bluuebunny/crossref_metadata_2025_split huggingface dataset:
Reactive power valuation for DFIGs based on power loss and power capacity limitations
Authors: Deylami Fazel Pourmirzaei , Darabi Ahmad 
Published: January 2025

AbstractThis article presents a detailed method for reactive power valuation exclusively from the generator‐side point of view. This study is performed on a 1.5 MVA doubly‐fed induction generator and two main generator‐side parameters are introduced as the effective variables. These variables include power loss and active power capacity. In this article, in constant apparent power, the produced/consumed reactive power of the generator in exchange with the power grid is studied in detail and the operational characteristics of the generator including active/reactive power, power factor, and efficiency are reported. Using these functional characteristics, the power loss increment factor and the active power capacity reduction factor are proposed. Finally, the reactive power valuation factor that is the main output of this article is presented and the results are compared with the current reactive power pricing method in Iran. The results show that the proposed method can be useful for a more accurate analysis of reactive power valuation problem from the generator point of view.
Directional power transfer and data communication using magnetic resonance wireless charging system
Authors: Chien Yi‐Chi , Huang Chih‐Cheng , Lin Chun‐Liang 
Published: January 2025

AbstractThis research proposes bidirectional power and data transfer which implements electric vehicle (EV) battery charge/discharging using magnetic resonance coupling wireless power transfer (MRC‐WPT) with inner communication by binary phase‐shift keying (BPSK) technology between the primary side and the secondary side. The MRC‐WPT‐BPSK can implement grid‐to‐vehicle, vehicle‐to‐grid, or vehicle‐to‐load (V2L) functions due to inner communication capability without additional wireless communication modules such as Wi‐Fi, Bluetooth, ZigBee, and so on. In the experiment result, charging power up to 300 W can reach an efficiency of 85%. The data communication accuracy (quality) of this system can be almost 100% within the air gap from 0 to 50 mm. In addition, the V2L function can provide energy up to 1000 Wh, such as emergency power, backup power, or light sources in the parking lot. Therefore, MRC‐WPT‐BPSK like a smart grid aids the user to obtain benefits from charging or discharging the battery according to time‐of‐use list.
Bio-degradable smart nanocomposite fiber from Moringa oleifera fiber reinforced with safely functionalized carbon nanotubes
Authors: Kalam Md Abul , Mustakim Salvin , Rahman Mohammad Jellur , Uddin Md Tushar , Alam Md Ashraful , Rahman Mohammad Mahbubur 
Published: January 2025

In this work, we present an innovative approach to fabricating biodegradable smart nanocomposite fibers by incorporating functionalized carbon nanotubes (f-CNTs) onto the surface of Moringa oleifera fibers (MOFs) by using a simple dipping–drying technique. The objective of this research is to enhance the thermal, mechanical, and electrical properties of MOFs through a scalable, non-destructive modification process, making them suitable for advanced applications. A facile radio frequency oxygen plasma processing method is employed to modify CNTs with hydrophilic oxygen-containing groups. The fibers are treated with an alkaline solution coupled with plasma treatment to enhance the interaction between the fiber and the matrices. The performance of the fiber is evaluated by measuring the surface morphology, structural, thermal, mechanical, and electrical properties of the uncoated and f-CNTs-coated fibers. Field emission scanning electron microscope micrographs show that CNTs are uniformly integrated onto the surfaces of the treated MOFs. The results also indicate that interaction between the treated MOFs and f-CNTs, thermal stability, flame retardancy, crystallinity, and mechanical strength of the fibers are increased remarkably with the incorporation of f-CNTs. The resistance per meter of the f-CNTs/treated MOFs fiber drops dramatically from 1.5 MΩ to 13 Ω with the addition of f-CNTs. The current density of the sample increases by about 1000 times, and conductivity rises to 80 S m−1 under the applied voltage of 50 V, which also increases with temperature, indicating the semiconducting nature of CNTs. Therefore, these fibers are suitable for use in various electrical and electronic devices as well as conductive fillers in composite technologies.
Comparative analysis of modified Johnson–Cook model and artificial neural network for flow stress prediction in BN-reinforced AZ80 magnesium composite
Authors: Elajjani Ayoub , Feng Shaochuan , Sun Chaoyang 
Published: January 2025

Abstract
Boron nitride (BN), renowned for its exceptional optoelectrical properties, mechanical robustness, and thermal stability, has emerged as a promising two-dimensional material. Reinforcing AZ80 magnesium alloy with BN can significantly enhance its mechanical properties. To investigate and predict this enhancement during hot deformation, we introduce two independent modeling approaches a modified Johnson–Cook constitutive model and an artificial neural network (ANN). These models aim to capture both linear and nonlinear deformation characteristics. Hot compression tests conducted across various temperatures and strain rates provided a comprehensive dataset for model validation. The MJCC model, accounting for strain rate and temperature effects, achieved a correlation coefficient R of 0.96 and an average absolute relative error (AARE) of 6.28%. In contrast, the ANN, trained on experimental data, improved the correlation coefficient to R of 0.99 and reduced the AARE to below 1.5%, significantly enhancing predictive accuracy. These results indicate that while the modified J–C model provides reliable predictions under moderate conditions, the ANN more effectively captures complex behaviors under extreme deformation conditions. By comparing these modeling approaches, our study offers valuable insights for accurately predicting the rheological behavior of BN-reinforced AZ80 magnesium composite, aiding process optimization in industrial applications.
Anomalous and large topological Hall effects in β-Mn chiral compound Co6.5Ru1.5Zn8Mn4: electron electron interaction facilitated quantum interference effect
Authors: Ahmed Afsar , Bhattacharya Arnab , Das I 
Published: January 2025

Abstract

β-Mn-type chiral cubic Co
x
Zn
y
Mn
z
(x + y + z = 20) alloys present a intriguing platform for exploring topological magnetic orderings with promising spintronic potential. This study examines the magnetotransport properties of Co6.5Ru1.5Zn8Mn4, a skyrmion-hosting β-Mn-type chiral compound. The longitudinal resistivity (ρ

xx
) exhibits field-insensitive low-temperature minima due to quantum interference effects, driven by

T

1

/

2

-dependent electron–electron interactions. We observe a substantial intrinsic anomalous Hall conductivity, unaffected by quantum interference. Additionally, a pronounced topological Hall effect is observed at the metastable skyrmionic state, persisting up to

T
C

and achieving notable magnitudes for stoichiometric compounds. These results position the Co
x
Zn
y
Mn
z
family favourably to leverage the rich pallete of emergent magnetotransport properties for spintronic applications.
Dynamical analysis of a tri-stable piezoelectric energy harvester with stops driven by narrow-band random noise
Authors: Zhao Zhen , Jin Yanfei , Zhang Tingting 
Published: January 2025

Abstract
The tri-stable vibrational energy harvester (VEH) is commonly designed to achieve the large-orbit motion, but it is always trapped in one potential well and performs an intra-well motion under weak vibration excitations. To solve this problem, a tri-stable piezoelectric VEH with stops is proposed in this paper. The analytic expressions of the first-order and second-order moments for system response are derived based on the multiple scale method. It is found that the harvesting frequency bandwidth of the tri-stable VEH becomes wider under an optimal impact distance. Compared with the tri-stable VEH without stops, the consideration of stops can help the system achieve the multiple high-energy solutions. The choice of high-stiffness stops at high frequencies can enhance the harvesting efficiency of the tri-stable VEH with stops. Moreover, the experimental results and analysis also are presented to verify the obtained theoretical results. It is found that the energy harvesting efficiency can be increased to 140% of the unrestricted state at the optimal impact distance. Compared with the tri-stable VEH with rubber and stiff springs, the one with silica gel stops has a higher output voltage.
A CNN-BiLSTM short-term wind power forecasting model incorporating adaptive boosting
Authors: Cai Jingkao , Wang Yang , Chen Zongchuan , Gao Yulun , Tang Guangyu 
Published: January 2025

Abstract
Aiming at the wind power signal with the characteristics of intermittency, nonlinearity, volatility, non-stationarity and uncertainty, this paper establishes a wind power prediction model based on the combination of variational modal decomposition (VMD), convolutional neural network (CNN), bi-directional long and short-term memory network (BILSTM) and adaptive boosting mechanism (AdaBoost). In terms of data processing, the core parameters of VMD such as decomposition modulus number and penalty factor affect the data decomposition ability, thus the core parameters of VMD are optimized using the multi-strategy mutation sand cat swarm optimization (SSCSO). The global search ability and convergence speed of SSCSO algorithm are enhanced by integrating cubic mapping, spiral search strategy and sparrow alert mechanism, etc., and are applied to optimize the core parameters of VMD, so as to effectively improve the data decomposition performance of VMD; in terms of the prediction model, for the existence of a single deep neural network model with slow arithmetic speed, artificial parameter tuning, etc., which affects the overall prediction accuracy of the model, thus CNN-BiLSTM combination prediction model with the introduction of AdaBoost is adopted. The CNN-BiLSTM is repeatedly trained as a weak predictor and outputs the prediction results, and the weights are calculated and the errors are corrected according to the prediction error values of each weak predictor. Finally, the strong predictor is obtained by combining several groups of weak predictors after several rounds of training, and the output predicted values are superimposed to obtain the final predicted values, which further improves the overall prediction accuracy of the model, and the strong predictor composed of the CNN-BiLSTM model trained in several rounds is able to process the data more adaptively, and improves the operation speed to a certain extent under the premise of guaranteeing the prediction accuracy. The experimental results show that the root mean square error (RMSE), mean absolute error (MAE) The experimental results show that the RMSE, MAE, correlation coefficient and running time of the proposed model are better than those of SSCSO-VMD-CNN-BiLSTM, SSCSO-VMD-CNN-LSTM and SSCSO-VMD-CNN-GRU, VMD-CNN-BiLSTM-Adaboost, SABO-VMD-CNN-BiLSTM-Adaboost, DBO-VMD-CNN-BiLSTM-Adaboost and WOA-VMD-CNN-BiLSTM-Adaboost prediction models. Therefore, the combined model proposed in this paper has better prediction accuracy and running speed.
Topology optimization design of the ‘Beam Shaping Assembly’ of an AB-BNCT facility—application to the case of glioblastoma treatment
Authors: Chabod Sébastien , Santos Daniel , Sauzet Nadine 
Published: January 2025

Abstract

Objective. This study aims to determine the optimal structure of the Beam Shaping Assembly (BSA) for an accelerator-based boron neutron capture therapy (BNCT) facility. The aim is to maximize the possible depth of treatment for glioblastoma while ensuring that a treatment time constraint is not exceeded. Approach. To achieve this goal, we utilize a new optimization procedure known as topology optimization. This technique can accurately identify the most optimal structure of a nuclear device, in this case a BSA, to be identified among 9 × 101206 possible structures for the example given in this study. The exploration of such a vast space of configurations is inaccessible to any other method available to date. Main results. The topology optimization generated Air-AlF3–LiF–LiFPE BSA has an original structure that differs significantly from the structures previously tested by the BNCT community. This structure, which combines a ring collimator and a filter cone to mimic the effect of multi-field treatment, generates unprecedented treatment depths, with a treatable depth TD = 10.01 cm and an advantage depth AD = 12.48 cm (for 15 ppm of Boron-10 in blood, with a 3.5 tumor-to-blood Boron-10 concentration ratio), or TD = 10.30 cm and AD = 12.69 cm (for 18 ppm of Boron-10). These depths are much greater than any other design proposed to date by the community. The structure also verifies the latest proposed radiation protection constraints, which set limit values on its out-of-field leakages. Significance. The findings of this study indicate that topology optimization procedures are highly beneficial for the design of BSAs. In particular, the use of ring collimators could significantly improve the quality of BNCT treatments of brain tumors.
Dual-modality flow phantom for ultrasound and optical flow measurements
Authors: Kallweit Chris M , Chee Adrian J Y , Yiu Billy Y S , Peterson Sean D , Yu Alfred C H 
Published: January 2025

Abstract
As ultrasound-compatible flow phantoms are devised for performance testing and calibration, there is a practical need to obtain independent flow measurements for validation using a gold-standard technique such as particle image velocimetry (PIV). In this paper, we present the design of a new dual-modality flow phantom that allows ultrasound and PIV measurements to be simultaneously performed. Our phantom’s tissue mimicking material is based on a novel hydrogel formula that uses propylene glycol to lower the freezing temperature of an ultrasound-compatible poly(vinyl) alcohol cryogel and, in turn, maintain the solution’s optical transparency after thermocycling. The hydrogel’s optical attenuation {1.56 dB cm−1 with 95% confidence interval (CI) of [1.512 1.608]}, refractive index {1.337, CI: [1.340 1.333]}, acoustic attenuation {0.038 dB/(cm × MHz
b
), CI: [0.0368 0.0403]; frequency dependent factor of 1.321, CI: [1.296 1.346]}, and speed of sound {1523.6 m s−1, CI: [1523.8 1523.4]} were found to be suitable for PIV and ultrasound flow measurements. As an application demonstration, a bimodal flow phantom with spiral lumen was fabricated and used in simultaneous flow measurements with PIV and ultrasound color flow imaging (CFI). Velocity fields and profiles were compared between the two modalities under a constant flow rate (2.5 ml s−1). CFI was found to overestimate flow speed compared to the PIV measurements, with a 14%, 10%, and 6% difference between PIV and ultrasound for the 60°, 45°, and 30° angles measured. These results demonstrate the new phantom’s feasibility in enabling performance validation of ultrasound flow mapping tools.
Gadolinium oxide nanoparticles as a multimodal contrast enhancement agent for pre-clinical proton imaging
Authors: Würl Matthias , Liubchenko Grigory , Hu Guyue , Schnürle Katrin , Meyer Sebastian , Bortfeldt Jonathan , Landry Guillaume , Käsmann Lukas , Lauber Kirsten , Granja Carlos , Oancea Cristina , Verroi Enrico , Tommassino Francesco , Parodi Katia 
Published: January 2025

Abstract
Orthotopic tumor models in pre-clinical translational research are becoming increasingly popular, raising the demands on accurate tumor localization prior to irradiation. This task remains challenging both in x-ray and proton computed tomography (xCT and pCT, respectively), due to the limited contrast of tumor tissue compared to the surrounding tissue. We investigate the feasibility of gadolinium oxide nanoparticles as a multimodal contrast enhancement agent for both imaging modalities. We performed proton radiographies at the experimental room of the Trento Proton Therapy Center using a MiniPIX-Timepix detector and dispersions of gadolinium oxide nanoparticles in sunflower oil with mass fractions up to 8wt%. To determine the minimum nanoparticle concentration required for the detectability of small structures, pCT images of a cylindrical water phantom with cavities of varying gadolinium oxide concentration were simulated using a dedicated FLUKA Monte Carlo framework. These findings are complemented by simulating pCT at dose levels from 80 mGy to 320 mGy of artificially modified murine xCT data, mimicking different levels of gadolinium oxide accumulation inside a fictitious tumor volume. To compare the results obtained for proton imaging to x-ray imaging, cone-beam CT images of a cylindrical PMMA phantom with cavities of dispersions of oil and gadolinium oxide nanoparticles with mass fractions up to 8wt% were acquired at a commercial pre-clinical irradiation setup. For proton radiography, considerable contrast enhancement was found for a mass fraction of 4wt%. Slightly lower values were found for the simulated pCT images at imaging doses below 200 mGy. In contrast, full detectability of small gadolinium oxide loaded structures in xCT at comparable imaging dose is already achieved for 0.5wt%. Achieving such concentrations required for pCT imaging inside a tumor volume in in-vivo experiments may be challenging, yet it might be feasible using different targeting and/or injection strategies.
An unsupervised automatic texture classification method for ultrasound images of thyroid nodules
Authors: Lu Chenzhuo , Fu Zhuang , Fei Jian , Xie Rongli , Lu Chenyue 
Published: January 2025

Abstract

Objective. Ultrasound is the predominant modality in medical practice for evaluating thyroid nodules. Currently, diagnosis is typically based on textural information. This study aims to develop an automated texture classification approach to aid physicians in interpreting ultrasound images of thyroid nodules. However, there is currently a scarcity of pixel-level labeled datasets for the texture classes of thyroid nodules. The labeling of such datasets relies on professional and experienced doctors, requiring a significant amount of manpower. Therefore, the objective of this study is to develop an unsupervised method for classifying nodule textures. Approach. Firstly, we develop a spatial mapping network to transform the one-dimensional pixel value space into a high-dimensional space to extract comprehensive feature information. Subsequently, we outline the principles of feature selection that are suitable for clustering. Then we propose a pixel-level clustering algorithm with a region growth pattern, and a distance evaluation method for texture sets among different nodules is established. Main results. Our algorithm achieves a pixel-level classification accuracy of 0.931 for the cystic and solid region, 0.870 for the hypoechoic region, 0.959 for the isoechoic region, and 0.961 for the hyperechoic region. The efficacy of our algorithm and its concordance with human observation have been demonstrated. Furthermore, we conduct calculations and visualize the distribution of different textures in benign and malignant nodules. Significance. This method can be used for the automatic generation of pixel-level labels of thyroid nodule texture, aiding in the construction of texture datasets, and offering image analysis information for medical professionals.
3D printable piezoelectric composites manufactured via scalable and sustainable solvent-free multi-extrusion process
Authors: Kompelli Gopi , Dauksevicius Rolanas , Kleiva Arunas , Turczyn Roman , Krukiewicz Katarzyna 
Published: January 2025

Abstract
The study presents the development of 3D printable lead-free particulate piezocomposites by implementing a solvent-free multi-extrusion process (MEP) to address the scalability limitations and safety concerns of solvent-based processing commonly used with highly resilient fluoropolymer polyvinylidene fluoride (PVDF) and its copolymers (e.g. with hexafluoropropylene (HFP)). Composite filaments of PVDF-HFP with ferroelectric barium titanate (BTO) particles at 20, 40 and 60 wt% were manufactured for fused filament fabrication (FFF) by applying the melt-based process consisting of effectively composed multiple extrusion and granulation cycles. The results from TGA, DSC, FTIR, XRD, EDS-SEM and tensile tests indicate that physical and mechanical properties of the re-extruded and printed PVDF-HFP are largely preserved. The process ensures homogeneous BTO dispersion within the consistently printable piezocomposites, which demonstrate satisfactory levels of piezoresponse and flexibility together with filler-reinforcing and high-field poling capabilities. The FFF-printed piezocomposites tested at higher strain rates (up to 0.17 s−1) exhibit 30%–40% increase in tensile strength at the expense of reduced ductility. Brief thermal poling at 80 °C and 20 kV mm−1 is observed to improve coefficient d
33 through more effective BTO polarization compared to room-temperature poling (up to 7.3 pC/N is measured for the 40/60 wt% PVDF-HFP/BTO). Thermal poling also enhances piezoresponse stability by minimizing depolarization (d
33 decay) regardless of poling duration. Increase in BTO content results in stronger dependence of piezoresponse on poling field, temperature and duration, as well as weaker dependence of ductility characteristics on the strain rate. The MEP approach is environmentally and economically sustainable manufacturing method that is accessible to a wide FFF user community. It is also scalable to high-throughput production of functional composites based on thermally resistant materials to enable 3D printing of customizable piezoelectric sensing devices.
Efficient digital quadratic unconstrained binary optimization solvers for SAT problems
Authors: Fong Robert Simon , Song Yanming , Yosifov Alexander 
Published: January 2025

Abstract
Boolean satisfiability (SAT) is a propositional logic problem of determining whether an assignment of variables satisfies a Boolean formula. Many combinatorial optimization problems can be formulated in Boolean SAT logic—either as k-SAT decision problems or Max k-SAT optimization problems, with conflict-driven clause learning (CDCL) solvers being the most prominent. Despite their ability to handle large instances, CDCL-based solvers have fundamental scalability limitations. In light of this, we propose recently-developed quadratic unconstrained binary optimization (QUBO) solvers as an alternative platform for 3-SAT problems. To utilize them, we implement a 2-step [3-SAT]-[Max 2-SAT]-[QUBO] conversion procedure and present a novel approach using linear systems and Diophantine equation to calculate the number of both satisfied and violated clauses of the original 3-SAT instance from the transformed Max 2-SAT formulation. We then demonstrate, through numerical simulations on several benchmark instances, that digital QUBO solvers can achieve state-of-the-art accuracy on 78-variable 3-SAT benchmark problems. Our work facilitates the broader use of quantum annealers on noisy intermediate-scale quantum devices, as well as their quantum-inspired digital counterparts, for solving 3-SAT problems.