bluuebunny/crossref_metadata_2025 huggingface dataset:
Research on Multitarget Recognition and Detection Based on Computer Vision
Authors: Zhang XiaoYing , Chen Yiran
Published: April 2022

Information is obtained from human eyes for thinking divergence, and further associated with computer equipment, so human beings endow computers with the ability of “vision” to convey and feel information. This field has developed for many years, and many aspects can be in line with other research directions, such as artificial intelligence, which has become popular in recent years, and pattern recognition, which has been applied a lot. In order to sort out the structure and content of multitarget recognition smoothly, this paper starts from the perspective of shallow vision, uses theory and practical experiments, and chooses the core technology with the largest weight from massive computer technologies, so that the recognition algorithm can compare with the recognition algorithm. The research shows that (1) CNN shows its unique feature ability and incomparable detection accuracy from many models, and the error rate can be reduced from 28.07% to 18.40%. (2) The method of candidate region is complex, and the larger the region, the more difficult it is to calculate. The method based on regression is far beyond it in both precision and speed and is more suitable for the research of this subject. (3) When the mAP increases, the speed is forced to slow down. If the image resolution is high with the same model, the mAP will be high (SSD and YOLO models are often used). Experiments show that the recognition effect is obvious. At the end of the article, the advantages and disadvantages of this study are summarized. In the field of computer vision, people need to do more in-depth research. Follow-up can optimize multitarget recognition and detection and strive to improve the accuracy.
A Novel Model for Accessibility of Bus Station and Its Application to Bottlenecks Identification: A Case Study in Harbin
Authors: Sun Chao , Quan Wei
Published: April 2022

Accessibility plays a crucial role in evaluating and optimizing the service quality of public transport systems. Traditionally, bus accessibility research studies mainly focus on the evaluation from the perspective of the aggregate model and analyze the service radius of the site considering the front end of travel. This paper aims to model microlevel bus accessibility calculation and accurate bottleneck station identification according to the opportunity method with AVL (Automatic Vehicle Location) data. The research featured the following control elements: (1) redefining bus accessibility with bus station as the research object; (2) running time between interstation sections is selected as the control index; (3) A new measure index of accessibility. The farthest direct reachable distance is proposed. Moreover, by introducing the quality control algorithm, the study establishes a model of upper and lower limits of accessibility based on the probability index of bilateral norms, which can be employed to identify the bottleneck stations according to the significance level. Harbin City is selected as the case study for methodology verification and shows that the accessibility method is effective and reliable. Additionally, by taking the travel demand in the region as the adjustment parameter of the quality control algorithm, the study demonstrates the ideal accessibility interval of stations and accurately locates all bottlenecks. The study presented here adds to our understanding of accessibility in the construction and management of buses and provides an essential reference for the following optimization.
Evidence-Based Prediction of COVID-19 Severity in Hospitalized Children
Authors: Armin Shahnaz , Mirkarimi Mohammadreza , Pourmoghaddas Zahra , Tariverdi Marjan , Shamsizadeh Ahmad , Alisamir Mohsen , Mohammadian Maryam , Rahmati Mohammad Bagher , Rafiei Tabatabaei Sedigheh , Mansour Ghanaiee Roxana , Fahimzad Seyed Alireza , Yaraghi Ayeh , Hoseini-Alfatemi Seyedeh Mahsan , Marhamati Noushin , Esmaeili Tarki Farzad , Shirvani Armin , Karimi Abdollah
Published: April 2022

Objective. In this study, by using clinical and paraclinical characteristics, we have aimed to predict the severity of the disease in hospitalized COVID-19 children. Method. This cross-sectional study was conducted on medical records about epidemiologic data, underlying diseases, symptoms, and laboratory tests from March to October, 2020, on 238 hospitalized confirmed COVID-19 paediatric cases in several children’s hospitals of Tehran, Ahwaz, Isfahan, and Bandar Abbas. Results. From 238 patients, 140 (59%) were male and most of them were in the age group of 1 to 5 years (34.6%). Among all hospitalized patients, 38% had an underlying disease and in total, 5% of cases were expired. Conclusion. Determining patient severity is essential for appropriate clinical decision making; our results showed that in hospitalized pediatric patients, by using several variables such as SGOT, CRP, ALC, LDH, WBC, O2sat, and ferritin, we can use clinical and paraclinical characteristics for predicting the severity of COVID-19.
Factors That Affect Maternal Mortality in Rwanda: A Comparative Study with India and Bangladesh
Authors: Patrick Mugenzi , Zaman Muhammad Sami uz , Afzal Ghazala , Mahsud Minhas , Hanifatu Mumuni Napari
Published: April 2022

Healthcare sector is one of the most pivotal pillars of the administrative setup of a country. It addresses one of the most important dilemmas that countries have to face: provision of quality healthcare to public in affordable prices. Africa lags behind in many health indicators. One of the contemporary health issues faced by countries, especially for those in sub-Sahara countries, is maternal mortality rate (MMR). It has had a significant part to play in the social conditions of the population and needs immediate attention. In spite of many years of civil war and the terrible genocide in the mid-1990s, as of late, Rwanda is showing signs of improvement in healthcare sector. This research is aimed at studying the current state of maternal mortality rate in Rwanda and the factors behind its performance, in a comparative study with India and Bangladesh for a cross-section of time mainly between 1990 and 2015. After a literature review, pivotal indicators that affect healthcare are shortlisted and a comparative analysis of the three countries is made on the basis of these indicators. A regression is run between historical MMR data and these indicators. A directly significant relationship is found between MMR and healthcare expenditure per capita and government commitment to health, closely followed by female literacy and healthcare infrastructure.
Identifying the Mechanism of Polygoni Cuspidati Rhizoma et Radix in Treating Acute Liver Failure Based on Network Pharmacology and Molecular Docking
Authors: Hong Jing , Ding Jie , Hong Han-han , Xu Xiao-wan , Pan Bo , Ruan Yi , Zhai Xiao-feng
Published: April 2022

Background and Objective. Acute liver failure (ALF) is a rare clinical syndrome with a poor prognosis and leads to multiple organ failure. Polygoni Cuspidati Rhizoma et Radix (PCRR) is a commonly used Chinese medicine, which is recognized as a potential therapeutic herb against ALF. This study aimed to explore the pharmacological mechanisms of the therapeutic effect of PCRR in ALF via network pharmacology and molecular docking. Materials and Methods. The potential bioactive compounds of PCRR and their targets were collected from TCMSP, TCMID, and BATMAN-TCM databases with absorption, distribution, metabolism, and excretion protocols (oral bioavailability ≥30% and drug-likeness ≥0.18). The ALF-related target genes were identified using the GeneCards and OMIM databases. A protein-protein interaction (PPI) network among these targets was constructed using the Cytoscape software to obtain the core targets. The genes associated with ALF were analyzed via Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to identify the signaling pathways related to the therapeutic effect of PCRR in ALF. Results. In total, 10 bioactive compounds of PCRR and 200 targets related to them were obtained, and 2913 ALF-related target genes were identified. PPI network analysis pinpointed 15 core targets, namely, TP53, AKT1, JUN, HSP90AA1, MAPK1, RELA, TNF, ESR1, IL6, MYC, MAPK14, FOS, RB1, CDKN1A, and EGFR. GO enrichment and KEGG pathway analyses revealed that the therapeutic mechanisms of PCRR in ALF are related to cell metabolism, oxidative stress, inflammation, and hepatocyte apoptosis. Conclusion. This is the first study to explore the therapeutic mechanisms of PCRR in ALF via network pharmacology and molecular docking. This study provides a research platform with candidate ALF-related targets of PRCC for the development of therapeutics against ALF.
Rule-Based Classification Based on Ant Colony Optimization: A Comprehensive Review
Authors: Hossain Sayed Kaes Maruf , Ema Sajia Afrin , Sohn Hansuk
Published: April 2022

The Ant Colony Optimization (ACO) algorithms have been well-studied by the Operations Research community for solving combinatorial optimization problems. A handful of researchers in the Data Science community have successfully implemented various ACO methodologies for rule-based classification. This family of ACO algorithms is referred to as AntMiner algorithms. Due to the flexibility of the framework, and the availability of alternative strategies at the modular level, a systematic review on the AntMiner algorithms can benefit the broader community of researchers and practitioners interested in highly interpretable classification techniques. In this paper, we provided a comprehensive review of each module of the AntMiner algorithms. Our motivation is to provide insight into the current practices and future research scope in the context of the rule-based classification. Our discussions address ACO methodologies, rule construction strategies, candidate selection metrics, rule quality evaluation functions, rule pruning strategies, methods to address continuous attributes, parameter selection, and experimental settings. This review also reports a summary of real-life implementations of the rule-based classifiers in diverse domains including medical, genetics, portfolio analysis, geographic information system (GIS), human-machine interaction (HMI), autonomous driving, ICT, quality, and reliability engineering. These implementations demonstrate the potential application domains that can be benefitted from the methodological contributions to the rule-based classification technique.
Numerical Modeling of Rock Panels Subjected to Blast Loadings
Authors: Wang Haijun , Xiong Daiyu , Duan Yun , Cao Weiqin , Zhang Wenru , Jiao Peng , Wang Bonan , Zhao Xulin , Miao Yonggang , Zhou Le
Published: April 2022

In rock engineering problems, the rock is usually subjected to dynamic loads induced by drill, blast and rockburst. Therefore, understanding the dynamic response of rock benefit the safety and productivity of excavation activities. In this study, the response of rock panels under blast loadings is numerically investigated. The uniaxial and triaxial compression tests are firstly conducted in the laboratory to obtain the material property of four types of rocks, including Chuanshan limestone, Gaolishan sandstone, Mineralized limestone and Xixia limestone. Then, a total of 16 numerical simulations are carried out in which each type of rock panel is subjected to TNT blast loadings with four scaled distances, i.e., 0.15 m/kg1/3, 0.30 m/kg1/3, 0.60 m/kg1/3, 1.20 m/kg1/3. The damage distribution, which is characterized by the effective plastic strain, within the rock panels is quantitatively and qualitatively analyzed. The modelling results demonstrate that the effect of blast loading on the rock is material dependent. For a given scaled distance, the Gaolishan sandstone damages most severely, followed by Xixia limestone, Chuanshan limestone, and mineralized limestone. A critical scaled distance is observed on the limestone panels. When the scaled distance is smaller than the critical value, the damage of limestone panels increases with increasing the scaled distance. Once exceeding the critical value, the damage of limestone panels decreases with the increase of scaled distance. However, such a transitional scaled distance is not observed in the sandstone panels, of which damage decreases gradually with the increases of scaled distance.
Analysis of Coordinated Pricing Model of Closed‐Loop Supplying Chain Based on Game Theory in E‐Commerce Environment
Authors: Zhou Zhaoquan , Gu Bin
Published: April 2022

Although the research of closed‐loop supply chains has attracted great attention and some research results have appeared, it has not formed a complete theoretical system. People have studied many pricing and coordination models by modeling the closed‐loop supply chain system, but most of these models are based on the research under the conditions of market demand determination and information symmetry, which is far from the practice of the operation of the closed‐loop supply chain system, so it is difficult for these studies to improve its theoretical guidance ability. Starting from reality, this paper fully considers various situations closer to reality, such as multichannel recycling, a conflict between sales channels and recycling channels, and double trust in a complex practical environment. This paper constructs the corresponding pricing decision‐making model for analysis and research and further discusses the coordination and incentive mechanism of each node enterprise in the closed‐loop supply chain system, so as to realize the overall optimization of the closed‐loop supply chain. In this paper, the pricing strategy of the closed‐loop supply chain and the coordination of interests among participants are studied by using game theory under the practical e‐commerce model. Firstly, the traditional sales channel is considered, and the different situations of centralized decision‐making and decentralized decision‐making are compared. In short, cooperation can increase profits and achieve win‐win results. Finally, this paper puts forward some methods of construction coordination of closed‐loop supply chain to guide the closed‐loop supply chain to reduce costs, improve competitive advantage, and make the development of closed‐loop supply chain more practical significance.
Study on the Nonlinear Damage Creep Model of the Weak Interlayer
Authors: Wei Erjian , Hu Bin , Tian Kunyun , Cen Peishan , Zhang Zhen , Wang Zeqi , Chang Shuxiang
Published: April 2022

The weak interlayer has become a weak link in slope engineering due to its rheological effect. It is of great significance to study the nonlinear creep model of weak interlayer for long‐term stability of the slope. In this paper, based on the creep curve characteristics of weak interlayer and considering the influence of aging damage, the nonlinear improvement of a classical viscoplastic body under stress and time‐double threshold conditions is carried out, so that it can more accurately reflect the accelerated creep characteristics of the weak interlayer. By analyzing the relationship between failure load and time, the accelerated creep time threshold of the weak interlayer is obtained. On this basis, a nonlinear damage creep constitutive model of the weak interlayer is constructed and its creep equation is derived. By using the self‐defined function fitting tool of Origin software and the Levenberg–Marquardt optimization algorithm, the creep test data of weak interlayer are fitted and compared. The fitting curve is in good agreement with the test data, which shows the rationality and applicability of the nonlinear creep model. The results show that the nonlinear damage creep model constructed in this paper can well describe the creep characteristics of the weak interlayer and the model has important theoretical reference significance for the study of long‐term stability of slope with the weak interlayer.
Design of an Artificial Intelligence Algorithm Teaching System for Universities Based on Probabilistic Neuronal Network Model
Authors: Zhu Xianyou , Tang Songlin
Published: April 2022

Intelligence is gradually becoming an important tool for solving difficult problems with the development of computers. This article takes the design of university teaching systems as the research context to establish an artificial intelligence network research and learning platform. A probabilistic process neuron network model is proposed, which combines the Bayesian probabilistic classification mechanism with the dynamic signal processing method of process neuron networks, and achieves dynamic classification based on Bayesian rules by adding a pattern unit layer to the feed-forward process neuron network as well as adopting a normalised exponential excitation function. Artificial intelligence prediction based on probabilistic neural networks is verified by MATLAB as having good convergence and fault tolerance as well as data processing capability. The article also analyses the functions of the university intelligent teaching system and realises the optimal design of the university intelligent teaching system.
Toward an Effective Bug Triage System Using Transformers to Add New Developers
Authors: Zaidi Syed Farhan Alam , Woo Honguk , Lee Chan-Gun
Published: April 2022

As defects become more widespread in software development and advancement, bug triaging has become imperative for software testing and maintenance. The bug triage process assigns an appropriate developer to a bug report. Many automated and semiautomated systems have been proposed in the last decade, and some recent techniques have provided direction for developing an effective triage system. However, these techniques still require improvement. Another open challenge related to this problem is adding new developers to the existing triage system, which is challenging because the developers have no listed triage history. This paper proposes a transformer-based bug triage system that uses bidirectional encoder representation from transformers (BERT) for word representation. The proposed model can add a new developer to the existing system without building a training model from scratch. To add new developers, we assumed that new developers had a triage history created by a manual triager or human triage manager after learning their skills from the existing developer history. Then, the existing model was fine-tuned to add new developers using the manual triage history. Experiments were conducted using datasets from well-known large-scale open-source projects, such as Eclipse and Mozilla, and top-k accuracy was used as a criterion for assessment. The experimental outcome suggests that the proposed triage system is better than other word-embedding-based triage methods for the bug triage problem. Additionally, the proposed method performs the best for adding new developers to an existing bug triage system without requiring retraining using a whole dataset.
Energy Audit of a Residential Building to Reduce Energy Cost and Carbon Footprint for Sustainable Development with Renewable Energy Sources
Authors: Darshan Anagha , Girdhar Naman , Bhojwani Rohan , Rastogi Kanish , Angalaeswari S. , Natrayan L. , Paramasivam Prabhu
Published: April 2022

Electricity is a crucial part of our everyday lives. A limited number of resources can be utilized to generate power; hence, one must save these resources or electricity for future utilization. This is only possible by using energy most efficiently. For sustainable development and energy conservation, energy auditing plays an indispensable role. The energy audit is an extensive study that helps to identify energy use among different services and provides opportunities for energy conservation. The literature showed various energy audits conducted at various locations with its analysis. Few were shown that the appliances are responsible for high energy consumption, and few talked about the cost analysis with energy‐saving methodologies. This work is proposed to combine the integration of renewable energy sources in a building and the cost‐saving due to energy‐efficient appliances. This paper tries to observe, infer, and analyze the patterns of energy usage of a residential complex and various measures to reduce energy consumption and cost‐saving. A case study is included to analyze the reduction in consumption of energy per unit to make the building energy efficient. A feasibility study is executed to observe the increments in costs. Calculations for auditing will reduce the building’s carbon footprint and benefit residents in the form of cost savings in the long run.
Counting of Distinct Equivalence Classes of Circuits in PSL2, Z‐Space
Authors: Alolaiyan Hanan , Aamir Muhammad , Yousaf Awais , Razaq Abdul
Published: April 2022

Graham Higman was the first who studied the transitive actions of the extended modular group over PL(Fq) = Fq∪{∞} graphically and named it as coset diagram. In these sorts of graphs, a closed path of edges and triangles is known as a circuit. Coset diagrams evolve through the joining of these circuits. In a coset diagram, a circuit is termed as a length‐l circuit if its one vertex is fixed by , and it is denoted by (π1, π2, π3, …, πl). In this study, we shall formulate combinatorial sequences and find the number of distinct equivalence classes of a length‐6 circuit (π1, π2, π3, π4, π5, π6) for a fixed number of triangle Δ of class Π.
Spanish Airport Network Structure: Topological Characterization
Authors: Trobajo M. T. , Carriegos M. V.
Published: April 2022

Applied complex network theory has become an interesting research field in the last years. Many papers have appeared on this subject dealing with the topological description of real transport systems, from small networks like the Italian airport network to the worldwide air transportation network. A comprehensive topological description of those critical structures is relevant in order to understand their dynamics, capacities, and vulnerabilities. In this work, for the first time, we describe the Spanish airport network (SAN) as a complex network. Nodes are airports, and links are flight connections weighed by traffic flow. We study its topological features and traffic dynamics. Our analysis shows that SAN has complex dynamics similar to small-size air transportation networks of other developed economies. It shares properties of small-world and scale-free networks, and it is highly connected and efficient and has a disassortative pattern for high-degree nodes.
Improved Capsule Network Optimization Hierarchical Convolution Algorithm for Mental Health Recognition
Authors: Bhowmik Tapas , Bhusnurmath Rohini A. , Sahu Deepti , Jyotsna  , Babu K. Suresh , Alqahtani Abdullah
Published: April 2022

To address the shortcomings of standard convolutional neural networks (CNNs), the model structure is complex, the training period is lengthy, and the data processing technique is single. A modified capsule network is presented to optimize hierarchical convolution—the algorithm for identifying mental health conditions. To begin, two types of data processing are performed on the original vibration data: wavelet noise reduction and wavelet packet noise reduction; this retains more valuable information for mental health identification in the original signal; secondly, the CNN employs the concept of hierarchical convolution, and three distinct scaled convolution kernels are utilized to extract features from numerous angles; ultimately, the convolution kernel’s extracted features are fed into the pruning strategy’s capsule network for mental health diagnosis. The enhanced capsule network has the potential to significantly speed up mental health identification while maintaining accuracy. It is time to address the issue of the CNN structure being too complex and the recognition impact being inadequate. The experimental findings indicate that the suggested algorithm achieves a high level of recognition accuracy while consuming a small amount of time.
Digital Design of Chinese Classic Literary Works Based on Virtual Reality Technology
Authors: Jiao Yanna
Published: April 2022

In order to improve the digital communication effect of Chinese classic literary works, this paper combines virtual reality technology to carry out the digital system design of Chinese classic literary works, improve the digital design effect of Chinese classic literary works, and develop an augmented reality interaction method based on the HoloLens platform. Moreover, this paper analyzes the lighting model in computer graphics and the rendering process of three-dimensional objects in combination with the digitization requirements of literary works and realizes the rendering of special effects by customizing the lighting model in the shader. The research shows that the digital design system of Chinese classic literary works based on virtual reality technology proposed in this paper has a good effect on the digital protection of Chinese classic literary works.
Systematic Thinking and Evaluation of Construction Quality Management Standardization of Power Engineering in China
Authors: Yuan Tai-Ping , Ou Jing-Feng , Wang Juan , Zhang Wei
Published: April 2022

Power engineering is complex, involving many participants, numerous management factors, and different stages. To improve the quality management level of power engineering and realize the standardization of power engineering quality management, power engineering construction quality management standardization (CQMS) is evaluated using the order relation analysis method (G1 method) and the cloud model method. Firstly, based on systematic thinking and practice in China, the CQMS framework is proposed, and the evaluation index system including organizational performance, management behaviour performance, and entity performance related to power engineering construction quality is established. Subsequently, the G1 method is used to determine the weight of each index, and the cloud model method is used to implement the evaluation process. The results are divided into five grades, representing the five grades of construction quality management. Finally, taking a practical multiterminal DC demonstration project in China as an example, the CQMS performance is described and evaluated. The evaluation results directly reflect the level and difference of CQMS and show that this evaluation method can provide a reference for CQMS in power engineering.
Microdeletion of 4p16.2 in Children: A Case Report and Literature Review
Authors: Qian Yanjie , Wang Xiaoying , Tang Wei , Zou Chaochun
Published: April 2022

Copy number variations (CNV) are thought to play an important role in causing human diseases, including congenital anomalies, psychiatric disorders, and intellectual disabilities. We report here a one-year-old boy presented to our clinic as developmental delay. He presented a birth weight of 4.5 kg, motor delay, mental retardation, mild hypertonia, and some dysmorphic features (mild frontal bossing, hypertelorism, epicanthus, concave nasal ridge, slightly sparse hair, short hands, and mild nail dysplasia). The brain MRI indicated brain abnormalities; the Gross Motor Function Measure-66 score was 23.37; the Gesell test result showed the development quotient was 50, suggesting mental retardation. Chromosomal microarray analysis showed an approximately 97 kb microdeletion at 4p16.2 (4p16.2 CNV), including part of EVC and EVC2 genes, which were associated with Ellis-van Creveld syndrome (EvC) and Weyers acrofacial dysostosis (WAD). This report suggests 4p16.2 microdeletion may be associated with multiple developmental abnormalities, including motor delay and mental retardation.
Internet of Things Network Topology Discovery Algorithm Based on Wireless Sensors
Authors: Sun Hao
Published: April 2022

Remote sensor network is a multijump self-coordinating organization framework shaped by countless energy-restricted miniature sensor hubs sent in the observing region. Be that as it may, the correspondence conventions of customary organization configuration cannot be straightforwardly applied to remote sensor organizations. The improvement of committed correspondence conventions and estimation techniques has turned into a pressing exploration subject in the field of remote sensor organizations. The research purpose of this paper is to research the Internet of Things network topology discovery algorithm based on wireless sensors. This paper analyzes and mines an adaptive neighbor discovery scheduling algorithm with lower latency, acquires the information of potential neighbor nodes based on existing neighbor nodes, discovers potential neighbor nodes by proactively waking up, and studies the information recommendation mechanism between neighbor nodes and compares. The intimacy between neighbor nodes (such as common neighbor rate) is used to selectively receive recommended information from neighbor nodes, thereby filtering redundant data information, lessening hub energy utilization, and accomplishing the motivation behind broadening the organization life cycle. A geography disclosure calculation in light of versatile specialists is proposed to tackle the geography revelation issue under the various leveled geography structure in remote sensor organizations. The calculation joins the qualities of portable specialist innovation and various leveled Internet of Things network geography and simultaneously further develops the relocation methodology of versatile specialists for information assortment, intermittent undertaking assignment, and organization status observing. Research shows that in the investigation of the effect of hub obligation cycle on framework execution, the energy utilization of the DDC-Group calculation is marginally expanded contrasted and the gathering calculation, and the increase is not more than 1%, but compared to the CNR-Group increase more.
Image Recognition Method of Agricultural Pests Based on Multisensor Image Fusion Technology
Authors: Zeng Xianfeng , Huang Changjiang , Zhan Liuchun
Published: April 2022

With the rise and development of precision agriculture and smart agriculture concepts, traditional agricultural pest detection and identification methods have become increasingly unable to meet current agricultural production requirements due to their slow recognition speed, low recognition accuracy, and strong subjectivity need. This article aims to combine multifeature fusion technology with sensors to apply to crop pest detection and build crop pest detection services based on image recognition. In terms of image recognition, the use of image denoising methods based on median filtering, image preprocessing methods based on the maximum between-class error method (Otsu), image segmentation methods based on super green features, and feature extraction methods based on multiparameter features and based on the one-to-one elimination strategy and the M-SVM multiclass recognition algorithm fused with the kernel function, it realizes the identification and detection of six soybean leaf borers. The system uses the ARM920T series S3C2440 chip as the central processing unit. Through the temperature and humidity sensor and infrared, the multisensor module composed of sensors collects real-time information on the agricultural greenhouse. After normalizing the information, the central processing unit performs judgment processing and information fusion. And through experimental data, it is finally verified that the image recognition method used in this paper improves the recognition rate and effectiveness by nearly 65% in the detection of soybean leaf moth pests.
Using Machine Learning for Performance Classification and Early Fault Detection in Solar Systems
Authors: Refaee Eshrag A.
Published: April 2022

The steady increase in the world’s population has directly influenced global climate change, resulting in catastrophic environmental consequences. This has created an immediate need for scientists from interdisciplinary domains like clean technology innovation in solar energy and computer science to join in the effort to save the world for future generations. As such, the United Nations has set a goal to ensure global access to affordable, sustainable, and clean energy. As a leading influential G20 economy, Saudi Arabia has recently established the Green Saudi initiative to align with the UN goal for enhancing the use of green energy. However, research in this area is sparse and greater effort is still required. This work is among the first to address the issue of enhancing and expanding the use of clean energy by means of studying the data collected from solar plants around Saudi. We used machine learning-based methods to assess the energy output performance of solar plants and employed the collected data to train the models to make early detection of faults. Our models achieved the highest performance at an accuracy score of 98.85% and 0.98 weighted F-score using the J48 model trained on a publicly available dataset of 874 instances collected from 26 different sites across Saudi. We anticipate that the findings of this work to serve as testbed to facilitate further research in this area and enhance the early fault detection in solar energy stations.
Increased B Cell-Activating Factor Expression Is Associated with Postoperative Recurrence of Chronic Rhinosinusitis with Nasal Polyps
Authors: Zhang Fang , Xu Zhenhang , He Xi , Sun Yi , Zhao Chong , Zhang Jianhui
Published: April 2022

Background. Chronic rhinosinusitis with nasal polyps (CRSwNP) is a common upper airway inflammatory disease with a high postoperative recurrence rate. This study is aimed at exploring the expression of B cell-activating factor (BAFF) in CRSwNP and its association with postoperative recurrence. Methods. A total of 80 CRSwNP patients, including 40 primary CRSwNP patients and 40 recurrent CRSwNP patients, 40 chronic rhinosinusitis without nasal polyps (CRSsNP) patients, and 40 healthy controls (HC) were enrolled in this study, and the serum and tissue samples were collected. The circulating and tissue BAFF expressions were detected by enzyme-linked immunosorbent assay reverse transcription-polymerase chain reaction and immunohistochemistry. Their clinical values for predicting postoperative recurrence of CRSwNP were evaluated. Results. We determined serum levels of BAFF were remarkably increased in the CRSwNP group than the CRSsNP and HC groups (

P
<
0.05

), and higher concentrations of BAFF were associated with peripheral eosinophil percentage (

r
=
0.614

,

P
<
0.001

). The serum BAFF concentrations were significantly higher in the recurrent CRSwNP group in comparison with the primary group (

P
<
0.05

). Multivariate analysis and receiver operating characteristic (ROC) curve presented that serum BAFF levels were associated with the postoperative recurrence in CRSwNP patients (

P
<
0.05

). Moreover, tissue BAFF levels were significantly increased in the CRSwNP group than the HC group, especially in the recurrent CRSwNP group (

P
<
0.05

), and enhanced BAFF RNA expressions were correlated with serum BAFF levels (

r
=
0.703

,

P
<
0.001

). Conclusion. Our results elucidated that the BAFF expression was enhanced in CRSwNP patients and associated with postoperative recurrence. BAFF could be a serologic biomarker for predicting postoperative recurrence in CRSwNP patients.
Performance of Doubled Haploid Elite Rice (Oryza sativa L.) Germplasm for Grain Yield and Associated Traits in Harare, Zimbabwe
Authors: Chitanda Lovemore , Mativavarira Munyaradzi , Manjeru Pepukai , Kang Kyung-Ho , Nzuma Jean K. , Kamunhukamwe Trymore , Hove Tendai A. , Madzingaidzo Leonard
Published: April 2022

A field experiment was conducted in 2018/2019 growing season at the Scientific Industrial Research and Development Center under irrigated conditions using the simple alpha lattice (7

∗

7) design, with three replications. The objective of the study was to determine the performance of 44 elite rice lines that were selected and shared from elite doubled haploid germplasm and five cultivars were used as checks. All evaluated genotypes exhibited significant variation in the traits measured except for number of grains/panicle, total tillers/plant, and effective tillers/plant. Elite line 70462 was found to be the highest yielding, with yield advantage of 66% over the high yielding local check (Nerica 7). High values for broad sense heritability were recorded for days to 50% heading, panicle length, culm length, number of filled grains/panicle, 1000-grain weight, grain length, grain shape, and grain yield and indicated lesser influence of environment in expression of these traits, hence amenable to simple selection. Correlation analysis revealed that grain yield manifested significantly positive correlation with filled grains/panicle contributing the highest correlation (r = 0.784), followed by culm length (r = 0.605), spikelet fertility/panicle (r = 0.677), grain length (r = 0.551), 1000-grain weight (r = 0.518), panicle length (r = 0.449), and number of grains/panicle (r = 0.328). Based on grain yield, number of filled grains/panicle and spikelet fertility/panicle, panicle length, and earliness, lines 70462, 60143, 70383, Arica 3, Sahel 177, 6040, 70537, 60409, and 70476 had the best performance. Selection of these traits would be more effective for yield improvement in rice and these promising lines could be used in the varietal development and can be tested in multilocational trials and on-farm trials in Zimbabwe with the possibility of release and commercialization.
An Attention Encoder-Decoder Dual Graph Convolutional Network with Time Series Correlation for Multi-Step Traffic Flow Prediction
Authors: Zhao Shanchun , Li Xu
Published: April 2022

Accurate traffic prediction is a powerful factor of intelligent transportation systems to make assisted decisions. However, existing methods are deficient in modeling long series spatio-temporal characteristics. Due to the complex and nonlinear nature of traffic flow time series, traditional methods of prediction tasks tend to ignore the heterogeneity and long series dependencies of spatio-temporal data. In this paper, we propose an attentional encoder-decoder dual graph convolution model with time-series correlation (AED-DGCN-TSC) for solving the spatio-temporal sequence prediction problem in the traffic domain. First, the time-series correlation module calculates the sequence similarity by fast Fourier transform and inverse fast Fourier transform, while obtaining multiple possible lengths as possible solutions for the sequence period length. Then, K possible periods fetches are selected and the corresponding sequences are weighted and aggregated to the target sequence. Then, the gated dual graph convolution recurrent unit uses the graph convolution operation, which combines the ideas of node embedding, and dual graph, as an operation inside the gated recurrent structure to capture the spatio-temporal heterogeneity relationship of long sequences. The gated decomposition recurrent module decomposes the time series into the period and trend terms, which are modelled by convolutional gated recurrent unit (ConvGRU) and then fused with features, respectively, and output after graph convolution. Finally, multi-step prediction of future traffic flow is performed in the form of encoder-decoder. Experimental evaluations are conducted on two real traffic datasets, and the results demonstrate the effectiveness of the proposed model.
Fault Detection Method Based on Improved Faster R-CNN: Take ResNet-50 as an Example
Authors: Renjun Xie , Junliang Yuan , Yi Wu , Mengcheng Shu
Published: April 2022

In view of the low accuracy of existing tomographic detection methods, in order to improve the accuracy of tomographic detection, a tomographic detection method based on residual network and Faster R-CNN is proposed. First, input the image into the ResNet-50 feature extraction network to obtain the corresponding feature map, then use the RPN structure to generate the candidate frame, and project the candidate frame generated by the RPN to the feature map to obtain the corresponding feature matrix, and finally, through the ROI pooling layer, each of the feature matrix is scaled to a fixed-size feature map, and then the feature map is flattened through a series of fully connected layers to obtain the prediction result. ResNet-50 mainly solves the problem of network degradation and overfitting caused by deepening of the network layer when extracting the deep features of faults. Faster R-CNN realizes end-to-end training, combines the advantages of ResNet-50 and Faster R-CNN, and has a precise positioning efficiency. The accuracy of detecting faults reaches 90%. The data enhancement is further optimized, the generalization ability of the network is improved, the detection results of the network are optimized, and the accuracy of fault detection is effectively improved, and the feasibility of the method is verified by actual seismic data.
Inhibition of Galectin-3 Impairs Antifungal Immune Response in Fungal Keratitis
Authors: Xiao Yichen , Yang Jiahui , Fu Zhenyuan , Xiong Zhile , Zhang Chao , He Dalian , Zhou Zhenwen , Li Naiyang , Yuan Jin
Published: April 2022

Galectin-3 is one of the galectin family members which are master regulators of immune homeostasis, especially in infectious diseases. However, its mechanism of immune regulation in fungal keratitis has not been thoroughly studied. Our study is aimed at clarifying the role of galectin-3 in the fungal keratitis mouse model in vivo, thereby providing a new biomarker of antifungal therapy. In our study, aspergillus, the most common pathogenic fungi of fungal keratitis, was identified and isolated by corneal tissue fungus culture. Then, the RNA expression levels of galectin family members in corneas of the mouse model with aspergillus fumigatus keratitis were screened by transcriptome sequencing (RNA-seq). The expression of the galectin-3 was detected by quantitative real-time Polymerase Chain Reaction (qPCR), enzyme-linked immunosorbent assay (ELISA), and immunofluorescence in the corneal tissue of the fungal keratitis mouse model. Recruitment of neutrophils and the co-immunofluorescence of galectin-3 and related markers in corneal tissue were determined by flow cytometry analysis and immunofluorescence staining. The regulatory role of galectin-3 for proinflammatory cytokines and neutrophils was validated by the knockout mouse model. Galectin-3 knockout deteriorated the condition for the inhibition of galectin-3 was benefecial for fungi to survive and thrive in corneal lesions. These results demonstrated that in the ocular fungal infection, galectin-3 is capable of regulating the pathogenesis of fungal keratitis by modulating neutrophil recruitment. The deterioration of fungal keratitis and increased fungal load in corneal lesions of galectin-3 knockout mice proved the regulatory role of galectin-3 in fungal keratitis. In conclusion, galectin-3 is going to be an essential target to modulate neutrophil recruitment and its related antifungal immune response in fungal keratitis.
Simulation Evaluation of Calligraphy Art Style Relying on Fuzzy Support Vector Machine Scientific Calculation Algorithm
Authors: Cai Wei
Published: April 2022

With the comprehensive follow-up of the information age and the rise of digital technology, the collection of calligraphy art can be preserved in the form of image data. As a testimony to the Chinese 5,000-year history, calligraphy has formed its own genre in the long river of time and its style prevailed in different periods. Nowadays, the recognition technology of characters has become advanced, but there is no better method for the recognition and classification of the artistic style of calligraphy. In addition, there are more and more counterfeiting methods, and computers alone cannot completely replace human identification; therefore, the two need to be integrated and cooperated in the identification work. This article proposes an improved fuzzy support vector machine algorithm based on the aggregation algorithm, which uses the skeleton extraction of calligraphic fonts as the main method. It uses the extracted font style and font morphological characteristics to identify the style of the work and whether it is genuine. In the article, the improvement strategy of the traditional fuzzy support vector machine is proposed, and its detection accuracy is compared with that of the traditional fuzzy support vector machine. The experimental results of this article show that the discrimination rate of authenticity cannot reach 100% and there is a certain degree of error. Through repeated experiments for many times, the value has a certain change, and when the step size is 0.1, calculating the average value can get the value of the false rejection rate and the false recognition rate.
The Association between Regulatory T Cell Subpopulations and Severe Pneumonia Post Renal Transplantation
Authors: Zhuang Quan , Cai Haozheng , Yang Min , Peng Bo , Luo Yulin , Zhang Ying , Ming Yingzi
Published: April 2022

Severe pneumonia accounts for the majority of morbidity and mortality in renal allograft recipients due to immunosuppressant maintenance. Regulatory T cells (Tregs), which are involved in tackling infections under immunosuppressive conditions, are rarely uncovered. We aimed to investigate the relationship between various Treg subpopulations and severe pneumonia after kidney transplantation (KTx). KTx recipients with pneumonia were divided into severe pneumonia and mild pneumonia groups. The frequencies and absolute numbers (Ab No.) of total Tregs (CD4+CD25+FoxP3+), six subsets of Tregs (Helios+/-, CD39+/-, and CD45RA+/-), and T cells, B cells, and NK cells were assessed from peripheral blood via flow cytometry using the

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or Mann-Whitney test and receiver operating curve analysis. We also determined the median fluorescence intensity (MFI) of human leukocyte antigen- (HLA-) DR on monocytes and CD64 on neutrophils. Logistic regression was used to identify the risk factors of disease progression, and Pearson’s correlation analysis was performed to identify relationships between the measured immune indices and patients’ clinical information. Our research indicated that Treg subpopulations were strongly associated with severe pneumonia progression post KTx. Based on the monitoring of Treg subpopulations, better-individualized prevention and therapy might be achieved for patients with severe pneumonia post KTx.
The Modeling of Super Deep Learning Aiming at Knowledge Acquisition in Automatic Driving
Authors: Liang Yin , Gu Zecang , Zhang Zhaoxi
Published: April 2022

In this paper, we proposed a new theory of solving the multitarget control problem by introducing a machine learning framework in automatic driving and implementing the acquisition of excellent drivers’ knowledge. Nowadays, there still exist some core problems that have not been fully realized by the researchers in automatic driving, such as the optimal way to control the multitarget objective functions of energy saving, safe driving, headway distance control, and comfort driving. It is also challenging to resolve the networks that automatic driving is relied on and to improve the performance of GPU chips on complex driving environments. According to these problems, we developed a new theory to map multitarget objective functions in different spaces into the same one and thus introduced a machine learning framework of SDL (super deep learning) for optimal multitarget control based on knowledge acquisition. We will present in this paper the optimal multitarget control by combining the fuzzy relationship of each multitarget objective function and the implementation of excellent drivers’ knowledge acquired by machine learning. Theoretically, the impact of this method will exceed that of the fuzzy control method used in the automatic train.