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-contractive condition on multi-valued mappings in a complete strong b-metric space. The theorems presented in this paper extend, generalize, and improve various existing results in the literature. To demonstrate the superiority of the results, we present multiple examples throughout this article and two applications: one in dynamic programming and another in ordinary differential equations. Moreover, the proposed results provide stronger and more general conclusions compared to several well-known fixed point theorems in the literature. In particular, our findings highlight the novelty and superiority of the

α

ψ

-contractive framework in the setting of strong b-metric spaces, offering broader applicability and deeper insight into both theoretical and applied contexts. Full article

(This article belongs to the Special Issue Fixed Point, Optimization, and Applications: 3rd Edition)

20 pages, 1698 KB

Open AccessArticle

Calibration and Testing of Discrete Element Simulation Parameters for the Presoaked Cyperus esculentus L. Rubber Interface Using EDEM

by Zhenyu Liu, Jianguo Yan, Fei Liu and Lijuan Wang

Agronomy 2025, 15(10), 2440; https://doi.org/10.3390/agronomy15102440 - 21 Oct 2025

Abstract

To address the challenges in precision seeding of Cyperus esculentus L. seeds caused by their irregular shape and uneven surface, this study investigates the effect of soaking pretreatment on seed germination and adopts rubber-based seed suction holes to improve adsorption performance. Subsequently, calibration and experiments on discrete element simulation parameters were carried out. Initially, by setting four soaking time gradients (0, 24, 48, and 72 h), the optimal soaking duration was determined. Furthermore, through free-fall collision tests, static friction tests, and rolling friction tests, combined with the Plackett–Burman design, steepest ascent experiments, and Box–Behnken response surface methodology, the contact parameters between seeds and between seeds and rubber suction holes were calibrated and optimized. The results showed that the static friction coefficient (D) between seeds, the rolling friction coefficient (E) between seeds, and the rolling friction coefficient (H) between seeds and rubber have significant effects on the stacking angle. The optimal parameter combination obtained was D = 0.592, E = 0.325, H = 0.171. Validation tests on the dynamic stacking angle demonstrated that the relative error between the simulated and physical test values was only 1.89%, confirming the accuracy of the parameters. This study provides reliable parameter references for the design and simulation optimization of precision seed metering devices for C. esculentus after soaking pretreatment. Full article

(This article belongs to the Section Precision and Digital Agriculture)

23 pages, 4482 KB

Open AccessArticle

D²T² Analysis of a Loss of Main Feed Water Accident

by Silvia Tolo and John Andrews

Systems 2025, 13(10), 927; https://doi.org/10.3390/systems13100927 - 21 Oct 2025

Abstract

The availability of accurate models capturing the realistic behaviour of complex systems is critical for the safe operation and optimal management of nuclear installations. However, the dynamic nature of such systems and the resulting dense network of interdependencies existing among their parts are no match for current risk modelling techniques, which rely on oversimplifying premises. Dependencies are often simplified or ignored, with conservative assumptions introduced to compensate, leading to results of uncertain realism. Alternative methods address these limitations but often remain difficult to scale, interpret, or integrate into established Probabilistic Safety Assessment practice. The Dynamic and Dependent Tree Theory (

D^{2} T^{2}

) offers a bridging framework that preserves the familiar FT/ET structure while enabling dependencies to be represented directly at the basic-event, intermediate, or subsystem level through compact submodels. This paper applies

D^{2} T^{2}

to a loss of main feed water accident in a boiling water reactor, capturing dependencies from maintenance strategies to subsystem interactions. Results show that

D^{2} T^{2}

improves reliability predictions compared with conventional FT/ET, aligns closely with dynamic benchmarks, and remains computationally tractable. Beyond accuracy, the approach makes modelling assumptions explicit and transparent, promoting deeper system understanding while lowering barriers to adoption in safety-critical applications. Full article

(This article belongs to the Special Issue Advances in Reliability Engineering for Complex Systems)

20 pages, 1922 KB

Open AccessArticle

On the Use of Machine Learning Methods for EV Battery Pack Data Forecast Applied to Reconstructed Dynamic Profiles

by Joaquín de la Vega, Jordi-Roger Riba and Juan Antonio Ortega-Redondo

Appl. Sci. 2025, 15(20), 11291; https://doi.org/10.3390/app152011291 - 21 Oct 2025

Abstract

Lithium-ion batteries are essential to electric vehicles, so it is crucial to continuously monitor and control their health. However, since today’s battery packs consist of hundreds or thousands of cells, monitoring all of them is challenging. Additionally, the performance of the entire battery pack is often limited by the weakest cell. Therefore, developing effective monitoring techniques that can reliably forecast the remaining time to depletion (RTD) of lithium-ion battery cells is essential for safe and efficient battery management. However, even in robust systems, this data can be lost due to electromagnetic interference, microcontroller malfunction, failed contacts, and other issues. Gaps in voltage measurements compromise the accuracy of data-driven forecasts. This work systematically evaluates how different voltage reconstruction methods affect the performance of recurrent neural network (RNN) forecast models trained to predict RTD through quantile regression. The paper uses experimental battery pack data based on the behavior of an electric vehicle under dynamic driving conditions. Artificial gaps of 500 s were introduced at the beginning, middle, and end of each discharge phase, resulting in over 4300 reconstruction cases. Four reconstruction methods were considered: a zero-order hold (ZOH), an autoregressive integrated moving average (ARIMA) model, a gated recurrent unit (GRU) model, and a hybrid unscented Kalman filter (UKF) model. The results presented here reveal that the UKF model, followed by the GRU model, outperform alternative reconstruction methods. These models minimize signal degradation and provide forecasts similar to the original past data signal, thus achieving the highest coefficient of determination and the lowest error indicators. The reconstructed signals were fed into LSTM and GRU RNNs to estimate RTD, which produced confidence intervals and median values for decision-making purposes. Full article

(This article belongs to the Special Issue AI-Based Machinery Health Monitoring)

21 pages, 1264 KB

Open AccessArticle

DMSR: Dynamic Multipath Secure Routing Against Eavesdropping in Space-Ground Integrated Optical Networks

by Guan Wang and Xingmei Wang

Photonics 2025, 12(10), 1039; https://doi.org/10.3390/photonics12101039 - 21 Oct 2025

Abstract

With the continuous growth of global communication demands, the space-ground integrated optical network (SGION), composed of the satellite optical network (SON) and terrestrial optical network (TON), has gradually become a critical component of global communication systems due to its wide coverage, low latency, and large bandwidth. However, although the high directivity of laser communication can significantly enhance the security of data transmission, it still carries the risk of being eavesdropped on during the process of service routing. To resist eavesdropping attacks during service transmission in the SGION, this paper proposes a secure routing scheme named dynamic multipath secure routing (DMSR). In DMSR, a metric called the service eavesdropping ratio (SER) is defined to quantify the service leakage severity. The objective of DMSR is to reduce each service’s SER by switching its routing path proactively. To realize DMSR, heuristic algorithms are developed to sequentially search for optimal routing paths for service path switching in the TON and SGION. Finally, simulation results demonstrate that DMSR can achieve trade-offs between secure service transmission and network performance at different levels by adjusting its system parameters. Furthermore, the DMSR scheme significantly reduces the SER compared to the baseline schemes, while introducing acceptable increases in computation overhead and service latency. Full article

(This article belongs to the Special Issue Advancements and Future Perspectives in All-Optical Detection and Reliability Improvement Technologies)

43 pages, 8058 KB

Open AccessArticle

Biomechanical Design and Adaptive Sliding Mode Controlof a Human Lower Extremity Exoskeleton for Rehabilitation Applications

by Sk K. Hasan and Nafizul Alam

Robotics 2025, 14(10), 146; https://doi.org/10.3390/robotics14100146 - 21 Oct 2025

Abstract

The human lower extremity plays a vital role in locomotion, posture, and weight-bearing through coordinated motion at the hip, knee, and ankle joints. These joints facilitate essential functions including flexion, extension, and internal and external rotation. To address mobility impairments through personalized therapy, this study presents the design, dynamic modeling, and control of a four-degree-of-freedom (4-DOF) lower limb exoskeleton robot. The system actuates hip flexion–extension and internal–external rotation, knee flexion–extension, and ankle dorsiflexion–plantarflexion. Anatomically aligned joint axes were incorporated to enhance biomechanical compatibility and reduce user discomfort. A detailed CAD model ensures ergonomic fit, modular adjustability, and the integration of actuators and sensors. The exoskeleton robot dynamic model, derived using Lagrangian mechanics, incorporates subject-specific anthropometric parameters to accurately reflect human biomechanics. A conventional sliding mode controller (SMC) was implemented to ensure robust trajectory tracking under model uncertainties. To overcome limitations of conventional SMC, an adaptive sliding mode controller with boundary layer-based chattering suppression was developed. Simulations in MATLAB/Simulink demonstrate that the adaptive controller achieves smoother torque profiles, minimizes high-frequency oscillations, and improves tracking accuracy. This work establishes a comprehensive framework for anatomically congruent exoskeleton design and robust control, supporting the future integration of physiological intent detection and clinical validation for neurorehabilitation applications. Full article

(This article belongs to the Section Neurorobotics)

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12 pages, 3612 KB

Open AccessArticle

A Broad-Temperature-Range Wavelength Tracking System Employing a Thermistor Monitoring Circuit and a Tunable Optical Filter

by Ju Wang, Manyun Liu, Hao Luo, Xuemin Su, Chuang Ma and Jinlong Yu

Photonics 2025, 12(10), 1038; https://doi.org/10.3390/photonics12101038 - 21 Oct 2025

Abstract

A broad-temperature-range wavelength tracking system employing a thermistor monitoring circuit and a tunable optical filter is proposed and experimentally demonstrated. In this scheme, a thermistor monitoring circuit is utilized to acquire the real-time resistance values of a distributed feedback laser diode (DFB-LD). When the mapping relationship curve among thermistor resistance, temperature, and center wavelength of the DFB-LD is established, the drive voltage of the narrowband tunable optical filter is dynamically adjusted to regulate its filter window. Therefore, wavelength tracking is achieved by matching the filter window and the center wavelength of the DFB-LD. The experimental results show that the proposed system can achieve adaptive wavelength tracking within the operation band of 1539.4 nm to 1548.6 nm across a temperature range from −40 °C to 60 °C. The wavelength detection resolution and the minimum step of wavelength control are better than 0.79 pm and 0.1 nm, respectively. By exploiting the conversion characteristics between the thermistor and the center wavelength of the DFB-LD, this approach transforms laser wavelength detection into a low-cost, real-time electrical measurement, significantly enhancing transmission stability and reliability of laser sources in complex thermal environments. Full article

(This article belongs to the Special Issue Microwave Photonics: Advances and Applications)

26 pages, 32868 KB

Open AccessArticle

Low-Altitude Multi-Object Tracking via Graph Neural Networks with Cross-Attention and Reliable Neighbor Guidance

by Hanxiang Qian, Xiaoyong Sun, Runze Guo, Shaojing Su, Bing Ding and Xiaojun Guo

Remote Sens. 2025, 17(20), 3502; https://doi.org/10.3390/rs17203502 - 21 Oct 2025

Abstract

In low-altitude multi-object tracking (MOT), challenges such as frequent inter-object occlusion and complex non-linear motion disrupt the appearance of individual targets and the continuity of their trajectories, leading to frequent tracking failures. We posit that the relatively stable spatio-temporal relationships within object groups (e.g., pedestrians and vehicles) offer powerful contextual cues to resolve such ambiguities. We present NOWA-MOT (Neighbors Know Who We Are), a novel tracking-by-detection framework designed to systematically exploit this principle through a multi-stage association process. We make three primary contributions. First, we introduce a Low-Confidence Occlusion Recovery (LOR) module that dynamically adjusts detection scores by integrating IoU, a novel Recovery IoU (RIoU) metric, and location similarity to surrounding objects, enabling occluded targets to participate in high-priority matching. Second, for initial data association, we propose a Graph Cross-Attention (GCA) mechanism. In this module, separate graphs are constructed for detections and trajectories, and a cross-attention architecture is employed to propagate rich contextual information between them, yielding highly discriminative feature representations for robust matching. Third, to resolve the remaining ambiguities, we design a cascaded Matched Neighbor Guidance (MNG) module, which uniquely leverages the reliably matched pairs from the first stage as contextual anchors. Through MNG, star-shaped topological features are built for unmatched objects relative to their stable neighbors, enabling accurate association even when intrinsic features are weak. Our comprehensive experimental evaluation on the VisDrone2019 and UAVDT datasets confirms the superiority of our approach, achieving state-of-the-art HOTA scores of 51.34% and 62.69%, respectively, and drastically reducing identity switches compared to previous methods. Full article

(This article belongs to the Special Issue Multi-Object Detection and Feature Extraction of Remote Sensing Images)

25 pages, 5852 KB

Open AccessArticle

ADEmono-SLAM: Absolute Depth Estimation for Monocular Visual Simultaneous Localization and Mapping in Complex Environments

by Kaijun Zhou, Zifei Yu, Xiancheng Zhou, Ping Tan, Yunpeng Yin and Huanxin Luo

Electronics 2025, 14(20), 4126; https://doi.org/10.3390/electronics14204126 - 21 Oct 2025

Abstract

Aiming to address the problems of scale uncertainty and dynamic object interference in monocular visual simultaneous localization and mapping (SLAM), this paper proposes an absolute depth estimation network-based monocular visual SLAM method, namely, ADEmono-SLAM. Firstly, some detail features including oriented fast and rotated brief (ORB) features of input image are extracted. An object depth map is obtained through an absolute depth estimation network, and some reliable feature points are obtained by a dynamic interference filtering algorithm. Through these operations, the potential dynamic interference points are eliminated. Secondly, the absolute depth image is obtained by using the monocular depth estimation network, in which a dynamic point elimination algorithm using target detection is designed to eliminate dynamic interference points. Finally, the camera poses and map information are obtained by static feature point matching optimization. Thus, the remote points are randomly filtered by combining the depth values of the feature points. Experiments on the karlsruhe institute of technology and toyota technological institute (KITTI) dataset, technical university of munich (TUM) dataset, and mobile robot platform show that the proposed method can obtain sparse maps with absolute scale and improve the pose estimation accuracy of monocular SLAM in various scenarios. Compared with existing methods, the maximum error is reduced by about 80%, which provides an effective method or idea for the application of monocular SLAM in the complex environment. Full article

(This article belongs to the Special Issue Digital Intelligence Technology and Applications, 2nd Edition)

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24 pages, 3676 KB

Open AccessArticle

Open-Access Simulation Platform and Motion Control Design for a Surface Robotic Vehicle in the VRX Environment

by Brayan Saldarriaga-Mesa, Julio Montesdeoca, Dennys Báez, Flavio Roberti and Juan Marcos Toibero

Robotics 2025, 14(10), 147; https://doi.org/10.3390/robotics14100147 - 21 Oct 2025

Abstract

This work presents an open-source simulation framework designed to extend the capabilities of the VRX environment for developing and validating control strategies for surface robotic vehicles. The platform features a custom monohull, kayak-type USV with four thrusters in differential configuration, represented with a complete graphical mockup consistent with its physical design and modeled with realistic dynamics and sensor integration. A thrust mapping function was calibrated using manufacturer data, and the vehicle’s behavior was characterized using a simplified Fossen model with parameters identified via Least Squares estimation. Multiple motion controllers, including velocity, position, trajectory tracking, and path guidance, were implemented and evaluated in a variety of wave and wind scenarios designed to test the full vehicle dynamics and closed-loop behavior. In addition to extending the VRX simulator, this work introduces a new USV model, a calibrated thrust response, and a set of model-based controllers validated in high-fidelity marine conditions. The resulting framework constitutes a reproducible and extensible resource for the marine robotics community, with direct applications in robotic education, perception, and advanced control systems. Full article

(This article belongs to the Section Sensors and Control in Robotics)

15 pages, 613 KB

Open AccessArticle

Telomere Length and COVID-19 Severity: A Comparative Cross-Sectional Study Across the Clinical Spectrum

by Flora Bacopoulou, Anastasios Tentolouris, Eleni Koniari, Dimitrios Kalogirou, Dimitrios Basoulis, Ioanna Eleftheriadou, Pinelopi Grigoropoulou, Vasiliki Efthymiou, Konstantina K. Georgoulia, Ioanna A. Anastasiou, Stavroula Papadodima, George Chrousos and Nikolaos Tentolouris

Healthcare 2025, 13(20), 2656; https://doi.org/10.3390/healthcare13202656 - 21 Oct 2025

Abstract

Background: Telomere attrition has been implicated in immune function and vulnerability to infectious diseases. However, the relation between telomere length and COVID-19 severity remains unclear. Methods: In this cross-sectional study, patients aged 30–75 years, with confirmed SARS-CoV-2 infection, as well as age- and BMI-matched controls without COVID-19, were recruited over a period of 1 year (2021–2022) from the outpatient clinics and wards of the General Hospitals “Laiko” and “Elpis” in Athens, Greece. Telomere length, expressed as a telomere to single-copy gene (T/S) ratio, was measured in all participants using a quantitative PCR-based method. Participants’ clinical, biochemical, demographic, and respiratory parameters were assessed in relation to their telomere length. Results: Study participants included a total of 139 individuals divided into three groups: controls (n = 34), patients with non-severe COVID-19 (n = 50), and patients with severe COVID-19 (n = 55). Patients with severe COVID-19 had significantly shorter telomeres when compared to both the non-severe COVID-19 group and controls (p < 0.001). Logistic regression analysis confirmed that telomere length was independently associated with disease severity (p < 0.001). Females demonstrated longer telomeres than males (p = 0.039), but no significant correlation was found between telomere length and age. When patients with non-severe and severe COVID-19 were analyzed together, no significant difference in telomere length was observed compared to controls (p = 0.727). Conclusions: Shortened telomeres may be linked to more severe forms of COVID-19, suggesting a potential role for telomere biology in disease progression. Results highlight the need for further research into telomere dynamics as a biomarker for disease susceptibility and outcome in viral infections. Full article

28 pages, 1757 KB

Open AccessArticle

Numerical Prediction of the NPSH Characteristics in Centrifugal Pumps

by Matej Štefanič

Fluids 2025, 10(10), 274; https://doi.org/10.3390/fluids10100274 - 21 Oct 2025

Abstract

This study focuses on the numerical analysis of a centrifugal pump’s suction capability, aiming to reliably predict its suction performance characteristics. The main emphasis of the research was placed on the influence of different turbulence models, the quality of the computational mesh, and the comparison between steady-state and unsteady numerical approaches. The results indicate that steady-state simulations provide an unreliable description of cavitation development, especially at lower flow rates where strong local pressure fluctuations are present. The unsteady k–ω SST model provides the best overall agreement with experimental NPSH₃ characteristics, as confirmed by the lowest mean deviation (within the ISO 9906 tolerance band, corresponding to an overall uncertainty of ±5.5%) and by multiple operating points falling entirely within this range. This represents one of the first detailed unsteady CFD verifications of NPSH prediction in centrifugal pumps operating at high rotational speeds (above 2900 rpm), achieving a mean deviation below ±5.5% and demonstrating improved predictive capability compared to conventional steady-state approaches. The analysis also includes an evaluation of the cavitation volume fraction and a depiction of pressure conditions on the impeller as functions of flow rate and inlet pressure. In conclusion, this study highlights the potential of advanced hybrid turbulence models (such as SAS or DES) as a promising direction for future research, which could further improve the prediction of complex cavitation phenomena in centrifugal pumps. Full article

(This article belongs to the Section Mathematical and Computational Fluid Mechanics)

32 pages, 607 KB

Open AccessArticle

How Does Digital Transformation Catalyze New-Quality Productivity? Unraveling the Path Through Green Innovation and the Role of Digital Financial Inclusion

by Lingling Tan, Kehui Wang and Huifang Zhang

Sustainability 2025, 17(20), 9351; https://doi.org/10.3390/su17209351 - 21 Oct 2025

Abstract

In the pursuit of sustainable economic development, fostering new-quality productivity (NQP) is both an inherent requirement and a strategic priority for advancing a green economy, while digital transformation has emerged as a pivotal driver in reconciling economic growth with environmental protection. Grounded in the Dual-Factor Theory of Productivity, this study empirically examines the impact of digital transformation on corporate NQP, with a focus on its heterogeneous effects, using panel data from China’s A-share listed firms (2013–2022). We further investigate the mediating role of green innovation—encompassing both technological and managerial dimensions—and the moderating effect of digital financial inclusion (DFI). The results show that digital transformation significantly enhances NQP, a finding robust to multiple endogeneity tests and alternative model specifications. Mechanism analysis indicates that digitalization fosters NQP by promoting green technological and managerial innovations, while DFI amplifies this effect. Heterogeneity analysis reveals stronger impacts in state-owned enterprises, non-manufacturing sectors, firms in developed regions, and highly competitive industries. These findings advance theoretical understanding of dynamic control mechanisms in environmental economics, provide empirical evidence on how digital transformation drives sustainable productivity through green innovation, and offer actionable insights for policymakers and firms seeking to align economic growth with environmental sustainability. Full article

(This article belongs to the Special Issue Environmental Economics, Policies and Sustainable Economic Development)

20 pages, 434 KB

Open AccessArticle

Symmetric Equilibrium Bagging–Cascading Boosting Ensemble for Financial Risk Early Warning

by Yao Zou, Yuan Yuan, Chen Zhu and Chenhui Yu

Symmetry 2025, 17(10), 1779; https://doi.org/10.3390/sym17101779 - 21 Oct 2025

Abstract

Financial risk early warning systems provide critical corporate financial status information to stakeholders, including corporate managers, investors, regulatory agencies, and other interested parties, enabling informed decision-making. This study proposes a corporate financial risk early warning model based on a bagging–cascading–boosting architecture, which can be used to predict the financial risk of a firm. The model performance is improved by integrating the residual fitting characteristics of LightGBM, the variance suppression mechanism of bagging, and the adaptive expansion ability of the cascade framework. Evaluated on 46 financial indicators from 2826 A-share-listed companies, the model demonstrates superior performance in AUC and F1-score metrics, outperforming traditional statistical methods and standalone machine-learning models. The methodological innovation lies in its tripartite mechanism: LightGBM ensures low-bias prediction, bagging controls variance, and the cascading structure dynamically adapts to data complexity, maintaining 94.09% AUC robustness, even when training data is reduced to 50%. Empirical results confirm this “ensemble-of-ensembles” framework effectively identifies Special Treatment (ST) firms, delivering early risk alerts for management while supporting investment decisions and regulatory risk mitigation. Full article

(This article belongs to the Section Computer)

18 pages, 10300 KB

Open AccessArticle

Assessment and Validation of FAPAR, a Satellite-Based Plant Health and Water Stress Indicator, over Uganda

by Ronald Ssembajwe, Amina Twah, Godfrey H. Kagezi, Tuula Löytty, Judith Kobusinge, Anthony Gidudu, Geoffrey Arinaitwe, Qingyun Du and Mihai Voda

Remote Sens. 2025, 17(20), 3501; https://doi.org/10.3390/rs17203501 - 21 Oct 2025

Abstract

This study aimed to assess, compare, and validate a satellite-based plant health and water stress indicator: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) over Uganda. We used a direct agricultural drought indicator—the Standardized Precipitation and Evapotranspiration Index at scale 3 (SPEI-03)—and a plant water stress indicator—the crop water stress index (CWSI)—for the period of 1983–2013. Novel approaches such as spatial variability and trend analysis, along with correlation analysis, were used to achieve this. The results showed that there are six classes of highly variable photosynthetic activity over Uganda, dominated by class 4 (0.36–0.45). This dominant class encompassed 45% of the total land area, mainly spanning cropland. In addition, significant increases in monthly photosynthetic activity (FAPAR) and FAPAR-centered stress indicators (SFI < −1) were observed over 85% and 60% of total land area, respectively. The Standardized FAPAR Index (SFI) had a strong positive correlation with SPEI-03 over cropland, grassland, and forest lands, while SFI had a strong negative correlation with CWSI over 80% of the total area. These results highlight the state and variation in plant health and water stress, generate insights on ecosystem dynamics and functionality, and weigh in on the usability and reliability of satellite-based variables such as FAPAR in plant water monitoring over Uganda. We thus recommend satellite-based FAPAR as a robust proxy for vegetation health and water stress monitoring over Uganda, with potential application in crop yield prediction and irrigation management to inform effective agricultural planning and improve productivity. Full article

(This article belongs to the Special Issue Enhancing Vegetation and Water Use Management Through Earth Observation)

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