Search Results (9,985)

To address voltage over-limit and transformer overload issues in distribution grids caused by large-scale distributed PV integration, this paper proposes a distributed cooperative-based intelligent load optimization technique for distribution grids. First, by analyzing the limitations of traditional centralized control in communication burden, response speed, and fault tolerance, the necessity of distributed cooperative control is demonstrated. Subsequently, leveraging the bidirectional power regulation capability of energy storage systems, a distributed PV-storage system cooperative control model based on a consensus algorithm is constructed. This model comprehensively considers PV output fluctuations, energy storage state of charge, and grid regulation demands. Through multi-node information exchange and iterative updates of consensus variables, the model achieves coordinated power allocation among systems and voltage overlimit mitigation. Simulation results demonstrate that the proposed method effectively smooths PV fluctuations and alleviates local overloads in distribution grids. It simultaneously accommodates capacity differences and operational constraints across energy storage systems, enhancing system response speed and robustness. This provides effective technical support for the safe operation of distribution grids under high penetration of distributed renewable energy. Full article

In this paper, we study matrix transformations on spaces of generalized almost convergent double sequences with powers. Extending classical results of Lorentz, Maddox, and Nanda, we characterize several classes of infinite matrices that map between Maddox’s double sequence spaces and spaces of almost convergent (to zero) double sequences with powers. Our results generalize earlier characterizations for single sequence spaces obtained by the authors in previous work, providing a structured framework for studying summability and convergence in higher dimensions. Full article

Voltage sags pose severe risks to sensitive equipment in modern industries, requiring power quality monitoring equipment to possess fast and accurate sag detection capabilities. The traditional second-order generalized integrator (SOGI) will have oscillation phenomena in the case of DC offset, low-frequency harmonics, and high-frequency impulse noise. This study introduces a strong detection algorithm that combines Adaptive Composite Morphological Filtering (ACMF) with an improved Mixed Second- and Third-Order Generalized Integrator (MSTOGI). First, the ACMF pre-filtering module dynamically adjusts the scale of composite structuring elements through periodic parameter optimization, effectively filtering high-frequency random impulses while preserving the sharp transitions of abrupt voltage changes. Second, MSTOGI eliminates DC offset, and optimizes the gain coefficient to achieve the best dynamic response speed. Ultimately, a cascaded notch filter (CNF) module focuses on and removes even-order harmonic ripples caused by the synchronous reference frame transformation. Simulation results indicate that under severe grid conditions involving multiple composite distortions, the proposed architecture reduces the sag detection time to within 1.0 ms under typical operating conditions, with steady-state phase errors strictly controlled within a ±2° range. This method provides a reliable solution for DVR and UPS. Full article

Reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access (NOMA) has emerged as a promising technique to enhance spectral efficiency and coverage in fifth- and sixth-generation wireless networks. However, asymmetric indoor propagation conditions characterized by heterogeneous line-of-sight (LoS) and non-line-of-sight (NLoS) links often degrade user fairness. This paper investigates a downlink RIS-assisted NOMA system under the standardized 3GPP indoor office (InH) channel model to address fairness-oriented design under realistic link-budget constraints. We formulate an optimization problem for max–min fairness that jointly considers discrete RIS element partitioning and NOMA power allocation to achieve a symmetrical allocation of quality of service (QoS). To enable efficient computation, the non-convex problem is transformed into an epigraph form and solved using a low-complexity, bisection-based quasi-convex optimization framework combined with enumeration over RIS partitions. Numerical results demonstrate significant fairness gains; for instance, doubling the RIS array size yields a substantial improvement in the ergodic max–min rate, corresponding to approximately a 66% gain at moderate transmit power levels. Furthermore, by accounting for practical impairments such as imperfect successive interference cancellation (iSIC), imperfect channel state information (iCSI), and RIS implementation losses, the results reveal that fairness-optimal operation consistently prioritizes the far user to overcome severe indoor NLoS attenuation. The proposed framework is also compared with alternating optimization (AO)-based RIS-NOMA, conventional RIS beamforming without partition and RIS-assisted orthogonal multiple access (OMA) schemes. Simulation results confirm that the proposed framework achieves low computational complexity, making it suitable for practical indoor wireless environments. Full article

In this paper, we investigate the (1 + 1)-dimensional nonlinear truncated M-fractional FitzHugh–Nagumo model. The main objective is to analyze the dynamical behavior and obtain exact solutions for the model. First, a fractional transformation is applied to convert the governing partial differential equation into an ordinary differential equation. Subsequently, a Galilean transformation is employed to reduce the resulting equation to a dynamical system. The bifurcation structure and chaotic dynamics of the model are then examined. The presence of chaos is further confirmed through the phase portrait, basin of attraction, return map, Lyapunov exponent, permutation entropy, Poincaré map, power spectrum, attractor, fractal dimension, multistability, time analysis, and recurrence plot. In addition, the sensitivity of the system to the initial conditions is analyzed. Finally, exact solutions for the model are constructed using the unified Riccati equation expansion method. The obtained results are illustrated using two-dimensional, three-dimensional, and contour plots. Full article

Metagenomic and targeted next-generation sequencing (NGS) technologies are rapidly transforming diagnosis and management for infectious diseases. This review comprehensively examines the current applications of metagenomic NGS (mNGS) and targeted NGS (tNGS) in clinical microbiology, highlighting their roles in pathogen detection, antimicrobial resistance profiling, virulence characterization, and outbreak investigation—particularly in complex cases such as pneumonia, critical illness with pulmonary infections, and pediatric acute respiratory illnesses. We discuss the diagnostic performance, advantages, and limitations of these approaches, including challenges related to sensitivity, specificity, standardization, bioinformatic complexity, and cost-effectiveness. Furthermore, we explore emerging opportunities for integrating NGS-based surveillance with public health strategies, such as wastewater epidemiology, to monitor healthcare-associated infections (HAIs) and antimicrobial resistance (AMR) at the population level. Finally, we outline key steps needed to translate these powerful genomic tools from research settings into routine clinical and public health practice. Full article

An analytical model of frictional heat transfer during the uniform sliding of two layers is proposed. One layer is composed of a functionally graded material (FGM) with a thermal conductivity coefficient that varies exponentially across its thickness, while the second layer is homogeneous, with constant thermophysical properties. The thermal problem of friction is formulated as an initial boundary value problem of heat conduction, accounting for the thermal contact conductance and convective heat exchange with the environment. An exact solution for constant friction power was obtained using the Laplace integral transform, supplemented by an asymptotic form for the initial stage of heating. Based on these analytical solutions, a comprehensive study was carried out for a frictional system comprising a ceramic–metal FGM composite in contact with a homogeneous friction material. A dimensional analysis allowed for both a qualitative and quantitative investigation into the influence of contact conductance, convective heat exchange, layer thickness and the FGM gradient parameter on the temperature evolution and distribution, as well as the time to reach the steady state. It was demonstrated that the implementation of an appropriately graded material can substantially improve thermal operating conditions by enhancing heat dissipation into the material bulk and intensifying convective cooling. Full article

Background/Objectives: Wound management presents a substantial clinical challenge due to the rising incidence of chronic wounds, infections, and the limitations of conventional dressings in creating an ideal healing microenvironment. This review aims to provide a comprehensive overview of advanced smart hydrogel platforms designed to improve wound healing outcomes, focusing on their capacity to respond adaptively to physiological and external stimuli. Methods: This article analyzes the core characteristics of smart hydrogels, specifically examining stimuli-responsive systems (pH, temperature, enzyme, light, and electricity). The review evaluates advanced configurations—including injectable, self-healing, and 3D-printable systems—and functionalized hydrogels integrated with antimicrobials, drugs, and nanocomposites. Additionally, essential characterization methodologies, biological assessments, and regulatory considerations for clinical translation are synthesized. Results: The literature, which is predominantly preclinical in nature, indicates that functionalized hydrogels significantly enhance tissue regeneration, angiogenesis, and infection control compared to traditional methods. Conductive hydrogels utilizing bioelectrical signals show particular promise in accelerating the healing process. While current clinical applications and commercial products demonstrate efficacy, significant barriers remain regarding large-scale manufacturing and regulatory approval. Conclusions: Smart hydrogels represent a transformative approach to precision wound management, offering superior adaptability and therapeutic delivery. To achieve widespread clinical adoption, future research must address manufacturing scalability and focus on emerging trends, such as the integration of biosensors and AI-powered monitoring systems, to create fully intelligent wound care solutions. Full article

All forms of neural communications, from cognition to emotion, are regulated by neurotransmitters, which are otherwise the chemical language of the brain. Precise detection of these neurotransmitters is essential for the perception of neurophysiology and diagnosis of neurodegenerative diseases as well. Among the existing techniques for the detection of these molecules, fluorescence sensing is evolving as a powerful approach in terms of high sensitivity, rapid response, and real-time visualization of the chemical events occurring in the neural system. In recent years, nanomaterials have transformed this field by integrating tunable optical properties, excellent photostability, and modifiable surface chemistry into biocompatible nanostructures. We summarize the recent advances of these architectures to show how the material type and dimensionality, as well as the surface functionality, play roles in sensing through the mechanisms of Förster resonance energy transfer (FRET), photoinduced electron transfer (PET), inner filter effect (IFE), and aggregation-induced emission (AIE). The discussion has also been extended to the correlation of fluorescence modulation with the selectivity and sensitivity in the mechanism-to-function relationship. The potential utility of such innovative technologies, including artificial intelligence, spectral deconvolution analysis via big data algorithms, and chip-integrated sensing, was explored as a means to enable real-time neurochemical detection. This converging area of nanotechnology and neuroscience leaves a mark not just in analytical accuracy, but also parallels human brain rhythms. Full article

Hyperspectral image (HSI) classification faces significant challenges due to the spatial–spectral heterogeneity of land covers and the geometric rigidity of standard convolutions. Although Transformers offer powerful global modeling capabilities, their quadratic computational complexity limits practical efficiency. To address these limitations, this paper proposes a novel hierarchical framework named MDS³-Net (Multiscale Deformable Spectral–Spatial Sequence Network). Specifically, we design a Multiscale Spectral-Deformable Convolution (MSDC) module that adopts a cascaded strategy to sequentially extract discriminative spectral features and adaptively align spatial receptive fields with irregular object boundaries. To capture long-range dependencies efficiently, a Spectral–Spatial Sequence (S³) Encoder is introduced based on a gated large-kernel convolution mechanism, achieving global context modeling with linear complexity. Furthermore, a Dual-Path Feature Extraction (DPFE) module is proposed to perform semantics-preserving dimension reduction via spectral reorganization and spatial attention. Experimental results on four public datasets demonstrate that the proposed MDS³-Net achieves state-of-the-art classification performance and exhibits superior robustness under limited training samples compared to existing methods. Full article

With the rapid development of smart cities, coordinating diverse distributed energy resources through storage-centric shared management has become a critical challenge. This paper proposes a bi-level energy management framework to support peer-to-peer energy trading among multiple virtual power plants (VPPs) under multidimensional uncertainties. The interaction is modeled as a Stackelberg–Nash equilibrium framework, in which OK, we will make the necessary revisions as per the requirements.a public energy storage operator and a natural gas company act as leaders to maximize social welfare and design differentiated trading strategies for VPPs. The VPPs act as followers and participate in cooperative energy trading based on a generalized Nash equilibrium scheme, sharing surplus energy and allocating cooperative benefits according to their contributions. To address uncertainty, Conditional Value at Risk (CVaR) is adopted to quantify the expected loss of the upper-level decision makers. The lower-level VPP problem is formulated as a three-stage stochastic robust optimization model considering renewable generation uncertainty. To solve the resulting nonlinear bi-level problem, a two-stage solution approach combining particle swarm optimization and KKT-based reformulation is developed to transform it into a tractable mixed-integer linear programming model. Numerical case studies verify the effectiveness of the proposed framework. Full article

In this paper, a novel scheme is introduced that combines a traveling wave tube (TWT) with a metasurface to generate high-power E-band vortex electromagnetic waves. The TE₁₀ mode electromagnetic wave emitted by the TWT is initially converted into a plane wave via a horn antenna and subsequently transformed into a vortex electromagnetic wave by the metasurface. The metasurface is designed and simulated, and the results show that this approach can convert the TE₁₀ mode from the TWT into vortex electromagnetic waves with a specific topological charge of $l=+1$ within the 71–76 GHz frequency range, achieving a remarkable mode purity of up to 97%. The experiment at 73.5 GHz was successfully carried out, generating vortex electromagnetic waves with the designated topological charge of $l=+1$ using this method. Although the experimentally measured mode purity was limited to 30.6%, this outcome confirms the effectiveness of the proposed method. Full article

The Hyakuzahōdan Kikigakishō (百座法談聞書抄, hereafter Hyakuza 百座), compiled in the late Heian period, is an important Buddhist document that records a hundred-day lecture series on the Lotus Sutra (法華経). While previous scholarship has recognized the constructed nature of the text as a kikigaki (聞書), it has predominantly focused on content analysis, implicitly treating the text as a transparent window into the actual preaching event. To move beyond this limitation, this study proposes the analytical framework of dual performativity and, drawing on Diana Taylor’s theory of the archive and the repertoire, reexamines the text’s generative logic and political implications. This study argues that the Hyakuza embodies two interrelated forms of performance: first, the performativity of the hōdan (法談) as a live ritual, understood as a repertoire performance that constructs immediate authority through body, voice, and situational dynamics; second, the performativity of the kikigaki as textual construction, understood as an archival performance that transforms the ephemeral oral event into an authoritative, transmissible text through formulaic rhetoric, localized adaptation, and systematic arrangement. Integrating methodologies from textual history, rhetorical analysis, ritual theory, and intellectual history, this study demonstrates that the Hyakuza is not a neutral transcript of sermons but a meticulous, intentional act of writing with two fundamental aims: on a cultural level, to hierarchically integrate shinbutsu shūgō (神仏習合) through narrative appropriation; on a social level, to symbolically bind Buddhist merit with the institutional identities of aristocrats such as naishinnō (内親王), ultimately serving the self-affirmation internal cohesion, and cultural demarcation of the elite community from the masses, while simultaneously contributing to the state’s project of constructing a unified ideology in the late Heian period. By examining both cross-civilizational universal logic and specific historical context, this study reveals how the Hyakuza’s dual performativity produces and categorizes knowledge narratives while embedding political power dynamics, offering a critical path for the study of kikigaki-genre literature from discourse analysis to politics of textuality. Full article

The growing importance of integrating renewable energy sources (RESs) into mainline railway traction networks stems from the sector’s substantial electricity demand, which is traditionally met by carbon-intensive thermal generation. This paper addresses the potential of wind power to enhance energy efficiency and reduce emissions in rail transport. It details the development of digital models for simulating DC traction power systems (TPSs) coupled with RESs, specifically wind turbines. Given the complexity of TPSs, effective integration requires digital modeling that accounts for their unique properties. The proposed methodology, based on phase coordinate algorithms, offers a universal and comprehensive framework. It enables the identification of various operational modes (normal, emergency, and special) for diverse network components, including traction networks, transmission lines, and transformers. These models were used to simulate real-world train operations, generating data on electrical parameter dynamics and transformer thermal conditions. The results confirm that wind integration can improve energy efficiency, validating the methodology’s practical applicability for RES projects in DC traction networks, including advanced high-voltage systems. Full article

Against the growing global focus on green development, government subsidies are widely recognized as a crucial policy tool to promote firms’ green transformation. In competitive markets, green investment decisions are jointly shaped by supply chain power structures, and different research and development (R&D) leadership can yield distinct policy outcomes. This study develops a Bertrand competition model of a green supply chain with one manufacturer and two competing retailers, comparing two structures: manufacturer-led R&D (SM) and retailer-led R&D (SR). We examine how these policies affect pricing decisions, product greenness, and revenues. Under the retailer-led R&D, a green cost-sharing ratio is introduced to capture the interaction between internal coordination and government support. The results show that subsidy effects depend on consumer green awareness. When green awareness is low, subsidies mainly raise prices through cost pass-through. When green awareness is high, subsidies can lower prices by stimulating demand. In addition, the interaction between subsidy intensity and cost sharing leads to non-monotonic changes in retailers’ revenues. By comparing different market structures and parameter settings, we identify the conditions under which SM or SR dominates in terms of prices, product greenness, and revenues, providing guidance for more flexible green subsidy design. Full article