bluuebunny/crossref_metadata_2025 huggingface dataset:
RNA: A Flexible and Efficient Accelerator Based on Dynamically Reconfigurable Computing for Multiple Convolutional Neural Networks
Authors: Yang Chen , Hou Jia , Wang Yizhou , Zhang Haibo , Wang Xiaoli , Geng Li
Published: June 2022

The increasingly complicated and versatile convolutional neural networks (CNNs) models bring challenges to hardware acceleration in terms of performance, energy efficiency and flexibility. This paper proposes a reconfigurable neural accelerator (RNA) for flexible and efficient CNN acceleration. To provide hardware flexibility, RNA employs dynamically reconfigurable computing framework to rapidly configure data path between processing elements (PE) at run-time, as well as an interlaced data access mechanism for multi-bank RAM. To achieve high energy efficiency, three optimization mechanisms, including image row broadcasting dataflow (IRBD), tile-by-tile computing (TTC), and zero detection technology (ZDT), are dedicatedly designed for RNA to exploit data reuse and decrease memory bandwidth requirement, which is the key to improving performance and saving power consumption. To save hardware overhead, an online dynamic adaptive data truncation (DADT) mechanism is designed to compensate accuracy loss of multiplication results so that the computational precision in RNA can be reduced from 16-bit to 8-bit, which contributes to reducing the area of data path. The RNA architecture is implemented on Xilinx XC7Z100 FPGA and works at 250[Formula: see text]MHz. Experimental results show that the performance of running LeNet, AlexNet and VGG are 500 GOPS, 598 GOPS and 660 GOPS, respectively. Compared to previous FPGA-based designs, RNA achieves [Formula: see text] performance speedup and [Formula: see text] improvements on energy efficiency.
Unitarity and quantum resolution of gravitational singularities
Authors: Gielen Steffen , Menéndez-Pidal Lucía
Published: June 2022

Here, we explore the consequences of requiring that quantum theories of gravity be unitary, mostly focusing on simple cosmological models to illustrate the main points. We show that unitarity for a clock that encounters a classical singularity at finite time implies quantum singularity resolution, but for a clock that encounters future infinity at finite time leads to a quantum recollapse. We then find that our starting point — assuming the general covariance of general relativity — is actually incompatible with general quantum unitarity: singularity resolution in quantum gravity can always be engineered by choosing the right clock, or avoided by using a different one.
Technical perspective: DFI: The Data Flow Interface for High-Speed Networks
Authors: Alonso Gustavo
Published: June 2022

Optimizing data movement has always been one of the key ways to get a data processing system to perform efficiently. Appearing under different disguises as computers evolved over the years, the issue is today as relevant as ever. With the advent of the cloud, data movement has become the bottleneck to address in any data processing system. In the cloud, compute and storage are typically disaggregated, with a network in between. In addition, cloud systems are scale-out, i.e., performance is obtained by parallelizing across machines, which also involves network communication. And while it is possible to use machines with large amounts of memory, the pricing models and the virtualized nature of the cloud tends to favor clusters of smaller computing nodes. Nowadays, the problem of optimizing data movement has become the problem of using the network as efficiently as possible.
Technical Perspective
Authors: Kemper Alfons
Published: June 2022

With the emergence of (geographically) distributed data mangement in cloud infrastructures the key value systems were promoted as so-called NoSQL systems. In order to achieve maximum availability and performance these KV stores sacrificed the "holy grail" of database consistency and relied on relaxed consistency models, such as eventual consistency.
Bipartite Matching
Authors: Abeywickrama Tenindra , Liang Victor , Tan Kian-Lee
Published: June 2022

The Kuhn-Munkres (KM) algorithm is a classical combinatorial optimization algorithm that is widely used for minimum cost bipartite matching in many real-world applications, such as transportation. For example, a ride-hailing service may use it to find the optimal assignment of drivers to passengers to minimize the overall wait time. Typically, given two bipartite sets, this process involves computing the edge costs between all bipartite pairs and finding an optimal matching. However, existing works overlook the impact of edge cost computation on the overall running time. In reality, edge computation often significantly outweighs the computation of the optimal assignment itself, as in the case of assigning drivers to passengers which involves computation of expensive graph shortest paths. Following on from this, we also observe common real-world settings exhibit a useful property that allows us to incrementally compute edge costs only as required using an inexpensive lower-bound heuristic. This technique significantly reduces the overall cost of assignment compared to the original KM algorithm, as we demonstrate experimentally on multiple real-world data sets and workloads. Moreover, our algorithm is not limited to this domain and is potentially applicable in other settings where lower-bounding heuristics are available.
Model Counting Meets Distinct Elements in a Data Stream
Authors: Pavan A. , Vinodchandran N. V. , Bhattacharyya Arnab , Meel Kuldeep S.
Published: June 2022

Constraint satisfaction problems (CSPs) and data stream models are two powerful abstractions to capture a wide variety of problems arising in different domains of computer science. Developments in the two communities have mostly occurred independently and with little interaction between them. In this work, we seek to investigate whether bridging the seeming communication gap between the two communities may pave the way to richer fundamental insights. To this end, we focus on two foundational problems: model counting for CSPs and computation of zeroth frequency moments (F0) for data streams.
Timing and kinematics of horizontal within-blink saccades measured by EOG
Authors: Kirchner Johannes , Watson Tamara , Busch Niko A. , Lappe Markus
Published: June 2022

This study observed that people make large dynamic overshoots when making a saccadic eye movement within a blink but their eyes are back on target by the time the eyelids are open. We used electrooculography (EOG) to measure eye movements even when the lid is down and introduced a novel procedure to subtract blink-related EOG components. These findings challenge the current view that within-blink saccades are programmed as slow but straight saccades.
Constructing a Security System for Classified Computer Information Using Distributed Parallel Computing
Authors: Wang Leng , Yang Li
Published: June 2022

Information security can ensure an efficient application of classified computers. At present, in some classified computers, there exist problems and deficiencies in the field of information security management. It is not conducive to the control of information security. A network parallel computing system is a network-based system that has a high potential to resolve these issues. In recent years, with the development of cost-effective CPU and high-speed network technology, this system has gradually attracted the attention of people. Based on the research on distributed parallel computing, this paper abstractly analyzes the network parallel computing environment. It also discusses how a security protection system solution based on a distributed enterprise network environment helps rectify this problem. After research and analysis of classified computer information systems, this paper uses distributed parallel computing for building an advanced security system. Experimental results show that this method can improve the security status of the system to a much higher extent compared to the existing network security technologies. It has strong practicality and good social and economic value. The purpose of this paper is to provide information management tools for enterprises to improve the secrecy of management-level enterprises.
High‐Efficiency Power Generation Device of Magnetic Declination Thermoelectric Material and Multisource Coordination Optimization of Distribution Network
Authors: Zhang Lifeng , Wu Xiaofang
Published: June 2022

The first‐order phase transition compounds have attracted widespread attention from refrigeration enterprises and scientists due to their giant magnetocaloric effect and refrigeration temperature region spanning room temperature. Magnetic substances are crystal structures composed of magnetic ions or atoms with a certain thermal motion or vibration. This research mainly discusses the multisource coordination optimization of magnetic declination thermoelectric material efficient power generation device and distribution network. In this study, large doses of MnFe(P,Si) compounds with different ratios were prepared. The reason for choosing a large dose is that the properties and structures of the samples prepared in a large amount and a small amount will be more or less the same for the samples fired in the same proportion and in the same process. At the same time, mass production also prepares for future mass production. Thermomagnetic power generation described in this study is a form of power generation that converts thermal energy directly into electrical energy. The conversion of thermal energy to electric energy is realized by the change of magnetization in the closed coil by the thermomagnetic material in the magnetic field environment, and then, the change of the magnetic flux in the closed coil is realized. The distribution network refers to the power network that receives electrical energy from the transmission network or regional power plants and distributes it locally through distribution facilities or distributes it to various users step by step according to the voltage. It is composed of overhead lines, cables, towers, distribution transformers, isolating switches, reactive power compensators, and some ancillary facilities. A network that plays an important role in distributing electrical energy in the power grid. This research starts with the active distribution network scheduling priority and the distribution network scheduling coordination control framework. Combined with the scheduling and operation characteristics of each component in the distribution network, the coordinated optimization scheme of the distribution network and the source‐network load‐storage coordinated control framework are investigated and analyzed. The scheduling resources of the model include the power of the substation flowing into the distribution network, the output of the adjustable distributed power source, the output value of the energy storage element, the closing status of the tie switch, and the section switch. In the study, it was found that when the Fe content was 0.63, the Curie temperature of the compound could reach 273 K at the highest. Scientifically designing a thermomagnetic power generation demonstration device is an important part of the realization of thermomagnetic power generation. The magnetic declination thermoelectric materials designed in this study will help to improve the power generation efficiency.
Overexpression of LINC00936 Inhibits the Proliferation and Invasion of Endometrial Carcinoma Cells
Authors: Sun Haizhu , Li Peiling
Published: June 2022

Objective. Endometrial carcinoma (EC) is one of the most common malignancies leading to death in women and poses a serious threat to women’s health. Therefore, exploring the molecular mechanisms affecting EC progression and metastasis is a clinical research hotspot. It has been shown that lncRNAs play an important role in the pathogenesis of EC. It is important to investigate the role of lncRNAs in the growth of EC to improve diagnosis and find new therapeutic targets of EC. Methods. The expression of LINC00936 in 36 EC tissues, paracancerous tissues, and cell lines was measured by fluorescence quantitative PCR. The relationship between LINC00936 expression and clinicopathological characteristics of patients was analyzed. The effects of overexpression of LINC00936 on proliferation, invasion, and migration of EC cells were examined by CCK-8 and transwell assays. Colony formation assay was also performed to evaluate the colony forming ability of EC cells. The effect of overexpression of LINC00936 on the expression of EMT-related proteins in EC cells was examined by western blot. In addition, the effect of LINC00936 overexpression on the growth of EC in vivo was examined by using tumorigenicity assay in the nude mouse. Results. LINC00936 was expressed at a low level in EC tissues and cell lines and significantly correlated with tumor size and lymphatic metastasis of patients. Overexpression of LINC00936 significantly inhibited the proliferation, invasion, and migration, as well as colony formation ability of EC cells. Western blot analysis showed that overexpression of LINC00936 significantly promoted the expression of E-cadherin and inhibited the expression of N-cadherin and vimentin in EC cells. Tumorigenic assays in the nude mouse demonstrated that overexpression of LINC00936 inhibited the growth of EC in vivo by suppressing Ki-67 and promoting E-cadherin expression. Conclusion. LINC00936 was expressed at a low level in EC tissues and significantly correlated with tumor size and lymphatic metastasis of patients. Overexpression of LINC00936 significantly inhibited the proliferation, invasion, and migration, as well as colony formation ability of EC cells. Therefore, LINC00936 could be a new target for the early diagnosis and treatment of EC patients.
Seismic Performance-Based Evaluation of Cold-Rolled Steel Structures
Authors: Zekri Samira , Salajegheh Javad
Published: June 2022

Today, lighter buildings with a smaller share of seismic force are needed in the construction industry. Given the attractiveness of this construction method for researchers, it led them to conduct more and more accurate experiments to better understand these structures and, in particular, to study the lateral behavior of these structures. The aim of this study has been to calculate reliability index and probability of failure. In addition to this sources of uncertainty in structural system of cold-formed frames with sharp bracing have been ranked by application of reliability analysis method. To do this, considering sources of seismic and structural uncertainty such as spectral acceleration, live load, cross-sectional dimension, and modulus elasticity of steel has been selected. Then reliability analysis methods, FOSM and FORM, have been utilized to estimate the probability of failure based on limit-state function which is defined as relative displacement of floor to allowed limit. Results show that failure probability following a decreasing trend up to two story-floor frames increased up to five-floor frames. As a result of the analysis, it was found that, in the FOSM method, the reliability index increases with increasing floors in cold-rolled steel structures. In the FORM method, by increasing the floors up to the two-story frame, the reliability index has an ascending trend and up to the five-story frame has a descending trend with a very gentle slope. By increasing the distance, the value of the FOSM method reliability index is different from the value of this index in the FORM method to the extent that, in the Type 5 framework, this difference reaches five units. The probabilities of failure in one-story and three-story frames are the highest and the lowest, respectively. Uncertainty in the load applied to the structure was identified as the most important parameter. The uncertainty in the geometry of the members decreases with the increase of classes.
Artificial Intelligence Segmentation Algorithm-Based Optical Coherence Tomography Image in Evaluation of Binocular Retinopathy
Authors: Shen Jiemei
Published: June 2022

On account of optical coherence tomography (OCT) images with intelligent segmentation algorithm, this article investigated the clinical efficacy and safety of docetaxel combined with fluorouracil. In this study, 60 patients with retinopathy treated in hospital were selected as the research objects. There were 30 cases in each group, the control group was treated with conventional images, and the observation group was treated with algorithm-based OCT images. Intelligent segmentation boundary detection algorithm, boundary tracking, and contour localization were proposed and applied to the OCT images of patients to analyze features and measure corneal thickness in OCT images with high signal-to-noise ratio and noise and artifacts. Objects in the control group were treated with semiconductor laser, and those in the observation group were treated with OCT images with algorithm in addition to the treatment of the control group. The results showed that the number of images with relative error of 2 was more, and the number of images with relative error of -2 was the least. The average thickness of high-quality images was 562.7 μm, and the average thickness of images with noise and artifacts was 573.8 μm. The total effective rate of the observation group was 96.67%, which was significantly higher than that of the control group (80%), and the curative effect and physical improvement rate of the observation group were significantly better than that of the control group (

P
<
0.05

). All in all, the feature extraction of OCT images and corneal measurement proposed in this study had a good measurement effect, and the method had the advantages of strong anti-interference ability and high measurement accuracy.
Neutrosophic Simulated Annealing Algorithm and Its Application in Operation Optimization in Dangerous Goods Warehouse
Authors: Zhang Fangwei , Chen Zhenrui , Ye Jun , Han Bing
Published: June 2022

In the classical simulated annealing algorithm (SAA), the iteration feasible solution is mainly based on a certain random probability. In the process of iteration, there is a lack of comparison between individuals and the whole population of feasible solutions, and the indicators to measure the change of state of individuals are too absolute to achieve the overall control of the algorithm. To deal with uncertain information that individuals encounter in iteration, this study introduces the idea of neutrosophic decision‐making and establishes a kind of neutrosophic fuzzy set (NFS) to describe the time‐varying iterative state of individuals according to the change of individual state, the change of population state, and the number of iteration. The biggest feature of this study is to propose a neutrosophic simulated annealing algorithm that combines the idea of NFS with simulated annealing. The biggest contribution of this study is to propose a novel entropy measurement method using the NFS and combine it with the simulated annealing algorithm to handle the optimization process. Finally, the effectiveness of the novel algorithm is verified by an example of warehouse optimization.
A Two‐Level Metaheuristic for the Job‐Shop Scheduling Problem with Multipurpose Machines
Authors: Pongchairerks Pisut
Published: June 2022

This paper proposes a two‐level metaheuristic consisting of lower‐ and upper‐level algorithms for the job‐shop scheduling problem with multipurpose machines. The lower‐level algorithm is a local search algorithm used for finding an optimal solution. The upper‐level algorithm is a population‐based metaheuristic used to control the lower‐level algorithm’s input parameters. With the upper‐level algorithm, the lower‐level algorithm can reach its best performance on every problem instance. Most changes of the proposed two‐level metaheuristic from its original variants are in the lower‐level algorithm. A main purpose of these changes is to increase diversity into solution neighborhood structures. One of the changes is that the neighbor operators of the proposed lower‐level algorithm are developed to be more adjustable. Another change is that the roulette‐wheel technique is applied for selecting a neighbor operator and for generating a perturbation operator. In addition, the proposed lower‐level algorithm uses an adjustable delay‐time limit to select an optional machine for each operation. The performance of the proposed two‐level metaheuristic was evaluated on well‐known benchmark instances. The evaluation’s results indicated that the proposed two‐level metaheuristic performs well on most benchmark instances.
A Comparison between Multiple-Input Multiple-Output and Multiple-Input Single-Output Radar Configurations for Through-the-Wall Imaging Applications
Authors: Pisa Stefano , Cicchetti Renato , Piuzzi Emanuele , Testa Orlandino
Published: June 2022

The performances of a multiple-input multiple-output (MIMO) radar, employing 16 equivalent antennas, and multiple-input single-output (MISO) radar, employing 10 antennas, for through-the-wall imaging applications are analyzed. In particular, imaging algorithms based on the Fourier transform (FT) and the multiple signal classification (MUSIC) available in the literature are compared with the FT-MUSIC hybrid algorithm recently developed by the authors. Three different investigations have been performed. The first, performed analytically, refers to a scenario in which a point scatterer is placed in free space, and the second, addressed numerically using the CST full-wave software, refers to a scenario in which two targets are present, while the last was executed in a real scenario where a metal panel is placed behind a tuff wall. All the algorithms and radar configurations were found to be suitable for accurately reconstructing the position of the investigated target. In particular, applying the FT technique, the MISO configuration has a lower cross-range half-power beamwidths (HPBW) than the MIMO one, while the range HPBW is the same for the two radar configurations. Despite the different number of elements present in the two radar configurations, similar range and cross-range HPBW are obtained for both configurations when MUSIC and FT-MUSIC techniques are employed. The field of view for FT and FT-MUSIC is about 45°, while it is less than 15° for the MUSIC algorithm. The HPBWs obtained with the experimental setup are very close to those obtained in the analytical study. Finally, the proposed experimental MISO radar acquires the data in half the time required by the MIMO one. The numerical results, confirmed by the experimental measurements, seem to indicate in the FT-MUSIC technique the one that provides the best performance for the considered radar configurations.
Application of Fuzzy Clustering Model in the Classification of Sports Training Movements
Authors: Song Bo
Published: June 2022

In order to accurately analyze the movements of sports training using artificial intelligence techniques, an improved fuzzy clustering model is proposed in this study. The fuzzy C-means is used to granulate the multilabel space, and the correlation degree between different variable labels is obtained through information gain. Aiming at the problem of multilabel information classification, an appropriate membership function is selected, which is used to map all information samples and obtain the membership degree of its category. Considering the slow training efficiency of fuzzy support vector machine, the clustering method is used to optimize the fuzzy support vector machine, establish the optimal hyperplane, and complete the classification according to their respective attributes in high-dimensional space. Finally, the proposed algorithm and other algorithms are experimentally compared on the published KTH and Weizmann human behavior data sets. Experimental results show that the proposed method is effective and robust.
The Effect of Humidity and Temperature on Indoor and Outdoor COVID-19 Infections
Authors: Salih Jalal M.
Published: June 2022

Environmental conditions and their association with COVID-19 have significantly attracted scientists’ attention. The current study links COVID-19 with climate indicators by comparing two configurations: indoor infections in a University of Duhok (UOD) building and outdoor infections within the boundaries of the Duhok Governorate (DG). The collected data included temperature and relative humidity (RH) and confirmed cases for indoor and outdoor configurations over 5 and 11 months, respectively. For the indoor infections, data were collected over the period of 5 weekdays, while for the outdoor infections, they were collected on the days when statistics were published. The prospective cross-section design was used for different statistical analyses. The overall indoor infections were very low, and the maximum values for RH and temperature were approximately <24% and <20°C, respectively; in the one-sample t-test, the results were significantly correlated (

p

value <0.05) with the confirmed COVID-19 cases. For outdoor infections, using the correlation bivariate method, the study found that the RH and temperature results significantly correlated (

p

value <0.05) with the confirmed COVID-19 cases. However, for indoor configuration, other than for Tmax, the results were not associated. As for the outdoor infections, the RH and temperature averages were high enough to put in groups to employ the one-way repeated ANOVA and general linear model with the same results. The means of the RHlow, RHmedium, and RHh groups were significantly correlated (

p

value <0.05) with COVID-19. However, the means of the medium RH and high RH groups were not significantly associated with the increasing outdoor infections. This study will contribute to the reduction of overall COVID-19 infections.
Comprehensive Evaluation Method of Digital Economy Development Level Based on Complex Network Model
Authors: Shi Xingjun
Published: June 2022

In today’s economic globalization and artificial intelligence modern society, the rapid economic growth mainly relies on the vigorous development of digital information technology. Simultaneously, digital technology promotes economic revolution and produces a digital economy, which has become an important concept of China’s economic development. In order to more scientifically and accurately describe the development status of my country’s industrial structure and digital economy industry, this paper takes each industry of the national economy as the research object and puts forward the idea of using complex network theory to study the relationship between input and output. Based on the complex network theory, this paper analyzes the interrelationships between various industries in the complex system. By referring to the relevant data of the national economic input and output in recent years, the input-output correlation network model between the various industries in the national economic system is established. Using the method of combining statistical analysis and comparative analysis, quantitative analysis and qualitative analysis, carry out detailed and in-depth statistical analysis from the input-output correlation network model and model attributes so as to point out the status and role of various industries in the national economy and so as to analyze and study the industrial structure of the national economy.
The Relationship between Benthic Macroinvertebrate Assemblages and Water Quality Parameters in the Sanyati Basin, Lake Kariba, Zimbabwe
Authors: Makumbe Peter , Kanda Artwell , Chinjani Tambudzai
Published: June 2022

Biological monitoring of reservoirs is important in assessing aquatic health. This study aimed at assessing the structure of benthic macroinvertebrate communities in relation to physicochemical parameters along Sanyati basin shoreline in Lake Kariba, Zimbabwe. Six sites (S1 to S6) characterized by various human disturbances were sampled for physicochemical parameters and benthic macroinvertebrates from January to March 2018. We computed macroinvertebrate metrics and classified them into functional feeding groups (FFGs). A canonical correspondence analysis (CCA) triplot was constructed to assess species-physicochemical relations. Significant differences across the sampling sites were observed for pH, electrical conductivity, turbidity, nitrate-nitrogen (NO3-N), ammonium nitrogen (NH4-N), total nitrogen (TN), phosphate-phosphorus (PO4-P), total phosphates (TP), and dissolved oxygen (DO). The results from CCA highlighted that S1 was generally associated with high pH, NH4-N, and TN, and Oligochaeta, Syrphidae, and Hydrophilidae families. The highest percentage of EPT taxa (39.83%) was recorded at S6, while the lowest was recorded at S1. The taxa were made up of 50% predators, 26% collector-gatherers, 6% scrappers, 6% shredders, and 3% collector-filters with 3 taxa (Chironomidae, Hydropsychidae, and Leptoceridae) having more than two FFGs. Site S1 had a significantly higher mean abundance of collector-gatherers than the other sites. A high correlation between water parameters and SASS and ASPT scores was observed indicating their ability to detect environmental changes. These findings suggest that macroinvertebrate communities are good candidates for delineating the effects of industrial pollution on water quality.
Feedback Delay of Sports Intelligent Learning System Based on Model Predictive Control
Authors: Lv Haidong
Published: June 2022

In this paper, a trajectory tracking controller based on linear time-varying model prediction is developed, and the model predictive control theory based on the six degrees of freedom dynamic model and tire model is applied. Combined with the soft constraint of turning angle and the control algorithm to ensure the stability, the trajectory tracking is realized. The terminal node is a wearable device. The terminal node is equipped with a pressure sensor and attitude sensor network to collect human data, and then the terminal node sends the data to the ZigBee network. The data in the sensor network will be received by the gateway, and the data will be processed and displayed on the PC software after being received by the gateway. Finally, the data is saved to the remote server for archiving. The application of intelligent learning systems in sports makes up for many shortcomings of traditional sports. The teaching of the course consists of seven types of intelligent courses, which not only conform to the spirit of high-quality education but also combine the shortcomings of traditional physical education learning and combines several intelligent theories and the need for time development. The purpose is to develop a new sports model suitable for students’ all-round development. Under the guidance of the theory of intelligent learning system, this paper establishes a new and diverse movement model, including various educational models, educational contents, educational methods, student-based learning model, and multidimensional evaluation. By comparing and analyzing the results of the two groups, the multiple intelligences physical education is more suitable for the development of modern students’ various intelligences. According to the physical education learning questionnaire, students also learn more methods and contents and accept diversified teaching evaluation, which expands the development direction of students.
A Method for Financial System Analysis of Listed Companies Based on Random Forest and Time Series
Authors: Zhang Chi , Zhong Huaigong , Hu Aiping
Published: June 2022

The world economy has recently moved in a fresh era, where the financial world is rapidly developing. Various economic crises, such as banking, economic, and currency crises, impose high economic costs, and harm the entire society. This necessitates the creation of an early warning system for financial crisis that can be adaptively analyzed using past information. Early warning systems could prevent the occurrence of business and economic crises by providing a systematic prediction of unfavorable events. Early warning systems are mainly used to detect crises before they do damage and to reduce false alarms of impending crises. Because of the above, this paper studies early warning of the financial crisis of listed companies based on random forest and time series. Besides, it constructs a random forest and Boruta-Random forest (BRF) model with Benford factor to deal with the impact of financial data quality on the financial risk early-warning model. Our model can effectively improve the prediction accuracy of the financial early warning model. The experiments show that, in comparison to RF, BRF can increase the accuracy of financial risk early warning, expand the applicability of RF, as well as provide a fresh perspective for research on listed company financial risk early warning.
Online English Teaching System under the Background of Epidemic Situation Based on Intelligent Feature Recognition Technology
Authors: Chen Luoyun , Wang Weiwei
Published: June 2022

In order to improve the effect of online English teaching in the context of the epidemic, this paper combines intelligent feature recognition technology to carry out an online English teaching system in the context of the epidemic and greatly reduces the number of variants by selecting mutation operators with excellent performance. Moreover, in this paper, the mutation adequacy and the number of mutation operators are regarded as two objective functions, and the selection problem of mutation operators is generated into a two-stage optimization problem, and the above problems are solved by a genetic algorithm. In addition, this paper sorts the mutation branches and preferentially covers the mutation branches of the mutants corresponding to the mutation operators with higher mutation scores. The experimental study shows that the online English teaching system based on intelligent feature recognition technology proposed in this paper meets the actual needs of online English teaching in the context of the epidemic.
A Two-Level Integrated Scheduling Strategy for Vehicle-Network Synergy considering New Energy Consumption
Authors: Gao Yifei
Published: June 2022

With the substantial increase in the number of electric vehicles, the charging of electric vehicles without regulation and scale control will bring about problems, such as overloading of distribution transformers; the proportion of new energy power generation is also increasing year by year, and the access of new energy to the power grid will cause volatility. In order to solve the problems above, this paper proposed a coordinated and orderly scheduling strategy considering new energy consumption, which protects the interests of both users and the integrated power grid. First, a two-level vehicle-network interaction model considering both supply and demand sides was established. The upper-level model optimized the indicators on the distribution grid side, and a term of charge-discharge margin as well as grid-side load variance model was proposed. The lower-level optimization model was set based on the users’ condition. The average discharge rate index was defined to evaluate the battery loss satisfaction in the scheduling strategy, which fully considered users’ charging and discharging cost, and finally achieved a win-win situation between the power grid and the user. Secondly, the fast nondominated sorting genetic algorithm (NSGA-II) was used to figure out the effect of the strategy proposed in this paper, and a community is taken as an example for simulation. The results confirmed the economy and rationality of the above strategy, by rationally scheduling the charging and discharging behavior of electric vehicles, consuming new energy, restraining the fluctuation of the remaining new energy power generation, realizing the dynamic balance between the charging and discharging load and the output of new energy in a certain area, and finally effectively suppressing the fluctuation of the power grid load while improving the availability of clean energy.
An Exploratory Study on the Design and Management Model of Traditional Chinese Medicine Quality Safety Traceability System Based on Blockchain Technology
Authors: Li Dacan , Gong Yuanyuan , Zhang Xianhui , Huang Min
Published: June 2022

Aiming at the problems of long life cycle complex roles of participants, diverse risk factors, poor supervision coverage and difficult information traceability of TCM (Traditional Chinese Medicine) supply chain, this paper constructs a TCM quality safety traceability system based on blockchain technology by analyzing the business process and supervision characteristics of TCM supply chain. Blockchain technology is a new application mode of computer technology such as distributed data storage, peer-to-peer transmission, consensus mechanism, encryption algorithm, and other computer technologies. It has the characteristics of “decentralization, nontampering, transparent and open, and data traceability.” The function of nontampering, hash function, and time stamp can effectively solve the traceability problem of TCM supply chain. Through the analysis of the blockchain TCM traceability principle and network structure, combined with the actual business process of TCM traceability, this paper designs the blockchain TCM quality safety traceability system, analyzes its overall structure, data storage mode, digital signature, consensus algorithm, and main functions, and preliminarily constructs a new model of TCM quality safety traceability management based on blockchain technology. Using blockchain technology to realize the quality safety traceability of TCM, we can complete the omnidirectional, multiangle, and wide coverage of the data and information in whole supply chain of TCM breeding and seedling raising, planting, innovation and research and development, processing of TCM pieces, circulation of TCM, and consumption, so as to realize that the main body of responsibility of TCM can be put on record, the production records can be queried, product flow can be traced, quality safety can be forewarned, main responsibility can be identified, regulatory information can be shared, and product source can be traced.
Intelligent Control Method for Loss Distribution Balance of High-Power Photovoltaic Grid-Connected Inverters
Authors: Xu Yi , Zou ErYong , Tang FengPing
Published: June 2022

Aiming at the problem that the loss distribution balance control effect of high-power photovoltaic grid-connected inverter is poor due to the complex loss factors, this paper proposes a loss distribution analysis and balance intelligent control method for high-power photovoltaic grid-connected inverter. The topology structure of high-power photovoltaic grid-connected inverter is constructed and the overall control scheme is designed. The loss of inductance, resonant frequency, harmonic attenuation, and damping resistance in the circuit of PV grid-connected inverter is analyzed, respectively. On this basis, a two-stage loss control model of high-power PV grid-connected inverter is constructed, and the generalized linear decision rule is adopted to schedule PV power supply and fluctuating load. A two-stage loss control model for high-power photovoltaic grid-connected inverter was established and the optimal loss control value was obtained. Experimental results show that the proposed method can accurately suppress voltage and current losses of grid-connected photovoltaic inverters and has strong resonance resistance and robustness.
Optimization of Virtual Network Mapping Based on Improved Ant Colony Algorithm in Complex Network Environments
Authors: Zhang Ran , Sheng Jie
Published: June 2022

Financial marketing is a method of risky investment, but in a shorter duration, the individual can expect a profit or loss, depending on the current market rate for the organization. Though it has a higher risk of getting a profit, many individuals are ready to take a chance to benefit. In the current era, much research is going on to provide a better prediction rate. Most researchers prefer Ant Colony Optimization (ACO) as a more trustworthy optimization algorithm among the existing optimization algorithms. In this research work, a novel optimization algorithm is proposed; it is a hybrid algorithm with Ant Colony Optimization (ACO) and financial marketing crisis prediction (FMCP) and is termed ACO-FMCP. This hybrid algorithm was applied to the economic sanctions dataset, and the results were generated. The results were compared with ACO and FMCP algorithms and it was found that the hybrid ACO-FMCP shows improved performance than the other two algorithms.
A Closure Jacking Force Calculation Algorithm for Curved Prestressed Concrete Continuous Rigid-Frame Bridges with Asymmetric Cantilevers and Piers
Authors: Chen Chuang , Wang Xuyi , Lin Qidong , Wang Yinhui
Published: June 2022

Concrete continuous rigid-frame bridges are the most commonly used to cross exciting highway and railway. Before the bridge closure, the closure jacking force (CJF) needs to be determined firstly, which plays an important role in mid-span deflection controlling and the whole bridge stress state. This study proposed a closure jacking force calculation algorithm for curved prestressed concrete continuous rigid-frame bridges with asymmetric cantilevers and piers. Firstly, the mechanics method was used to analyze the relationship between the CJF and normal stress of main-girder and the pier. In addition, an actual bridge was taken as a numerical simulation case to determine the influence of the deformation and the stress induced by concrete shrinkage and creep of the main-girder and the jacking force. Finally, in order to obtain the optimal stress state of bridge piers, the multi-objective linear programming method was proposed to calculate the CJF. The results show that curved prestressed concrete continuous rigid-frame bridges usually suffered longitudinal and transverse bending and torsion under the concrete shrinkage and creep of the main-girder and the CJF, and the CJF significantly affected the normal stress of the pier with a low anti-push rigidity. The normal stress of piers was obviously linear with the CJF during whole construction and operation state of bridges at the following three states: the jacking process, bridge completion, and ten years of bridge operation. The multi-objective linear programming algorithm was an effective way to determine the optimal CJF and can fully reflect both longitudinal and transverse bending of piers.
Study on Sunshine Stress Effect of Long-Span and Wide Concrete Box Girder
Authors: Cheng Wang , Chentian Zhang , Shuo Li , Jianhua Li
Published: June 2022

In order to further clarify the sunshine stress effect of long-span and wide concrete box girder, the study selected a concrete box girder with

4
×
42
 
m

span and 33.5 m width, formed the temperature field load through a field test and numerical simulation, established a numerical analysis model by using the finite element program of ANSYS, and comprehensively analyzed the longitudinal temperature stress distribution of a long-span and wide concrete box girder under sunshine. The midspan section of the first span of the box girder is selected for field stress measure, and the field measure results and numerical analysis results are compared and analyzed. The research shows that the results of field measurement and numerical simulation are basically consistent. Sunshine temperature has a great influence on long-span and wide concrete box girder, which should be paid enough attention.
Analysis of ∼106 Spiral Galaxies from Four Telescopes Shows Large-Scale Patterns of Asymmetry in Galaxy Spin Directions
Authors: Shamir Lior
Published: June 2022

The ability to collect unprecedented amounts of astronomical data has enabled the nomical data has enabled the stu scientific questions that were impractical to study in the pre-information era. This study uses large datasets collected by four different robotic telescopes to profile the large-scale distribution of the spin directions of spiral galaxies. These datasets cover the Northern and Southern hemispheres, in addition to data acquired from space by the Hubble Space Telescope. The data were annotated automatically by a fully symmetric algorithm, as well as manually through a long labor-intensive process, leading to a dataset of nearly

10

6

galaxies. The data show possible patterns of asymmetric distribution of the spin directions, and the patterns agree between the different telescopes. The profiles also agree when using automatic or manual annotation of the galaxies, showing very similar large-scale patterns. Combining all data from all telescopes allows the most comprehensive analysis of its kind to date in terms of both the number of galaxies and the footprint size. The results show a statistically significant profile that is consistent across all telescopes. The instruments used in this study are DECam, HST, SDSS, and Pan-STARRS. The paper also discusses possible sources of bias and analyzes the design of previous work that showed different results. Further research will be required to understand and validate these preliminary observations.
Improvement on the Key Element of Flexible U‐Shaped Steel Support and Its Field Applications
Authors: Su Zhili , Jing Shengguo , Xie Wenbing , Tang Qingteng , Wang Xingkai
Published: June 2022

In China, there are a significant numbers of soft rocks roadways in underground coal mines. The roofs of these roadways are generally highly fractured, and traditional bolt installation does not provide sufficient supporting capacity. Hence, U‐shaped steel support has gained popularity, and the key influential factor of steel support is the lock unit between its mechanical structures. The commonly used two‐slotted splint lock unit can experience eversion and splitting and resulted in overall instability. To overcome the limitation, a “surface snap‐in” lock unit was developed. Based on experimental results, it was found that the relationships of bearing capacity and deformation of two lock unit types were similar under same loading conditions. The ultimate bearing capacity of surface snap‐in lock unit was lower than the two slotted splint unit. Although, its overall stability and flexibility were better. On the other hand, screw bolts of both unit types exhibited increase or decrease with the compressional deformation of steel support. The maximum stress along the screw bolt of two‐slotted splint lock unit was 1.6 to 1.8 times of the surface snap‐in lock unit. Based on the findings, the combination of two lock unit types was proposed and used at Peigou coal mine. Results show that the proposed design can effectively manage the roadway deformation while preventing the steel support damage. This in turn can reduce the associated supporting cost as the steel support can be recycled.