bluuebunny/crossref_metadata_2025 huggingface dataset:
A probabilistic numerical method for a class of mean field games
Authors: Sahar Ben Aziza , Salwa Toumi
Published: November 2021

The Mean Field Games PDEs system is at the heart of the Mean Field Games theory initiated by [J.-M. Lasry and P.-L. Lions, Jeux à champ moyen. I–le cas stationnaire, C. R. Math. 343 (2006) 619–625; J.-M. Lasry and P.-L. Lions, Jeux à champ moyen. II–horizon fini et contrôle optimal, C. R. Math. 343 (2006) 679–684; J.-M. Lasry and P.-L. Lions, Mean field games, Jpn. J. Math. 2 (2007) 229–260] which constitutes a seminal contribution to the modeling and analysis of games with a large number of players. We propose here a numerical method of resolution of such systems based on the construction of a discrete mean field game where the controlled state-variable is a Markov chain approximating the controlled stochastic differential equation [H. Kushner and P. G. Dupuis, Numerical Methods for Stochastic Control Problems in Continuous Time, Stochastic Modeling and Applied Probability, Vol. 24 (Springer Science & Business Media, 2013)]. In particular, existence and uniqueness properties of the discrete MFG are investigated with convergence results under adequate assumptions.
Optimal Trained Deep Neural Network for Localization in Wireless Sensor Network
Authors: Kagi Shivakumar , Mathapati Basavaraj S.
Published: November 2021

Wireless Sensor Network (WSN) localization has been bloomed as an active area of research in this era. In fact, WSN differs from the traditional network in diverse aspects and therefore requires novel algorithms for addressing specific challenges like the identification of the unknown node location in hazardous environments. In this paper, a new localization model is introduced by the range-based localization approach via an optimization-assisted deep learning model. The proposed work undergoes two major phases: (a) training phase and (b) localization phase. The trained Deep Neural Network (DNN) with the measured distance-based features like the “Angle of Arrival (AoA) and Received Signal Strength Indicator (RSSI)” find out the place of the unknown node more precise. Further, to enhance the localization accuracy, the weight of DNN is tuned via a novel hybrid optimization algorithm named as Lion-Assisted Firefly Algorithm (LAFA) model. The proposed LAFA is the concept of both the Lion Algorithm (LA) and Firefly Algorithm (FF). Finally, the evaluation of the presented work is done in terms of error measures.
A Precise Time-Domain Expanding Crack-Tip Element for 3D Planar and Axial Dynamic Cracks
Authors: Hu Xiaofei , Li Jiaxi , Chen Weihua , Bui Tinh Quoc , Yao Weian
Published: November 2021

Unlike two-dimensional (2D) crack problems of which the analytical symplectic eigensolutions are available, the analytical symplectic solution of three-dimensional (3D) planar and axial dynamic cracks has not been derived yet. We thus propose a trial displacement field for the 3D planar and axial crack problem using the existing solutions of the 2D crack problems and construct a new crack-tip element on that basis. The recently developed precise integration scheme for 3D planar and axial cracks under dynamic loading (Hu, X. F., Chen, W. H., Zhang, P. and Yao, W. A. [2021] “An explicit crack-tip element for stationary dynamic cracks,” Theor. Appl. Fract. Mech. 112, 102886) is further enhanced. A precise time-domain expanding algorithm is employed for the temporal discretization. All the field variables are expanded using a series expansion scheme, and the dynamic problem is then transformed into a series of quasi-static elastic problems. All the sub-elastic problems are related in a recursive manner. Taking advantage of a smart automatic truncating approach, the number of expanding terms can be determined automatically subjected to a prescribed error tolerance. A few numerical examples are investigated to demonstrate the developed method, numerical solutions indicate that the time dependent stress intensity factors (SIFs) can be calculated in a simple way and the present method has higher efficiency than the recently developed time integration scheme while having comparable accuracy.
Classical and quantum cosmology in f(T)-gravity theory: A Noether symmetry approach
Authors: Bhaumik Roshni , Dutta Sourav , Chakraborty Subenoy
Published: November 2021

In the framework of [Formula: see text]-gravity theory, classical and quantum cosmology has been studied in this work for Friedmann Lemaitre Robertson Walker Metric (FLRW) space-time model. The Noether symmetry, a point-like symmetry of the Lagrangian, is used to the physical system and a specific functional form of [Formula: see text] is determined. A point transformation in the 2D augmented space restricts one of the variables to be cyclic so that the Lagrangian as well as the field equations are simplified so that they are solvable. Lastly, for quantum cosmology, the WD equation is constructed and a possible solution has been evaluated.
Sensitivity of Rainfall to Cumulus Parameterization Schemes from a WRF Model over the City of Douala in Cameroon
Authors: Tanessong Roméo S. , Mbienda A. J. Komkoua , Guenang G. M. , Kaissassou S. , Djiotang Lucie A. Tchotchou , Vondou Derbetini A. , Lekina H. Bita , Balla R. H. Mvondo , Pokam Wilfried M. , Igri P. Moudi
Published: November 2021

With the recurrence of extreme weather events in Central Africa, it becomes imperative to provide high-resolution forecasts for better decision-making by the Early warning systems. This study assesses the performance of the Weather Research and Forecasting (WRF) model to simulate heavy rainfall that affected the city of Douala in Cameroon during 19–21 August 2020. The WRF model is configured with two domains with horizontal resolutions of 15 and 5[Formula: see text]km, 33 vertical levels using eight cumulus parameterization schemes (CPSs). The WRF model performance is assessed by investigating the agreement between simulations and observations. Categorical and deterministic statistics are used, which include the probability of detection (POD), the success ratio (SR), the equitable threat score (ETS), the pattern correlation coefficient (PCC), the root mean square error (RMSE), the mean absolute error (MAE), and the BIAS. K-index is finally used to assess the capacity of the WRF model to predict the instability of the atmosphere in Douala during the above-mentioned period. It is found that (1) The POD, SR and ETS decrease when the threshold increases, showing the difficulty of the WRF model to predict and locate heavy rainfall events; (2) There are important differences in the rainfall area simulated by the eight CPSs; (3) The BIAS is negative for the eight CPSs, implying that all of the CPSs tested underestimate the rainfall over the study area; (4) Some of the CPSs have good agreement with observations, especially the new modifed Tiedtke and the Betts–Miller–Janjic schemes; (5) The K-index, an atmospheric instability index, is well predicted by the eight CPSs tested in this work. Overall, the WRF model exhibits a strong ability for rainfall simulation in the study area. The results point out that heavy rainfall events in tropical areas are very sensitive to CPSs and study domain. Therefore, sensitivity tests studies should be multiplied in order to identify most suitable CPSs for a given area.
Passenger Mobility Prediction via Representation Learning for Dynamic Directed and Weighted Graphs
Authors: Wang Yuandong , Yin Hongzhi , Chen Tong , Liu Chunyang , Wang Ben , Wo Tianyu , Xu Jie
Published: November 2021

In recent years, ride-hailing services have been increasingly prevalent, as they provide huge convenience for passengers. As a fundamental problem, the timely prediction of passenger demands in different regions is vital for effective traffic flow control and route planning. As both spatial and temporal patterns are indispensable passenger demand prediction, relevant research has evolved from pure time series to graph-structured data for modeling historical passenger demand data, where a snapshot graph is constructed for each time slot by connecting region nodes via different relational edges (origin-destination relationship, geographical distance, etc.). Consequently, the spatiotemporal passenger demand records naturally carry dynamic patterns in the constructed graphs, where the edges also encode important information about the directions and volume (i.e., weights) of passenger demands between two connected regions. aspects in the graph-structure data. representation for DDW is the key to solve the prediction problem. However, existing graph-based solutions fail to simultaneously consider those three crucial aspects of dynamic, directed, and weighted graphs, leading to limited expressiveness when learning graph representations for passenger demand prediction. Therefore, we propose a novel spatiotemporal graph attention network, namely
Gallat
(

G

raph prediction with

all
at

tention) as a solution. In Gallat, by comprehensively incorporating those three intrinsic properties of dynamic directed and weighted graphs, we build three attention layers to fully capture the spatiotemporal dependencies among different regions across all historical time slots. Moreover, the model employs a subtask to conduct pretraining so that it can obtain accurate results more quickly. We evaluate the proposed model on real-world datasets, and our experimental results demonstrate that Gallat outperforms the state-of-the-art approaches.
Origin-Aware Location Prediction Based on Historical Vehicle Trajectories
Authors: Chen Meng , Liu Qingjie , Huang Weiming , Zhang Teng , Zuo Yixuan , Yu Xiaohui
Published: November 2021

Next location prediction is of great importance for many location-based applications and provides essential intelligence to various businesses. In previous studies, a common approach to next location prediction is to learn the sequential transitions with massive historical trajectories based on conditional probability. Nevertheless, due to the time and space complexity, these methods (e.g., Markov models) only utilize the just passed locations to predict next locations, neglecting earlier passed locations in the trajectory. In this work, we seek to enhance the prediction performance by incorporating the travel time from all the passed locations in the query trajectory to each candidate next location. To this end, we propose a novel prediction method, namely the Travel Time Difference Model, which exploits the difference between the shortest travel time and the actual travel time to predict next locations. Moreover, we integrate the Travel Time Difference Model with a Sequential and Temporal Predictor to yield a joint model. The joint prediction model integrates local sequential transitions, temporal regularity, and global travel time information in the trajectory for the next location prediction problem. We have conducted extensive experiments on two real-world datasets: the vehicle passage record data and the taxi trajectory data. The experimental results demonstrate significant improvements in prediction accuracy over baseline methods.
geoTangle
: Interactive Design of Geodesic Tangle Patterns on Surfaces
Authors: Nazzaro Giacomo , Puppo Enrico , Pellacini Fabio
Published: November 2021

Tangles are complex patterns, which are often used to decorate the surface of real-world artisanal objects. They consist of arrangements of simple shapes organized into nested hierarchies, obtained by recursively splitting regions to add progressively finer details. In this article, we show that 3D digital shapes can be decorated with tangles by working interactively in the intrinsic metric of the surface. Our tangles are generated by the recursive application of only four operators, which are derived from tracing the isolines or the integral curves of geodesics fields generated from selected seeds on the surface. Based on this formulation, we present an interactive application that lets designers model complex recursive patterns directly on the object surface without relying on parametrization. We reach interactive speed on meshes of a few million triangles by relying on an efficient approximate graph-based geodesic solver.
DiffPD: Differentiable Projective Dynamics
Authors: Du Tao , Wu Kui , Ma Pingchuan , Wah Sebastien , Spielberg Andrew , Rus Daniela , Matusik Wojciech
Published: November 2021

We present a novel, fast differentiable simulator for soft-body learning and control applications. Existing differentiable soft-body simulators can be classified into two categories based on their time integration methods: Simulators using explicit timestepping schemes require tiny timesteps to avoid numerical instabilities in gradient computation, and simulators using implicit time integration typically compute gradients by employing the adjoint method and solving the expensive linearized dynamics. Inspired by
Projective Dynamics
(
PD
), we present
Differentiable Projective Dynamics
(
DiffPD
), an efficient differentiable soft-body simulator based on PD with implicit time integration. The key idea in DiffPD is to speed up backpropagation by exploiting the prefactorized Cholesky decomposition in forward PD simulation. In terms of contact handling, DiffPD supports two types of contacts: a penalty-based model describing contact and friction forces and a complementarity-based model enforcing non-penetration conditions and static friction. We evaluate the performance of DiffPD and observe it is 4–19 times faster compared with the standard Newton’s method in various applications including system identification, inverse design problems, trajectory optimization, and closed-loop control. We also apply DiffPD in a
reality-to-simulation
(
real-to-sim
) example with contact and collisions and show its capability of reconstructing a digital twin of real-world scenes.
Research of Inonotus obliquus Oligosaccharide in Prevention of Hyperlipidemia
Authors: Wu Dawei , Zhang Yanrong , Wang Dawei , Liu Tingting , Zhang Shanshan , Yang Chenhe
Published: November 2021

In this study, hot water was used to extract Inonotus obliquus oligosaccharide. DEAE-cellulose and Sepharose G-200 were used to purify Inonotus obliquus oligosaccharide. Inonotus obliquus oligosaccharide IOP-2A was obtained. Its molecular weight Mw is about 1000 Da. The monosaccharide composition and molar ratio were glucose : xylose : galactose : mannose = 54.1 : 13.6 : 13.2 : 6.7. In addition, it also contains a small amount of galactose, gluconic acid, rhamnose, and fucose. IOP-2A contained mainly β-glycosidic bonds. Among them, 1,4-glycosidic bonds accounted for 9.2%, and 1,6-glycosidic bonds accounted for 85.1%. Oligosaccharide macromolecules formed a layered structure. Mouse experiments showed that IOP-2A had the function of preventing hyperlipidemia. At the same time, IOP-2A had a certain protective effect on the liver and kidney. The mechanism of IOP-2A in preventing hyperlipidemia was obtained from the perspective of mouse intestinal flora.
Solitary Nitric Oxide Signaling Mediates Mild Stress-Induced Anxiety and Norepinephrine Release in the Bed Nucleus of the Stria Terminalis during Protracted Ethanol Withdrawal
Authors: Zhao Zhenglin , Kim Sang Chan , Jiao Yu , Wang Yefu , Lee Bong Hyo , Kim Hee Young , Lee Chul Won , Yang Chae Ha , Zhao Rongjie
Published: November 2021

Ethanol withdrawal (EtOHW) alters the pattern of neurohormonal and behavioral response toward internal and external stimuli, which mediates relapse to alcohol use even after a long period of abstinence. Increased noradrenergic signaling from the nucleus tractus solitarius (NTS) to the bed nucleus of the stria terminalis (BNST) during EtOHW underlies withdrawal-induced anxiety, while nitric oxide synthase (NOS) inhibitors injected into the periaqueductal area attenuate EtOHW-induced anxiety. Therefore, this study investigated the involvement of NOS within the NTS in anxiety and increased norepinephrine (NE) release in the BNST during protracted EtOHW in rats exposed to a mild stress. Rats were intraperitoneally administered 3 g/kg/day EtOH for 21 days followed by 28 days of withdrawal, and on the 28th day of withdrawal, the rats were subjected to restraint stress for 7 minutes. The elevated plus maze test was employed to evaluate anxiety-like behavior in rats, and in vivo microdialysis was used to measure the extracellular NE level in the BNST. In elevated plus maze tests, EtOHW rats but not EtOH-naive rats exhibited anxiety-like behavior when challenged with 7-minute mild restraint stress, which was, respectively, mitigated by prior intra-NTS infusion of the nitric oxide scavenger 2-(4-carboxyphenyl)-4,4,5,5-tetramethylimidazoline-1-oxyl-3-oxide (carboxy-PTIO), nonselective NOS inhibitor NG-nitro-L-arginine methyl ester (L-NAME), or selective neuronal NOS (nNOS) inhibitor 7-nitroindazole (7-NI). Each of these agents also decreased the plasma corticosterone levels in EtOHW rats. In in vivo microdialysis, prior intra-NTS infusion of carboxy-PTIO, L-NAME, or 7-NI attenuated the mild stress-induced NE release in the BNST of EtOHW rats. Additionally, EtOHW rats showed increased solitary nNOS gene and protein expression. Moreover, the anxiolytic effect of intra-NTS administration of 7-NI was abolished by subsequent intra-NTS administration of sodium nitroprusside. These results suggest that elevation of solitary nitric oxide signaling derived from nNOS mediates stress-precipitated anxiety and norepinephrine release in the BNST during protracted EtOHW.
Evaluation of Ecological Water Consumption in Yanhe River Basin Based on Big Data
Authors: Guo Ting , Yu Huiying
Published: November 2021

Starting from the main eco‐environmental problems faced by water environment, taking Yanhe River Basin as an example, this paper discusses the theoretical connotation and evaluation calculation method of eco‐environmental water consumption. In order to study the eco‐environmental water consumption of Yanhe River Basin, a runoff driving factor mining method based on big data analysis is established in this paper. Aiming at the problem that the statistical law and genetic law of runoff change frequently in changing environment, the mining technology method of runoff key driving factors is proposed by combining traditional methods with big data analysis. The characteristic factors that have no significant impact on runoff change are removed, the implicit characteristic factors affecting runoff change are extracted, the driving relationship of hydrological, meteorological, and vegetation characteristic factors on ecological water consumption change is identified, and the key driving factors of ecological water consumption change are extracted, which lays a data foundation for ecological water consumption prediction based on machine learning. The economic water consumption based on eco‐environmental water consumption in Yanhe River Basin in the future is predicted (including water demand in three aspects of industry, agriculture, and life); that is, the prediction is to meet the economic water demand on the basis of ensuring that the water consumption of ecological environment will not be occupied, which can effectively ensure the improvement of ecological environment function in Yanhe River Basin and is conducive to the sustainable utilization of water resources in Yanhe River Basin. The research is only based on a small watershed such as Yanhe River Basin, and the purpose of the research is to provide a reference for ecological environment protection and sustainable utilization of water resources in the Loess Plateau, even in the arid, semiarid, and semihumid areas of North China.
PIK3R1, SPNB2, and CRYAB as Potential Biomarkers for Patients with Diabetes and Developing Acute Myocardial Infarction
Authors: Zheng Yue , Lang Yuheng , Qi Zhenchang , Gao Wenqing , Hu Xiaomin , Li Tong
Published: November 2021

Background. Young patients with type 2 diabetes mellitus (DM) and acute myocardial infarction (AMI) have high long-term all-cause and cardiovascular mortality rates. We aimed to investigate the differentially expressed genes (DEGs) that might be potential targets for DM patients with AMI. Methods. Gene datasets GSE775, GSE19322, and GSE97494 were meta-analyzed to obtain DEGs of the left ventricle myocardium in infarcted mice. Gene datasets including GSE3313, GSE10617, and GSE136948 were meta-analyzed to identify DEGs in diabetes mice. A Venn diagram was used to obtain the overlapping DEGs. KEGG and GO pathway analyses were performed, and hub genes were obtained. Pivotal miRNAs were predicted and validated using the miRNA dataset in GSE114695. To investigate the cardiac function of the screened genes, a MI mouse model was constructed; echocardiogram, qPCR, and ELISA of hub genes were performed; ELISA of hub genes in human blood samples was also utilized. Results. A total of 67 DEGs were identified, which may be potential biomarkers for patients with DM and AMI. GO and KEGG pathway analyses were performed, which were mainly enriched in response to organic cyclic compound and PI3K-Akt signaling pathway. The expression of PIK3R1 and SPNB2 increased in the MI group and was negatively correlated to left ventricular ejection fraction (LVEF), whereas that of CRYAB decreased and was positively correlated to LVEF. Patients with high CRYAB expression demonstrated a short hospital stay and the area under the curves of the three protein levels before and after treatment were 0.964, 0.982, and 0.918, suggesting that PIK3R1, SPNB2, and CRYAB may be diagnostic and prognostic biomarkers for the diabetes patients with AMI. Conclusion. The screened hub genes, PIK3R1, SPNB2, and CRYAB, were validated as credible molecular biomarkers and may provide a novel therapy for diabetic cardiac diseases with increased proteotoxic stress.
Quality Assessment of Five Different Brands of Chyawanprash Marketed in Nepal
Authors: Aryal Babita , Paudyal Prem , Ghimire Krishma , Sharma Sabitra , Khanal Dharma Prasad
Published: November 2021

Chyawanprash, a polyherbal formulation, is popular for its nutritional value and is widely used as a tonic, rejuvenator, immunomodulator, and enhancer. This study aimed to evaluate the quality profile of five different brands of Chyawanprash marketed in Nepal. Proximate analysis, estimation of heavy metal, physicochemical analysis, determination of microbial contamination, estimation of vitamin C, and detection of heavy metals were carried out to assess the therapeutical values and compare the quality control parameters. All the analyses were carried out based on standard procedures. The data analysis revealed that the presence of moisture content (11.83 ± 1.61%–19.90 ± 0.92%), ash content (1.2–2.3%), energy (316.12 ± 1.96–363 ± 11.90 Kcal/100g), acid-insoluble ash content (0.12 ± 0.05–1.47 ± 0.26%), pH (5.12–5.34), vitamin C (9.92–28.58 mg/100g), and heavy metals was not detected in ppm in any of the brands. Chyawanprash holds tremendous amounts of nutritional value suitable for treating different types of illness. Thus, stringent quality control parameters should be implemented from the government level for product consistency and to reduce variation among different brands.
A Short-Term Solar Photovoltaic Power Optimized Prediction Interval Model Based on FOS-ELM Algorithm
Authors: Ramkumar G. , Sahoo Satyajeet , Amirthalakshmi T. M. , Ramesh S. , Prabu R. Thandaiah , Kasirajan Kasipandian , Samrot Antony V. , Ranjith A.
Published: November 2021

Solar energy conversion efficiency has improved by the advancement technology of photovoltaic (PV) and the involvement of administrations worldwide. However, environmental conditions influence PV power output, resulting in randomness and intermittency. These characteristics may be harmful to the power scheme. As a conclusion, precise and timely power forecast information is essential for the power networks to engage solar energy. To lessen the negative impact of PV electricity usage, the offered short-term solar photovoltaic (PV) power estimate design is based on an online sequential extreme learning machine with a forgetting mechanism (FOS-ELM) under this study. This approach can replace existing knowledge with new information on a continuous basis. The variance of model uncertainty is computed in the first stage by using a learning algorithm to provide predictable PV power estimations. Stage two entails creating a one-of-a-kind PI based on cost function to enhance the ELM limitations and quantify noise uncertainty in respect of variance. As per findings, this approach does have the benefits of short training duration and better reliability. This technique can assist the energy dispatching unit list producing strategies while also providing temporal and spatial compensation and integrated power regulation, which are crucial for the stability and security of energy systems and also their continuous optimization.
The Impact of Employee Stock Ownership Plan on the Cost of Equity Capital: Evidence from China
Authors: Cheng Fu , Ji Shanshan
Published: November 2021

Due to the immaturity of bond market and the defects of internal governance structure, Chinese-listed companies have a strong preference for equity financing. How to reduce the cost of equity capital is particularly important for Chinese-listed companies. As an equity incentive system, employee stock ownership plan (ESOP) can reduce the agency conflicts among shareholders, executives, and employees to some extent. These reduced conflicts will, in an efficient capital market, be reflected in a lower cost of equity capital. This paper investigates whether the implementation of ESOP in a new era in China affects the cost of equity capital and further explores whether the impact of ESOP on the cost of equity capital is affected by the ownership nature, the firm size, and the contract design of ESOP. The results show that the implementation of ESOP reduces the cost of equity capital of enterprises. Compared with state-owned enterprises and large enterprises, the implementation of ESOP is more likely to reduce the cost of equity capital in non-state-owned enterprises and small enterprises. Furthermore, the reduction effect of ESOP on the cost of equity capital is influenced by the contract design of ESOP. This study not only enriches the literature on the relationship between employee stock ownership and the cost of equity capital but also provides a new idea for listed companies to reduce the cost of equity financing.
First-Principles Investigation of Structural, Electronic, and Room Temperature Ferromagnetism in Si-Doped Monolayer BN
Authors: Ajaybu Ahemedin Abedea , Hailemariam Sintayehu Mekonnen
Published: November 2021

We performed spin-polarized density functional theory (DFT) to investigate the structural, electronic, and magnetic properties of silicon- (Si-) doped monolayer boron nitride (BN). The present study revealed that structural parameters like bond length, bond angle, and lattice parameters increase as Si-doped in the B site of monolayer BN. However, the bandgap of monolayer BN is reduced in the presence of the Si dopant. Moreover, the obtained magnetic moment and analysis of the total density of states (TDOS) show that Si-doped monolayer BN displays ferromagnetism. The calculated ferromagnetic transition temperature (Tc) value for Si concentration of 12.5% is 476 K which exceeds room temperature. The findings are avenues to enhance the application of monolayer BN for spintronics.
Microneedling in Combination with Topical Pimecrolimus 1% versus Topical Pimecrolimus 1% for the Treatment of Refractory Stable Vitiligo: A Randomized Clinical Trial
Authors: Iraji Fariba , Asilian Ali , Talebzadeh Zahra , Saber Mina , Mokhtari Fatemeh , Siadat Amirhossein , Hosseini Seyed Mohsen
Published: November 2021

Objective. Vitiligo is a common, autoimmune disease that results in the destruction of the melanocytes and manifests as depigmented macules on various areas of the skin. Numerous treatment options have been proposed for vitiligo. The purpose of this study was to compare the efficacy of microneedling plus topical pimecrolimus 1% versus the sole use of topical pimecrolimus 1% for the treatment of vitiligo. Methods. This clinical trial was conducted on 30 skin lesions on 15 Al-Zahra hospital patients. Each patient had two similar lesions in the limb area, and each lesion was considered a separate treatment group. The left or right side of the patient’s lesion was randomly assigned to receive microneedling plus topical pimecrolimus for three months, while the other side received only topical pimecrolimus 1%. As part of the follow-up, digital photography was taken at the baseline and biweekly for three months after treatment and six months’ follow-up. The following methods were used to evaluate the results: DLQI questionnaires, patient satisfaction questionnaires, and two independent dermatologists comparing the improvement rate for each group. Results. Topical pimecrolimus 1% treatment led to unsatisfactory results, whereas the combination of microneedling and topical pimecrolimus1% treatment produced a more favorable overall outcome (

P
<
0.001

). Conclusion. This study established that combination therapy results in more significant patient improvement. Additionally, one patient experienced mild skin irritation as a side effect of topical pimecrolimus.
Fractional Analysis of MHD Boundary Layer Flow over a Stretching Sheet in Porous Medium: A New Stochastic Method
Authors: Khan Imran , Ullah Hakeem , AlSalman Hussain , Fiza Mehreen , Islam Saeed , Shoaib Muhammad , Raja Muhammad Asif Zahoor , Gumaei Abdu , Ikhlaq Farkhanda
Published: November 2021

In this article, an effective computing approach is presented by exploiting the power of Levenberg-Marquardt scheme (LMS) in a backpropagation learning task of artificial neural network (ANN). It is proposed for solving the magnetohydrodynamics (MHD) fractional flow of boundary layer over a porous stretching sheet (MHDFF BLPSS) problem. A dataset obtained by the fractional optimal homotopy asymptotic (FOHA) method is created as a simulated data simple for training (TR), validation (VD), and testing (TS) the proposed approach. The experiments are conducted by computing the results of mean-square-error (MSE), regression analysis (RA), absolute error (AE), and histogram error (HE) measures on the created dataset of FOHA solution. During the learning task, the parameters of trained model are adjusted by the efficacy of ANN backpropagation with the LMS (ANN-BLMS) approach. The ANN-BLMS performance of the modeled problem is verified by attaining the best convergence and attractive numerical results of evaluation measures. The experimental results show that the approach is effective for finding a solution of MHDFF BLPSS problem.
Research on Fundus Image Mosaic Method Based on Genetic Algorithm
Authors: Wang Shuang
Published: November 2021

Retinal image mosaic is the key to detect common diseases, and the existing image mosaic methods are difficult to solve the problems of low contrast of fundus images and geometric distortion between images in different fields of view. To solve the problem of noise in retinal fundus images, an image mosaic algorithm based on the genetic algorithm was proposed. Firstly, a series of morphological pretreatment was performed on the fundus images. Then, the vascular network is extracted by obtaining the maximum entropy of the image to determine the threshold value. The similarity of the image to be spliced is a feature, and the genetic algorithm is used to solve the optimal parameters to achieve the maximum similarity. By smoothing the image, a clear image with minimum noise is obtained. Experimental results show that the proposed algorithm can effectively realize the image mosaic of the fundus. The method proposed in this paper can provide support for high-precision automatic stitching of multiple single-mode color fundus images.
Influencing Factors of Academic Entrepreneurship-Based Knowledge Mapping: A Comparative Analysis of Chinese and International Literature
Authors: Ziyu Liu , Lixia Zhao
Published: November 2021

In order to analyze the hot frontier of academic entrepreneurship and study the influencing factors of academic entrepreneurship, By using citespace V information visualization software and content analysis, 32 papers in CNKI (China National Knowledge Infrastructure) database are retrieved which are indexed by CSSCI (Chinese Social Sciences Citation Index), and 66 papers are retrieved in Web of Science database which are indexed by SSCI (Social Sciences Citation Index); the Chinese and international sample papers were compared and analyzed, and a comparative analysis between Chinese and international papers was made. The results show that the influencing factors of academic entrepreneurship are divided into personal, environmental, and organizational factors. There are many researches on personal factors in China and external factors such as environment, organization, and system in international countries. Chinese research tends to the macrolevel, while international research pays more attention to the microlevel. The research frontier at home and abroad is the research of academic entrepreneurial process at microlevel. Finally, some suggestions are given to academic entrepreneurship: academic exchanges should be strengthened, more support platforms should be set up in university institutions, and the state should introduce incentive policies to enhance the strength and level of academic entrepreneurship.
Intelligent Informatization Early Warning Analysis of Agricultural Economy Based on Support Vector Sequential Regression Model
Authors: Yang Ying
Published: November 2021

The development of science and technology has laid a solid foundation for the economic informatization of agriculture, and at the same time it brought technical guarantee for the development of agriculture, and the development of agriculture has provided an important material foundation for the development of science and technology. How to identify and deeply study agricultural economic informatization, give early warning to risk information, and ensure the steady development of the whole industry has become a key issue in the application of Internet technology in the field of agricultural development. This paper studies the present situation of agricultural economy informatization development process and applies support vector machine to forecast regional economic development level. The warning limit of agricultural economic growth rate is obtained on the basis of warning situation and warning indicator in early warning index system. The economic early warning model is established based on the support vector sequential regression method, and then the data is trained by MATLAB software to verify the rationality of the early warning model, and the accuracy and corresponding error of the model are given. Experimental results show that the prediction accuracy is 99.3%, the error is less than 0.05, and the prediction effect is relatively ideal, for agricultural economic intelligence information to provide accurate warning and agricultural economic research agricultural commercial development to provide support.
Study on Time Factors in the Smoke Control Process of Highway Tunnel Fires
Authors: Zheng Guoping , Xue Dapeng , Zhuang Yizhou , Zhu Yusheng
Published: November 2021

Fire is the most deadly risk during tunnel operation. Early rapid response and a reasonable smoke control plan are very important to evaluate tunnel fire performance. In order to study the relevant time factors for smoke management in a highway tunnel, firstly, the logical sequence and time of the fire alarm system (FAS) startup are investigated and analyzed. Then, according to the one‐dimensional fluid mechanics model, the time rule of adjusting the airflow field in the tunnel from the normal operation stage to the emergency ventilation state is analyzed theoretically. Finally, the abovementioned theoretical formulas are verified through the employment of model experiments. The analysis shows that the time that passes from the start of the fire to when the exhaust fan is activated is close to 3 minutes. The time required to form a stable critical wind speed, however, is close to 7 minutes, which is longer than the 5 minutes it takes for the fire to reach its maximum temperature. Due to inertia, it takes about 0.5 to 2 minutes for the air velocity in tunnels of different lengths to drop from the traffic piston wind speed to the critical wind speed. If reverse smoke extraction is required, however, the duration is between 3 and 8 minutes. The conclusion is of guiding significance for the preparation of the emergency linkage control scheme for tunnels, as well as for the setting of initial boundary conditions for CFD fire simulations.
Awareness Modeling and Computing for Quality-Aware Coordination
Authors: Liu Qing , Liu Charles Z. , Li Lan-lan , Gambino Maria T.
Published: November 2021

In this paper, we address the issues of the trade-off between QoS and QoE with an analytical analysis based on mathematical modeling under a unified normalization measurement. We model through computation the awareness of QoS and QoE with a strategy of quality-aware QoE-QoS coordination. A balanced coordination is proposed using modeling correlations between user experience and service performance. The main contributions of this paper include three main parts. First, a comprehensive mapping is modeled in a close form to illustrate the analytic correlations between QoS, QoE, and data communication. Second, an analytical method to analyze and coordinate the nonlinear trade-off between QoE and QoS is proposed based on the theoretical proof with discussions on necessary-sufficient conditions. Third, an algorithmic framework is provided to perform QoE-QoS coordination based on quality-awareness computing with a test proof. An assessment model for user experience quantification is built with the mean opinion score (MOS) test. Quality-aware QoE and QoS models are built based on the subspace learning strategy. Simulations are given to prove the feasibility and effectiveness of the proposed method. The results show that the operations with the proposed solution can be obtained analytically with balanced efficiency in both user experience performance and network performance.
Impact of Organic Matter, Inorganic, and Biofertilizer Combination on Soybean Yield in Entisol Soil of Indonesia
Authors: Sucahyono Didik , Widodo Yudi , Purwaningrahayu Runik D. , Kuntyastuti Henny , Pratiwi Herdina , Wahyuningsih Sri , Sundari Titik , Soehendi Rudy , Arsana I. Gusti K. D. , Mejaya Made J.
Published: November 2021

The study aimed to investigate an effect of organic fertilizer applied to rice in the first planting season for unfertilized soybean as second crop followed by inorganic and biofertilizer applied in the third season on soybean growth and yield under the rice-soybean-soybean cropping pattern in 2016/2017. The main plot was organic: (1) without organic amendment, (2) 10 t·ha−1 of chicken manure, and (3) 10 t·ha−1 precomposted rice straw. The subplot was inorganic N and P fertilizers and commercial biofertilizer (consisting of Rhizobium, nitrogen-fixing bacteria, and P-solubilizing bacteria) applied at planting of soybeans in the second dry season: (1) control (no inorganic and biofertilizer), (2) 50 kg urea ha−1, (3) 100 kg SP36·ha−1, (4) 50 kg urea + 100 kg SP36 ha−1, (5) biofertilizer, (6) biofertilizer + 25 kg urea ha−1, (7) biofertilizer + 75 kg SP36 ha 1, and (8) biofertilizer + 25 kg urea + 75 kg SP36 ha−1. Soybean planted on the first dry season after rice harvested was not fertilized (untreated). The results showed that the chicken manure amendment increased grain yield of soybean in the second season, i.e., from 1.03 t·ha−1 (without organic amendment) to 1.27 t·ha−1, an increase of 23%. There was no effect of rice straw on soybean grain yield. In the third season, however, the residual effect of straw compost or chicken manure increased soybean grain yield by 8% and 20%, respectively. Both straw compost and chicken manure also showed a positive effect on the use of inorganic and biofertilizers in increasing soybean productivity.
Clinical Outcomes of COVID-19 and Impact on Disease Course in Patients with Inflammatory Bowel Disease
Authors: Wetwittayakhlang Panu , Albader Farah , Golovics Petra A , Hahn Gustavo Drügg , Bessissow Talat , Bitton Alain , Afif Waqqas , Wild Gary , Lakatos Peter L
Published: November 2021

Background and Aims. The impact of COVID-19 has been of great concern in patients with inflammatory bowel disease (IBD) worldwide, including an increased risk of severe outcomes and/or possible flare of IBD. This study aims to evaluate prevalence, outcomes, the impact of COVID-19 in patients with IBD, and risk factors associated with severe COVID-19 or flare of IBD activity. Methods. A consecutive cohort of IBD patients who were diagnosed with COVID-19 infection and followed up at the McGill University Health Care Centre was obtained between March 1, 2020, and April 30, 2021. Demographics, comorbidities, IBD (type, treatments, pre- and post-COVID-19 clinical activity, biomarkers, and endoscopic activity), and COVID-19-related outcomes (pneumonia, hospitalization, death, and flare of IBD disease) were analyzed. Results. A cohort of 3,516 IBD patients was included. 82 patients (2.3%) were diagnosed with COVID-19 infection (median age: 39.0 (IQR 27.8–48.0), 77% with Crohn’s disease, 50% were female). The prevalence of COVID-19 infection in IBD patients was significantly lower compared to the general population in Canada and Quebec (3.5% versus 4.3%,

p
<
0.001

). Severe COVID-19 occurred in 6 patients (7.3%); 2 patients (2.4%) died. A flare of IBD post-COVID-19 infection was reported in 8 patients (9.8%) within 3 months. Biologic therapy was held during active COVID-19 infection in 37% of patients. Age ≥55 years (odds ratio (OR): 11.1, 95% CI: 1.8–68.0), systemic corticosteroid use (OR: 4.6, 95% CI: 0.7–30.1), active IBD (OR: 3.8, 95% CI: 0.7–20.8), and comorbidity (OR: 4.9, 95% CI: 0.8–28.6) were factors associated with severe COVID-19. After initial infection, 61% of IBD patients received COVID-19 vaccinations. Conclusion. The prevalence of COVID-19 infection among patients with IBD was lower than that in the general population in Canada. Severe COVID-19, mortality, and flare of IBD were relatively rare, while a large proportion of patients received COVID-19 vaccination. Older age, comorbidities, active IBD disease, and systemic corticosteroid, but not immunosuppressive or biological therapy, were associated with severe COVID-19 infection.
[Retracted] Research on Digital Economy of Intelligent Emergency Risk Avoidance in Sudden Financial Disasters Based on PSO‐BPNN Algorithm
Authors: Liu Lulu
Published: November 2021

In recent years, disasters have seriously affected the normal development of financial business in some regions. At the time of disaster, how to effectively integrate resources of all parties, deal with sudden financial disasters efficiently, and restore financial services in time has become an important task. Therefore, this paper adopts Particle Swarm Optimization (PSO) to improve the traditional BP Neural Network (BPNN) and finally constructs a Particle Swarm Optimization powered BP Neural Network (PSO‐BPNN) model for the intelligent emergency risk avoidance of sudden financial disasters in digital economy. At the same time, the proposed algorithm is also compared to GA‐BPNN and BPNN algorithms, which are also intelligent algorithms. Experimental results show that the hybrid PSO‐BPNN algorithm is superior to GA‐BPNN algorithm and BPNN algorithm in simulation and prediction effect. It can accurately predict the sudden financial disaster in recent period, so the model has a good application prospect.
Consensus Conditions for a Class of Fractional-Order Nonlinear Multiagent Systems with Constant and Time-Varying Time Delays
Authors: Zhang Xiaorong , Shi Min
Published: November 2021

The consensus problem for a class of fractional-order nonlinear multiagent systems with a distributed protocol containing input time delay is investigated in this paper. Consider both cases of constant time delay and time-varying delay, the delay-independent consensus conditions are obtained to achieve the consensus of the systems, respectively, by adopting the linear matrix inequality (LMI) methods and stability theory of fractional-order systems. As illustrated by the numerical examples, the proposed theoretical results work well and accurately.
Morphological Bias of Ancient Artifacts: A Case Study of Incense Burners in Ming and Qing Dynasties
Authors: Chen Yu-Fu , Wei Jie
Published: November 2021

This study verifies the relevance of the combination of traditional Chinese artifacts and perceptual semantic research. It provides new research ideas to study the Chinese artifact culture. Moreover, it helps people to understand the cultural spirit better and design crystallization of traditional artifacts. This study considered Chinese traditional incense burners in Ming and Qing dynasties to adopt morphological analysis and affinity diagram to select representative experimental samples. Furthermore, this research applied the perceptual engineering theory to explore the relation between design group’s description of perceptual semantics and the shape of incense burners. The focuses were the design group on the shape and style of ancient artifacts in aesthetic consideration. According to the results of semantic principal component analysis, the perceptual semantic bias of the design group towards incense burners was concentrated, which is related to the style acceptance of incense burners. Among these related incense burner styles, the design group paid more attention to the proportional design of “incense burner foot” in the perceptual bias of incense burner shape and preferred the proportional incense burner shape of “long foot.”
Recent Advances in the Endogenous Brain Renin-Angiotensin System and Drugs Acting on It
Authors: Urmila Aswar , Rashmi Patil , Nilam Ghag , Subhash Bodhankar
Published: November 2021

The RAS (renin-angiotensin system) is the part of the endocrine system that plays a prime role in the control of essential hypertension. Since the discovery of brain RAS in the seventies, continuous efforts have been put by the scientific committee to explore it more. The brain has shown the presence of various components of brain RAS such as angiotensinogen (AGT), converting enzymes, angiotensin (Ang), and specific receptors (ATR). AGT acts as the precursor molecule for Ang peptides—I, II, III, and IV—while the enzymes such as prorenin, ACE, and aminopeptidases A and N synthesize it. AT1, AT2, AT4, and mitochondrial assembly receptor (MasR) are found to be plentiful in the brain. The brain RAS system exhibits pleiotropic properties such as neuroprotection and cognition along with regulation of blood pressure, CVS homeostasis, thirst and salt appetite, stress, depression, alcohol addiction, and pain modulation. The molecules acting through RAS predominantly ARBs and ACEI are found to be effective in various ongoing and completed clinical trials related to cognition, memory, Alzheimer’s disease (AD), and pain. The review summarizes the recent advances in the brain RAS system highlighting its significance in pathophysiology and treatment of the central nervous system-related disorders.