Search Results (265)

Accurate extraction of typical load curves (TLCs) is essential for electricity market trading, demand-side management, and optimal design of energy storage systems. However, conventional methods are highly sensitive to anomalous consumption days caused by equipment failures or maintenance, which can distort normal electricity consumption patterns. To address this issue, this paper proposes a two-stage unsupervised framework that integrates a deep sequence model with an anomaly detection algorithm for robust TLC extraction. First, a Transformer-based autoencoder is employed to learn complex temporal dependencies and intrinsic patterns from historical daily load data, extracting robust periodic features by reconstructing the input load sequences. Subsequently, the reconstruction error of each daily load curve is computed as an anomaly assessment metric. These reconstruction error features are then fed into an Isolation Forest algorithm to identify anomaly loads that significantly deviate from the learned normal patterns, without requiring predefined thresholds or labeled data. Validation using real-world commercial and industrial electricity consumption data demonstrates that the proposed method effectively filters out various anomalies (e.g., spikes, troughs, and shape distortions) that conventional methods fail to exclude. The extracted TLCs exhibit improved robustness and representativeness. Further case studies indicate that adopting purified TLCs to guide electricity procurement in market trading facilitates more scientific trading strategies and avoids increased electricity costs caused by distorted load patterns. In summary, the proposed Transformer-Isolation Forest hybrid framework provides an effective data-driven solution for robust TLC extraction. The resulting TLCs can be directly used to guide day-ahead market bidding, optimize power purchase contract decomposition, and assess user demand response potential. Full article

Stretchable conductive composites are important for soft electronics, wearable systems, and adaptive electromechanical devices, yet the mechanisms governing strain-dependent electrical transport remain insufficiently understood, particularly in hybrid filler systems. In this work, the strain-dependent electromechanical behavior of graphite–silicone and hybrid graphite–copper–silicone composites was investigated under uniaxial tensile deformation up to 60% strain. Electrical measurements revealed distinct transport behaviors governed by filler composition and conductive network structure. Graphite-only composites containing 50 wt% and 60 wt% graphite exhibited monotonic resistance increases with increasing strain due to progressive widening of inter-particle tunneling gaps between neighboring graphite platelets. In contrast, hybrid graphite–copper composites showed monotonic resistance decreases under deformation, which is attributed to Poisson-ratio-driven transverse contraction, tunneling-gap reduction, and strain-assisted formation of Cu–Cu and Cu–graphite conductive pathways. Representative volume element (RVE)-based simulations further supported the proposed transport interpretation. From an engineering design perspective, the results show that filler composition and conductive network architecture can be used as design variables to tune strain-dependent electrical responses in stretchable conductive composites. These findings provide design guidance for developing silicone-based conductive composites with tunable electromechanical functionality for soft electronics, wearable sensors, and adaptive devices. Full article

Authentication is essential to hold users accountable across online services. Conventional authentication systems rely on centralized architectures or third-party identity providers, which, however, introduce single points of failure, privacy concerns, and limited user autonomy. Conversely, fully decentralized authentication frameworks often struggle to provide reliable identity attestation mechanisms. This makes them vulnerable to Sybil attacks and self-asserted claims, while limiting their interoperability with trust-based systems. This paper presents dAuth, a hybrid blockchain-based authentication architecture based on Ethereum smart contracts to provide cryptographic tokens that enable authentication to services. These tokens, anchored to the smart contract, are derived by users from institutionally certified base credentials issued by an accredited verifying authority and enable authentication to services without further involvement of the authority. Each token is cryptographically bound to a specific service, constrained in scope and duration, and verifiable off-chain through data and cryptographic commitments provided by the user. No plaintext personal information is published on-chain: identity attributes are committed as cryptographic digests, which anchor certified identity data on-chain while keeping the underlying personal information private and auditable. This design removes the verifying authority from the authentication process, as all authentication steps are assisted by the user-controlled smart contract. The verifying authority’s role is limited to initial identity certification and exceptional update procedures. The result is a privacy-preserving and verifiable hybrid authentication framework that leverages the cryptographic security properties of the underlying blockchain infrastructure and inherits its scalability characteristics. The proposed design has been implemented and experimentally evaluated on the Ethereum platform, addressing public blockchain-specific challenges such as scalability constraints and transaction costs to ensure practical deployment. Full article

Faculty governance in higher education depends on transparent participation, reliable quorum enforcement, accountable record keeping, and strict alignment with institutional regulations. Conventional departmental council processes provide formal authority and academic deliberation, but they often rely on manual documentation, fragmented records, and procedural enforcement that is difficult to verify after the fact. This work presents an integrated hybrid Decentralized Autonomous Organization (DAO) framework for faculty governance that combines regulatory alignment analysis, a working smart-contract prototype, and scenario-based simulation. The framework is designed for university departmental councils and is structured across three layers: off-chain community governance, on-chain protocol governance, and off-chain execution governance. It expands prior conceptual work by incorporating governance dimensions related to roles, incentives, membership, communication, decision-making, identity, auditability, conflict-of-interest handling, and institutional ratification. The evaluation simulates 1488 proposals across twelve scenarios covering four faculty sizes (15, 30, 50, and 100 members) and three adoption levels (low, moderate, and high). Scenario results indicate that adoption intensity is the dominant driver of governance performance: mean participation increases from about 33% under low usage to about 85% under high usage, quorum achievement rises from about 6% to about 96%, and execution rises from about 19% to about 70%. Relative to a modeled conventional workflow baseline, the DAO-supported process reduces decision-cycle time by about 76%, improves audit completeness by about 30%, and increases traceability from about 0.63 to 1.00. The results indicate that DAO-assisted faculty governance can strengthen transparency, procedural consistency, and auditability while preserving legally mandated university authority, but its practical value depends on sustained participation, privacy safeguards, cost control, and clearly defined hybrid control points. Full article

Agricultural cooperatives in developing economies struggle with capital access and typically depend on subsidized credit with rigid repayment schedules that create vulnerability during low-production cycles. In this paper, we present a mathematical framework implemented through a smart contract to tokenize cooperative capital. Our mathematical framework uses magnified accumulators (scaled accumulator variables) to maintain temporal fairness, allocating dividends proportionally based on token holding periods through correction factors. The dividend distribution model operates with $O\left(1\right)$ computational complexity, regardless of cooperative size. The CooperativeToken smart contract combines ERC20 standards with automated dividend distribution, democratic governance mechanisms, and a hybrid payment architecture supporting both cryptocurrency and fiat transactions. Deployment verification and a gas analysis demonstrate operational viability with consistent performance and minimal transaction costs, enabling scalability from small to large cooperatives. The proposed system offers agricultural cooperatives a debt-free alternative to conventional financing, democratizing access to tokenized capital structures that were previously restricted to large agribusinesses. While the model is validated via Ethereum Sepolia testnet simulation, real-world deployment and field testing in active cooperatives remain necessary to confirm practical feasibility. This study provides the algorithmic and economic foundation for such pilots. Full article

Metamaterials with tailored structural architectures enable negative thermal expansion through geometric mechanisms that counteract constituent-level positive expansion. This study evaluates the thermomechanical performance and structural limits of polymer and hybrid NTE lattices. We systematically classify the dominant kinematic mechanisms, including bimetallic bending, rotational squares, and re-entrant honeycombs, and quantify the inherent trade-offs between effective thermal contraction, structural stiffness, and mass efficiency. The analysis demonstrates that reliance on idealized linear–elastic and rigid-lever models leads to significant predictive discrepancies when evaluating the physical response of polymeric and hybrid prototypes. We establish that these deviations are fundamentally governed by localized stress singularities at multi-material interfaces and the profound thermoviscoelastic softening of polymers as they approach the glass transition temperature ($T_g$). We conclude that accurate prediction of the cyclic lifespan and dimensional stability of these systems requires a transition to coupled multiphysics frameworks. Specifically, integrating temperature-dependent cohesive zone modeling and time–temperature superposition principles is essential for capturing interfacial delamination and thermal ratcheting in high-performance polymeric NTE metamaterials. Full article

Ezrin, expressed by the EZR gene, is a member of the ERM protein family that connects the plasma membrane to the actin cytoskeleton, participating in processes such as cell adhesion, migration, and signaling. However, its role in cardiac morphogenesis remains incompletely understood. In zebrafish (Danio rerio), two ezrin homologs, ezra and ezrb, are present. CRISPR/Cas9 gene editing technology was used to generate ezra knockout lines, and the simultaneous knockdown of ezra and ezrb was induced via morpholino oligonucleotides (MOs). To investigate the molecular mechanisms, transcriptome sequencing and bioinformatic analysis were conducted on 48 h post-fertilization (hpf) ezrin–MO embryos, with subsequent validation using a real-time quantitative polymerase chain reaction (RT-qPCR) and whole-mount in situ hybridization (WISH) experiment. The results showed that ezra^−/− exhibited a compensatory upregulation of ezrb without overt developmental defects, whereas ezrin–MO embryos presented with pericardial edema, reduced cardiac chamber size, and atrioventricular valve malformations at 48 hpf. RNA-seq revealed that myocardial contraction-related genes were significantly dysregulated and apoptotic signaling pathways were activated in ezrin–MO embryos. These findings demonstrate that ezra and ezrb are functionally redundant in cardiac development and that the loss of ezrin function may lead to cardiac developmental defects and impaired myocardial contractility via the activation of apoptotic signaling pathways. Full article

We investigate chaos control and hybrid synchronization in a variable-order fractional Arneodo system by treating the differentiation order $\alpha \left(t\right)$ as a closed-loop control variable. A hybrid chaos indicator, combining a tracking error with a windowed estimate of the largest Lyapunov exponent, drives both static and dynamic order modulation laws. The presence and uniqueness of solutions are demonstrated through two distinct methodologies: a piecewise constant-order decomposition with an explicit convergence rate and a direct contraction-mapping argument on the variable-order Volterra operator. Local stability is analyzed via Matignon’s spectral criterion under a quasi-static (frozen-time) approximation. The modulation laws are designed to steer $\alpha \left(t\right)$ below the critical order ${\alpha }_c\approx 0.8632$, at which the nontrivial equilibria $E_{1,2}=(\pm \sqrt{5.5},0,0)$ become locally asymptotically stable. A second-order predictor–corrector scheme attains its expected convergence rate. A controlled ablation study over 200 Monte Carlo runs demonstrates that the proposed laws reduce the terminal tracking error by 81% relative to the best fixed-order baseline, while requiring approximately eight orders of magnitude less control effort than classical active control. Hybrid synchronization (complete in $(u,v)$ and anti-synchronization in w) is successfully achieved in the variable-order setting. Full article

Binary neutron star mergers and proto-neutron stars provide unique environments where dense matter is hot, lepton-rich, and potentially undergoes a transition from hadronic to deconfined quark matter. We investigate the thermodynamics and stellar properties of hybrid matter under such conditions. The hadronic phase is described within a covariant density functional framework, while the quark phase is modeled using a Nambu–Jona-Lasinio (NJL) model that includes repulsive vector interactions, the axial $U_A\left(1\right)$-breaking ’t Hooft determinant interaction, and two-flavor color-superconducting (2SC) pairing. The phase transition between hadronic and quark matter is constructed using a mixed-phase prescription that enforces baryon and lepton number conservation, allowing us to follow thermodynamic trajectories at fixed entropy per baryon and a fixed lepton fraction. We analyze the phase structure of dense matter at a finite temperature and study the composition of the hadronic, mixed, and quark phases in both neutrino-trapped and neutrino-free regimes. Our results show that neutrino trapping significantly modifies the particle composition and shifts the onset of deconfinement to higher densities. The mixed phase exhibits a density-dependent pressure due to the presence of multiple conserved charges. Using the resulting equations of state, we compute static stellar configurations and examine the influence of the temperature and lepton content on the mass–radius relation in hybrid stars. Hot, neutrino-rich configurations are found to have larger radii and slightly higher maximum masses than their cold counterparts. As the star cools and deleptonizes, its radius contracts at an approximately constant baryonic mass, potentially triggering changes in the internal phase structure. These results highlight the roles of color superconductivity, lepton trapping, and thermal effects in shaping the structure and evolution of hybrid stars in transient astrophysical environments. Full article

This paper proposes a standards-aligned hybrid artificial intelligence–digital twin (DT) framework for predictive maintenance (PdM) in the maritime domain under conditions of data scarcity and heterogeneous sensor environments. The proposed framework adopts a DT-ready reference architecture centered on an ISO 19848-aligned data contract enabling consistent signal naming across vessels and equipment. On this foundation, the prognostics module is designed as a Domain-Knowledge Enhanced LSTM (DK-LSTM), a constraint-regularized sequence model in which three domain-informed constraints—(i) RUL non-negativity, (ii) monotonic degradation, and (iii) operating-range upper bounds—are formulated within the learning objective. Constraints (i) and (iii) are active throughout, while constraint (ii) is reserved for future work due to the structural limitation of batch-sort approximation in single-output architectures. An asymmetric safety penalty further suppresses hazardous over-predictions. Scenario-based virtual experiments are conducted using the NASA C-MAPSS turbofan degradation benchmark, evaluated under (1) sensor missingness via masking indicators and (2) structural domain shift comprising operational-condition shift (E3a: FD001 → FD002) and fault-mode shift (E3b: FD001 → FD003). Through systematic ablation of loss weights and stabilization techniques across multi-seed verification (seeds 0, 42, 123), the final stabilized configuration (DK-LSTM-v4) demonstrates robust safety-critical prediction in zero-shot domain-shift scenarios: 43.7% NASA Score improvement over the strongest baseline (GRU) under E3a and 20.8% improvement under E3b. The model trades modest in-domain performance for substantial cross-domain robustness, aligning with the core requirement of safety-critical maritime and defense applications where target-domain training data is unavailable. Full article

Machine learning models increasingly rely on continuously generated sensor data for automated decision-making in Internet of Things (IoT) environments. The distributed and often insecure nature of IoT infrastructures introduces risks related to data manipulation, lack of traceability, and unverifiable model evolution. Existing solutions typically address isolated aspects such as data security or access control but do not provide end-to-end provenance across the machine learning lifecycle. This paper proposes a tamper-evident data and model provenance framework for IoT-based machine learning that integrates blockchain with off-chain storage. The framework records cryptographic hashes and metadata of data, preprocessing outputs, and trained models on-chain while maintaining large artifacts off-chain to ensure scalability. Smart contracts establish verifiable linkage among lifecycle artifacts and automate provenance registration. The framework is evaluated in a simulated IoT–ML pipeline under integrity attack scenarios including data manipulation, model tampering, and metadata modification. Experimental results demonstrate reliable detection of unauthorized modifications with low verification latency and constant on-chain storage per record under controlled conditions. These findings indicate the feasibility of hybrid blockchain architectures for tamper-evident provenance in IoT-based machine learning systems, while highlighting the need for further validation in real-world deployments. Full article

Optimal allocation of distributed generation (DG) and feeder reconfiguration are critical strategies for improving the operational efficiency and voltage stability of modern radial distribution networks under increasing penetration of renewable resources. However, the simultaneous optimization of DG placement, sizing, and network topology constitutes a highly nonlinear multi-objective problem subject to electrical, operational, and radiality constraints. Unlike existing studies that treat DG allocation and feeder reconfiguration as separate or weakly coupled problems, this work introduces a unified mixed-integer nonlinear optimization framework that captures their strong interdependency. In addition, a hybrid Big Bang–Big Crunch (HBB-BC) algorithm is proposed, combining stochastic contraction with adaptive learning mechanisms to improve convergence robustness in highly nonlinear search spaces. This contribution addresses the limitations of conventional metaheuristics in handling coupled topology–generation optimization problems and provides a scalable solution for modern active distribution networks. We propose a coordinated optimization framework for optimal DG placement and feeder reconfiguration aimed at minimizing real power losses while enhancing voltage stability and reducing both operational cost and environmental impact. The problem is formulated as a constrained multi-objective optimization model and solved using an improved hybrid Big Bang–Big Crunch metaheuristic algorithm which integrates exploration and exploitation mechanisms to achieve fast convergence and robust global search performance. The proposed method is validated on both IEEE 33-bus and IEEE 69-bus radial distribution systems under multiple operational scenarios. The results demonstrate that the coordinated optimization consistently achieves significant performance improvements across different network scales, confirming the robustness and scalability of the proposed framework. Full article

This paper establishes a rigorous analysis of a coupled hybrid fractional differential system involving a generalized Hilfer operator under integral and antiperiodic boundary conditions. The existence and uniqueness of solutions are proved using Dhage’s fixed point theorem for existence and the Banach contraction principle for uniqueness. Furthermore, we establish Ulam–Hyers stability by deriving the following explicit and computable bound estimate: $\left(\begin{array}{c}\parallel \widehat{u}-u\parallel \\ \parallel \widehat{v}-v\parallel \end{array}\right)\le {(I-\mathit{\chi })}^{-1}\left(\begin{array}{c}{C}_1{ϵ}_1\\ C_2{ϵ}_2\end{array}\right)$, where $C_1$ and $C_2$ are positive constants depending on the system parameters, ${ϵ}_1,{ϵ}_2$ denote the perturbation bounds, and $\mathit{\chi }$ is the associated Lipschitz matrix. This formulation provides a more detailed stability description than scalar criteria, as it captures the interactions among the system components through the entries of $\mathit{\chi }$, leading to a more informative stability estimate. Two illustrative examples confirm the theoretical results and demonstrate their potential applicability for modeling real-world phenomena where memory effects are present. Full article

This paper investigates a new class of mixed impulsive fractional boundary value problems (BVPs) in which the mixing occurs both in the governing fractional differential equations—through the combined presence of $\psi$-Caputo and quantum (q-difference) fractional derivatives—and in the boundary conditions formulated via fractional integral constraints. By incorporating two distinct operators within the same dynamical framework, the proposed model is capable of capturing both memory effects and discrete-scale behaviors inherent in complex hybrid systems. Using the Banach contraction mapping principle and the Leray–Schauder nonlinear alternative, sufficient conditions ensuring the existence and uniqueness of solutions are established. The theoretical results unify and extend several known fractional models. Owing to its flexible structure, the proposed framework may serve as a useful mathematical tool for modeling impulsive phenomena in systems where non-local memory and scale-transition mechanisms coexist, such as in engineering, physics, and applied sciences. Finally, numerical examples are provided to illustrate the applicability and qualitative behavior of the solutions. Full article

Modern company activities depend greatly on inventory management, which covers demand forecasting and inventory optimization to guarantee operational effectiveness and customer happiness. This paper presents a new method fusing blockchain technology with cutting-edge deep learning to overcome these restrictions for better inventory management. Initially, the data are preprocessed using Zmin–max normalization (ZMM), and then feature extraction follows. To extract the spatiotemporal features and capture long-term temporal dependencies in demand data, a hybrid deep learning architecture is presented, built on a Deep Convolutional Koopman Network (CKN) integrated with a Coordinate Attention-Based Gated Recurrent Unit (CKN-CGRU).Genetic Secretary Bird Optimization (GSBO) is used to further tune the model automatically. While the CKN captures complex spatial temporal correlations, the GRU effectively models sequential dependencies. Blockchain architecture with smart contracts and improved Proof-of-Stake consensus is integrated to guarantee data integrity and transparency in stock transactions. This makes it possible to securely, automatically, and in a tamper-proof way record inventory projections, orders, and stock updates. The suggested system improves the stakeholder trust in decentralized inventory management by ensuring complete traceability and real-time auditability throughout the process. Experimental outcomes show the efficiency of the proposed model strategy, with an accuracy of 99.94% and precision of 99.93%. Full article