bluuebunny/crossref_metadata_2025 huggingface dataset:
Economic Growth, Energy Use, and Greenhouse Gases Emission in Macao SAR, China
Authors: TO Wai Ming , LAM King Hang
Published: March 2022

A city’s economic structure and energy mix would change when the city is developed to accommodate more residents, visitors, and activities. This paper reviews Macao’s economic growth, energy use, and greenhouse gases (GHG) emission from 1985 to 2020. Specifically, Macao’s gross domestic product (GDP), energy use, and GHG emission have surged after the gaming industry was liberalized in 2002. The official data show that Macao’s GDP was MOP 11 billion in 1985, increased by four-fold to MOP 54 billion in 2000, and then surged rapidly to MOP 445 billion in 2019. Additionally, Macao’s total energy use increased from 8,840[Formula: see text]TJ in 1985 to 48,330[Formula: see text]TJ in 2019 while Macao’s GHG emission increased from 0.70[Formula: see text]Mt of CO2-equivalent in 1985 to 6.13[Formula: see text]Mt of CO2-equivalent in 2019. Macao’s GHG emission from all local sources per capita and GDP per capita exhibit an inverted U-shaped relationship, showing an environmental Kuznets curve. Due to the negative impact of COVID-19 pandemic, Macao’s GDP dropped by 56% to MOP 194 billion while its total energy use and GHG emission dropped by 33% and 17% to 32,198[Formula: see text]TJ and 5.06[Formula: see text]Mt of CO2-equivalent, respectively, in 2020.
Roadmap for edge AI
Authors: Ding Aaron Yi , Peltonen Ella , Meuser Tobias , Aral Atakan , Becker Christian , Dustdar Schahram , Hiessl Thomas , Kranzlmüller Dieter , Liyanage Madhusanka , Maghsudi Setareh , Mohan Nitinder , Ott Jörg , Rellermeyer Jan S. , Schulte Stefan , Schulzrinne Henning , Solmaz Gürkan , Tarkoma Sasu , Varghese Blesson , Wolf Lars
Published: March 2022

Based on the collective input of Dagstuhl Seminar (21342), this paper presents a comprehensive discussion on AI methods and capabilities in the context of edge computing, referred as Edge AI. In a nutshell, we envision Edge AI to provide adaptation for data-driven applications, enhance network and radio access, and allow the creation, optimisation, and deployment of distributed AI/ML pipelines with given quality of experience, trust, security and privacy targets. The Edge AI community investigates novel ML methods for the edge computing environment, spanning multiple sub-fields of computer science, engineering and ICT. The goal is to share an envisioned roadmap that can bring together key actors and enablers to further advance the domain of Edge AI.
Overview of assets 2021
Authors: Feng Jinjuan Heidi , Hwang Faustina , Lazar Jonathan
Published: March 2022

The 23rd International ACM SIGACCESS Conference on Computers and Accessibility (ASSETS 2021) was successfully held between October 18th to 22nd, 2021. The ASSETS conference is the premier computing research conference exploring the design, evaluation, and use of computing and information technologies with and for people with disabilities and older adults. This year, the ASSETS conference continues its tradition of presenting innovative research on mainstream and specialized assistive technologies, accessible computing, and assistive applications of computer, network, and information technologies. 424 attendees from 24 countries and 6 continents (no one from Antarctica yet!) attended the conference, setting a new attendance record for ASSETS.
The use of automatic text simplification to provide reading assistance to deaf and hard-of-hearing individuals in computing fields
Authors: Alonzo Oliver
Published: March 2022

Automatic Text Simplification (ATS) aims to rewrite text in a way that reduces its linguistic complexity while preserving its original meaning. While some prior research has explored using ATS to provide reading assistance to different user groups, relatively little work has investigated its use for Deaf and Hard-of-hearing (DHH) adults or readers in a particular domain. In this project, we investigate the use of ATS-based reading assistance tools for DHH individuals in the computing and information technology (IT) fields, motivated by prior work suggesting that computing professions often require reading about new technologies in order to stay current in the profession. Employing a variety of research methods, we investigate questions including the needs and interests of DHH individuals in the computing and IT fields for ATS-based reading assistance tools and their preferences for different interface parameters of these tools. We also investigate how to evaluate these technologies with this particular user group and how they may benefit from using these tools. This summary presents the motivation for this work, positions it in the context of the related literature, and outlines the proposed solution, our current progress and the project's contributions.
SIGACCESS
Authors: Freeman Jason
Published: March 2022

As part of the 50th anniversary of SIGACCESS, a historical research project was conducted to investigate the establishment and history of the SIG. Based on interviews with key figures in the history of SIGACCESS, as well as examination of publications and documents related to the SIG, this article provides an overview of four major eras of SIGACCESS's history, including its founding, a "precarious" era in which the SIG's fate was uncertain, a period of revitalization, and a modern era focused on inclusion.
Glucose Determination Using Non-Enzymatic Sensors Based on Fe2O3 Nanoparticles
Authors: Hernández Ramírez Daniel , Álvarez Romero Giaan Arturo , Mendoza Huizar L. Humberto , Galan-Vidal Carlos Andres , Aguilar-Lira Guadalupe Yoselin
Published: March 2022

In this work, Fe2O3 nanoparticles were used for the construction of a modified carbon paste electrode for the electrochemical determination of glucose in an alkaline solution. Differential pulse voltammetry (DPV) was used as a quantitative analytical technique and a Box-Behnken design to optimize the variables related to this technique. The Fe2O3-NPs/CPE sensor showed excellent electro-catalytic performance towards glucose oxidation with a wide linear range of 0.0003 mM - 0.7 mM, a detection limit of 4.42x10-5 mM, and a quantification limit of 1.47x10-4 mM. The sensor also showed good reproducibility and repeatability, excellent selectivity (in the presence of ascorbic acid, uric acid, lactose, caffeine, and paracetamol), and satisfactory applicability for glucose detection in commercial electrolyte drink and human urine samples. Fe2O3 nanostructures are promising for the development of effective non-enzymatic electrochemical sensors for glucose determination in complex samples.
Characterization By Modified Cyclic Biomass Voltammetry for the Reduction of Chromium in Solution with Carbon Paste Electrodes
Authors: Jiménez Pineda Bryan , Balderas-Hernández Patricia , Morales Gabriela Roa , Olivares Julián Cruz , Barrera Diaz Carlos Eduardo
Published: March 2022

Chromium is one of the elements that can be found in wastewater from a wide variety of industrial processes. Its toxicity depends on the state of oxidation and concentration in which it is found, being of great importance the elimination of hexavalent chromium present in aqueous systems such as wastewater from the tannery industry, chrome plating etc., for its recognized carcinogenic character. (1) The proposal of this work consists of characterizing the biomaterial (Pepper Dioica L. Merrill), in its different stages of adsorption of Cr(VI),that is, tests were carried out before and after being in contact with solution of this metal. This in order to be able to recognize as a method of removing Cr(VI) the Contact Technique type Batch. It was possible to achieve the characterization by means of cyclic Voltammetry, obtaining particular signals, to which the Cr(VI) signal is assumed.
Numerical Simulation of the Electrosynthesis of Br2 from a Highly Concentrated KBr Solution
Authors: Ocampo-Lazcarro Xochitl , González Gutiérrez Ana Gabriela , Rosales Abraham Sainz , Hernández Alan Plazola , Casillas Norberto
Published: March 2022

This work is aimed to investigate the electrolysis of a highly concentrated KBr solution on platinum electrodes to produce Br2(l) in a two-electrode cell without separator. The liquid bromine produced on the anode surface increases the local density up to 212% with respect to the 1 M KBr original solution. This increase in density promotes a natural Br2(l) convection flux toward the bottom of the cell with a Grashof number <1x109. The highly dense, slightly soluble, and stable liquid bromine produced at the anode surface absorbs visible light, it exhibits an intense reddish-brown color at high concentrations and a yellowish color at more dilute ones. It facilitates mass transfer studies, diffusion layer monitoring and natural convective flux inspection by video recording. The results presented in this study include a qualitative comparison between the concentration profiles, the current and potential distribution, and the calculated velocity profiles compared with those obtained experimentally.
Electrochemical Evaluation of 15-5PH Stainless Steel Passivated in Citric Acid.
Authors: Martinez Brisa , Gaona Tiburcio Citlalli , Bastidas David , Lara-Banda Maria , Samaniego Oliver , Almeraya Calderon Facundo
Published: March 2022

Most of the current aircraft are made of metal, which are exposed throughout their useful life to extreme meteorological conditions, defying the design factors in the aircraft, where the fight against corrosion has always been a determining factor. One of the protection methods used in aircraft components made of stainless steel is passivation. The study carried out consisted of the evaluation of 15-5PH stainless steel, passivated. Citric acid was used as passivating agent for passivation for 30 and 90 min with a temperature of 25 ° C. The electrochemical evaluation was carried out through the techniques of Cyclic Potentiodynamic Polarization Curves in the electrolytes of NaCl at 3.5% wt and H2SO4 at 1% wt.
Comparison between Ibuprofen and Acetaminophen in the Treatment of Infectious Fever in Children: A Meta-Analysis
Authors: Yin Fengrui , Liu Yu , Guo Hongxian
Published: March 2022

Background. To evaluate ibuprofen and acetaminophen in the treatment of infectious fever in children. Methods. We searched randomized controlled trials and retrospective cohort studies comparing ibuprofen and acetaminophen in the treatment of infectious fever among children. Data were extracted from eligible studies. We sought to evaluate temperature after taking drugs in 1, 2, and 4 hours, respectively, and analyzed the adverse events. Results were expressed as mean difference(MD) and risk ratio (RR) with 95% confidence intervals. Results. Ten studies were included in this study with a total of 1773 patients. The results showed that acetaminophen had higher temperature than ibuprofen after 1 hour (The overall mean difference was −0.29 with 95% CI (−0.49, −0.09). The P value of the overall effect was = 0.004, I2 = 91%), 2 hours (MD = −0.46, 95% CI (−0.67, −0.25), P < 0.00001, I2 = 95%), and 4 hours (MD = -0.57, 95% CI (−0.82, −0.33), P < 0.00001, I2 = 97%). There is no difference between two drugs in the adverse events (RR = 0.8, 95% CI (0.52, 1.24), P = 0.32, I2 = 0%). The sensitivity analysis and funnel plot showed that our study was robust and had low publication bias. Conclusion. Our study suggested that ibuprofen is more effective than acetaminophen in children with infectious fever.
Alteration of Phenolic and Volatile Compounds of Tea Leaf Extract by Tyrosinase and β-Glucosidase during Preparation of Ready-to-Drink Tea on Farm
Authors: Faustina Dea Risfika , Gunadi Rachmad , Fitriani Aprilia , Supriyadi Supriyadi
Published: March 2022

The manufacturing of ready-to-drink black tea was through a long process, up to 14-18 hours. There was an alternative way to produce RTD black tea directly on the farm to reduce time production and improve the quality of black tea using exogenous enzymes, i.e., tyrosinase and β-glucosidase. Tyrosinase was investigated for the ability to improve the color of tea extract by oxidize the phenolic content of green tea leaves to theaflavin and thearubigin, and β-glucosidase can enhance the volatile compounds by hydrolyze glycosidic bonds in tea leaves. Incubation of tea leaf extract with tyrosinase produces a high content of theaflavin and good color of tea extract but lowered the antioxidant activity. According to the TF/TR ratio values, tyrosinase treated tea leaf extract was in the best quality tea range. The use of β-glucosidase showed an increase in the proportion of good volatile compounds of linalool, linalool oxide, methyl salicylate, and β-damascenone.
Dynamic Monitoring of Football Training Based on Optimization of Computer Intelligent Algorithm
Authors: Gang Jin
Published: March 2022

Nowadays, with the development of computer science and technology, computer intelligent algorithms are more and more widely used in various industries. Every calculation formula in the computer intelligent algorithm has systematic logic and singleness, in order to expound the dynamic algorithm of football training optimized by the computer intelligent algorithm in detail. In this paper, the monitoring system using the computer intelligent algorithm can dynamically observe people or objects and systematically analyze them. This paper mainly studies the research of a football training dynamic monitoring system based on the computer intelligent algorithm and the design and optimization of the computer intelligent dynamic monitoring system in football training. Finally, the overall composition of the computer intelligent dynamic monitoring system and the application of the optimized computer intelligent dynamic monitoring system to the analysis of sample data are studied.
An Efficient Stacked Deep Transfer Learning Model for Automated Diagnosis of Lyme Disease
Authors: AlZubi Ahmad Ali , Tiwari Shailendra , Walia Kuldeep , Alanazi Jazem Mutared , AlZobi Firas Ibrahim , Verma Rohit
Published: March 2022

Lyme disease is one of the most common vector-borne infections. It typically causes cardiac illnesses, neurologic illnesses, musculoskeletal disorders, and dermatologic conditions. However, most of the time, it is poorly diagnosed due to many similarities with other diseases such as drug rash. Given the potentially serious consequences of unnecessary antimicrobial treatments, it is essential to understand frequent and uncommon diagnoses that explain symptoms in this population. Recently, deep learning models have been used for the diagnosis of various rash-related diseases. However, these models suffer from overfitting and color variation problems. To overcome these problems, an efficient stacked deep transfer learning model is proposed that can efficiently distinguish between patients infected with Lyme (+) or infected with other infections. 2nd order edge-based color constancy is used as a preprocessing approach to reduce the impact of multisource light from images acquired under different setups. The AlexNet pretrained learning model is used for building the Lyme disease diagnosis model. To prevent overfitting, data augmentation techniques are also used to augment the dataset. In addition, 5-fold cross-validation is also used. Comparative analysis indicates that the proposed model outperforms the existing models in terms of accuracy, f-measure, sensitivity, specificity, and area under the curve.
A Blind Source Separation Method Using Denoising Strategy Based on ICEEMDAN and Improved Wavelet Threshold
Authors: Feng Lu , Li Jiong , Li Changqing , Liu Yang
Published: March 2022

Traditional blind source separation (BSS) methods often want no additional noise effects. But in practice, noise is ubiquitous, and there are even cases with low signal-to-noise ratios (SNR). Unfortunately, these blind source separation methods have a significant impact on system performance in the presence of noise. This study proposes a blind source separation method to eliminate noise and extract signal information to the maximum extent. The method is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and improved wavelet threshold (IWT) hierarchical decomposition to improve the signal separation effect. First, the signal is decomposed via ICEEMDAN to obtain the noise-dominated and information-dominated intrinsic mode function (IMF) components. Then, the noise-dominated IMF components are again decomposed by ICEEMDAN, and each component’s scale function is obtained via detrended fluctuation analysis (DFA). Next, the noise-dominated IMF components generated in the second decomposition round undergo an improved wavelet threshold denoising to obtain useful signal information. Finally, the information-dominated IMF components generated in the two rounds of decomposition and the resulting signal of wavelet denoising yield a reconstructed signal, which is then decomposed using the BSS algorithm to obtain the source signal. The experimental results demonstrate that the proposed method can accurately extract signal information and can better separate the source signal than the existing algorithm.
Maslinic Acid Protects against Streptozotocin-Induced Diabetic Retinopathy by Activating Nrf2 and Suppressing NF-κB
Authors: Alsabaani Nasser A. , Osman Osama M. , Dallak Mohamed A. , Morsy Mohamed D. , Al-Dhibi Hassan A.
Published: March 2022

This study tested the protective effect of maslinic acid (MA) against diabetic retinopathy (DR) in rats with type 1 diabetes mellitus (T1DM) and investigated possible mechanisms of action. DM was introduced by streptozotocin (STZ) (65 mg/kg, i.p.). Control and STZ (T1DM) were divided into 2 subgroups, which received either the vehicle or MA (80 mg/kg). Serum, pancreases, and retinas were collected for further use. MA significantly reduced fasting glucose levels in the control and T1DM rats but enhanced fasting insulin levels and partially increased the size of the islets of Langerhans and the number of β-cells in T1DM rats. In addition, MA significantly improved the retina structure by preventing the reduction in the area between the inner and outer limiting membranes (ILM and OLM, respectively) and increasing the number of cells forming the ganglion cell layer (GCL), inner nuclear layer (INL), and outer nuclear layer (ONL). Associated with these effects, MA significantly reduced the total levels of tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6), as well as the nuclear levels of NF-κB p65, mRNA levels of Bax, and protein levels of cleaved caspase-3 in the retinas of T1DM rats. However, MA significantly lowered levels of reactive oxygen species (ROS) and malondialdehyde (MDA) but significantly increased the nuclear levels of Nrf2, protein levels of Bcl2, and total levels of superoxide dismutase (SOD) and reduced glutathione (GSH) in the retinas of the control and T1DM rats. In conclusion, MA prevents DR by antioxidant potential mediated by the activation of Nrf2.
Construction of College English Data Resources and Change of Teacher Positioning Using QoS Constraints
Authors: Hu Aijie
Published: March 2022

Cultivating students' autonomous learning abilities has become a research hotspot in CE (College English) reform, and the new CE autonomous learning teaching mode with students at the center also requires teachers to change traditional teaching ideas. The traditional teaching mode and teacher orientation have changed dramatically as a result of the new situation of CE teaching reform. The design and implementation of a CE data resource base based on a web database demonstrates that these technologies provide people with greater convenience and practicality. A GA (Genetic Algorithm) based on a QoS (Quality of Service) constraint is proposed on this basis. This algorithm can find a suitable resource node in the computing resources for each subtask that meets the constraint conditions and assign it to the corresponding task. Information-based education has become a powerful driving force for educational revolution, with computer technology and network information technology making English classroom teaching richer, more flexible in form, rich in content, and searchable in data.
A Brief History of Listening Comprehension in Second Language Teaching and Learning
Authors: Nazarieh Mehrdad , Razmi Mohammad Hasan , Azizian Mahdieh , Ghani Muhammad Usman
Published: March 2022

Listening, as an overlooked aspect of a foreign language acquisition, was regarded as a passive procedure to be acquired by itself, during the early days of second language teaching. As the days of second language teaching were going by, the listening difficulties and problems ensued to get in the way of second language acquisition/learning. The automatic acquisition of listening comprehension seemed to be a failed assumption. The theorists and researchers in the field, consequently, came up with the idea of developing rigorous theories to initially redefine the listening process and furthermore set clear-cut and definite standards to help the learners improve their comprehension in a specific listening context. Looking at the listening comprehension with a new viewpoint, at a specific time in the history of second language acquisition, was not at a distance from the common views over learning and teaching at that time. Therefore, the prevailing views in a specific era exerted a powerful influence over the views on teaching listening comprehension skills. The theoretical definitions of listening comprehension along with a brief history of L2 listening comprehension from the very early days of second language teaching to the present time have been discussed in the subsequent sections.
Identification of Potential Key Biomarkers of Atrial Fibrillation and Their Correlation with Immune Infiltration in Atrial Tissue
Authors: Liu Jie , Liu Meilin , Chen Xiahuan
Published: March 2022

Objective. To identify potential key biomarkers and characterize immune infiltration in atrial tissue of patients with atrial fibrillation (AF) through bioinformatics analysis. Methods. Differentially expressed genes (DEGs) were identified by the LIMMA package in Bioconductor, and functional and pathway enrichment analyses were undertaken using GO and KEGG. The LASSO logistic regression and BORUTA algorithm were employed to screen for potential novel key markers of AF from all DEGs. Gene set variation analysis was also performed. Single-sample gene set enrichment analysis was employed to quantify the infiltration levels for each immune cell type, and the correlation between hub genes and infiltrating immune cells was analyzed. Results. A total of 52 DEGs were identified, including of 26 downregulated DEGs and 26 upregulated DEGs. DEGs were primarily enriched in the Major Histocompatibility Complex class II protein complex, glucose homeostasis, protein tetramerization, regulation of synapse organization, cytokine activity, heart morphogenesis, and blood circulation. Three downregulated genes and three upregulated genes were screened by LASSO logistic regression and the BORUTA algorithm. Finally, immune infiltration analysis indicated that the atrial tissue of AF patients contained significant infiltration of APC_co_inhibition, Mast_cell, neutrophils, pDCs, T_cell_costimulation, and Th1_cells compared with paired sinus rhythm (SR) atrial tissue, and the three downregulated genes were negatively correlated with the six kinds of immune cells mentioned above. Conclusion. The hub genes identified in this study and the differences in immune infiltration of atrial tissue observed between AF and SR tissue might help to characterize the occurrence and progression of AF.
The Effectiveness of Laser Peripheral Iridotomy in Adolescent Eyes with Ocular Hypertension and Concave Configuration of the Peripheral Iris
Authors: Bakunowicz-Łazarczyk Alina , Urban Beata , Krętowska Małgorzata
Published: March 2022

Purpose. To evaluate the efficacy of laser peripheral iridotomy (LPI) in preventing deterioration in eyes with ocular hypertension (OHT) and concave configuration of the iris. Materials and Methods. This was a retrospective study, which was carried out within a period of 3–5 years. Twenty-four patients with OHT and concave irises were treated with LPI and followed up periodically. IOP, central corneal thickness (CCT), anterior chamber depth (ACD), scleral spur angle (SSA), global neuroretinal rim (NRR) thickness, and global retinal nerve fiber layer (RNFL) were examined before and after LPI. Results. The average age of the 24 patients was 14.21 ± 1.41 (13–17.5) years on admission. The initial IOP of the 48 eyes was 23.21 ± 1.56 mmHg in RE and 22.96 ± 2.1 mmHg in LE before LPI. All 48 eyes had concave irises in both eyes. All eyes treated with LPI have shown iris flattening, which has persisted during follow-up (mean 1.54 ± 0.9 years). At the last follow-up visit, the average IOP was 17.58 ± 2.63 (14–21) mmHg in RE and 17.58 ± 2.86 (14–21) mmHg in LE, which was statistically lower than that of the baseline (

p
<
0.001

). There were significant changes in SSA in both eyes and global RNFL in RE after LPI. Conclusions. In the current study, LPI resulted in an IOP-lowering effect and iris flattening in adolescent eyes with a concave configuration of the peripheral iris.
Eye State Identification Utilizing EEG Signals: A Combined Method Using Self-Organizing Map and Deep Belief Network
Authors: Ahmadi Neda , Nilashi Mehrbakhsh , Minaei-Bidgoli Behrouz , Farooque Murtaza , Samad Sarminah , Aljehane Nojood O. , Zogaan Waleed Abdu , Ahmadi Hossein
Published: March 2022

Measuring brain activity through Electroencephalogram (EEG) analysis for eye state prediction has attracted attention from machine learning researchers. There have been many methods for EEG analysis using supervised and unsupervised machine learning techniques. The tradeoff between the accuracy and computation time of these methods in performing the analysis is an important issue that is rarely investigated in the previous research. This paper accordingly proposes a new method for EEG signal analysis through Self-Organizing Map (SOM) clustering and Deep Belief Network (DBN) approaches to efficiently improve the computation and accuracy of the previous methods. The method is developed using SOM clustering and DBN, which is a deep layer neural network with multiple layers of Restricted Boltzmann Machines (RBMs). The results on a dataset with 14980 instances and 15 attributes representing the values of the electrodes demonstrated that the method is efficient for EEG analysis. In addition, compared with the other supervised methods, the proposed method was able to significantly improve the accuracy of the EEG prediction.
Green Supply Chain Innovation Strategies, considering Government Subsidy and Altruistic Preference
Authors: Liu Guangdong , Chen Jinggui , Li Ziyang , Zhu Huagui
Published: March 2022

To study the impact of government subsidies and altruistic preferences on green supply chain innovation, Stackelberg game theory and numerical simulation are used to analyze and verify the optimal decision-making of enterprises under different decision-making models. The results show that the two methods of government subsidies can improve product greenness, corporate profits, and overall supply chain performance. While only the unilateral altruistic preference of the manufacturer or retailer can improve product greenness, the profit of the other member, and the overall profit of the green supply chain, doing so will reduce its own profit. When the two members have the same degree of altruistic preference, the retailer’s altruistic preference is more conducive to improving product greenness. Increasing government subsidies can strengthen, to a certain extent, the effect of altruistic preference on product greenness, the profit of the other member, and the profit of the green supply chain. However, when the manufacturer implements altruistic preference, if the government subsidy exceeds a certain range, then the increase in the government subsidy will accelerate the decline of the manufacturer’s own profit.
Comparison of Visual Outcomes and Quality of Life in Patients with High Myopic Cataract after Implantation of AT LISA Tri 839MP and LS-313 MF30 Intraocular Lenses
Authors: Shen Jiying , Zhang Limei , Ni Shuang , Cai Lei , Guo Haike , Yang Jin
Published: March 2022

Purpose. To investigate the benefits of multifocal lens in patients with high myopic cataract and compare the clinical effects between AT LISA tri 839MP and MPlus LS-313 MF30 intraocular lenses (IOLs) in high myopic eyes. Methods. This retrospective cohort study analyzed 60 eyes with axial length >26 mm in 40 patients. Thirty eyes were implanted with MF30, and the remaining 30 eyes were implanted with 839MP. Postoperative uncorrected distance visual acuity (UDVA), best corrected distance visual acuity (BCDVA), uncorrected intermediate visual acuity (UIVA) and uncorrected near visual acuity (UNVA), defocus curve, modulation transfer function (MTF) curve, Strehl ratio (SR), and complications were compared between the two groups. Results. All vision outcomes were significantly improved in both groups (

p
<
0.05

). There was no significant between-group difference in UDVA at 1 and 3 months postoperatively (

p
>
0.05

). However, UIVA and UNVA were significantly better in the 839MP group (

p
<
0.05

). The VF-14 score, especially for near vision quality, was significantly higher in the MF30 group (2.2 ± 0.9 vs. 0.8 ± 0.7;

p
≤
0.001

). The SR of both groups significantly increased postoperatively (

p
<
0.05

). All the 3-month MTF curve values (MTF 10 total, MTF 10 internal, MTF 30 total, and MTF 30 internal) were significantly better in the 839MP group (

p
<
0.05

). Meanwhile, all the high-order aberration values (coma, spherical aberration, and trefoil) were significantly greater in the MF30 group (

p
<
0.05

). Conclusion. Multifocal IOL implantation achieves good quality of distance, intermediate, and near vision in patients with high myopia, improving their quality of life. Both 839MP and MF30 IOLs can provide good distance vision, but 839MP performs better in near and intermediate vision. However, for some patients with an extra-long optic axis, MF30 may be a good choice because of its wider range of degrees.
Intelligent Processing and Classification of Multisource Health Big Data from the Perspective of Physical and Medical Integration
Authors: Tang Haiou
Published: March 2022

With the development of computer science and information technology, human society is gradually stepping into the Internet and big data. The medical and health industry can realize the integration and readjustment of existing resources, improve the operation efficiency of the industry, and tap the huge potential of the industry with the support of big data technology. However, the medical data in the new era has the characteristics of massive, high latitude, complex structure, and complex information, which is not conducive to the direct classification of health data. The preprocessing of health data can improve the quality of dataset, reduce the size of data, and improve the efficiency and accuracy of data classification. Based on this and according to the characteristics of health dataset and the existing pretreatment technology, this paper analyzes and improves the algorithm of abnormal data detection and data protocol in the process of reprocessing data cleaning. This paper analyzes and studies feature selection algorithms based on Bayesian inference algorithm and focuses on feature selection algorithms based on random forest. In order to solve the problem that the original algorithm ignored the relationship between the importance degrees of each feature in a single tree, a feature importance degree calculation method based on local importance degree was proposed. Through experimental analysis and comparison, the improved algorithm can select better feature subset and improve the performance of the classification model. Then, TAN classifier, BAN classifier, and MBN classifier were constructed based on preprocessed hypothyroidism data, and the performances of these four classifiers were compared through experiments. The final results show that BAN classifier has the best average classification effect.
A Network Sampling Strategy Inspired by Epidemic Spreading
Authors: Dong Qiang , Yu En-Yu , Li Wen-Jun
Published: March 2022

Nowadays, network sampling has become an indispensable premise and foundation for large-scale network analysis, and its effectiveness determines to a large extent the reliability and practicability of the subsequent network analysis results. In this paper, we propose a network sampling algorithm inspired by an epidemic spreading model named the contact process. The contact process is similar to the random walk process but different from it in two key points. First, at each time step, a randomly selected sampled node rather than the latest sampled node is responsible for recruiting a new node from its neighborhood. Second, the responsible node recruits one of its neighbor nodes with a probability inversely proportional to the degree of this neighbor node, instead of equal probability. Experiments on nine indiscriminately selected real-world networks show that our proposed sampling algorithm has a significant advantage in preserving two basic network properties, the degree distributions and clustering coefficient distributions of original networks, compared with seven classical sampling methods.
Effect and Role of miR-196b in Ectopic Pregnancy
Authors: Deng Shan , Su Xiaofeng , Li Xiaoling , Shen Xiaomiao , Chen Shengru , Lin Xiaoman , Ye Mushi
Published: March 2022

Ectopic pregnancy (EP) is associated with significant morbidity and mortality, but the molecular mechanism of this condition is still unclear. miR-196b, a hot research direction for the past few years, participates in the occurrence of various diseases but whether it plays a regulatory role in EP is still unclear. This research was set to investigate the expression and potential value of miR-196b in EP. qRT-PCR was utilized to determine the relative expression of miR-196b in peripheral blood of EP patients and to observe the expression changes of miR-196b before and after treatment. Correlation analysis of miR-196b with HCG and progesterone was performed. Logistic regression analysis was applied to independent risk factors affecting EP patients. TargetScan was utilized to predict the downstream target genes of miR-196b, and GO and KEGG analysis was carried out using the R language pack. qRT-PCR showed that miR-196b expression in peripheral blood of EP patients was lower than that of normal people. miR-196b expression in patients after treatment was notably higher than that before treatment. In addition, correlation analysis showed that miR-196b was positively correlated with the expression of HCG, progesterone, and estradiol. Risk factor analysis revealed that abortion history, pelvic inflammatory disease history, lower abdominal surgery history, and miR-196b were independent risk factors for EP, and the AUC of the combined ROC curve was 0.899. GO function enrichment and KEGG signal pathway enrichment found 10 potential functions and 2 potential signal pathways of miR-196b. miR-196b is expressed in EP patients, is differentially expressed according to the change in EP condition, and is expected to become a promising index for clinical diagnosis of EP.
Research on Rapid Dynamic Rendering and Modeling Technology of Water Landscape Based on 3D Image Technology
Authors: Sun Li
Published: March 2022

With the development of computer graphics technology, the application of 3D visualization simulation technology has become increasingly widespread. This paper develops a three-dimensional visualization simulation system for water bodies by studying the general model of regional landscape space and water quality purification. This paper mainly does research on rapid dynamic rendering and modeling technology of water landscape based on 3D image technology and uses the 3ds Max Material Editor to map the enhanced 3D landscape design to complete the design, and the designed landscape feature data will be saved in the system data statistics unit for later query. This article also expounds the entire process of alternative simulation construction and confirms the feasibility and superiority of alternative modeling methods through multiple case comparisons. Alternative modeling methods are used as the main way to construct waterfall scenes, that is, simple models are combined with texture mapping, particle effects, and other forms to replace complex models, while ensuring the simulation effect, and the complex models are presented in the most simplified way to make the virtual scene. It is well performed in terms of authenticity and real time. The research results show that the designed system design renderings are extremely clear and of good quality; they have a high score in the water landscape design. Moreover, the system application cost is low, energy consumption is low, and operation efficiency is high.
A Novel Fault Diagnosis Approach for Rolling Bearing Based on CWT and Adaptive Sparse Representation
Authors: Yuan Xing , Zhang Huijie , Liu Hui
Published: March 2022

Extraction and enhancement of weak impulse signature is the key of rolling bearing fault prognostics in which case the features are often weak and covered by noise. Tunable Q-factor wavelet transform (TQWT), as an emerging wavelet construction theory developed in a frequency domain explicitly, has the advantages of matching with the specific oscillation behavior of signal components. In this article, an adaptive sparse representation (ASR) method is proposed, which integrates the sparse code shrinkage (SCS) and parameter optimization into TQWT. However, direct application of ASR is difficult to extract fault signatures at the early stage or low-speed operation due to weak fault symptoms and background noise. A novel fault diagnosis strategy based on continuous wavelet transform (CWT) and ASR is investigated. CWT owns significant advantages on multiscale subdivision and weak signal detection. The results of simulated and experimental vibration signal analyses verify the effectiveness of the proposed method in accurately extracting weak impulse features from the noise environment.
Application of Key Technologies of Distributed Storage Based on the Internet of Things in Urban Fire Protection
Authors: Yin Weibin , Han Zhongjie , Feng Yueliang , Zhou Gang , Shen Yunchao
Published: March 2022

Due to the rapid development of science and technology in the current era, fires occur more frequently, and the relationship between various economic activities and things is becoming more and more frequent. The need for real-time monitoring and remote monitoring of various firefighting facilities in buildings has become very urgent; this is a task that must be put on the agenda. The existing urban fire remote monitoring system has fewer intelligent networks, so it has high requirements for the firefighters on duty in the fire control room, which is no longer sufficient for the firefighting needs of the modern society. This paper proposes a wireless city fire remote monitoring system based on Internet of Things technology, NB-IoT technology, and cloud computing technology and studies core technologies such as designing wireless monitoring nodes at the perception layer. In the urban fire protection environment of distributed storage, several important theories, key technologies, and related algorithms are being studied in detail. After a variety of experimental verification results, the spatial data engine and adaptive spatial data model of the metadata database are developed. This provides a distributed storage virtual city geographic environment, multilevel, multiregional design and development of a simulated prototype platform and storage, management, sharing, and visualization of urban geospatial data. After technical analysis, a visualization framework was installed, which has the characteristics of global vector grid integration and distributed spatial data. Through the research on the key technology of distributed storage of the Internet of Things, this paper applies it to urban fire protection and promotes the intelligent development of urban fire protection. The application of IoT storage technology in urban fire protection can solve the shortcomings of the current urban fire remote monitoring system and automatic fire alarm system. Wireless city fire remote monitoring system, real-time collection, and transmission and storage of working status information of various fire facilities in the building are provided. The functions of real-time monitoring, real-time alarm, real-time search query, record query, maintenance management, route guidance, and user management are also realized.
Discussing Total Electron Content over the Solar Wind Parameters
Authors: Eroglu Emre
Published: March 2022

Modeling and forecasting of Total Electron Content (TEC) values by an Artificial Neural Network model (ANNm) have high agreement on November 2003, 2004 superstorms. The work discusses Solar Wind Parameters (SWp) from OMNI (Operating Missions as a Node on the Internet) and TEC (TECU) data (International Reference Ionosphere) IRI-2012, IRI-2016 on November 20, 2003 (Dst = –422 nT) and on November 08, 2004 (Dst = –374 nT) Geomagnetic Storms (GSs). The paper commences with a 120-hour GS exhibition of SWp and proceeds with the correlation data of the variables, their hierarchical tracks, and inner dispersions. The ANNm with SWp as the input and TEC data as the output are introduced. The performance of the ANNm for 2003 and 2004 superstorms is adequate. The Correlation Coefficient (R) and Root Mean Square Error (RMSE) of the ANNm are 97.5%, 1.17 TECU (IRI-2012), and 97.9%, 1.09 TECU (IRI-2016) for the 2003 GS and 97.0%, 0.89 TECU (IRI-2012), and 98.0%, 1.61 TECU (IRI-2016) for 2004 GS. Parameters effect of the R constant of TEC data points out to the dynamic pressure (nPa), the magnetic field Bz component (nT), the flow speed (km/s), and the proton density (1/cm3). Besides, the absolute total error and the variance of the predicted TEC data for November 2003 and November 2004 GSs are 0.06 (0.30%) with 0.013 variance (IRI-2012), 0.09 (0.49%) with 0.016 variance (IRI-2016) for 2003 storm and 0.13 (0.73%) with 0.033 variance (IRI-2012), and 0.11 (1.06%) with 0.035 variance (IRI-2016) for 2004. It means that the paper models TEC data with considerable consistency over the SWp.
Research on Human Behavior Modeling of Sports Culture Communication in Industrial 4.0 Intelligent Management
Authors: Li Zhihui
Published: March 2022

With the advent of the information age, Internet technology and computer technology are gradually applied in various fields. Human daily life is inseparable from the help of information technology. The progress and development of the times are also changing our industrial environment. The Internet of things, artificial intelligence, and digital production have been widely used in the industrial field. We need to integrate the new intelligent management concept in the industrial 4.0 era into the modern industrial system and media communication. In the communication of sports culture, how to integrate the intelligent technology under the background of industry 4.0 and realize the spatiotemporal modeling and analysis of human behavior and behavior characteristics is the main content of our research. Firstly, this study briefly analyzes the development and historical process of industry 4.0 era and explores the impact of Internet of things and information technology on sports culture communication. This study analyzes the development process of Chinese sports culture communication and probes into the influence and significance of sports culture communication on human behavior. Under the background of industrial 4.0 management, a model of human temporal and spatial behavior change in sports culture communication is established. Human behavior trajectory is analyzed by data mining, human behavior recognition algorithm, behavior quantitative analysis method, and behavior feature model. The results show that under the background of industry 4.0 management, human behavior modeling can clearly describe the basic characteristics and behavior state in the process of sports culture communication and can predict and analyze the human behavior performance of such social activities and phenomena. Through data mining, data preprocessing, and behavior quantitative analysis, human behavior trajectory is studied. It has a positive impact on the behavior trend in human daily life. The main factor affecting behavior change is whether to receive sports culture news.
A Minimum Variance Hedging Ratio Model Based on Nonlinear Grey Classification Model
Authors: Gao Xia , Zhao Zhanxing
Published: March 2022

The risk transfer function of futures market is mainly realized by hedging strategy. Futures price yield and spot price yield tend to show different fluctuations before and during hedging, which leads to the distortion of hedging ratio, that is, the calculated hedging effect is weaker than the traditional hedging effect. On the basis of MV (minimum variance) hedging model, this paper introduces NGCM (nonlinear grey classification model) to solve the nonlinear correlation between futures and spot returns, which can improve the hedging effect. The results show that, due to the existence of basis, the price change model violates the linear assumption of OLS (ordinary least squares) parameters, and there is a problem of model missetting. It is estimated that HR (hedging ratio) should choose the price model, which can better depict the linkage between futures price and spot price. The effectiveness of HR in this study is higher than that of existing models. Applying this model to hedge can effectively avoid the spot price risk. Investors can reasonably choose the hedging model according to their own needs.
Characteristics and Visual Prognosis of Pediatric Open Globe Injury in Shanghai, China
Authors: Guo Yue , Guo Yu , Gu Ruiping , Yang Qian , Zhu Ke , Shu Qinmeng , Gan Dekang
Published: March 2022

Introduction: The aim of the study was to describe the characteristics of open globe injury (OGI) and the relationship between the complications and visual outcomes in children with this type of injury. Methods: This was a retrospective chart review of 1,664 children, under the age of 16 years, who were hospitalized for OGI between January 1, 2007, and December 31, 2015. Each patient’s age, sex, cause and agent of injury, complications, visual acuity, and classification of ocular trauma were collected for review and analysis. Results: The mean age was 5.6 ± 3.4 years. Right eyes were particularly vulnerable to injury (right eye:left eye ratio = 1.2:1). Traumatic cataract was the most common complication. The average initial and final best corrected visual acuity were logarithm of the minimum angle of resolution (logMAR) 2.04 ± 0.78 and logMAR 1.74 ± 0.88, respectively. Logistic regression analysis showed that hyphema (odds ratio [OR] = 1.850), iris prolapse (OR = 1.702), vitreous hemorrhage (OR = 9.703), retinal detachment (OR = 11.938), endophthalmia (OR = 5.377), intraocular foreign body (OR = 3.346), and initial visual acuity <0.05 (OR = 9.017) were risk factors for visual acuity <0.05 at hospital discharge. Conclusion: OGI was most frequent in preschool children and boys. Right eyes were more vulnerable than left eyes. Poor visual outcomes were associated with hyphema, iris prolapse, vitreous hemorrhage, retinal detachment, endophthalmia, intraocular foreign body, and an initial visual acuity <0.05.