bluuebunny/crossref_metadata_2025_split huggingface dataset:
Management of the Middle Vault
Authors: Pruthi Asheema , Dobratz Eric , Dougherty William 
Published: August 2023

AbstractEarly reductive rhinoplasty techniques focused on hump reduction and tip plasty with minimal focus on treating or preserving the integrity and width of the middle vault. With time, rhinoplasty surgeons noted the aesthetic and functional complications of the deformities that may occur in the middle vault with reduction techniques and developed methods to treat and also avoid these complications. Thus, the importance of protecting the integrity of the middle third of the nose has been increasingly emphasized over the years. Primary deformities of the middle vault that result in nasal obstruction require attention of the rhinoplasty surgeon, as well as preservation of support structures of the middle vault and internal nasal valve to minimize secondary deformities and functional compromise after rhinoplasty surgery.
Evaluation of the efficacy of peripheral nerve block alone in episodic and chronic migraine patients
Authors: Evcili Gokhan , Yabalak Ahmet 
Published: August 2023

AbstractBackground Peripheral nerve block (PNB) is usually performed in patients with migraine who are resistant to treatment with medications.Objective To compare the efficacy of PNB alone and PNB combined with prophylactic medications in migraine patients.Method The data on migraine patients who underwent PNB in our clinic between November 2019 and January 2022 were retrospectively reviewed. Blocks of the greater occipital nerve (GON), lesser occipital nerve (LON) and supraorbital nerve (SON) were performed upon admission and in the second week.Results The study included 116 patients. While 21 out of 39 episodic migraine (EM) patients continued to use prophylactic medications, 18 were followed up with PNB alone. While 49 out of 77 chronic migraine (CM) patients continued to use prophylactic medications, 28 were followed up with PNB alone. Comparison of the admission and second-month data of the patients who only underwent PNB and those who continued the drug treatment together with PNB in both the EM and the CM group showed that the number of days with pain, number of analgesics taken and scores on the Visual Analog Scale (VAS) and the Migraine Disability Assessment (MIDAS) were significantly reduced in both groups (p < 0.01). Comparison of the second-month data of the patients followed up with PNB alone and those followed up with PNB together with prophylactic medications showed that there was no significant difference between the EM and CM patients (p > 0.05).Conclusion Bilateral GON, LON and SON block with lidocaine injection seems to be an effective treatment on its own, without the need for prophylactic medications, in both EM and CM patients during a two-month follow-up.
A Soft Wire Chinese Finger Grip
Authors: Prakash Satya 
Published: August 2023

SummaryThe soft wire Chinese finger grip offers a convenient mode of applying traction on fingers, pulling them apart if necessary and helps in application of plaster case in the region. It can also be used for suspending the upper limb during surgery and for elevation of the hand. It is durable and can be easily sterilized. It is almost inexpensive and has been used by the author for more than 25 years without any regrets.
Unstable Elbow-Skin Shortage a Stabilising Factor
Authors: Prakash Satya 
Published: August 2023

SummaryA 56-year old patient had his elbow trapped in his turned over car 21 years ago. The lower articular surfaces of the humerus were grossly comminuted and just fell out during dressings. The bone gap was large. The large raw area was covered with skin graft which only partially survived. Healing occurred by scarification and development of contracture. The contracted grafted area stabilized the joint which is still functional.
Luxury tourism and purchase intention factors: a mixed approach applied to luxury goods
Authors: Gil-Cordero Eloy , Ledesma-Chaves Pablo , Yoo Sunghoon , Han Heesup 
Published: August 2023

AbstractThis research explores the key drivers of purchase intention for luxury goods in the new tourism era after the pandemic via constructs associated with the behavior of tourist consumers of luxury goods and the business strategy regarding them. The mixed methodology allowed for a twofold objective with the investigation of purchase intention to clearly establish what the most important determinants that affect purchase intention are, and, secondly, to determine to some extent the order, importance, and combination of these determinants. The PLS-SEM demonstrated price, vanity, communication, and social influence as increasing purchase intention. The QCA analysis evidences value and vanity as the necessary conditions for intention.
What happens in the prefrontal cortex? Cognitive processing of novel and familiar stimuli in soccer: An exploratory fNIRS study
Authors: Schmaderer Lena F. , Meyer Mathilda , Reer Rüdiger , Schumacher Nils 
Published: August 2023

ABSTRACTThe importance of both general and sport‐specific perceptual‐cognitive abilities in soccer players has been investigated in several studies. Although these perceptual‐cognitive skills could contribute significantly to soccer players’ expertise, the underlying cortical mechanisms have not been clarified yet. Examining activity changes in the prefrontal cortex under different cognitive demands may help to better understand the underlying mechanisms of sports expertise. The aim of this study was to analyse the prefrontal activity of soccer experts during general and sport‐specific cognitive tasks. For this purpose, 39 semi‐professional soccer players performed four perceptual‐cognitive tests, two of which assessed general cognition, the other two assessed sport‐specific cognition. Since soccer is a movement‐intensive sport, two tests were performed in motion. While performing cognitive tests, prefrontal activity was recorded using functional near‐infrared spectroscopy (fNIRS) (NIRSport, NIRx Medical Technologies, USA). Differences of prefrontal activity in general and sport‐specific cognitive tasks were analysed using paired t‐tests. The results showed significant increases in prefrontal activity during general cognitive tests (novel stimuli) compared to sport‐specific tests (familiar stimuli). The comparatively lower prefrontal activity change during sport‐specific cognition might be due to learned automatisms of experts in this field. These results seem in line with previous findings on novel and automated cognition, “repetition suppression theory” and “neural efficiency theory”. Furthermore, the different cortical processes could be caused by altered prefrontal structures of experts and might represent a decisive factor for expertise in team sports. However, further research is needed to clarify the prefrontal involvement on expertise in general and sport‐specific cognition.
Critical screening parameters of one-electron systems with screened Coulomb potentials: circular Rydberg states
Authors: Xu Lei , Jiao Li Guang , Liu Aihua , Wang Yuan Cheng , Montgomery Jr Henry E , Ho Yew Kam , Fritzsche Stephan 
Published: August 2023

Abstract
The critical screening parameters (λ

c
) of one-electron systems in circular states under short-range screened Coulomb potentials (SCPs) are investigated using the generalized pseudospectral method. High-precision values of λ

c
are obtained for bound states with the principal quantum number n up to a thousand. The polynomial fittings based on the numerical values indicate that the high-Rydberg limits of the n
2-scaled critical screening parameters, i.e.

n

2

λ

c

, are equal to

2

/

e

,

1.295
220
475
783
830

, and

0.543
564
037
493
402

, respectively, for the Debye–Hückel, Hulthén, and exponential cosine SCPs. It is surprisingly found that these high-Rydberg limits can be well-reproduced from Bohr’s correspondence principle together with the semi-classical model of the hydrogen atom. We further show that both the present numerical calculations and the semi-classical analysis can be extended to other short-range potentials such as the generalized exponential, Lennard–Jones, modified Pöschl–Teller, shifted Morse, Woods–Saxon, and Manning–Rosen potentials that attract wide interest in atomic, molecular, and nuclear physics.
Annealing effects on the magnetic and magnetotransport properties of iron oxide nanoparticles self-assemblies
Authors: Fabris Fernando , Lima Enio , Nuñez Jorge Martín , Troiani Horacio E , Aguirre Myriam H , Leborán Victor , Rivadulla Francisco , Winkler Elin L 
Published: August 2023

Abstract
In magnetic tunnel junctions based on iron oxide nanoparticles the disorder and the oxidation state of the surface spin as well as the nanoparticles functionalization play a crucial role in the magnetotransport properties. In this work, we report a systematic study of the effects of vacuum annealing on the structural, magnetic and transport properties of self-assembled ∼10 nm Fe3O4 nanoparticles. The high temperature treatment (from 573 to 873 K) decomposes the organic coating into amorphous carbon, reducing the electrical resistivity of the assemblies by 4 orders of magnitude. At the same time, the 3.Fe2+/(Fe3++Fe2+) ratio is reduced from 1.11 to 0.13 when the annealing temperature of the sample increases from 573 to 873 K, indicating an important surface oxidation. Although the 2 nm physical gap remains unchanged with the thermal treatment, a monotonous decrease of tunnel barrier width was obtained from the electron transport measurements when the annealing temperature increases, indicating an increment in the number of defects and hot-spots in the gap between the nanoparticles. This is reflected in the reduction of the spin dependent tunneling, which reduces the interparticle magnetoresistance. This work shows new insights about influence of the nanoparticle interfacial composition, as well their the spatial arrangement, on the tunnel transport of self-assemblies, and evidence the importance of optimizing the nanostructure fabrication for increasing the tunneling current without degrading the spin polarized current.
Learnable PM diffusion coefficients and reformative coordinate attention network for low dose CT denoising
Authors: Zhang Haowen , Zhang Pengcheng , Cheng Weiting , Li Shu , Yan Rongbiao , Hou Ruifeng , Gui Zhiguo , Liu Yi , Chen Yang 
Published: August 2023

Abstract

Objective. Various deep learning methods have recently been used for low dose CT (LDCT) denoising. Aggressive denoising may destroy the edge and fine anatomical structures of CT images. Therefore a key issue in LDCT denoising tasks is the difficulty of balancing noise/artifact suppression and edge/structure preservation. Approach. We proposed an LDCT denoising network based on the encoder-decoder structure, namely the Learnable PM diffusion coefficient and efficient attention network (PMA-Net). First, using the powerful feature modeling capability of partial differential equations, we constructed a multiple learnable edge module to generate precise edge information, incorporating the anisotropic image processing idea of Perona–Malik (PM) model into the neural network. Second, a multiscale reformative coordinate attention module was designed to extract multiscale information. Non-overlapping dilated convolution capturing abundant contextual content was combined with coordinate attention which could embed the spatial location information of important features into the channel attention map. Finally, we imposed additional constraints on the edge information using edge-enhanced multiscale perceptual loss to avoid structure loss and over-smoothing. Main results. Experiments are conducted on simulated and real datasets. The quantitative and qualitative results show that the proposed method has better performance in suppressing noise/artifacts and preserving edges/structures. Significance. This work proposes a novel edge feature extraction method that unfolds partial differential equation into neural networks, which contributes to the interpretability and clinical application value of neural network.
A generalized AI method for pathology cancer diagnosis and prognosis prediction based on transfer learning and hierarchical split
Authors: Yin Pengzhi , Zhou Zehao , Liu Jingze , Jiang Nan , Zhang Junchao , Liu Shiyu , Wang Feiyang , Wang Li 
Published: August 2023

Abstract

Objective. This study aims to propose a generalized AI method for pathology cancer diagnosis and prognosis prediction based on transfer learning and hierarchical split. Approach. We present a neural network framework for cancer diagnosis and prognosis prediction in pathological images. To enhance the network’s depth and width, we employ a hierarchical split block (HS-Block) to create an AI-aided diagnosis system suitable for semi-supervised clinical settings with limited labeled samples and cross-domain tasks. By incorporating a lightweight convolution unit based on the HS-Block, we improve the feature information extraction capabilities of a regular network (RegNet). Additionally, we integrate a Convolutional Block Attention Module into the first and last convolutions to optimize the extraction of global features and local details. To address limited sample labels, we employ a dual-transfer learning (DTL) mechanism named DTL-HS-Regnet, enabling semi-supervised learning in clinical settings. Main results. Our proposed DTL-HS-Regnet model outperforms other advanced deep-learning models in three different types of cancer diagnosis tasks. It demonstrates superior feature extraction ability, achieving an average sensitivity, specificity, accuracy, and F1 score of 0.9987, 1.0000, 1.0000 and 0.9992, respectively. Furthermore, we evaluate the model’s capability to directly extract prognosis prediction information from pathological images by constructing patient cohorts. The results show that the correlation between DTL-HS-Regnet predictions and the presence of cancer-associated fibroblasts is comparable to that of pathologists. Significance. Our proposed AI method offers a generalized approach for cancer diagnosis and prognosis prediction in pathology. The outstanding performance of the DTL-HS-Regnet model demonstrates its potential for improving current practices in image digital pathology, expanding the boundaries of cancer treatment in two critical areas.
Superdense coding for V-shaped channel and cylindrical geometry
Authors: Tayyab Ansha , Javed Seerat , Shaukat Muzzamal I 
Published: August 2023

Abstract
We have examined the possibility of quantum dense coding for the V-shaped channel and cylindrical geometry of plasmonic waveguides by assuming certain initial states at different dipole-dipole distances. It is found that the dense coding capacity initially decreases and then gradually increases until it becomes steady (χ = 1) at later time. We also revealed the optimal time valid for super-dense coding regarding each initial state. It is worth noting that dense coding capacity is valid for all other states for a time less than optimal time (t < τ

o
) except for pure state. The estimated optimal time for a V-shaped channel is prominent due to greater β-factor accomplishing it as a prosperous geometry for Superdense coding. The greater optimal time for V-shaped channel entitles this geometry a benchmark for the practical applications of quantum information technology.
Magnetic ordering in Ce substituted zirconite-YCrO4 nanostructures: focus on tunable energy bandgap and local-atomic environment
Authors: Dokala R K , Das S , Mishra P K , Thota S 
Published: August 2023

Abstract
This study presents a comprehensive investigation of the structural, magnetic, electronic, and optical spectroscopic properties of tetragonal Zirconite Y1−x
Ce
x
CrO4 polycrystals that were synthesized at various temperature ranges. The YCrO4 phase in its pristine state demonstrates ferromagnetic (FM) ordering, characterized by a Curie temperature, T
C ∼ 9.3 K. Nevertheless, the introduction of magnetic Ce3+ in place of Y3+ ions results in a reduction of the T
C and the appearance of a weak antiferromagnetic (AFM) behaviour, as indicated by the presence of a negative Curie–Weiss temperature (−10 K ≲ θ
CW ≲ −30 K). It is observed that the introduction of moderate levels of Ce substitution leads to a significant increase in thermal irreversibility in the magnetization difference between field-cooled and zero-field-cooled states ΔM (M
FC − M
ZFC). This phenomenon indicates the substantial influence of magneto-crystalline anisotropy that exists within the system. Field-dependent magnetization data exhibits higher saturation magnetization values (M
S ≳ (2.7 to 4.4) × 103 emu mole−1) and higher magneto-crystalline anisotropy (K
1 ∼ 2.18 to 3.41 J m−3) for the dilute dispersion of Ce (x ≤ 0.05). Local atomic environment probed by the electron spin resonance clearly indicates the presence of hyperfine splitting of Cr5+ spectra having a single d-electron (

t

2
g

1

e

g

0

) with orbital configuration bearing the possible doublet:

F
=
1
,

2

I
=

3
2

J
=

1
2

.

On the other hand, the rare-earth Ce3+ spectra show hyperfine-doublets as

7
2

and

9
2

.

Finally, the energy band-gap (E
g = 2.14 to 2.38 eV, for x ∼ 0 to 0.1) determined from the diffuse reflectance spectroscopy reveals the emergence of intermediate localized states upon substitution of Ce3+.
Detection of Ischemic Brain Stroke using Deep Learning
Authors: Sharmila C , Santhiya S , Poongundran M , Sanjeeth S , Pranesh S 
Published: August 2023

Abstract
Stroke is a severe disease that is currently the country’s fourth greatest reason of death. Ischaemic stroke is a medical emergency that necessitates prompt triage, diagnosis, and treatment. A patient’s stroke lesion site must be identified as soon as feasible and properly to assess the risks and give more early and efficient treatment. It is essential to develop automated methods for identifying and segmenting stroke lesions. In this work, MRI is sliced into two-dimensional network groups and used each division set to train sessions and predict individually. Finally, before slicing and splitting to produce a 3D lesion result, two-dimensional slices results of all groups are arranged according to the position order in the MRI.
Self-Supervised Approach to Addressing Zero-Shot Learning Problem
Authors: Okerinde Ademola , Hoggatt Sam , Lakkireddy Divya Vani , Brubaker Nolan , Hsu William , Shamir Lior , Spiesman Brian 
Published: August 2023

Abstract
In recent years, self-supervised learning has had significant success in applications involving computer vision and natural language processing. The type of pretext task is important to this boost in performance. One common pretext task is the measure of similarity and dissimilarity between pairs of images. In this scenario, the two images that make up the negative pair are visibly different to humans. However, in entomology, species are nearly indistinguishable and thus hard to differentiate. In this study, we explored the performance of a Siamese neural network using contrastive loss by learning to push apart embeddings of bumblebee species pair that are dissimilar, and pull together similar embeddings. Our experimental results show a 61% F1-score on zero-shot instances, a performance showing 11% improvement on samples of classes that share intersections with the training set.
Medical Image Segmentation Method Based on Improved Unet
Authors: Ge Shufan , Wang Zhaorui , Li Ruixuan , Shao Tianhao 
Published: August 2023

Abstract
This paper is an attempt to apply 3DUnet system to medical image segmentation. The classifying model is built based on actual medical images from MSD Cardiac dataset. The 3DUnet system, which is developed on Convolutional Neural Networks will be of great significance in medical diagnosis and analysis. The paper analyzes and contains details of the 3DUnet system we built and the algorithms we applied. We applied the method of cutting the input images into patches and K-fold cross validation to obtain better results. The final average value of Dice is over 87%. Codes are available: https://github.com/LRXuanxuan/jubilant-chainsaw.
Breast Cancer Prediction Feature Selection Using ML Algorithms
Authors: Jiang Lin 
Published: August 2023

Abstract
Breast cancer is a disease that breast epithelial cells uncontrolled hyperplasia under the effects of several different carcinogens. The early symptoms of breast cancer are breast lesions, nipple discharge and axillary fossa lymphadenectasis. Breast cancer can directly threat the life of patient by casing various organ lesions and metastasis of cancer cells in the late stage of suffering from this disease. The method of classifying tumors into malignant or benign is the main challenge that needed to be dealt with to treat patient in a correct way. This paper shows the different classification models to predict the kind of breast cancer. All data used to distinguish the type of breast cancer in the research comes from UCI repository. Some simple classifiers are employed in the research, for instance, Decision Tree Classifier, Extreme Gradient Boosting, Naïve Bayes Classifier, Gradient Boosting, Support Vector Machine. The results show that using Support Vector Machine algorithm the prediction accuracy of breast cancer reaches 99.7%.
Multiple Object Tracking Based on YOLOv5 and Optimized DeepSORT Algorithm
Authors: Bai Tianyou 
Published: August 2023

Abstract
As a widely discussed issue in academic and industrial fields, multiple object tracking (MOT) has a huge impact on various aspects, such as video surveillance, human-machine interaction, viral reality and autonomous driving. Among all the MOT algorithms, DeepSORT enjoys a high reputation for its speed, accuracy and robustness to frequent occlusion circumstance. With the help of DeepSORT, the main objective of this research work is to propose an algorithm with a better performance on frequent occlusion and long-time occlusion issues. To accomplish such a mission, an improved deep appearance descriptor was constructed and trained off-line. To achieve a higher performance, the object detector YOLOv5, which has been proved to be faster than previous detectors, is employed in this research as a substitution of the original detector Faster R-CNN.
Detector challenges of the strong-field QED experiment LUXE at the
European XFEL
Authors: Santra A. 
Published: August 2023

Abstract
The LUXE experiment aims at studying high-field QED in electron-laser and
photon-laser interactions, with the 16.5 GeV electron beam of the European XFEL
and a laser beam with power of up to 350 TW. This experiment will use one
electron bunch out of 2700 bunches per train from XFEL. The experiment will
measure the multiplicity spectra of electrons, positrons and photons in expected
ranges of 10-3 to 109 particles per 1 Hz
bunch crossing, depending on the laser power and focus. These measurements have
to be performed in the presence of low-energy high radiation-background. To meet
these challenges, for high-rate electron and photon fluxes, the experiment will
use Cherenkov radiation detectors, scintillator screens, sapphire sensors as
well as lead-glass monitors for backscattering off the beam-dump. A four layer
silicon-pixel tracker and a compact electromagnetic tungsten calorimeter with
GaAs sensors will be used to measure the positron spectra. The layout of the
experiment and the expected performance under the harsh radiation conditions
will be presented. Beam tests for the Cherenkov detector and the electromagnetic
calorimeter were performed at DESY recently and results will be presented. The
experiment received a stage 0 critical approvement (CD0) from the DESY
management and is in the process of preparing its technical design report (TDR).
It is expected to start running in 2025/6.