bluuebunny/crossref_metadata_2025_split huggingface dataset:
Severe immune checkpoint inhibitor-associated gastritis: A case series and literature review
Authors: Sugiyama Yuya , Tanabe Hiroki , Matsuya Taisuke , Kobayashi Yu , Murakami Yuki , Sasaki Takahiro , Kunogi Takehito , Takahashi Keitaro , Ando Katsuyoshi , Ueno Nobuhiro , Kashima Shin , Moriichi Kentaro , Tanino Mishie , Mizukami Yusuke , Fujiya Mikihiro , Okumura Toshikatsu 
Published: April 2022

Abstract
Background and study aims Recent advances in cancer treatment have involved the clinical application of immune checkpoint inhibitors (ICIs) for various type of cancers. The adverse events associated with ICIs are generally referred to as immune-related adverse events (irAEs). Gastrointestinal irAEs are a major disorder, but gastritis is not frequently observed. The aims of this study were to elucidate the clinical, endoscopic, and histological characteristics of irAE gastritis.
Patients and methods Information on patients treated with ICIs were collected from a single institute over 3 years. IrAE gastritis was identified based on the clinical course and endoscopic and histopathological findings. Of the 359 patients treated with ICIs, four cases of irAE gastritis were identified in clinical records from the endoscopy unit. The endoscopic and histopathological findings were analyzed, and further immunohistochemical studies with immune subtype markers and programmed cell death ligand-1 (PD-L1) antibody were conducted.
Results Among four patients with irAE gastritis, the remarkable endoscopic characteristics were network-pattern erosion, erythematous and edematous mucosa with thick purulent discharge, and fragile mucosa. Corresponding histological features were fibrinopurulent exudate, severe inflammatory cell infiltration, and epithalaxia, respectively. The PD-L1 expression rate was ≥ 1 % in the gastric tissue of all patients with gastritis. These patients were treated with prednisolone (PSL) and their symptoms improved within a few days to 2 weeks.
Conclusions IrAE gastritis were characterized by specific endoscopic findings. The appropriate endoscopic diagnosis may lead to effective treatment with PSL.
Dietary Patterns during Pregnancy and Gestational Weight Gain: A Systematic Review
Authors: Ferreira Larissa Bueno , Lobo Cecília Viana , Miranda Aline Elizabeth da Silva , Carvalho Brenda da Cunha , Santos Luana Caroline dos 
Published: April 2022

AbstractThe present systematic review (PROSPERO: CRD42020148630) hypothesizes the association of excessive weight gain during pregnancy with dietary patterns composed of ultraprocessed foods. Thus, the objective was to investigate the association between dietary patterns after analysis and weight gain during pregnancy. The search for articles was performed in nine databases. Two reviewers selected the articles in the databases and extracted from them the data used in the review. Two scales were used to evaluate the quality of the selected studies: New Castle-Ottawa Quality Assessment for cohort-based studies and Appraisal tool for Cross-Sectional Studies (AXIS) for cross-sectional-based studies. In total, 11 studies were identified with sample size variation (n = 173–5,733). Women presenting more adherence to healthy and traditional patterns (fruits, vegetables, salads, nuts, and dairy) recorded less excessive gestational weight gain (GWG). Higher intake of mixed patterns and western patterns rich in ultraprocessed foods were associated with a higher prevalence of excessive GWG (24.48–55.20%). Gestational dietary patterns a posteriori-derived that have presented ultraprocessed components rich in fat and sugars presented association with high GWG; healthy and traditional dietary patterns were related to better mother-child health conditions, such as adequate GWG.
Loudness Scaling Revisited
Authors: Elberling Claus 
Published: April 2022

AbstractThe present work was undertaken in an attempt to evaluate whether it is reasonable to expect that categorical loudness scaling can provide useful information for nonlinear hearing aid fitting. Normative data from seven scaling procedures show that the individual procedures relate the perceptual categories differently to sound level and with a substantial between-subject variance. Hearing-impaired data from four studies demonstrate that the inverse slope of the loudness function varies linearly with hearing loss and with a constant variance. In relation to hearing aid fitting, the slope can, in most cases, be predicted from the hearing loss with an accuracy within the range of a normal finetuning. For the fitting of nonlinear hearing aids, the statistical properties of both normal and impaired loudness functions are equally important. The present analysis strongly suggests that categorical loudness scaling cannot, in general, provide significant information for the fitting process.
Abbreviations: α = slope of fitted straight line, CB = critical band, COVXY = covariance between variables χ and y, CR = compression ratio, GL = hearing aid insertion gain at input level L, HTL = hearing threshold level, I/O = input/output, LGOB = loudness growth in 1/2–octave bands, RETSPL = reference threshold sound pressure level, S = slope of loudness growth function, VARX = variance of variable x, WDRC = wide dynamic range compression.
Pure nematic quantum critical point accompanied by a superconducting dome
Authors: Ishida Kousuke , Onishi Yugo , Tsujii Masaya , Mukasa Kiyotaka , Qiu Mingwei , Saito Mikihiko , Sugimura Yuichi , Matsuura Kohei , Mizukami Yuta , Hashimoto Kenichiro , Shibauchi Takasada 
Published: April 2022

SignificanceThe notion of the quantum critical point (QCP) is at the core of modern condensed matter physics. Near a QCP of the symmetry-breaking order, associated quantum-mechanical fluctuations are intensified, which can lead to unconventional superconductivity. Indeed, dome-shaped superconducting phases are often observed near the magnetic QCPs, which supports the spin fluctuation–driven superconductivity. However, the fundamental question remains as to whether a nonmagnetic QCP of electronic nematic order characterized by spontaneous rotational symmetry breaking can promote superconductivity in real materials. Here, we provide an experimental demonstration that a pure nematic QCP exists near the center of a superconducting dome in nonmagnetic FeSe1−xTex. This result evidences that nematic fluctuations enhanced around the nematic QCP can boost superconductivity.
Fast and accurate obstacle detection of manipulator in complex human–machine interaction workspace
Authors: Cui Yuming , Jiang Linheng , Liu Songyong , Jiang Hongxiang 
Published: April 2022

Abstract
In order to solve the problems of a large amount of calculation, poor real-time performance, and insufficient detection accuracy in an existing robot obstacle detection system, this paper proposes an improved lightweight You Only Look Once Version 3 algorithm for obstacle detection and tracking, by combining the DeepSort algorithm with a three-dimensional model velocity estimation method. First, the depthwise separable convolution and convolution kernel pruning methods are used to lighten the network. Second, to meet the requirement of dynamic obstacle detection, the DeepSort method is used to track dynamic obstacles and estimate their moving speed. Third, a method of bounding-sphere modeling and discrete marking is proposed to simplify the obstacle model. The experimental results show that: the model size is reduced by 92.11%, which suggests that the proposed lightweight detection system is more suitable for industrial applications; the detection speed in the actual scene reaches 118 frames per second and the average detection accuracy is 92.6%, while the multiple objects tracking accuracy and multiple objects tracking precision are all above 0.8 in the dynamic obstacle tracking process. Static and dynamic obstacles in the workspace can be detected simultaneously with an average positioning accuracy error of 1.5 cm. The proposed model can not only effectively improve the detection efficiency of obstacles in the manipulator workspace but also enhance the safety of industrial robot systems.
Nanosynthesis by atmospheric arc discharges excited with pulsed-DC power: a review
Authors: Corbella Carles , Portal Sabine , Kundrapu Madhusudhan N , Keidar Michael 
Published: April 2022

Abstract
Plasma technology is actively used for nanoparticle synthesis and modification. All plasma techniques share the ambition of providing high quality, nanostructured materials with full control over their crystalline state and functional properties. Pulsed-DC physical/chemical vapour deposition, high power impulse magnetron sputtering, and pulsed cathodic arc are consolidated low-temperature plasma processes for the synthesis of high-quality nanocomposite films in vacuum environment. However, atmospheric arc discharge stands out thanks to the high throughput, wide variety, and excellent quality of obtained stand-alone nanomaterials, mainly core–shell nanoparticles, transition metal dichalcogenide monolayers, and carbon-based nanostructures, like graphene and carbon nanotubes. Unique capabilities of this arc technique are due to its flexibility and wide range of plasma parameters achievable by modulation of the frequency, duty cycle, and amplitude of pulse waveform. The many possibilities offered by pulsed arc discharges applied on synthesis of low-dimensional materials are reviewed here. Periodical variations in temperature and density of the pulsing arc plasma enable nanosynthesis with a more rational use of the supplied power. Parameters such as plasma composition, consumed power, process stability, material properties, and economical aspects, are discussed. Finally, a brief outlook towards future tendencies of nanomaterial preparation is proposed. Atmospheric pulsed arcs constitute promising, clean processes providing ecological and sustainable development in the production of nanomaterials both in industry and research laboratories.
A perspective on thermal stability and mechanical properties of 2D Indium Bismide from ab initio molecular dynamics
Authors: Lundgren Christoffer , Kakanakova-Georgieva Anelia , Gueorguiev Gueorgui K 
Published: April 2022

Abstract
Identification and synthesis of 2D topological insulators is particularly elusive. According to previous ab initio predictions 2D InBi (Indium Bismide) is a material exhibiting topological properties which are combined with a band gap suitable for practical applications. We employ ab initio molecular dynamics (AIMD) simulations to assess the thermal stability as well as the mechanical properties such as elastic modulus and stress–strain curves of 2D InBi. The obtained new knowledge adds further characteristics appealing to the feasibility of its synthesis and its potential applications. We find that pristine 2D InBi, H-InBi (hydrogenated 2D InBi) as well as 2D InBi heterostructures with graphene are all stable well above room temperature, being the calculated thermal stability for pristine 2D InBi 850 K and for H-InBi in the range above 500 K. The heterostructures of 2D InBi with graphene exhibit thermal stability exceeding 1000 K. In terms of mechanical properties, pristine 2D InBi exhibits similarities with another 2D material, stanene. The fracture stress for 2D InBi is estimated to be ∼3.3 GPa (∼3.6 GPa for stanene) while elastic modulus of 2D InBi reads ∼34 GPa (to compare with ∼23 GPa for stanene). Overall, the thermal stability, elastic, and fracture resistant properties of 2D InBi and its heterostructures with graphene appear as high enough to motivate future attempts directed to its synthesis and characterization.
Automatic sleep stage classification based on a two-channel electrooculogram and one-channel electromyogram
Authors: Li Yanjun , Xu Zhi , Zhang Yu , Cao Zhongping , Chen Hua 
Published: April 2022

Abstract

Objective. Sleep monitoring by polysomnography (PSG) severely degrades sleep quality. In order to reduce the load of sleep monitoring, an approach to automatic sleep stage classification without an electroencephalogram (EEG) was proposed. Approach. A total of 124 records from the public dataset ISRUC-Sleep incorporating American Academy of Sleep Medicine (AASM) standards were used: 10 records were from the healthy group while the others were from sleep disorder groups. The 124 records were collected from 116 subjects (eight subjects had two records each, the others had one record each) with ages ranging from 20 to 85 years. A total of 108 features were extracted from the two-channel electrooculograms (EOGs) and six features were extracted from the one-channel electromyogram (EMG). A novel ‘quasi-normalization’ method was proposed and used for feature normalization. Then the random forest algorithm was used to classify five stages, including wakefulness, rapid eye movement sleep, N1 sleep, N2 sleep and N3 sleep. Main results. Using 114 normalized features from the combination of EOG (108 features) and EMG (6 features) data, Cohen’s kappa coefficient was 0.749 and the accuracy was 80.8% by leave-one-out cross-validation. As a reference for AASM standards using a computer-assisted method, Cohen’s kappa coefficient was 0.801 and the accuracy was 84.7% for the same dataset based on 438 normalized features from a combination of EEG (324 features), EOG (108 features) and EMG (6 features) data. Significance. A combination of EOG and EMG can reduce the load of sleep monitoring, and achieves comparable performance to the ‘gold standard’ signals of EEG, EOG and EMG for sleep stage classification.
Effects of soft and hard magnetic particles on the mechanical performance of ultra-soft magnetorheological elastomers
Authors: Moreno-Mateos M A , Lopez-Donaire M L , Hossain M , Garcia-Gonzalez D 
Published: April 2022

Abstract
Magnetorheological elastomers (MREs) mechanically respond to external magnetic stimuli by changing their mechanical properties and/or changing their shape. Recent studies have shown the great potential of MREs when manufactured with an extremely soft matrix and soft-magnetic particles. Under the application of an external magnetic field, such MREs present significant mechanical stiffening, and when the magnetic field is off, they show a softer response, being these alternative states fully reversible. Although soft-magnetic particles are suitable for their high magnetic susceptibility, they require the magnetic actuation to remain constant in order to achieve the magneto-mechanical stiffening. Here, we present an alternative solution based on hard-magnetic MREs to provide stiffening responses that can be sustained along time without the need of keeping the external magnetic field on. To this end, we manufacture novel extremely soft hard-magnetic MREs (stiffness in the order of 1 kPa) and characterise them under magneto-mechanical shear and confined magnetic expansion deformation modes, providing a comparison framework with the soft-magnetic counterparts. The extremely soft nature of the matrix allows for easily activating the magneto-mechanical couplings under external magnetic actuation. In this regard, we provide a novel approach by setting the magnetic actuation below the fully magnetic saturating field. In addition, free deformation tests provide hints on the microstructural transmission of torques from the hard-magnetic particles to the viscoelastic matrix, resulting in macroscopic geometrical effects and intricate shape-morphing phenomena.
Hebbian plasticity rules abrupt desynchronization in pure simplicial complexes
Authors: Kachhvah Ajay Deep , Jalan Sarika 
Published: April 2022

Abstract
This letter investigates the upshots of adaptive development of pure two- and three-simplicial complexes (triad and tetrad) on the nature of the transition to desynchrony of the oscillator ensembles. The adaptation exercised in the pure simplicial coupling takes a cue from the Hebbian learning rule, i.e., the coupling weight of a triad (tetrad) is prone to increase if the oscillators forming it are in phase and decrease if they are out of phase. The coupling weights in these pure simplicial complexes experiencing such adaptation give rise to first-order routes to desynchronization, whose onsets are entirely characterized by respective Hebbian learning parameters. Mean-field analyses presented for the order parameters for the adaptive two- and three-simplicial complexes strongly corroborate with the respective numerical assessments.
Changeover phenomenon in randomly colored Potts models
Authors: Schreiber Nir , Cohen Reuven , Amir Gideon , Haber Simi 
Published: April 2022

Abstract
A hybrid Potts model where a random concentration p of the spins assume q
0 states and a random concentration 1 − p of the spins assume q > q
0 states is introduced. It is known that when the system is homogeneous, with an integer spin number q
0 or q, it undergoes a second or a first order transition, respectively. It is argued that there is a concentration p* such that the transition nature of the model is changed at p*. This idea is demonstrated analytically and by simulations for two different types of interaction: the usual square lattice nearest neighboring and mean field (MF) all-to-all. Exact expressions for the second order critical line in concentration-temperature parameter space of the MF model together with some other related critical properties, are derived.
Interactive and Fast Low-Light Image Enhancement Algo-rithm and Application
Authors: Shi Yangming , Wu Xiaopo , Zhu Ming 
Published: April 2022

Abstract
To obtain personalized outcomes for the low-light image enhancement, a novel interactive algorithm based on the well-designed Gamma Curve is proposed to enrich the enhancement techniques. Different from the previous works trying to enhance the image in solely brightness or naturalness by a specific designed deep network, the proposed method is capable of controlling the output results according to the user’s preferences by the same framework with different parameters. There would be three main advantages brought by the proposed network: 1) Interactivity, which allows to generate enhancements results according to users’ preferences in human-interactive manners; 2) Convenience, wherein the model only needs to train for once without using any reference images, and then can obtain results with different brightness during testing by adjusting the hyper-parameter. 3) Fastness, which results from the lightweight network and the excellent properties of the Gamma Curve to make the network operate in extraordinary high speed. Experiments demonstrate the superiority of our algorithm relative to the previous work. In addition, a multi-platform low-illumination enhancement software is explored to facilitate its application for the public.
The weighted fusion prediction algorithm of acoustic interval optimized by PCA-PSO-BP and MLRM
Authors: Hu Tao , Li Xisheng , You Jia 
Published: April 2022

Abstract
Acoustic interval is widely used in drilling operation, but the actual drilling operation is limited by well depth, geological conditions and economic cost, so it is difficult to directly measure the value of acoustic interval. A model between logging data and acoustic interval can be constructed to predict acoustic interval. In view of the shortcomings of traditional algorithms, this paper proposes a weighted fusion algorithm based on the combination optimization of the PCA, PSO, BP and MLRM, carries out the PCA and PSO combination optimization the BP prediction for non-linear logging data, the MLRM regression prediction for linear logging data, and then weights and fuses two groups of prediction results to obtain the final result. Through the test and comparison of several groups of logging data experimental results, the prediction effect of the fusion algorithm is better than the BP, and the prediction effect of the PCA and the PSO optimized the BP verifies the feasibility of the fusion algorithm proposed in this paper.
A computational optimization research on ant colony optimization for the traveling salesman problem
Authors: Sheng Yufan 
Published: April 2022

Abstract
The traveling salesman problem (TSP) is one of typical combinatorial optimization problems. Ant colony optimization (ACO) is an effective method to solve the traveling salesman problem, but there are some non-negligible shortcomings hidden in the original algorithm. The primary objective of this research is to optimize the ACO to produce quality work throughout solving TSP. To this end, the hybrid SOS-MMAS algorithm is proposed. Concretely, apply the advanced Max-Min Ant System (MMAS) as the basic algorithm to raise task scheduling efficiency, meanwhile introduce symbiotic organisms search (SOS) into the MMAS to optimize the key parameters. Experiments were carried out on typical TSP instances of different scales, and the SOS-ACO and ACO algorithms were compared with SOS-MMAS, which proved the excellent performance of SOS-MMAS in solving TSP. Rationality of the algorithm design and high performance has been illuminated by experimentation. In addition, the model also could serve to suggest further research of TSP or other related areas.
Research on Bi-LSTM machine reading comprehension algorithm based on attention mechanism
Authors: Shi Tong , Li Xiaohui , Liu Zhonglin , Wang Licai 
Published: April 2022

Abstract
Machine reading comprehension is a very important research field in Natural Language Processing (NLP), but it is not sufficient for military research, and traditional machine reading comprehension algorithms do not fully explore every word in different contexts. In response to the above problems, this paper proposes a machine reading comprehension model that incorporates a multi-attention mechanism. It is brave to solve machine reading comprehension tasks in the military field. The model in this paper performs word embedding, part-of-speech embedding, and knowledge base embedding for each word in the article. Among them, knowledge embedding uses military knowledge bases to make up for the lack of information on military entities and relationships in military articles, and independently carries out part-of-speech embedding to solve the problem of part of speech judgment of polysemous words in specific contexts. The experimental results show that the machine reading comprehension model with multi-attention mechanism proposed in this paper can achieve better results on machine reading comprehension tasks, and the accuracy rate is improved by 1.6% compared with ROUGE-L.
An Contract Theory based Federated Learning Aggregation Algorithm in IoT Network
Authors: Tian Mengmeng 
Published: April 2022

Abstract
Taking IoT devices as the edge nodes is one of the powerful way to offloading the federated task since IoT devices are closer to the data generation end. The aggregation efficiency of federated learning in the IoT environment is inefficiency since the server of federated learning can not know the data quality of heterogeneous IoT device. How to encourage IoT edge clients to participate in federated learning and maximize the aggregation effect of the global model is an important problem. This paper proposes a federated learning aggregation model based on contract theory incentive mechanism. Our experimental results show that the proposed algorithm effectively improves the aggregation efficiency of federated learning compared with FedAvg algorithm.