bluuebunny/crossref_metadata_2025 huggingface dataset:
Lorentz–Darboux rectifying surfaces and osculating surfaces along frontal curves on timelike surfaces
Authors: Jiang Jiakun , Liu Siyao , Wang Zhigang , Liu Haiming
Published: April 2022

In this paper, we consider two kinds of developable surfaces along a timelike frontal curve lying in a timelike surface in Minkowski 3-space, the Lorentz–Darboux rectifying surfaces and the Lorentz–Darboux osculating surfaces. Meanwhile, we also consider two curves generated by such a timelike frontal curve. We give two new invariants of the frontal curve which characterize singularities of the Lorentz–Darboux developable surfaces and Lorentz–Darboux rectifying and osculating curves. Unlike the regular curves, the frontal curves may have singular points. Using the methods of the unfolding theory in singularity theory, we complete the classifications of the singular points of these two surfaces and two curves. The main results indicate that compared with developable surfaces along a regular curve, there exists a more complicated construction for the singularities of the developable surfaces along a timelike frontal curve, there will appear an extra locus, arising by the singular point of the timelike frontal curve, for the singularities of the Lorentz–Darboux rectifying surface, whereas the Lorentz–Darboux osculating surface did not. In addition, we investigate the geometric properties of the timelike frontal curve, it is shown that the timelike frontal curve can be regarded as the envelope of a family of timelike frontal rectifying curves. Finally, we provide several examples to illustrate the theoretical results.
An Innovative Microwave-Assisted One-Step Green Synthetic Approach of Biowaste Derived Fluorescent Carbon-Dot Invisible Ink for Currency Anti-Counterfeiting Applications
Authors: Dinake Pogisego , Phokedi Gothatamang Norma , Mokgadi Janes , Mokibe Tumisang , Tlhako Mogomotsi , Botlhomilwe Mmamiki Ayanda , Kelebemang Rosemary , Motswetla Obakeng , Present Bokang
Published: April 2022

The quest for the design and synthesis of carbon dots with anti-counterfeit properties that are derived via green, environmentally friendly and economical procedures is a continuous process. Carbon dots (C-dots) derived from biowaste are cheap to synthesize, possess good photo-stability and high synthetic yield, making them applicable in the anti-counterfeiting of currency. Herein, we report a novel eco-friendly, cheaper, and faster method for the synthesis of carbon dots with strong photoluminescence properties from monkey orange fruit (Strychnos spinosa) biowaste. The presence of the hydroxyl and carbonyl functional groups of the carbon dots were determined by the Fourier transform infrared spectroscopy (FTIR). The carbon dots showed strong blue emission fluorescence (emission wavelength of 452[Formula: see text]nm) when excited at 330[Formula: see text]nm. The morphology and size were determined by the atomic force microscopy (AFM) which indicated amorphous and spherical nanoparticles with an average size of less than 2[Formula: see text]nm. The no-crystallinity of the as-prepared carbon dots was confirmed using X-ray diffraction which showed the graphite-like structure. The carbon dots were produced and demonstrated good photo and chemical stability as well as high covert properties. The anti-counterfeiting of currency application by the synthesized carbon dots was demonstrated when the subsequent gel ink printed on the currency showed excellent chemical stability when exposed to washing with water, ethanol, and acetone. It also showed superior photostability when exposed to UV light at 365[Formula: see text]nm and daylight for an extended period of up to 6[Formula: see text]h. This work provides a facile, economical, and green approach for large scale production of carbon dots from the abundant biowaste.
Proposing a Set of Far-Field Records for Time History Analysis in Tehran City and Comparison with FEMA-P695 Set Records
Authors: Hassani Sokhtesaraei M. , Ghassemieh M. , Mirghaderi S. R.
Published: April 2022

The aim of this paper is to prepare a list of appropriate far-field records for the purpose of time history analysis and structural design assessment in Tehran. Accordingly, the study focused on considering all parameters affecting the choice of accelerogram pair for Tehran. For this purpose, a total of 1000 accelerogram pairs of the earthquakes that occurred in Iran during 1978–2010 were studied in terms of the parameters affecting the choice of appropriate accelerogram pair for Tehran, including distance to the epicenter, magnitude, frequency content, earthquake mechanism, soil properties, and configuration of ground layers. An important objective followed by preparing such a list was to appropriately match it to susceptibility and performance levels set in local design standards. For this purpose, seismic, statistical, and analytic studies were performed, finally ending up with a list of appropriate accelerogram pairs for time history analysis and design assessment in Tehran. In this research, to provide a proper understanding of the proposed list’s applied demands on the region’s structures, numerical and statistical studies and comparisons with the FEMA-P695 list have been done.
Hardness of uncertain segment cover, contiguous SAT and visibility with uncertain obstacles
Authors: Alipour Sharareh , Parsa Salman
Published: April 2022

In this paper, we prove that the uncertain segment cover problem is NP-hard. The uncertain segment cover problem, which appears in solving the question of visibility of a segment in the plane from a given point in the presence of uncertain obstacles, asks if a given interval can be covered by a collection of uncertain sub-intervals having two possible positions. This is done by showing that this problem is equivalent to a special version of the SAT problem that we will call contiguous SAT. In a contiguous SAT problem a CNF formula with a given ordering on its clauses is given such that any literal appears contiguously. We prove that contiguous SAT problem is NP-hard and as a consequence it follows that the uncertain segment cover problem is NP-hard. The approximation solution of this problem and its hardness are also studied.
Semantics Foundation for Cyber-physical Systems Using Higher-order UTP
Authors: Xu Xiong , Talpin Jean-Pierre , Wang Shuling , Zhan Bohua , Zhan Naijun
Published: April 2022

Model-based design has become the predominant approach to the design of hybrid and cyber-physical systems (CPSs). It advocates the use of mathematically founded models to capture heterogeneous digital and analog behaviours from domain-specific formalisms, allowing all engineering tasks of verification, code synthesis, and validation to be performed within a single semantic body. Guaranteeing the consistency among the different views and heterogeneous models of a system at different levels of abstraction, however, poses significant challenges. To address these issues, Hoare and He’s Unifying Theories of Programming (UTP) proposes a calculus to capture domain-specific programming and modelling paradigms into a unified semantic framework. Our goal is to extend UTP to form a semantic foundation for CPS design.
Higher-order UTP
(HUTP) is a conservative extension to Hoare and He’s theory that supports the specification of discrete, real-time, and continuous dynamics, concurrency and communication, and higher-order quantification. Within HUTP, we define a calculus of
normal hybrid designs
to model, analyse, compose, refine, and verify heterogeneous hybrid system models. In addition, we define respective formal semantics for Hybrid Communicating Sequential Processes and Simulink using HUTP.
perCLTV: A General System for Personalized Customer Lifetime Value Prediction in Online Games
Authors: Zhao Shiwei , Wu Runze , Tao Jianrong , Qu Manhu , Zhao Minghao , Fan Changjie , Zhao Hongke
Published: April 2022

Online games make up the largest segment of the booming global game market in terms of revenue as well as players. Unlike games that sell games at one time for profit, online games make money from in-game purchases by a large number of engaged players. Therefore, Customer Lifetime Value (CLTV) is particularly vital for game companies to improve marketing decisions and increase game revenues. Nowadays, as virtual game worlds are becoming increasingly innovative, complex, and diverse, the CLTV of massive players is highly personalized. That is, different players may have very different patterns of CLTV, especially on churn and payment. However, current solutions are inadequate in terms of personalization and thus limit predictive performance. First, most methods just attempt to address either task of CLTV, i.e., churn or payment, and only consider the personalization from one of them. Second, the correlation between churn and payment has not received enough attention and its personalization has not been fully explored yet. Last, most solutions around this line are conducted based on historical data where the evaluation is not convincing enough without real-world tests. To tackle these problems, we propose a general system to predict personalized customer lifetime value in online games, named perCLTV. To be specific, we revisit the personalized CLTV prediction problem from the two sub-tasks of churn prediction and payment prediction in a sequential gated multi-task learning fashion. On this basis, we develop a generalized framework to model CLTV across games in distinct genres by heterogeneous player behavior data, including individual behavior sequential data and social behavior graph data. Comprehensive experiments on three real-world datasets validate the effectiveness and rationality of perCLTV, which significantly outperforms other baseline methods. Our work has been implemented and deployed in many online games released from NetEase Games. Online A/B testing in production shows that perCLTV achieves a prominent improvement in two precision marketing applications of popup recommendation and churn intervention.
HAMLET: A Hierarchical Agent-based Machine Learning Platform
Authors: Esmaeili Ahmad , Gallagher John C. , Springer John A. , Matson Eric T.
Published: April 2022

Hierarchical Multi-agent Systems provide convenient and relevant ways to analyze, model, and simulate complex systems composed of a large number of entities that interact at different levels of abstraction. In this article, we introduce HAMLET (Hierarchical Agent-based Machine LEarning plaTform), a hybrid machine learning platform based on hierarchical multi-agent systems, to facilitate the research and democratization of geographically and/or locally distributed machine learning entities. The proposed system models machine learning solutions as a hypergraph and autonomously sets up a multi-level structure of heterogeneous agents based on their innate capabilities and learned skills. HAMLET aids the design and management of machine learning systems and provides analytical capabilities for research communities to assess the existing and/or new algorithms/datasets through flexible and customizable queries. The proposed hybrid machine learning platform does not assume restrictions on the type of learning algorithms/datasets and is theoretically proven to be sound and complete with polynomial computational requirements. Additionally, it is examined empirically on 120 training and 4 generalized batch testing tasks performed on 24 machine learning algorithms and 9 standard datasets. The provided experimental results not only establish confidence in the platform’s consistency and correctness but also demonstrate its testing and analytical capacity.
Profile Decomposition Based Hybrid Transfer Learning for Cold-Start Data Anomaly Detection
Authors: Li Ziyue , Yan Hao , Tsung Fugee , Zhang Ke
Published: April 2022

Anomaly detection is an essential task for quality management in smart manufacturing. An accurate data-driven detection method usually needs enough data and labels. However, in practice, there commonly exist newly set-up processes in manufacturing, and they only have quite limited data available for analysis. Borrowing the name from the recommender system, we call this process a cold-start process. The sparsity of anomaly, the deviation of the profile, and noise aggravate the detection difficulty.

Transfer learning could help to detect anomalies for cold-start processes by transferring the knowledge from more experienced processes to the new processes. However, the existing transfer learning and multi-task learning frameworks are established on task- or domain-level relatedness. We observe instead, within a domain, some components (background and anomaly) share more commonality, others (profile deviation and noise) not. To this end, we propose a more delicate component-level transfer learning scheme, i.e., decomposition-based hybrid transfer learning (
DHTL
): It first decomposes a domain (e.g., a data source containing profiles) into different components (smooth background, profile deviation, anomaly, and noise); then, each component’s transferability is analyzed by expert knowledge; Lastly, different transfer learning techniques could be tailored accordingly. We adopted the Bayesian probabilistic hierarchical model to formulate parameter transfer for the background, and “
L
2,1
+
L
1
”-norm to formulate low dimension feature-representation transfer for the anomaly. An efficient algorithm based on Block Coordinate Descend is proposed to learn the parameters. A case study based on glass coating pressure profiles demonstrates the improved accuracy and completeness of detected anomaly, and a simulation demonstrates the fidelity of the decomposition results.
Advantages of a Statistical Estimation Approach for Clock Frequency Estimation of Heterogeneous and Irregular CGRAs
Authors: Wolf Dennis Leander , Spang Christoph , Diener Daniel , Hochberger Christian
Published: April 2022

Estimating the maximum clock frequency of homogeneous Coarse Grained Reconfigurable Arrays/Architectures (CGRAs) with an arbitrary number of Processing Elements (PE) is difficult. Clock frequency estimation of highly heterogeneous CGRAs takes additional factors into account, thus is even more difficult. Main challenges are the heterogeneous set of operators for each Processing Element (PE) and the irregular interconnect (connecting a CGRA’s PEs). Multiple estimation approaches could be reasonable. We propose an optimized statistical estimator, which is based on our prior work. We demonstrate its superiority to state-of-the-art neural networks in terms of accuracy and robustness, especially in situations with a sparse set of training data.
Do Volatilities Matter in the Interconnectedness between World Energy Commodities and Stock Markets of BRICS?
Authors: Amoako Gilbert K. , Asafo-Adjei Emmanuel , Mintah Oware Kofi , Adam Anokye M.
Published: April 2022

Financial markets integration has resulted in high interconnectedness among the BRICS stock markets, which minimizes diversification potentials. This has increased investors’ interest in the financialization of commodities to minimize their portfolio risks. However, the comovements between these assets do not operate in a vacuum, which requires that the role of volatilities be considered in tandem. The purpose of this study is to explore the interdependencies between energy commodities and stock markets of BRICS in the midst of relevant volatilities. For this reason, the wavelet techniques, biwavelet and partial wavelet, are employed. We find that positive comovements between energy commodities and stock markets of BRICS become stronger in the long‐term. Furthermore, volatility has a long‐term impact on the correlations between energy commodities and the BRICS stock market. We argue that the US Volatility Index, which measures investor anxiety and volatility in stock markets, has the biggest impact on the relationship between energy commodities and BRICS stock markets. Surprisingly, the correlations between energy commodities and Russian stock markets were strong enough to withstand the effects of volatilities. Hence, investors can use volatilities to hedge portfolio risks in energy commodities and stock markets in Brazil, India, China, and South Africa.
Effects of Changes in Osmolarity on the Biological Activity of Human Normal Nucleus Pulposus Mesenchymal Stem Cells
Authors: Zhang Hua , Li Wei , Wu YaoHong , Zhang Shengquan , Li Jie , Han Letian , Chen Haoyu , Wang Ziyu , Shen Cailiang , Zhang Yinshun , Tao Hui
Published: April 2022

The expansion and maintenance of the NPMSC (nucleus pulposus mesenchymal stem cell) phenotype are considered as potential therapeutic tools for clinical applications in intervertebral disc tissue engineering and regenerative medicine. However, the harsh microenvironment within the intervertebral disc is the main limitation of its regeneration. The osmolarity of the intervertebral disc is higher than that of other tissues, which has an important influence on the biological characteristics of NPMSCs. In this study, we observed the effect of different osmolarities on the biological characteristics of human normal NPMSCs cultured in vitro and explored the role of osmolarity in intervertebral disc degeneration. Our data demonstrated that the change in osmotic pressure has an important effect on the biological activity of NPMSCs, and this effect may occur through the P16INK4A/Rb pathway. This study provides a theoretical basis for the future treatment of intervertebral disc degeneration.
A MultiModal Detection Method for UHV Substation Faults Based on Robot Inspection and Deep Learning
Authors: Meng Rong , Wang Zhao-lei , Zhao Zhi-long , Li Jian-peng , Fu Wei-ping
Published: April 2022

Aiming at the problem of multi-modal fault detection of different equipment in ultrahigh voltage (UHV) substations, a method for based on robot inspection and deep learning is proposed. First, the inspection robot is used to collect the image data of different devices in the station and the source data is preprocessed by standard image augmentation and image aliasing augmentation. Then, the HSV color space model based on saliency area detection is used to extract equipment defect areas, which improves the accuracy of defect image classification. Finally, the traditional YOLOv3 network is improved by combining the residual network and the K-means clustering algorithm, and the detailed flow of the corresponding detection method is proposed. The proposed detection method and the other three methods were compared and analyzed under the same conditions through simulation experiments. The results show that the detection accuracy and recall rate of the method proposed in this study are the largest, which are 95.9% and 91.3%, respectively. The average detection accuracy under multiple intersection ratio thresholds is also the highest, and the performance is better than the other three comparison algorithms.
On the Performance of Cooperative Relaying Systems with NOMA in
κ
−
μ
Fading Environment
Authors: Khatalin Sari , Miqdadi Hadeel
Published: April 2022

Nonorthogonal multiple access (NOMA) is considered to be a promising solution for the fifth generation (5G) wireless networks. The performance of the 5G wireless networks in terms of outage probability, achievable rate, and other important network performance metrics can be remarkably improved by the integration of NOMA with cooperative relaying transmission technique. In this paper, we investigate the performance of the cooperative relaying system integrated with NOMA in terms of outage probability and achievable rate over

κ
−
μ

fading channels. Analytical expressions for the outage probability and the achievable rate are obtained for the system mentioned above. Moreover, corresponding expressions for Rician and Rayleigh fading channels are also presented in this paper as special cases of

κ
−
μ

fading channels. Some numerical examples are presented here for illustration purposes. The analytical results obtained in this paper are all validated via simulation.
GC-MS Analysis of Bioactive Compounds in Methanolic Extracts of Papaver decaisnei and Determination of Its Antioxidants and Anticancer Activities
Authors: Jabbar Ahmed Aj. , Abdullah Fuad O. , Abdulrahman Kamaran K. , Galali Yaseen , Sardar Abdullah Sh.
Published: April 2022

The Papaver L. plant (Papaver decaisnei) has ethnobotanical records in many countries including Iraqi Kurdistan. The current study investigates the methanol (99.9%) extracts (10 μg/mL) of roots, leaves, and flowers of Papaver decaisnei in terms of phytochemistry by gas chromatography-mass spectrophotometry GC-MS, in vitro antioxidant activity by radical scavenging and reducing power assays, and finally, the anticancer actions as IC50 (inhibitory concentration at 50%) against human colorectal adenocarcinoma (Caco-2), mammary cancer cells (MCF-7), and human cervical carcinoma (HeLa) cells. The results showed 22, 19, and 17 chemicals for roots, leaves, and flowers of P. decaisnei, respectively. The prevalent organic compounds of P. decaisnei were alkaloids (62.03%), phenolics (55.43%), fatty acids (42.51%), esters (32.08%), terpenoids (25.59%), and phytosterols (15.68%), namely, roemerine (70.44%), 9,12,15-octadecatrien-1-ol (37.45%), hexadecanoic acid (33.72%), decarbomethoxytabersonine (24.49%), and γ-sitosterol (11.22%). The antioxidant activity of plant organs was within 39.1–143.5 μg/mL for DPPH, 135.4–276.4 μg/mL for ABTS, 12.4–34.3 μg/mL for FRAP, and 42.6–75.8 μg/mL for CUPRAC assays. The anticancer of P. decaisnei was found as 125.3–388.4 μg/mL against all tested cell lines (Caco-2, MCF-7, and HeLa). The detected alkaloids and bioactivity of P. decaisnei encourage future isolation of those remarkable alkaloids (reomerine) for potential usage in the pharmaceutical industry.
Financial Crisis Warning for Listed Manufacturing Companies in China
Authors: Wang Yang , Liu Weimin , Fu Yuwei
Published: April 2022

In this paper, we have first selected 28 indicators based on the selection principle of financial indicators adopted in relevant studies both at home and abroad from seven aspects, which include profitability, long-term and short-term solvency, company development capacity, operating capacity, cash flow generation capability, equity characteristics, and board characteristics. Subsequently, we have conducted the comparative analysis and comprehensive study on the logistic early warning model and BP neural network to provide reference for managers and stakeholders to select the optimal model. In addition, through our study on the dynamic early warning of BP neural network, we intend to convey the concept of constantly updating the model to both managers and stakeholders. Therefore, this paper provides ideas for the research on the model of financial crisis early warning for China’s manufacturing industry. The study is of significance for guiding the research of related issues in the manufacturing sector and can also provide reference for the early warning of other industries.
Prioritization of Susceptible Watershed to Sediment Yield and Evaluation of Best Management Practice: A Case Study of Awata River, Southern Ethiopia
Authors: Kefay Temesgen , Abdisa Tolera , Chelkeba Tumsa Bekan
Published: April 2022

Soil erosion is currently a global problem that causes land degradation and long-lasting challenges in Ethiopia. Sediment yield is influenced by the watershed characteristics such as land cover, soil class, and slope, which are considered the drivers of soil erosion in the basin. The middle Awata watershed is highly susceptible to soil erosion due to its topographical features. This study is therefore aimed at estimating sediment yield, examining its spatial distribution, and evaluating the selected Best Management Practices (BMPs) to reduce soil erosion-prone areas at downstream. The model simulation was done by dividing the total watershed area of 1912 km2 into 37 subbasins and 294 hydrologic response units (HRUs) for 31 years (1988–2018). The model’s uncertainty evaluation was carried out on monthly basis using the Sequential Uncertainty Fitting (SUFI-2) algorithm. The performance of the model was evaluated by statistical parameters that gave R2 = 0.76, NSE = 0.75, RSR = 0.51, and PBIAS = 5.6% for calibration and R2 = 0.75, NSE = 0.74, RSR = 0.51, and PBIAS = 2.7% for validation of streamflow. Meanwhile, sediment yield in the watershed was also simulated with R2 = 0.69, NES = 0.66, RSR = 0.58, and PBIAS = 3.7% for calibration and R2 = 0.67, NES = 0.65, RSR = 0.61, and PBIAS = 5.6% for validation of sediment distribution. The simulated annual average sediment yield was 34.543 × 103 ton/year at the outlet of the middle Awata watershed. The developed spatial distribution of sediment yield identified the first twelve upstream subwatersheds as being soil erosion-prone areas. Following an evaluation of the four selected BMPs, it was determined that parallel terracing is the most recommended method for soil erosion reduction option in all critical subbasins found in the watershed.
Statistics and Analysis of Targeted Poverty Alleviation Information Integrated with Big Data Mining Algorithm
Authors: Gao Meizhen , Li Li , Gao Yetong
Published: April 2022

To gain a more comprehensive and systematic understanding of the impact of government assistance to poor households on poverty reduction targets, a targeted poverty alleviation information statistics and analysis integrated with big data mining algorithm is proposed. Combined with the big data knowledge of the new era, according to the machine learning (ML) pipeline module in spark, a big data computing framework, combined with known data mining algorithms, massive sample data are used to replace random stratified sampling data for modeling and analysis, and random forest model, logistic model, and newly proposed waterfall model are constructed for poor households. Finally, through the comparative evaluation of several poor household identification models, the results show that when 100 real data test the accuracy of the three poor household models, the random forest model and logistic model are slightly reduced, which are 82% and 72%, respectively, but the waterfall model is basically unchanged, which is 83%, and the three models have little change. The new waterfall design proposed in this article has the advantage of a high percentage of sample reuse and can effectively prevent overfitting, and there is no need for massive data. It is a stable and reliable new model. The combination of targeted poverty reduction algorithms and big information technology and mining data can get the most common causes more accurate and convincing results. The right rib trunk and rib are often separated from the common cause because of the population.
Somatic Alteration Characteristics of Early-Onset Gastric Cancer
Authors: Zhou Qiyang , Tao Feng , Qiu Liqing , Chen Hanlin , Bao Hua , Wu Xue , Shao Yang , Chi Liangjie , Song Hu
Published: April 2022

Gastric cancer is one of the most common and deadly cancer types worldwide, which brings millions of dollars of economic loss each year. Patients diagnosed with early-onset gastric cancer were reported to have a worse prognosis compared to other gastric cancer patients, while the mechanisms behind such phenomenon are unknown. To identify age-dependent somatic alternations in gastric cancer, next-generation sequencing targeting 425 genes was performed on 1688 gastric tumor tissues and corresponding plasma samples. In our study, the microsatellite instability (MSI) and chromosomal instability score (CIS) values increased along with the age of patients, which indicates that older patients display a less genomic stability pattern. The differences of somatic alternations between young and old groups were compared. Somatic mutations CDH1 and copy number gains of FGFR2 were identified to enrich in the younger gastric cancer patients, which may contribute to the worse prognosis of early-onset gastric cancer patients.
Prognostic Factors for Visual Outcomes in Closed Idiopathic Macular Holes after Vitrectomy: Outcomes at 4 Years in a Monocentric Study
Authors: Lachance Alexandre , Hébert Mélanie , You Eunice , Rozon Jean-Philippe , Bourgault Serge , Caissie Mathieu , Tourville Éric , Dirani Ali
Published: April 2022

Purpose. To identify predictive factors of visual outcomes in the eyes after successful macular hole (MH) surgery. Methods. It is a retrospective monocentric study of the eyes that underwent successful vitrectomy for full-thickness MH in an academic, tertiary care center (CHU de Québec–Université Laval, Québec, Canada) between 2014 and 2018. We included a single eye per patient and excluded the eyes with ocular comorbidities. Clinical and anatomical features of patients were collected, including demographics, MH duration, baseline MH size, baseline visual acuity (VA), and final VA. Multiple logistic regressions were performed to determine predictive factors of VA ≥70 ETDRS letters (Snellen equivalent: 20/40) and VA gain ≥15 ETDRS letters at final follow-up. Areas under the receiver operating characteristic curve (AUC) were used to determine the performance of each model and identify the Youden index maximizing performance at a given threshold. Results. A total of 460 eyes were included in this study; 274/460 eyes (60%) achieved final VA ≥70 ETDRS letters and 304/460 eyes (66%) had a VA gain ≥15 ETDRS letters at 24 months follow-up. Multiple logistic regression analyses showed that the main predictive factors for final VA ≥70 ETDRS letters (model AUC = 0.716) were baseline VA (OR = 1.064;

p
<
0.001

), MH duration (OR = 0.950;

p
=
0.005

), and age (OR = 0.954;

p
=
0.004

). Predictors of VA gain ≥15 ETDRS letters at final follow-up (model AUC = 0.615) were baseline VA (OR = 0.878;

p
<
0.001

), MH duration (OR = 0.940;

p
<
0.001

), and MH size (OR = 0.998;

p
=
0.036

). Thresholds for the final VA ≥70 ETDRS letters model and the VA gain ≥15 ETDRS letters model were VA ≥55.5 ETDRS letters (Snellen equivalent: 6/30) and MH size of 237 μm, respectively. Conclusion. The eyes with shorter MH duration, smaller MH size, and higher preoperative VA achieved better visual outcomes after successful MH surgery.
An Optimization and Adjustment Method of Pile Orientation for a Pile‐Supported Wharf Based on a Genetic Algorithm
Authors: Cui Guan-Chen , Liang Ting , Han Guo-Feng
Published: April 2022

The pile‐supported wharf is widely used in port wharf design due to its excellent adaptability. However, it may intersect in space because a large number of batter piles are used. In the design process of the pile‐supported wharfs, pile orientation adjustment is an essential process to avoid pile intersection. The arrangement of pile orientation in a structural section often takes several days manually. Sometimes, designers cannot avoid pile intersections after many attempts. At present, the software can only check pile intersections, but it cannot optimize pile orientations. Considering the priority order of adjusting the pile slope, the rotation angle, and avoiding a pile intersection, the objective function for optimization is designed, and the pile orientation is optimized and adjusted by a genetic algorithm. Then the performance of the algorithm is analyzed. Finally, the algorithm is applied to two practical cases of pile‐supported wharfs. One case involves the designers who failed to arrange the pile orientation. Moreover, satisfactory results of pile orientations are obtained in a few minutes. Therefore, the proposed method greatly improves labor efficiency.
Geotechnical Characteristic Assessments of Floodplain Soils Using SCPTU Data in Nanjing, China
Authors: Zhang Mingfei , Tong Liyuan , Wang Qiang
Published: April 2022

In order to improve the understanding of such floodplain sediments and determining the validity of the tests, an extensive series of multifunctional seismic piezocone tests with pore pressure dissipation phase have been performed and supplemented with conventional borings, standard penetration, laboratory testing, and so forth. Sounding results from SCPTU were used to determine the stratigraphic profiles and the soil characteristics of two anchorage sites. A comparison of the boring and laboratory results with the CPTU profiles showed that the CPTU provided excellent information on soil stratigraphy and good guidance for determination of behavior and engineering implications of recent Yangtze River floodplain. At an area where local correlations based on modern SCPTU do not exist, methods for estimating coefficient of earth pressure at rest (K0), hydraulic permeability (kh), and equivalent stiffness (G0) associated with bridge foundation design are presented, compared, and verified. Results also illustrate the complexity and variability of the floodplain stratigraphy and soil properties, which means that the suggestions in this study should be updated when more local experience is obtained. This case study suggests that such enhanced seismic piezocone test should be considered as a potential tool and the instrument of first choice in site characterization programs for design of bridges founded on complicated soils in China.
Darbo Fixed Point Criterion on Solutions of a Hadamard Nonlinear Variable Order Problem and Ulam-Hyers-Rassias Stability
Authors: Rezapour Shahram , Bouazza Zoubida , Souid Mohammed Said , Etemad Sina , Kaabar Mohammed K. A.
Published: April 2022

The existence aspects along with the stability of solutions to a Hadamard variable order fractional boundary value problem are investigated in this research study. Our results are obtained via generalized intervals and piecewise constant functions and the relevant Green function, by converting the existing Hadamard variable order fractional boundary value problem to an equivalent standard Hadamard fractional boundary problem of the fractional constant order. Further, Darbo’s fixed point criterion along with Kuratowski’s measure of noncompactness is used in this direction. As well as, the Ulam-Hyers-Rassias stability of the proposed Hadamard variable order fractional boundary value problem is established. A numerical example is presented to express our results’ validity.
Comparing the Prognoses of Breast-Conserving Surgeries for Differently Aged Women with Early Stage Breast Cancer: Use of a Propensity Score Method
Authors: Bao Shurui , He Guijin
Published: April 2022

Background. To explore the effect of age on the prognosis of patients with early stage breast cancer after breast-conserving surgery (BCS) and to provide references for young patients. Methods. All clinical data of patients with early breast cancer undergoing BCS who were treated at Shengjing Hospital of China Medical University from January 2011 to May 2016 were obtained. The primary endpoints were local recurrence (LR) and distant recurrence, and the secondary endpoint was breast cancer-specific survival (BCSS). Chi-squared tests and Fisher’s exact tests were used for statistical analysis. Disease-free survival (DFS) and BCSS were calculated by Kaplan–Meier survival analysis and compared using log-rank tests. Logistic regression was used for multivariable analysis of the effect of age in different subgroups. Propensity score matching (PSM) was used to reduce the bias confounding factors on oncological outcomes. Results. Younger patients had higher Ki-67 expression (

P
=
0.048

) and larger tumors (

P
=
0.042

) compared to older patients. No other clinical features were significantly different between age groups. There was no significant difference between the two groups in BCSS (

P
=
0.186

); however, DFS was significantly different before PSM (

P
=
0.012

). Triple-negative breast cancer and Ki-67 positivity combined with younger age at diagnosis were associated with a higher risk of recurrence (

P
=
0.018

and

P
=
0.046

, respectively). After PSM, there were no significant differences in BCSS nor DFS between the two age groups (

P
=
0.559

and

P
=
0.261

, respectively). Conclusion. BCS for young patients is not associated with increased DFS nor BCSS. However, young patients with triple-negative breast cancer and/or Ki-67 positivity have a poor prognosis. In sum, BCS may be appropriate for a subgroup of young patients.
Design, Experiment, and Commissioning of the Spent Fuel Conveying and Loading System of HTR-PM
Authors: Wu Bin , Wang Jinhua , Li Yue , Wang Haitao , Ma Tao
Published: April 2022

The Chinese high-temperature gas-cooled reactor pebble-bed module, HTR-PM, began fuel loading in August 2021. The reactor refuels continuously, while the spent fuel is discharged from the core. The spent fuel conveying and loading system was designed to convey the spent fuel pebbles to the spent fuel building and load them into dry canisters for on-site interim storage. This study describes the operating principles of the main functions and introduces the experiments and commissioning tests of the system. Functional tests were carried out to indicate the items of mechanical and electrical equipment are functioning in accordance with the designed requirements. Experience learned from commissioning activities was also presented as feedback for future operation and design improvement.
Design of Multimedia English Online Teaching Platform under Wireless Network Communication Technology
Authors: Dan He
Published: April 2022

The traditional online teaching platform has poor compatibility due to the high data delay in practical applications. Therefore, a Multimedia English online teaching platform under wireless network communication technology is designed. Aiming at the actual functional requirements of online teaching, the overall architecture of the Multimedia English online teaching platform is designed. In this architecture, the hardware of the data collector, memory, and main controller is deployed to build the platform. Based on the wireless network communication technology, the software modules are divided into two parts. It is made of a number of functional modules. The design of the platform functional modules is mainly based on the main functions of the administrator submodule, the teacher submodule, and the student submodule. At the same time, the weight of the indicators is determined by combining the analytic hierarchy process and the evaluation domain for the quality evaluation of Multimedia English online teaching is established. The level model completes the platform design. The experimental results show that the test results of the designed platform meet the expected goals and can effectively improve the quality and efficiency of Multimedia English online teaching. The teaching quality is always higher than 95%, and the average teaching efficiency is 96.27%.
Real-Time Regulation Model of Physical Fitness Training Intensity Based on Wavelet Recursive Fuzzy Neural Network
Authors: Shen Zhaoxin , Yang Ying
Published: April 2022

It has been widely used in signal processing, image processing, speech recognition and synthesis, pattern recognition, machine vision, machinery fault diagnosis and monitoring, and other scientific and technological fields and has achieved great results. The application potential in nonlinear system identification is increasing. According to the theory of “overload recovery” and “functional reserve”, the mathematical model of “load-fitness state” is established to understand the adaptation characteristics and individual characteristics of athletes to sports training. The model is used to simulate the values and time required to reach the maximum fitness state for four types of precompetition reduction plans and to provide a reference for the development of precompetition training plans. The data required for parameter estimation were the actual training data of six outstanding basketball athletes (mean age 18.2 ± 0.75, mean training years 4.6 ± 0.49). And the coaches’ training plan was not intervened during the test. In order to further reduce the biaxial synchronization error of the sports platform and improve the stability of the system, the wavelet transformation capable of time-varying signal analysis and the recursive structure with dynamic capability were combined with the fuzzy neural network, and the learning ability of the neural network was used to learn and adjust the scaling and translation factors in the wavelet function, the mean and standard deviation in the fuzzy structure, and the connection weights between the layers, according to the biaxial synchronization. The simulation results show that the designed global sliding mode controller can improve the convergence speed of tracking error and ensure the single-axis tracking accuracy of the H-type motion platform compared with the traditional sliding mode controller, and the tracking accuracy and synchronization accuracy of the system can be further improved after adding the cross-coupled synchronization controller, but the improvement of synchronization control accuracy is not very satisfactory due to the fixed selection of the parameters of the cross-coupled controller. Further improvement is needed.
Intelligent Diagnosis Model of Traction Seat of Urban Rail Vehicle Based on Harris Hawks Optimization
Authors: Chang Qi , Zheng Minglei , Luo Jiaxin , Lin Li  , Man Junfeng , Shen Yiping , Liu Yi
Published: April 2022

Traction seat is an important connecting part of urban rail vehicle, which plays an important role in maintaining smooth running and power transmission of the vehicle body. Timely diagnosis of early failure of traction seat is the key to ensure the safe operation of urban rail vehicles. In order to realize the intelligent diagnosis of traction seat, a multialgorithm fusion scheme based on the Harris Hawk algorithm (HHO) is proposed to realize the fault diagnosis of traction seat. Firstly, the early mechanism of traction seat was studied, and the simulation experiment platform of urban rail vehicle traction seat was built to obtain the vibration data of the early crack traction seat model, so as to facilitate the simulation experiment research. Then, the vibration data of the traction seat were processed by HHO optimized variational mode decomposition (HVMD) to obtain several intrinsic mode functions (IMFs). Secondly, the multiscale permutation entropy (MPE) of each IMF is quantified and its average value is used to construct the energy characteristic vector. Finally, feature vectors are input into the HHO optimized support vector machine (HSVM) model to train a pattern recognizer. Through Python simulation verification, the results show that the model can accurately extract the characteristic information of traction seat and accurately identify the fault type, and the recognition rate reaches 100%.
[Retracted] Recognition of Protein Network for Bioinformatics Knowledge Analysis Using Support Vector Machine
Authors: Kaur Arshpreet , Chitre Abhijit , Wanjale Kirti , Kumar Pankaj , Miah Shahajan , Alguno Arnold C.
Published: April 2022

Protein is the material foundation of living things, and it directly takes part in and runs the process of living things itself. Predicting protein complexes helps us understand the structure and function of complexes, and it is an important foundation for studying how cells work. Genome‐wide protein interaction (PPI) data is growing as high‐throughput experiments become more common. The aim of this research is that it provides a dual‐tree complex wavelet transform which is used to find out about the structure of proteins. It also identifies the secondary structure of protein network. Many computer‐based methods for predicting protein complexes have also been developed in the field. Identifying the secondary structure of a protein is very important when you are studying protein characteristics and properties. This is how the protein sequence is added to the distance matrix. The scope of this research is that it can confidently predict certain protein complexes rapidly, which compensates for shortcomings in biological research. The three‐dimensional coordinates of C atom are used to do this. According to the texture information in the distance matrix, the matrix is broken down into four levels by the double‐tree complex wavelet transform because it has four levels. The subband energy and standard deviation in different directions are taken, and then, the two‐dimensional feature vector is used to show the secondary structure features of the protein in a way that is easy to understand. Then, the KNN and SVM classifiers are used to classify the features that were found. Experiments show that a new feature called a dual‐tree complex wavelet can improve the texture granularity and directionality of the traditional feature extraction method, which is called secondary structure.
Influences of Extrusion and Silver Content on the Degradation of Mg‐Ag Alloys In Vitro and In Vivo
Authors: Liu Guanqi , Han Jianmin , Yu Xiaodong , Yuan Shenpo , Nie Zhihua , Qiu Tiancheng , Yan Ziyu , Tan Chengwen , Guo Chuanbin
Published: April 2022

Binary magnesium‐silver (Mg‐Ag) alloys were designed as antibacterial materials for biomedical implant applications. In the present study, we focused on the effects of extrusion (extrusion ratio (ER): 1, 7.1, and 72.2) and Ag content (Ag = 0, 3, and 6 wt.%) on the degradation of Mg‐Ag alloys in vitro and in vivo via microstructure characterization and corrosion/degradation measurements. The results showed that the Ag promoted a galvanic reaction with the Mg matrix to accelerate degradation or formed a protective oxide mesh texture to inhibit degradation, especially in vivo. Ag might also be beneficial for product crystallization, biomineralization, and organic matter deposition. For pure Mg, extrusion produced a more refined grain and decreased the degradation rate. For the Mg‐Ag alloys, a low extrusion ratio (7.1) accelerated the degradation caused by the increase in the proportion of the precipitate. This promoted the release of Mg2+ and Ag+, which led to more deposition of organic matter and calcium phosphate, but also more H2 bubbles, which led to disturbance of product deposition in some local positions or even inflammatory reactions. Extrusion at a higher ratio (72.2) dissolved the precipitates. This resulted in moderate degradation rates and less gas production, which promoted osteogenesis without an obvious inflammation reaction.
Modification and Validation of Ritchie Model for Evaporation of Maize Field in Saline Alkali Soil
Authors: Zheng Xin , Xu Wenbao , Zheng Chunyu , Xie Hengyan , Li Bo
Published: April 2022

In order to study the evaporation law of the soil between the corn trees of Daqing salt-alkali soil, the evaporation simulation value of different growth stages in the experimental period was compared with the measured value according to the Ritchie model theory, and the model fit was not well matched by using a homemade microsteaming seepage instrument to study the evaporation of the whole fertility period of corn in the green wind garden test area of Daqing High-tech Zone, as well as rainfall and soil moisture content. The accuracy of the two parameters in Ritchie’s model directly determines the accuracy of the model’s prediction of evaporation. The first

U

value and the second-stage

α

values were revised, respectively, and the model verification was based on the different growth-and-long-term values of corn, and the analysis showed that the average monthly rainfall was a function relationship with the average monthly evaporation. The modified Ritchie model has a maximum evaporation fit of 0.96 for maturity and a minimum fit of 0.91 for seedling. It provides an accurate evaporation prediction model for the accurate prediction of evaporation between saline soil corn classes in Daqing area and provides a theoretical basis for water quantity management and soil salinization prevention and control in the corn planting process.