bluuebunny/crossref_metadata_2025 huggingface dataset:
The GAPS programme at TNG
Authors: Maldonado J. , Colombo S. , Petralia A. , Benatti S. , Desidera S. , Malavolta L. , Lanza A. F. , Damasso M. , Micela G. , Mallonn M. , Messina S. , Sozzetti A. , Stelzer B. , Biazzo K. , Gratton R. , Maggio A. , Nardiello D. , Scandariato G. , Affer L. , Baratella M. , Claudi R. , Molinari E. , Bignamini A. , Covino E. , Pagano I. , Piotto G. , Poretti E. , Cosentino R. , Carleo I.
Published: April 2022

Context. Active-region evolution plays an important role in the generation and variability of magnetic fields on the surface of lower main sequence stars. However, determining the lifetime of active-region growth and decay as well as their evolution is a complex task. Most previous studies of this phenomenon are based on optical light curves, while little is known about the chromosphere and the transition region.

Aims. We aim to test whether or not the lifetime of active-region evolution shows any dependency on stellar parameters, particularly stellar age.

Methods. We identified a sample of stars with well-defined ages via their kinematics and membership to young stellar associations and moving groups. We made use of high-resolution échelle spectra from HARPS at La Silla 3.6m-telescope and HARPS-N at TNG to compute rotational velocities, activity levels, and emission excesses. We used these data to revisit the activity-rotation-age relationship. The time-series of the main optical activity indicators, namely Ca II H and K, Balmer lines, Na I D1, D2, and He I D3, were analysed together with the available photometry using state-of-the-art Gaussian processes to model the stellar activity of these stars. Autocorrelation functions of the available photometry were also analysed. We used the derived lifetimes of active-region evolution to search for correlations with stellar age, spectral type, and activity level. We also used the pooled variance technique to characterise the activity behaviour of our targets.

Results. Our analysis confirms the decline of activity and rotation as a star ages. We also confirm that the rotation rate decays with age more slowly for cooler stars and that, for a given age, cooler stars show higher levels of activity. We show that F- and G-type young stars also depart from the inactive stars in the flux–flux relationship. The Gaussian process analysis of the different activity indicators does not seem to provide any useful information on the lifetime and evolution of active regions. On the other hand, the lifetimes of active regions derived from the light-curve analysis might correlate with stellar age and temperature.

Conclusions. Although we suggest caution because of small number statistics, our results suggest that active regions seem to live longer on younger, cooler, and more active stars.
Delirium Tremens and Central Pontine Myelinolysis in a Patient with
Alcohol Use Disorder and Pneumonia: a Case Report and a Narrative
Review
Authors: Danışman Mustafa , İspir Gamze Zengin , Oğur Begüm
Published: April 2022

Introduction Delirium tremens (DT) is a serious condition occurring in
alcohol withdrawal syndrome. Alcohol consumption may also cause additional
health problems, such as respiratory infections or neuropsychiatric conditions
such as central pontine myelinolysis. In this clinical scenario, managing DT can
be expected to be more compelling and complex. Alcohol decreases coughing and
mucociliary clearance and disrupts the immunity of the respiratory system.

Case Here we report on a middle-aged man with alcohol use disorder who had
developed DT due to alcohol withdrawal and comorbid pneumonia.

Discussion and Conclusion In this paper, DT, the relation between
respiratory infections and alcohol intake, and the correlation of alcohol
consumption and central pontine myelinolysis (CPM) are discussed. Also, the
literature on alcohol consumption and the additional respiratory and neurologic
problems resulting from it are presented.
Wichtige Differentialdiagnosen des Beinödems – ein kurzer Praxisleitfaden
Authors: Koller Anna , Trautinger Franz
Published: April 2022

Das differenzialdiagnostische Spektrum der Beinödeme ist vielfältig und umfasst sowohl phlebologische, entzündliche, traumatologische, medikamentöse und andere systemische und regionale Ursachen und Erkrankungen.

Um die Ursachen für Beinödeme bestmöglich abzuklären, sind eine ausführliche Anamnese und klinische Untersuchung entscheidend. Die Lokalisation der Beinschwellung, Beginn und Dauer der Beschwerden, Schmerzsymptomatik, Allgemeinsymptome, Vorerkrankungen und bestehende Hautveränderungen können wichtige Hinweise auf mögliche zugrunde liegende Ursachen liefern. Nach Eingrenzung der möglichen Differenzialdiagnosen können gezielte apparative und laborchemische Untersuchungen ergänzend angeschlossen werden, um die Diagnose zu sichern und die richtige Therapie einzuleiten.
Bromide as the Directing Group for β-Arylation of Thiophenes
Authors: Zhang Hong-Hai , Wang Cai-Xia , Sheng Fei-Fei , Liu Kai-Hui , Gu Jian-Guo , Shen Kang , Sun Zheng-Yi , Hong Kunlun
Published: April 2022

Direct β-arylation of thiophene derivatives with bromide as directing group is disclosed. The reaction is conducted with PdCl2/(p-tolyl)3P as catalyst, silver carbonate as additive, and aryl iodide as coupling partner, affording brominated biaryl compounds as product. Control experiments indicated that the presence of bromide group enhances the reactivity of the C–H bond, enabling β-arylation. Furthermore, the C–Br bond can be easily converted into many useful functional groups through a wide range of methodologies. The mechanistic study suggests that silver salt plays a key role in the C–H bond-activation step.
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

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

The 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

The 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.
Revealing the doping density in perovskite solar cells and its impact on device performance
Authors: Peña-Camargo Francisco , Thiesbrummel Jarla , Hempel Hannes , Musiienko Artem , Le Corre Vincent M. , Diekmann Jonas , Warby Jonathan , Unold Thomas , Lang Felix , Neher Dieter , Stolterfoht Martin
Published: April 2022

Traditional inorganic semiconductors can be electronically doped with high precision. Conversely, there is still conjecture regarding the assessment of the electronic doping density in metal-halide perovskites, not to mention of a control thereof. This paper presents a multifaceted approach to determine the electronic doping density for a range of different lead-halide perovskite systems. Optical and electrical characterization techniques, comprising intensity-dependent and transient photoluminescence, AC Hall effect, transfer-length-methods, and charge extraction measurements were instrumental in quantifying an upper limit for the doping density. The obtained values are subsequently compared to the electrode charge per cell volume under short-circuit conditions (CUbi/eV), which amounts to roughly 1016 cm−3. This figure of merit represents the critical limit below which doping-induced charges do not influence the device performance. The experimental results consistently demonstrate that the doping density is below this critical threshold (∼1012 cm−3, which means ≪ CUbi/eV) for all common lead-based metal-halide perovskites. Nevertheless, although the density of doping-induced charges is too low to redistribute the built-in voltage in the perovskite active layer, mobile ions are present in sufficient quantities to create space-charge-regions in the active layer, reminiscent of doped pn-junctions. These results are well supported by drift–diffusion simulations, which confirm that the device performance is not affected by such low doping densities.
Extended wall-adapting local eddy-viscosity model for the large-eddy simulations of multiscale flows
Authors: Dai Shaoshi , Tang Dan , Younis Bassam A.
Published: April 2022

We report on progress made in improving the performance of the well-established wall-adapting local eddy-viscosity model in an important class of multiscale flows, namely, that of unsteady, massively separated flows at high Reynolds number. While this model succeeds in capturing the asymptotic near-wall behavior of the eddy viscosity that enters the formulation of the model for the sub-grid scale correlations, its performance in these flows has proved to be unsatisfactory due to high levels of dissipation leading to poor predictions in the separated wakes. We have sought to improve the performance of this model by combining it with another, the regularized variational multiscale model, which better represents the interactions that occur in multiscale flows. This combination, which was implemented in OpenFOAM, was validated against experimental data for the challenging case of vortex shedding from circular cylinders at the high Reynolds number. Distinct improvements over the original model were obtained.
Al0.7Sc0.3N butterfly-shaped laterally vibrating resonator with a figure-of-merit (kt2·Qm) over 146
Authors: Luo Zhifang , Shao Shuai , Liu Kangfu , Lu Yuan , Mazzalai Andrea , Tosi Carlo , Wu Tao
Published: April 2022

This work presents the laterally vibrating Lamb wave resonators (LVRs) based on a 30% aluminum scandium nitride (Al0.7Sc0.3N) thin film with three interdigited transducer pairs operating in the S0 mode. In order to reduce the anchor loss, perfect matched layer-based finite element analysis simulations are utilized to design and optimize the device. Thanks to the high quality AlScN using magnetron sputtering with a single alloy target, vertical etching profile, and designed device structure, 1-μm-thick Al0.7Sc0.3N-based LVRs with high performance are fabricated. The resonator equivalent electric parameters are extracted utilizing the modified Butterworth–Van Dyke model. The best Al0.7Sc0.3N LVR achieves an electromechanical coupling coefficient (kt2) of 9.7% and a loaded quality factor (Qr) of 1141.5 operating at approximately 305 MHz. The same resonator shows a motional quality factor (Qm) of 1507.2, resulting in a high figure-of-merit (FoM = kt2 · Qm) of 146.2. A 1.8 MHz tuning range is measured for an Al0.7Sc0.3N LVR by applying DC voltage in the range of −40 to 40 V due to the ferroelectric property of high Sc doping in Al0.7Sc0.3N. With the high FoM, Qr, Qm, and low motional resistance (Rm), the Al0.7Sc0.3N-based LVRs show strong potential in applications of radio frequency communications and piezoelectric transducers.
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

Significance

The 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.
The archaeal glutamate transporter homologue GltPh shows heterogeneous substrate binding
Authors: Reddy Krishna D. , Ciftci Didar , Scopelliti Amanda J. , Boudker Olga
Published: April 2022

Integral membrane glutamate transporters couple the concentrative substrate transport to ion gradients. There is a wealth of structural and mechanistic information about this protein family. Recent studies of an archaeal homologue, GltPh, revealed transport rate heterogeneity, which is inconsistent with simple kinetic models; however, its structural and mechanistic determinants remain undefined. Here, we demonstrate that in a mutant GltPh, which exclusively populates the outward-facing state, at least two substates coexist in slow equilibrium, binding the substrate with different apparent affinities. Wild type GltPh shows similar binding properties, and modulation of the substate equilibrium correlates with transport rates. The low-affinity substate of the mutant is transient following substrate binding. Consistently, cryo-EM on samples frozen within seconds after substrate addition reveals the presence of structural classes with perturbed helical packing of the extracellular half of the transport domain in regions adjacent to the binding site. By contrast, an equilibrated structure does not show such classes. The structure at 2.2-Å resolution details a pattern of waters in the intracellular half of the domain and resolves classes with subtle differences in the substrate-binding site. We hypothesize that the rigid cytoplasmic half of the domain mediates substrate and ion recognition and coupling, whereas the extracellular labile half sets the affinity and dynamic properties.
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

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

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

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

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

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

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

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

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

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

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

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

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.
Traffic Sign Detection Algorithm Based On Improved YOLOv4
Authors: Wu Xiru , Cao Haozhe
Published: April 2022

This paper proposes a traffic sign detection algorithm using improved lightweight YOLOv4 in order to solve the problems of large parameters quantity, poor real-time performance, and low accuracy for the unmanned driving system. Firstly, we use the MobileNetV2 network to replace the original feature extraction network in YOLOv4 to improve the detection speed by reducing the number of model parameters. Secondly, we add an attention mechanism to the backbone network to effectively extract feature information. Trim the network layer of the prediction part and add residual structure, so that feature reuse enhancement can avoid the gradient disappearing when the network layer is deeper. The size of the improved network model is reduced by 79.5%. The experimental results show that the average accuracy of the network model is 2.5% lower than that of the YOLOV4 network, and the detection speed is 58.8% higher than that of the YOLOv4 network.
A hierarchy superpixel algorithm dealing with stripe
Authors: Zhou ZhenDong , Chen Yu
Published: April 2022

The hierarchy superpixel algorithm, in computer vision, aims to generate multi-scale superpixels with boundary perception. The commonly used strategy to obtain hierarchical structure is utilize the similarity between adjacent finer superpixels, then iteratively generate coarser superpixels based on finer ones. However, these methods may not achieve desired result when dealing with stripe targets. The alternating parts of the stripes are too different in image features, thus computer believes these parts are unrelated. In human visual perception, however, these parts actually belong to one striped object. We propose a new ISF-based method to solve this problem. Firstly, we present a improved ISF algorithm in finest scale segmentation with the help of sliding window strategy, which runs faster without reducing accuracy. Then, in order to generate hierarchy superpixels of stripe images, we design a new cross-neighbor path-cost function and add a stripe-pattern detector. Experimental results demonstrate that the proposed algorithm is accurate and efficient.
GA-based fuzzy neural network control for ROV
Authors: Lu Ziling , Xin Shaojie
Published: April 2022

Aiming at the problem of uncertainty and static instability of underwater robot system, a motion control algorithm of underwater robot based on fuzzy neural network optimized by genetic algorithm is proposed in this paper. The algorithm makes use of the characteristics of neural network which can predict and approximate the target value well and the complementarity of fuzzy function to neural network, and uses genetic algorithm to optimize the parameters of fuzzy neural network membership function, so as to reduce the computation of neural network. The experimental results show that the controller has good robustness and enhances the stability of the control system.
Overview of Crow Search Algorithm
Authors: Wang Tingting , Zhang Chengming , He Ankun , Dong Wei
Published: April 2022

Crow Search Algorithm (CSA) is a swarm intelligence optimization resolving schemes. Its optimization method simulates the behavior of crows tracking each other. The paper elaborates the basic principle of the algorithm, discusses the main parameters affecting the algorithm performance, and analyzes the search mechanism of the algorithm. The improvement strategies of the algorithm are summarized. Examples are given to illustrate the application of the algorithm in 0-1 knapsack problem, Image processing, Scheduling issues, feature selection and parameter optimization. Finally, according to the research and application status of crow search algorithm, the future research and development direction are prospected.
Application of UAV path planning based on parameter optimization GA-PSO fusion algorithm
Authors: Lin Chaoxiong , Zhang Xuecong
Published: April 2022

Aiming at the complexity of the unmanned aerial vehicle (UAV) path planning problem and the great influence of genetic algorithm parameters on the stability of the results, a fusion algorithm based on parameter optimization is proposed in this paper. In the iterative process, the GA-PSO fusion algorithm uses particle swarm optimization algorithm to search the optimal value of crossover rate and mutation rate in genetic algorithm, which makes the algorithm convergence speed is fast and search ability is strong. In addition, the core part of the algorithm fusion framework is realized by introducing the optimal adaptive value of the population after crossover and mutation as the adaptive value of the parameter particle. Finally, we design two groups of experiments and compare the proposed fusion algorithm with the classical particle swarm optimization algorithm (PSO), ant colony algorithm (ACA), genetic algorithm (GA), artificial fish swarm algorithm (AFSA), Wolf pack algorithm (WPA), artificial bee colony algorithm (ABC) and the improved algorithm through experimental simulation. The experimental results show that: In general, the path planned by the GA-PSO fusion algorithm in this paper is 10% shorter than that planned by other algorithms on average. Simulation results also show that the convergence speed of the fusion algorithm in this paper is faster, and the final search path is smoother.
An effective metaheuristic for the robust distributed flowshop scheduling problem
Authors: Guo Hengwei , Sang Hongyan , Sun Xiaobin , Zhao Yasheng , Tian Mengxi , Li Zhongkai
Published: April 2022

This paper discusses the distributed permutation flowshop scheduling problem under uncertain processing time and considers the sequence-dependent setup times (denoted as DPFSP_UPT_SDST). Uncertain processing times are described by interval scenarios. An expectation-standard deviation robust model is used to describe the DPFSP_UPT_SDST. Based on the characteristics of the DPFSP_UPT_SDST, four heuristics are proposed. In addition, a discrete Fruit Fly optimization algorithm with an age mechanism is proposed, which helps the algorithm to avoid local optimum and increases the diversity of solutions. The parameters of the proposed algorithm are calibrated by a detailed design experiment. Finally, the effectiveness of the proposed heuristics and metaheuristic is demonstrated by a comprehensive comparison with the closely related and state-of-the-art algorithms.
HRT-YOLO: A transformer-based high-resolution representation model for face flaw detection
Authors: Sun Yage , Li Xueming , Zhang Xianlin
Published: April 2022

Nowadays, it is imperative to accurately detect and repair face flaws to obtain high-resolution, detail-preserving face images in social life. However, there exists a significant gap between face flaws and natural scene features. In this paper, we propose a framework of HRT-YOLO to erase the problems of small scale, irregular shape, and regional overlap of face flaws. Based on YOLOv5, we introduce high-resolution representation, where the main network of high-resolution feature maps is gradually added in parallel to the sub-network of low-resolution feature maps, allowing for more accurate localization of flaws. We also implement transformer detection heads, which use a multi-head attention to mine contextual features and improve the defect-intensive regions detection. Finally, we present a high-resolution face flaw dataset to evaluate our approach. The evaluation results show that HRT-YOLO has excellent performance on face flaw detection, with 3.23% improvement in mAP and 5.33% improvement in accuracy compared to the baseline (YOLOv5).
Physiological Signal Emotion Recognition Based on Temporal Convolutional Networks
Authors: Sheng Yong , Hu Qinghong , Cao Jun
Published: April 2022

Human emotion recognition has become an important research field. Because of its objectivity, physiological signals have become one of the most robust cues for emotion recognition. In recent years, deep learning methods have made great progress in the field of emotion recognition, especially the superiority of recurrent neural networks (RNN) in time series models, and more and more tasks are completed based on RNN. However, RNN has problems such as time-consuming and gradient disappearance and explosion, and the feature input and sentiment output of RNN are not aligned. To avoid these problems, this paper is based on the temporal convolutional networks (TCN) model and the connectionist temporal classification (CTC) algorithm to process the emotion recognition task. First, generate a spectrogram representation of the physiological signal in each channel; Second, use the TCN to learn long-term dynamic features, and use CTC to align the dynamic features and their sentiment labels, and then feed the learned deep features into the neural network to predict the sentiment of each channel; Finally, take the best result as the final emotion representation. Experimental results on the AMIGOS dataset show that the proposed method outperforms existing methods.
Multi-view Attention Mechanism Learning for POI Recommendation
Authors: Zhu Fujian , Dai Shaojie
Published: April 2022

POI(point of interest) recommendation is a very necessary research field in both academic and commerce, however, predicting users’ potential points of interest is always faced with the problems of data sparsity and context semantics. Some studies have shown that graph embedding technology alleviates the problem of data sparsity to a certain extent. However, neither graph embedding techniques nor unsupervised learning models can adaptively learn the different effects of multiple relations between users and POIs, respectively. In view of this, we leverage the contextual information of users and POIs to build the multi-view affinity graphs(e.g. User-User, POI-POI and User-POI), and learn the latent representations of users and POIs based on the Graph Embedding technology and Attention mechanism, namely the GEA model. In particular, we first construct multi-view affinity graphs by using user’s social relationship, geographical distance and check-in behaviour, and embed them into a low dimensional shared space to learn the latent representation of users and POIs. Afterwards, in order to take advantage of the different effects of multiple relationships in the final recommendation task, we exploit the attention mechanism to obtain the fused latent representation and make recommendation according users’ potential preferences. Finally, we design a multi-task objective function for joint optimization to obtain more accurate recommendation results. Extensive experiments on Gowalla have verified the effectiveness of our model.
Optimal scheduling model based on unmanned collector truck arrival time variation field bridge under balanced strategy
Authors: Chen JiaHong , Zhou JiaJing , Zheng YuTing , Ma RongZhe
Published: April 2022

The scheduling of container yard bridges has an important impact on the operational efficiency of the terminal. For the future automated terminal, to reasonably schedule the field bridges, under a given batch of tasks, considering the change of the arrival time of unmanned trucks and the balance of the workload of each field bridge, a balanced strategy is established based on the change of the arrival time of unmanned trucks. The optimal scheduling model of field bridges is given, and an example of field bridge scheduling is given through experiments. Provides a scientific solution for the future automated dock, yard, and bridge scheduling
Person identification using a new CNN-based method and radar gait micro-Doppler signatures
Authors: Huang Yu , Jiang Enshuo , Xu Haodong , Zhang Guangbo
Published: April 2022

The use of gait micro-Doppler signatures to identify a person is a hot topic of research. In this paper, we present a new CNN-based method called Multi-Scale CNN (MS-CNN) to obtain features at multiple scales. It extracts shallow features at low-level multi-scale blocks by using multiple kernels at the same time, then extracts deep features and fuses multi-branch embedding features at high-level multi-branch blocks. Experimental results reveal that our method outperforms other commonly used CNN algorithms in terms of accuracy, allowing it to be used for personal identification.
Hybrid Electric Vehicle Energy Online Allocation Strategy Based on Fuzzy Reinforcement Learning
Authors: Xu Dong
Published: April 2022

To lessen fuel consumption of hybrid electric vehicle, energy online distribution strategy based on fuzzy reinforcement learning is proposed. Vehicle dynamic model and key component model are built, so that hybrid power system simulation model is given. Energy distribution problem of hybrid electric vehicle is transferred to constrained optimization problem of stochastic dynamic system through modelling, and solving problem based on fuzzy reinforcement learning is put forwarded. Fuzzy reinforcement learning is used to optimize fuzzy inference system real-time, which makes fuzzy inference system adjust with driving cycle adaptively, so that optimal control under any driving cycle come true. Clarified by simulation, fuel consumption under the control of rule-based is 3.89L/km, and it is 3.45L/km under the control of fuzzy reinforcement learsning, which saves 11.31% fuel consumption compared with rule-based. The data above proves validity of fuzzy reinforcement learning on hybrid electric vehicle energy management.