bluuebunny/crossref_metadata_2025_split huggingface dataset:
Factors influencing charge transport at perovskite–charge transport layer interfaces: current strategies, challenges, and perspectives based on first-principles studies
Authors: Bhattacharya Labanya , Caspary Toroker Maytal 
Published: May 2024

Abstract
Charge transport at the interface between a perovskite absorbing layer and the charge transport layer (CTL) has a major role in determining perovskite solar cell device efficiency and long-term stability. Hence, a detailed operando characterization of the factors influencing charge transport at perovskite/CTL interfaces needs to be discussed. This perspective summarizes recent studies involving charge transport through perovskite and CTL interfaces. An overview is provided of the recent advances in the fundamental understanding of how these interfaces influence device performance. Based on recently reported articles, some of the crucial factors such as energy-level alignment, defects, and non-radiative recombination are identified. The identification is made from an atomistic point of view as these factors have a strong influence on charge transport at perovskite/CTL interfaces. Furthermore, we share our perspective on calculating said factors influencing interfacial charge transport from different approaches using density functional theory (DFT) and beyond.
Understanding the phase stability in a multi-principal-component AlCuFeMn alloy
Authors: Swarnakar Palash , Ghosh M , Mahato B , De Partha Sarathi , Roy Amritendu 
Published: May 2024

Abstract
Method(s) that can reliably predict phase evolution across thermodynamic parameter space, especially in complex systems, are of critical significance in academia as well as in the manufacturing industry. In the present work, the phase stability in an equimolar AlCuFeMn multi-principal-component alloy (MPCA) was predicted using complementary first-principles density functional theory calculations and ab initio molecular dynamics (AIMD) simulations. The temperature evolution of completely disordered, partially ordered, and completely ordered phases was examined based on the Gibbs free energy. Configurational, electronic, vibrational, and lattice mismatch entropies were considered to compute the Gibbs free energy of the competing phases. Additionally, elemental segregation was studied using AIMD. The predicted results at 300 K align well with room-temperature experimental observations using x-ray diffraction and scanning and transmission electron microscopy on a sample prepared using commercially available pure elements. The adopted method could help in predicting plausible phases in other MPCA systems with complex phase stability.
Enhanced performance of normally-OFF GaN HEMTs with stair-shaped p-GaN cap layer
Authors: Ye Yankai , Zhang Haochen , Xing Zhanyong , Yang Lei , Wang Hu , Zhang Mingshuo , Zuo Chengjie , Sun Haiding 
Published: May 2024

Abstract
The p-GaN-gated E-mode HEMTs (P-HEMTs) are being extensively studied for emerging power electronics. However, the devices still suffer from performance trade-off among threshold voltage (V
TH), output drain current (I
DS), and breakdown voltage (V
BD). Herein, we propose a P-HEMT with a stair-like p-GaN cap layer to boost their performance. The p-GaN cap layer is composed of four discrete p-GaN staircases with the decreased thickness to the right (Right-Stair P-HEMT) or to the left (Left-Stair P-HEMT). It is found that the RSP-HEMT simultaneously achieves the 1.3 times increased I
DS, 160 V enhanced V
BD, and improved V
TH stability against drain-induced barrier lowering effects, compared with the LSP-HEMT and the conventional BaSeline P-HEMT. These device merits should be attributed to the effective manipulation of the lateral electric field (E
F) under all bias conditions by the unique band structures enabled by the RSP configuration. Such E
F manipulation strategies offer us helpful guidance and insights to further propel the development of high-performance E-mode P-HEMTs.
An experimental study on low-temperature plasma tissue ablation and its thermal effect
Authors: Chen Liuxiao , Xie Lu , Wu Tong , Xu Qun , Liu Yangzhi , Xin Lin , Mao Lin , Song Chengli 
Published: May 2024

Abstract
Low-temperature plasma ablation has been recently used for minimally invasive surgeries. However, more research is still needed on its generation process during tissue ablation and the underlying mechanism of tissue thermal damage. In this paper, high-speed camera footage, voltage–current signal collection, temperature analysis, and histological analysis were used to investigate the dynamic process of plasma tissue ablation and its thermal effect of dual-needle electrodes immersed in normal saline, which were driven by a high-frequency DC power supply with an output voltage ranging from 220 V to 320 V and a squire wave of 100 kHz. Microbubbles occurred around the ground electrode and merged to form a vapor layer that could completely cover the ground electrode. Plasma capable of ablating tissue would occur in the vapor layer between the ground electrode and tissue. The effect of electrical parameters on plasma generation and its thermal effect are analyzed by statistical results. The experimental results indicated that the voltage applied to the electrodes significantly influenced both the generation and stability of plasma, as well as the heat generation and tissue damage around the electrodes. Furthermore, under the same voltage, the existence of biological tissue promotes the formation of a vapor layer around the electrode, thereby facilitating the generation and stability of plasma. Notably, the temperature rise around the ground electrode is much higher than that around the powered electrode. These results have direct application to the design of plasma tissue ablation systems, which could achieve tissue ablation effects with minimal thermal damage.
Characteristics of electric breakdown in repeated frequency pulse with microcavity effect
Authors: Zhang Tianliang , Wang Wenjing , He Feng 
Published: May 2024

Abstract
The electric breakdown characteristics in microcavity structure under repeated frequency pulse (RFP) were studied, and the physical mechanism was investigated quantitatively based on the full statistical distribution of breakdown time delay obtained in step rectangular pulse (SRP). Experimentally, microcavity heights of 300, 800, and 2000 μm were used. In RFP, the occurrence of breakdown becomes probabilistic when the time delay t
d and pulse width t
PW satisfy the condition t
s-min < t
PW < t
s-max. The breakdown probability increases with pulse width, and the probability distributions are roughly exponential and Gaussian at pulse frequencies of 3 and 1000 Hz, respectively. We found the results are attributed to the similar distributions of time delay in RFP and SRP with similar afterglow time and pulse voltage, and the equal distributions of breakdown probability (with pulse width) and cumulative probability of t
d in RFP. The microcavity effect will decrease the breakdown probability under given pulse width and voltage. Additionally, it is found that in RFP the increase of pulse width from 1 to 1000
μ
s will decrease the threshold voltages at 0% and 100% breakdown probabilities, and the threshold voltage difference will decrease simultaneously to around 0, which results in the transition of breakdown feature from probability to certainty. This phenomenon is due to that the reduction of pulse voltage will increase the time delay significantly and meanwhile the variation rate of time delay with pulse voltage Δt
d/ΔU
w decreases sharply. The microcavity effect will cause the increase of threshold breakdown voltages at a given pulse width and frequency. Finally, it is found that in RFP the breakdown voltage will decrease with the rise of pulse frequency from 10° to 104 Hz, which is consistent with the variation of time delay with afterglow time (from 10−1 to 103 ms) in the memory curve measured in SRP under similar afterglow time. Overall, the microcavity effect will enhance the adsorption of charged and excited species by dielectric walls during afterglow period and enlarge the time delay in the following pulse breakdown, and then influence the RFP breakdown characteristics.
Scaling and universality at ramped quench dynamical quantum phase transitions
Authors: Zamani Sara , Naji J , Jafari R , Langari A 
Published: May 2024

Abstract
The nonequilibrium dynamics of a periodically driven extended XY model, in the presence of linear time dependent magnetic field, is investigated using the notion of dynamical quantum phase transitions (DQPTs). Along the similar lines to the equilibrium phase transition, the main purpose of this work is to search fundamental concepts such as scaling and universality at the ramped quench DQPTs. We have shown that the critical points of the model, where the gap closing occurs, can be moved by tuning the driven frequency and consequently the presence of or absence of DQPTs can be flexibly controlled by adjusting the driven frequency. We have uncovered that, for a ramp across the single quantum critical point, the critical mode at which DQPTs occur is classified into three regions: the Kibble–Zurek (KZ) region, where the critical mode scales linearly with the square root of the sweep velocity, the pre-saturated (PS) region, and the saturated (S) region where the critical mode makes a plateau versus the sweep velocity. While for a ramp that crosses two critical points, the critical modes disclose just the KZ and PS regions. On the basis of numerical simulations, we find that the dynamical free energy scales linearly with time, as approaches to DQPT time, with the exponent

ν
=
1
±
0.01

for all sweep velocities and driven frequencies.
Theoretical study of the nonlinear magnon–phonon coupling in CoF2
Authors: Liu Shuang , Wang Qian , Long Meng-Qiu , Wang Yun-Peng 
Published: May 2024

Abstract
The coupling and interplay between magnon and phonon are important topics for spintronics and magnonics. In this work we studied the nonlinear magnon–phonon coupling in CoF2. First-principles calculations demonstrate that the antiferromagnetic resonance magnon drives a phonon with B1g character; the oscillating driving force has a frequency twice of that of the magnon. Comparing with similar materials indicates a strong correlation between the strength of nonlinear magnon–phonon coupling and the orbital magnetic moment of the magnetic ion. This work pave the way for theoretical study of nonlinear magnon–phonon coupling.
Spatio-temporal features based deep learning model for depression detection using two electrodes
Authors: Choudhary Shubham , Kumar Bajpai Manish , Kumari Bharti Kusum 
Published: May 2024

Abstract
Deep learning has made significant contributions to the medical field and has shown great potential in various applications. Its ability to process vast amounts of data and extraction of patterns has enabled breakthroughs in medical research, diagnosis and treatment. The application of deep learning plays a vital role in depression detection. Depression is a neurological disorder characterized by persistent feelings of sadness, hopelessness and a lack of interest. The prevalence of depression is a significant factor contributing to the rise in suicide cases on a global scale. The electroencephalogram (EEG) is a non-invasive technique used to detect depression. It records brain activity using multiple electrodes. The number of EEG electrodes used for measurement directly affects the instrumentation and measurement complexity of the experiment. The present manuscript proposes a deep learning model for depression detection, focusing on two electrodes named FP1 and FP2. The purpose of employing two electrodes is to enhance the system’s portability while reducing data acquisition time and system cost. EEG is spatio-temporal data and possesses inherent spatial and temporal features. The present manuscript proposes a methodology for extracting temporal and spatial features. The temporal feature extraction module extracts temporal features in the time domain and the spatial module extracts spatial features in the spatial domain. This manuscript presents a study on the applicability of two electrodes for depression detection. This research can enhance accessibility, user-friendliness and easier data collection and analysis. The proposed deep learning model is evaluated on two benchmark datasets. It achieves 93.41% classification accuracy, 92.54% precision, 93.23% recall, 93.06% F1 score and 97.80% area under the curve (AUC) for Hospital University Sains Malaysia dataset and for Multi-modal Open Dataset for Mental-disorder Analysis dataset it achieves 79.40% accuracy, 81.18% precision, 67.73% recall, 73.80% F1 score and 85.66% AUC.
Fractional differentiation-based variational level set model for noisy image segmentation without contour initialization
Authors: Zhong Zhengyang , Wang Bo , Hao Can , Wang Ying 
Published: May 2024

Abstract
Noisy image segmentation is a hot topic in image analysis. In this paper, we present a novel methodology for tackling this issue through the integration of fractional differentiation in the frequency domain with a variational level set model (VLSM), which eliminates user-selected initial contours by incorporating the convex energy function. Additionally, the fractional differentiation reduces noises while preserving more detail information. Experiments on synthetic and real noisy images demonstrate that our proposed model surpasses other denoising VLSMs in terms of noise reduction, segmentation accuracy, and efficiency.
DMOT-SLAM: visual SLAM in dynamic environments with moving object tracking
Authors: Wang Kesai , Yao Xifan , Ma Nanfeng , Ran Guangjun , Liu Min 
Published: May 2024

Abstract
Visual simultaneous localization and mapping (SLAM) in dynamic environments has received significant attention in recent years, and accurate segmentation of real dynamic objects is the key to enhancing the accuracy of pose estimation in such environments. In this study, we propose a visual SLAM approach based on ORB-SLAM3, namely dynamic multiple object tracking SLAM (DMOT-SLAM), which can accurately estimate the camera’s pose in dynamic environments while tracking the trajectories of moving objects. We introduce a spatial point correlation constraint and combine it with instance segmentation and epipolar constraint to identify dynamic objects. We integrate the proposed motion check method into DeepSort, an object tracking algorithm, to facilitate inter-frame tracking of dynamic objects. This integration not only enhances the stability of dynamic features detection but also enables the estimation of global motion trajectories for dynamic objects and the construction of object-level semi-dense semantic maps. We evaluate our approach on the public TUM, Bonn, and KITTI dataset, and the results show that our approach has a significant improvement over ORB-SLAM3 in dynamic scenes and performs better compared to other state-of-the-art SLAM approaches. Moreover, experiments in real-world scenarios further substantiate the effectiveness of our approach.
Mirror-assisted 360° panoramic 3D measurement system based on rotary laser profilometer
Authors: Yao Chuan-Wei , Han Yu-Chen , Zhou Peng , Wang Heng-Yu , Wang Yao , Lin Bin 
Published: May 2024

Abstract
The full surface digitization of real samples plays a crucial role in fields such as object recognition, industrial quality control, and reverse modeling. To achieve panoramic three-dimensional (3D) reconstruction of samples in complex scenes, we propose an advanced panoramic laser scanning system, consisting of a laser rotation scanning system and a pair of plane mirrors. With the assistance of mirrors, the projected line laser beam can illuminate the sample from full angles in a single scanning sequence, and the camera can capture the 3D information of the sample from several specific viewpoints. The scanning method of rotating allows us to achieve more efficient and larger-scale 3D scanning. After obtaining the sample’s 3D information from different viewpoints using the same calibration parameters, the sample’s full-surface 3D data in the global coordinate can be obtained using mirror reflections. We conducted precision and effectiveness tests on samples with different sizes, materials, shapes, and textures to demonstrate the ability of the proposed method to perform full-surface 3D reconstruction under different scenarios. The experimental results of accuracy evaluation demonstrate that the proposed system can achieve a measurement accuracy at the level of 100 μm. Additionally, the robust experimental results in different scenarios also indicate that the proposed panoramic laser scanning system is capable of accurately obtaining full-surface 3D data of various types of samples, providing a cost-effective, easy-to-use, efficient, high-resolution, and effective approach for achieving panoramic 3D reconstruction.
Nominal state determination and its effect on remaining useful life prediction
Authors: Mochammad Solichin , Kim Nam Ho , Noh Yoojeong 
Published: May 2024

Abstract
In machinery operation, a prolonged healthy or nominal state often lacks prognostic significance, causing challenges like data overload, biased predictions, and complex models. Moreover, many prediction methods utilize the complete history of monitoring data from the machine’s startup to its failure; however, prognostics mostly relies on data from the degradation stage. To address this, this study proposes a method to identify and exclude the prolonged period of the nominal state, thereby enhancing the prediction performance of remaining useful life (RUL). A health index (HI) is formulated by integrating acceleration signals from multiple time windows, with deviations computed as the disparity between the HI and its root mean squares. The identification of start and end times for the nominal state, determined by the intersection of consecutive deviation curves, leads to its exclusion from degradation behaviour modelling. The utilization of polynomial degradation trends from HI data after the nominal state’s end time, incorporating a positive slope constraint, aids in mitigating extrapolation uncertainty. The method’s efficiency is demonstrated in three defect cases, highlighting improved RUL predictions without the nominal state’s inclusion.
Underwater image enhancement via color correction and multi-feature image fusion
Authors: Ke Ke , Zhang Biyun , Zhang Chunmin , Yao Baoli , Guo Shiping , Tang Feng 
Published: May 2024

Abstract
The light attenuation underwater causes the actual underwater images to suffer from color cast, low contrast, and weak illumination. To address these issues, an effective fusion-based method is proposed, which realizes color correction (CC), brightness adjustment, contrast, and detail enhancement of underwater images. Concretely, we first design an adaptive CC method via dominant color channel judgment and lower color channel compensation. Then, we detect the brightness of each input image and propose a gamma correction function based on the gradient of the cumulative histogram to adjust the brightness of the low-light images. Subsequently, global histogram stretching and adaptive fractional differentiation techniques are employed to process the brightness-adjusted image, and then the global contrast-enhanced version and detail-enhanced version are generated respectively. To integrate the advantages of both versions, a channel fusion method based on the Lab color space is used to fuse the luminance and color of the two versions separately. The experimental results demonstrate the effectiveness of the proposed method in improving the color and illumination of underwater images, as well as enhancing the clarity of images. Moreover, the testing results on multiple datasets validate the excellent stability of this method.
FDS-MOMEDA: optimization-blind deconvolution in finite high-dimensional spaces for extracting pulse signal in rolling bearing fault diagnosis
Authors: Zhang Meng 
Published: May 2024

Abstract
Rolling bearing fault diagnosis is crucial for ensuring the safe and reliable operation of mechanical equipment. Detecting faults directly from measurement signals is challenging due to severe noise and interference. Blind deconvolution (BD), as a preferred method, effectively recovers periodic pulses from the measured vibration signals of faulty bearings. This study introduces a simulated annealing-based BD approach to enhance the pulse signal components reflecting faults in vibration signals measured on rolling bearings. This method iteratively searches for the optimal coordinates in a high-dimensional orthogonal optimization space, where the optimal coordinates reflect the combination of the inverse filter coefficients. Compared to the generalized spherical optimization space used in the ‘Optimization-Blind Deconvolution’ method in previous works, the proposed finite high-dimensional optimization space helps overcome the problem of inverse filter coefficient convergence, allowing for the design of inverse filters without limit of its shape. To better accommodate the cyclostationarity characteristics of bearing signal measured in reality, the proposed method employs a target vector that allows for uncertainty in pulse occurrence instants, thus overcomes challenges introduced by pseudo-periodic phenomena resulting from bearing slippage. Numerical simulations and experimental results on real bearing vibration signals confirm that the proposed method can design more flexible filters to enhance pulse-like patterns in signals, effectively utilize limited filter resources. Its capacity to tolerate inaccurate fault period estimates, high background noise, and pulse randomness enables it to effectively address vibration measurement signals in real-world scenarios.
Generalized nonlinear hybrid-norm parallel sparse filtering for bearing fault diagnosis under complex interference
Authors: Wang Jinrui , Jia Min , Zhang Zongzhen , Jiang Xingxing , Han Baokun , Bao Huaiqian 
Published: May 2024

Abstract
Considerable attention has been garnered by the bearing fault diagnostic method, founded on sparse feature learning, for its exceptional robustness and adaptability to noise. Being an effective sparse learning method, parallel sparse filtering (PSF) has found successful applications in intelligent fault diagnosis. However, influenced by mechanical structural collisions and complex transmission paths, the obtained bearing signals come with abnormal impacts and noise, resulting in a reduced reliability of the PSF. Additionally, PSF constrains features and samples with

l

1

/

2

norm, which is significantly disturbed by noise. To address these issues, generalized nonlinear hybrid-norm PSF (GNHNPSF) is proposed. Firstly, the Sigmoid nonlinear function is examined to suppress the amplitude of the anomalous shock. Subsequently, the hybrid-norm constraints, which involve the application of a

l

1

/

2

norm to the column features of the input signal and a

l

1

/

3

norm to the row features, can be employed to effectively extract distinctive information from the original data. The GNHNPSF is demonstrated to improve the accuracy of bearing fault diagnosis under complex interference, as evidenced by the results of simulation and experiments.