Search Results (2,727)

This study investigates whether combining singular value decomposition with wavelet analysis can provide new insights into the spatiotemporal complementarity between wind turbine sites, surpassing previous findings. Earlier studies predominantly relied on various forms of correlation analysis to quantify complementarity. While correlation analysis offers a way to compute global metrics summarizing the relationship between entire time series, it inherently overlooks localized and time-specific patterns. The proposed approach overcomes these limitations by enabling the identification of spatially explicit and temporally resolved complementarity patterns across a large number of wind turbine sites in the study area. Because complementarity information is derived from orthogonal components obtained through singular value decomposition of a wind power density matrix, there is no need to adjust for phase shifts between sites. Moreover, the complementary contributions of these components to overall wind power density are expressed in watts per square meter, directly reflecting the magnitude of the analyzed data. This facilitates a site-specific, complementarity-optimized strategy for further wind energy expansion. Full article

Traditional antenna arrays for direction-of-arrival (DOA) estimation typically require numerous elements to achieve target performance, increasing system complexity and cost. Reconfigurable intelligent surfaces (RISs) offer a promising alternative, yet their performance critically depends on phase coding design. To address this, we propose a phase coding design method for RIS-aided DOA estimation with a single receiving channel. First, we establish a system model where averaged received signals construct a power-based formulation. This transforms DOA estimation into a compressed sensing-based sparse recovery problem, with the RIS far-field power radiation pattern serving as the measurement matrix. Then, we derive the decoupled expression of the measurement matrix, which consists of the phase coding matrix, propagation phase shifts, and array steering matrix. The phase coding design is then formulated as a Frobenius norm minimization problem, approximating the Gram matrix of the equivalent measurement matrix to an identity matrix. Accordingly, the phase coding design problem is reformulated as a Frobenius norm minimization problem, where the Gram matrix of the equivalent measurement matrix is approximated to an identity matrix. The phase coding is deterministically constructed as the product of a unitary matrix and a partial Hadamard matrix. Simulations demonstrate that the proposed phase coding design outperforms random phase coding in terms of angular estimation accuracy, resolution probability, and the requirement of coding sequences. Full article

Harmonics are a common phenomenon widely present in power systems. The presence of harmonics not only increases the energy consumption of equipment but also poses hidden risks to the safety and stealth performance of large ships. Thus, there is an urgent need for a detection method for the harmonic characteristics of time series. We propose a novel harmonic feature estimation method, termed Harmonic Aggregation Entropy (HaAgEn), which effectively discriminates against background noise. The method is based on bispectrum analysis; utilizing the distribution characteristics of harmonic signals in the bispectrum matrix, a new Diagonal Bi-directional Integral Bispectrum (DBIB) method is employed to effectively extract harmonic features within the bispectrum matrix. This approach addresses the issues associated with traditional time–frequency analysis methods, such as the large computational burden and lack of specificity in feature extraction. The integration results, $I_x$ and $I_y$, of DBIB on different frequency axes are calculated using cross-entropy to derive HaAgEn. It is verified that HaAgEn is significantly more sensitive to harmonic components in the signal compared to other types of entropy, thereby better addressing harmonic detection issues and reducing feature redundancy. The detection accuracy of harmonic components in the shaft-rate electromagnetic field signal, as evidenced by sea trial data, reaches 96.8%, which is significantly higher than that of other detection methods. This provides a novel technical approach for addressing the issue of harmonic detection in industrial applications. Full article

As China’s economy grows and travel demand increases, its aviation market has evolved to become the second-largest in the world. This study presents a pioneering analysis of China’s aviation network evolution (1990–2024) by integrating temporal dynamics into a network density matrix theory, addressing critical gaps in prior static network analyses. Unlike conventional studies focusing on isolated topological metrics, we introduce a triangulated methodology: ① a network sequence analysis capturing structural shifts in degree distribution, clustering coefficient, and path length; ② novel redundancy–entropy coupling quantifying complexity evolution beyond traditional efficiency metrics; and ③ economic-network coordination modeling with spatial autocorrelation validation. Key innovations reveal previously unrecognized dynamics: ① Time-embedded density matrices ($\rho$) demonstrate how sparsity balances information propagation efficiency ($\eta$) and response diversity, resolving the paradox of functional yet sparse connectivity. ② Redundancy–entropy synergy exposes adaptive trade-offs. Entropy ($H$) rises 18% (2000–2024), while redundancy ($R$) rebounds post-2010 (0.25→0.33), reflecting the strategic resilience enhancement after early efficiency-focused phases. ③ Economic-network coupling exhibits strong spatial autocorrelation ($Moran’sI>0.16$, $p<0.05$), with eastern China achieving “primary coordination”, while western regions lag due to geographical constraints. The empirical results confirm structural self-organization. Power-law strengthening, route growth exponentially outpacing cities, and clustering ($C$) rising 16% as the path length ($L$) increases, validating the hierarchical hub formation. These findings establish aviation networks as dynamically optimized systems where economic policies and topological laws interactively drive evolution, offering a paradigm shift from descriptive to predictive network management. Full article

Magnetorheological (MR) foams represent a class of smart materials with unique tunable viscoelastic properties when subjected to external magnetic fields. Combining porous structures with embedded magnetic particles, these materials address challenges such as leakage and sedimentation, typically encountered in conventional MR fluids while offering advantages like lightweight design, acoustic absorption, high energy harvesting capability, and tailored mechanical responses. Despite their potential, challenges such as non-uniform particle dispersion, limited durability under cyclic loads, and suboptimal magneto-mechanical coupling continue to hinder their broader adoption. This review systematically addresses these issues by evaluating the synthesis methods (ex situ vs. in situ), microstructural design strategies, and the role of magnetic particle alignment under varying curing conditions. Special attention is given to the influence of material composition—including matrix types, magnetic fillers, and additives—on the mechanical and magnetorheological behaviors. While the primary focus of this review is on MR foams, relevant studies on MR elastomers, which share fundamental principles, are also considered to provide a broader context. Recent advancements are also discussed, including the growing use of artificial intelligence (AI) to predict the rheological and magneto-mechanical behavior of MR materials, model complex device responses, and optimize material composition and processing conditions. AI applications in MR systems range from estimating shear stress, viscosity, and storage/loss moduli to analyzing nonlinear hysteresis, magnetostriction, and mixed-mode loading behavior. These data-driven approaches offer powerful new capabilities for material design and performance optimization, helping overcome long-standing limitations in conventional modeling techniques. Despite significant progress in MR foams, several challenges remain to be addressed, including achieving uniform particle dispersion, enhancing viscoelastic performance (storage modulus and MR effect), and improving durability under cyclic loading. Addressing these issues is essential for unlocking the full potential of MR foams in demanding applications where consistent performance, mechanical reliability, and long-term stability are crucial for safety, effectiveness, and operational longevity. By bridging experimental methods, theoretical modeling, and AI-driven design, this work identifies pathways toward enhancing the functionality and reliability of MR foams for applications in vibration damping, energy harvesting, biomedical devices, and soft robotics. Full article

As semiconductor technologies continue to scale aggressively, electromigration (EM) has become critical in modern VLSI design. Since traditional EM assessment methods fail to accurately capture the complex behavior of multi-segment interconnects, recent physics-based models have been developed to provide a more accurate representation of EM-induced stress evolution. However, numerical methods for these models result in large-scale systems, which are computationally expensive and impractical for complex interconnect structures. Model order reduction (MOR) has emerged as a key enabler for scalable EM analysis, with moment-matching (MM) techniques offering a favorable balance between efficiency and accuracy. However, conventional Krylov-based approaches often suffer from limited frequency resolution or high computational cost. Although the extended Krylov subspace (EKS) improves frequency coverage, its symmetric structure introduces significant overhead in large-scale scenarios. This work introduces a novel MOR technique based on the asymmetric extended Krylov subspace (AEKS), which improves upon the conventional EKS by incorporating a sparsity-aware and computationally efficient projection strategy. The proposed AEKS-based moment-matching framework dynamically adapts the Krylov subspace construction according to matrix sparsity, significantly reducing runtime without sacrificing accuracy. Experimental evaluation on IBM power grid benchmarks demonstrates the high accuracy of our method in both frequency-domain and transient EM simulations. The proposed approach delivers substantial runtime improvements of up to 15× over full-order simulations and 100× over COMSOL, while maintaining relative errors below 0.5%, even under time-varying current inputs. Full article

In the process of machining an aluminum matrix silicon carbide (SiCp/Al) composite material using wire electric discharge machining (WEDM), the thermal conductivity and dielectric properties of working fluid, such as discharge medium and cool carrier, directly determine the material removal rate (MRR) and surface roughness (R_a). In this paper, graphene-working fluid is innovatively used as working medium to optimize the discharge process due to its high thermal conductivity and field emission characteristics. The single-factor experiments show that graphene can increase the MRR by 11.16% and decrease the R_a by 29.96% compared with traditional working fluids. In order to analyze the multi-parameter coupling effect, an L16 (4⁴) orthogonal test is further designed, and the effects of the pulse width (T_on), duty cycle (DC), power tube number (PT), and wire speed (WS) on the MRR and R_a are determined using a signal-to-noise analysis. Based on a gray relational grade analysis, a multi-objective optimization model was established, and the priority of the MRR and R_a was determined using an AHP, and finally the optimal parameter combination (T_on = 22 μs, DC = 1:4, PT = 3, WS = 2) was obtained. Full article

To enhance the reliability of unmanned hydro-mechanical transmission tractors, a fault diagnosis method for their power-shift system was developed. First, fault types were identified, and sample data was collected via a test bench. Next, a feature extraction method for data dimensionality reduction and a deep learning network called W_SCBAM were introduced for fault diagnosis. Both W_SCBAM and conventional algorithms were trained 20 times, and their performance was compared. Further testing of W_SCBAM was conducted in various application scenarios. The results indicate that the feature extraction method reduces the sample length from 46 to 3. The fault diagnosis accuracy of W_SCBAM for the radial-inlet clutch system has an expectation of 98.5% and a variance of 1.6%, respectively, outperforming other algorithms. W_SCBAM also excels in diagnosing faults in the axial-inlet clutch system, achieving 97.6% accuracy even with environmental noise. Unlike traditional methods, this study integrates the update of a dimensionality reduction matrix into network parameter training, achieving high-precision classification with minimal input data and lightweight network structure, ensuring reliable data transmission and real-time fault diagnosis of unmanned tractors. Full article

Colorectal cancer (CRC) incidence increases markedly with age, yet chronological age is an inadequate proxy for the complex biological processes involved. Colon aging, the intrinsic biological aging of the colonic tissue, is emerging as a crucial, active driver of CRC development. This review comprehensively analyzes the interplay between colon aging and CRC pathogenesis by examining fundamental hallmarks of aging—such as altered tissue homeostasis, epigenetic dysregulation, and microenvironmental shifts including chronic inflammation (inflammaging), gut microbiome dysbiosis, and extracellular matrix remodeling—manifest specifically within the aging colon to synergistically foster a pro-tumorigenic environment. Key findings synthesized from the literature highlight the critical roles of impaired colonic stem cell function, epithelial barrier disruption (“leaky gut”), persistent low-grade inflammation, and altered microbial communities and their metabolites in promoting CRC initiation and progression. Translating this mechanistic understanding holds significant promise: insights from colon aging research can inform novel biomarkers for improved early detection and risk stratification, guide the development of personalized preventative strategies and therapeutic interventions targeting aging pathways, and underpin public health initiatives focused on healthy colon aging. Ultimately, recognizing colon aging as a modifiable contributor to CRC offers a powerful avenue to potentially reduce CRC incidence and enhance patient outcomes, particularly in the vulnerable aging population. Full article

This study aims to explore a new concept for a Power to Heat (P2H) device and demonstrate its effectiveness compared to a thermal heating method. The proposed concept is a medium-temperature system where electro-thermal conversion occurs via the Joule effect in a metallic tube (resistive element). This tube also serves as a heat exchange surface between the heat transfer fluid and the thermal storage medium. The heat storage material here proposed consists of base concrete formulated on purpose to ensure its operation at high temperatures, good performance and prolongated thermal stability. The addition of 10%_wt phase change material (i.e., solar salts) stabilized in shape through a diatomite porous matrix allows the energy density stored in the medium itself to increase (hybrid sensible/latent system). Testing of the heat storage module has been conducted within a temperature range of 220–280 °C. An experimental comparison of charging times has demonstrated that electric heating exhibits faster dynamics compared to thermal heating. In both electrical and thermal heating methods, the concrete module has achieved 86% of its theoretical storage capacity, limited by thermal losses. In conclusion, this study successfully demonstrates the viability and efficiency of the proposed hybrid sensible/latent P2H system, highlighting the faster charging dynamics of direct electrical heating compared to conventional thermal methods, while achieving a comparable storage capacity despite thermal losses. Full article

Nanotechnology has revolutionized dermocosmetic innovation by improving the stability, bioavailability, and efficacy of active ingredients. In this study, we developed and optimized a novel xanthan gum-based hydrogel containing quercetin-loaded chitosan lactate nanoparticles for antioxidant and antimicrobial skincare applications. Chitosan was converted to its lactate form to enhance water solubility and enable nanoparticle formation at physiological pH via ionic gelation with citric acid. The formulation was optimized using Box–Behnken response surface methodology to achieve minimal particle size and maximal zeta potential. The final gel was structured with xanthan gum as the gelling polymer, into which the optimized nanoparticles were incorporated to create a stable and bioactive hydrogel system. Encapsulation efficiency was measured separately to assess the effectiveness of drug loading. The optimized nanoparticles exhibited a mean diameter of 422.02 nm, a zeta potential of +29.49 mV, and a high quercetin encapsulation efficiency (76.9%), corresponding to the proportion of quercetin retained in the nanoparticle matrix relative to the total amount initially used in the formulation. Antioxidant assays (TAC, DPPH, and reducing power) confirmed superior radical-scavenging activity of the nanoformulation compared to the base hydrogel. Antibacterial tests showed strong inhibition against Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus, with MIC values comparable to streptomycin. Accelerated stability studies demonstrated excellent physicochemical and microbiological stability over 60 days. This natural, bioactive, and eco-friendly formulation represents a promising platform for next-generation cosmeceuticals targeting oxidative stress and skin-related pathogens. Full article

This study presents advanced control and energy management strategies for uncertain wind energy systems using a Takagi–Sugeno (T-S) fuzzy modeling framework. To address key challenges, such as system uncertainties, external disturbances, and input delays, the study integrates a fuzzy H∞ robust control approach with a neural network-based delay compensation mechanism. A fuzzy observer-based H∞ tracking controller is developed to enhance robustness and minimize the impact of disturbances. The stability conditions are rigorously derived using a quadratic Lyapunov function, H∞ performance criteria, and Young’s inequality and are expressed as Linear Matrix Inequalities (LMIs) for computational efficiency. In parallel, a neural network-based controller is employed to compensate for the input delays introduced by online learning processes. Furthermore, an energy management layer is incorporated to regulate the power flow and optimize energy utilization under varying operating conditions. The proposed framework effectively combines control and energy coordination to improve the systems’ performance. The simulation results confirm the effectiveness of the proposed strategies, demonstrating enhanced stability, robustness, delay tolerance, and energy efficiency in wind energy systems. Full article

With the widespread application of optical technology in numerous fields, the polarization performance of transmissive optical components has become increasingly crucial. The extinction ratio, an important indicator for evaluating their polarization characteristics, holds great significance for its precise detection. Aiming at the measurement of the extinction ratio of a transparent component, this study proposes a measurement method for solving the extinction ratio based on measuring the Mueller matrix of the transparent component. The purpose is to analyze the worst position of the extinction ratio of the transmissive component. The extinction ratio of the sample is obtained according to the phase retardation derived from the Stokes vector of the incident light and the Mueller matrix of the optical component, and a theoretical analysis and simulation of this method are carried out. The simulation results verify the feasibility of the theoretical derivation of this method. To further verify the accuracy of the measurement method, experimental verification is conducted. A standard transparent sample with a phase retardation of 13 nm is selected for actual measurement. The data of independent experiments on the transparent sample under different powers are analyzed, and the extinction ratio of the transparent sample is further obtained. When using this method, the relative error is less than 2%, indicating good accuracy. Full article

An algorithm is proposed to recover out-of-service loads (OOSLs) in smart distribution systems (SDSs) after exposure to cyber-attacks (CAs) resulting in interruptions of in-service loads (INSLs). The proposed algorithm is implemented in three steps. The first step is based on building the SDS in matrix form to be data input to the proposed algorithm. The second step is concerned with classifying the SDS into three zones: the attacked zone, the primary neighbor zone, and the secondary neighbor zone. The third step is performing five maneuvering processes (MPs) to recover the OOSL without breaking the electric limitations (ELs). The ELs are related to the maximum branch current, the node voltage, the load priority, the radiality maintenance of the SDS, the minimum system total power loss, the instruction sequence of the automatic-communication-switches (ACS), and the minimum number of ACSs. The proposed algorithm was tested under a 70-bus SDS with four electric supply feeders. The proposed algorithm achieved supply recovery for all OOSLs with efficiency of 100% after the occurrence of a CA on a single or double ACS without breaking the ELs. The proposed algorithm succeeded in achieving supply recovery for 97.6%, 97.1%, and 96.4% of the OOSLs after the simultaneous occurrence of a CA on three, four, and five ACSs, respectively, without breaking the ELs. The advantages of the proposed algorithm are a lack of dependency on the system size, a short electric supply recovery time within the range of 190–199 ms, a lack of dependency on distributed generation (DG), and the achievement of self-healing in the SDS following a single and two simultaneous CAs, as well as almost achieving self-healing under exposure to three, four, and five simultaneous CAs. Full article

This paper analyzed the behavior of polymer composite materials reinforced with randomly oriented short natural fibers (hemp, flax, etc.) subjected to external stresses under quasistatic contact conditions with dry Coulomb friction. We presumed the composite body, a 2D flat rectangular plate, being in frictional contact with a rigid foundation for the quasistatic case. The manuscript proposes the finite element method approximation in space and the finite difference approximation in time. The problem of quasistatic frictional contact is described with a special finite element, which can analyze the state of the nodes in the contact area, and their modification, between open, sliding, and fixed contact states, in the analyzed time interval. This finite element also models the Coulomb friction law and controls the penetrability according to a power law. Moreover, the quasi-static case analyzed allows for the description of the load history using an incremental and iterative algorithm. The discrete problem will be a static and nonlinear one for each time increment, and in the case of sliding contact, the stiffness matrix becomes non-symmetric. The regularization of the non-differentiable term comes from the modulus of the normal contact stress, with a convex function and with the gradient in the sub-unit modulus. The non-penetration condition was achieved with the penalty method, and the linearization was conducted with the Newton–Raphson method. Full article