bluuebunny/crossref_metadata_2025 huggingface dataset:
INFLUENCE OF WIRE-EDM PARAMETERS ON SURFACE CHARACTERISTICS OF SUPERALLOY MONEL 400
Authors: ARUN KUMAR N. E. , BABU A. SURESH , SUBRAMANIAN M. , KUMAR C. PRADEEP
Published: January 2022

Progressive development in the industrial field leads to the increasing demand for superalloys with enhanced mechanical properties, such as toughness, hardness, ductility, damping strength, tensile strength and improved surface finish. Monel 400, one of such superalloys, with the majority of its application in aerospace and marine fields demands a good super finish. There arises the need for some nonconventional processes like WEDM. This process is more effective to obtain complex shapes to close tolerance. This research focuses on clear understanding of the machining strategies with proper parametric combinations to achieve an improved surface finish, subsequently reducing the time and expense involved in the superfinishing procedure. The surface qualities of the selected samples are validated with the help of roughness profile and topography images. This study has proven that the increasing input current and wire feed rate (WFR) consistently decreases the surface roughness (SR; [Formula: see text] of the specimen. This paper also explains the effect of topographic parameters and microstructure over the resulting SR. In addition, the consistent contribution of WFR and input current toward the lower SR is established. The relationship between morphological behavior and parametric deviations is evaluated. A significant correlation found to exist between the rate of wire feed and the height parameters of SR such as [Formula: see text], [Formula: see text], etc.
Quadratic-exponential functionals of Gaussian quantum processes
Authors: Vladimirov Igor G. , Petersen Ian R. , James Matthew R.
Published: January 2022

This paper is concerned with exponential moments of integral-of-quadratic functions of quantum processes with canonical commutation relations of position-momentum type. Such quadratic-exponential functionals (QEFs) arise as robust performance criteria in control problems for open quantum harmonic oscillators (OQHOs) driven by bosonic fields. We develop a randomised representation for the QEF using a Karhunen–Loeve expansion of the quantum process on a bounded time interval over the eigenbasis of its two-point commutator kernel, with noncommuting position-momentum pairs as coefficients. This representation holds regardless of a particular quantum state and employs averaging over an auxiliary classical Gaussian random process whose covariance operator is specified by the commutator kernel. This allows the QEF to be related to the moment-generating functional of the quantum process and computed for multipoint Gaussian states. For stationary Gaussian quantum processes, we establish a frequency-domain formula for the QEF rate in terms of the Fourier transform of the quantum covariance kernel in composition with trigonometric functions. A differential equation is obtained for the QEF rate with respect to the risk sensitivity parameter for its approximation and numerical computation. The QEF is also applied to large deviations and worst-case mean square cost bounds for OQHOs in the presence of statistical uncertainty with a quantum relative entropy description.
Design of Computer Multimedia Intelligent Platform Using Big Data Analysis
Authors: Chen Zhenna
Published: January 2022

This exploration aims to transfer, process and store multimedia information timely, accurately and comprehensively through computer comprehensive technology processing, and organically combine various elements under the background of big data analysis, so as to form a complete intelligent platform design for multimedia information processing and application. In this exploration, the intelligent vehicle monitoring system is taken as an example. Data acquisition, data transmission, real-time data processing, data storage and data application are realized through the real-time data stream processing framework of [Formula: see text] of big data technology. Data interaction is realized through Spring, Spring MVC, VUE front-end framework, and Ajax asynchronous communication local update technology. Data storage is achieved through Red is cache database, and intelligent vehicle operation supervision system is achieved through multimedia information technology processing. Its purpose is to manage the vehicle information, real-time monitor the running state of the vehicle and give an alarm when there are some problems. The basic functions of vehicle operation monitoring and management system based on big data analysis are realized. The research on the design of vehicle operation monitoring and management system based on big data analysis shows that big data technology can be applied to the design of computer multimedia intelligent platform, and provides a reference case for the development of computer multimedia intelligent platform based on big data analysis.
Urban Traffic Dynamics Prediction—A Continuous Spatial-temporal Meta-learning Approach
Authors: Zhang Yingxue , Li Yanhua , Zhou Xun , Luo Jun , Zhang Zhi-Li
Published: January 2022

Urban traffic status (e.g., traffic speed and volume) is highly dynamic in nature, namely, varying across space and evolving over time. Thus, predicting such traffic dynamics is of great importance to urban development and transportation management. However, it is very challenging to solve this problem due to spatial-temporal dependencies and traffic uncertainties. In this article, we solve the traffic dynamics prediction problem from Bayesian meta-learning perspective and propose a novel continuous spatial-temporal meta-learner (cST-ML), which is trained on a distribution of traffic prediction tasks segmented by historical traffic data with the goal of learning a strategy that can be quickly adapted to related but unseen traffic prediction tasks. cST-ML tackles the traffic dynamics prediction challenges by advancing the Bayesian black-box meta-learning framework through the following new points: (1) cST-ML captures the dynamics of traffic prediction tasks using variational inference, and to better capture the temporal uncertainties within tasks, cST-ML performs as a rolling window within each task; (2) cST-ML has novel designs in architecture, where CNN and LSTM are embedded to capture the spatial-temporal dependencies between traffic status and traffic-related features; (3) novel training and testing algorithms for cST-ML are designed. We also conduct experiments on two real-world traffic datasets (taxi inflow and traffic speed) to evaluate our proposed cST-ML. The experimental results verify that cST-ML can significantly improve the urban traffic prediction performance and outperform all baseline models especially when obvious traffic dynamics and temporal uncertainties are presented.
Earth Imagery Segmentation on Terrain Surface with Limited Training Labels: A Semi-supervised Approach based on Physics-Guided Graph Co-Training
Authors: He Wenchong , Sainju Arpan Man , Jiang Zhe , Yan Da , Zhou Yang
Published: January 2022

Given earth imagery with spectral features on a terrain surface, this paper studies surface segmentation based on both explanatory features and surface topology. The problem is important in many spatial and spatiotemporal applications such as flood extent mapping in hydrology. The problem is uniquely challenging for several reasons: first, the size of earth imagery on a terrain surface is often much larger than the input of popular deep convolutional neural networks; second, there exists topological structure dependency between pixel classes on the surface, and such dependency can follow an unknown and non-linear distribution; third, there are often limited training labels. Existing methods for earth imagery segmentation often divide the imagery into patches and consider the elevation as an additional feature channel. These methods do not fully incorporate the spatial topological structural constraint within and across surface patches and thus often show poor results, especially when training labels are limited. Existing methods on semi-supervised and unsupervised learning for earth imagery often focus on learning representation without explicitly incorporating surface topology. In contrast, we propose a novel framework that explicitly models the topological skeleton of a terrain surface with a contour tree from computational topology, which is guided by the physical constraint (e.g., water flow direction on terrains). Our framework consists of two neural networks: a
convolutional neural network (CNN)
to learn spatial contextual features on a 2D image grid, and a
graph neural network (GNN)
to learn the statistical distribution of physics-guided spatial topological dependency on the contour tree. The two models are co-trained via variational EM. Evaluations on the real-world flood mapping datasets show that the proposed models outperform baseline methods in classification accuracy, especially when training labels are limited.
Data-driven Targeted Advertising Recommendation System for Outdoor Billboard
Authors: Wang Liang , Yu Zhiwen , Guo Bin , Yang Dingqi , Ma Lianbo , Liu Zhidan , Xiong Fei
Published: January 2022

In this article, we propose and study a novel data-driven framework for Targeted Outdoor Advertising Recommendation (TOAR) with a special consideration of user profiles and advertisement topics. Given an advertisement query and a set of outdoor billboards with different spatial locations and rental prices, our goal is to find a subset of billboards, such that the total targeted influence is maximum under a limited budget constraint. To achieve this goal, we are facing two challenges: (1) it is difficult to estimate targeted advertising influence in physical world; (2) due to NP hardness, many common search techniques fail to provide a satisfied solution with an acceptable time, especially for large-scale problem settings. Taking into account the exposure strength, advertisement matching degree, and advertising repetition effect, we first build a targeted influence model that can characterize that the advertising influence spreads along with users mobility. Subsequently, based on a divide-and-conquer strategy, we develop two effective approaches, i.e., a master–slave-based sequential optimization method, TOAR-MSS, and a cooperative co-evolution-based optimization method, TOAR-CC, to solve our studied problem. Extensive experiments on two real-world datasets clearly validate the effectiveness and efficiency of our proposed approaches.
Increasingly Intelligent Micromachines
Authors: Huang Tian-Yun , Gu Hongri , Nelson Bradley J.
Published: January 2022

Intelligent micromachines, with dimensions ranging from a few millimeters down to hundreds of nanometers, are miniature systems capable of performing specific tasks autonomously at small scales. Enhancing the intelligence of micromachines to tackle the uncertainty and variability in complex microenvironments has applications in minimally invasive medicine, bioengineering, water cleaning, analytical chemistry, and more. Over the past decade, significant progress has been made in the construction of intelligent micromachines, evolving from simple micromachines to soft, compound, reconfigurable, encodable, multifunctional, and integrated micromachines, as well as from individual to multiagent, multiscale, hierarchical, self-organizing, and swarm micromachines. The field leverages two important trends in robotics research—the miniaturization and intelligentization of machines—but a compelling combination of these two features has yet to be realized. The core technologies required to make such tiny machines intelligent include information media, transduction, processing, exchange, and energy supply, but embedding all of these functions into a system at the micro- or nanoscale is challenging. This article offers a comprehensive introduction to the state-of-the-art technologies used to create intelligence for micromachines and provides insight into the construction of next-generation intelligent micromachines that can adapt to diverse scenarios for use in emerging fields.
Brainstem Circuits for Locomotion
Authors: Leiras Roberto , Cregg Jared M. , Kiehn Ole
Published: January 2022

Locomotion is a universal motor behavior that is expressed as the output of many integrated brain functions. Locomotion is organized at several levels of the nervous system, with brainstem circuits acting as the gate between brain areas regulating innate, emotional, or motivational locomotion and executive spinal circuits. Here we review recent advances on brainstem circuits involved in controlling locomotion. We describe how delineated command circuits govern the start, speed, stop, and steering of locomotion. We also discuss how these pathways interface between executive circuits in the spinal cord and diverse brain areas important for context-specific selection of locomotion. A recurrent theme is the need to establish a functional connectome to and from brainstem command circuits. Finally, we point to unresolved issues concerning the integrated function of locomotor control.
Capturing Atom-Specific Electronic Structural Dynamics of Transition-Metal Complexes with Ultrafast Soft X-Ray Spectroscopy
Authors: Jay Raphael M. , Kunnus Kristjan , Wernet Philippe , Gaffney Kelly J.
Published: January 2022

The atomic specificity of X-ray spectroscopies provides a distinct perspective on molecular electronic structure. For 3 d metal coordination and organometallic complexes, the combination of metal- and ligand-specific X-ray spectroscopies directly interrogates metal–ligand covalency—the hybridization of metal and ligand electronic states. Resonant inelastic X-ray scattering (RIXS), the X-ray analog of resonance Raman scattering, provides access to all classes of valence excited states in transition-metal complexes, making it a particularly powerful means of characterizing the valence electronic structure of 3 d metal complexes. Recent advances in X-ray free-electron laser sources have enabled RIXS to be extended to the ultrafast time domain. We review RIXS studies of two archetypical photochemical processes: charge-transfer excitation in ferricyanide and ligand photodissociation in iron pentacarbonyl. These studies demonstratefemtosecond-resolution RIXS can directly characterize the time-evolving electronic structure, including the evolution of the metal–ligand covalency.
Simultaneous Adsorptive Stripping Voltammetric Analysis of Heavy Metals at Graphenated Cupferron Pencil Rods
Authors: Sanga Nelia A. , Jahed Nazeem , Leve Zandile , Iwuoha Emmanuel I. , Pokpas Keagan
Published: January 2022

Electroanalysis of heavy metal ions in the presence of cupferron ligands has been extensively studied due to its ability to form stable metallic coordination complexes. Herein, electrochemically reduced graphene oxide (ERGO) sheets were for the first time employed in conjunction with low-cost, disposable pencil graphite rods and in situ plated thin mercury films (HgF) for the simultaneous detection of Cd2+, Cu2+, Pb2+, and Zn2+ in the presence of cupferron as a chelating agent by square-wave adsorptive cathodic stripping voltammetry (SW-AdCSV). The technique is based on the catalytic reduction of adsorbed cupferron-metal ion complexes at the surface of the ERGO-HgF-PGE at 0.1 V for 60 s in 0.1 M acetate buffer solution (pH 4.6). Owing to the improved electronic and surface effects associated with ERGO inclusion, improved sensitivity was further achieved. Under optimized conditions, the ERGO-HgF-PGE showed a linear relationship from 20 to 200 μg.l−1 with detection limits below the US-EPA of 0.17 μg.l−1, 0.02 μg.l−1, 0.17 μg.l−1 and 0.14 μg.l−1 for Cd2+, Cu2+, Pb2+ and Zn2+, respectively at a deposition time of 60 s. The ERGO-HgF-PGE exhibited highly reproducible results with negligible intermetallic interferences and applied successfully to the determination of trace metals in tap water with satisfactory results.
Reducing sarcolipin expression improves muscle metabolism in mdx mice
Authors: Balakrishnan Rekha , Mareedu Satvik , Babu Gopal J.
Published: January 2022

Duchenne muscular dystrophy (DMD) is an inherited muscle wasting disease. Metabolic impairments and oxidative stress are major secondary mechanisms that severely worsen muscle function in DMD. Here, we sought to determine whether germline reduction or ablation of sarcolipin (SLN), an inhibitor of sarco/endoplasmic reticulum (SR) Ca2+ ATPase (SERCA), improves muscle metabolism and ameliorates muscle pathology in the mdx mouse model of DMD. Glucose and insulin tolerance tests show that glucose clearance rate and insulin sensitivity were improved in the SLN haploinsufficient mdx ( mdx:sln+/−) and SLN-deficient mdx ( mdx:sln−/−) mice. The histopathological analysis shows that fibrosis and necrosis were significantly reduced in muscles of mdx:sln+/− and mdx:sln−/− mice. SR Ca2+ uptake, mitochondrial complex protein levels, complex activities, mitochondrial Ca2+ uptake and release, and mitochondrial metabolism were significantly improved, and lipid peroxidation and protein carbonylation were reduced in the muscles of mdx:sln+/− and mdx:sln−/− mice. These data demonstrate that reduction or ablation of SLN expression can improve muscle metabolism, reduce oxidative stress, decrease muscle pathology, and protects the mdx mice from glucose intolerance.
Maternal obesogenic diet regulates offspring bile acid homeostasis and hepatic lipid metabolism via the gut microbiome in mice
Authors: Thompson Michael D. , Kang Jisue , Faerber Austin , Hinrichs Holly , Özler Oğuz , Cowen Jamie , Xie Yan , Tarr Phillip I. , Davidson Nicholas O.
Published: January 2022

We utilized a mouse model of maternal obesogenic diet exposure to evaluate the effect on offspring microbiome and bile acid homeostasis. We identified shifts in the offspring microbiome associated with changes in cecal bile acid levels. Transfer of the microbiome from maternal obesogenic diet-exposed offspring to microbiome-depleted mice altered bile acid homeostasis and increased fructose-induced hepatic steatosis.
Carbonic anhydrase and soluble adenylate cyclase regulation of cystic fibrosis cellular phenotypes
Authors: Boyne Kathleen , Corey Deborah A. , Zhao Pan , Lu Binyu , Boron Walter F. , Moss Fraser J. , Kelley Thomas J.
Published: January 2022

Several aspects of the cell biology of cystic fibrosis (CF) epithelial cells are altered including impaired lipid regulation, disrupted intracellular transport, and impaired microtubule regulation. It is unclear how the loss of cystic fibrosis transmembrane conductance regulator (CFTR) function leads to these differences. It is hypothesized that the loss of CFTR function leads to altered regulation of carbonic anhydrase (CA) activity resulting in cellular phenotypic changes. In this study, it is demonstrated that CA2 protein expression is reduced in CF model cells, primary mouse nasal epithelial (MNE) cells, excised MNE tissue, and primary human nasal epithelial cells ( P < 0.05). This corresponds to a decrease in CA2 RNA expression measured by qPCR as well as an overall reduction in CA activity in primary CF MNEs. The addition of CFTR-inhibitor-172 to WT MNE cells for ≥24 h mimics the significantly lower protein expression of CA2 in CF cells. Treatment of CF cells with l-phenylalanine (L-Phe), an activator of CA activity, restores endosomal transport through an effect on microtubule regulation in a manner dependent on soluble adenylate cyclase (sAC). This effect can be blocked with the CA2-selective inhibitor dorzolamide. These data suggest that the loss of CFTR function leads to the decreased expression of CA2 resulting in the downstream cell signaling alterations observed in CF.
Differentiating children with sepsis with and without acute respiratory distress syndrome using proteomics
Authors: Yehya Nadir , Fazelinia Hossein , Taylor Deanne M. , Lawrence Gladys G. , Spruce Lynn A. , Thompson Jill M. , Margulies Susan S. , Seeholzer Steven H. , Worthen G. Scott
Published: January 2022

Both sepsis and acute respiratory distress syndrome (ARDS) rely on imprecise clinical definitions leading to heterogeneity, which has contributed to negative trials. Because circulating protein/DNA complexes have been implicated in sepsis and ARDS, we aimed to develop a proteomic signature of DNA-bound proteins to discriminate between children with sepsis with and without ARDS. We performed a prospective case-control study in 12 children with sepsis with ARDS matched to 12 children with sepsis without ARDS on age, severity of illness score, and source of infection. We performed co-immunoprecipitation and downstream proteomics in plasma collected ≤ 24 h of intensive care unit admission. Expression profiles were generated, and a random forest classifier was used on differentially expressed proteins to develop a signature which discriminated ARDS. The classifier was tested in six independent blinded samples. Neutrophil and nucleosome proteins were over-represented in ARDS, including two S100A proteins, superoxide dismutase (SOD), and three histones. Random forest produced a 10-protein signature that accurately discriminated between children with sepsis with and without ARDS. This classifier perfectly assigned six independent blinded samples as having ARDS or not. We validated higher expression of the most informative discriminating protein, galectin-3-binding protein, in children with ARDS. Our methodology has applicability to isolation of DNA-bound proteins from plasma. Our results support the premise of a molecular definition of ARDS, and give preliminary insight into why some children with sepsis, but not others, develop ARDS.
Superficial peroneal neuromodulation of nonobstructive urinary retention induced by prolonged pudendal afferent activity in cats
Authors: Chen Jialiang , Zhong Yihua , Shen Bing , Wang Jicheng , Shen Zhijun , Beckel Jonathan , de Groat William C. , Tai Changfeng
Published: January 2022

The purpose of this study is to determine whether superficial peroneal nerve stimulation (SPNS) can improve nonobstructive urinary retention (NOUR) induced by prolonged pudendal nerve stimulation (PNS). In this exploratory acute study using eight cats under anesthesia, PNS and SPNS were applied by nerve cuff electrodes. Skin surface electrodes were also used for SPNS. A double lumen catheter was inserted via the bladder dome for bladder infusion and pressure measurement and to allow voiding without a physical urethral outlet obstruction. The voided and postvoid residual (PVR) volumes were also recorded. NOUR induced by repetitive (4–13 times) application of 30-min PNS significantly ( P < 0.05) reduced voiding efficiency by 49.5 ± 16.8% of control (78.3 ± 7.9%), with a large PVR volume at 208.2 ± 82.6% of control bladder capacity. SPNS (1 Hz, 0.2 ms) at 1.5–2 times threshold intensity (T) for inducing posterior thigh muscle contractions was applied either continuously (SPNSc) or intermittently (SPNSi) during cystometrograms to improve the PNS-induced NOUR. SPNSc and SPNSi applied by nerve cuff electrodes significantly ( P < 0.05) increased voiding efficiency to 74.5 ± 18.9% and 67.0 ± 15.3%, respectively, and reduced PVR volume to 54.5 ± 39.0% and 88.3 ± 56.0%, respectively. SPNSc and SPNSi applied noninvasively by skin surface electrodes also improved NOUR similar to the stimulation applied by a cuff electrode. This study indicates that abnormal pudendal afferent activity could be a pathophysiological cause for the NOUR occurring in Fowler’s syndrome and a noninvasive superficial peroneal neuromodulation therapy might be developed to treat NOUR in patients with Fowler’s syndrome.
Motor imagery helps updating internal models during microgravity exposure
Authors: Rannaud Monany D. , Barbiero M. , Lebon F. , Babič J. , Blohm G. , Nozaki D. , White O.
Published: January 2022

Gravity strongly affects the way movements are performed. How internal models process this information to adapt behavior to novel contexts is still unknown. The microgravity environment itself does not provide enough information to optimally adjust the period of natural arm swinging movements to microgravity. However, motor imagery of the task while immersed in microgravity was sufficient to update internal models. These results show that actually executing a task is not necessary to update graviception.
Crossed reflex responses to flexor nerve stimulation in mice
Authors: Laflamme Olivier D. , Ibrahim Marwan , Akay Turgay
Published: January 2022

Insights into the mechanisms of crossed reflexes are essential for understanding coordinated leg movements that maintain stable locomotion. Advances in mouse genetics allow for the selective manipulation of spinal interneurons and provide opportunities to understand crossed reflexes. Crossed reflexes in mice, however, are poorly described. Here, we describe crossed reflex responses in mice initiated by stimulation of the common peroneal nerve, which serves as a starting point for investigating crossed reflexes at the cellular level.
Pinocembrin relieves hip fracture-induced pain by repressing spinal substance P signaling in aged rats
Authors: Xing Baorui , Feng Nana , Zhang Juan , Li Yunmei , Hou Xiuxiu , Wu Hao , Liu Wendong , Han Guangpu
Published: January 2022

Substance P signaling contributes to the nociceptive and vascular changes observed in hip fracture, which could be alleviated by PCN.
Research on the Effect of Online Marketing Based on Multimodel Fusion and Artificial Intelligence in the Context of Big Data
Authors: Zhao Hongyu , Lyu Fang , Luo Yalan
Published: January 2022

Traditional online marketing methods use a single model to predict the advertising conversion rate, but the prediction results are not accurate, and users are not satisfied with the recommendation results. Therefore, this paper proposes an online marketing method based on multimodel fusion and artificial intelligence algorithms under the background of big data. First, it introduces big data technology and analyzes the characteristics of network advertising marketing model (RTB). Second, combined with multitask learning and fusion technology to improve the single model in advertising conversion rate prediction effect, prediction results to further improve the accuracy of results. Then, tF-IDF technology in artificial intelligence algorithm is used to measure the importance of advertising words in online marketing and calculate the contribution degree. Finally, according to XGBoost technology, the multitask fusion model of online marketing effect is classified. Experiments are used to analyze the effect of online marketing. Experimental results show that the proposed method can improve the accuracy of advertising conversion rate prediction and online sales of goods.
Novel Analysis of Fuzzy Physical Models by Generalized Fractional Fuzzy Operators
Authors: Alaoui Mohammed Kbiri , Alharbi F. M. , Zaland Shamsullah
Published: January 2022

The present article correlates with a fuzzy hybrid technique combined with an iterative transformation technique identified as the fuzzy new iterative transform method. With the help of Atangana-Baleanu under generalized Hukuhara differentiability, we demonstrate the consistency of this method by achieving fuzzy fractional gas dynamics equations with fuzzy initial conditions. The achieved series solution was determined and contacted the estimated value of the suggested equation. To confirm our technique, three problems have been presented, and the results were estimated in fuzzy type. The lower and upper portions of the fuzzy solution in all three examples were simulated using two distinct fractional orders between 0 and 1. Because the exponential function is present, the fractional operator is nonsingular and global. It provides all forms of fuzzy solutions occurring between 0 and 1 at any fractional-order because it globalizes the dynamical behavior of the given equation. Because the fuzzy number provides the solution in fuzzy form, with upper and lower branches, fuzziness is also incorporated in the unknown quantity. It is essential to mention that the projected methodology to fuzziness is to confirm the superiority and efficiency of constructing numerical results to nonlinear fuzzy fractional partial differential equations arising in physical and complex structures.
Regulatory Networks of Prognostic mRNAs in Pediatric Acute Myeloid Leukemia
Authors: Zhang Hao , Cheng Liqin , Liu Cong
Published: January 2022

Acute myeloid leukemia (AML) in children refers to a malignant tumor caused by the abnormal proliferation of immature myeloid cells in the bone marrow and peripheral blood. The prognosis of patients with pediatric acute myeloid leukemia (AML) remains poor, highlighting the need for improved targeted therapy. The expression data of lncRNAs, mRNAs, and miRNAs and survival information of pediatric AML patients were collected from The Cancer Genome Atlas (TCGA) database. Cox regression analysis was used to screen the lncRNAs, mRNAs, and miRNAs that significantly affect the overall survival (OS) of patients as OS-related genes (included lncRNAs, mRNAs, and miRNAs). Enrichment analysis and protein-protein interaction (PPI) network construction were performed for the OS-related mRNAs. We further established a ceRNAs regulatory network. In addition, the potential prognostic role of genes was further evaluated by risk score. We have identified 5275 lncRNAs, 176 miRNAs, and 6221 mRNAs that significantly affect the prognosis of pediatric AML patients. It is worth noting that OS-related mRNAs are mainly involved in ribosome, RNA transport, and spliceosome. We identified the top 10 most connected mRNAs in the PPI network as important mRNAs and constructed a ceRNAs regulatory network (including NCBP2, RPLP0, UBC, RPS2, and RPS9). The risk score and nomogram results suggest that NCBP2 may be a risk factor for pediatric AML, while RPLP0, UBC, RPS2, and RPS9 may be protective factors. Our results construct 5 gene signals as new prognostic indicators for predicting the survival of pediatric AML patients. Our research has demonstrated the ceRNAs regulatory network may become a new target for pediatric AML treatment.
Smart Financial Management System Based on Data Ming and Man-Machine Management
Authors: Lai Maotao
Published: January 2022

To begin, the architecture of an intelligent financial management system is thoroughly investigated, and a new architecture of an intelligent financial management support system based on data mining is developed. Second, it goes over the definition and structure of a data warehouse and data mining, as well as how to use data mining strategy and technology in financial management. Data mining in relation to technology is being investigated, as is the development of an intelligent data mining algorithm. The flaws of the intelligent data mining algorithm are discovered through an analysis and summary of the algorithm, and an improved algorithm is proposed to address the flaws. Related mining experiments are carried out on the improved algorithm, and the experiment shows that it has certain advantages. Then, using an intelligent forecasting financial management decision as an example, the intelligent financial management based on data mining is thoroughly investigated, the basic design framework for intelligent financial management is established, and the application of a data mining model in decision support system is introduced.
[Retracted] Effects of Caffeine Intake on Cardiopulmonary Variables and QT Interval after a Moderate‐Intensity Aerobic Exercise in Healthy Adults: A Randomized Controlled Trial
Authors: Yu JaeHo , Lim Jeong-Hun , Seo Sang-Woo , Lee DongYeop , Hong JiHeon , Kim JinSeop , Kim SeongGil , Nekar Daekook M. , Kang HyeYun
Published: January 2022

Caffeine is considered a widely consumed natural and legal psychoactive stimulant with several effects on the body. The present study attempted to investigate the effects of caffeine consumed before and after a physical exercise on cardiovascular and cardiorespiratory functions in healthy adults. 36 healthy adult males were recruited and randomly allocated to one of the three (3) groups: group I (exercise without caffeine consumption), group II (caffeine beverage intake before exercise), and group III (caffeine beverage intake immediately after exercise). The heart rate (HR), QTc interval, blood pressure (BP), respiratory rate (RR), oxygen consumption (VO₂), and carbon dioxide emission (VCO₂) were measured at 0, 5, 10, and 15 min after the exercise. We observed a significant difference in all measured outcomes during the different recovery times in all the groups (p < 0.05). HR, RR, SBP, VO2, and VCO2 gradually decreased with time, DBP contrarily increased with time, and the QTc showed an irregular pattern. We can affirm that ingestion of caffeine before and after moderate aerobic exercise slows down the parasympathetic stimulation, heart rate recovery, and the recovery of HR and QTc with no major effects on BP, RR, VO₂, and VCO₂ in healthy adult men.
Analysis of Related Factors of Mother-to-Child Transmission of AIDS and Evaluation of Measures to Prevent Mother-to-Child Transmission
Authors: Liu Yuhuan , Zhang Yulan , Pang Lin
Published: January 2022

AIDS is still a major public health facing the world. With the implementation of AIDS prevention projects and the continuous maturity of technology, more and more HIV-positive women choose to have children. However, the children born to these women are a special group. Exposure to HIV and antiviral drugs during the fetal period can increase the success rate of children’s elimination of the mother, which is worthy of attention and research. This article focuses on the analysis of the related factors of mother-to-child transmission of AIDS and the evaluation of mother-to-child blocking measures, using the method of field research to conduct experiments on AIDS patients in this city who are pregnant and provide them with antidrug treatment and some barrier measures. Then, the mother-to-child transmission rate was recorded, and the experimental results showed that the antidrug treatment plan is related to mother-to-child transmission. After antidrug treatment, the transmission rate of single-drug treatment is reduced by 5%, and the transmission rate of combined drug treatment is reduced by 10%. It can be seen from this that antidrug treatment is an effective measure to block mothers and babies.
Electromagnetic Field Analysis and Shielding Method of Underground Variable Frequency Power Cable
Authors: Zhang Jianwei , Yang Zhuojing
Published: January 2022

The transmission and radiation of underground variable frequency electromagnetic waves will seriously interfere with the operation of the power cable and its surrounding environment. At present, the test methods for power cables basically require the impedance of the test system to match the characteristic impedance of the cable. The defect is that the process of designing and making the impedance matching impedance network is relatively complex and requires high manufacturing accuracy. In order to solve these problems, this paper puts forward the electromagnetic field fast detection formula and electromagnetic field shielding method of underground variable frequency power cable. The research method of this paper is the principle of shielding electromagnetic field materials and the suppression principle of shielding layer for electromagnetic coupling. The function of the two principles is to study the reflection, absorption, and multiple reflection of electromagnetic waves and to study the cut‐off frequency of the nonmagnetic shielding layer. These two principles guide the experiment. In this paper, the measurement formula of the shielding performance of mismatched cables is derived through experiments. The results show that the error of the measurement formula is no more than 8 dB. Then, through the experiment of restraining the interference of magnetic materials on the electromagnetic field, it is concluded that the magnetic field shielding performance can reach 20 dB. Then, through the performance test of electromagnetic field shielding materials, the shielding efficiency of metal fiber antiradiation materials is the largest, and the average efficiency reaches 76.4 dB.
Systematic Understanding of Mechanism of Danggui Shaoyao San against Ischemic Stroke Using a Network Pharmacology Approach
Authors: Li Sijie , Yang Yong , Zhang Wei , Li Haiyan , Yu Wantong , Gao Chen , Xu Jiali , Zhao Wenbo , Ren Changhong
Published: January 2022

Purpose. Danggui Shaoyao San (DSS) was developed to treat the ischemic stroke (IS) in patients and animal models. The purpose of this study was to explore its active compounds and demonstrate its mechanism against IS through network pharmacology, molecular docking, and animal experiment. Methods. All the components of DSS were retrieved from the pharmacology database of TCM system. The genes corresponding to the targets were retrieved using OMIM, CTD database, and TTD database. The herb-compound-target network was constructed by Cytoscape software. The target protein-protein interaction network was built using the STRING database. The core targets of DSS were analyzed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Then, we achieved molecular docking between the hub proteins and the key active compounds. Finally, animal experiments were performed to verify the core targets. Triphenyltetrazolium chloride (TTC) staining was used to calculate the infarct size in mice. The protein expression was determined using the Western blot. Results. Compound-target network mainly contained 51 compounds and 315 corresponding targets. Key targets contained MAPK1, SRC, PIK3R1, HRAS, AKT1, RHOA, RAC1, HSP90AA1, and RXRA FN1. There were 417 GO items in GO enrichment analysis (

p
<
0.05

) and 119 signaling pathways (

p
<
0.05

) in KEGG, mainly including negative regulation of apoptosis, steroid hormone-mediated signaling pathway, neutrophil activation, cellular response to oxidative stress, and VEGF signaling pathway. MAPK1, SRC, and PIK3R1 docked with small molecule compounds. According to the Western blot, the expression of p-MAPK 1, p-AKT, and p-SRC was regulated by DSS. Conclusions. This study showed that DSS can treat IS through multiple targets and routes and provided new insights to explore the mechanisms of DSS against IS.
Low-Frequency rTMS over Contralesional M1 Increases Ipsilesional Cortical Excitability and Motor Function with Decreased Interhemispheric Asymmetry in Subacute Stroke: A Randomized Controlled Study
Authors: Luk Ka Yan , Ouyang Hui Xi , Pang Marco Yiu Chung
Published: January 2022

Objective. To determine the long-term effects of low-frequency repetitive transcranial magnetic stimulation (LF-rTMS) over the contralesional M1 preceding motor task practice on the interhemispheric asymmetry of the cortical excitability and the functional recovery in subacute stroke patients with mild to moderate arm paresis. Methods. Twenty-four subacute stroke patients were randomly allocated to either the experimental or control group. The experimental group underwent rTMS over the contralesional M1 (1 Hz), immediately followed by 30 minutes of motor task practice (10 sessions within 2 weeks). The controls received sham rTMS and the same task practice. Following the 2-week intervention period, the task practice was continued twice weekly for another 10 weeks in both groups. Outcomes were evaluated at baseline (T0), at the end of the 2-week stimulation period (T1), and at 12-week follow-up (T2). Results. The MEP (paretic hand) and interhemispheric asymmetry, Fugl-Meyer motor assessment, Action Research Arm Test, and box and block test scores improved more in the experimental group than controls at T1 (

p
<
0.05

). The beneficial effects were largely maintained at T2. Conclusion. LF-rTMS over the contralesional M1 preceding motor task practice was effective in enhancing the ipsilesional cortical excitability and upper limb function with reducing interhemispheric asymmetry in subacute stroke patients with mild to moderate arm paresis. Significance. Adding LF-rTMS prior to motor task practice may reduce interhemispheric asymmetry of cortical excitabilities and promote upper limb function recovery in subacute stroke with mild to moderate arm paresis.
Lipopolysaccharide Enhances Genotoxicity by Activating GADD45G and NF-κB in Human Corneal Epithelial Cells
Authors: Samivel Ramachandran , Subramanian Umadevi , Ali Khan Adnan , Kirat Omar , Masmali Ali , Almubrad Turki , Akhtar Saeed
Published: January 2022

As the prevalence of microbial keratitis increases, it creates an environment conducive to genotoxicity response. A potential connection between growth arrest and DNA-damage-inducible 45 gamma (GADD45G) gene expression has not been proven in the corneal epithelial cells. The aim of this study was to determine whether lipopolysaccharide (LPS) enhances genotoxicity, DNA damage, and inflammatory responses in human corneal epithelial cells (HCECs) in vitro. In a set of parameters, cytotoxicity, reactive oxygen species, mitochondrial membrane potential, DNA damage, inflammatory response, and apoptosis were assessed. LPS (1, 5, and 10 μg/mL) treated HCECs were increased reactive oxygen species formation, mitochondrial membrane depolarization, and genotoxicity in a concentration-dependent manner. Similarly, NF-κB, PARP1, and TP53 were also overexpressed in the LPS treated HCECs. 24 hours after LPS induction, micronucleus scoring, and proapoptotic factors were also increased. Among them, the GADD45G, NF-κB, and γH2AX were overexpressed both on the mRNA and protein levels in LPS (10 μg/mL) treated HCECs. In our study, we show that the GADD45G signaling can trigger genotoxic instability in HCECs exposed to LPS. Therefore, understanding the factors contributing to infectious keratitis, such as GADD45G, NF-κB, and γH2AX signaling, may help to develop antigenotoxic and anti-inflammatory therapies for corneal dystrophy and epithelial cell remodeling.
Application of Web 2.0 Technology to Cooperative Learning Environment System Design of Football Teaching
Authors: Lin Hui
Published: January 2022

In general, web 2.0 technology serves as an educational tool for teaching and learning aspects. The study is aimed at exploring the interactive system of football teaching in the information technology era. The coach and players will utilize the mobile learning resources to get effective learning about the fun. Using mobile learning technology, the coach has to implement different modes to make the players learn about the game. The study implemented the convolutional neural network (CNN) algorithm to evaluate the accuracy of using web 2.0 technology to cooperative learning environment system design of football teaching. The results show that the network teaching interactive learning system of football courses based on web 2.0 can achieve the intended function of the college educational administration management system.
A Cooperative Positioning Method of Connected and Automated Vehicles with Direction-of-Arrival and Relative Distance Fusion
Authors: Wang Faan , Xu Liwei , Jin Xianjian , Yin Guodong , Liu Ying
Published: January 2022

The rapid development of science and technology has created favorable conditions for Connected and Automated Vehicles (CAVs). Accurate localization is one of the fundamental functions of CAV to realize some advanced operations such as vehicle platooning. However, complicated urban traffic environments, such as the flyover, significantly influence vehicular positioning accuracy. The inability of CAV to accurately perceive self-localization information has become an urgent issue to be addressed. This paper proposed a novel cooperative localization method by introducing the relative Direction-of-Arrival (DOA) and Relative Distance (RD) into CAV to improve the localization accuracy of CAV in the multivehicle environment. First, the three-dimensional positioning error model of the host vehicle concerning adjacent vehicles in azimuth angle and pitch angle and intervehicle distances under the vehicle-to-vehicle communication was established. Second, two least-squares estimation algorithms, linear and nonlinear, are established to decrease the position errors by combining relative DOA and RD measurement information. To verify the proposed algorithm's effect, the PreScan-Simulink joint simulation is carried out. The results show that the host vehicle's localization accuracy by the proposed method can be improved by 25% compared with direct linearization. Besides, by combining relative DOA and relative RD measurement, the locating capability of the least-square-based nonlinear optimization method can be enhanced by 22%.
[Retracted] A Multiobjective Optimization Model for Prevention and Control of Coronavirus Disease 2019 in China
Authors: Li Xiaocheng , Liu Zhaoli , Ge Fangzhen
Published: January 2022

It is a global issue to set up a practical, sensitive, and useful model to eradicate or mitigate the coronavirus disease 2019 (COVID‐19). Taking Central China’s Hubei Province for example, three models were established. Firstly, a susceptible‐probable‐infectious‐recovered (SPIR) model was proposed to predict the monthly number of confirmed and susceptible cases in each city. Next, an epidemic prefecture clustering model was set up to find proper vaccine delivery sites, according to the distance of each city. Finally, a dynamic material delivery optimization model was established for multiple epidemic prefectures, aiming to speed up vaccine production and storage in each delivery site.
Double Sumudu Transform Iterative Method for One-Dimensional Nonlinear Coupled Sine-Gordon Equation
Authors: Deresse Alemayehu Tamirie
Published: January 2022

In this paper, the combined double Sumudu transform with iterative method is successfully implemented to obtain the approximate analytical solution of the one-dimensional coupled nonlinear sine-Gordon equation (NLSGE) subject to the appropriate initial and boundary conditions which cannot be solved by applying double Sumudu transform only. The solution of the nonlinear part of this equation was solved by a successive iterative method, the proposed technique has the advantage of producing an exact solution, and it is easily applied to the given problems analytically. Two test problems from mathematical physics were taken to show the liability, accuracy, convergence, and efficiency of the proposed method. Furthermore, the results indicate that the introduced method is promising for solving other types of systems of NLPDEs.
Research on Face Recognition Classification Based on Improved GoogleNet
Authors: Yu Zhigang , Dong Yunyun , Cheng Jihong , Sun Miaomiao , Su Feng
Published: January 2022

Face recognition is a relatively mature technology, which has some applications in many aspects, and now there are many networks studying it, which has indeed brought a lot of convenience to mankind in all aspects. This paper proposes a new face recognition technology. First, a new GoogLeNet-M network is proposed, which improves network performance on the basis of streamlining the network. Secondly, regularization and migration learning methods are added to improve accuracy. The experimental results show that the GoogLeNet-M network with regularization using migration learning technology has the best performance, with a recall rate of 0.97 and an accuracy of 0.98. Finally, it is concluded that the performance of the GoogLeNet-M network is better than other networks on the dataset, and the migration learning method and regularization help to improve the network performance.