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This study examines how the newly established United States pursued economic development through diplomatic and commercial initiatives with the Ottoman Empire, navigating regional powers and the era’s political-economic conditions. It analyzes using American archival sources how America endeavored to establish commercial and diplomatic relations with the Ottoman Empire in the Mediterranean and Black Sea regions, which it viewed as critical markets in the late 18th and early 19th centuries, before signing any formal agreement. The research tracks how these early efforts laid foundations for what would become one of the world’s largest economies. The study analyzes America’s diplomatic efforts to secure an agreement with the Ottoman Empire prior to the 7 May 1830 trade agreement—which laid the foundation for bilateral relations—alongside the reactions of regional powers, the prevailing conditions of the period, and the Ottoman administration’s reluctance due to various factors, based on U.S. archival sources that, to the best of our knowledge, have not previously been utilized in existing studies. Full article

(This article belongs to the Section Political, Institutional, and Economy History)

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16 pages, 2036 KiB

Open AccessArticle

Scalable Chemical Vapor Deposition of Silicon Carbide Thin Films for Photonic Integrated Circuit Applications

by Souryaya Dutta, Alex Kaloyeros, Animesh Nanaware and Spyros Gallis

Appl. Sci. 2025, 15(15), 8603; https://doi.org/10.3390/app15158603 (registering DOI) - 2 Aug 2025

Abstract

Highly integrable silicon carbide (SiC) has emerged as a promising platform for photonic integrated circuits (PICs), offering a comprehensive set of material and optical properties that are ideal for the integration of nonlinear devices and solid-state quantum defects. However, despite significant progress in nanofabrication technology, the development of SiC on an insulator (SiCOI)-based photonics faces challenges due to fabrication-induced material optical losses and complex processing steps. An alternative approach to mitigate these fabrication challenges is the direct deposition of amorphous SiC on an insulator (a-SiCOI). However, there is a lack of systematic studies aimed at producing high optical quality a-SiC thin films, and correspondingly, on evaluating and determining their optical properties in the telecom range. To this end, we have studied a single-source precursor, 1,3,5-trisilacyclohexane (TSCH, C₃H₁₂Si₃), and chemical vapor deposition (CVD) processes for the deposition of SiC thin films in a low-temperature range (650–800 °C) on a multitude of different substrates. We have successfully demonstrated the fabrication of smooth, uniform, and stoichiometric a-SiCOI thin films of 20 nm to 600 nm with a highly controlled growth rate of ~0.5 Å/s and minimal surface roughness of ~5 Å. Spectroscopic ellipsometry and resonant micro-photoluminescence excitation spectroscopy and mapping reveal a high index of refraction (~2.7) and a minimal absorption coefficient (<200 cm⁻¹) in the telecom C-band, demonstrating the high optical quality of the films. These findings establish a strong foundation for scalable production of high-quality a-SiCOI thin films, enabling their application in advanced chip-scale telecom PIC technologies. Full article

(This article belongs to the Section Materials Science and Engineering)

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33 pages, 12598 KiB

Open AccessArticle

OKG-ConvGRU: A Domain Knowledge-Guided Remote Sensing Prediction Framework for Ocean Elements

by Renhao Xiao, Yixiang Chen, Lizhi Miao, Jie Jiang, Donglin Zhang and Zhou Su

Remote Sens. 2025, 17(15), 2679; https://doi.org/10.3390/rs17152679 (registering DOI) - 2 Aug 2025

Abstract

Accurate prediction of key ocean elements (e.g., chlorophyll-a concentration, sea surface temperature, etc.) is imperative for maintaining marine ecological balance, responding to marine disaster pollution, and promoting the sustainable use of marine resources. Existing spatio-temporal prediction models primarily rely on either physical or data-driven approaches. Physical models are constrained by modeling complexity and parameterization errors, while data-driven models lack interpretability and depend on high-quality data. To address these challenges, this study proposes OKG-ConvGRU, a domain knowledge-guided remote sensing prediction framework for ocean elements. This framework integrates knowledge graphs with the ConvGRU network, leveraging prior knowledge from marine science to enhance the prediction performance of ocean elements in remotely sensed images. Firstly, we construct a spatio-temporal knowledge graph for ocean elements (OKG), followed by semantic embedding representation for its spatial and temporal dimensions. Subsequently, a cross-attention-based feature fusion module (CAFM) is designed to efficiently integrate spatio-temporal multimodal features. Finally, these fused features are incorporated into an enhanced ConvGRU network. For multi-step prediction, we adopt a Seq2Seq architecture combined with a multi-step rolling strategy. Prediction experiments for chlorophyll-a concentration in the eastern seas of China validate the effectiveness of the proposed framework. The results show that, compared to baseline models, OKG-ConvGRU exhibits significant advantages in prediction accuracy, long-term stability, data utilization efficiency, and robustness. This study provides a scientific foundation and technical support for the precise monitoring and sustainable development of marine ecological environments. Full article

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23 pages, 872 KiB

Open AccessArticle

Performance Optimization of Grounding System for Multi-Voltage Electrical Installation

by Md Tanjil Sarker, Marran Al Qwaid, Md Sabbir Hossen and Gobbi Ramasamy

Appl. Sci. 2025, 15(15), 8600; https://doi.org/10.3390/app15158600 (registering DOI) - 2 Aug 2025

Abstract

Grounding systems are critical for ensuring electrical safety, fault current dissipation, and electromagnetic compatibility in power installations across different voltage levels. This research presents a comparative study on the optimization of grounding configurations for 400 V, 10 kV, and 35 kV electrical installations, focusing on key performance parameters such as grounding resistance, step and touch voltages, and fault current dissipation efficiency. The study employs computational simulations using the finite element method (FEM) alongside empirical field measurements to evaluate the influence of soil resistivity, electrode materials, and grounding configurations, including rod electrodes, grids, deep-driven rods, and hybrid grounding systems. Results indicate that soil resistivity significantly affects grounding efficiency, with deep-driven rods providing superior performance in high-resistivity conditions, while grounding grids demonstrate enhanced fault current dissipation in substations. The integration of conductive backfill materials, such as bentonite and conductive concrete, further reduces grounding resistance and enhances system reliability. This study provides engineering insights into optimizing grounding systems based on installation voltage levels, cost considerations, and compliance with IEEE Std 80-2013 and IEC 60364-5-54. The findings contribute to the development of more resilient and cost-effective grounding strategies for electrical installations. Full article

20 pages, 12851 KiB

Open AccessArticle

Evaluation of a Vision-Guided Shared-Control Robotic Arm System with Power Wheelchair Users

by Breelyn Kane Styler, Wei Deng, Cheng-Shiu Chung and Dan Ding

Sensors 2025, 25(15), 4768; https://doi.org/10.3390/s25154768 (registering DOI) - 2 Aug 2025

Abstract

Wheelchair-mounted assistive robotic manipulators can provide reach and grasp functions for power wheelchair users. This in-lab study evaluated a vision-guided shared control (VGS) system with twelve users completing two multi-step kitchen tasks: a drinking task and a popcorn making task. Using a mixed methods approach participants compared VGS and manual joystick control, providing performance metrics, qualitative insights, and lessons learned. Data collection included demographic questionnaires, the System Usability Scale (SUS), NASA Task Load Index (NASA-TLX), and exit interviews. No significant SUS differences were found between control modes, but NASA-TLX scores revealed VGS control significantly reduced workload during the drinking task and the popcorn task. VGS control reduced operation time and improved task success but was not universally preferred. Six participants preferred VGS, five preferred manual, and one had no preference. In addition, participants expressed interest in robotic arms for daily tasks and described two main operation challenges: distinguishing wrist orientation from rotation modes and managing depth perception. They also shared perspectives on how a personal robotic arm could complement caregiver support in their home. Full article

(This article belongs to the Special Issue Intelligent Sensors and Robots for Ambient Assisted Living)

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25 pages, 19905 KiB

Open AccessArticle

Assessing Urban Park Accessibility via Population Projections: Planning for Green Equity in Shanghai

by Leiting Cen and Yang Xiao

Land 2025, 14(8), 1580; https://doi.org/10.3390/land14081580 (registering DOI) - 2 Aug 2025

Abstract

Rapid urbanization and demographic shifts present significant challenges to spatial justice in green space provision. Traditional static assessments have become increasingly inadequate for guiding park planning, which now requires a dynamic, future-oriented analytical approach. To address this gap, this study incorporates population dynamics into urban park planning by developing a dynamic evaluation framework for park accessibility. Building on the Gaussian-based two-step floating catchment area (Ga2SFCA) method, we propose the human-population-projection-Ga2SFCA (HPP-Ga2SFCA) model, which integrates population forecasts to assess park service efficiency under future demographic pressures. Using neighborhood-committee-level census data from 2000 to 2020 and detailed park spatial data, we identified five types of population change and forecast demographic distributions for both short- and long-term scenarios. Our findings indicate population decline in the urban core and outer suburbs, with growth concentrated in the transitional inner-suburban zones. Long-term projections suggest that 66% of communities will experience population growth, whereas short-term forecasts indicate a decline in 52%. Static models overestimate park accessibility by approximately 40%. In contrast, our dynamic model reveals that accessibility is overestimated in 71% and underestimated in 7% of the city, highlighting a potential mismatch between future population demand and current park supply. This study offers a forward-looking planning framework that enhances the responsiveness of park systems to demographic change and supports the development of more equitable, adaptive green space strategies. Full article

(This article belongs to the Special Issue Spatial Justice in Urban Planning (Second Edition))

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21 pages, 7537 KiB

Open AccessArticle

Variable Step-Size FxLMS Algorithm Based on Cooperative Coupling of Double Nonlinear Functions

by Jialong Wang, Jian Liao, Lin He, Xiaopeng Tan and Zongbin Chen

Symmetry 2025, 17(8), 1222; https://doi.org/10.3390/sym17081222 (registering DOI) - 2 Aug 2025

Abstract

Based on the principle of symmetry, we propose a variable step-size FxLMS algorithm with double nonlinear functions cooperative coupling (DNVSS-FxLMS), aiming to optimize the contradiction between convergence rate and steady-state error in the active pressure pulsation control system of hydraulic systems. The algorithm innovatively couples two types of nonlinear mechanisms (rational-fractional and exponential-function-based), constructing a refined error-step mapping relationship to achieve a balance between rapid convergence and low steady-state error. Simulation experiments were conducted considering the complex time-varying operating environment of a simulation-based hydraulic system. The results demonstrate that, when the system undergoes unstable random changes, the DNVSS-FxLMS algorithm converges at least twice as fast as traditional and existing variable step size algorithms, while reducing steady-state error by 2–5 dB. The proposed DNVSS-FxLMS algorithm exhibits significant advantages in convergence rate, steady-state error reduction, and tracking capability, providing a highly efficient and robust solution for real-time active control of hydraulic system pressure pulsation under complex operating conditions. Full article

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23 pages, 3120 KiB

Open AccessArticle

Bee Swarm Metropolis–Hastings Sampling for Bayesian Inference in the Ginzburg–Landau Equation

by Shucan Xia and Lipu Zhang

Algorithms 2025, 18(8), 476; https://doi.org/10.3390/a18080476 (registering DOI) - 2 Aug 2025

Abstract

To improve the sampling efficiency of Markov Chain Monte Carlo in complex parameter spaces, this paper proposes an adaptive sampling method that integrates a swarm intelligence mechanism called the BeeSwarm-MH algorithm. The method combines global exploration by scout bees with local exploitation by worker bees. It employs multi-stage perturbation intensities and adaptive step-size tuning to enable efficient posterior sampling. Focusing on Bayesian inference for parameter estimation in the soliton solutions of the two-dimensional complex Ginzburg–Landau equation, we design a dedicated inference framework to systematically compare the performance of BeeSwarm-MH with the classical Metropolis–Hastings algorithm. Experimental results demonstrate that BeeSwarm-MH achieves comparable estimation accuracy while significantly reducing the required number of iterations and total computation time for convergence. Moreover, it exhibits superior global search capabilities and adaptive features, offering a practical approach for efficient Bayesian inference in complex physical models. Full article

17 pages, 901 KiB

Open AccessArticle

Tuning the Activity of NbOPO₄ with NiO for the Selective Conversion of Cyclohexanone as a Model Intermediate of Lignin Pyrolysis Bio-Oils

by Abarasi Hart and Jude A. Onwudili

Energies 2025, 18(15), 4106; https://doi.org/10.3390/en18154106 (registering DOI) - 2 Aug 2025

Abstract

Catalytic upgrading of pyrolysis oils is an important step for producing replacement hydrocarbon-rich liquid biofuels from biomass and can help to advance pyrolysis technology. Catalysts play a pivotal role in influencing the selectivity of chemical reactions leading to the formation of main compounds in the final upgraded liquid products. The present work involved a systematic study of solvent-free catalytic reactions of cyclohexanone in the presence of hydrogen gas at 160 °C for 3 h in a batch reactor. Cyclohexanone can be produced from biomass through the selective hydrogenation of lignin-derived phenolics. Three types of catalysts comprising undoped NbOPO₄, 10 wt% NiO/NbOPO₄, and 30 wt% NiO/NbOPO₄ were studied. Undoped NbOPO₄ promoted both aldol condensation and the dehydration of cyclohexanol, producing fused ring aromatic hydrocarbons and hard char. With 30 wt% NiO/NbOPO₄, extensive competitive hydrogenation of cyclohexanone to cyclohexanol was observed, along with the formation of C₆ cyclic hydrocarbons. When compared to NbOPO₄ and 30 wt% NiO/NbOPO₄, the use of 10 wt% NiO/NbOPO₄ produced superior selectivity towards bi-cycloalkanones (i.e., C₁₂) at cyclohexanone conversion of 66.8 ± 1.82%. Overall, the 10 wt% NiO/NbOPO₄ catalyst exhibited the best performance towards the production of precursor compounds that can be further hydrodeoxygenated into energy-dense aviation fuel hydrocarbons. Hence, the presence and loading of NiO was able to tune the activity and selectivity of NbOPO₄, thereby influencing the final products obtained from the same cyclohexanone feedstock. This study underscores the potential of lignin-derived pyrolysis oils as important renewable feedstocks for producing replacement hydrocarbon solvents or feedstocks and high-density sustainable liquid hydrocarbon fuels via sequential and selective catalytic upgrading. Full article

(This article belongs to the Special Issue Recent Advances in Biomass Energy Torrefaction, Pyrolysis and Gasification Technologies)

15 pages, 1258 KiB

Open AccessArticle

Synthesis and Evaluation of Sunflower-Oil-Based Esters as Biolubricant Base Oils Using Ca/TEA Alkoxide Catalyst

by Dimosthenis Filon, George Anastopoulos and Dimitrios Karonis

Lubricants 2025, 13(8), 345; https://doi.org/10.3390/lubricants13080345 (registering DOI) - 2 Aug 2025

Abstract

This study evaluates the production of base oils for biolubricants using fatty acid methyl esters (FAMEs) derived from sunflower oil as the raw material. The production process involved the synthesis of oleochemical esters through a single-step alkaline transesterification reaction with a high-molecular-weight polyol, such as trimethylolpropane (TMP). To assess the effectiveness of the developed catalytic system in conducting the transesterification reactions and its impact on the properties of the final product, two types of alkaline catalysts were used. Specifically, the reactions were carried out using either Ca/TEA alkoxide or sodium methoxide as catalysts in various configurations and concentrations to determine the optimal catalyst concentration and reaction conditions. Sodium methoxide served as the commercial benchmark catalyst, while the Ca/TEA alkoxide was prepared in the laboratory. The optimal concentration of Ca/TEA was determined to be 3.0% wt. in the presence of iso-octane and 3.5% wt. under vacuum, while the corresponding concentrations of CH₃ONa for both cases were determined to be 2.0% wt. The synthesized biolubricant esters exhibit remarkable performance characteristics, such as high kinematic viscosities and low pour points—ranging from 33–48 cSt at 40 °C, 7.68–10.03 cSt at 100 °C, to −14 to −7 °C, respectively—which are comparable to or improved over those of mineral oils such as SN-150 or SN-500, with the Ca/TEA alkoxide-catalyzed systems showing superior oxidation stability and reduced byproduct formation. Full article

(This article belongs to the Special Issue Tribological Properties of Biolubricants)

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14 pages, 5954 KiB

Open AccessArticle

Mapping Wet Areas and Drainage Networks of Data-Scarce Catchments Using Topographic Attributes

by Henrique Marinho Leite Chaves, Maria Tereza Leite Montalvão and Maria Rita Souza Fonseca

Water 2025, 17(15), 2298; https://doi.org/10.3390/w17152298 (registering DOI) - 2 Aug 2025

Abstract

Wet areas, which are locations in the landscape that consistently retain moisture, and channel networks are important landscape compartments, with key hydrological and ecological functions. Hence, defining their spatial boundaries is an important step towards sustainable watershed management. In catchments of developing countries, wet areas and small order channels of river networks are rarely mapped, although they represent a crucial component of local livelihoods and ecosystems. In this study, topographic attributes generated with a 30 m SRTM DEM were used to map wet areas and stream networks of two tropical catchments in Central Brazil. The topographic attributes for wet areas were the local slope and the slope curvature, and the Topographic Wetness Index (TWI) was used to delineate the stream networks. Threshold values of the selected topographic attributes were calibrated in the Santa Maria catchment, comparing the synthetically generated wet areas and drainage networks with corresponding reference (map) features, and validated in the nearby Santa Maria basin. Drainage network and wet area delineation accuracies were estimated using random basin transects and multi-criteria and confusion matrix methods. The drainage network accuracies were 67.2% and 70.7%, and wet area accuracies were 72.7% and 73.8%, for the Santa Maria and Gama catchments, respectively, being equivalent or higher than previous studies. The mapping errors resulted from model incompleteness, DEM vertical inaccuracy, and cartographic misrepresentation of the reference topographic maps. The study’s novelty is the use of readily available information to map, with simplicity and robustness, wet areas and channel initiation in data-scarce, tropical environments. Full article

(This article belongs to the Section Hydrogeology)

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19 pages, 4169 KiB

Open AccessFeature PaperArticle

Magnetic Coil’s Performance Optimization with Nonsmooth Search Algorithms

by Igor Reznichenko, Primož Podržaj and Aljoša Peperko

Mathematics 2025, 13(15), 2490; https://doi.org/10.3390/math13152490 (registering DOI) - 2 Aug 2025

Abstract

This research is concerned with design optimization of control systems. Our case study deals with magnetic levitation, in which an essential part is a solenoid. Its dimensions, along with controller parameters, form the optimization variables. We present a novel way of writing the explicit expression of the solenoid’s force acting on a magnetic dipole, as well as its first derivatives. Numerical tests using non-gradient search algorithms show the difference in optimal designs provided by these methods. Since such optimization depends on output signals, a comparison of step response analysis methods is presented. Full article

(This article belongs to the Special Issue Advances in Metaheuristic Optimization Algorithms)

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24 pages, 3172 KiB

Open AccessArticle

A DDPG-LSTM Framework for Optimizing UAV-Enabled Integrated Sensing and Communication

by Xuan-Toan Dang, Joon-Soo Eom, Binh-Minh Vu and Oh-Soon Shin

Drones 2025, 9(8), 548; https://doi.org/10.3390/drones9080548 (registering DOI) - 1 Aug 2025

Abstract

This paper proposes a novel dual-functional radar-communication (DFRC) framework that integrates unmanned aerial vehicle (UAV) communications into an integrated sensing and communication (ISAC) system, termed the ISAC-UAV architecture. In this system, the UAV’s mobility is leveraged to simultaneously serve multiple single-antenna uplink users (UEs) and perform radar-based sensing tasks. A key challenge stems from the target position uncertainty due to movement, which impairs matched filtering and beamforming, thereby degrading both uplink reception and sensing performance. Moreover, UAV energy consumption associated with mobility must be considered to ensure energy-efficient operation. We aim to jointly maximize radar sensing accuracy and minimize UAV movement energy over multiple time steps, while maintaining reliable uplink communications. To address this multi-objective optimization, we propose a deep reinforcement learning (DRL) framework based on a long short-term memory (LSTM)-enhanced deep deterministic policy gradient (DDPG) network. By leveraging historical target trajectory data, the model improves prediction of target positions, enhancing sensing accuracy. The proposed DRL-based approach enables joint optimization of UAV trajectory and uplink power control over time. Extensive simulations validate that our method significantly improves communication quality and sensing performance, while ensuring energy-efficient UAV operation. Comparative results further confirm the model’s adaptability and robustness in dynamic environments, outperforming existing UAV trajectory planning and resource allocation benchmarks. Full article

(This article belongs to the Special Issue Recent Developments in Artificial Intelligence and Interdisciplinary Research for UAV Application)

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17 pages, 5839 KiB

Open AccessArticle

Hydrogen Bond-Regulated Rapid Prototyping and Performance Optimization of Polyvinyl Alcohol–Tannic Acid Hydrogels

by Xiangyu Zou and Jun Huang

Gels 2025, 11(8), 602; https://doi.org/10.3390/gels11080602 (registering DOI) - 1 Aug 2025

Abstract

Traditional hydrogel preparation methods typically require multiple steps and certain external stimuli. In this study, rapid and stable gelation of polyvinyl alcohol (PVA)-tannic acid (TA)-based hydrogels was achieved through the regulation of hydrogen bonds. The cross-linking between PVA and TA is triggered by the evaporation of ethanol. Rheological testing and analysis of the liquid-solid transformation process of the hydrogel were performed. The gelation onset time (GOT) could be tuned from 10 s to over 100 s by adjusting the ethanol content and temperature. The addition of polyhydroxyl components (e.g., glycerol) significantly enhances the hydrogel’s water retention capacity (by 858%) and tensile strain rate (by 723%), while concurrently increasing the gelation time. Further studies have shown that the addition of alkaline substances (such as sodium hydroxide) promotes the entanglement of PVA molecular chains, increasing the tensile strength by 23% and the fracture strain by 41.8%. The experimental results indicate that the optimized PVA-TA hydrogels exhibit a high tensile strength (>2 MPa) and excellent tensile properties (~600%). Moreover, the addition of an excess of weakly alkaline substances (such as sodium acetate) reduces the degree of hydrolysis of PVA, enabling the system to form a hydrogel with extrudable characteristics before the ethanol has completely evaporated. This property allows for patterned printing and thus demonstrates the potential of the hydrogel in 3D printing. Overall, this study provides new insights for the application of PVA-TA based hydrogels in the fields of rapid prototyping and strength optimization. Full article

(This article belongs to the Special Issue Synthesis and Applications of Hydrogels (3rd Edition))

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Graphical abstract

18 pages, 5389 KiB

Open AccessArticle

Novel Method of Estimating Iron Loss Equivalent Resistance of Laminated Core Winding at Various Frequencies

by Maxime Colin, Thierry Boileau, Noureddine Takorabet and Stéphane Charmoille

Energies 2025, 18(15), 4099; https://doi.org/10.3390/en18154099 (registering DOI) - 1 Aug 2025

Abstract

Electromagnetic and magnetic devices are increasingly prevalent in sectors such as transportation, industry, and renewable energy due to the ongoing electrification trend. These devices exhibit nonlinear behavior, particularly under signals rich in harmonics. They require precise and appropriate modeling for accurate sizing. Identifying model-specific parameters, which depend on frequency, is crucial. This article focuses on a specific frequency range where a circuit model with series resistance and inductance, along with a parallel resistance to account for iron losses (

R_{iron}

), is applicable. While the determination of series elements is well documented, the determination of

R_{iron}

remains complex and debated, with traditional methods neglecting operating conditions such as magnetic saturation. To address these limitations, an innovative experimental method is proposed, comprising two main steps: determining the complex impedance of the magnetic device and extracting

R_{iron}

from the model. This method aims to provide a more precise and representative estimation of

R_{iron}

, improving the reliability and accuracy of electromagnetic and magnetic device simulations and designs. The obtained values of the iron loss equivalent resistance are different by at least 300% than those obtained by an impedance analyzer. The proposed method is expected to advance the understanding and modeling of losses in electromagnetic and magnetic devices, offering more robust tools for engineers and researchers in optimizing device performance and efficiency. Full article

(This article belongs to the Section F1: Electrical Power System)

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