bluuebunny/crossref_metadata_2025 huggingface dataset:
On Wiener Index of Unit Graph Associated with a Commutative Ring
Authors: Asir T. , Rabikka V. , Su Huadong
Published: April 2022

Let [Formula: see text] be a ring with non-zero identity. The unit graph of [Formula: see text], denoted by [Formula: see text], is an undirected graph with all the elements of [Formula: see text] as vertices and where distinct vertices [Formula: see text], [Formula: see text] are adjacent if and only if [Formula: see text] is a unit of [Formula: see text]. In this paper, we investigate the Wiener index and hyper-Wiener index of [Formula: see text] and explicitly determine their values.
On Annihilating Graphs Associated with Modules over Commutative Rings
Authors: Raja Rameez , Pirzada Shariefuddin
Published: April 2022

Let [Formula: see text] be a commutative ring with unity, [Formula: see text] be a unitary [Formula: see text]-module and [Formula: see text] be a simple connected graph. We examine different equivalence relations on subsets [Formula: see text], [Formula: see text] and [Formula: see text] of [Formula: see text], where [Formula: see text] is the set of full-annihilators, [Formula: see text] is the set of semi-annihilators and [Formula: see text] is the set of star-annihilators in [Formula: see text]. We prove that elements [Formula: see text] are neighborhood similar in the annihilating graph [Formula: see text] if and only if the submodules [Formula: see text] and [Formula: see text] of [Formula: see text] are equal. We study the isomorphism of annihilating graphs arising from [Formula: see text] and the tensor product [Formula: see text], where [Formula: see text], [Formula: see text].
Magnetic Behaviors of the Kesterite and the Stannite Nanostructures: Monte Carlo Study
Authors: Saber N. , Fadil Z. , Mhirech A. , Kabouchi B. , Bahmad L. , Benomar W. Ousi
Published: April 2022

In this work, the Monte Carlo simulations have been performed to investigate the magnetic properties of the kesterite and the stannite nanostructures with mixed spins. The Blume–Capel model has been adopted to study the Cu2ZnSnSe4 nanostructures. Furthermore, we have examined different transitions as a function of the temperature, the crystal field, and the exchange coupling interactions. Also, the behavior of the magnetizations and magnetic susceptibilities are investigated as a function of the different physical parameters. A comparison of the magnetic properties of each nanostructure has been studied and discussed.
Relating random matrix map enumeration to a universal symbol calculus for recurrence operators in terms of Bessel–Appell polynomials
Authors: Ercolani Nicholas M. , Waters Patrick
Published: April 2022

Maps are polygonal cellular networks on Riemann surfaces. This paper analyzes the construction of closed form general representations for the enumerative generating functions associated to maps of fixed but arbitrary genus. The method of construction developed here involves a novel asymptotic symbol calculus for difference operators based on the relation between spectral asymptotics for Hermitian random matrices and asymptotics of orthogonal polynomials with exponential weights. These closed form expressions have a universal character in the sense that they are independent of the explicit valence distribution of the cellular networks within a broad class. Nevertheless the valence distributions may be recovered from the closed form generating functions by a remarkable unwinding identity in terms of Appell polynomials generated by Bessel functions. Our treatment reveals the generating functions to be solutions of nonlinear conservation laws and their prolongations. This characterization enables one to gain insights that go beyond more traditional methods that are purely combinatorial. Universality results are connected to stability results for characteristic singularities of conservation laws that were studied by Caflisch, Ercolani, Hou and Landis, Multi-valued solutions and branch point singularities for nonlinear hyperbolic or elliptic systems, Commun. Pure Appl. Math. 46 (1993) 453–499, as well as directly related to universality results for random matrix spectra.
ActivFORMS: A Formally Founded Model-based Approach to Engineer Self-adaptive Systems
Authors: Weyns Danny , Iftikhar Usman M.
Published: April 2022

Self-adaptation equips a computing system with a feedback loop that enables it to deal with change caused by uncertainties during operation, such as changing availability of resources and fluctuating workloads. To ensure that the system complies with the adaptation goals, recent research suggests the use of formal techniques at runtime. Yet, existing approaches have three limitations that affect their practical applicability: (i) they ignore correctness of the behavior of the feedback loop, (ii) they rely on exhaustive verification at runtime to select adaptation options to realize the adaptation goals, which is time- and resource-demanding, and (iii) they provide limited or no support for changing adaptation goals at runtime. To tackle these shortcomings, we present ActivFORMS (Active FORmal Models for Self-adaptation). ActivFORMS contributes an end-to-end approach for engineering self-adaptive systems, spanning four main stages of the life cycle of a feedback loop: design, deployment, runtime adaptation, and evolution. We also present ActivFORMS-ta, a tool-supported instance of ActivFORMS that leverages timed automata models and statistical model checking at runtime. We validate the research results using an IoT application for building security monitoring that is deployed in Leuven. The experimental results demonstrate that ActivFORMS supports correctness of the behavior of the feedback loop, achieves the adaptation goals in an efficient way, and supports changing adaptation goals at runtime.
There’s no Such Thing as a Free Lunch: Lessons Learned from Exploring the Overhead Introduced by the Greenkeeper Dependency Bot in Npm
Authors: Rombaut Benjamin , Cogo Filipe R. , Adams Bram , Hassan Ahmed E.
Published: April 2022

Dependency management bots are increasingly being used to support the software development process, for example, to automatically update a dependency when a new version is available. Yet, human intervention is often required to either accept or reject any action or recommendation the bot creates. In this article, our objective is to study the extent to which dependency management bots create additional, and sometimes unnecessary, work for their users. To accomplish this, we analyze 93,196 issue reports opened by
Greenkeeper
, a popular dependency management bot used in open source software projects in the
npm
ecosystem. We find that
Greenkeeper
is responsible for half of all issues reported in client projects, inducing a significant amount of overhead that must be addressed by clients, since many of these issues were created as a result of
Greenkeeper
taking incorrect action on a dependency update (i.e., false alarms). Reverting a broken dependency update to an older version, which is a potential solution that requires the least overhead and is automatically attempted by
Greenkeeper
, turns out to not be an effective mechanism. Finally, we observe that 56% of the commits referenced by
Greenkeeper
issue reports only change the client’s dependency specification file to resolve the issue. Based on our findings, we argue that dependency management bots should (i) be configurable to allow clients to reduce the amount of generated activity by the bots, (ii) take into consideration more sources of information than only the pass/fail status of the client’s build pipeline to help eliminate false alarms, and (iii) provide more effective incentives to encourage clients to resolve dependency issues.
Resource-Efficient Continual Learning for Sensor-Based Human Activity Recognition
Authors: Leite Clayton Frederick Souza , Xiao Yu
Published: April 2022

Recent advances in deep learning have granted unrivaled performance to sensor-based
human activity recognition (HAR)
. However, in a real-world scenario, the HAR solution is subject to diverse changes over time such as the need to learn new activity classes or variations in the data distribution of the already-included activities. To solve these issues, previous studies have tried to apply directly the continual learning methods borrowed from the computer vision domain, where it is vastly explored. Unfortunately, these methods either lead to surprisingly poor results or demand copious amounts of computational resources, which is infeasible for the low-cost resource-constrained devices utilized in HAR. In this paper, we provide a resource-efficient and high-performance continual learning solution for HAR. It consists of an expandable neural network trained with a replay-based method that utilizes a highly-compressed replay memory whose samples are selected to maximize data variability. Experiments with four open datasets, which were conducted on two distinct microcontrollers, show that our method is capable of achieving substantial accuracy improvements over baselines in continual learning such as Gradient Episodic Memory, while utilizing only one-third of the memory and being up to 3× faster.
Challenges in Deploying Machine Learning: A Survey of Case Studies
Authors: Paleyes Andrei , Urma Raoul-Gabriel , Lawrence Neil D.
Published: April 2022

In recent years, machine learning has transitioned from a field of academic research interest to a field capable of solving real-world business problems. However, the deployment of machine learning models in production systems can present a number of issues and concerns. This survey reviews published reports of deploying machine learning solutions in a variety of use cases, industries, and applications and extracts practical considerations corresponding to stages of the machine learning deployment workflow. By mapping found challenges to the steps of the machine learning deployment workflow, we show that practitioners face issues at each stage of the deployment process. The goal of this article is to lay out a research agenda to explore approaches addressing these challenges.
RBF-Based 3D Visual Detection Method for Chinese Martial Art Wrong Movements
Authors: Wang Xi , Pan Yi-Hsiang , Li Zongbai , Li Bing
Published: April 2022

The accuracy of action detection is limited by the extracted action, and there are problems of high processing complexity and low efficiency. Therefore, a three-dimensional visual detection method of martial art wrong action based on RBF is proposed. After noise reduction and weighting processing of martial art action video images, a martial art action 3D visual transformation model is established. According to the 3D visual model, C3D features are used to represent martial art actions. The video is segmented using sparse coding to determine the detection range. RBF neural network model is established, and the combination of the above 3D visual model and network parameters is obtained by sample training to detect martial art wrong actions. The test method of the experimental results shows the detection of the research under the condition of different degrees of precision, an average of at least 5%, and the method of detection of high efficiency and stability.
Plant‐Based Bimetallic Silver‐Zinc Oxide Nanoparticles: A Comprehensive Perspective of Synthesis, Biomedical Applications, and Future Trends
Authors: Ehsan Maria , Waheed Abdul , Ullah Abd , Kazmi Abeer , Ali Amir , Raja Naveed Iqbal , Mashwani Zia-ur-Rehman , Sultana Tahira , Mustafa Nilofar , Ikram Muhammad , Li Huanyong
Published: April 2022

The universal emphasis on the study of green nanotechnology has led to biologically harmless uses of wide‐ranged nanomaterials. Nanotechnology deals with the production of nanosized particles with regular morphology and properties. Various researches have been directed on nanomaterial synthesis by physical, chemical, and biological means. Understanding the safety of both environment and in vivo, a biogenic approach particularly plant‐derived synthesis is the best strategy. Silver‐zinc oxide nanoparticles are most effective. Moreover, these engineered nanomaterials via morphological modifications attain improved performance in antimicrobial, biomedical, environmental, and therapeutic applications. This article evaluates manufacturing strategies for silver‐zinc oxide nanoparticles via plant‐derived means along with highlighting their broad range of uses in bionanotechnology.
Lignin as Green Filler in Polymer Composites: Development Methods, Characteristics, and Potential Applications
Authors: Ridho Muhammad Rasyidur , Agustiany Erika Ayu , Rahmi Dn Muslimatul , Madyaratri Elvara Windra , Ghozali Muhammad , Restu Witta Kartika , Falah Faizatul , Rahandi Lubis Muhammad Adly , Syamani Firda Aulya , Nurhamiyah Yeyen , Hidayati Sri , Sohail Asma , Karungamye Petro , Nawawi Deded Sarip , Iswanto Apri Heri , Othman Nadras , Mohamad Aini Nor Anizah , Hussin M. Hazwan , Sahakaro Kannika , Hayeemasae Nabil , Ali Muhammad Qasim , Fatriasari Widya
Published: April 2022

After cellulose, lignin is the most commonly used natural polymer in green biomaterials. Pulp and paper mills and emerging cellulosic biorefineries are the main sources of technical lignin. However, only 2–5% of lignin has been converted into biomaterials. Making lignin-based polymer biocomposites to replace petroleum-based composites has piqued the interest of many researchers worldwide due to the positive environmental impact of traditional composites over time. In composite development, lignin is being used as a filler in commercial polymers to improve biodegradability and possibly lower production costs. As a natural polymer, lignin may have different properties depending on the isolation method and source, affecting polymer-based composites. The application has been affected by the characteristics of lignin and the uniform distribution of lignin in polymers. The review’s goal was to provide an overview of technical lignin extraction, properties, and its potential appropriate utilization. It was also planned to revisit the lignin-based composites’ preparation procedure as well as their composite characteristics. Solvent casting and extrusion methods are used to fabricate lignin from polymeric matrices such as polypropylene, epoxy, polyvinyl alcohol, polylactic acid, starch, wood fiber, natural rubber, and chitosan. Packaging, biomedical materials, automotive, advanced biocomposites, flame retardant, and other applications for lignin-based composites has existed. As a result, the technology is still being refined to increase the performance of lignin-based biocomposites in several applications. This review could assist explain lignin’s position as a composite additive, which could lead to more efficient processing and application strategies.
Improved Deep Neural Network for Cross-Media Visual Communication
Authors: Miao Yubo
Published: April 2022

Cross-media visual communication is an extremely complex task. In order to solve the problem of segmentation of visual foreground and background, improve the accuracy of visual communication scene reconstruction, and complete the task of visual real-time communication. We propose an improved generative adversarial network. We take the generative adversarial network as the basis and add a combined codec package to the generator, while configuring the generator and discriminator as a cascade structure, preserving the feature upsampling and downsampling convolutional layers of visual scenes with different layers through correspondence. To classify features with different visual scene layers, we add a new auxiliary classifier based on convolutional neural networks. With the help of the auxiliary classifier, similar visual scenes with different feature layers have a more accurate recognition rate. In the experimental part, to better distinguish foreground and background in visual communication, we perform performance tests on foreground and background using separate datasets. The experimental results show that our method has good accuracy in both foreground and background in cross-media communication for real-time visual communication. In addition, we validate the efficiency of our method on Cityscapes, NoW, and Replica datasets, respectively, and experimentally demonstrate that our method performs better than traditional machine learning methods and outperforms deep learning methods of the same type.
Assessment of Status of Climate Change and Determinants of People’s Awareness to Climate-Smart Agriculture: A Case of Sarlahi District, Nepal
Authors: Adhikari Sandhya , Rawal Sara , Thapa Sandesh
Published: April 2022

This study aims to analyze the status of climate change and determinants of people’s awareness of climate change in Sarlahi district, Nepal. A total of 102 respondents were selected randomly from the study area and interviewed using a semistructured questionnaire from May 12, 2021, to May 23, 2021. Along with the determinants, this survey emphasized finding climate-smart alternatives favoring not only the population or sectors like agriculture but also the climate itself. The chi-square test was conducted to measure the relationship between the operational variables, which revealed that there was no significant relationship between gender and knowledge of climate change, occupation and knowledge of climate change, land ownership and knowledge of climate change, guardian and knowledge of climate change, and decision role and knowledge on climate change. However, education, family size, and age had a significant effect on the knowledge of climate change. The binary logit model reported that age, years of schooling, training related to climate change, and involvement with cooperatives were found to have a significant effect on people’s awareness of climate change. Thus, improving people’s adoption of climate-smart agriculture in the education system of the study area and training the people in the study area should be a prime concern.
Analysis on Optimization of Agricultural Products Supply Chain Based on Dynamic System
Authors: Guo Yuhan , Yin Yue
Published: April 2022

To thoroughly implement the spirit of the Fifth Plenary Session of the 19th CPC Central Committee, this paper optimizes the supply chain of agricultural products based on dynamic system. In the complex supply chain of agricultural products, they are scattered upstream and downstream. It is difficult to form agglomeration scale effect, information asymmetry between supply chain nodes, and poor hardware environment of supply chain. Logistics operation is difficult. By analyzing the supply chain of agricultural products, optimize the supply chain by using the push‐pull supply chain. According to the change of market demand, the push mode and the pull mode are properly allocated and the government’s attention to the supply chain is increased, intentionally the Internet is combined with the supply chain, the stability of the supply chain is increased, and there is a certain adaptability to market demand fluctuations. Problems can be fed back to the suppliers and final buyers of agricultural products in time, and the delivery rate of the whole supply chain should be improved.
An Expanded Concept and Graph of Zadeh’s Algebraic Operations for 3‐Dimensional Generalized Triangular Fuzzy Sets
Authors: Yun Yong Sik
Published: April 2022

Algebraic operators for fuzzy sets have been studied in 2‐dimensional space. We define the algebraic operators between two 3‐dimensional fuzzy sets and calculate the operators for the generalized 3‐dimensional triangular fuzzy sets. To facilitate an understanding of the results in 3D, the values of membership function are displayed in graphs using color density. We give various types of graphs of α‐cuts.
Emotion Analysis Model of Microblog Comment Text Based on CNN-BiLSTM
Authors: Li Aichuan , Yi Shujuan
Published: April 2022

Aiming at the problems of over reliance on labor and low generalization of traditional emotion analysis methods based on dictionary and machine learning, an emotion analysis model of microblog comment text based on deep learning is proposed. Firstly, text is obtained through microblog crawler program. After data preprocessing, including data cleaning, Chinese word segmentation, removal of stop words, and so on, the Skip-gram model is used for word vector training on a large-scale unmarked corpus, and then the trained word vector is used as the text input of CNN-BiLSTM model, which combines Bidirectional Long-Short Term Memory (BiLSTM) neural network and Convolution Neural Network (CNN). Considering the historical context information and the subsequent context information, BiLSTM can better use the temporal relationship of text to learn sentence semantics. CNN can extract hidden features from the text and combine them. Finally, after Adamax optimization training, the emotion type of microblog comment text is output. The proposed model combines the learning advantages of BiLSTM and CNN. The overall accuracy of text emotion analysis has been greatly improved, with an accuracy of 0.94 and an improvement of 8.51% compared with the single CNN model.
Single-Cell RNA-Sequencing Analyses Revealed Heterogeneity and Dynamic Changes of Metabolic Pathways in Astrocytes at the Acute Phase of Ischemic Stroke
Authors: Ma Hongyu , Zhou Yu , Li Zifu , Zhu Luojiang , Li He , Zhang Guanghao , Wang Jing , Gong Haiyi , Xu Da , Hua Weilong , Liu Pei , Zhang Xiaoxi , Zhang Yongxin , Zhang Lei , Hong Bo , Zhou Wang , Yang Pengfei , Liu Jianmin
Published: April 2022

Astrocyte plays important roles in the pathogenesis of ischemic stroke and reperfusion injury. They intensively participate in the energy metabolism of the brain, while their heterogeneity and function after ischemic stroke remain controversial. By employing single-cell sequencing of mice cortex at 12 h after transient middle cerebral artery occlusion (tMCAO) and comparing with the similar published datasets of 24h after tMCAO, we uncover the cellular phenotypes and dynamic change of astrocytes at the acute phase of ischemic stroke. In this study, we separately identified 3 major subtypes of astrocytes at the 12 h-tMCAO-system and 24 h-tMCAO-system, indicated the significant differences in the expression of genes and metabolic pathways in the astrocytes between the two time nodes after ischemic stroke, and detected the major change in the energy metabolism. These results provided a comprehensive understanding of the characteristic changes of astrocytes after ischemic stroke and explored the potential astrocytic targets for neuroprotection.
A Traffic Scheduling Method Based on SDN
Authors: Wang Li
Published: April 2022

The rise of big data has brought severe challenges to data storage, which also means that computing resources and storage resources have become centralized. In the past, business data could end up after being processed locally or at the secondary node. Now, all data must be uploaded to each data center for unified processing. Data collection also requires users to access data center services and then send them back, which undoubtedly increases the pressure on the backbone network. This also means that the annual private line cost of the enterprise will increase with the growth of network traffic. Large enterprises often use multiple lines between network nodes to improve link utilization and network reliability. However, in most cases, the line utilization of the backup line is usually low, because the traditional routing algorithm forwards according to the optimal path. Load sharing or traffic scheduling is achieved by adjusting interface cost values ​​or BGP parameters. In large networks, it often affects the entire body. Each adjustment requires detailed planning and validation. At the same time, due to the diversity of applications, different applications have different business characteristics. If the network can distinguish different applications, sense network conditions, and adjust the QoS and traffic paths of applications as needed, the link utilization of leased lines and the network quality of applications will be significantly improved. SDN separates the control plane from the data plane and intelligently controls the network forwarding policy through the global controller, which improves the flexibility and security of the network. Based on the traditional large-scale enterprise WAN architecture, this paper proposes a complete traffic scheduling solution based on SDN to achieve secure, controllable, and flexible scheduling of enterprise WAN network resources.
Design of Motion Damage Estimation Model Based on Improved Recursive Neural Network Algorithm
Authors: Zhu Jiaju , Hu Song , Zhang Meng
Published: April 2022

In recent years, the China’s rapid economic development and living standards of people have been improved, and the usage of clothing, food, housing, and transportation has increased. However, due to the prevalence of sports injuries and their high frequency of incidence, sports injury incidents do occur from time to time. Sports injury will not only affect the normal training of athletes and sports enthusiasts but also adversely affect their health. An estimation model of sports injury is designed based on an improved recursive neural network algorithm to reduce the incidence of sports injury in the process of physical exercise so that athletes and sports enthusiasts can reduce sports injury, which is conducive to physical health and sports health.
Perioperative and Survival Outcomes of Robotic-Assisted Surgery, Comparison with Laparoscopy and Laparotomy, for Ovarian Cancer: A Network Meta-Analysis
Authors: Tang Qin , Liu Weichu , Jiang Dan , Tang Junying , Zhou Qin , Zhang Jing
Published: April 2022

Objective. We aimed to compare the perioperative and survival outcomes of robotic-assisted surgery, traditional laparoscopy, and laparotomy approaches in ovarian cancer. Methods. PubMed, Cochrane Library, Embase, Web of Science, and Chinese National Knowledge Infrastructure (CNKI) were searched using multiple terms for ovarian cancer surgeries, including comparative studies in Chinese and English. Literatures are published before August 31, 2021. The outcomes include operating time, estimated blood loss, length of hospital stay, postoperative/intraoperative/total complications, pelvic/para-aortic/total lymph nodes, transfusion, and five-year overall survival rate. The dichotomous data, continuous data, and OS data were pooled and reported as relative risk, standardized mean differences, and hazard ratio HRs with 95% confidence intervals, respectively. The Newcastle–Ottawa Scale was used to evaluate the risk of bias of included studies. Results. Thirty-eight studies, including 8,367 patients and three different surgical approaches (robotic-assisted laparoscopy surgery, traditional laparoscopy, or laparotomy approaches), were included in this network meta-analysis. Our analysis shows that the operating time of laparotomy was shorter than laparoscopy. The robotic-assisted laparoscopy has the least estimated blood loss during the surgery, followed by laparoscopy, and finally laparotomy. Compared with laparotomy, the incidence of blood transfusion was lower in the robotic-assisted laparoscopy and laparoscopy groups, and the length of hospital stay is shorter. Laparotomy had a significantly higher incidence of total complications than robotic-assisted laparoscopy and laparoscopy and higher postoperative complications than laparoscopy. For the number of pelvic/para-aortic/total lymph nodes removed by different surgical approaches, our analysis revealed no statistical difference. Our analysis also revealed no significant differences in intraoperative complications and 5-year OS among the three surgical approaches. Conclusion. Compared with laparotomy, robotic-assisted laparoscopy and laparoscopy had a shorter hospital stay, decreased blood loss, fewer complications, and transfusion happened. The 5-year OS of ovarian cancer patients has no difference between robotic-assisted laparoscopy, laparoscopy, and laparotomy groups.
Fuzzy Adaptive Teaching Learning-Based Optimization for Solving Unconstrained Numerical Optimization Problems
Authors: Din Fakhrud , Khalid Shah , Fayaz Muhammad , Gwak Jeonghwan , Zamli Kamal Z. , Mashwani Wali Khan
Published: April 2022

Teaching learning-based optimization is one of the widely accepted metaheuristic algorithms inspired by teaching and learning within classrooms. It has successfully addressed several real-world optimization problems, but it may still be trapped in local optima and may suffer from the problem of premature convergence in the case of solving some challenging optimization problems. To overcome these drawbacks and to achieve an appropriate percentage of exploitation and exploration, this study presents a new modified teaching learning-based optimization algorithm called the fuzzy adaptive teaching learning-based optimization algorithm. The proposed fuzzy adaptive teaching learning-based optimization algorithm uses three measures from the search space, namely, quality measure, diversification measure, and intensification measure. As the 50-50 probabilities for exploitation and exploration in the basic teaching learning-based optimization algorithm may be counterproductive, the Mamdani-type fuzzy inference system of the new algorithm takes these measures as a crisp inputs and generates selection as crisp output to choose either exploitation or exploration based on the current search requirement. This fuzzy-based adaptive selection helps to adequately balance global search or exploration and local search or exploitation operations during the search process as these operations are intrinsically dynamic. The performance of the fuzzy adaptive teaching learning-based optimization is evaluated against other metaheuristic algorithms including basic teaching learning-based optimization on 23 unconstrained global test functions. Moreover, adaptive teaching learning-based optimization is used to search for near-optimal values for the four parameters of the COCOMO II model, which are then tested for validity on a software project of NASA. Analysis and comparison of the obtained results indicate the efficiency and competitiveness of the proposed algorithm in addressing unconstrained continuous optimization tasks.
Analysis of Transient Response of ZPW-2000A Jointless Track Circuit Considering Frequency Variation
Authors: Zhao Bin , Yu Guanghao , Wang Dong , Chen Lei , Ou Jingning
Published: April 2022

In order to accurately analyze the influence of electromagnetic transient signals on the jointless track circuit when the electromagnetic transient signal propagates in the rail, it is necessary to consider the frequency-variable load terminated in the ZPW-2000A jointless track circuit and the frequency-variable loss inside the rail. A method is proposed for calculating the transient response of transmission lines system with frequency-variable end load of jointless track circuit. Firstly, the transmission lines model of jointless track circuit is established, based on multiconductor transmission lines theory, the model equation is deduced and discretized by finite difference time domain (FDTD). The vector fitting method is used to express the admittance of the tuning region in the track circuit, and the rational approximation function of the tuning region is derived from the poles, residues, and constants. The voltage and current at nodes in the tuning region are calculated by piecewise linear recursive convolution algorithm. Combined with the discrete transmission line equation, the current and voltage expression of the transient electromagnetic signal at the receiving end of the track circuit in time domain is obtained. Compared with state variable method, the error is less than 6%, which verifies the correctness of the proposed method. Finally, this paper studies the influence laws of different factors on the overvoltage at the receiving end of jointless track circuit and the weak links of jointless track circuit under the influence of transient electromagnetic signal. It provides theoretical reference for fault research and anti-interference analysis of ZPW-2000A jointless track circuit.
Risk Assessment Method of Agricultural Management Investment Based on Genetic Neural Network
Authors: Zhao JingJing
Published: April 2022

At present, the economy has entered the new normal. In the environment of economic slowdown, agricultural operation and investment risk management is of great significance in investment activities. In order to avoid the related risks of the agricultural operation and investment, improve the stability of agricultural operation and investment risk assessment, and ensure the effect of the agricultural operation and investment risk assessment, an agricultural operation and investment risk assessment method based on genetic neural network is proposed. This method fully considers the global optimization capability of the genetic algorithm and optimizes the weights and thresholds of the BP neural network. According to the construction principle of the index system, from the perspective of systemic risk and nonsystematic risk, a risk assessment index system for agricultural business investment has been established. Based on the establishment of risk evaluation index system, using the genetic neural network method, the agricultural operation investment risk evaluation model is established to realize the agricultural operation investment risk evaluation. The experimental results show that the proposed method has a good effect on the risk assessment of the agricultural operation and investment and can effectively improve the stability of the risk assessment of agricultural operation and investment.
More than an Incidentaloma: The Nonreportable NIPT
Authors: Jay Allison M. , Mason Brian , Lebovic Daniel , Chuba Paul
Published: April 2022

Noninvasive prenatal testing (NIPT), which utilizes a maternal blood sample to detect fetal gender and screen for fetal aneuploidy (abnormal chromosomes), is widely used in obstetrics to screen for Trisomies 21, 18, and 13. Per the literature, approximately 0.3% of pregnant woman’s results are nonreportable. Reasons include low fetal fraction, insufficient DNA, vanishing twin, twin pregnancy, clonal mosaicism, and maternal neoplasia. Here, we describe a 25-year-old G2P1 pregnant woman who had two nonreportable NIPT results and subsequently was diagnosed with lymphoma. We discuss the importance of clinical exam in correlation with the results to offer comprehensive evaluation of the patient with a nonreportable finding, given malignancy occurs in 1/1000 pregnant women. This report overviews proposed management guidelines for pregnant women with a nonreportable result and helps to address discomfort the treating physician may feel in discussing this result with their patient.
Open Search-Based Proteomics Reveals Widespread Tryptophan Modifications Associated with Hypoxia in Lung Cancer
Authors: Chen Jinfeng , Zhang Lei , Sun Zhao , Li Hongyi , Li Jingyi , Xue Xinli , Zhu Qingqing , Dong Bowen , Wang Yuanyuan , Yang Yang , Dong Yongqiang , Guo Guangyu , Jiang Hongqiang , Zhang An , Zhang Guoqing , Hou Zhichao , Li Xiangnan , Yang Jing-Hua
Published: April 2022

The tryptophan residue has a large hydrophobic surface that plays a unique role in the folded protein conformation and functions. Tryptophan modifications are presumably to be readily detected in proteins due to the vulnerability of the indole structure to electrophilic attacks. In this study, we report a systematic identification of sequence variations at tryptophan, termed tryptophan variants, from the proteome of patients with nonsmall cell lung cancer (NSCLC). Using shotgun proteomics and a modified open search algorithm, 25 tryptophan variants on 2481 sites in over 858 proteins were identified. Among these, 6 tryptophan variants are previously identified, 15 are newly annotated, and 4 are still unknown, most of which are involved in the cascade of oxidation in the blood microparticle. Remarkably, Trp313 of glyceraldehyde-3-phosphate dehydrogenase (GAPDH) was up-oxidized whereas Trp16 and Trp38 of hemoglobin (HBB) were down-oxidized in NCSLC tissues. The results were further supported by an independent cohort of 103 lung adenocarcinoma samples, reflecting a negative feedback and potential detoxification mechanism against tumor glycolysis and hypoxia. Overall, the study reports a quick approach to explore tryptophan variants at the proteomic scale. Our findings highlight the predominant role of tryptophan oxidation in regulating the redox balance of cancer cells and its potential role as prognostic biomarker for patients with NSCLC.
Evaluation of Sustainable Development of an Agricultural Economy Based on the DPSIR Model
Authors: Zhang Yanan , Wang Ying , Wei Jinghong
Published: April 2022

With today’s rapid economic growth, the role of agriculture in production and life has come to the forefront. Many problems in China’s agricultural economy have seriously constrained the vitality of Chinese agriculture. Therefore, it is necessary to further strengthen the study of an agricultural economy, inject new impetus into an agricultural development, develop targeted solutions, and lay a solid foundation for the sustainable development of China’s agricultural economy. Therefore, in this paper, based on the theory of sustainable development of an agricultural economy, this paper constructs a sustainable agricultural development index system in Jiangsu province from five aspects of the DPSIR model: driving force, pressure, state, impact and response and uses principal component analysis to make a comprehensive evaluation of the level of sustainable agricultural development in Jiangsu province from 2010 to 2016. The analysis results show that Jiangsu’s agricultural economy is overall better and steadily improving, and it has made a huge leap in the seven-year period from 2010 to 2016, showing better sustainability. We found that the state subsystem exposed some social problems after analyzing the subsystem, and if relevant measures are not taken in time to solve the emerged problems, it will affect the other subsystems and the sustainability of the agricultural economy.
A Study of Combinations of Factors in the Marketization of Open Data of Local Governments in China on the Perspective of the Configurations
Authors: Wei Lili , Gu Bin
Published: April 2022

The Chinese Government and Statistics Departments have collected huge amounts of data. They use these government open data to conduct applied research and technological studies, including the establishment of a market‐based system for data elements. These data are put into e‐government to build the construction of digital government and improve the level of government services continuously. This paper uses the perspective of the configurations to discuss the combinations of factors in the marketization of open data of local governments in China with fsQCA method; this paper is based on the TOE framework, and the indices of the data layer of the government open data forest in 16 provinces are taken as the research object. The conclusions are as follows: (1) the market‐oriented utilization paths of local government open data are categorized as technology‐led, management‐resource‐led, economic and technology‐driven, and location‐advantaged. (2) Under certain conditions, when the number of data developers is small, and there is no dedicated government data management agency, optimizing and improving the government open data platform could be an alternative path to promote the effective use of local government open data in the market.
Consensus of Second‐Order Heterogeneous Hybrid Multiagent Systems via Event‐Triggered Protocols
Authors: Zhang Hong , Li Yanhan , Zheng Ying
Published: April 2022

This paper investigates the event‐based consensus problem for the heterogeneous hybrid multiagent system (MAS). First, the heterogeneous hybrid MAS is proposed which contains continuous and discrete‐time subsystems with second‐order and first‐order heterogeneous dynamics. Second, the event‐triggered protocols are proposed, which mainly include the event‐based control laws and event‐triggered conditions for different kinds of agents. Then, the consensus conclusions of fixed topology and switching topologies are obtained based on graph theory and nonnegative matrix theory, which include the constraints on control parameters, coupling gains, and sampling interval to guarantee consensus. Finally, a simulation example is given to verify the efficiency of the proposed protocols.
Optimization of Grid-Connected Photovoltaic Power Generation Technology Based on Nonlinear Back-Stepping Controller
Authors: Xuan Feng , Han Mengyang , Kumar Avinash , Vishwakarma Satyendra , Kannan Nithiyananthan , Jain Rituraj
Published: April 2022

To address the issue of energy scarcity and to use solar photovoltaic energy as a renewable source, a three-phase grid-connected photovoltaic inverter system with uncertain system model parameters is investigated, which converts DC power into AC power, feeds it into the grid, and maintains the grid-connected part’s quality. An enhanced back-stepping approach is proposed to explore the control strategy of three-phase solar grid-connected inverter, which includes adaptive control, dissipative theory, sliding mode control, and rapid terminal sliding mode control, respectively. It is verified that the system is subjected to external interference F = 5 during 0.5 s–0.8 s. As per the simulation results, the improved adaptive terminal sliding mode control method can suppress the interference to a certain extent and has strong robustness to the external interference. When compared with the adaptive sliding mode control approach, the anti-interference ability is better, and the system stability is improved. Then, to improve the above approach, a high speed terminal sliding mode control method is proposed, which can swiftly converge in finite time and the estimated value of uncertain parameters is closer to the actual value, thereby boosting the system’s robustness.
Da Vinci Robot-Assisted Video Image Processing under Artificial Intelligence Vision Processing Technology
Authors: Cheng Qiangli , Dong Yajun
Published: April 2022

This research was aimed to explore the application value of intelligent algorithm-based digital images in Da Vinci robot-assisted treatment of patients with gastric cancer surgery. 154 patients were included as the research objects, with 89 cases in the control group underwent laparoscopic surgery, and 65 cases in the experimental group underwent robotic surgery. According to the propensity score, the patients in two groups were pair matched (1: 1), of which 104 cases (52 cases in each group) were successfully matched. The general data of patients, the changes in the images before and after the algorithm processing, the intraoperative and postoperative conditions, the pathological examination results, and the follow-up information were observed after matching. Compared with the original images, the images processed by the thread image edge detection algorithm had the significantly improved clarity, as well as highly reduced artifacts and noises. The sensitivity, specificity, and accuracy of image-assisted diagnosis were improved remarkably, showing the differences of statistical significance (

P
<
0.05

). The total time of surgery, intraoperative bleeding, CRP (1d and 3d after surgery), and postoperative total abdominal drainage showed the significant differences as well (

P
<
0.05

). The surgeries of patients in both groups met R0 resection (no tumor infiltration within 1 mm of the surgical margin), but there was a significant difference in the number of lymph node dissections (

P
<
0.05

). The overall survival rates of patients in the experimental group and the control group were 83.0% and 76.1%, respectively, 2 years after surgery, with no significant difference (

P
>
0.05

). The thread image edge detection algorithm produced a better processing effect on the images, which greatly improved the diagnostic sensitivity, specificity, and accuracy. Compared with endoscopic surgery, robotic surgery has better postoperative recovery, safety and reliability, and obvious advantages of minimally invasive surgery.
Associations between Serum Interleukins (IL‐1β, IL‐2, IL‐4, IL‐6, IL‐8, and IL‐10) and Disease Severity of COVID‐19: A Systematic Review and Meta‐Analysis
Authors: Chang Yuanmin , Bai Mengru , You Qinghai
Published: April 2022

Background. To investigate the association between interleukins (IL‐1β, IL‐2, IL‐4, IL‐6, IL‐8, and IL‐10) and the disease severity of coronavirus disease 2019 (COVID‐19). Materials and Methods. We systematically searched records investigating the role of interleukins (IL‐1β, IL‐2, IL‐4, IL‐6, IL‐8, and IL‐10) in COVID‐19 patients in Web of Science, Pubmed, and Embase through December 2020. Data were extracted and pooled, and the weighted mean difference (WMD) and its 95% confidence interval (CI) were calculated. The funnel plot and the nonparametric trim and fill method were used to visualize and adjust the publication bias. Results. In total, 61 studies enrolled 14,136 subjects (14,041 patients and 95 healthy subjects) were enrolled in this meta‐analysis. Our results showed that serum IL‐2, IL‐4, IL‐6, and IL‐10 levels were elevated in COVID‐19 patients compared to healthy controls, and IL‐6, IL‐8, and IL‐10 levels were increased in severe COVID‐19 cases compared to nonsevere patients. Additionally, the levels of IL‐1β, IL‐6, and IL‐8 were elevated in nonsurvivor patients compared to survivors. For patients in the intensive care unit (ICU), IL‐6 and IL‐8 levels were increased than that in non‐ICU patients. Conclusions. Elevated levels of IL‐6, IL‐8, and IL‐10 were associated with the disease severity of COVID‐19, and elevated levels of IL‐1β, IL‐6, and IL‐8 were related to the prognosis of COVID‐19 patients, which could be used to evaluate COVID‐19 patients’ disease severity and prognosis.
Comprehensive Analysis of Subtypes and Identification of Key lncRNAs Based on Glutamine Metabolism-Related Long Noncoding RNAs
Authors: Feng Yuwei , Sun Xiaowei , Yang Tiangu , Han Jingqi , Zhou Dapeng , Ren Haitao , Sheng Yulong , Wang Yanhua
Published: April 2022

Background. Long noncoding RNAs (lncRNAs) are becoming a critical class of metabolic regulate molecule in cancer. Glutamine is a regulator that contributes to each of the core metabolic tasks in proliferating tumor cells. Thus, we aimed to evaluate the association of lncRNAs with glutamine metabolism in lung adenocarcinoma (LUAD). Methods. Using single-sample gene set enrichment analysis (ssGSEA), LUAD specimens were assigned scores based on glutamine metabolism-related genes, and the shared common glutamine metabolism-related lncRNAs in three different LUAD data cohorts were identified. ConsensusClusterPlus was used to perform unsupervised clustering analysis in patients with LUAD. Key glutamine metabolism-related lncRNAs were identified by first-order partial correlation analysis. Results. A total of 11 shared glutamine metabolism-associated lncRNAs were identified in three LUAD data cohorts, and LUAD patients were classified into three glutamine metabolism subtypes based on the expressions of the related genes. C1 exhibited shorter overall survival (OS), poor genomic instability, and inadequate infiltration of immune cell types in the tumor microenvironment (TME) and was representative of the immunodeficiency phenotype. C2 represented the immunosuppressive phenotype while C3 represented the immune activation phenotype, exhibiting the highest sensitivity to immunotherapy. Nine of the 11 lncRNAs were localized to the nucleus. Finally, three key lncRNAs, significantly enriched in multiple metabolic pathways, were screened and found to be remarkably related to the OS of LUAD. Conclusion. We identified three glutamine metabolism subtypes of LUAD, which reflected different OS, genomic, and TME features, and identified three key glutamine metabolism-associated lncRNAs may contribute to further study of lncRNAs in cancer metabolism.