bluuebunny/crossref_metadata_2025_split huggingface dataset:
First-principles study of the electronic structures and optical properties of g-ZnO/CdX (X = S, Se, Te) van der Waals heterostructures
Authors: Ren Xinle , Huang Yan , Zhang Xiaozhe , Su Junhong , Sun Shaodong 
Published: September 2024

Abstract
The stability, electronic structures and optical properties of g-ZnO/CdX (X = S, Se, Te) heterostructures are studied by density functional theory. It is found that the stable monolayers spacing of the corresponding heterostructure decreases with the increase of the X atomic radius in the CdX monolayers. The constructed g-ZnO/CdX heterostructures all belong to direct band gap, 2.12 eV, 2.09 eV and 1.99 eV, respectively. Electrostatic potential results show that the two monolayers form an internal electric field at the heterostructure interface, and can inhibit the recombination of photogenerated electron hole pairs, and effectively extend the carrier lifetime. Charge density difference analysis shows that charge redistribution mainly occurs in the interfacial region. The optical properties show that the absorption of g-ZnO in the visible range is achieved by heterostructure. In general, with the smallest band gap and the strongest built-in electric field, g-ZnO/CdTe could have the best carrier separation efficiency. And the optical property analysis proves that the g-ZnO/CdTe heterostructure system has the highest utilization ratio of visible light. The above results show that the electronic structure and optical properties of g-ZnO/CdTe heterostructure are the best, and it can be inferred that this heterostructure will be the most beneficial to improve the photocatalytic activity of g-ZnO, providing a new direction for its application in the field of photocatalysis.
Deep learning-driven UFIR filter for enhanced GNSS/PDR pedestrian localization
Authors: Dou Jie , Hu Fen , Dou Lei 
Published: September 2024

Abstract
Integrating pedestrian dead reckoning with the global navigation satellite system enhances the reliability of pedestrian navigation. However, such an integration is typically based on the Kalman Filter, faces challenges due to noise disturbances and model uncertainties. To address these issues, we have introduced a robust integration framework, which updates the state by leveraging the most recent N measurements within a finite horizon window, using an unbiased finite impulse response (UFIR) filter. Additionally, to mitigate disturbances arising from system uncertainties, deep learning techniques are employed instead of conventional model-driven methods to estimate the generalized noise power gain of the UFIR filter. Extensive field experiments, including normal walking, walk-to-run transitions, and scenarios with noise interference, were conducted to validate the efficacy of the approach. The results show that, compared to AKF, FGO, and UFIR, the mean 2D positioning error is reduced by 34.60%, 30.18%, and 25.70%, respectively.
Model of oil pipeline tiny defects detection based on DDPM gated parallel convolutional swin transformer
Authors: Yuan Kaixin , Lang Xianming , Cao Jiangtao , Zhang He 
Published: September 2024

Abstract
To address the challenges of difficult detection of minute magnetic flux leakage (MFL) defects, insufficient inspection data, and low detection accuracy, the denoising diffusion probabilistic model (DDPM) gate dilated parallel convolution swin transformer (DGPST) is proposed. First, we introduce a DDPM-based data generation model, successfully generating a large quantity of diverse and rich MFL defect samples. Second, a gated parallel convolution layer is introduced into the backbone network. This strategy uses the characteristics of dilated convolution to broaden the receptive field of the model, thus enhancing the integration ability of global information. The addition of gating mechanism enables the model to adjust the calculation of attention weight based on broader context information in advance, which not only complicates the shortcomings of window self-attention in global dependence understanding, but also effectively suppress irrelevant calculation. Finally, the loss function of H Intersection over Union is introduced to improve the mean average precision. Following these enhancements, DGPST attains a satisfactory outcome in detecting tiny defects within the MFL problem. Experimental data indicates the accuracy of the algorithm reaches 95.6% and the delay is reduced to 7.6 ms.
AEFF-SSC: an attention-enhanced feature fusion for 3D semantic scene completion
Authors: Shen Yehu , Sheng Yangyang , Niu Xuemei , Jiang Quansheng , Zhu Qixin , Li Jingbin 
Published: September 2024

Abstract
Three-dimensional (3D) occupancy perception technology aims to enable autonomous vehicles to observe and understand dense 3D environments. Estimating the complete geometry and semantics of a scene solely from visual images is challenging. However, humans can easily conceive the complete form of objects based on partial key information and their own experience. This ability is crucial for recognizing and interpreting the surrounding environment. To equip 3D occupancy perception systems with a similar capability, a 3D semantic scene completion method called AEFF-SSC is proposed. This method deeply explores boundary and multi-scale information in voxels, aiming to reconstruct 3D geometry more accurately. We have specifically designed an attention-enhanced feature fusion module that effectively fuses image feature information from different scales and focuses on feature boundary information, thereby more efficiently extracting voxel features. Additionally, we introduce a semantic segmentation module driven by a 3D attention-UNet network. This module combines a 3D U-Net network with a 3D attention mechanism. Through feature fusion and feature weighting, it aids in restoring 3D spatial information and significantly improves the accuracy of segmentation results. Experimental verification on the SemanticKITTI dataset demonstrates that AEFF-SSC significantly outperforms other existing methods in terms of both geometry and semantics. Specifically, within a 12.8 m × 12.8 m area ahead, our geometric occupancy accuracy has achieved a significant improvement of 71.58%, and at the same time, the semantic segmentation accuracy has also increased remarkably by 54.20%.
Ultrathin nanocapacitor assembled via atomic layer deposition
Authors: Lopez Medina Javier Alonso , Mejía-Salazar J Ricardo , Carvalho William O F , Lopez Mercado Cesar , Nedev N , Reyes Gómez Faustino , Oliveira Jr Osvaldo N , Farías M H , Tiznado Hugo 
Published: September 2024

Abstract
We fabricated ultrathin metal–oxide–semiconductor (MOS) nanocapacitors using atomic layer deposition. The capacitors consist of a bilayer of Al2O3 and Y2O3 with a total thickness of ∼10 nm, deposited on silicon substrate. The presence of the two materials, each slab being ∼5 nm thick and uniform over a large area, was confirmed with transmission electron microscopy and x-ray photoelectron spectroscopy. The capacitance in accumulation varied from 1.6 nF (at 1 MHz) to ∼2.8 nF (at 10 kHz), which is one to two orders of magnitude higher than other nanocapacitors. This high capacitance is attributed to the synergy between the dielectric properties of ultrathin Al2O3 and Y2O3 layers. The electrical properties of the nanocapacitor are stable within a wide range of temperatures, from 25 °C to 150 °C, as indicated by capacitance–voltage (C–V). Since the thickness-to-area ratio is negligible, the nanocapacitor could be simulated as a single parallel plate capacitor in COMSOL Multiphysics, with good agreement between experimental and simulation data. As a proof-of-concept we simulated a MOS field effect transistor device with the nanocapacitor gate dielectric, whose drain current is sufficiently high for micro and nanoelectronics integrated circuits, including for applications in sensing.
Auriferous nanozymes: advances in diagnostic and therapeutic applications
Authors: Deshwal Akanksha , Tripathi Ravi Mani , Saxena Kirti , Sheikh Faheem A , Mishra Prashant 
Published: September 2024

Abstract
Nanozymes are a group of nanomaterials that garnered significant attention due to their enzyme-mimicking properties and their catalytic activities comparable to those of natural enzymes. The ability of nanozymes to emulate crucial biological processes which can conquer the drawbacks of natural enzymes, such as their restricted thermostability as well as substrate range. Auriferous (gold) nanozymes possess remarkable enzyme-like properties, such as reductase, peroxidase, superoxide dismutase, oxidase, and catalase. This characteristic makes them a strong competitor for possible applications in the fields of biomedicine as well as biochemical analysis, especially when compared to natural enzymes, along with their simple manufacturing, adaptable features, biocompatibility, and affordability. This review evaluates the factors that affect the catalytic activity of auriferous nanozymes. We offer a thorough investigation of their diagnostic applications, including detecting cancer, microorganisms, glucose, cysteine, and uric acid. Furthermore, we delve into the applications of gold nanozyme in therapeutics including chemodynamic therapy, radiotherapy, and photothermal therapy. In contrast to previous review, our review highlights various advantages of auriferous nanozymes in diagnostics and therapies and provides novel insights into the diverse applications of gold nanozymes encompassing current research studies.
Influence of UV irradiation on the luminescence properties of CsPbBr3 perovskite quantum dots and CsPbBr3 perovskite quantum dots/PVDF composite film—for white LED application
Authors: V Kamalarasan , C Venkateswaran 
Published: September 2024

Abstract
Perovskite quantum dots (QDs) have been widely investigated for their excellent properties such as high color purity in displays, tunable emission wavelength, and high photoluminescence quantum yield. For device applications, improving the stability is an area of interest. In this study, the effects of UV irradiation on the structural and luminescence properties of CsPbBr3 perovskite QDs (CPB QDs) excited at 365 nm were investigated. To overcome the effects of UV irradiation, a CPB QDs/PVDF composite flexible film was prepared. It exhibits high structural and optical stability under UV irradiation and emits a highly intense green color. The emission wavelength and intensity were observed for three years, and the stability of the temperature-dependent emission intensity up to 400 K has been reported. In addition, it is stable in water. A white LED, fabricated by integrating a blue LED with CPB QDs/PVDF composite film and red phosphor, produces bright natural white light [(CIE x, CIE y) = (0.3704, 0.3611), and CCT = 4177 K] with a color gamut area coverage of 86.4% of the standard NTSC (1953) color space.
Investigating deep learning strategies for fast denoising of 5D cardiac photon-counting micro-CT images
Authors: Nadkarni Rohan , Clark Darin P , Allphin Alex J , Badea Cristian T 
Published: September 2024

Abstract

Objective. Photon-counting detectors for CT imaging use energy thresholds to simultaneously acquire projections at multiple energies, making them suitable for spectral imaging and material decomposition. Unfortunately, setting multiple energy thresholds results in noisy analytical reconstructions due to low photon counts in high-energy bins. Iterative reconstruction provides high quality photon-counting CT (PCCT) images but requires enormous computation time for 5D (3D + energy + time) in vivo cardiac imaging. Approach. We recently introduced UnetU, a deep learning (DL) approach that accurately denoises axial slices from 4D (3D + energy) PCCT reconstructions at various acquisition settings. In this study, we explore UnetU configurations for 5D cardiac PCCT denoising, focusing on singular value decomposition (SVD) modifications along the energy and time dimensions and alternate network architectures such as 3D U-net, FastDVDNet, and Swin Transformer UNet. We compare our networks to multi-energy non-local means (ME NLM), an established PCCT denoising algorithm. Main results. Our evaluation, using real mouse data and the digital MOBY phantom, revealed that all DL methods were more than 16 times faster than iterative reconstruction. DL denoising with SVD along the energy dimension was most effective, consistently providing low root mean square error and spatio-temporal reduced reference entropic difference, alongside strong qualitative agreement with iterative reconstruction. This superiority was attributed to lower effective rank along the energy dimension than the time dimension in 5D cardiac PCCT reconstructions. ME NLM sometimes outperformed DL with time SVD or time and energy SVD, but lagged behind iterative reconstruction and DL with energy SVD. Among alternate DL architectures with energy SVD, none consistently outperformed UnetU Energy (2D). Significance. Our study establishes UnetU Energy as an accurate and efficient method for 5D cardiac PCCT denoising, offering a 32-fold speed increase from iterative reconstruction. This advancement sets a new benchmark for DL applications in cardiovascular imaging.
Enhancing timing performance of heterostructures with double-sided readout
Authors: Pagano Fiammetta , Kratochwil Nicolaus , Lowis Carsten , Choong Woon-Seng , Paganoni Marco , Pizzichemi Marco , Cates Joshua W , Auffray Etiennette 
Published: September 2024

Abstract

Objective. Heterostructured scintillators offer a promising solution to balance the sensitivity and timing in TOF-PET detectors. These scintillators utilize alternating layers of materials with complementary properties to optimize performance. However, the layering compromises time resolution due to light transport issues. This study explores double-sided readout—enabling improved light collection and Depth-of-Interaction (DOI) information retrieval—to mitigate this effect and enhance the timing capabilities of heterostructures. Approach. The time resolution and DOI performances of 3 × 3 × 20 mm3 BGO&EJ232 heterostructures were assessed in a single and double-sided readout (SSR and DSR, respectively) configuration using high-frequency electronics. Main results. Selective analysis of photopeak events yielded a DOI resolution of 6.4 ± 0.04 mm. Notably, the Coincidence Time Resolution (CTR) improved from 262 ± 8 ps (SSR) to 174 ± 6 ps (DSR) when measured in coincidence with a fast reference detector. Additionally, symmetrical configuration of two identical heterostructures in coincidence was tested, yielding in DSR a CTR of 254 ± 8 ps for all photopeak events and 107 ± 5 ps for the fastest events. Significance. By using high-frequency double-sided readout, we could measure DOI resolution and improve the time resolution of heterostructures of up to 40%. The DOI information resulted intrinsically captured in the average between the timestamps of the two SiPMs, without requiring any further correction.
A study on GaN-based high-efficiency class E power amplifier circuits at medium-low frequencies
Authors: Liu Chang , Huang Xuegong 
Published: September 2024

Abstract
In this paper, a high-power amplifier is designed to address the challenges of low transmission power and poor efficiency in wireless energy transmission technology at low frequencies. The design is based on a class E power amplifier circuit. The theoretical analysis is conducted to determine transistor selection and the static DC operating point. Load traction technology is employed to match the load impedance of the power amplifier, ensuring highly efficient and unified power additional efficiency, and output power. To enhance system stability and feedback compensation, gate series resistance and parallel capacitors are incorporated. Simulation results demonstrate that the designed power amplifier achieves an output power of 48 dBm with a power additive efficiency (PAE) of 85%. Furthermore, the third-order intermodulation component reaches 70 dBm, indicating concentrated output energy and high linearity in the amplifier system. Experimental results validate the consistency between theoretical analysis and model simulation, confirming the designed power amplifier circuit’s stability and successfully achieving energy-efficient, high-power transmission.
Cosmic topology. Part IVa. Classification of manifolds using machine learning: a case study with small toroidal universes
Authors: Tamosiunas Andrius , Cornet-Gomez Fernando , Akrami Yashar , Anselmi Stefano , Carrón Duque Javier , Copi Craig J. , Eskilt Johannes R. , Güngör Özenç , Jaffe Andrew H. , Kosowsky Arthur , Martin Barandiaran Mikel , Mertens James B. , Mihaylov Deyan P. , Pereira Thiago S. , Saha Samanta , Samandar Amirhossein , Starkman Glenn D. , Taylor Quinn , Vardanyan Valeri ,   The COMPACT collaboration
Published: September 2024

Abstract
Non-trivial spatial topology of the Universe may give rise to potentially measurable signatures in the cosmic microwave background.
We explore different machine learning approaches to classify harmonic-space realizations of the microwave background in the test case of Euclidean E
1 topology (the 3-torus) with a cubic fundamental domain of a size scale significantly smaller than the diameter of the last scattering surface.
This is the first step toward developing a machine learning approach to classification of cosmic topology and likelihood-free inference of topological parameters.
Different machine learning approaches are capable of classifying the harmonic-space realizations with accuracy greater than 99%
if the topology scale is half of the diameter of the last-scattering surface and orientation of the topology is known.
For distinguishing random rotations of these sky realizations from realizations of the covering space, the extreme gradient boosting classifier algorithm performs best with an accuracy of 88%. Slightly lower accuracies of 83% to 87% are obtained with the random forest classifier along with one- and two-dimensional convolutional neural networks. The techniques presented here can also accurately classify non-rotated cubic E
1 topology realizations with a topology scale slightly larger than the diameter of the last-scattering surface, if enough training data are provided.
While information compressing methods like most machine learning approaches cannot
exceed the statistical power of a likelihood-based approach that captures
all available information, they potentially offer a computationally cheaper alternative.
A principle challenge appears to be accounting for arbitrary orientations of a given
topology, although this is also a significant hurdle for likelihood-based approaches.
Influence of feedback transparency on motor imagery neurofeedback performance: the contribution of agency
Authors: Dussard Claire , Pillette Léa , Dumas Cassandra , Pierrieau Emeline , Hugueville Laurent , Lau Brian , Jeunet-Kelway Camille , George Nathalie 
Published: September 2024

Abstract

Objective. Neurofeedback (NF) is a cognitive training procedure based on real-time feedback (FB) of a participant’s brain activity that they must learn to self-regulate. A classical visual FB delivered in a NF task is a filling gauge reflecting a measure of brain activity. This abstract visual FB is not transparently linked—from the subject’s perspective—to the task performed (e.g., motor imagery (MI)). This may decrease the sense of agency, that is, the participants’ reported control over FB. Here, we assessed the influence of FB transparency on NF performance and the role of agency in this relationship. Approach. Participants performed a NF task using MI to regulate brain activity measured using electroencephalography. In separate blocks, participants experienced three different conditions designed to vary transparency: FB was presented as either (1) a swinging pendulum, (2) a clenching virtual hand, (3) a clenching virtual hand combined with a motor illusion induced by tendon vibration. We measured self-reported agency and user experience after each NF block. Main results. We found that FB transparency influences NF performance. Transparent visual FB provided by the virtual hand resulted in significantly better NF performance than the abstract FB of the pendulum. Surprisingly, adding a motor illusion to the virtual hand significantly decreased performance relative to the virtual hand alone. When introduced in incremental linear mixed effect models, self-reported agency was significantly associated with NF performance and it captured the variance related to the effect of FB transparency on NF performance. Significance. Our results highlight the relevance of transparent FB in relation to the sense of agency. This is likely an important consideration in designing FB to improve NF performance and learning outcomes.
Simultaneous modulation of pulse charge and burst period elicits two differentiable referred sensations
Authors: Benigni T R , Pena A E , Kuntaegowdanahalli S S , Abbas J J , Jung R 
Published: September 2024

Abstract

Objective. To investigate the feasibility of delivering multidimensional feedback using a single channel of peripheral nerve stimulation by complementing intensity percepts with flutter frequency percepts controlled by burst period modulation. Approach. Two dimensions of a distally referred sensation were provided simultaneously: intensity was conveyed by the modulation of the pulse charge rate inside short discrete periods of stimulation referred to as bursts and frequency was conveyed by the modulation of the period between bursts. For this approach to be feasible, intensity percepts must be perceived independently of frequency percepts. Two experiments investigated these interactions. A series of two alternative forced choice tasks (2AFC) were used to investigate burst period modulation’s role in intensity discernibility. Magnitude estimation tasks were used to determine any interactions in the gradation between the frequency and intensity percepts. Main results. The 2AFC revealed that burst periods can be individually differentiated as a gradable frequency percept in peripheral nerve stimulation. Participants could correctly rate a perceptual scale of intensity and frequency regardless of the value of the second, but the dependence of frequency differentiability on charge rate indicates that frequency was harder to detect with weaker intensity percepts. The same was not observed in intensity differentiability as the length of burst periods did not significantly alter intensity differentiation. These results suggest multidimensional encoding is a promising approach for increasing information throughput in sensory feedback systems if intensity ranges are selected properly. Significance. This study offers valuable insights into haptic feedback through the peripheral nervous system and demonstrates an encoding approach for neural stimulation that may offer enhanced information transfer in virtual reality applications and sensory-enabled prosthetic systems. This multidimensional encoding strategy for sensory feedback may open new avenues for enriched control capabilities.