Search Results (571)

In complex Unmanned Aerial Vehicle (UAV) trajectory tracking scenarios, conventional Interacting Multiple Model (IMM) algorithms face challenges such as slow model switching rates and insufficient tracking accuracy. To address these limitations, this paper proposes an enhanced algorithm named Adaptive Interacting Multiple Model-Improved Adaptive Kalman Filter (AIMM-IAKF). The AIMM component dynamically adjusts the model transition probability matrix based on real-time model probability differences, overcoming the limitation of a fixed matrix in traditional IMM. Furthermore, the conventional Kalman filter is replaced with an Improved Adaptive Kalman Filter (IAKF), which introduces a convergence criterion and a suboptimal fading factor to optimize noise statistics. Simulation results demonstrate that, compared to the traditional IMM algorithm, the proposed AIMM-IAKF algorithm improves tracking accuracy by approximately 69%, achieves a faster model switching response, and exhibits superior stability with lower error fluctuation. The proposed framework provides a highly accurate and robust solution for tracking highly maneuvering UAVs. Full article

Multifractality in time series analysis characterizes the presence of multiple scaling exponents, indicating heterogeneous temporal structures and complex dynamical behaviors beyond simple monofractal models. In the context of digital currency markets, multifractal properties arise due to the interplay of long-range temporal correlations and heavy-tailed distributions of returns, reflecting intricate market microstructure and trader interactions. Incorporating multifractal analysis into the modeling of cryptocurrency price dynamics enhances the understanding of market inefficiencies. It may also improve volatility forecasting and facilitate the detection of critical transitions or regime shifts. Based on the multifractal cross-correlation analysis (MFCCA) whose spacial case is the multifractal detrended fluctuation analysis (MFDFA), as the most commonly used practical tools for quantifying multifractality, we applied a recently proposed method of disentangling sources of multifractality in time series to the most representative instruments from the digital market. They include Bitcoin (BTC), Ethereum (ETH), decentralized exchanges (DEX) and non-fungible tokens (NFT). The results indicate the significant role of heavy tails in generating a broad multifractal spectrum. However, they also clearly demonstrate that the primary source of multifractality encompasses the temporal correlations in the series, and without them, multifractality fades out. It appears characteristic that these temporal correlations, to a large extent, do not depend on the thickness of the tails of the fluctuation distribution. These observations, made here in the context of the digital currency market, provide a further strong argument for the validity of the proposed methodology of disentangling sources of multifractality in time series. Full article

This study examines the descriptive trajectories through which service innovation is associated with customer exit dynamics after service failures, drawing on a three-wave panel of 532 online travel agency users and employing partial least squares structural equation modeling with predictive assessment. We analyze how innovation is associated with switching intentions via brand hate and brand distrust over time. Results reveal distinct temporal patterns: service innovation is linked to consistent reductions in both hate and distrust, yet only hate emerges as a salient mediator whose marginal association with switching intensifies over time. In contrast, distrust, although mitigated by innovation, remains relatively stable and behaviorally inert. Rather than asserting a causal explanation, we document temporal associations—labelled here as a “dilution effect”—to indicate that innovation coincides with weakening negative emotions but only partial attenuation of their behavioral correlates. By distinguishing between the fading but influential role of hate and the persistent yet inert nature of distrust, this study clarifies differentiated pathways through which negative states coincide with customer exit. For managers, the results highlight the need for staged innovation strategies to dissipate hate, complemented by long-term trust-repair initiatives to address enduring distrust and reduce customer churn. Full article

This work presents a closed-form approximation to the symbol error probability (SEP) for cross-quadrature amplitude modulation (cross-QAM) schemes over $\mathit{\kappa }$–$\mathit{\mu }$ fading channels. The proposed formulation enables accurate performance evaluation while avoiding computationally expensive numerical integration. The analysis covers millimeter-wave (mmWave) frequencies at 55, 60, and 65 GHz, under both line-of-sight (LoS) and non-line-of-sight (nLoS) conditions, and for multiple transmitter–receiver polarization configurations. A key contribution of this work is the experimental validation of the theoretical expression with real channel-measurement data, which confirms the applicability of the $\mathit{\kappa }$–$\mathit{\mu }$ model in realistic mmWave scenarios. Furthermore, we perform a detailed parametric study to quantify the influence of $\mathit{\kappa }$ and $\mathit{\mu }$ on adaptive modulation performance, providing practical insights for 5G and future 6G systems. The proposed framework bridges theoretical analysis and experimental validation, offering a computationally efficient and robust tool for the design and evaluation of advanced modulation schemes in generalized fading environments. Full article

This study introduces a novel mathematical framework for a machine learning algorithm tailored to address linear regression problems in the presence of non-Gaussian estimation noise. In particular, we focus on Bernoulli–Gaussian noise, which frequently occurs in practical scenarios such as wireless communication channels and signal processing systems. We apply our framework within the context of wireless systems, particularly emphasizing its utility in channel estimation tasks. This article demonstrates the efficacy of linear regression in estimating wireless channel fading coefficients under the influence of additive Bernoulli–Gaussian noise. Through comparative analysis with Gaussian noise scenarios, we underscore the indispensability of the proposed framework. Additionally, we evaluate the performance of the maximum-likelihood estimator using gradient descent, highlighting the superiority of estimators tailored to non-Gaussian noise assumptions over those relying solely on simplified Gaussian models. Full article

Electrochemical processes within a lithium-ion battery cause electrode expansion and gas generation, thus resulting in battery swelling and, in severe cases, reliability and safety issues. This paper presents the mechanisms responsible for swelling, including thermal expansion, lithium intercalation, electrode interphase layer growth, lithium plating, and gas generation, while highlighting their dependence on material properties, design considerations, C-rate, temperature, state of charge (SoC), and voltage. The paper then discusses how swelling correlates with capacity fade, impedance rise, and thermal runaway, and demonstrates the potential of using swelling as a diagnostic and prognostic metric for battery health. Swelling models that connect microscopic mechanisms to macroscopic deformation are then presented. Finally, the paper presents strategies to mitigate swelling, including materials engineering, surface coatings, electrolyte formulation, and mechanical design modifications. Full article

Magnoliids represent one of the most basal lineages within angiosperms, and their ancestral floral morphology provides crucial insights into the evolution of flowers in angiosperms. MCM1-AGAMOUS-DEFICIENS-SRF (MADS)-box transcription factors play crucial roles in specifying floral organs. To understand their evolutionary history and functional divergence in magnoliids, we identified MADS-box genes, and conducted phylogenetic and expression analysis in 33 magnoliids and 8 other angiosperm plants. A total of 1310 MADS-box genes were identified and classified into Type I and Type II. The expansion of MADS-box genes in magnoliids mainly arose from whole-genome duplication events. In Liriodendron chinensis and Chimonanthus praecox, we identified floral homeotic MADS-box genes that are orthologous to the ABCDE model genes of floral organ identity determination. The broad expression pattern of A and B genes in floral organs and overlapping activity of ABCDE-model genes are consistent with the “shifting−fading borders” scheme proposed in basally diverging angiosperm lineages. Our results not only elucidate the driving forces underlying the diversification of MADS-box genes in magnoliids, but also shed light on the evolutionary models of floral development in angiosperms. Full article

Free-space optical (FSO) communication systems face significant challenges from atmospheric turbulence, which induces time-correlated fading and burst errors that critically affect link reliability. This paper presents a comprehensive end-to-end CCSDS O3K simulation platform with detailed atmospheric channel and pointing error modeling, enabling realistic performance evaluation. The atmospheric channel model follows ITU-R P.1622-1 recommendations and incorporates amplitude scintillation with temporal correlation using Ornstein–Uhlenbeck processes, while the pointing error model captures beam misalignment effects inherent in satellite optical links. Through extensive Monte Carlo simulations, we investigate the impact of coherence time, and interleaving depth on system performance. Results show that deeper interleaving significantly improves reliability under realistic channel conditions, providing valuable design guidance for CCSDS-compliant optical communication systems. This study does not propose new algorithms or protocols; rather, it delivers the first end-to-end CCSDS-compliant simulation framework under realistically modeled turbulence and pointing errors. Accordingly, the results offer meaningful reference value and practical benchmarks for inter-satellite optical communication research and system design. Full article

Automatic modulation classification (AMC) of received unknown signals is critical in modern communication systems, enabling intelligent signal interception and spectrum management. In this paper, we propose a wavelet-based spectrum convolutional neural network (WS-CNN) model that integrates signal processing techniques with deep learning to achieve robust classification under challenging conditions, including noise, fading, and Doppler effects. The WS-CNN model is based on wavelet analysis and a convolutional neural network (CNN). Specifically, the proposed wavelet analysis, including wavelet threshold denoising, median filtering, and continuous wavelet transformation, is used for signal preprocessing to extract features and generate a compact 2D diagram. The 2D diagram is subsequently fed into the CNN for classification. The simulation results show that the proposed WS-CNN model achieves higher classification rates across a wide range of signal-to-noise ratios (SNRs) compared with existing methods. Full article

This study presents a multiphysics simulation approach to optimize graphite-buffered bilayer anodes for enhanced energy density in lithium-ion batteries, assessing the electrochemical impact of diverse inner-layer materials, including silicon, hard carbon, lithium titanate (LTO), and metallic lithium, in pure and graphite-composite forms. A coupled finite-element model implemented in COMSOL Multiphysics 6.2 was used to integrate spherical lithium diffusion, charge conservation, and the solid electrolyte interphase (SEI) formation kinetics. The evaluated anode structure consisted of a 60 µm-thick bilayer: a 30 µm graphite surface layer coupled with a 30 µm inner layer of alternative active materials. Simulations were performed using an NMC622 cathode, LiPF₆ in EC:EMC electrolyte, at room temperature, under a charge rate of 1 C, considering realistic particle sizes (graphite: 2.5 µm; Si: 0.1 µm; hard carbon: 2.5 µm; LTO: 0.2 µm; Li metal: 0.5 µm), and evaluated over 2000 cycles. The hard carbon/graphite configuration exhibited a capacity fade of 5.8% compared with 7.1% in pure graphite. Additionally, the SEI thickness decreased to 0.20 µm (from 0.25 µm), the overpotential dropped to −17 mV (from −59 mV), and the electrolyte consumption was reduced to 20.8% (from 42.9%). The analysis highlights hard carbon and LTO inner layers as optimal trade-offs between capacity and cycle stability, whereas silicon and lithium metal significantly increased the initial capacity but accelerated SEI formation and impedance growth. These findings demonstrate the graphite-buffered bilayer’s potential to decouple interfacial degradation from high-capacity materials, providing valuable guidelines for the design of advanced lithium-ion battery anodes. Full article

This paper presents an analytical framework for the channel capacity evaluation of simultaneous wireless information and power transfer (SWIPT)-enabled opportunistic amplify-and-forward (OAF) relaying systems over Rayleigh fading channels. For the SWIPT, we employ a power splitter (PS) at the relay, which splits the received signal into the information transmission and the energy-harvesting parts. By modeling the partial channel state information (P-CSI)-based SWIPT OAF system as an equivalent non-SWIPT OAF configuration, a semi-lower bound and a new upper bound on the ergodic channel capacity are derived. A refined approximation is then obtained by averaging these bounds, yielding a simple yet accurate analytical estimate of the true capacity. Simulation results confirm that the proposed approximations closely track the actual performance across a wide range of signal-to-noise ratios (SNRs) and relay configurations. They further demonstrate that SR-based relay selection provides higher capacity than RD-based selection, primarily due to its direct influence on energy harvesting efficiency at the relay. In addition, diversity advantages manifest mainly as SNR improvements, rather than as gains in diversity order. The proposed framework thus serves as a practical and insightful tool for the capacity analysis and design of SWIPT-enabled cooperative networks, with direct relevance to energy-constrained Internet of Things (IoT) and wireless sensor applications. Full article

Leader–follower neural networks deployed over wireless platforms are subject to parameter uncertainties and stochastic channel fading. The combined impact of these effects on quasi-synchronization control remains largely unexplored. The paper addresses the problem of quasi-synchronization performance degradation in discrete-time leader–follower neural networks caused by randomly occurring parameter uncertainties and stochastic channel fading. Discrete leader–follower neural networks are constructed in state-space form. Randomly occurring parameter uncertainties in the leader neural networks are described using a Bernoulli probability distribution and time-varying parameter matrices. Channel fading is represented by a finite-state Markovian model that captures state switching. For the follower neural networks, an intermittent impulsive control strategy is designed based on linear matrix inequalities and the Lyapunov stability principle. A computable bound on the synchronization error is derived as well. A simulation study validates that the proposed impulsive control strategy effectively suppresses synchronization error caused by parameter uncertainties and Markovian channel fading, thereby ensuring mean-square boundedness. Compared with an existing method, the proposed approach consumes less control energy but achieves better performance in terms of synchronization error. The average norms of the synchronization error and the control input signal are reduced by 24.00% and 58.64%, respectively. Full article

This study explores the transmission characteristics between the links of UV(ultraviolet)-network communication under mobile conditions. Utilizing the prevalent UV-network communication network topology as a foundation, a UV-network communication model tailored to mobile scenarios was developed. This model includes a method for calculating the impulse response of the system, focusing specifically on three common network topology structures: two parallel links, co-address of the originating link, and co-address of the receiving link. The simulation and analysis conducted in this study examine the impact of various factors on the system’s impulse response, such as receiver movement speed, geometric parameters of the receivers, link spacing, and the angle between links. The results indicate that receiver movement speed significantly influences pulse response fading, with faster speeds resulting in more severe fading. Additionally, in parallel links, smaller link spacing results in stronger impulse response. Furthermore, a smaller angle between the originating and receiving co-addresses results in increased inter-link interference. The study findings in this paper will lay the foundation for the study of UV mobile self-organizing networks. Full article

The membranes of halophilic archaea are a source of novel biomaterials, mainly of isoprenoid nature, with therapeutic properties practically unraveled. Here, we explored the antitumoral activity of neutral archaeolipids (NAs, such as bacterioruberin, astaxanthin, and dihydrosqualene) present in the total archaeolipids (TAs) (a fraction from the first step of lipid extraction by the modified Blight and Dyer technique) extracted from halophilic archaea Halorubrum tebenquichense, and formulated as TA-nanoarchaeosomes (TA: polar archaeolipids (PAs): Tween 80, 5:5:4 w:w:w, TA-nanoARC). The structure of 300.3 ± 84.2 nm TA-nanoARC of 0.59 ± 0.12 polydispersity index and −20 ± 3.7 mV ζ potential as determined by SAXS modelling, revealed that NA reduced the hydrophobic core and enlarged its hydrophilic section in comparison to TA-lacking bilayers (nanoARC), while preserving the width (~50 Å) and unilamellarity. Stable to storage and nebulization, TA-nanoARC was cytotoxic on A549 cells after 48 h, with an IC50 expressed as [bacterioruberin] of 0.15 μg/mL (~0.20 µM), comparable to or lower than the IC50 of docetaxel or cisplatin. Such cytotoxicity was exerted at a concentration harmless to macrophages (mTHP-1 cells). Besides, the conditioned medium from TA-nanoARC nebulized on A549 cells reduced the expression of the CD204/SRA-1, an M2 phenotype marker, and induced pro-inflammatory activity, comparable to or to a greater extent than that induced by lipopolysaccharide, including IL-6 and TNF-α, in mTHP-1 as a model of tumor-associated macrophages. The endocytosis of TA-nanoARC by A549 cells induced Lysotracker red fluorescence to fade and blur. This suggested the internalization of the highly viscous and ordered TA-nanoARC rich in NAs and subsequent lysosomal dysfunction (and not its antioxidant activity), as responsible for the selective damage on A549 cells. These are the first results showing that nebulized TA-nanoARC, lethal to A549 cells and modulating mTHP-1 cell phenotype, may act as antitumorals in the absence of cytotoxic drugs. Full article

Accurate indoor localization is vital for smart environments and the Internet of Things (IoT) applications. Received signal strength indicator (RSSI)-based methods suffer from multipath fading, signal attenuation, and missing data. To address these issues, we developed quantum random forest indoor localization (QRF-IL), a quantum-inspired machine learning method that combines quantum random forests (QRFs) with weighted centroid regression. Each quantum decision tree in QRF uses a quantum support vector machine (QSVM) with Nyström quantum kernel estimation for efficient and accurate learning. On a public dataset, QRF-IL showed an average localization error of 2.3 m, which was reduced by 9% over a standalone QRF model and 21% over an adaptive path loss model (ADAM). Full article