we establish the local well-posedness of mild and strong solutions using semigroup theory. We then use a potential-well argument to prove global existence for initial data in the stable set. Under an additional decay condition on the relaxation kernels, an appropriate Lyapunov functional is constructed to establish the exponential decay of the energy. Finally, numerical simulations based on a finite-difference scheme and a physics-informed neural network (PINN) are used to illustrate the predicted decay behavior. Full article

The integration of semantic communication with unmanned aerial vehicles (UAVs) and intelligent reflecting surfaces (IRSs) offers a promising approach for next-generation wireless systems. However, jointly optimizing semantic reliability, physical-layer security, and energy efficiency remains challenging. This paper investigates an IRS-assisted UAV semantic secure communication system in the presence of a potential eavesdropper, with the objective of maximizing semantic secure energy efficiency (SSEE). We first introduce a semantic similarity-based secure energy efficiency metric to capture the trade-off among transmission reliability, physical-layer security, and UAV energy consumption. The semantic symbol number, UAV trajectory, transmit power, and IRS phase shifts are jointly considered under mobility, secrecy, and energy constraints. To efficiently solve the resulting mixed discrete-continuous optimization problem, we develop a hierarchical optimization framework: the outer layer exhaustively searches over a finite set of candidate semantic symbol numbers, while the inner layer solves the continuous resource allocation problem using a heuristic-guided soft actor–critic (HG-SAC) algorithm. Simulation results show that the proposed framework achieves noticeable SSEE gains over several benchmark schemes. Full article

When smart tourism data are shared across scenic areas, hotels, transportation platforms, and regulatory authorities, existing access control schemes often struggle to simultaneously support batch authorization for data object sets, multi-authority collaboration, revocation-version binding, and low-latency online encryption. To address these challenges, this paper proposes a Threshold Distributed Batch Identity-Based Encryption scheme for object-level authorization of smart tourism data, termed TD-BIBE. The scheme jointly binds data object identities, batch labels, authorization periods, revocation versions, and authorization contexts to ciphertext access control. It employs Shamir secret sharing for threshold distributed key generation, preventing any single authorization authority from obtaining the complete master secret key. Set-polynomial digests and membership witnesses are used to support batch authorization over identity sets and object-level membership verification. A revocation-version binding mechanism restricts the validity scope of authorization materials, while an offline/online encryption structure reduces the online computational overhead of data providers. Formal security analyses are conducted in the random oracle model with respect to data confidentiality, object-level authorization, batch-label binding, revocation-version binding, resistance to unauthorized key-combination attacks, and threshold authorization security. Experimental results demonstrate that TD-BIBE is suitable for cross-departmental, multi-object, and on-demand data sharing in smart tourism scenarios. Full article

Fuel cell systems require high-efficiency DC–DC interfaces capable of regulating rapid voltage variations while respecting the operational constraints of proton-exchange membrane fuel cells (PEMFCs). The floating interleaved boost converter (FIBC) is a strong candidate for this purpose due to its reduced current ripple, improved power sharing, and lower component stress. The design of control strategies for FIBCs supplied by PEMFCs remains challenging because explicitly enforcing fuel cell operational constraints under fast converter dynamics is inherently difficult, particularly when detailed fuel cell models are unavailable or undesirable, as reflected in existing approaches such as classical linear regulators and single-model predictive schemes. Therefore, this paper proposes a multi-model finite control set model-based predictive control (MM-FCS-MPC) strategy for FIBC converters supplied by PEMFCs. The method employs multiple discrete prediction models with cost functions defined by the converter switching mode, integrates a fuel cell-aware reference-generation mechanism to ensure nominal and safe PEMFC operation by enforcing current and power constraints within the predictive framework, and enables fast, accurate output-voltage regulation. Detailed modelling of the FIBC, component sizing, and PEMFC characteristics is provided. Obtained results under load disturbances and reference variations validate the proposed control scheme, demonstrating improved transient dynamics, reduced steady-state error, and enhanced current-sharing performance. Obtained results under load disturbances and reference variations validate the proposed control scheme, demonstrating improved transient dynamics, reduced steady-state error, and enhanced current-sharing performance, with a rise time of approximately 4.4 ms, a $\pm 2\%$ settling time of 10.3 ms, a maximum overshoot of only 0.056%, and a phase delay of approximately 4.26${}^{\circ }$, compared with 9.6${}^{\circ }$ for the conventional PI voltage-tracking baseline. Full article

With the rapid development of the Internet of Medical Things (IoMT), wearable sensors and intelligent medical terminals continue to generate a large amount of sensitive medical data, which needs to be uploaded to the cloud platform for storage and sharing. However, IoMT devices usually have the characteristics of limited computing resources and limited energy, and it is difficult for traditional high-complexity encryption schemes to meet the requirements of security and efficiency. In addition, the existing Proxy Re-Encryption (PRE) scheme has the risk of authorization transfer, and it is difficult to achieve fine-grained and secure sharing of medical data. In response to the above problems, this paper proposes a lightweight non-pairing proxy re-encryption enhancement scheme SM2-PRE+ based on the SM2 algorithm, which is used for the secure sharing of IoMT medical sensing data. The scheme combines the SM2 elliptic curve cipher algorithm and the SM4 symmetric encryption algorithm to achieve data protection through a double-layer key structure, and it uses the re-encryption mechanism of message binding to enhance the authorization control ability. Compared with the traditional PRE scheme, the proposed scheme avoids bilinear pairing operations, reduces the computing overhead of resource-limited equipment, and supports collusion-resistant and authorization non-transferability. Security analysis shows that the scheme meets the security requirements of IND-CCA under the Random Oracle Model (ROM). Performance analysis results show that SM2-PRE+ has low computing overhead and storage burden, which is suitable for wearable medical devices, intelligent sensing terminals, and cloud-assisted IoMT data sharing scenarios. Full article

In 1970, Dana Scott proposed his highly influential “mathematical theory of computation” to define the relationship between the text of a program and what the program computes (or denotes)—the “semantics” of the program. Scott used a complete lattice based on the “information ordering”, with the bottom element representing undefined—a program failing to terminate normally, thus producing no information. The top element, however, was unused. Hence most subsequent applications of denotational semantics have used mathematical structures that avoid top elements. We suggest that the information ordering is relevant not only to semanticists but also to working programmers as a basis for determining if a program component or a computation is correct according to their intentions. We also suggest that a return to the use of complete lattices is called for, to broaden formal semantics and allow it to encompass programmer intentions. That is because often those intentions permit more than one runtime behaviour for a given input. In this paper we explore the connections between the information ordering, correctness of computations and programs, and debugging. We present a general theory and describe several instances where the intention for what our logic/functional code computes plus what it actually computes can be described by elements in a complete lattice. For correct code, the information order relates (1) what is intended and what is computed, (2) successive states of a computation, and (3) the left and right sides of program component definitions. For bugs, the information order is violated. The technical results are a reasonably straightforward extension to previous denotational semantics work, but the scheme aligns much better with practical programming and software tools. The theory extends both the theoretical basis and practical flexibility of declarative debugging and reasoning about partial correctness and gives an attractive mathematical framework that encompasses our intentions, our programs and what they compute. Full article

Research on low-carbon construction in power transmission and distribution (PTD) projects has mainly focused on carbon accounting and emission-reduction assessment, with limited consideration of construction method differences and shared resource constraints. This study proposes a multi-objective optimization method for resource-constrained construction scheduling considering prefabricated components. The project is decomposed into construction units and procedures, and candidate construction methods are defined by carbon emissions, cost, duration, prefabrication rate, and resource demand. The lower level generates representative schemes from procedure-level method combinations, while the upper-level mixed-integer linear programming (MILP) model determines construction-unit schemes, resource input levels, and start times under precedence and shared resource constraints. An ε-constraint method is used to obtain a carbon-prioritized scheme. A 110 kV PTD case study shows that, compared with the cast-in-place baseline, the carbon-prioritized scheme reduces carbon emissions and duration by 17.45% and 14.66%, respectively, while maintaining comparable cost. Compared with the minimum-carbon scheme, it increases carbon emissions by only 0.21%, but shortens duration by 10.00% and slightly reduces cost. The results demonstrate that the proposed method can shorten construction duration and reduce cost while keeping carbon emissions close to the minimum level. Full article

This paper introduces a shared gamma frailty Birnbaum–Saunders model for clustered lifetime data under progressive Type-II censoring. The frailty term acts on the scale parameter and accounts for unobserved variation among clusters. Likelihood-based and Bayesian formulations are developed, and posterior estimation and prediction are carried out using a Metropolis-within-Gibbs algorithm. A simulation study considers different shape parameters, frailty levels, numbers of clusters, and censoring schemes. The proposed method estimates the shape parameter accurately and gives generally satisfactory results for the frailty variance. Increasing the number of clusters improves estimation, and the frailty model gives lower prediction errors in almost all valid comparisons with the ordinary BS model. A real-data application to kidney catheter infection times illustrates the shared-frailty component of the proposed model. Full article

Person–Job Fit (PJF) serves as a core task of intelligent recruitment recommendation. However, existing graph-based PJF models rely on a fixed, single-path aggregation scheme, thereby failing to simultaneously capture global interaction statistics and local competency-matching signals from candidate–job bipartite graphs. To address this limitation, this work proposes Bi-path Graph Convolutional Neural Network with Gate Fusion (BiGCNG), a dual-path graph convolutional network with global learnable gate fusion, composed of three coordinated modules. First, the Shared Text Embedding Pre-processing Module (STEPM) generates unified node embeddings by fusing structured attributes and BERT contextual text features. Second, the Bi-path Graph Convolution Module (BiGCM) extracts multi-granularity graph representations via separate sum and max aggregation paths. Third, the lightweight Gate Fusion Module (GFM) balances two feature streams via a learnable global scalar gate. The model is optimized with regularized Bayesian Personalized Ranking (BPR) loss on highly sparse recruitment data (99.97% sparsity). BiGCNG is evaluated on the Zhilian dataset, a real-world Chinese recruitment dataset, and outperforms five mainstream baselines notably, increasing MRR@5 by 7.67% and NDCG@5 by 5.48% on the Candidate subset, 2.30% and 0.61% on the Job subset against the best baseline, respectively. Several visualizations and hyperparameter analysis jointly validate the effectiveness and robustness of dual-path propagation and gate fusion. This work provides an effective multi-granularity graph learning paradigm for intelligent talent recruitment matching. Full article

Digital transformation can improve supply chain sensing, but recovery depends on whether reliable information is converted into coordinated operational action. This study develops a simulation-based case study that integrates data-quality-adjusted indicator fusion, a six-dimensional resilience index, system dynamics, and phase-based constrained policy search. The case represents a component-intensive discrete-manufacturing network with 28 tier-1 suppliers, nine qualified alternatives, seven logistics nodes, and five product families. A reproducible 36-month supplier-product panel (5040 unit-month records) is generated from a fixed seed and an explicit machine-readable configuration; it is not presented as confidential company data. Completeness, timeliness, cross-source consistency, and out-of-sample predictive contribution are defined explicitly, and an event-preserving gate prevents reliability shrinkage from attenuating logged disruption signals. Twelve indicators measure robustness, redundancy, agility, visibility, collaboration, and adaptive recovery; time-to-recovery is reserved as an outcome rather than included in the input index. The dynamic model is solved at a 0.25-month step over a 24-month policy horizon. Under a compound supplier-capacity, demand, and logistics shock, the balanced phase-based portfolio increases minimum resilience from 0.490 to 0.680 and reduces time-to-recovery from 8.4 to 3.5 months relative to the efficiency baseline. At an equal 6.9% incremental-cost budget, the integrated portfolio retains a 0.029–0.071 advantage in minimum resilience over single-mechanism alternatives. Holdout replay, alternative weighting schemes, event-gate tests, parameter perturbations, and unseen shock combinations establish numerical robustness but do not constitute external empirical validation. The findings indicate, for this specified model and case, that visibility creates resilience value when coupled with response authority, supplier coordination, flexible capacity, and targeted buffers. Full article

Addressing the challenges associated with partial renovation of existing residential interiors, including ordinary homeowners’ incomplete expression of design needs, limited access to professional design support, and the inability of conventional AIGC tools to accurately interpret complex design requirements, this study proposes an AI Agent-assisted design method for partial interior renovation. An AI Agent workflow was developed to establish an integrated process encompassing requirement acquisition, design semantic mapping, and design-scheme generation. The workflow automatically transforms natural-language requirements into structured design information and improves the quality and consistency of generated designs through multimodal information analysis and prompt-weight optimization. Experimental results show that the AI Agent-assisted method achieves improvements in requirement alignment, spatial structure preservation, spatial aesthetics, and generation stability. SUS analysis indicates that this method can provide relatively accessible design decision support for non-professional users. Overall, this study demonstrates that AI Agent-assisted human–AI collaborative design has the potential to support homeowners in independently designing and refining partial residential interior renovation schemes. Although this study did not directly measure indicators such as material consumption, embodied carbon, or service-life extension, the proposed workflow may also offer a potential decision-support pathway for more efficient and incremental renovation of existing homes. Full article

Under high renewable-energy penetration, regional power systems face increasing operational variability caused by wind and photovoltaic generation. Capacity planning for multi-energy complementary systems must therefore consider both investment economy and operation-related reliability costs under practical grid-access constraints. This study proposes a substation-constrained bi-level capacity-planning framework for a regional multi-energy power system in Ningxia. In the upper level, the installed capacities of wind, photovoltaic, thermal, and energy-storage resources are optimized by minimizing the annualized comprehensive cost subject to regional substation capacity limits. In the lower level, a mixed-integer linear programming dispatch model evaluates the operating cost of each candidate capacity scheme under representative seasonal scenarios, including thermal fuel cost, net grid-exchange cost, and reliability-related penalty costs associated with load curtailment, reserve shortage, and power imbalance. An Improved Whale Optimization Algorithm is used as the outer planning optimizer and is coupled with the lower-level MILP dispatch model. The case study uses normalized hourly load, wind power, and photovoltaic power data from Ningxia to construct an equivalent three-region planning system. The results show that the proposed bi-level framework reduces the annualized comprehensive cost by 28.3% compared with the single-level model, with the minimum total system cost reaching 2.71 × 10⁸ CNY. Compared with the standard WOA, PSO, and GA under the same case setting, the proposed IWOA obtains a lower objective value and smaller variation across repeated runs. The reliability-related terms in the model are interpreted as economic operational proxies rather than direct probabilistic reliability indices. The proposed framework provides a planning-oriented method for evaluating multi-energy capacity allocation under substation capacity constraints and representative operational scenarios. Full article

Facial biometric templates are permanent identifiers: once exposed, the underlying identity cannot be reissued, so single-copy storage is a critical single point of failure. This study protects facial templates by combining a non-invertible BioHashing transform and authenticated encryption with a threshold secret-sharing layer that distributes the protected record across independent storage nodes and reconstructs it only when a quorum of shares is collected. Two threshold schemes, previously proposed by our group for fingerprint and for general confidential data, are, for the first time, applied to facial templates and compared on a common platform as a resource-aware architecture: a non-positional polynomial notation scheme with the Chinese remainder theorem, and a verifiable multivariable-function scheme. Both reconstruct the template exactly and, across 2000 trials per attack, resist or detect every attack in our evaluation (for example, malicious-share tampering is detected in 100% of 2000 trials and stolen-token recovery succeeds in 0 of 2000), whereas a single read breach of one-copy storage discloses the template in full. Below the threshold they differ: the polynomial scheme is a compact, deterministic ramp scheme for edge and Internet-of-Things nodes that discloses only ciphertext bytes and, under separate key and token storage, neither the biometric nor the key; the multivariable scheme adds native share verification and, in its randomized single-secret mode, provides information-theoretic perfect secrecy for a single high-value secret, while for the packed record it is a verified ramp. Rather than ranking the schemes, we quantify this trade-off. Because the protection layer is lossless, recognition accuracy is inherited unchanged from the face encoder; on the LFW verification protocol, the complete pipeline attains an equal error rate of 2.12% ± 0.57% (95% CI [1.77%, 2.47%]) against a per-fold raw-embedding cosine baseline of 1.37% ± 0.62%. Full article