bluuebunny/crossref_metadata_2025_split huggingface dataset:
Resolving full-wave through-wall transmission effects in multi-static synthetic aperture radar
Authors: Watson F M , Andre D , Lionheart W R B 
Published: June 2024

Abstract
Through-wall synthetic aperture radar (SAR) imaging is of significant interest for security purposes, in particular when using multi-static SAR systems consisting of multiple distributed radar transmitters and receivers to improve resolution and the ability to recognise objects. Yet there is a significant challenge in forming focused, useful images due to multiple scattering effects through walls, whereas standard SAR imaging has an inherent single scattering assumption. This may be exacerbated with multi-static collections, since different scattering events will be observed from each angle and the data may not coherently combine well in a naive manner. To overcome this, we propose an image formation method which resolves full-wave effects through an approximately known wall or other arbitrary obstacle, which itself has some unknown ‘nuisance’ parameters that are determined as part of the reconstruction to provide well focused images. The method is more flexible and realistic than existing methods which treat a single wall as a flat layered medium, whilst being significantly computationally cheaper than full-wave methods, strongly motivated by practical considerations for through-wall SAR.
Microfluidic carbon cloth-based enzymatic glucose biofuel cell for sustainably powering a microelectronic circuit
*
Authors: Vanmathi S , Goel Sanket 
Published: June 2024

Abstract
Enhancing enzymatic microfluidic biofuel cells (EBFCs) devices has garnered significant attention due to the development of microfluidic ultra-low power energy-gathering techniques. To facilitate the ability to create microfluidic EBFCs, a carbon cloth (CC) has been considered since they are effective renewable energy sources and utilized as the ideal paper-based substitute for traditional power supplies for a variety of tiny devices due to their inherent qualities and exceptional performance. The developed microfluidic EBFC utilized glucose as a fuel, carbon cloth as the bioelectrode, Glucose oxidase for the anode, and laccase for the cathode. The maximum stable open circuit voltage of CC-EBFC was measured to be 475 mV with a peak power density of 85 µW cm−2 at 300 mV and a current density of 484 µA cm−2. The power performance of the device was improved by bovine serum albumin and a booster circuit, which was also coated and connected to the load to stabilize the performance. The novelty of the work is that using a flexible substrate of carbon cloth, with a microfluidic channel, has an added advantage in the biofuel cell. LTC3108EDE DC–DC booster was used to increase energy and attain a high charging voltage of 5 V to operate a digital watch up to 3 V. With minimal weight and flexibility; this minuscule device opens up new possibilities to sustainably power wearable and portable microelectronic devices.
Effect of external electric field on copper/silica contact electrification and adhesion: insight from first-principles and molecular mechanics investigations
Authors: Wu Jun , Cao Jiapeng , Bi Hailin , Zhu Wenming , Rong Junyi , Xu Yang 
Published: June 2024

Abstract
The electrostatic force induced by charge transfer during contact electrification is one of the main components of adhesion force at the solid interface. Some studies found that the magnitude of charge transfer and the consequently electrostatic force can be tuned by the external electric field. However, the detailed mechanism is still lacking in understanding. In this study, the effect of external electric field on copper/silica contact electrification and adhesion is studied via first-principles and molecular mechanics calculations and the mechanism is revealed by electrostatic potential and adhesion energy analysis. It is proved that the external electric field can affect the contact potential difference, which is the driving force of contact electrification, thus influencing the magnitude of charge transfer and electrostatic force. When the electric field direction is the same as the electron transfer direction, the contact electrification can be suppressed, leading to the decrease in the ratio of electrostatic force to van der Waals force. In particularly, the contact electrification and electrostatic force can be completely eliminated when applying a specific electric field intensity. This can provide an inspiration for quantitatively studying the source of adhesion force at solid interface.
Majorana CP-violating phases and NSI effects in neutrino decay
Authors: Alok Ashutosh Kumar , Chundawat Neetu Raj Singh , Mandal Arindam , Soni Bhavya 
Published: June 2024

Abstract
In this work, we investigate the impact of neutrino decay in the presence of non-standard interactions (NSI) along with the effects of the Majorana phase on neutrino decay in matter in the context of two-flavor neutrino oscillations. These effects are studied on neutrino oscillation probabilities P

α
β
≡ P(ν

α
→ ν

β
), and the difference

Δ

P

α
β

≡
P
(

ν

α

→

ν

β

)
−
P
(

ν

¯

α

→

ν

¯

β

)

for several accelerator and reactor neutrino experiments. We find that for P

α
β
, the influence of the Majorana phase on decay in matter can be replicated by the simultaneous presence of both decay and NSI. However, precise measurements of the P

α
β
and ΔP

α
β
observables have the potential to unequivocally identify the presence of the Majorana phase by discerning its effects from the concurrent presence of both decay and NSI.
All-optical spin switching on an ultrafast time scale
Authors: Hübner Wolfgang , Lefkidis Georgios , Zhang G P 
Published: June 2024

Abstract
Information technology revolution demands bigger and faster magnetic storage. All-optical spin switching (AOS) may offer a solution, where an ultrafast laser pulse alone can switch magnetization from one direction to another faithfully within 1–10 ps, free of a magnetic field. There are two types of switching: One is the helicity-dependent all-optical spin switching (HD-AOS) and the other the helicity-independent all-optical spin switching (HID-AOS). In a few alloys, one single laser pulse, with sufficient fluence, can switch spin, but the majority of magnetic materials requires multiple pulses. Both material-specific and laser-specific properties strongly affect the switching process. However, the underlying mechanism is still under debate. As the entire research field moves toward applications, it is very appropriate to review what has been achieved in the last decade. This review covers some of the major experimental and theoretical developments within the last decade, and serves as an introduction to the uninitiated reader in this field and a summary for the seasoned researchers.
Comparative analysis of molecular dynamics and method of moments in two-dimensional concentric circular layers
Authors: Salazar Robert , Cobos Cristian , Jaramillo Diego , Bayona-Roa Camilo 
Published: June 2024

Abstract
In this manuscript, we undertake an examination of a classical plasma deployed on two finite co-planar surfaces: a circular region

Ω
in

into an annular region

Ω
out

with a gap in between. It is studied both from the point of view of statistical mechanics and the electrostatics of continua media. We employ a dual perspective: the first one is by using molecular dynamics (MD) simulations to find the system’s positional correlation functions and velocity distributions. That by modeling the system as a classical two-dimensional Coulomb plasma of point-like charged particles q
1 and q
2 on the layers

Ω
in

and

Ω
out

respectively with no background density. The second one corresponds to a finite Surface Electrode (SE) composed of planar metallic layers displayed on the regions

Ω
in

,

Ω
out

at constant voltages

V
in

,

V
out

considering axial symmetry. The surface charge density is calculated by the Method of Moments (MoM) under the electrostatic approximation. Point-like and differential charges elements interact via a

1

/

r

—electric potential in both cases. The thermodynamic averages of the number density, and electric potential due to the plasma depend on the coupling and the charge ratio

ξ
=

q
1

/

q
2

once the geometry of the layers is fixed. On the other hand, the fields due to the SE depend on the layer’s geometry and their voltage. In the document, is defined a protocol to properly compare the systems. We show that there are values of the coupling parameter, where the thermodynamic averages computed via MD agree with the results of MoM for attractive

ξ
=
−
1

and repulsive layers ξ = 1.
Ultrafast dynamics of exciton-polariton fluids at non-zero momenta
Authors: Zhao Xianyan , Ye Ziyu , Chen Fei , Zhou Hang , Jia Haoyuan , Xu Huailiang , Li Hui , Wu Jian 
Published: June 2024

Abstract
In this study, we have explored the ultrafast formation and decay dynamics of exciton-polariton fluids at non-zero momenta, non-resonantly excited by a small-spot femtosecond pump pulse in a ZnO microcavity. Using the femtosecond angle-resolved spectroscopic imaging technique, multidimensional dynamics in both the energy and momentum degrees of freedom have been obtained. Two distinct regions with different decay rate in the energy dimension and various decay-channels in the momentum dimension can be well-resolved. Theoretical simulations based on the generalized Gross–Pitaevskii equation can reach a qualitative agreement with the experimental observations, demonstrating the significance of the initial potential barrier induced by the pump pulse during the decay process. The finding of our study can provide additional insights into the fundamental understanding of exciton-polariton condensates, enabling further advancements for controlling the fluids and practical applications.
Genetically optimised SMOTE-based adversarial discriminative domain adaptation for rotor fault diagnosis at variable operating conditions
Authors: Rajagopalan Sudhar , Purohit Ashish , Singh Jaskaran 
Published: June 2024

Abstract
For safety, reliability, and uninterrupted output of gas turbines, aviation engines, power-generating equipment, pumps, gears, compressors etc, rotor mass imbalance must be detected and diagnosed to avoid catastrophic failure. Industry 4.0 relies on predictive digital maintenance and deep learning-based convolutional neural network (CNN), which predicts defects but fails if the operating conditions change. Research studies in various fields indicate that the domain shift issue occurs due to source and target samples being from different domains, which reduces prediction capability. Moreover, research studies are scarce in examining prediction capability under varying operating speeds for rotor mass imbalance. Hence, this research proposes the adversarial discriminative domain adaptation (ADDA) technique which predicts machine failures under various operational conditions. The efficacy of ADDA has been explored by introducing 1D-CNN as a source and a target encoder inside ADDA’s architecture to take advantage of CNN’s feature extraction capability. Further, this research effectively tackles CNN’s inherent issues of overfitting and hyperparameters value selection. Furthermore, The real-world scenario has more healthy samples than fault condition samples, causing a multiclass imbalance in sample data, which affects the classification decision boundary and causes biased prediction. Hence, the proposed methodology first addresses the class imbalance through synthetic minority oversampling (SMOTE), then genetic algorithm optimizes 1D-CNN’s hyperparameters, and the effective dropout layer positioning solves the overfitting. Finally, the deep learning-based SMOTE_ADDA_GO-1D-CNN decreases domain discrepancy with ADDA. The proposed methodology’s efficacy has been explored through F1-Score, which is used as multiclass evaluation metrics, and it has been benchmarked against standard machine learning and deep learning algorithms. The test results of the proposed methodology surpassed all of them with maximum prediction accuracy. Thus, this study contributes to rotor massimbalance detection and diagnosis for multiclass imbalanced data under varying operational conditions by successfully overcoming potential challenges during fault prediction.
Depth extraction of 3D defects on curved surfaces with multi-line lasers
Authors: Jin Lei , Li Siyuan , Xu Ke 
Published: June 2024

Abstract
The adoption of computer vision technology has significantly impacted surface defect inspection by providing a non-contact, cost-effective solution that has been widely accepted. Among the various techniques available, three-dimensional (3D) defect inspection using multi-line lasers is notable for its simplicity, high detection speed, and extensive coverage. The accuracy of this method is significantly constrained by the precision of laser stripe extraction. In industrial environments, achieving accurate extraction is hindered by the intricate surface geometries of objects and the challenge of maintaining uniform brightness in multi-line laser stripes. To address these challenges, we propose a novel approach to extract the depth of 3D defects on surfaces using multi-line lasers. Our method combines guided filtering and the Frankle-McCann Retinex algorithms to improve the quality of captured images. We have refined the laser stripe extraction process and proposed an advanced adaptive threshold segmentation technique that utilizes the OTSU method to determine threshold coefficients, followed by secondary segmentation based on a neighborhood search. The extracted laser strips are then processed using the quadratic weighted gray gravity method. Additionally, we proposed an innovative region-growth segmentation method based on neighborhood search that effectively segments individual laser strips. We also design a strategy for determining 3D defect depths in situations where precise camera calibration is challenging. The efficacy of our proposed method was rigorously tested on a hot-rolled seamless steel tube with a diameter of 145 mm. The resulting 3D defect depth exhibited an error of less than 0.5 mm, meeting the stringent standards required for practical applications.
A fuzzy q-closest alignment model
Authors: Mucha Piotr B , Peszek Jan 
Published: June 2024

Abstract
The paper examines the issue of well-posedness of the Cucker-Smale model with communication restricted to the q-closest neighbors, known also as the Cucker-Dong model. With agents oscillating on the boundary of different clusters, the system becomes difficult to precisely define, which leads to further problems with kinetic limits as the number of agents tends to infinity. We introduce the fuzzy q-closest system, which circumvents the issues with well-posedness. For such a system we prove a stability estimate for measure-valued solutions and perform the kinetic mean-field limit.
Highly accelerated parameter mapping using model-based alternating reconstruction coupling fitting
Authors: Li Shaohang , Wang Lili , Priest Andrew N , Horvat-Menih Ines , Mendichovszky Iosif A , Gallagher Ferdia A , Wang He , Li Hao 
Published: June 2024

Abstract

Objective. A model-based alternating reconstruction coupling fitting, termed Model-based Alternating Reconstruction COupling fitting (MARCO), is proposed for accurate and fast magnetic resonance parameter mapping. Approach. MARCO utilizes the signal model as a regularization by minimizing the bias between the image series and the signal produced by the suitable signal model based on iteratively updated parameter maps when reconstructing. The technique can incorporate prior knowledge of both image series and parameters by adding sparsity constraints. The optimization problem is decomposed into three subproblems and solved through three alternating steps involving reconstruction and nonlinear least-square fitting, which can produce both contrast-weighted images and parameter maps simultaneously. Main results. The algorithm is applied to T
2 mapping with extended phase graph algorithm integrated and validated on undersampled multi-echo spin-echo data from both phantom and in vivo sources. Compared with traditional compressed sensing and model-based methods, the proposed approach yields more accurate T
2 maps with more details at high acceleration factors. Significance. The proposed method provides a basic framework for quantitative MR relaxometry, theoretically applicable to all quantitative MR relaxometry. It has the potential to improve the diagnostic utility of quantitative imaging techniques.
Addressing the sources of inter-subject variability in E-field parameters in anodal tDCS stimulation over motor cortical network
Authors: Franco-Rosado Pablo , Callejón M Amparo , Reina-Tosina Javier , Roa Laura M , Martin-Rodriguez Juan F , Mir Pablo 
Published: June 2024

Abstract

Objetive: . Although transcranial direct current stimulation constitutes a non-invasive neuromodulation technique with promising results in a great variety of applications, its clinical implementation is compromised by the high inter-subject variability reported. This study aims to analyze the inter-subject variability in electric fields (E-fields) over regions of the cortical motor network under two electrode montages: the classical C3Fp2 and an alternative P3F3, which confines more the E-field over this region. Approach. Computational models of the head of 98 healthy subjects were developed to simulate the E-field under both montages. E-field parameters such as magnitude, focality and orientation were calculated over three regions of interest (ROI): M1S1, supplementary motor area (SMA) and preSMA. The role of anatomical characteristics as a source of inter-subject variability on E-field parameters and individualized stimulation intensity were addressed using linear mixed-effect models.Main results. P3F3 showed a more confined E-field distribution over M1S1 than C3Fp2; the latter elicited higher E-fields over supplementary motor areas. Both montages showed high inter-subject variability, especially for the normal component over C3Fp2. Skin, bone and CSF ROI volumes showed a negative association with E-field magnitude irrespective of montage. Grey matter volume and montage were the main sources of variability for focality. The curvature of gyri was found to be significantly associated with the variability of normal E-fields.Significance. Computational modeling proves useful in the assessment of E-field variability. Our simulations predict significant differences in E-field magnitude and focality for C3Fp2 and P3F3. However, anatomical characteristics were also found to be significant sources of E-field variability irrespective of electrode montage. The normal E-field component better captured the individual variability and low rate of responder subjects observed in experimental studies.
Deep learning-based voxel sampling for particle therapy treatment planning
Authors: Quarz A , Volz L , Antink C Hoog , Durante M , Graeff C 
Published: June 2024

Abstract

Objective. Scanned particle therapy often requires complex treatment plans, robust optimization, as well as treatment adaptation. Plan optimization is especially complicated for heavy ions due to the variable relative biological effectiveness. We present a novel deep-learning model to select a subset of voxels in the planning process thus reducing the planning problem size for improved computational efficiency. Approach. Using only a subset of the voxels in target and organs at risk (OARs) we produced high-quality treatment plans, but heuristic selection strategies require manual input. We designed a deep-learning model based on P-Net to obtain an optimal voxel sampling without relying on patient-specific user input. A cohort of 70 head and neck patients that received carbon ion therapy was used for model training (50), validation (10) and testing (10). For training, a total of 12 500 carbon ion plans were optimized, using a highly efficient artificial intelligence (AI) infrastructure implemented into a research treatment planning platform. A custom loss function increased sampling density in underdosed regions, while aiming to reduce the total number of voxels. Main results. On the test dataset, the number of voxels in the optimization could be reduced by 84.8% (median) at <1% median loss in plan quality. When the model was trained to reduce sampling in the target only while keeping all voxels in OARs, a median reduction up to 71.6% was achieved, with 0.5% loss in the plan quality. The optimization time was reduced by a factor of 7.5 for the total AI selection model and a factor of 3.7 for the model with only target selection. Significance. The novel deep-learning voxel sampling technique achieves a significant reduction in computational time with a negligible loss in the plan quality. The reduction in optimization time can be especially useful for future real-time adaptation strategies.
Static and dynamic analysis of cerebral blood flow in fifty-six large arterial vessel networks
Authors: Chou Yuan-Chung , Payne Stephen 
Published: June 2024

Abstract

Objective. The cerebral vasculature is formed of an intricate network of blood vessels over many different length scales. Changes in their structure and connection are implicated in multiple cerebrovascular and neurological disorders. In this study, we present a novel approach to the quantitative analysis of the cerebral macrovasculature using computational and mathematical tools in a large dataset. Approach. We analysed a publicly available vessel dataset from a cohort of 56 (32/24F/M) healthy subjects. This dataset includes digital reconstructions of human brain macrovasculatures. We then propose a new mathematical model to compute blood flow dynamics and pressure distributions within these 56-representative cerebral macrovasculatures and quantify the results across this cohort. Main results. Statistical analysis showed that the steady state level of cerebrovascular resistance (CVR) gradually increases with age in both men and women. These age-related changes in CVR are in good agreement with previously reported values. All subjects were found to have only small phase angles (<6°) between blood pressure and blood flow at the cardiac frequency. Significance. These results showed that the dynamic component of blood flow adds very little phase shift at the cardiac frequency, which implies that the cerebral macrocirculation can be regarded as close to steady state in its behaviour, at least in healthy populations, irrespective of age or sex. This implies that the phase shift observed in measurements of blood flow in cerebral vessels is caused by behaviour further down the vascular bed. This behaviour is important for future statistical models of the dynamic maintenance of oxygen and nutrient supply to the brain.
Study on a p-GaN HEMT with composite passivation and composite barrier layers
Authors: Cheng Junji , Wang Queyang , Liu Yikai , Ding Guo , Zhang Minming , Yi Bo , Huang Haimeng , Yang Hongqiang 
Published: June 2024

Abstract
A novel structure of p-GaN high-electron-mobility transistor (HEMT) is proposed and studied. It features two composite layers. One is the composite passivation (CP) layer consisting of Si3N4 and high-permittivity (HK) film. The other is a composite barrier (CB) layer consisting of Al
x
Ga1−x
N/AlN/Al0.23Ga0.77N. Due to the coordinated effect of CP and CB, the specific on-resistance (R
ON, SP) can be reduced under the premise of ensuring breakdown voltage (BV). Meanwhile, since the HK film in CP introduces a mechanism to automatically compensate the hot electrons trapped by surface states, the current collapse effect could be suppressed. According to the simulation results, in comparison with the conventional p-GaN HEMT, the proposed one using TiO2 as the HK material and using Al-component of 0.35 for Al
x
Ga1−x
N gains a 29.5% reduction in R
ON, SP while getting a 9.8% increase in BV, which contributes to a 50.5% decrease in the energy loss during one cycle at 200 kHz. It is also demonstrated by the simulation results that the current collapse in the proposed device is reduced by 28.6%. Thereby, a promising p-GaN HEMT with improved performance and reliability is invented.