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Ensuring data integrity and adaptability in aircraft health monitoring (AHM) is vital for safety-critical aviation systems. Traditional digital twin (DT) and federated learning (FL) frameworks, while effective in enabling distributed, privacy-preserving fault detection, lack mechanisms to remove the influence of corrupted or adversarial data once these have been integrated into global models. This paper proposes a novel FL–DT–FU framework that combines digital twin-based subsystem modeling, federated learning for collaborative training, and federated unlearning (FU) to support the post hoc correction of compromised model contributions. The architecture enables real-time monitoring through local DTs, secure model aggregation via FL, and targeted rollback using gradient subtraction, re-aggregation, or constrained retraining. A comprehensive simulation environment is developed to assess the impact of sensor drift, label noise, and adversarial updates across a federated fleet of aircraft. The experimental results demonstrate that FU methods restore up to 95% of model accuracy degraded by data corruption, significantly reducing false negative rates in early fault detection. The proposed system further supports auditability through cryptographic logging, aligning with aviation regulatory standards. This study establishes federated unlearning as a critical enabler for resilient, correctable, and trustworthy AI in next-generation AHM systems. Full article

(This article belongs to the Special Issue Artificial Intelligence-Driven Emerging Applications)

26 pages, 13445 KiB

Open AccessArticle

StMAPKK1 Enhances Thermotolerance in Potato (Solanum tuberosum L.) by Enhancing Antioxidant Defense and Photosynthetic Efficiency Under Heat Stress

by Xi Zhu, Yasir Majeed, Kaitong Wang, Xiaoqin Duan, Nengkang Guan, Junfu Luo, Haifei Zheng, Huafen Zou, Hui Jin, Zhuo Chen and Yu Zhang

Plants 2025, 14(15), 2289; https://doi.org/10.3390/plants14152289 - 24 Jul 2025

Abstract

The functional role of MAPKK genes in potato (Solanum tuberosum L.) under high-temperature stress remains unexplored, despite their critical importance in stress signaling and yield protection. We characterized StMAPKK1, a novel group D MAPKK localized to plasma membrane/cytoplasm. Quantitative real-time polymerase chain reaction (qRT-PCR) revealed cultivar-specific upregulation in potato (‘Atlantic’ and ‘Desiree’) leaves under heat stress (25 °C, 30 °C, and 35 °C). Transgenic lines overexpressing (OE) StMAPKK1 exhibited elevated antioxidant enzyme activity, including ascorbate peroxidase (APX), catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD), mitigating oxidative damage. Increased proline and chlorophyll accumulation and reduced oxidative stress markers, hydrogen peroxide (H₂O₂) and malondialdehyde (MDA), indicate improved cellular redox homeostasis. The upregulation of key antioxidant and heat stress-responsive genes (StAPX, StCAT1/2, StPOD12/47, StFeSOD2/3, StMnSOD, StCuZnSOD1/2, StHSFA3 and StHSP20/70/90) strengthened the enzymatic defense system, enhanced thermotolerance, and improved photosynthetic efficiency, with significant improvements in net photosynthetic rate (Pn), transpiration rate (E), and stomatal conductance (Gs) under heat stress (35 °C) in StMAPKK1-OE plants. Superior growth and biomass (plant height, plant and its root fresh and dry weights, and tuber yield) accumulation, confirming the positive role of StMAPKK1 in thermotolerance. Conversely, RNA interference (RNAi)-mediated suppression of StMAPKK1 led to a reduction in enzymatic activity, proline content, and chlorophyll levels, exacerbating oxidative stress. Downregulation of antioxidant-related genes impaired ROS scavenging capacity and declines in photosynthetic efficiency, growth, and biomass, accompanied by elevated H₂O₂ and MDA accumulation, highlighting the essential role of StMAPKK1 in heat stress adaptation. These findings highlight StMAPKK1’s potential as a key genetic target for breeding heat-tolerant potato varieties, offering a foundation for improving crop resilience in warming climates. Full article

(This article belongs to the Special Issue Cell Physiology and Stress Adaptation of Crops)

21 pages, 2784 KiB

Open AccessArticle

BIM-Based Adversarial Attacks Against Speech Deepfake Detectors

by Wendy Edda Wang, Davide Salvi, Viola Negroni, Daniele Ugo Leonzio, Paolo Bestagini and Stefano Tubaro

Electronics 2025, 14(15), 2967; https://doi.org/10.3390/electronics14152967 - 24 Jul 2025

Abstract

Automatic Speaker Verification (ASV) systems are increasingly employed to secure access to services and facilities. However, recent advances in speech deepfake generation pose serious threats to their reliability. Modern speech synthesis models can convincingly imitate a target speaker’s voice and generate realistic synthetic audio, potentially enabling unauthorized access through ASV systems. To counter these threats, forensic detectors have been developed to distinguish between real and fake speech. Although these models achieve strong performance, their deep learning nature makes them susceptible to adversarial attacks, i.e., carefully crafted, imperceptible perturbations in the audio signal that make the model unable to classify correctly. In this paper, we explore adversarial attacks targeting speech deepfake detectors. Specifically, we analyze the effectiveness of Basic Iterative Method (BIM) attacks applied in both time and frequency domains under white- and black-box conditions. Additionally, we propose an ensemble-based attack strategy designed to simultaneously target multiple detection models. This approach generates adversarial examples with balanced effectiveness across the ensemble, enhancing transferability to unseen models. Our experimental results show that, although crafting universally transferable attacks remains challenging, it is possible to fool state-of-the-art detectors using minimal, imperceptible perturbations, highlighting the need for more robust defenses in speech deepfake detection. Full article

(This article belongs to the Special Issue Selected Papers from Young Researchers in Signal/Image/Video Coding and Processing, 2nd Edition)

18 pages, 6818 KiB

Open AccessArticle

Deep Learning-Based Min-Entropy-Accelerated Evaluation for High-Speed Quantum Random Number Generation

by Xiaomin Guo, Wenhe Zhou, Yue Luo, Xiangyu Meng, Jiamin Li, Yaoxing Bian, Yanqiang Guo and Liantuan Xiao

Entropy 2025, 27(8), 786; https://doi.org/10.3390/e27080786 - 24 Jul 2025

Abstract

Secure communication is critically dependent on high-speed and high-security quantum random number generation (QRNG). In this work, we present a responsive approach to enhance the efficiency and security of QRNG by leveraging polarization-controlled heterodyne detection to simultaneously measure the quadrature amplitude and phase fluctuations of vacuum shot noise. To address the practical non-idealities inherent in QRNG systems, we investigate the critical impacts of imbalanced heterodyne detection, amplitude–phase overlap, finite-size effects, and security parameters on quantum conditional min-entropy derived from the entropy uncertainty principle. It effectively mitigates the overestimation of randomness and fortifies the system against potential eavesdropping attacks. For a high-security parameter of

10^{- 20}

, QRNG achieves a true random bit extraction ratio of 83.16% with a corresponding real-time speed of 37.25 Gbps following a 16-bit analog-to-digital converter quantization and 1.4 GHz bandwidth extraction. Furthermore, we develop a deep convolutional neural network for rapid and accurate entropy evaluation. The entropy evaluation of 13,473 sets of quadrature data is processed in 68.89 s with a mean absolute percentage error of 0.004, achieving an acceleration of two orders of magnitude in evaluation speed. Extracting the shot noise with full detection bandwidth, the generation rate of QRNG using dual-quadrature heterodyne detection exceeds 85 Gbps. The research contributes to advancing the practical deployment of QRNG and expediting rapid entropy assessment. Full article

(This article belongs to the Section Quantum Information)

27 pages, 1438 KiB

Open AccessArticle

Techno-Economic Analysis of Hydrogen Hybrid Vehicles

by Dapai Shi, Jiaheng Wang, Kangjie Liu, Chengwei Sun, Zhenghong Wang and Xiaoqing Liu

World Electr. Veh. J. 2025, 16(8), 418; https://doi.org/10.3390/wevj16080418 - 24 Jul 2025

Abstract

Driven by carbon neutrality and peak carbon policies, hydrogen energy, due to its zero-emission and renewable properties, is increasingly being used in hydrogen fuel cell vehicles (H-FCVs). However, the high cost and limited durability of H-FCVs hinder large-scale deployment. Hydrogen internal combustion engine hybrid electric vehicles (H-HEVs) are emerging as a viable alternative. Research on the techno-economics of H-HEVs remains limited, particularly in systematic comparisons with H-FCVs. This paper provides a comprehensive comparison of H-FCVs and H-HEVs in terms of total cost of ownership (TCO) and hydrogen consumption while proposing a multi-objective powertrain parameter optimization model. First, a quantitative model evaluates TCO from vehicle purchase to disposal. Second, a global dynamic programming method optimizes hydrogen consumption by incorporating cumulative energy costs into the TCO model. Finally, a genetic algorithm co-optimizes key design parameters to minimize TCO. Results show that with a battery capacity of 20.5 Ah and an H-FC peak power of 55 kW, H-FCV can achieve optimal fuel economy and hydrogen consumption. However, even with advanced technology, their TCO remains higher than that of H-HEVs. H-FCVs can only become cost-competitive if the unit power price of the fuel cell system is less than 4.6 times that of the hydrogen engine system, assuming negligible fuel cell degradation. In the short term, H-HEVs should be prioritized. Their adoption can also support the long-term development of H-FCVs through a complementary relationship. Full article

(This article belongs to the Special Issue Electric Vehicle Technology Development, Energy and Environmental Implications, and Decarbonization: 2nd Edition)

25 pages, 19515 KiB

Open AccessArticle

Towards Efficient SAR Ship Detection: Multi-Level Feature Fusion and Lightweight Network Design

by Wei Xu, Zengyuan Guo, Pingping Huang, Weixian Tan and Zhiqi Gao

Remote Sens. 2025, 17(15), 2588; https://doi.org/10.3390/rs17152588 - 24 Jul 2025

Abstract

Synthetic Aperture Radar (SAR) provides all-weather, all-time imaging capabilities, enabling reliable maritime ship detection under challenging weather and lighting conditions. However, most high-precision detection models rely on complex architectures and large-scale parameters, limiting their applicability to resource-constrained platforms such as satellite-based systems, where model size, computational load, and power consumption are tightly restricted. Thus, guided by the principles of lightweight design, robustness, and energy efficiency optimization, this study proposes a three-stage collaborative multi-level feature fusion framework to reduce model complexity without compromising detection performance. Firstly, the backbone network integrates depthwise separable convolutions and a Convolutional Block Attention Module (CBAM) to suppress background clutter and extract effective features. Building upon this, a cross-layer feature interaction mechanism is introduced via the Multi-Scale Coordinated Fusion (MSCF) and Bi-EMA Enhanced Fusion (Bi-EF) modules to strengthen joint spatial-channel perception. To further enhance the detection capability, Efficient Feature Learning (EFL) modules are embedded in the neck to improve feature representation. Experiments on the Synthetic Aperture Radar (SAR) Ship Detection Dataset (SSDD) show that this method, with only 1.6 M parameters, achieves a mean average precision (mAP) of 98.35% in complex scenarios, including inshore and offshore environments. It balances the difficult problem of being unable to simultaneously consider accuracy and hardware resource requirements in traditional methods, providing a new technical path for real-time SAR ship detection on satellite platforms. Full article

(This article belongs to the Topic Radar Signal and Data Processing with Applications, 2nd Edition)

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17 pages, 633 KiB

Open AccessReview

Passenger Service Time at the Platform–Train Interface: A Review of Variability, Design Factors, and Crowd Management Implications Based on Laboratory Experiments

by Sebastian Seriani, Vicente Aprigliano, Vinicius Minatogawa, Alvaro Peña, Ariel Lopez and Felipe Gonzalez

Appl. Sci. 2025, 15(15), 8256; https://doi.org/10.3390/app15158256 - 24 Jul 2025

Abstract

This paper reviews the variability of passenger service time (PST) at the platform–train interface (PTI), a critical performance indicator in metro systems shaped by the infrastructure design, affecting passenger behavior and accessibility. Despite its operational importance, PST remains underexplored in relation to crowd management strategies. This review synthesizes findings from empirical and experimental research to clarify the main factors influencing PST and their implications for platform-level interventions. Key contributors to PST variability include door width, gap dimensions, crowd density, and user characteristics such as mobility impairments. Design elements—such as platform edge doors, yellow safety lines, and vertical handrails—affect flow efficiency and spatial dynamics during boarding and alighting. Advanced tracking and simulation tools (e.g., PeTrack and YOLO-based systems) are identified as essential for evaluating pedestrian behavior and supporting Level of Service (LOS) analysis. To complement traditional LOS metrics, the paper introduces Level of Interaction (LOI) and a multidimensional LOS framework that captures spatial conflicts and user interaction zones. Control strategies such as platform signage, seating arrangements, and visual cues are also reviewed, with experimental evidence showing that targeted design interventions can reduce PST by up to 35%. The review highlights a persistent gap between academic knowledge and practical implementation. It calls for greater integration of empirical evidence into policy, infrastructure standards, and operational contracts. Ultimately, it advocates for human-centered, data-informed approaches to PTI planning that enhance efficiency, inclusivity, and resilience in high-demand transit environments. Full article

(This article belongs to the Special Issue Research Advances in Rail Transport Infrastructure)

27 pages, 8594 KiB

Open AccessArticle

An Explainable Hybrid CNN–Transformer Architecture for Visual Malware Classification

by Mohammed Alshomrani, Aiiad Albeshri, Abdulaziz A. Alsulami and Badraddin Alturki

Sensors 2025, 25(15), 4581; https://doi.org/10.3390/s25154581 - 24 Jul 2025

Abstract

Malware continues to develop, posing significant challenges for traditional signature-based detection systems. Visual malware classification, which transforms malware binaries into grayscale images, has emerged as a promising alternative for recognizing patterns in malicious code. This study presents a hybrid deep learning architecture that combines the local feature extraction capabilities of ConvNeXt-Tiny (a CNN-based model) with the global context modeling of the Swin Transformer. The proposed model is evaluated using three benchmark datasets—Malimg, MaleVis, VirusMNIST—encompassing 61 malware classes. Experimental results show that the hybrid model achieved a validation accuracy of 94.04%, outperforming both the ConvNeXt-Tiny-only model (92.45%) and the Swin Transformer-only model (90.44%). Additionally, we extended our validation dataset to two more datasets—Maldeb and Dumpware-10—to strengthen the empirical foundation of our work. The proposed hybrid model achieved competitive accuracy on both, with 98% on Maldeb and 97% on Dumpware-10. To enhance model interpretability, we employed Gradient-weighted Class Activation Mapping (Grad-CAM), which visualizes the learned representations and reveals the complementary nature of CNN and Transformer modules. The hybrid architecture, combined with explainable AI, offers an effective and interpretable approach for malware classification, facilitating better understanding and trust in automated detection systems. In addition, a real-time deployment scenario is demonstrated to validate the model’s practical applicability in dynamic environments. Full article

(This article belongs to the Special Issue Cyber Security and AI—2nd Edition)

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17 pages, 362 KiB

Open AccessArticle

Fractional Time-Scales Noether’s Theorem for Non-Standard Birkhoffian System

by Zhenyu Wu and Chuanjing Song

Fractal Fract. 2025, 9(8), 489; https://doi.org/10.3390/fractalfract9080489 - 24 Jul 2025

Abstract

In this work, Noether symmetries and conserved quantities of a non-standard Birkhoffian system based on the Caputo

Δ

Pfaff–Birkhoff principle on time scales are studied. Firstly, equations of motion for Caputo

Δ

non-standard Birkhoffian systems are set up from Caputo

Δ

variational principle. [...] Read more.

In this work, Noether symmetries and conserved quantities of a non-standard Birkhoffian system based on the Caputo

Δ

Pfaff–Birkhoff principle on time scales are studied. Firstly, equations of motion for Caputo

Δ

non-standard Birkhoffian systems are set up from Caputo

Δ

variational principle. Secondly, invariance of Caputo non-standard Pfaff action on time scales is demonstrated, thus giving rise to Noether symmetry criterions which establish Noether’s theorems for the corresponding system. The validity of the methods and results presented in the paper is illustrated by means of examples provided at the end of the article. Full article

(This article belongs to the Section Mathematical Physics)

15 pages, 2636 KiB

Open AccessArticle

Chest Compression Skill Evaluation System Using Pose Estimation and Web-Based Application

by Ryota Watanabe, Jahidul Islam, Xin Zhu, Emiko Kaneko, Ken Iseki and Lei Jing

Appl. Sci. 2025, 15(15), 8252; https://doi.org/10.3390/app15158252 - 24 Jul 2025

Abstract

It is critical to provide life-sustaining treatment to OHCA patients before ambulance care arrives. However, incorrectly performed resuscitation maneuvers reduce the chances of survival and recovery for the victims. Therefore, we must train regularly and learn how to do it correctly. To facilitate regular chest compression training, this study aims to improve the accuracy of a chest compression evaluation system using posture estimation and to develop a web application. To analyze and enhance accuracy, the YOLOv8 posture estimation was used to examine compression depth, recoil, and tempo, and its accuracy was compared to that of the manikin, which has evaluation systems. We conducted comparative experiments with different camera angles and heights to optimize the accuracy of the evaluation. The experimental results showed that an angle of 30 degrees and a height of 50 cm produced superior accuracy. For web application development, a system has been designed to allow users to upload videos for analysis and obtain appropriate compression parameters. The usability evaluation of the application confirmed its ease of use and accessibility, and positive feedback was obtained. In the conclusion, these findings suggest that optimizing recording conditions significantly improves the accuracy of posture-based chest compression evaluation. Future work will focus on enhancing real-time feedback functionality and improving the user interface of the web application. Full article

(This article belongs to the Special Issue Machine Learning in Biomedical Applications)

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15 pages, 819 KiB

Open AccessArticle

Edge-FLGuard+: A Federated and Lightweight Anomaly Detection Framework for Securing 5G-Enabled IoT in Smart Homes

by Manuel J. C. S. Reis

Future Internet 2025, 17(8), 329; https://doi.org/10.3390/fi17080329 - 24 Jul 2025

Abstract

The rapid expansion of 5G-enabled Internet of Things (IoT) devices in smart homes has heightened the need for robust, privacy-preserving, and real-time cybersecurity mechanisms. Traditional cloud-based security systems often face latency and privacy bottlenecks, making them unsuitable for edge-constrained environments. In this work, we propose Edge-FLGuard+, a federated and lightweight anomaly detection framework specifically designed for 5G-enabled smart home ecosystems. The framework integrates edge AI with federated learning to detect network and device anomalies while preserving user privacy and reducing cloud dependency. A lightweight autoencoder-based model is trained across distributed edge nodes using privacy-preserving federated averaging. We evaluate our framework using the TON_IoT and CIC-IDS2018 datasets under realistic smart home attack scenarios. Experimental results show that Edge-FLGuard+ achieves high detection accuracy (≥95%) with minimal communication and computational overhead, outperforming traditional centralized and local-only baselines. Our results demonstrate the viability of federated AI models for real-time security in next-generation smart home networks. Full article

(This article belongs to the Special Issue Edge-Cloud Computing and Federated-Split Learning in Internet of Things—Second Edition)

14 pages, 1765 KiB

Open AccessArticle

Microfluidic System Based on Flexible Structures for Point-of-Care Device Diagnostics with Electrochemical Detection

by Kasper Marchlewicz, Robert Ziółkowski, Kamil Żukowski, Jakub Krzemiński and Elżbieta Malinowska

Biosensors 2025, 15(8), 483; https://doi.org/10.3390/bios15080483 - 24 Jul 2025

Abstract

Infectious diseases poses a growing public health challenge. The COVID-19 pandemic has further emphasized the urgent need for rapid, accessible diagnostics. This study presents the development of an integrated, flexible point-of-care (POC) diagnostic system for the rapid detection of Corynebacterium diphtheriae, the pathogen responsible for diphtheria. The system comprises a microfluidic polymerase chain reaction (micro-PCR) device and an electrochemical DNA biosensor, both fabricated on flexible substrates. The micro-PCR platform offers rapid DNA amplification overcoming the time limitations of conventional thermocyclers. The biosensor utilizes specific molecular recognition and an electrochemical transducer to detect the amplified DNA fragment, providing a clear and direct indication of the pathogen’s presence. The combined system demonstrates the effective amplification and detection of a gene fragment from a toxic strain of C. diphtheriae, chosen due to its increasing incidence. The design leverages lab-on-a-chip (LOC) and microfluidic technologies to minimize reagent use, reduce cost, and support portability. Key challenges in microsystem design—such as flow control, material selection, and reagent compatibility—were addressed through optimized fabrication techniques and system integration. This work highlights the feasibility of using flexible, integrated microfluidic and biosensor platforms for the rapid, on-site detection of infectious agents. The modular and scalable nature of the system suggests potential for adaptation to a wide range of pathogens, supporting broader applications in global health diagnostics. The approach provides a promising foundation for next-generation POC diagnostic tools. Full article

(This article belongs to the Special Issue Microfluidics for Sample Pretreatment)

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27 pages, 6134 KiB

Open AccessArticle

Research on BPNN-MDSG Hybrid Modeling Method for Full-Cycle Simulation of Surge in Altitude Test Facility Compressor System

by Yang Su, Xuejiang Chen and Xin Wang

Appl. Sci. 2025, 15(15), 8253; https://doi.org/10.3390/app15158253 - 24 Jul 2025

Abstract

Altitude Test Facility (ATF) compressor systems are widely used in aero-engine tests. These systems achieve the control of gas pressure and transport through complex operation processes. With advancements in the aviation industry, there is a growing demand for higher performance, greater safety, and more energy efficiency in digital ATF systems. Hybrid modeling is a technology that combines many methods and can meet these requirements. The Modular Dynamic System Greitzer (MDSG) compressor model, including mechanistic and data-driven modeling approaches, is combined with a neural network to obtain a BPNN-MDSG hybrid modeling method for the digital turbine system. The digital simulation is linked with the physical sensors of the ATF system to realize real-time simulation and monitoring. The steady and dynamic conditions of the actual system are simulated in virtual space. Compared with the actual results, the average error of steady mass flow is less than 3%, and the error of pressure is less than 1%. The average error of dynamic mass flow is less than 5%, and the error of pressure is less than 3%. The simulation and characteristic predictions are carried out in BPNN-MDSG virtual space. The anti-surge characteristics of the ATF system under start-up conditions are obtained. The full-condition anti-surge operation map of the system is obtained, which provides guidance for the actual operation of the ATF system. Full article

(This article belongs to the Special Issue Computational Fluid Dynamics for Aerospace and Aeronautical Applications)

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22 pages, 1486 KiB

Open AccessArticle

Research on the Data-Driven Identification of Control Parameters for Voltage Ride-Through in Energy Storage Systems

by Liming Bo, Jiangtao Wang, Xu Zhang, Yimeng Su, Xueting Cheng, Zhixuan Zhang, Shenbing Ma, Jiyu Wang and Xiaoyu Fang

Appl. Sci. 2025, 15(15), 8249; https://doi.org/10.3390/app15158249 - 24 Jul 2025

Abstract

The large-scale integration of wind power, photovoltaic systems, and energy storage systems (ESSs) into power grids has increasingly influenced the transient stability of power systems due to their dynamic response characteristics. Considering the commercial confidentiality of core control parameters from equipment manufacturers, parameter identification has become a crucial approach for analyzing ESS dynamic behaviors during high-voltage ride-through (HVRT) and low-voltage ride-through (LVRT) and for optimizing control strategies. In this study, we present a multidimensional feature-integrated parameter identification framework for ESSs, combining a multi-scenario voltage disturbance testing environment built on a real-time laboratory platform with field-measured data and enhanced optimization algorithms. Focusing on the control characteristics of energy storage converters, a non-intrusive identification method for grid-connected control parameters is proposed based on dynamic trajectory feature extraction and a hybrid optimization algorithm that integrates an improved particle swarm optimization (PSO) algorithm with gradient-based coordination. The results demonstrate that the proposed approach effectively captures the dynamic coupling mechanisms of ESSs under dual-mode operation (charging and discharging) and voltage fluctuations. By relying on measured data for parameter inversion, the method circumvents the limitations posed by commercial confidentiality, providing a novel technical pathway to enhance the fault ride-through (FRT) performance of energy storage systems (ESSs). In addition, the developed simulation verification framework serves as a valuable tool for security analysis in power systems with high renewable energy penetration. Full article

(This article belongs to the Section Electrical, Electronics and Communications Engineering)

37 pages, 2378 KiB

Open AccessArticle

A Quantile Spillover-Driven Markov Switching Model for Volatility Forecasting: Evidence from the Cryptocurrency Market

by Fangfang Zhu, Sicheng Fu and Xiangdong Liu

Mathematics 2025, 13(15), 2382; https://doi.org/10.3390/math13152382 - 24 Jul 2025

Abstract

This paper develops a novel modeling framework that integrates time-varying quantile-based spillover effects into a regime-switching realized volatility model. A dynamic spillover factor is constructed by identifying the most influential contributors to Bitcoin’s realized volatility across different quantile levels. This quantile-layered structure enables the model to capture heterogeneous spillover paths under varying market conditions at a macro level while also enhancing the sensitivity of volatility regime identification via its incorporation into a time-varying transition probability (TVTP) Markov-switching mechanism at a micro level. Empirical results based on the cryptocurrency market demonstrate the superior forecasting performance of the proposed TVTP-MS-HAR model relative to standard benchmark models. The model exhibits strong capability in identifying state-dependent spillovers and capturing nonlinear market dynamics. The findings further reveal an asymmetric dual-tail amplification and time-varying interconnectedness in the spillover effects, along with a pronounced asymmetry between market capitalization and systemic importance. Compared to decomposition-based approaches, the X-RV type of models—especially when combined with the proposed quantile-driven factor—offers improved robustness and predictive accuracy in the presence of extreme market behavior. This paper offers a coherent approach that bridges phenomenon identification, source localization, and predictive mechanism construction, contributing to both the academic understanding and practical risk assessment of cryptocurrency markets. Full article

(This article belongs to the Section E5: Financial Mathematics)

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