bluuebunny/crossref_metadata_2025 huggingface dataset:
Novel web application for self-assessment of distance visual acuity to support remote consultation: a real-world validation study in children
Authors: Allen Louise , Thirunavukarasu Arun James , Podgorski Simon , Mullinger Deborah
Published: September 2021

Objective

The difficulty in accurately assessing distance visual acuity (VA) at home limits the usefulness of remote consultation in ophthalmology. A novel web application, DigiVis, enables automated VA self-assessment using standard digital devices. This study aims to compare its accuracy and reliability in children with clinical assessment by a healthcare professional.

Methods and Analysis

Children aged 4–10 years were recruited from a paediatric ophthalmology service. Those with VA worse than +0.8 logMAR (Logarithm of the Minimum Angle of Resolution) or with cognitive impairment were excluded. Bland-Altman statistics were used to analyse both the accuracy and repeatability of VA self-testing. User feedback was collected by questionnaire.

Results

The left eyes of 89 children (median 7 years) were tested. VA self-testing showed a mean bias of 0.023 logMAR, with a limit of agreement (LOA) of ±0.195 logMAR and an intraclass correlation coefficient (ICC) of 0.816. A second test was possible in 80 (90%) children. Test–retest comparison showed a mean bias of 0.010, with an LOA of ±0.179 logMAR, an ICC of 0.815 and a repeatability coefficient of 0.012. 96% of children rated the test as good or excellent, as did 99% of their parents.

Conclusion

Digital self-testing gave comparable distance VA assessments with clinical testing in children and was well accepted. Since DigiVis self-testing can be performed under direct supervision using medical video consultation software, it may be a useful tool to enable a proportion of paediatric eye clinic attendances to be moved online, reducing time off school and releasing face-to-face clinical capacity for those who need it.
Cancer-related fatigue—pharmacological interventions: systematic review and network meta-analysis
Authors: Chow Ronald , Bruera Eduardo , Sanatani Michael , Chiu Leonard , Prsic Elizabeth , Boldt Gabriel , Lock Michael
Published: September 2021

Introduction

Cancer-related fatigue (CRF) is a very common symptom in patients with cancer, and one of the five areas of highest priority in cancer research. There is currently no consensus on pharmacologic interventions for treating CRF. The aim of this systematic review is to provide more clarity on which pharmacologic interventions may be most promising, for future clinical trials. The network meta-analysis provides the ability to compare multiple agents when no direct head-to-head trials of all agents have been performed.

Methods

Medline (PubMed), EMBASE and Cochrane Central Register of Controlled Trials were searched up until 5 March 2021. Studies were included if they reported on a pharmacologic intervention for CRF. Standardised mean differences and corresponding 95% CIs were computed using a random-effects maximum-likelihood model.

Results

This review reports on 18 studies and 2604 patients, the most comprehensive review of pharmacologic interventions for CRF at the time of this publication. Methylphenidate, modafinil and paroxetine were superior to placebo. Methylphenidate and modafinil were equivalent to one another. Paroxetine was superior to modafinil.

Conclusion

Paroxetine should be further studied in future trials. As well, more safety data are needed on pharmacologic interventions.
CD8+ TILs in NSCLC differentiate into TEMRA via a bifurcated trajectory: deciphering immunogenicity of tumor antigens
Authors: Lee Sung-Woo , Choi He Yun , Lee Gil-Woo , Kim Therasa , Cho Hyun-Ju , Oh In-Jae , Song Sang Yun , Yang Deok Hwan , Cho Jae-Ho
Published: September 2021

Background

CD8+ tumor-infiltrating lymphocytes (TILs) comprise phenotypically and functionally heterogeneous subpopulations. Of these, effector memory CD45RA re-expressing CD8+ T cells (Temra) have been discovered and characterized as the most terminally differentiated subset. However, their exact ontogeny and physiological importance in association with tumor progression remain poorly understood.

Methods

We analyzed primary tumors and peripheral blood samples from 26 patients with non-small cell lung cancer and analyzed their phenotypes and functional characteristics using flow cytometry, RNA-sequencing, and bioinformatics.

Results

We found that tumor-infiltrating Temra (tilTemra) cells largely differ from peripheral blood Temra (pTemra), with distinct transcriptomes and functional properties. Notably, although majority of the pTemra was CD27−CD28− double-negative (DN), a large fraction of tilTemra population was CD27+CD28+ double-positive (DP), a characteristic of early-stage, less differentiated effector cells. Trajectory analysis revealed that CD8+ TILs undergo a divergent sequence of events for differentiation into either DP or DN tilTemra. Such a differentiation toward DP tilTemra relied on persistent expression of CD27 and CD28 and was associated with weak T cell receptor engagement. Thus, a higher proportion of DP Temra was correlated with lower immunogenicity of tumor antigens and consequently lower accumulation of CD8+ TILs.

Conclusions

These data suggest a complex interplay between CD8+ T cells and tumors and define DP Temra as a unique subset of tumor-specific CD8+ TILs that are produced in patients with relatively low immunogenic cancer types, predicting immunogenicity of tumor antigens and CD8+ TIL counts, a reliable biomarker for successful cancer immunotherapy.
Immune cell and tumor cell-derived CXCL10 is indicative of immunotherapy response in metastatic melanoma
Authors: Reschke Robin , Yu Jovian , Flood Blake A , Higgs Emily F , Hatogai Ken , Gajewski Thomas F
Published: September 2021

A T cell-inflamed tumor microenvironment is characterized by the accumulation and local activation of CD8+ T cells and Bat3-lineage dendritic cells, which together are associated with clinical response to anti-programmed cell death protein 1 (anti-PD-1)-based immunotherapy. Preclinical models have demonstrated a crucial role for the chemokine CXCL10 in the recruitment of effector CD8+ T cells into the tumor site, and a chemokine gene signature is also seen in T cell-inflamed tumors from patients. However, the cellular source of CXCL10 in human solid tumors is not known. To identify the cellular source of CXCL10 we analyzed 22 pretreatment biopsy samples of melanoma metastases from patients who subsequently underwent checkpoint blockade immunotherapy. We stained for CD45+ and Sox10+ cells with multiparameter immunofluorescence staining, and RNA in situ hybridization technology was used in concert to identify CXCL10 transcripts. The results were correlated with the expression levels of CXCL10 transcripts from bulk RNA sequencing and the best overall response to immune checkpoint inhibition (anti-PD-1 alone or with anti-CTLA-4) in the same patients. We identified CD45+ cells as the major cellular source for CXCL10 in human melanoma metastases, with additional CXCL10 production seen by Sox10+ cells. Up to 90% of CD45+ cells and up to 69% of Sox10+ cells produced CXCL10 transcripts. The CXCL10 staining result was consistent with the level of CXCL10 expression determined by bulk RNA sequencing. The percentages of CD45+ CXCL10+ cells and Sox10+ CXCL10+ cells independently predicted response (p<0.001). The average number of transcripts per cell correlated with the CD45+ cell infiltrate (R=0.37). Immune cells and melanoma cells produce CXCL10 in human melanoma metastases. Intratumoral CXCL10 is a positive prognostic factor for response to immunotherapy, and the RNAscope technique is achievable using paraffin tissue. Strategies that support effector T cell recruitment via induction of CXCL10 should be considered as a mechanism-based intervention to expand immunotherapy efficacy.
Organisational values of National Health Service trusts in England: semantic analysis and relation to performance indicators
Authors: Waheed Amina , Presswood Edward , Scott Gregory
Published: September 2021

Background

Organisational values are widely assumed to have positive effects on performance and staff. National Health Service (NHS) trusts in England have accordingly chosen their own organisational values. However, there has been no survey of the values adopted, and there is little evidence that the choice of values per se has consequences for outcomes. We comprehensively described trusts’ organisational values, using natural language processing to identify common themes. We tested whether the choice of themes was associated with outcomes for patients and staff.

Methods

We collected data on trusts’ values (from their websites), performance (Summary Hospital-level Mortality Indicator (SHMI) statistics, Care Quality Commission (CQC) ratings), sickness absence rates (SAR) and staff opinions (NHS Staff Survey responses). We first characterised values based on lexical properties then progressed to semantic analysis, using Google’s Universal Sentence Encoder, to transform values to high-dimensional embeddings, and k-means clustering of embeddings to semantically cluster values into 12 common themes. We tested for associations between trusts’ use of these themes and outcomes.

Results

Organisational values were obtained for 221 of 228 NHS trusts, with 985 values in total (480 unique). Semantic clustering identified themes including ‘care’, ‘value respect’and ‘togetherness’. There was no significant association between themes and SHMI or CQC ratings. However, themes predicted trusts’ SAR (p=0.001, R2=0.159), with use of ‘care’,‘value respect’, ‘aspirational’and ‘people’all significant predictors ofincreasedsickness absence; themes also predicted staff opinions on ‘Equality, diversity and inclusion’(p=0.011, R2=0.116), but with ‘supportive’and ‘openness’predicting morenegativeresponses.

Conclusion

A trust’s adoption of individualised organisational values does not seem to make a positive difference to its patients or staff. These findings should give NHS managers pause for thought, challenging them to reconsider their reliance on value-defining initiatives, and to seek evidence that a focus on values has measurable benefits on outcomes.
Impact of smoking on the development of idiopathic pulmonary fibrosis: results from a nationwide population-based cohort study
Authors: Bae Won , Lee Chang-Hoon , Lee Jinwoo , Kim Young Whan , Han Kyungdo , Choi Sun Mi
Published: September 2021

Background

Smoking has been considered an important risk factor for idiopathic pulmonary fibrosis (IPF) incidence. However, there are no population-based large-scale studies demonstrating the effects of smoking on the development of IPF. We aimed to evaluate the effect of smoking on IPF development using a nationwide population-based cohort.

Methods

Using the Korean National Health Information Database, we enrolled individuals who had participated in the health check-up service between 2009 and 2012. Participants having a prior diagnosis of IPF were excluded. The history of smoking status and quantity was collected by a questionnaire. We identified all cases of incident IPF through 2016 on the basis of ICD-10 codes for IPF and medical claims. Cox proportional hazards models were used to calculate the adjusted HR (aHR) of the development of IPF.

Results

A total of 25 113 individuals (0.11%) with incident IPF were identified out of 23 242 836 participants registered in the database. The risk of IPF was significantly higher in current and former smokers than in never smokers, with an aHR of 1.66 (95% CI 1.61 to 1.72) and 1.42 (95% CI 1.37 to 1.48), respectively. Current smokers had a higher risk of IPF than former smokers (aHR 1.17, 95% CI 1.13 to 1.21). The risk of IPF development increased as the smoking intensity and duration increased.

Conclusion

Smoking significantly increased the risk of IPF development. Current smokers had a higher risk of IPF than former smokers. A dose–response relationship was observed between smoking and the development of IPF.
THE INFLUENCE OF GOVERNMENT’S EXPENDITURE IN THE ENVIRONMENTAL PROTECTION ON AIR POLLUTION IN TAIWAN
Authors: HUANG JR-TSUNG
Published: September 2021

This study explores the influence of government’s spending on environmental protection on air pollution in Taiwan. Using the panel data of 20 counties, county-level cities, and municipalities in Taiwan covering the period from 2013 to 2018 and the spatial econometric analysis due to considering the possible spatial dependence of air pollution represented by PM2.5 concentration and SO2 emissions, the primary finding is that government’s spending in the environmental protection can statistically significantly improve air pollution regardless of where the financial source is. However, rather than the local fiscal expenditure on environmental protection, subsidies of the air pollution control from the central government can play more important roles to effectively improve air quality of the local area in Taiwan.
DKP equation with smooth barrier
Authors: Langueur O. , Merad M. , Rassoul A.
Published: September 2021

In this paper, we study the Duffin–Kemmer–Petiau (DKP) equation in the presence of a smooth barrier in dimensions space–time (1+1) dimensions. The eigenfunctions are determined in terms of the confluent hypergeometric function [Formula: see text]. The transmission and reflection coefficients are calculated, special cases as a rectangular barrier and step potential are analyzed. A numerical study is presented for the transmission and reflection coefficients graphs for some values of the parameters [Formula: see text] are plotted.
HE-YOLO: Aerial Target Detection Based On Improved YOLOv3
Authors: Zhao Yuqing , Jia Jinlu , Liu Di , Qian Yurong
Published: September 2021

Aerial image-based target detection has problems such as low accuracy in multiscale target detection situations, slow detection speed, missed targets and falsely detected targets. To solve this problem, this paper proposes a detection algorithm based on the improved You Only Look Once (YOLO)v3 network architecture from the perspective of model efficiency and applies it to multiscale image-based target detection. First, the K-means clustering algorithm is used to cluster an aerial dataset and optimize the anchor frame parameters of the network to improve the effectiveness of target detection. Second, the feature extraction method of the algorithm is improved, and a feature fusion method is used to establish a multiscale (large-, medium-, and small-scale) prediction layer, which mitigates the problem of small target information loss in deep networks and improves the detection accuracy of the algorithm. Finally, label regularization processing is performed on the predicted value, the generalized intersection over union (GIoU) is used as the bounding box regression loss function, and the focal loss function is integrated into the bounding box confidence loss function, which not only improves the target detection accuracy but also effectively reduces the false detection rate and missed target rate of the algorithm. An experimental comparison on the RSOD and NWPU VHR-10 aerial datasets shows that the detection effect of high-efficiency YOLO (HE-YOLO) is significantly improved compared with that of YOLOv3, and the average detection accuracies are increased by 8.92% and 7.79% on the two datasets, respectively. The algorithm not only shows better detection performance for multiscale targets but also reduces the missed target rate and false detection rate and has good robustness and generalizability.
Sensorless Control Strategy for BLDC Motors Based on a Type-3 Phase Locked Loop
Authors: Abbes Mohamed , Chebbi Souad
Published: September 2021

This paper presents the design procedure of a high-performance sensorless control strategy for the widely used brushless DC (BLDC) motors. Generally, conventional sensorless techniques are based on detecting the zero-crossing instants (ZCP) of the back electromotives forces (back-EMFs) of the three phases. These methods, although widely adopted and marketed on an industrial level, involve many limitations such as filtering delays, difficulty to operate at low speeds and immunity against Electromagnetic Interferences (EMI). In this paper, the main objective is to develop a sensorless control technique integrally independent from the zero-crossing points of the back-EMFs. In the proposed method, a zero-delay adaptive filter was used to extract the fundamental and the quadrature components of the line-to-line voltage of the motor. Then, the Synchronous Reference Frame Phase Locked Loop (SRF-PLL) is used to estimate the electrical angle of phase-to-phase back-EMF along with the rotor speed. The conventional SRF-PLL was implemented using a second-order loop filter (type-3 PLL) in order to improve synchronization performances and for better rejection of voltage spikes induced in motor phases during commutations. The benefits of the control technique are brought to light through simulation results. An experimental prototype was designed to confirm the validity of the proposed method.
Infinitely many knots whose Whitehead doubles have the trivial first coefficient Kauffman polynomial
Authors: Takioka Hideo
Published: September 2021

We recall a skein relation of the first coefficient Kauffman polynomial for knots. By using the skein relation, we show that there exist infinitely many knots whose Whitehead doubles have the trivial first coefficient Kauffman polynomial.
On the potential functions for a link diagram
Authors: Yoon Seokbeom
Published: September 2021

Cho and Murakami defined the potential function for a link [Formula: see text] in [Formula: see text] whose critical point, slightly different from the usual sense, corresponds to a boundary-parabolic representation [Formula: see text]. They also showed that the volume and Chern–Simons invariant of [Formula: see text] can be computed from the potential function with its partial derivatives. In this paper, we extend the potential function to a representation that is not necessarily boundary-parabolic. We show that under a mild assumption it leads us to a combinatorial formula for computing the volume and Chern–Simons invariant of a [Formula: see text]-representation of a closed 3-manifold.
Spontaneous symmetry breaking in the late Universe and glimpses of the early Universe phase transitions à la baryogenesis
Authors: Sami M. , Gannouji Radouane
Published: September 2021

Spontaneous symmetry breaking is the foundation of electroweak unification and serves as an integral part of the model building beyond the standard model of particle physics and it also finds interesting applications in the late Universe. We review development related to obtaining the late cosmic acceleration from spontaneous symmetry breaking in the Universe at large scales. This phenomenon is best understood through Ginzburg–Landau theory of phase transitions which we briefly describe. Hereafter, we present elements of spontaneous symmetry breaking in relativistic field theory. We then discuss the “symmetron” scenario-based upon symmetry breaking in the late Universe which is realized by using a specific form of conformal coupling. However, the model is faced with “NO GO” for late-time acceleration due to local gravity constraints. We argue that the problem can be circumvented by using the massless [Formula: see text] theory coupled to massive neutrino matter. As for the early Universe, spontaneous symmetry breaking finds its interesting applications in the study of electroweak phase transition. To this effect, we first discuss in detail the Ginzburg–Landau theory of first-order phase transitions and then apply it to electroweak phase transition including technical discussions on bubble nucleation and sphaleron transitions. We provide a pedagogical exposition of dynamics of electroweak phase transition and emphasize the need to go beyond the standard model of particle physics for addressing the baryogenesis problem. Review ends with a brief discussion on Affleck–Dine mechanism and spontaneous baryogenesis. Appendixes include technical details on essential ingredients of baryogenesis, sphaleron solution, one-loop finite temperature effective potential and dynamics of bubble nucleation.
NEW DEVELOPMENTS IN WEIGHTED n-FOLD TYPE INEQUALITIES VIA DISCRETE GENERALIZED ℏ̂-PROPORTIONAL FRACTIONAL OPERATORS
Authors: RASHID SAIMA , ABOUELMAGD ELBAZ I. , SULTANA SOBIA , CHU YU-MING
Published: September 2021

This study explores some significant consequences of discrete [Formula: see text]-proportional fractional sums [Formula: see text] having an exponential function as a nonlocal kernel. Certain novel weighted versions comprising a group of positive mappings via [Formula: see text] are given. A variety of refinements can be derived by taking into account the extraction of the new estimates and the nabla [Formula: see text]-fractional sums. The suggested technique is a revolutionary formulation of conventional operators that may be used to design efficient mechanism descriptions in short time spans by provoking certain noteworthy properties of chaos theory. Moreover, novel generalizations of the discrete [Formula: see text]-fractional sum can be generated by the specific value of the proportionality index. Derived outcomes and investigation confirm that the proposed plan will offer gains in many modeling and chaotic framework applications.
MODELING AND ANALYSIS OF THE BOOK BORROWING OF STUDENTS IN THE LIBRARY USING PARTIAL DIFFERENTIAL EQUATIONS
Authors: WANG JIANGONG , ZHENG LIN , ALSULAMI HAMED , CHEN JIE
Published: September 2021

The purpose of this paper is to control and judge the big data of students’ learning and living conditions in college education. College students’ book-borrowing data are mined deeply from three aspects, the multi-source preprocessing of students’ borrowing data from university and college libraries, the quantification of students’ book borrowing, and academic performance prediction by learning and book borrowing. The data mining technology analyzes and processes students’ primary information, score information, and book-borrowing information. Students’ book borrowing is modeled and analyzed using the backpropagation neural network (BPNN) algorithm, and the constructed BPNN book-borrowing model’s loss function is optimized based on the partial differential equations. The library access control data and book-borrowing data are used for statistics of the learning behavior frequency. Data such as students’ stay duration in the learning area and attendance rate are input into the analysis model for experiments, and the average absolute error, the mean square error, and the determination coefficient evaluate the prediction results. The results show that as students’ booking borrowing frequency decreases, their scores decrease, and students who often borrow books have strong learning motivations. In Experiment 4, when [Formula: see text] reaches its maximum value, 0.594, the predicted scores by the students’ book-borrowing model have a high correlation with students’ actual scores, indicating that the BPNN algorithm has the best prediction results. The results show that the indicator of students’ book borrowing has significantly improved the model’s prediction performance, and the borrowed book number and book-borrowing frequency are significant in the prediction model construction.
RESEARCH ON STOCHASTIC FUZZY DIFFERENTIAL EQUATIONS IN MULTIPLE BLURRED IMAGE REPAIR MODELS
Authors: ZHAO JIAN , LI JIAMING , ALZAHRANI ABDULLAH K. , JIA JIAN
Published: September 2021

This paper aims to study the processing and repairing methods of blurred images, promote the development of partial differential equations in the field of image processing, and expand the application of stochastic fuzzy differential equations in the field of image processing and repair. This study starts with a typical blurred image repair method. First, a comparative analysis of several common blurred image repair methods including Wiener filtering restoration, inverse filtering restoration, and Lucy–Richardson (L-R) filtering restoration is performed. Second, based on the linear partial differential equation learning model (LPDE), the concept of fuzzy integral is introduced, and an improved stochastic fuzzy partial differential equation learning model (SFCPDE) is proposed. The effect of the learning model before and after improvement on blurred color image processing is compared and analyzed. Finally, based on the total variation (TV) blurred image repair algorithm, an improved TV blurred image repair algorithm is proposed. The comparison and analysis of the repair effects of several blurred image repair algorithms are performed. The results show that there are obvious differences in the repairing methods with or without noise. Inverse filtering works best when there is no noise. L-R filtering has the disadvantage of amplifying noise. Compared with LDPE, the training speed of SFCPDE is significantly improved, and the training error is less than LDPE. The SFCPDE learning model performs better in the processing of blurred color images. After 10 iterations, the improved TV algorithm is significantly better than the TV algorithm and the CDD algorithm in repairing blurred images. The PSNR value of the TV algorithm and the curvature-driven diffusion (CDD) algorithm after 10 iterations corresponds to about 60% of the PSNR value of the improved algorithm. The algorithms and models of stochastic fuzzy partial differential equations proposed in this paper have great application potential in the processing and repair of multiple blurred images.
ADOPTION OF VIBRATION OF FUZZY PARTIAL FRACTIONAL-ORDER EQUATION IN MONITORING OF NOISE BIG DATA UNDER INFINITE IMPULSE RESPONSE DIGITAL FILTER ALGORITHM
Authors: ZHANG LIWEI , ZHANG ZHANLING , ALSHOMRANI ALI SALEH , ZHANG YINGHUI , WANG LISHA
Published: September 2021

In order to study the application of fuzzy partial fractional-order equation based on infinite impulse response (IIR) digital filter in noise big data monitoring, in this study, first, the noise level is calculated through IIR filter to obtain the sound signal sequence after C weight and A weight; the weight equivalent continuous sound level of C, A, and Z is calculated. Then, the vibration condition of fuzzy partial fractional-order equation is studied; the vibration acceleration signal is converted into the vibration velocity and displacement signal; the corresponding effective values are calculated according to the root mean square (RMS), and the noise monitoring platform is designed from hardware and software aspects. Finally, the calibrated sound level value is compared with the standard signal value generated by the digital signal generator; the sequence of sound signal after weight of the original signal, the actual acceleration signal, velocity signal, and displacement signal are calculated; the acceleration signal, velocity signal, and theoretical signal values are compared at different frequencies. The results show that the error between the calculated and calibrated sound level and the standard signal value is very small, and the results of weight counting of C and A are good and meet the requirement of calculation; the error between the measured value of actual acceleration, velocity, and displacement signal and the theoretical value is small, which can realize the accurate acquisition of vibration signal; the error between the actual measured value and the theoretical value of acceleration and velocity at different frequencies is small, about 1% and 10%, respectively. Therefore, the fuzzy partial fractional-order equation based on IIR digital filter can effectively monitor the big noise data.
INFLUENCE OF MUSIC SIGNAL ON THE CHANGES IN BRAIN COGNITION ACTIVITY AND ITS NONLINEAR DYNAMIC SYSTEM
Authors: BAI YE , ALBARAKATI ABDULLAH
Published: September 2021

The nonlinear dynamic characteristics of human brain cognition and music stimulation are introduced based on the negative emotions of college students to explore the influence of music stimulation on human brain cognition activities and analyze the nonlinear dynamic characteristics of electroencephalogram (EEG) signals. First, the correlation between EEG signals and human brain cognition, as well as the nonlinear dynamics of cognition, are explained. The average correlation dimension and the maximum value of the Lyapunov exponent are selected to characterize the nonlinear dynamic characteristics of EEG signals. Second, the power spectrum and EEG coherence are considered, and an emotion assessment algorithm is proposed. Finally, music stimulation is introduced, and its role in adjusting college students’ negative emotions is discussed. Results demonstrate significant differences in the average correlation dimension and the maximum value of the Lyapunov component of the participants in different cognition states under normal physiological conditions. The cognition function state shows chaotic behaviors, explaining the nonlinear dynamic characteristics of EEG signals. After music stimulation, a significant increase in EEG relative power is mostly located in the frontal and temporal regions of the brain. The EEG coherence in the same brain region shows growth changes. After the intervention of music stimulation, the emotion assessment scores of the self-rating anxiety scale, the positive and negative affect schedule, and the self-rating depression scale are reduced, and the entire changing process presents statistical significance. In conclusion, music stimulation intervention can affect human brain cognition activity, playing a positive role in emotion regulation.
RESEARCH ON FUZZY SCHEDULING OF CLOUD COMPUTING TASKS BASED ON HYBRID SEARCH ALGORITHMS AND DIFFERENTIAL EVOLUTION
Authors: JIN MAOZHU , CHEN PENG , MALAIKAH HUNIDA , CHEN CHAO , LIU YIFENG
Published: September 2021

In the process of task scheduling, due to the large search space of resources, it takes a long time to allocate appropriate resources for tasks, which results in the increase of execution time and execution cost of algorithms. For this reason, this paper proposes a cloud computing task fuzzy scheduling strategy based on hybrid search algorithm and differential evolution, which incorporates the classification based on normal distribution and a variety of mutation strategies on the basis of standard differential evolution algorithm. In mutation strategy, the individual difference vector assigns priority to each task and arranges tasks according to resource allocation rules. It improves the slow convergence speed and easy to fall into local optimum of standard difference algorithm, and can effectively solve the task scheduling problem of cloud computing. A cloud computing task scheduling algorithm with time and cost constraints is designed and tested in a simulation environment. The experimental results show that the algorithm can not only shorten the task processing time and reduce the execution cost, but also fully satisfy the users’ actual quality of service requirements by adjusting the weights of time and cost factors.
STABILITY CONTROL OF ENERGY SAVING ELECTRIC VEHICLE USING DYNAMIC NONLINEAR SYSTEM STATE ESTIMATION
Authors: ZHANG LIANCHENG , AI WEIPING , LIU ZHIBIN , ZHANG LONG , TENG XINMIAO
Published: September 2021

The purpose is to study the handling stability and energy saving mode of energy saving electric vehicles. First, the differences between electric vehicles and traditional fuel vehicles are analyzed. Moreover, based on active safety technology, the control stability, handling stability and driving safety of electric vehicles are improved. Then, based on the vehicle dynamics theory and the proposed assumptions, the modular electric vehicle dynamics model is established, including the wheel model, tire model and driver model, which can effectively control the error of the vehicle model and reduce the difficulty and complexity of vehicle modeling. Next, based on the model predictive control theory, the vehicle steady-state control strategy is established, which can predict and control the state of the electric vehicle in the compound condition and improve the control stability of the vehicle. Finally, the vehicle dynamic model and steady-state control system are simulated and tested. The experimental results show that the simulation results of the established dynamic model of the electric vehicle in angle step condition and single line shifting condition are consistent with the results of professional software, which shows that the established dynamic model can accurately describe the dynamic state of electric vehicle and provides the basis for the research of vehicle stability control strategy. Experimental results of the control strategy under the condition of double line shifting show that the designed control strategy can accurately track the state of the system and constrain the deviation in a small range, so as to ensure the driving stability of the electric vehicle. This exploration provides a reference for the study of dynamic analysis and handling stability control strategy of electric vehicles.
ANIMATION DESIGN USING VIRTUAL REALITY MODELING AND FRACTAL MORPHING TECHNOLOGY
Authors: WANG YAN , ZHANG NING , CHEN DONGSHENG , VONGPHANTUSET JIRAWAT
Published: September 2021

The fractal Iterated Function System (IFS) algorithm is studied to simulate swinging plants’ animation effect under natural conditions. First, the collage method and the IFS attractor parameter morphing method render the fractal plant. Second, the IFS attractor parameter morphing is added on this basis. Third, the swinging plant’s animation morphing is achieved by combining the physics and adjusting the morphing parameters. Finally, the effects of leaves transforming into trees and swinging in the wind are simulated. The simulation results verify the effectiveness of the proposed algorithm. Compared with traditional methods, it reduces the time required to render images and overcomes interactive control limitations. The ability of attractors with parameter variables to control images is very stable. Continuous adjustment of parameters can effectively solve image distortion caused by matrix decomposition during fractal morphing. The proposed algorithm provides some references for applying fractal morphing technology in animation design and computer graphics.
THE USE OF NEURAL NETWORK IN DEFENSE AUDIT NONLINEAR DYNAMIC PROCESSING UNDER THE BACKGROUND OF BIG DATA
Authors: SUN HUIHUI , SHI HENGGUI , ALTERAZI HASSAN A. , ZHAN XUEGANG
Published: September 2021

The purpose is to further explore the application effect of the neural network algorithm in defense audit and improve the user information security performance. Based on the relevant theoretical basis of neural network in machine learning, the back propagation neural network (BPNN) algorithm model is constructed and optimized. Moreover, by comparing with the classification and prediction effect of the decision tree method, the application effect of BPNN is further clarified. Through statistical analysis, a total of six risk users are screened out. The test data are classified into non-risk user group and risk user group to study the prediction of classification. The specific results are as follows. The prediction accuracy of non-risk group is 99% by using the BPNN algorithm and that is improved to 99.5% by using the optimized BPNN; for risk group, the prediction accuracy of BPNN is only 50% and that of optimized BPNN is 83.3%. Meanwhile, the prediction error rate of the BPNN algorithm is significantly lower than that of the decision tree algorithm, which further verifies the good application effect of the BPNN algorithm. This study can provide scientific and effective reference for the follow-up research of defense audit.
MULTI-RESPONSE OPTIMIZATION ON ABRASIVE WATERJET MACHINING OF GLASS FIBER REINFORCED PLASTICS USING TAGUCHI METHOD COUPLED WITH TOPSIS
Authors: SHANMUGAM A. , MOHANRAJ T. , KRISHNAMURTHY K. , GUR ALI KAYA
Published: September 2021

This work aims to perform the multi-response optimization for abrasive waterjet machining (AWJM) of glass fiber reinforced plastics (GFRP). The experiments were conducted with AWJM factors like pressure (P), traverse speed (TS), and standoff distance (SOD) at three levels. Taguchi’s L9 orthogonal array (OA) was used to design the experiments. The influence of control factors was evaluated by measuring the surface roughness and taper angle while cutting GFRP. The optimum parameter for an individual response was obtained through Taguchi’s [Formula: see text]/[Formula: see text] and multi-response optimization was performed with TOPSIS. From TOPSIS, the optimal parameter of the pressure of 200 MPa, standoff distance (SOD) of 1.5[Formula: see text]mm, and traverse speed (TS) of 25[Formula: see text]mm/min were found. After optimization, the taper angle was decreased by 1.41%. The influence of cutting variables on the responses was statistically analyzed through analysis of variance. It was observed that the pressure has a significant effect on multi-response characteristics and a contribution of 85.90%. After, AWJM, the surface was examined using SEM analysis and found the deformation and pull-out of fibers.
Nonlinear Postbuckling of Auxetic-Core Sandwich Toroidal Shell Segments with CNT-Reinforced Face Sheets Under External Pressure
Authors: Van Tien Nguyen , Duc Vu Minh , Nam Vu Hoai , Phuong Nguyen Thi , Ho Lanh Si , Dong Dang Thuy , Ly Le Ngoc , Hung Dao , Minh Tran Quang
Published: September 2021

Nonlinear buckling analysis for honeycomb auxetic-core sandwich toroidal shell segments with CNT-reinforced face sheets surrounded by elastic foundations under the radial pressure is presented in this study. The basic equation system of shells is established based on the von Kármán–Donnell nonlinear shell theory, combined with Stein and McElman approximation. Meanwhile, the foundation-shell elastic interaction is simulated by the foundation model based on the Pasternak assumption. The Galerkin procedure is utilized to achieve the pre-buckling and post-buckling responses for the shell, from which the radially critical buckling load is determined. Numerical analysis shows the various influences of auxetic-core layer, CNT-reinforced face sheets, and elastic foundation on the pre-buckling and postbuckling behavior of sandwich shells with CNT reinforced face sheets.
Analysis of Fluctuation Patterns in Emotional States Using Electrodermal Activity Signals and Improved Symbolic Aggregate Approximation
Authors: Veeranki Yedukondala Rao , Ganapathy Nagarajan , Swaminathan Ramakrishnan
Published: September 2021

Analysis of fluctuations in electrodermal activity (EDA) signals is widely preferred for emotion recognition. In this work, an attempt has been made to determine the patterns of fluctuations in EDA signals for various emotional states using improved symbolic aggregate approximation. For this, the EDA is obtained from a publicly available online database. The EDA is decomposed into phasic components and divided into equal segments. Each segment is transformed into a piecewise aggregate approximation (PAA). These approximations are discretized using 11 time-domain features to obtain symbolic sequences. Shannon entropy is extracted from each PAA-based symbolic sequence using varied symbol size [Formula: see text] and window length [Formula: see text]. Three machine-learning algorithms, namely Naive Bayes, support vector machine and rotation forest, are used for the classification. The results show that the proposed approach is able to determine the patterns of fluctuations for various emotional states in EDA signals. PAA features, namely maximum amplitude and chaos, significantly identify the subtle fluctuations in EDA and transforms them in symbolic sequences. The optimal values of [Formula: see text] and [Formula: see text] yield the highest performance. The rotation forest is accurate (F-[Formula: see text] and 60.02% for arousal and valence dimensions) in classifying various emotional states. The proposed approach can capture the patterns of fluctuations for varied-length signals. Particularly, the support vector machine yields the highest performance for a lower length of signals. Thus, it appears that the proposed method might be utilized to analyze various emotional states in both normal and clinical settings.
The algebraic classification of nilpotent algebras
Authors: Kaygorodov Ivan , Khrypchenko Mykola , Lopes Samuel A.
Published: September 2021

We give the complete algebraic classification of all complex 4-dimensional nilpotent algebras. The final list has 234 (parametric families of) isomorphism classes of algebras, 66 of which are new in the literature.
CASA: An Alternative Smartphone-Based ADAS
Authors: Hina Manolo Dulva , Guan Hongyu , Soukane Assia , Ramdane-Cherif Amar
Published: September 2021

Advanced driving assistance system (ADAS) is an electronic system that helps the driver navigate roads safely. A typical ADAS, however, is suited to specific brands of vehicle and, due to proprietary restrictions, has non-extendable features. Project CASA is an alternative, low-cost generic ADAS. It is an app deployable on smartphone or tablet. The real-time data needed by the app to make sense of its environment are stored in the vehicle or on the cloud, and are accessible as web services. They are used to determine the current driving context, and, if needed, decide actions to prevent an accident or keep road navigation safe. Project CASA is an undertaking of a consortium of industrial and academic partners. A use case scenario is tested in the laboratory (virtual) and on the road (actual) to validate the appropriateness of CASA. It is a contribution to safe driving. CASA’s contribution also lies in its approach in the semantic modeling of the context of the environment, the vehicle and the driver, and on the modeling of rules for fusion of data and fission process yielding an action to be implemented. In addition, CASA proposes a secured means of transmitting data using light, via light fidelity (LiFi), itself an alternative means of wireless vehicle–smartphone communication.
Quality of Design Documentation in the Construction Industry: A Review Using Meta-Synthesis Approach
Authors: Agbaxode Peter Dodzi Kwasi , Dlamini Sitsabo , Saghatforoush Ehsan
Published: September 2021

A meta-synthesis approach was used to identify, synthesize, and categorize appropriately factors in the literature that contribute to design documentation quality. The categories include factors on quality attributes, quality indicators, and quality-influencing factors. Findings indicate that the quality of design documentation in practice is unsatisfactory, therefore, there is a need for collaboration between the owner, designers, and end-users to improve design documentation quality. The results offer pragmatic data on design documentation quality in the construction industry between the years 1992 and 2019. However, further research on the significant impacts of poor-quality design documentation on construction projects is recommended.
Conceptual Bases of Innovation Studies: An Analysis from the Micro- and Meso-Perspectives
Authors: Manjarrés-Henríquez Liney , Chams-Anturi Odette , Hervas-Oliver Jose Luis , Vega-Jurado Jaider
Published: September 2021

This paper analyzes the conceptual bases of innovation studies at the micro- and meso-levels of analysis. The analysis is carried out from a theoretical perspective and highlights the need to study the business unit (micro-unit) and the regional/local scope (meso-unit) as an indissoluble whole in which value creation and competitive advantages are reinforced and sustained, thereby creating winning regions. Likewise, this paper helps us understand the systemic aspect, nature, and dynamics of innovation, and the influence of the historical, social, economic, and technological contexts that affect it. Finally, this paper highlights the study of the micro- and meso-areas of innovation, including their main schools and research.
Effect of Graphene Composite on Biological Egg Protein Resistance Switching Memory Properties
Authors: Lu Junguo , Sun Yanmei , Wen Dianzhong
Published: September 2021

In this work, a resistive switching memory device was fabricated based on egg protein, a natural biomaterial. The effect of graphene composite on the resistive switching characteristics of the device was investigated. The experimental results show that both pure egg protein and graphene composite devices exhibit bipolar nonvolatile resistive conversion properties. Both devices have good data retention capability. Furthermore, the composite of graphene can effectively improve the device endurance and the consistency of the on-state current distribution of the device. Based on the theory of capture and de-capture of charge carrier, the mechanism of resistive switching is analyzed.
High-resolution polarization-sensitive optical coherence tomography and optical coherence tomography angiography for zebrafish skin imaging
Authors: Yang Di , Yuan Zhuoqun , Yang Zihan , Hu Muyun , Liang Yanmei
Published: September 2021

Zebrafish is an important animal model, which is used to study development, pathology, and genetic research. The zebrafish skin model is widely used in cutaneous research, and angiogenesis is critical for cutaneous wound healing. However, limited by the penetration depth, the available optical methods are difficult to describe the internal skin structure and the connection of blood vessels between the skin and subcutaneous tissue. By a homemade high-resolution polarization-sensitive optical coherence tomography (PS-OCT) system, we imaged the polarization contrast of zebrafish skin and the zebrafish skin vasculature with optical coherence tomography angiography (OCTA). Based on these OCT images, the spatial distribution of the zebrafish skin vasculature was described. Furthermore, we monitored the healing process of zebrafish cutaneous wounds. We think the high-resolution PS-OCT system will be a promising tool in studying cutaneous models of zebrafish.
CHINESE FOREIGN TRANSACTIONS: WHAT GETS THEM IN TROUBLE?
Authors: SINGH VIKKRAM
Published: September 2021

This study offers an understanding of the Chinese enterprises’ internationalization strategies using transaction data. A logit model estimates the determinants of Chinese transactions in trouble due to regulatory hurdles in understanding the prevailing trends. The results identify factors such as transaction size, sectors deemed national security, domestic competition (for the SOEs) and divergence in the perception of Chinese political influence. This study provides novel insights into the risks Chinese policymakers and firms need to recognize and formulate policies to prevent and mitigate them.
Exercise prevents whole body type 2 diabetes risk factors better than estradiol replacement in rats
Authors: Fritsch Luke J. , McCaulley Skylar J. , Johnson Colton R. , Lawson Nicholaus J. , Gorres-Martens Brittany K.
Published: September 2021

Exercise provides better protection than estradiol against type 2 diabetes when considering whole body measurements including adipose tissue weight, blood glucose levels, and serum insulin levels, which may be due to increased noninsulin-stimulated pAkt in the adipose tissue. However, at the cellular level, several molecular changes in fat metabolism and fat storage occurred in the liver and adipose tissue with estradiol treatment.