bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of oxygen vacancies on the electrical transport properties of conductive Y3Fe5O12 films at high temperature
Authors: Chen Yunhai , Luo Dianbing , Cao Xinyu , Wang Yifei , Aung Phyo , Jin Kexin , Wang Shuanhu 
Published: August 2023

Abstract
Oxygen vacancies doping in oxide materials is a very common means to modulate the electrical transport properties. In this work, Y3Fe5O12 (YIG) films with abundant oxygen vacancies were grown on Gd3Ga5O12 substrates by solution spin coating and high vacuum annealing method, and the effect of oxygen vacancies on the electrical transport properties was systematically studied. It was found that a large amount of oxygen vacancies doping could convert the YIG film from a good room-temperature insulator to an electrical conductor. At high temperature and high vacuum, a large number of oxygen vacancies increased the disorder of the system, resulting in the appearance of a band-tail state, thus forming a constant range hopping conduction. While when the sample was exposed to air, the oxygen vacancies in the sample would gradually recombine and disappear, and the conduction mechanism transferred to drift mode.
Active-matter isomorphs in the size-polydisperse Ornstein–Uhlenbeck Lennard–Jones model
Authors: Jespersen Daniel , Costigliola Lorenzo , Dyre Jeppe C , Saw Shibu 
Published: August 2023

Abstract
This paper studies size-polydisperse Lennard–Jones systems described by active Ornstein–Uhlenbeck particle (AOUP) dynamics. The focus is on the existence of isomorphs (curves of invariant structure and dynamics) in the model’s three-dimensional phase diagram. Isomorphs are traced out from a single steady-state configuration by means of the configurational-temperature method. Good isomorph invariance of the reduced-unit radial distribution function and the mean-square displacement as a function of time is demonstrated for three uniform-distribution polydispersities,

12
%

, 23%, and 29%. Comparing to active-matter isomorphs generated by the analytical direct-isomorph-check method, the latter have poorer invariance of the structure, but better invariance of the dynamics. We conclude that both methods can be used to quickly get an overview of the phase diagram of polydisperse AOUP models involving a potential-energy function obeying the hidden-scale-invariance property required for isomorph theory to apply.
A computational study of 2D group-III ternary chalcogenide monolayer compounds MNTe2 (M, N = In, Ga, Al)
Authors: Khengar S J , Parmar P R , Modi Nidhi , Thakor P B 
Published: August 2023

Abstract
First principle calculations of novel two-dimensional (2D) group-III ternary chalcogenide monolayer (G3TCM) compounds have been carried out using density functional theory. The 2D hexagonal structure has a honeycomb-like appearance from both the top and bottom views. Both pristine and G3TCM compounds are energetically favourable and have been found to be dynamically stable via phonon calculations. The ab-initio molecular dynamics calculations show the thermodynamical stability of the G3TCM compounds. The G3TCM compounds exhibit semiconductor behaviour with a decreased indirect bandgap compared to the pristine monolayers. Chalcogen atoms contribute mainly to the valence bands, while group-III atoms have a major contribution to the conduction band. A red shift has been observed in the absorption of light, mainly in the visible and ultraviolet regions, and the refractive index is increased compared to the pristine material. Both pristine and G3TCM compounds have been found to be more active in the ultraviolet region, and low reflection has been observed. In the 6–8 eV range of the ultraviolet region, zero reflection and the highest absorption are observed. The monolayer has shown potential applications in optoelectronics devices as an ultraviolet and visible light detector, absorber, coating material, and more. The band alignment of the 2D G3TCM monolayer is calculated to observe its photo-catalyst behaviour.
Theoretical upper limit of dislocation density in slightly-ductile single-crystal ceramics
Authors: Yasui Kyuichi , Hamamoto Koichi 
Published: August 2023

Abstract
Upper limit of dislocation density without fracture is numerically calculated for slightly- ductile single-crystal ceramics for which the Griffith criterion for fracture and the Bailey–Hirsch type relationship between applied stress and the dislocation density are nearly valid simultaneously in order to obtain useful information to improve functional, electrical, and mechanical properties of ceramics by the introduction of appropriate dislocations. Two models of fracture as a function of dislocation density are constructed; simple model and probability model. If the diameter of pre-existing microcracks is sufficiently small, the dislocation density could be as high as the crystallographic limit (

∼

10

18

m−2). Even if the typical diameter of pre-existing microcracks is not so small, there is some probability that the dislocation density could be as high as the crystallographic limit if the number of microcracks in the specimen is very small. Accordingly, the increase in ionic conductivity by several orders of magnitude without dendrite formation by introducing appropriate dislocations into single-crystal solid electrolytes with the dislocation density higher than about 1017 m−2 theoretically predicted by the authors may be practically possible.
Characteristic curves of the photodiode and phototransistor with Arduino
Authors: Chen Yu , Kim Hee Ra , Ahn Yu Jin , Kim Jung Bog 
Published: August 2023

Abstract
A simple circuit with Arduino for determining characteristic curves of the photodiode and phototransistor is described. The experimental results show that the measured I–V curves of the photodiode and phototransistor are consistent with the datasheet.
Vacuum-ultraviolet laser source for spectroscopy of trapped thorium ions
Authors: Thielking J , Zhang K , Tiedau J , Zander J , Zitzer G , Okhapkin M V , Peik E 
Published: August 2023

Abstract
A tunable vacuum-ultraviolet (VUV) laser source based on four-wave frequency mixing in xenon is presented. Using seed radiation from two continuous-wave lasers, the system allows for precise control of the VUV frequency and is developed for the resonant laser excitation of the Th-229 nucleus to its low-energy isomeric state. The system is prepared to operate in a wide scanning range from 148 nm to 155 nm. The source produces pulses of 6–10 ns duration with up to 40 µJ energy and is coupled via a vacuum beamline to a linear radiofrequency ion trap. In a first implementation of VUV laser spectroscopy of trapped Th+ ions we excite three previously unknown resonance lines near 149 nm wavelength to electronic levels that are close to the Th-229 isomer energy. The resonances are detected and analyzed via fluorescence of the excited Th+ ions. An analysis of the lineshape is used to estimate the linewidth of the VUV radiation to be in the range of

⩽

6 GHz, dominated by phase noise that is enhanced in harmonic generation and in the four-wave mixing process. The prospects for the use of the system in nuclear laser spectroscopy of Th-229 are discussed.
Impact of tetra ethyl ortho-silicate and magnesium nitrate hexahydrate on dielectric characteristics of forsterite powder
Authors: Pandey Ankur , Sahoo Swagatadeb 
Published: August 2023

Abstract
Forsterite powder with different compositions of tetra ethyl ortho-silicate (TEOS) and magnesium nitrate hexahydrate (MNH) is synthesized using the sol-gel method. X-ray Diffraction (XRD) technique is used for structural analysis and phase identification for forsterite powder. Surface morphology and elemental composition of forsterite samples are also investigated using scanning electron microscopy (SEM) and energy dispersive x-ray (EDX) techniques. In addition, an extensive study is performed to estimate the dielectric properties (dielectric constant, dielectric loss, alternating current conductivity and loss tangent) and their relationship with frequency at room temperature in the frequency range of 200 MHz–20 GHz using a vector network analyzer (VNA) assembly. Furthermore, the intense effect of TEOS and MNH on surface morphology and dielectric properties of forsterite powder is also discussed in detail by focusing on the application in medical implants.
Research on sparse identification method for aeroelastic dynamic response prediction
Authors: Yu Jiaming , Qi Hui , Li Xiangyu , Wang Kai , Guo Jing 
Published: August 2023

Abstract
Nonlinear aeroelastic system has the characteristics of complex structure, difficult modeling and difficult calculation of dynamic response. For the analysis of nonlinear aeroelastic systems, model identification is a very attractive method. However, the models identified by traditional methods are often relatively complex and limited in scope of use, so it is necessary to develop an interpretable equivalent reduced model. In this paper, sparse regression method and sequential threshold least squares technique are used to establish sparse identification method for complex aeroelastic systems. This method has the ability to identify reduced models containing only required nonlinear terms through measurement data. Then, the sparse identification method is used to identify the binary wing with dead zone nonlinearity and cubic stiffness nonlinearity. The obtained model can provide rapid and accurate prediction of the response of the system according to the sensor measurement, and can also be used as an explicit surrogate model for aeroelastic optimization design, thus verifying the superiority of the proposed method.
AutoEER: automatic EEG-based emotion recognition with neural architecture search
Authors: Wu Yixiao , Liu Huan , Zhang Dalin , Zhang Yuzhe , Lou Tianyu , Zheng Qinghua 
Published: August 2023

Abstract

Objective. Emotion recognition based on electroencephalography (EEG) is garnering increasing attention among researchers due to its wide-ranging applications and the rise of portable devices. Deep learning-based models have demonstrated impressive progress in EEG-based emotion recognition, thanks to their exceptional feature extraction capabilities. However, the manual design of deep networks is time-consuming and labour-intensive. Moreover, the inherent variability of EEG signals necessitates extensive customization of models, exacerbating these challenges. Neural architecture search (NAS) methods can alleviate the need for excessive manual involvement by automatically discovering the optimal network structure for EEG-based emotion recognition. Approach. In this regard, we propose AutoEER (Automatic EEG-based Emotion Recognition), a framework that leverages tailored NAS to automatically discover the optimal network structure for EEG-based emotion recognition. We carefully design a customized search space specifically for EEG signals, incorporating operators that effectively capture both temporal and spatial properties of EEG. Additionally, we employ a novel parameterization strategy to derive the optimal network structure from the proposed search space. Main results. Extensive experimentation on emotion classification tasks using two benchmark datasets, DEAP and SEED, has demonstrated that AutoEER outperforms state-of-the-art manual deep and NAS models. Specifically, compared to the optimal model WangNAS on the accuracy (ACC) metric, AutoEER improves its average accuracy on all datasets by 0.93%. Similarly, compared to the optimal model LiNAS on the F1 Ssore (F1) metric, AutoEER improves its average F1 score on all datasets by 4.51%. Furthermore, the architectures generated by AutoEER exhibit superior transferability compared to alternative methods. Significance. AutoEER represents a novel approach to EEG analysis, utilizing a specialized search space to design models tailored to individual subjects. This approach significantly reduces the labour and time costs associated with manual model construction in EEG research, holding great promise for advancing the field and streamlining research practices.
Two Applications of Manifold Regularization in Deep Learning Architectures
Authors: Yuan Weixuan , Zhang Chengyuan , Song Wenrui , Yang Siyi 
Published: August 2023

Abstract
With the rapid development of machine learning today, deep learning has shown great advantages in many fields. Due to the high cost of datasets with labels, its branch semi-supervised learning has become an important research area. However, existing semi-supervised learning methods perform poorly on extremely small datasets, for instance, only one labeled example for each class. Hence, we introduce methods that apply manifold approaches to DL(Deep Learning) and frameworks that implement the idea. Potential application scenarios were analyzed based on experiments on synthetic and real-world experiments.
An Automatic Wound Detection System Empowered by Deep Learning
Authors: Adnan Muhammad , Asif Muhammad , Ahmad Maaz Bin , Mahmood Toqeer , Masood Khalid , Ashraf Rehan , Nadeem Faisal C.M. 
Published: August 2023

Abstract
The wound can be defined as a breakdown in the protective function of the skin. Perceiving the types of wounds at the initial stages can be more viable in the treatment process. The paramedical staff usually examines the wound with its size and healing status is determined through visual assessment. Telemedicine and rest of the healthcare services delivered through digital means aim to ensure the availability of health facilities at doorstep. Especially, in COVID-19 period these services played significant role where physical examination of patient in hospital may enhance the spread of the disease. The remote assessment of wound demands high quality of medical images or videos and localization of wound area. The manual diagnosis is not very robust and reliable because of variability in the appearance of wound site. Whereas an automatic wound detection, localization and classification may assist a physician with more accuracy and robustness. In this work, an automatic wound detection technique using YOLO v3 model is proposed. The proposed technique detects, localizes and classifies the wound into four main categories containing stitch-wound, cut-wound, open-wound and normal-skin. The experimental results show that the proposed technique is more efficient and robust with 99% accuracy and outperforms other counter parts.
Medical Image Segmentation Based on U-Net
Authors: Chen Zhihao 
Published: August 2023

Abstract
Traditional statistical methods, although having a solid theoretical foundation, have been challenged in terms of their efficiency as well as their generalization ability in the face of the ever-increasing amount of massive data. With the rise of deep learning in recent years, the use of new tools such as convolutional neural networks to get information from data has become a new option. In particular, in the field of imaging, segmentation of medical images is important for tasks such as determining the type of disease and the location of lesions, which are excellent application areas for deep learning. U-Net is a particularly important deep model structure with good results for segmentation of medical images. However, there is a lack of discussions on the application of U-Net in the clinical field. In this paper, we introduce traditional image segmentation methods and U-Net, analyze the advantages of deep learning techniques in the field of image segmentation. In addition, we applied U-Net to the problem of cell segmentation and segmentation of covid-19 CT images, showing the potential of U-Net for clinical applications.
CAM-TMIL: A Weakly-Supervised Segmentation Framework for Histopathology based on CAMs and MIL
Authors: Feng Jiahao , Li Ce , Wang Jin 
Published: August 2023

Abstract
Semantic segmentation plays a significant role in histopathology by assisting pathologists in diagnosis. Although fully-supervised learning achieves excellent success on segmentation for histopathological images, it costs pathologists and experts great efforts on pixel-level annotation in the meantime. Thus, to reduce the annotation workload, we proposed a weakly-supervised learning framework called CAM-TMIL, which assembles methods based on class activation maps (CAMs) and multiple instance learning (MIL) to perform segmentation with image-level labels. By leveraging the MIL method, we effectively alleviate the influence caused by that CAMs only focus on discriminative regions. As a result, we achieved comparable performance with fully-supervised learning on Camelyon 16 only with image-level labels.
Improved Causal Bayesian Optimization Algorithm with Counter-noise Acquisition Function and Supervised Prior Estimation
Authors: Li Zheng , Li Yongxuan , Lian Zongqiang , Zheng Rui 
Published: August 2023

Abstract
Causality is an important element in decision-making and interventions are required to optimize results of target values. In this paper, based on the model of Causal Bayesian Optimization, a counter-noise version of acquisition function is proposed and new prior estimation algorithms including Support Vector Regression, Ridge Regression and Random Forest are evaluated. This paper provides an improved framework to facilitate causal inference and optimization processes.
Driving Assist: A Supervised-Learning Method for Parking Lot Detection
Authors: Huo Mingxiao 
Published: August 2023

Abstract
In the driving process, the parking lot detection can instruct drivers to get an appropriate place to get the car parking. Therefore, this study attempts to use computer vision methods to guide drivers to park. In this paper, we first divide all the parking lots into single parking spot by the Hough transform. Then a supervised learning method is proposed to classify every single parking spot into car spots and non-car spots. In this classification problem, we adopt the CNN (convolution neural network) method to extract every single spot’s features and put the spot into the corresponding category. For the whole network, VGG-19, which can extract a deeper feature, is adopted to get a more precise classification. Also, we adopt the Adam optimizer replacing the SGD optimizer in the former work, which relies little on the learning rate. It shows that the Adam optimizer gets better loss convergence in the training process. After collecting the results of the prediction for the test videos, it shows that the deeper network VGG-19 improves the accuracy of predicting a driver spot.
Logistics Robot Path Planning: A* Algorithm Operation and Analysis
Authors: Gui Yihan 
Published: August 2023

Abstract
With the rapid development of the e-commerce market, traditional logistics and warehousing methods are far from meeting the needs of the industry. Logistics robots have great potential in the area of intelligent warehousing. Inspired by this, this paper introduces the path planning principle of A* algorithm and the application of A* algorithm to find the path in the field of logistics robots. The principle of the A* algorithm is to select the grid with the least cost for each step from the starting point until the end point is reached. And it can be applied to the motion control of AGV and other logistics robots.
Predicting mortality index for ICU inpatients based on clinical data extracted from electronic health record
Authors: Dawood Aisha , Saba Tanzila 
Published: August 2023

Abstract
Predicting ICU inpatients mortality index needs to be improved to incorporate clinical data. It is also helpful to reflect the patient’s recovery and hospitals standards. In this research machine learning model LightGBM was trained and assessed. This study used a dataset for ICU admissions for adult patients from six countries. And a total of 130,000 patient records were included in the study. The final model achieved AUROC (95% CI) of 0.97, an accuracy of 0.95, and an F1 score of 0.81 on the dataset. Based on results, it is observed that machine learning models with the support of conventional mortality scoring indices can provide a successful and useful model for predicting the outcome of critical and severe cases in the ICU.
How should we be using biomarkers in trials of disease modification in Parkinson’s disease?
Authors: Vijiaratnam Nirosen , Foltynie Thomas 
Published: August 2023

Abstract
The recent validation of the α-synuclein seed amplification assay as a biomarker with high sensitivity and specificity for the diagnosis of Parkinson’s disease has formed the backbone for a proposed staging system for incorporation in Parkinson’s disease clinical studies and trials. The routine use of this biomarker should greatly aid in the accuracy of diagnosis during recruitment of Parkinson’s disease patients into trials (as distinct from patients with non-Parkinson’s disease parkinsonism or non-Parkinson’s disease tremors). There remain, however, further challenges in the pursuit of biomarkers for clinical trials of disease modifying agents in Parkinson’s disease, namely: optimizing the distinction between different α-synucleinopathies; the selection of subgroups most likely to benefit from a candidate disease modifying agent; a sensitive means of confirming target engagement; and the early prediction of longer-term clinical benefit. For example, levels of CSF proteins such as the lysosomal enzyme β-glucocerebrosidase may assist in prognostication or allow enrichment of appropriate patients into disease modifying trials of agents with this enzyme as the target; the presence of coexisting Alzheimer’s disease-like pathology (detectable through CSF levels of amyloid-β42 and tau) can predict subsequent cognitive decline; imaging techniques such as free-water or neuromelanin MRI may objectively track decline in Parkinson’s disease even in its later stages. The exploitation of additional biomarkers to the α-synuclein seed amplification assay will, therefore, greatly add to our ability to plan trials and assess the disease modifying properties of interventions. The choice of which biomarker(s) to use in the context of disease modifying clinical trials will depend on the intervention, the stage (at risk, premotor, motor, complex) of the population recruited and the aims of the trial. The progress already made lends hope that panels of fluid biomarkers in tandem with structural or functional imaging may provide sensitive and objective methods of confirming that an intervention is modifying a key pathophysiological process of Parkinson’s disease. However, correlation with clinical progression does not necessarily equate to causation, and the ongoing validation of quantitative biomarkers will depend on insightful clinical-genetic-pathophysiological comparisons incorporating longitudinal biomarker changes from those at genetic risk with evidence of onset of the pathophysiology and those at each stage of manifest clinical Parkinson’s disease.