bluuebunny/crossref_metadata_2025 huggingface dataset:
Vers une meilleure compréhension de la relation entraîneur-entraîné(s) : une revue systématique de la littérature issue du modèle des 3+1Cs
Authors: Roux Valentin , Trouilloud David
Published: March 2021

Depuis plus de 15 ans, le modèle des 3+1Cs (Jowett, 2007) propose d’analyser les processus psycho-sociologiques impliqués dans la relation entraîneur-entraîné (REE) en contexte sportif, à l’aide des concepts de proximité, d’engagement, de complémentarité, et de co-orientation. Cet article a pour objectif de présenter une revue systématique de la littérature scientifique basée sur ce modèle. Cinquante-neuf références ont été incluses, et analysées au regard de 3 thématiques principales : la mesure de la qualité de la REE, ses conséquences, et ses antécédents potentiels. Dans leur ensemble, les résultats indiquent que la perception des athlètes et des entraîneurs à propos de la qualité de leur relation est associée à des antécédents et des conséquences motivationnelles, affectives, interpersonnelles, et comportementales. Ces résultats confirment ainsi la pertinence du modèle des 3+1Cs pour l’analyse et la compréhension de la REE. Cette revue systématique a également permis d’identifier certaines limites et zones d’ombres existantes dans la littérature actuelle, appelant les travaux futurs à développer les connaissances à propos de la dynamique et du niveau de causalité existant entre la qualité de la REE et certaines variables, et à propos du rôle joué par le niveau d’interdépendance entre les points de vue de l’athlète et de l’entraîneur.
Electronic Media and Early Childhood: A Review
Authors: Paulus Frank W. , Möhler Eva , Recktenwald Franziska , Albert Amélie , Mall Volker
Published: March 2021

Background This review summarizes the state of knowledge of use of new media on the development in early childhood in 6 sections: descriptive utilization data, psychosocial and emotional development, cognition and language, motor development, nutrition and sleep, and influence of parental media consumption.

Methods The review is based on a literature search of this topic in peer-reviewed journals. We included 87 articles, books, and book chapters. The used literature data bases were ERIC, PsycARTICLES, PsycINFO and PSYNDEX.

Results Manifold studies describe in young children’s utilization data the pervasive nature of digital exposure and impressive usage times and availability. They confirm adverse influences of electronic media use (television, video games) on children’s emotional and behavioral problems and well-being, e. g. on physical activity, sleep and obesity. In general a positive effect in sense of knowledge transfer of age could be found for high quality educational media, however predominantly the impact of media use at younger age was negative.

Conclusions High frequent media use in early childhood is likely to have a negative impact on psychosocial development, positive effects such as knowledge transfer may be seen beyond the age of 18 months. As parental media use is a strong predictor of child media habits, reducing parental media use and enhancing parent–child interactions might be important areas to address when trying to change the media behavior of young children. In view of the scarcity of studies for early childhood, it is advisable to use digital play and communication devices cautiously and restrictively in this vulnerable development phase.
COVID-19-Pandemie – eine Ausnahmesituation für die Intensivpflege?
Authors: Hamacher Jochen , Klausmeier Johannes
Published: March 2021

Wie erlebten Pflegekräfte auf Intensivstationen ihre Arbeit im Frühjahr 2020 während der COVID-19-Pandemie? War diese Situation für Intensivpflegende, die es kennen, unter hohem Druck und schwierigen Bedingungen mit kritisch kranken Menschen zu arbeiten, überhaupt eine Ausnahmesituation und welche Erwartungen haben sie an die Zeit nach der Pandemie? Diesen Fragen gingen die Autoren mittels einer quantitativen Online-Befragung im Rahmen ihrer Masterarbeit an der Katholischen Hochschule Nordrhein-Westfalen nach.
Behavioral and Hemodynamic Changes Following Dichotic Training in Patients with Neurological Deficits of the Auditory Nervous System: A Case Series
Authors: Cline Trey , Colgrove Nicole , Bush Matthew , Lee Jessica , Powell David , Andersen Anders , Weihing Jeffrey , Musiek Frank , Shinn Jennifer
Published: March 2021

Abstract Background Dichotic listening occurs when one attends to different acoustical messages presented simultaneously to both ears. This is important for understanding speech in compromised listening situations, such as background noise. Deficits in dichotic
listening can be remediated by participating in auditory training. We present two patients with binaural integration deficits who underwent dichotic interaural intensity difference (DIID) training. Purpose The purpose of this investigation is to demonstrate improvement of dichotic
listening deficits following DIID training in neurological patients seen clinically for hearing issues. Research Design This was a case series utilizing a pre- and posttreatment design. Study Sample This case series utilized two female participants who demonstrated
binaural integration deficits during an auditory processing evaluation. Intervention The participants underwent a pretraining auditory processing evaluation and functional magnetic resonance imaging (fMRI). Participants then underwent 12, 30-minute DIID training sessions followed
by posttreatment auditory processing evaluations and fMRI. Data Collection and Analysis Data was collected at the pretreatment appointment and then immediately following the completion of the training. Results Each patient demonstrated varying degrees of improvement
on the posttreatment assessment. Case 1 showed significant improvement on the Speech-in-Noise-Revised (SPIN-R) test. fMRI showed changes in activation patterns following training. Case 2 demonstrated improved scores on the Dichotic Digits Test and SPIN-R and increased activation of the calcarine
sulcus following training. Conclusion Dichotic training can be an efficacious treatment for binaural integration deficits and may show evidence of improving speech understanding in noise. This case series demonstrates a promising therapy to help patients improve auditory function
by improving dichotic listening skills.
Methylene Chloride Burns: A Case Report and Review of Literature
Authors: Chauhan Neha , Prasad R. Shankar
Published: March 2021

Chemical burns account for a small percentage of burns but contribute to significant number of burn-related mortalities. The major challenge posed by chemical burns is difficulty in correct depth estimation, as the damage continues to progress until they are effectively neutralized. Besides the most common etiology of chemical burns by alkalis and acids, there are many other unique causes of chemical burns. The author describes a case of chemical burns by heavy duty paint remover, the main composition of which is methylene chloride. Although several studies about methylene chloride poisoning are available in literature, only one case report of burns due to methylene chloride exists in literature. These burns are unique in their presentation. The author describes the presentation and challenges in management of this burn.

Clinical trial registration Not applicable.
An improved higher-order moving particle semi-implicit method for simulations of two-dimensional hydroelastic slamming
Authors: Zha Ruosi , Peng Heather , Qiu Wei
Published: March 2021

An improved higher-order moving particle semi-implicit (MPS) method has been developed to solve the problem of fluid–structure interactions for an elastic wedge entering calm water. The structural responses of the wedge with a reinforced tip were computed during the water entry. In the present method, the pressure gradient is corrected to guarantee the first-order consistency and to satisfy the conservation of momentum. Different particle spacings are used for the fluid and the structure. Convergence studies were carried out on particle spacings for the fluid and the structure and on a time step. A particle convergence index method was applied to evaluate numerical uncertainties in the improved MPS method. Validation studies were carried out on two elastic wedges with deadrise angles of 30° and 20° entering water at various velocities. Numerical solutions were compared with the results from the original higher-order MPS method and experimental data. The improved higher-order MPS methods led to better agreement with experimental data than the original one and significantly reduced the oscillations in numerical solutions.
The impact of hemodynamic factors in a coronary main artery to detect the atherosclerotic severity: Single and multiple sequential stenosis cases
Authors: Hoque K. E. , Ferdows M. , Sawall S. , Tzirtzilakis E. E. , Xenos M. A.
Published: March 2021

A conventional invasive catheterization procedure is currently used to detect atherosclerotic severity in coronary arteries. However, it is still challenging to measure multiple consecutive stenoses (MCS) in coronary main arteries, a severe condition, by using the invasive method. In this paper, we report important hemodynamic properties such as wall shear stress (WSS) and velocity magnitude (VM) across different luminal areas of coronary stenosis in patient-based right coronary artery models of MCS using pulsatile heart flow simulations. The hemodynamic factors in coronary blood flow simulations of different degrees of stenosis indicated a relationship between the proximal moderate stenosis and distal severe stenosis models. The results show the physical effects of different hemodynamic factors including VM, mean arterial pressure difference, WSS, and virtual fractional flow reserve (vFFR), which allow for predicting the physiological computation in the MCS artery severity conditions. This study identifies the fundamental physics of coronary plaque with MCS and indicates the impact of these factors on vFFR measurements. These findings provide insights into and improvement of the pathophysiological assessment of MCS. The results reveal hemodynamic properties, which can be used to diagnose coronary irregularities using a visualization method.
The asymmetric optical bistability based on the one-dimensional photonic crystals composed of the defect layers containing the magnetized ferrite and nonlinear Kerr dielectric under the transverse electric polarization
Authors: Guo Si-Jia , Li Zhi-Jian , Li Fen-Ying , Xu Yi , Zhang Hai-Feng
Published: March 2021

Utilizing the modified transfer matrix method, under transverse electric polarization, asymmetric optical bistability is achieved by designing one-dimensional photonic crystals (PCs) with two Bragg reflector segments containing traditional dielectrics and asymmetric defect multilayers consisting of a magnetized ferrite and nonlinear Kerr dielectric. When the incident wave frequency equals the resonance frequency, owing to the breaking of symmetry in the defect layers and the Voigt magneto-optical effect generating in the magnetized ferrite layers together with the Kerr effect existing in the Kerr dielectric layers, the asymmetric optical modulations are presented as the bistable state in the forward propagation and the multistable state in the backward propagation. Also, the diverse energy localization distributions of the electric field in the proposed PCs from the two incident directions are graphically illustrated. Furthermore, the optical bistable switch-up and switch-down thresholds of the proposed resonator can be tailored flexibly by the external magnetic field, the incident angle, the thicknesses of different dielectrics, and the nonlinear coefficient of Kerr dielectric. This work provides a constructive proposal for the design of light modulators, such as the optical isolator, the optical triode, the all-optical diode, and the sensor.
Barriers and facilitators to pre-exposure prophylaxis among African migrants in high income countries: a systematic review
Authors: Mwaturura Chido , Traeger Michael , Lemoh Christopher , Stoove Mark , Price Brian , Coelho Alison , Mikola Masha , Ryan Kathleen E. , Wright Edwina
Published: March 2021

Background The aim of this review is to explore acceptability, barriers, and facilitators to PrEP use among African migrants in high-income countries. Methods: A systematic review was conducted to explore reasons that contribute to low PrEP uptake in this population. Three online databases, abstracts from key conferences and reference lists of relevant studies articles published between the 2 July 2018 and 3 March 2019 were searched. Narrative synthesis was performed on quantitative data and thematic synthesis was performed on qualitative data. Results: Of 1779 titles retrieved, two cross-sectional studies (United States (US) (n = 1), United Kingdom (UK) (n = 1)) and six qualitative studies (US (n = 2), UK (n = 3), Australia (n = 1)) met inclusion criteria. PrEP acceptability was reported in one cross-sectional article and two qualitative articles. Cross-sectional studies measured acceptability and willingness to use PrEP; in one study, 46% of African migrant men found PrEP use acceptable, and following PrEP education, another study categorised 60% of participants as willing to use PrEP if it were cost-free. Qualitative studies reported mixed acceptability, with higher acceptability reported for serodiscordant couples. Barriers and facilitators to PrEP use were coded into five themes: cultural aspects of stigma; knowledge gap in health literacy; risks unrelated to HIV transmission; practical considerations for PrEP use; and the impact of PrEP use on serodiscordant couples. Conclusions: Several common barriers to PrEP use, including stigma, health literacy and risk perception and cost, were identified. Findings were limited by there being no published data on uptake. Additional work is needed to understand PrEP acceptability and uptake among African migrants.
A narrow ear canal reduces sound velocity to create additional acoustic inputs in a microscale insect ear
Authors: Veitch Daniel , Celiker Emine , Aldridge Sarah , Pulver Christian , Soulsbury Carl D. , Jonsson Thorin , Woodrow Charlie , Montealegre-Z Fernando
Published: March 2021

Significance

The katydid tympanal ears have outer, middle, and inner ear components analogous to mammalian ears. Unlike mammals, each ear has two tympana exposed to sound both externally and internally, with a delayed internal version arriving via the gas-filled ear canal (EC). The two combined inputs in each ear play a significant role in directional hearing. Here, we demonstrate that the major factor causing the internal delay is the EC geometry. The EC bifurcates asymmetrically, producing two additional internal paths that impose different sound velocities for each tympanum. Therefore, various versions of the same signal reach the ears at various times, increasing the chance to pinpoint the sound source. Findings could inspire algorithms for accurate acoustic triangulation in detection sensors.
Phytophthora sojae
effector Avr1d functions as an E2 competitor and inhibits ubiquitination activity of GmPUB13 to facilitate infection
Authors: Lin Yachun , Hu Qinli , Zhou Jia , Yin Weixiao , Yao Deqiang , Shao Yuanyuan , Zhao Yao , Guo Baodian , Xia Yeqiang , Chen Qian , Wang Yan , Ye Wenwu , Xie Qi , Tyler Brett M. , Xing Weiman , Wang Yuanchao
Published: March 2021

Significance

Ubiquitination acts as a crucial regulator in plant immunity. Accordingly, microbial pathogens secrete effectors to hijack the host ubiquitination system. However, the molecular mechanisms by which effectors modulate the host ubiquitination system are not yet clear. Here, we found that the
Phytophthora sojae
effector Avr1d physically binds to the U-box-type E3 ligase GmPUB13, which proved to be a susceptibility factor. The crystal structure of Avr1d complexed with GmPUB13 revealed that Avr1d occupies the binding site in GmPUB13 for E2 ubiquitin conjugating enzyme and competes with E2 for binding to GmPUB13. Avr1d stabilized GmPUB13 by suppressing the self-ubiquitination activity of GmPUB13 and thereby promoting
Phytophthora
infection. This study reveals a structural basis for modulation of host targets by
Phytophthora
effectors.
Non–line-of-sight imaging over 1.43 km
Authors: Wu Cheng , Liu Jianjiang , Huang Xin , Li Zheng-Ping , Yu Chao , Ye Jun-Tian , Zhang Jun , Zhang Qiang , Dou Xiankang , Goyal Vivek K. , Xu Feihu , Pan Jian-Wei
Published: March 2021

Significance

Non–line-of-sight (NLOS) imaging can recover details of a hidden scene from the indirect light that has scattered multiple times. Despite recent advances, NLOS imaging has remained at short-range verifications. Here, both experimental and conceptual innovations yield hardware and software solutions to increase NLOS imaging from meter to kilometer range. This range is about three orders of magnitude longer than previous experiments. The results will open avenues for the development of NLOS imaging techniques and relevant applications to real-world conditions.
The impact of visible lip movements on silent pauses in simultaneous interpreting
Authors: Gieshoff Anne Catherine
Published: March 2021

Simultaneous interpreting requires interpreters to listen to a source text while producing the target text in a
second language. In addition, the interpreter needs to process various types of visual input, which may further increase the
already high cognitive load. A study with 14 students of interpreting was conducted to investigate the impact of a speaker’s
visible lip movements on cognitive load in simultaneous interpreting by analysing the duration of silent pauses in the target
texts. Background noise masking the source speech was introduced as a control condition for cognitive load. Silent pause durations
were shorter when interpreters saw the speaker’s lip movements, which indicates that interpreters benefitted from visual input.
Furthermore, silent pause durations were longer with noise, which suggests that comparative silent pause durations can indicate
changes in cognitive load.
Reducing 4DCBCT scan time and dose through motion compensated acquisition and reconstruction
Authors: Lau Benjamin K F , Reynolds Tess , Wallis Andrew , Smith Sandie , George Armia , Keall Paul J , Sonke Jan-Jakob , Vinod Shalini K , Dillon Owen , O’Brien Ricky T
Published: March 2021

Conventional 4DCBCT captures 1320 projections across 4 min. Adaptive 4DCBCT has been developed to reduce imaging dose and scan time. This study investigated reconstruction algorithms that best complement adaptive 4DCBCT acquisition for reducing imaging dose and scan time whilst maintaining or improving image quality compared to conventional 4DCBCT acquisition using real patient data from the first 10 adaptive 4DCBCT patients. Adaptive 4DCBCT was implemented in the ADaptive CT Acquisition for Personalized Thoracic imaging clinical trial. Adaptive 4DCBCT modulates gantry rotation speed and kV acquisition rate in response to the patient’s real-time respiratory signal, ensuring even angular spacing between projections at each respiratory phase. We examined the first 10 lung cancer radiotherapy patients that received adaptive 4DCBCT. Fast, 200-projection scans over 60–80 s, and slower, 600-projection scans over ∼240 s, were obtained after routine patient treatment and compared against conventional 4DCBCT acquisition. Adaptive 4DCBCT acquisitions were reconstructed using Feldkamp−Davis−Kress (FDK), McKinnon–Bates (MKB), Motion Compensated FDK (MCFDK) and Motion Compensated MKB (MCMKB) algorithms. Reconstructions were assessed via, Structural SIMilarity (SSIM), Signal-to-Noise-Ratio (SNR), Contrast-to-Noise-Ratio (CNR), Tissue Interface Sharpness of Diaphragm (TIS-D) and Tumor (TIS-T). The 200- and 600-projection adaptive 4DCBCT acquisition corresponded to 85% and 55% reduction in imaging dose, shorter and similar scan times of approximately 90 s and 236 s respectively, compared to conventional 4DCBCT acquisition. 200- and 600-projection adaptive 4DCBCT reconstructions achieved more than 0.900 SSIM relative to conventional 4DCBCT acquisition. Compared to conventional 4DCBCT acquisition, 200-projection adaptive 4DCBCT reconstructions achieved higher SNR, CNR, TIS-T, TIS-D with motion compensated algorithms, MCFDK (208%, 159%, 174%, 247%) and MCMKB (214%, 173%, 266%, 245%) respectively. The 200-projection adaptive 4DCBCT MCFDK- and MCMKB-reconstruction results show image quality improvements are possible even with 85% fewer projections acquired. We established acquisition-reconstruction protocols that provide substantial reductions in imaging time and dose whilst improving image quality.
Thermal drift and force convection analysis of nanofluid due to partially heated triangular fins in a porous circular enclosure
Authors: Haq Rizwan Ul , Shah Syed Saqib , Algehyne Ebrahem A
Published: March 2021

The main emphasis of this article is to analyze the simultaneous effects of thermal drift and force convection on water based single wall carbon nanotubes (SWCNTs) in a porous circular duct. Equilateral triangular fins which are enclosed in a circular cavity having vertical and horizontal side of triangular fins are moving. Internal heat generation/absorption is considered to determine the thermal drift. Impact of inclined Magnetohydrodynamics (MHD) has been introduced. The governed equations are solved through weighted residue method of Finite Element Method (FEM). Effect of various parameters, that is: Reynolds number, nanoparticles, porous medium and absorption/generation on flow patterns and heat transfer were worked out. The heat transfer rate at different heated fins has been investigated for various parameters. This study carries out significant impact of Q on temperature profile and Local Nusselt. Local Nusselt number against heated walls decreases with increase of porosity parameter and increases in case of heat generation. Streamlines decreases with increase of quantity of solid volume fraction in enclosure. Significant effect of Re observed in transformation of heat in circular duct.
Structural, electronic and optical properties of bulk and monolayer iron diselenide: A density functional study
Authors: Beiranvand Razieh , Mehrabi Vahid
Published: March 2021

Using the density functional theory (DFT), we study the electronic structure and linear optical properties of pyrite and monolayer structure of FeSe2. Calculated results show that pyrite FeSe2 is a non-magnetic semiconductor material, while hexagonal monolayer FeSe2 show completely metallic features. There have strong hybridizations between Fe-d orbitals and Se-p orbitals. All optical properties such as real and imaginary parts of dielectric function, absorption coefficient, reflectivity and loss function are obtained and analyzed. The results present obvious anisotropy in optical features of monolayer FeSe2 and excellent absorption properties in ultraviolet and visible light regions. Results show that the anti-ferromagnetic character in pyrite FeSe2 turn into ferromagnetic character for hexagonal monolayer and the main transitions near the Fermi level mainly contributed by down-spin carriers. From the density of states, we found thet the Fe-d orbitals play an important role in the interband and intraband transitions. These results made monolayer FeSe2 an ideal candidate for photovoltaic, opto-electric and spintronic applications.
Stochastic bifurcation for two-time-scale dynamical system with α-stable Lévy noise
Authors: Yuan Shenglan , Zeng Zhigang , Duan Jinqiao
Published: March 2021

This work focuses on stochastic bifurcation for a slow–fast dynamical system driven by non-Gaussian α-stable Lévy noise. We prove the main result for the stochastic equilibrium states for the original system and the reduced system based on the random slow manifold. Then, it is verified that the slow reduced system bears the stochastic bifurcation phenomenon inherited from the original system. Furthermore, we investigate the number and stability type of stochastic equilibrium states for dynamical systems through numerical simulations, and it is illustrated that the slow reduced system captures the stochastic bifurcation of the original system.
A generating function approach to Markov chains undergoing binomial catastrophes
Authors: Goncalves B , Huillet T
Published: March 2021

In a Markov chain population model subject to catastrophes, random immigration events (birth), promoting growth, are in balance with the effect of binomial catastrophes that cause recurrent mass removal (death). Using a generating function approach, we study two versions of such population models when the binomial catastrophic events are of a slightly different random nature. In both cases, we describe the subtle balance between the two birth and death conflicting effects.
The Impact of Super Resolution on detecting Covid-19 from CT Scans using VGG-16 Based Learning
Authors: Aburaed N , Panthakkan A , Al-Saad M , Al Mansoori S , Al Ahmad H
Published: March 2021

With the recent outbreak of the novel Coronavirus (COVID-19), the importance of early and accurate diagnosis arises, as it directly affects mortality rates. Computed Tomography (CT) scans of the patients’ lungs is one of the diagnosis methods utilized in some countries, such as China. Manual inspection of CT scans can be a lengthy process, and may lead to inaccurate diagnosis. In this paper, a Deep Learning strategy based on VGG-16 is utilized with Transfer Learning for the purpose of binary classification of CT scans; Covid and NonCovid. Additionally, it is hypothesized in this study that Single Image Super Resolution (SISR) can boost the accuracy of the networks’ performance. This hypothesis is tested by following the training strategy with the original dataset as well as the same dataset scaled by a factor of ×2. Experimental results show that SISR has a positive effect on the overall training performance.
Formation of the Structure and Training of a Multilayer Neural Network for the Analysis and Blood Glucose Level Prediction
Authors: Privezentsev D.G. , Zhiznyakov A.L. , Belyakova A.S. , Astafiev A.A.
Published: March 2021

The work aims to develop software for conducting research on the formation of the structure and training a multilayer neural network for analyzing and blood glucose level prediction. The paper analyzes the task, considers the tools, methods and algorithms for solving the problem. A model of an artificial neural network was investigated, the structure was developed and training was carried out. Summed up based on the results and assessed the effectiveness of such a system.
Low-code AutoML-augmented Data Pipeline – A Review and Experiments
Authors: Gain Ulla , Hotti Virpi
Published: March 2021

There is a lack of knowledge concerning the low-code autoML (automated machine learning) frameworks that can be used to enrich data for several purposes concerning either data engineering or software engineering. In this paper, 34 autoML frameworks have been reviewed based on the latest commits and augmentation properties of their GitHub content. The PyCaret framework was the result of the review due to requirements concerning adaptability by Google Colaboratory (Colab) and the BI (business intelligence) tool. Finally, the low-code autoML-augmented data pipeline from raw data to dashboards and low-code apps has been drawn based on the experiments concerned classifications of the “Census Income” dataset. The constructed pipeline preferred the same data to be a ground for different reports, dashboards, and applications. However, the constructed low-code autoML-augmented data pipeline contains changeable building blocks such as libraries and visualisations.
Decision-Making while Drilling Wells based on the Results of Modeling the Characteristics of Rocks using Probabilistic-Statistical Methods and Fuzzy Logic
Authors: Efendiyev Galib , Isayev Ramin , Piriverdiyev Igor
Published: March 2021

The current level of development of technical means and technologies creates favorable conditions for obtaining better information about the drilling process. To do this, it is necessary to analyze the information received, which in turn requires the use of methods that allow making decisions under uncertainty inherent in the drilling process. The operational information obtained in this case is very important and fundamental, which is of particular importance when drilling wells in areas for which there is no information or it is available in insufficient volume. The paper is devoted to increasing the efficiency of studying geological sections and improving the quality of information obtained in the process of drilling wells using probabilistic-statistical methods and fuzzy logic, which in turn contributes to improving the quality of decisions made. In two figures, a comparative analysis of changes in the properties of rocks and the rate of penetration was demonstrated, homogeneous intervals were identified according to the properties of rocks and drillability in general, which makes it possible to trace the intervals of possible complications.
Deep Learning for Binaural Sound Source Localization with Low Signal-to-noise Ratio
Authors: Zhao Fengnian , Li Ruwei , Pan Dongmei
Published: March 2021

A novel deep learning (DL) method is proposed for binaural sound source localization with low SNR. Firstly, the binaural sound signals are decomposed into several channels by using Gammatone filter. Secondly, the 4 feature parameters of Head-related Transfer Function, interaural time difference (ITD), interaural coherence (IC), interaural level difference (ILD), and interaural phase difference (IPD) are extracted. Thirdly, ITD and IC go through a Deep Belief Network (DBN) to determine the quadrant of the sound source and reduce the positioning range. Then, ITD, IC, ILD, and IPD go through a Deep Neural Network (DNN) to obtain the azimuthal angle within 90 degrees. Experimental results show that the proposed algorithm can solve the front-back confusion, and obtain a superior performance with lower complexity and higher precision under low SNR conditions.
Efficient Channel Attention Feature Fusion for Lightweight Single Image Super Resolution
Authors: Jiang Lingxiu , Zhou Yue
Published: March 2021

Recent advances in deep learning and convolution neural network have greatly improved the reconstruction performance of SISR compared with the traditional methods. However, complicated models and huge amount of parameters limit the application of those methods in real-world scenes. In our paper, we propose an efficient channel attention feature fusion method on the lightweight super-resolution network (ELSRN) for SISR. We reduce our network parameters through several modules, including binary cascading feature fusion. Besides, we propose to build efficient inverted residual block (EIRB) and stack several EIRBs to capture effective feature information of different scales. Last, we fuse multi-scale features in pairs step by step and finally refine final feature information with different scale features. Several experiments have proved that our EIRBs module and binary cascading method are effective and our network can achieve a great trade-off between reconstruction performance and model size.
Chinese Event Subject Extraction in the Financial Field Integrated with BIGRU and Multi-head Attention
Authors: Liu Yangwenhao , Liu Changhui , Wang Liya , Chen Zhe , Wu Yuan
Published: March 2021

Event subject extraction was to extract subjects of specific event types. For the traditional BiLSTM network, the threshold is complicated, the required parameters are many, and the time cost is high. This paper is oriented to the financial field and proposes a method of introducing a multi-head attention mechanism based on the BIGRU network to extract event subjects. First, the text is vectorized, and then the word vector obtained is input into the BIGRU network to learn the context features, and introduce a multi-head attention mechanism to extract the depth feature values of the text. Finally, a comparative experiment is conducted on the data set. The method in this paper achieves an Accuracy value of 80.47% and an F1 value of 89.18%. The result is better than the control group, indicating that the model proposed in this paper can effectively improve the accuracy of extracting Chinese event subjects.
Multi-objective Path Planning Based on K-D Fusion Algorithm
Authors: Kong Supeng , Chen Surong , Zhong Zhaoxi , Xu Bin , Shi Tianlei , Zhu Xiaohui , Yue Yong , Wang Wei
Published: March 2021

How to arrange sales staff to visit offline stores reasonably is a critical task in the Fast Moving Consumer Goods (FMCG) industry. Based on the K-means and Dijkstra algorithms (K-D fusion algorithm), this paper proposed an algorithm to automatically allocate offline stores for sales staff and optimize the visiting path, thereby improving management efficiency. A new initial cluster center selection approach was proposed for the K-means algorithm to select its initial clustering center with the consideration of outlier points. The sales staff’s visiting path to the store was planned by the Dijkstra algorithm. The performance of our K-D fusion algorithm was evaluated in terms of grouping rationality and path planning optimization. Experimental results show that our algorithm can comprehensively consider several factors such as the number of stores, store types, and geographical locations, and distribute the workload more evenly to all the sales staff. In addition, it can also optimize the visiting path for sales staff, which can effectively improve the efficiency of sales staff to visit offline stores.
Tensor Computations of Stochastic Dynamic Fields
Authors: Zhang Yinsheng
Published: March 2021

This article attempts to delve a kind of quantities—tensor for a stochastic dynamic domain, making its principles of computations such as gradient, divergence and interaction. The notation “stochastic dynamic field (SDF)”, proposed by the author, refers to such a dynamic field that it appears or annihilates, moves, acts or interacts at random. The study aims to consider not only randomness of vector fields in a continuous dynamic system, but also discontinuity with time variable t, i.e. more randomness for t, in a discontinuous dynamic system. To do so, representation forms of tensors areproperly transformed, which apply multiple-points tensor, and introduce the quantity of compound tensor like an expression of quantum state of combination of uncertainties. The work bears out some new results: (1) discontinuous mutation, even emerge or disappear, of a point in a tensor field, analyzed using a tensor with matrix structure, would not defer tensor computations of that field, while wouldn’t be computed commonly using accustomed representation of tensor with only a single-point structure. (2) Random quantity relations between phrases, no matter smooth nearby, non-connected or not, all can be computed. (3) Some classical physical laws analyzing field properties of space-relevant factors such as Euler equation and Gauss theorem are available in new forms with a random nature.
Study on the Segmentation Method of Multi-Phase CT Liver Tumor Based on Dual Channel U-Nets
Authors: Xu Changchang , Hu Dong , Zhang YaoDong , Pang Yuwei
Published: March 2021

As a common malignant tumor disease, hepatocellular carcinoma is the most common cancers in the world. The incidence of hepatocellular carcinoma in China is higher than that in the world. Therefore, it is very important for doctors to separate liver and tumor from CT images by means of computer-aided diagnosis and treatment. In this paper, a multiscale DC-CUNets network liver tumor segmentation method is proposed to enhance the fusion of multi-phase image features in CT, the scale of liver tumors, and the optimization of network training process. (1) A multistage CT liver tumor segmentation method based on two-channel cascaded U-Nets (DC-CUNets) is proposed. The liver was segmented using the first-order U-Net, and then the segmented area of interest of the liver was input into the second-order U-Net network to segment liver tumors. We designed two-channel U-Nets to learn the image characteristics of CT images in arterial and venous phases respectively, and to achieve two-channel feature fusion through feature cascade to improve the overall accuracy of liver tumor segmentation.(2) A multistage CT liver tumor segmentation method based on multiscale DC-CUNets was proposed. For the scale problem of liver tumors, we designed a two-layer multiscale void convolution module to obtain image features at different scales for large, medium and small tumors, and fuse the multiscale features at the output of the module. We have replaced the convolution layer of the fourth module in the second-order two-channel liver tumor segmentation U-Nets by the two-layer multiscale cavity convolution module to implement multiscale DC-CUNets.
Research on Application in Intelligent Vehicle Automatic Control System
Authors: Wang Yinghui , Sun Wenfu , Lu Yong
Published: March 2021

The traditional motor bridge circuit always has short circuit problems, and the smart car needs to be controlled by wire or remote control equipment. In order to study the application of automatic control technology in smart cars. Using the smart car model and simulation to study the smart car control system, with the mobile phone as the remote wireless control terminal, the smart car uses bluetooth communication and MCU as the intermediate bridge, the smart car achieves automatic direction control, voice control, gravity induction control, automatic tracking, automatic anti-collision and other functions. The motor circuit system is optimized by Simulation and experiment. It replaces the previous wired control and remote control equipment control, and effectively reduces the cost of equipment.
Crosstalk Analysis and Suppression of Single Chip SiC MOSFET Half-Bridge Circuit
Authors: Wang Yifan , Zhang Bin , Gong Jinlong , Wang Zun
Published: March 2021

For the hard-switch applications of the half-bridge circuit based on SiC MOSFET, the crosstalk generation mechanism of the half-bridge circuit in the switching on and off process is analysed. The influence of the driving resistance and stray inductance of driving circuit on the crosstalk is simulated and analysed. In the single-chip half-bridge circuit, the Miller clamp method with BJT + diode is used. By increasing the driving resistance and reducing the clamp circuit inductance, the crosstalk suppression effect can greatly improve. It is an effective way to optimize the clamping circuit and reduce the clamp circuit inductance without affecting the switch speed.
GIS-monitoring of Regional Transport Network Traffic as a Method to Study Commuting: Moscow Region Case
Authors: Mitroshin P A , Shitova Yu Yu , Shitov Yu A , Mitroshin A A , Vlasov D N
Published: March 2021

The paper is aimed to analyse the labour mobility in the region using GIS services. Within the framework of the project, a methodology for continuous automatic collection and accumulation of information on the state of the transport network and weather conditions was developed, tested and implemented. Based on this information, it is possible to analyse the important factors of the regional economy: temporary and financial losses of residents of the Moscow Region on the home-to-work paths, as well as identify patterns of traffic congestion from various factors. The first results of the analysis of the accumulated data are presented, which demonstrate the commuting effects in the regional transport network.
Multicriteria Algorithm for Multisensory Analysis of Coffee
Authors: Vianna Alexandre Magno da Rocha
Published: March 2021

The complexity of the new consumer market requires an equally complex investigative method to observe and analyze experiences, identifying neurophysiological patterns and their relationship with the choice of a product. The observation of the multisensory experience was made using fMRI, EEG and Fuzzy logic. For this project, the coffee consumption experience was analyzed and the neurophysiological responses observed, during the consumption of 3 different flavors, prepared in the same way. Based on the data and processes, a sensory mapping of the observer was generated in which the neurophysiological responses can be verified during the experiment. The data were processed and quantified in order to have a mathematical answer to the experience of each of the flavors and people, in addition to allowing an understanding of the importance of these standards for the selection of the winning option. Especially considering that a verbal response is not always the same as a neural response to the same stimulus.
Global virus lockdown and cybercrime rate trends: a routine activity approach
Authors: Govender I , Watson B W W , Amra J
Published: March 2021

Cybercrime perpetuates as a major threat to the reputation and economy of a nation. There is consensus in the media and academic journals alike, that cybercrimes have incremented over the years. During the coronavirus pandemic in the year 2020, a global lockdown imposed by governments forced people to be homebound, leading to a subsequent surge in online users. This resulted in cybersecurity risk hitting close to home, with an upsurge in cybercrimes. This article attempts to make sense of the elevated cybercrime landscape. For the last two decades, criminological research have attempted to explore variants of technology-enabled crime and have examined theories to account for offending. This study proposes ‘Routine Activity Theory’ to explain the magnitude of cybercrimes during this unprecedented period that reshaped people’s routine lifestyles. Routine activity theory predicts that changes in opportunity situations (increase in online users), potentially escalates the convergence of motivated offenders and suitable targets in the absence of capable guardianship. Guardianship (security measures) is pivotal in this theory, to protect the targets (online users) from offenders (cyber threat actors). Furthermore, this paper offers practical security measures, in light of the specified cyber-attacks during this unprecedented period. If this theory is empirically validated, it offers opportunities to mitigate the extent of cybercrimes with an upsurge of online users, and more so for cybercrimes in general.
Reliability assessment of the automatic plagiarism detection system for various editing patterns in documents containing complex mathematical notation
Authors: Bier Agnieszka , Sroczyński Zdzisław
Published: March 2021

The paper presents the idea and implementation details of a novel tool intended for detection of plagiarism in mathematical content of LATEX files. The proposed algorithm compares LTeX files and detects possible similarities in mathematical formulas, providing an objective similarity score. It offers two modes of comparison: direct (symbolic) mode and verbalized math mode, where the mathematical expressions are first verbalized to spoken language form and compared as such. The solution has been tested against identified various types of plagiarism specific to mathematical symbolic notation and LATeX features, and proved to be an efficient tool for plagiarism detection.
Metaheuristics for Sparse Index-Tracking Problem: A Case Study on FTSE 100
Authors: Yuen Man-Chung , Ng Sin-Chun , Leung Man-Fai
Published: March 2021

Passive management contributes a more stable return than an active management strategy over the long term. Index-tracking is one of the passive investment strategies that attempt to replicate market indexes to reproduce the performance. Sparse index-tracking considers a subset of market index stocks to minimize the difference between the market index and the replicated index. In this paper, two metaheuristics are applied to solve this problem. The sparse index-tracking problem formed by the objective function of the empirical tracking error with the penalty values that result in an NP-hard problem. The penalty value is used to restrict the numbers of the considered stocks. To show the performance of the metaheuristics, various penalty values are investigated, and they produce approximation solutions to the index-tracking problem. Among them, particle swarm optimization shows better or statistically similar performance to GA in solving the sparse index-tracking problem.
Research on Network Security Protection of Application-Oriented Supercomputing Center Based on Multi-Level Defense and Moderate Principle
Authors: Yang Boxiong , Yu Ying , Wang Zeyu , Li Shelei , Xiao Heng , Gao Hualing , Liang Zhiyong , Zhou Xue
Published: March 2021

High Performance Computing (HPC), also known as Supercomputing, is widely applied in various fields, which have the advantage of high processing capability and mass storage. Various open business services bring great threat to the network and data security of HPC cluster. This paper proposes a feasible scheme to realize supercomputing information security through multi-layer defense and overheads control according to the experience of operation, management and maintenance of Supercomputer Center for many years. First, this paper puts forward the principle of moderate risk, and gives the principles of design and the implementation for the security protection of Supercomputing Center. Second, this paper presents a security system architecture in coordination with deep collaborative defense. Finally, this paper designs a 4-layer protection scheme on account of the latest network security solutions and the operation and maintenance characteristic of our own supercomputer. Experiments on multi-level defense and overheads control show that the designed scheme takes into account the cost and network security, and obtain prospective results., which is especially suitable for the network security protection of small-scale or medium-scale high-performance computing cluster for engineering application.
Attack Sample Generation Algorithm Based on Dual Discriminant Model in Industrial Control System
Authors: Zhou Wen , Cao Xueying , Li Xiaoming , Sha Yun , Chen Jianping , Yan Yong , Liu Xuejun , Kong Xiangmin
Published: March 2021

The research of intrusion anomaly detection in industrial control system is facing the problem of few attack samples. This paper proposes an attack data sample generation algorithm based on dual discriminant model. Firstly, a few attack samples are generated by multiple attacks based on the original data set. Secondly, the sparse matrix algorithm is used to expand the data set, and the original data features are distributed to each extended data to ensure the authenticity of the seed data. Finally, a large number of attack samples are generated by the dual discriminant model to complete the sample expansion. The experimental results of Mississippi SCADA natural gas pipeline data set and an industrial control system data set show that, based on keeping the data in a reasonable range, the algorithm can obtain an effectively expanded sample data set. SVM, Random Forest and XGBOOST are used to classify the dataset. The results show that AUC index is better than the negative sample data set generated by traditional GAN algorithm, and is similar to the initial negative sample index.
Description and Implementation of Production Factory Ontology
Authors: Hardt Filip , Žáček Martin
Published: March 2021

The article follows the previous work of the authors. The paper aims to elaborate ontology into a solid form using directly for the production factory. The authors divided the article into two parts. The first part explains the basics of conceptualization, and the second part is devoted to creating and describing ontology using the RDF model. As an example of ontology’s practical use, there are codes in the programming language #C.