bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigating a Combination of Input Modalities, Canvas Geometries, and Inking Triggers on On-Air Handwriting in Virtual Reality
Authors: Venkatakrishnan Roshan , Venkatakrishnan Rohith , Chung Chih-Han , Wang Yu-Shuen , Babu Sabarish
Published: September 2022

Humans communicate by writing, often taking notes that assist thinking. With the growing popularity of collaborative
Virtual Reality (VR)
applications, it is imperative that we better understand aspects that affect writing in these virtual experiences. On-air writing in VR is a popular writing paradigm due to its simplicity in implementation without any explicit needs for specialized hardware. A host of factors can affect the efficacy of this writing paradigm and in this work, we delved into investigating the same. Along these lines, we investigated the effects of a combination of factors on users’ on-air writing performance, aiming to understand the circumstances under which users can both effectively and efficiently write in VR. We were interested in studying the effects of the following factors: (1) input modality: brush vs. near-field raycast vs. pointing gesture, (2) inking trigger method: haptic feedback vs. button based trigger, and (3) canvas geometry: plane vs. hemisphere. To evaluate the writing performance, we conducted an empirical evaluation with thirty participants, requiring them to write the words we indicated under different combinations of these factors. Dependent measures including the writing speed, accuracy rates, perceived workloads, and so on, were analyzed. Results revealed that the brush based input modality produced the best results in writing performance, that haptic feedback was not always effective over button based triggering, and that there are trade-offs associated with the different types of canvas geometries used. This work attempts at laying a foundation for future investigations that seek to understand and further improve the on-air writing experience in immersive virtual environments.
A Survey of 3D Ear Recognition Techniques
Authors: Ganapathi Iyyakutti Iyappan , Ali Syed Sadaf , Prakash Surya , Vu Ngoc-Son , Werghi Naoufel
Published: September 2022

Human recognition with biometrics is a rapidly emerging area of computer vision. Compared to other well-known biometric features such as the face, fingerprint, iris, and palmprint, the ear has recently received considerable research attention. The ear recognition system accepts 2D or 3D images as input. Since pose, illumination, and scale all affect 2D ear images, it is evident that they all impact recognition performance; therefore, 3D ear images are employed to address these issues. The geometric shapes of 3D ears are utilized as rich features to improve recognition accuracy. We present recent advances in several areas relevant to 3D ear recognition and provide directions for future research. To the best of our knowledge, no comprehensive review has been conducted on using 3D ear images in human recognition. This review focuses on three primary categories of 3D ear recognition techniques: (1) registration-based recognition, (2) local and global feature-based recognition, and (3) a combination of (1) and (2). Based on the above categorization and publicly available 3D ear datasets, this article reviews existing 3D ear recognition techniques.
Federated Route Leak Detection in Inter-domain Routing with Privacy Guarantee
Authors: Zeng Man , Li Dandan , Zhang Pei , Xie Kun , Huang Xiaohong
Published: September 2022

In the inter-domain network, route leaks can disrupt the Internet traffic and cause large outages. The accurate detection of route leaks requires the sharing of AS business relationship information. However, the business relationship information between ASes is confidential. ASes are usually unwilling to reveal this information to the other ASes, especially their competitors. In this paper, we propose a method named FL-RLD to detect route leaks while maintaining the privacy of business relationships between ASes by using a blockchain-based federated learning framework, where ASes can collaboratively train a global detection model without directly disclosing their specific business relationships. To mitigate the lack of ground-truth validation data in route leaks, FL-RLD provides a self-validation scheme by labeling AS triples with local routing policies. We evaluate FL-RLD under a variety of datasets including imbalanced and balanced datasets, and examine different deployment strategies of FL-RLD under different topologies. According to the results, FL-RLD performs better in detecting route leaks than the single AS detection, whether the datasets are balanced or imbalanced. Additionally, the results indicate that selecting ASes with the most peers to first deploy FL-RLD brings more significant benefits in detecting route leaks than selecting ASes with the most providers and customers.
Planning for Automated Vehicles with Human Trust
Authors: Sheng Shili , Pakdamanian Erfan , Han Kyungtae , Wang Ziran , Lenneman John , Parker David , Feng Lu
Published: September 2022

Recent work has considered personalized route planning based on user profiles, but none of it accounts for human trust. We argue that human trust is an important factor to consider when planning routes for automated vehicles. This article presents a trust-based route-planning approach for automated vehicles. We formalize the human-vehicle interaction as a partially observable Markov decision process (POMDP) and model trust as a partially observable state variable of the POMDP, representing the human’s hidden mental state. We build data-driven models of human trust dynamics and takeover decisions, which are incorporated in the POMDP framework, using data collected from an online user study with 100 participants on the Amazon Mechanical Turk platform. We compute optimal routes for automated vehicles by solving optimal policies in the POMDP planning and evaluate the resulting routes via human subject experiments with 22 participants on a driving simulator. The experimental results show that participants taking the trust-based route generally reported more positive responses in the after-driving survey than those taking the baseline (trust-free) route. In addition, we analyze the trade-offs between multiple planning objectives (e.g., trust, distance, energy consumption) via multi-objective optimization of the POMDP. We also identify a set of open issues and implications for real-world deployment of the proposed approach in automated vehicles.
The Doge of Wall Street: Analysis and Detection of Pump and Dump Cryptocurrency Manipulations
Authors: La Morgia Massimo , Mei Alessandro , Sassi Francesco , Stefa Julinda
Published: September 2022

Cryptocurrencies are increasingly popular. Even people who are not experts have started to invest in these assets, and nowadays, cryptocurrency exchanges process transactions for over 100 billion US dollars per month. Despite this, many cryptocurrencies have low liquidity and are highly prone to market manipulation. This paper performs an in-depth analysis of two market manipulations organized by communities over the Internet: The pump and dump and the crowd pump. The pump and dump scheme is a fraud as old as the stock market. Now, it has new vitality in the loosely regulated market of cryptocurrencies. Groups of highly coordinated people systematically arrange this scam, usually on Telegram and Discord. We monitored these groups for more than 3 years, detecting around 900 individual events. We report on three case studies related to pump and dump groups. We leverage our unique dataset of the verified pump and dumps to build a machine learning model able to detect a pump and dump in 25 seconds from the moment it starts, achieving the results of 94.5% of F1-score. Then, we move on to the crowd pump, a new phenomenon that hit the news in the first months of 2021, when a Reddit community inflated the price of the GameStop stocks (GME) by over 1,900% on Wall Street, the world’s largest stock exchange. Later, other Reddit communities replicated the operation on the cryptocurrency markets. The targets were DogeCoin (DOGE) and Ripple (XRP). We reconstruct how these operations developed and discuss differences and analogies with the standard pump and dump. We believe this study helps understand a widespread phenomenon affecting cryptocurrency markets. The detection algorithms we develop effectively detect these events in real-time and helps investors stay out of the market when these frauds are in action.
A Review of China's Financial Markets
Authors: Hu Grace Xing , Wang Jiang
Published: September 2022

The fast growth of China's economy has brought it not only to the center of the global economy but also to a transition point in its growth model, a transition from scale to efficiency, speed to sustainability, input driven to innovation led. How its financial markets can drive this new growth model and facilitate the transition are pressing challenges, for China and for the world. We provide a comprehensive review of China's financial markets, including government bonds, corporate/credit bonds, stocks, asset-backed securities, financial derivatives, investment management, and currency, focusing on their growth paths, distinct characteristics, and unique opportunities. Despite fast expansion at times, their development is often lagging behind market needs, uneven over time, and unbalanced across markets. This developmental pattern is driven mostly by the government's immediate policy objectives rather than by the markets' ultimate efficiency in serving their key functions, including liquidity provision, price discovery, and risk allocation.
Errors of the Egg: The Establishment and Progression of Human Aneuploidy Research in the Maternal Germline
Authors: Gruhn Jennifer R. , Hoffmann Eva R.
Published: September 2022

Meiosis, a key process in the creation of haploid gametes, is a complex cellular division incorporating unique timing and intricate chromosome dynamics. Abnormalities in this elaborate dance can lead to the production of aneuploid gametes, i.e., eggs containing an incorrect number of chromosomes, many of which cannot generate a viable pregnancy. For many decades, research has been attempting to address why this process is notoriously error prone in humans compared to many other organisms. Rapidly developing technologies, access to new clinical material, and a mounting public infertility crisis have kept the field both active and quickly evolving. In this review, we discuss the history of aneuploidy in humans with a focus on its origins in maternal meiosis. We also gather current working mechanistic hypotheses, as well as up-and-coming areas of interest that point to future scientific avenues and their potential clinical applications.
Core temperature responses to compensable versus uncompensable heat stress in young adults (PSU HEAT Project)
Authors: Cottle Rachel M. , Lichter Zachary S. , Vecellio Daniel J. , Wolf S. Tony , Kenney W. Larry
Published: September 2022

This study is the first to examine heat storage and the rate of change in core temperature above (uncompensable heat stress) and just below (compensable heat stress) critical environmental limits to human heat balance. Furthermore, we examine the influence of individual subject characteristics and seasonality on the change in core temperature in warm-humid versus hot-dry environments. We provide the rate of change in core temperature, enabling projections to be made to and from any hypothetical core temperature.
Cellular senescence and cardiovascular diseases: moving to the “heart” of the problem
Authors: Evangelou Konstantinos , Vasileiou Panagiotis V. S. , Papaspyropoulos Angelos , Hazapis Orsalia , Petty Russell , Demaria Marco , Gorgoulis Vassilis G.
Published: September 2022

Cardiovascular diseases (CVDs) constitute the prime cause of global mortality, with an immense impact on patient quality of life and disability. Clinical evidence has revealed a strong connection between cellular senescence and worse cardiac outcomes in the majority of CVDs concerning both ischemic and nonischemic cardiomyopathies. Cellular senescence is characterized by cell cycle arrest accompanied by alterations in several metabolic pathways, resulting in morphological and functional changes. Metabolic rewiring of senescent cells results in marked paracrine activity, through a unique secretome, often exerting deleterious effects on neighboring cells. Here, we recapitulate the hallmarks and key molecular pathways involved in cellular senescence in the cardiac context and summarize the different roles of senescence in the majority of CVDs. In the last few years, the possibility of eliminating senescent cells in various pathological conditions has been increasingly explored, giving rise to the field of senotherapeutics. Therefore, we additionally attempt to clarify the current state of this field with a focus on cardiac senescence and discuss the potential of implementing senolytics as a treatment option in heart disease.
Consumption Risk and Legal Response in B2C e-Commerce Based on Neural Network Algorithm
Authors: Chen Sisi
Published: September 2022

In the era of “Internet +,” the world economy is increasingly globalized and informatized, the development of China’s B2C e-commerce is facing unprecedented opportunities, but it is also constrained by consumption risks. Consumption risk will make consumers have a crisis of trust in e-commerce, which brings uncertainty to the development of B2C. Therefore, it is very necessary to predict and prevent consumption risks in B2C e-commerce and take corresponding legal countermeasures. It is well known that neural networks (NNs) have strong predictive ability, but there are also problems such as lack of stability. As a result, in order to improve the prediction ability of neural network, principal component analysis and particle swarm technology are proposed in this paper as well as its stability and prediction error. The risk prediction accuracy of the BP NN (BPNN) technique was the lowest at 60% and the maximum at 70%, according to the experimental results of this research. The GA-BP technique has the lowest risk prediction accuracy of 80 percent. The risk prediction accuracy of the PSO-BP method is the lowest with 90% and the highest with 100%. Although the NN before the improvement can effectively predict the consumption risk, the risk prediction ability of the improved NN combined with principal component analysis and particle swarm algorithm is higher. Therefore, in life, the relevant personnel can apply the GA-BP and PSO-BP methods to the consumption risk prediction in B2C e-commerce to reduce the risk and make the e-commerce develop better.
Hyper-Spherical Search Algorithm for Maximum Power Point Tracking of Solar Photovoltaic Systems under Partial Shading Conditions
Authors: Eetivand Kamran , Zangeneh Ali , Nabavi Seyed M. H.
Published: September 2022

Maximum Power Point Tracking (MPPT) for photovoltaic systems has widely been studied. However, studying the impact of partial shading (due to buildings, trees, clouds, adjacent arrays, and so on) on the MPPT and achieving the global maximum is still a challenging topic. This is because not only there is a global maximum power point (GMPP) but also there are several local maximums due to the non-linear nature of the power-voltage curve after partial shading. Therefore, the conventional MPPT methods fail to track the GMPP and are commonly trapped at one of the local maximum power points. This study utilizes the Hyper-Spherical Search (HSS) algorithm in MATLAB to achieve the GMPP while improving the efficiency, convergence speed, dynamic response, and reducing the losses. To track the GMPP using the HSS algorithm, the output power for the array (PPV) is defined as the objective function, and the duty cycle of the DC-DC converter is selected as the particles’ position (control variable). The performance of the proposed algorithm has been studied in three different partial shading patterns, and the simulation results confirm the capability of the algorithm in the rapid tracking of GMPP points. In addition, if the GMPP position changes over time, it will track the new GMPP with minimal oscillations. The proposed method along with PSO, P&O, ABC, and Dragon algorithms has been applied for various scenarios, and the obtained results using HSS have been compared with the four mentioned algorithms, which confirmed the effectiveness of the proposed method. Briefly, the advantages of the HSS algorithm in finding GMPP can be stated as simple implementation with few parameters, strong exploration, and exploitation during the tracking process, fast-tracking and low fluctuations during tracking, low oscillation at steady-state, high dynamic efficiency, and non-convergence to LMPP points.
Cultivating the Humanistic Quality of Nursing Undergraduates in the Internet Age
Authors: Lan Xianlan
Published: September 2022

Humanistic quality refers to the research ability, knowledge level of the humanities, and the human inner quality of the humanities. The purpose of this paper was to make a research on the cultivation of humanistic literacy of nursing undergraduates in the context of the Internet era. This paper first makes a general introduction of the classification of the Internet and the definition of humanistic literacy, analyzes the results of two rounds of expert consultation method, and then obtains the current situation of nursing undergraduates through the questionnaire survey of school nursing undergraduates. The experimental results showed that in the current survey situation, the expert support rate of humanistic care is as high as 90%, indicating that humanistic care is especially important for a nursing undergraduate. The total score of humanistic nursing ability of undergraduate nursing students was 127.03, which is in the middle level higher than that of most domestic studies, but lower than that of American nursing students.
Comparison of the Effects of Open Surgery and Minimally Invasive Surgery on the Achilles Tendon Rupture Healing Based on Angiogenesis
Authors: Gong Fan , Li Xiaoliang , Zhang Hanling , Wu Jianke , Ma Guoxu , Zhang Bowen , Gao Jian , Ding Yi , Huang Yonglu , Cheng Suoli , Zhou Xuebing , Zhao Fei
Published: September 2022

Objective. To compare the effect of three different surgical methods on rabbit Achilles tendon rupture. Methods. The Achilles tendon transection model was constructed by cutting off the inner half of the Achilles tendon. Rabbits were divided into 4 groups: model group, open surgery (OS) group, minimally invasive surgery (MS) group, and conservative treatment (CT) group. Biomechanical evaluation, H&E, and Picrosirius Red staining were applied to evaluate the histological changes and healing. RT-qPCR, Western blot, ELISA, and IHC staining were used to detect the expression of COLIII, IL-1β, TNF-α, IL-6, CD31, VEGF, bFGF, and TGF-β1. Results. Different surgery treatments significantly alleviated the histological changes in rabbits. The tension and elasticity of the Achilles tendon were significantly increased after surgery. In addition, surgery treatments notably alleviated the inflammatory responses in vivo via downregulation of IL-1β, TNF-α, and IL-6 and promoted the tube formation in tissues through upregulating VEGF, bFGF, TGF-β1, and CD31. Furthermore, MS exhibited best therapeutic efficiency on Achilles tendon rupture healing, compared with OS or CT. Conclusions. Our research revealed the superiority of MS in Achilles tendon rupture treatment at the molecular level compared with OS or CT.
Overexpression of CDCP1 is Associated with Poor Prognosis and Enhanced Immune Checkpoints Expressions in Breast Cancer
Authors: Zhao Jinlu , Mei Jie , Wang Fengxu , Zhao Xinyuan , Ren Yi , Zhao Xingyu , Li Wang , Gao Erli
Published: September 2022

CUB-domain containing protein 1 (CDCP1) is a transmembrane protein acting as an effector of SRC family kinases, which play an oncogenic role in multiple human cancers. However, its clinical and immune correlations in breast cancer (BrCa) have not been explored. To define the expression, prognostic value, and potential molecular role of CDCP1 in BrCa, multiple public datasets, and an in-house cohort were used. Compared with paratumor tissue, CDCP1 was remarkably upregulated in the tumor tissues at both mRNA and protein levels. In the in-house cohort, CDCP1 protein expression was related to several clinicopathological parameters, including age, ER status, PR status, molecular type, and survival status. Kaplan–Meier analysis and Cox regression analysis exhibited that CDCP1 was an important prognostic biomarker in BrCa. In addition, enrichment analysis uncovered that CDCP1 was not only involved in multiple oncogenic pathways, but correlated with overexpression of immune checkpoints. Overall, we reported that increased expression of CDCP1 is a favorable prognostic factor in patients with BrCa. In addition, the correlations between CDCP1 and immune checkpoints provide a novel insight into the adjuvant treatment for immune checkpoint blockade via targeting CDCP1.
Periodic Oscillations in MEMS under Squeeze Film Damping Force
Authors: Beron Juan , Rivera Andrés
Published: September 2022

We provide sufficient conditions for the existence of periodic solutions for an idealized electrostatic actuator modeled by the Liénard-type equation

x
¨

+

F

D

x
,

x
̇

+
x
=
β

V

2

t

/

1
−
x

2

,
x
∈

−

∞

,

1

with

β
∈

ℝ

+

,

V

∈

C

ℝ

/

T

ℤ

, and

F

D

x
,

x
̇

=
κ

x
̇

/

1
−
x

3

,

κ
∈

ℝ

+

(called squeeze film damping force), or

F

D

x
,

x
̇

=
c

x
̇

,

c
∈

ℝ

+

(called linear damping force). If

F

D

is of squeeze film type, we have proven that there exists at least two positive periodic solutions, one of them locally asymptotically stable. Meanwhile, if

F

D

is a linear damping force, we have proven that there are only two positive periodic solutions. One is unstable, and the other is locally exponentially asymptotically stable with rate of decay of

c
/
2

. Our technique can be applied to a class of Liénard equations that model several microelectromechanical system devices, including the comb-drive finger model and torsional actuators.
The Effect of Utilizing Business Model Canvas on the Satisfaction of Operating Electronic Business
Authors: Fakieh Bahjat , AL-Malaise AL-Ghamdi Abdullah S. , Ragab Mahmoud
Published: September 2022

The Business Model Canvas (BMC) is a strategic model for developing business organizations’ roadmap toward achieving their goals. While several organizations utilize the Business Model Canvas (BMC) to establish and operate their businesses well, the utilization of BMC seems to be limited in the local market of Saudi Arabia, especially when businesses utilize electronic business channels. This paper aims to explore the status of the utilization of BMC among Saudi SMEs, as a critical sector. The paper highlights the awareness and practice of BMC’s nine factors before and during the operation of e‐commerce stores by Saudi SMEs. Then, it aims to explore the significance of practicing those factors in increasing the satisfaction level of running businesses. In this paper, a quantitative survey was distributed conveniently to 200 SMEs in the two largest Saudi cities, which are Riyadh (the capital) and Jeddah (the country’s main seaport), resulting in 63 valid participations from different industries. After operating e‐commerce stores, most SMEs gained more knowledge of five BMC factors: key partners, value propositions, customer relationships, customer segments, and cost structures. Meanwhile, they found some issues in the other four factors: key activities, key resources, channels, and revenue streams. The proposed method exposes the variety of results and indicates a lack of understanding of BMC by the examined e‐commerce SME samples, as they depend on traditional methods of identifying the elements of each of the BMC factors.
COMMD3 Expression Affects Angiogenesis through the HIF1α/VEGF/NF-κB Signaling Pathway in Hepatocellular Carcinoma In Vitro and In Vivo
Authors: Zhu Tingting , Peng Xiaolin , Cheng Ziwei , Gong Xiuru , Xing Dongwei , Cheng Wei , Zhang Minguang
Published: September 2022

Background. High expression of copper metabolizing MURR1 domain (COMMD3) is significantly correlated with poor prognosis in hepatocellular carcinoma (HCC) patients. Here, we explored the mechanism by which COMMD3 affects HCC angiogenesis through the HIF1α/VEGF/NF-κB signaling pathway. Methods. SK-Hep1 and Hep-3B cell lines were transfected by COMMD3 overexpression and RNA interference lentivirus and verified using RT-qPCR and western blotting techniques. Using RNA sequencing, we analyzed differentially expressed genes in COMMD3-overexpressed and COMMD3-knockdown HCC cells. Altogether, colony formation assay, wound healing assay, transwell cell invasion assay, flow cytometry apoptosis experiments, HUVEC tube formation detection, phalloidin staining assay, western blotting, immunohistochemical staining, and a nude mouse xenograft model were used for experimental verification. Results. Lentivirus COMMD3 overexpression and knockdown were successfully established in HCC cells. COMMD3 overexpression significantly promoted the proliferation, angiogenesis, migration, and invasion capacities of HCC cells with no obvious effect on apoptosis versus controls while COMMD3 knockdown showed the opposite trend. The expression and protein levels of COMMD3 as well as HIF1α, VEGF, and NF-κB were increased in COMMD3-overexpressing HCC cells versus control cells, while they were reduced after COMMD3 knockdown. In addition, RNA-seq indicated that COMMD3 is an indispensable gene for HCC angiogenesis through HIF1α and NF-κB signaling pathways. Conclusion. This study showed that low expression of COMMD3 can inhibit HCC angiogenesis by suppressing the HIF1α/VEGF/NF-κB pathway. This implicates COMMD3 as a potential biomarker for improving the therapeutic outcome of HCC.
Risk Assessment and Analysis in Supply Chain Finance Based on Blockchain Technology
Authors: Meng Xue
Published: September 2022

With the development of the times, the financial industry is constantly changing. Blockchain technology continues to play an important role in supply chain finance. The security of the financial industry is very important. Blockchain technology can better protect the security of the financial industry, but there are also some risks. We have made a detailed investigation and research on the risk assessment and analysis of blockchain technology in supply chain finance. The research is as follows: (1) the risks under blockchain technology are introduced in detail, and the risks in many aspects are detailed. It is convenient for the public to do a good job in risk control in the use of blockchain finance. (2) The algorithm of the blockchain is used for mining. In finance, the security of privacy is the most important. The differential privacy in the algorithm and the Bc-ppkCa algorithm makes the financial privacy under the blockchain better. It is beneficial for the public to use blockchain technology more efficiently and safely. (3) The financial industry under the blockchain is very complex. Let us take the financial situation under industrial enterprises as an example. Investigate the company’s supply chain financial situation data, and compare the advantages of supply chain finance and the company’s financial situation. It also evaluates the risks generated by supply chain finance and makes corresponding analysis.
Study on the Changes and Significance of Immune State and Cycokines in Children with Adenovirus Pneumonia
Authors: Hou Yue , Liu Jing , Li Yanan , Chen Fang
Published: September 2022

Objective. To observe the difference between peripheral blood T lymphocytes subsets and cycokines in children with severe adenovirus pneumonia and nonsevere adenovirus pneumonia, and to investigate their clinical value in the prognosis of severe pneumonia. Methods. 215 children with adenovirus pneumonia and 30 healthy volunteers (which was set as the control group) in our hospital from January 2017 to May 2019 were enrolled in the study. There were 47 children with severe pneumonia in the severe group and 168 nonsevere pneumonia children in the nonsevere group. The flow cytometry and ELISA methods were used to detect the serum levels of CD3+, CD4+, CD8+ T cells and interleukin-2 (IL-2), IL-4, IL-6, IL-10, tumor necrosis factor-α (TNF-α), interferon-γ (IFN-γ). Results. (1) The levels of CD3 (%) T cells, CD4 (%) T cells, and CD4/CD8 ratio values of children with adenovirus pneumonia were lower than these of normal children (

P
<
0.05

). And the levels of CD3 (%) T cells, CD4 (%) T cells, and CD4/CD8 ratio values of children in the severe group were lower than these of children in the nonsevere group (

P
<
0.05

). (2) The levels of IL-2, IL-6, IL-10, TNF-α, and IFN-γ values of children with adenovirus pneumonia were lower than these of normal children (

P
<
0.05

). And the levels of IL-2, IL-6, IL-10, TNF-α, and IFN-γ of children in the severe group were higher than these of children in the nonsevere group (

P
<
0.05

). (3) Among the 47 children with severe adenoviral pneumonia, 39 received systematic treatment in our hospital. According to the treatment effect, 39 children were divided into the effective group (n = 25) and the ineffective group (n = 14). (4) The CD3 (%), CD4 (%), and CD4/CD8 ratios of the children in the effective group were higher than those in the ineffective group (

P
<
0.05

). (5) The levels of IL-2, IL-6, IL-10, TNF-α, and IFN-γ in the effective group were lower than those in the ineffective group (

P
<
0.05

). Conclusion. The immunophenotype of peripheral blood T lymphocytes and cycokines could be helpful to judge the severity of adenovirus pneumonia, which could be used as the objective indexes to evaluate the prognosis of children with severe adenovirus pneumonia.
Coupling Modeling and Adaptive Control for Piezoelectric-Actuated Positioning Stage
Authors: Li Liang , Zhang Shi-Xin , Wen Sheng-Jun
Published: September 2022

In this paper, a nonlinear coupling model with hysteresis, dynamics, and creep is proposed to describe accurately the complex characteristics of piezoelectric-actuated positioning stage, where a classic Hammerstein model in series with a fractional-order model is given. The fractional-order model is presented to express the nonlinear creep characteristics. Firstly, the Hammerstein structure model is composed of two blocks, where the former block is the classical PI model to describe the static hysteresis effects, and the latter block is the second-order discrete transfer function model to characterize the dynamic characteristics. In addition, the parameters of the coupling model are identified. Secondly, based on the built model, the inverse of fractional-order model and the inverse of PI model are implemented as the feedforward compensations, and an adaptive control is designed to adjust the tracking performance of the whole system. Finally, the effectiveness of the proposed coupling model and controllers are verified by the piezoelectric-actuated positioning experiment stage. Experimental results show that the established coupling model can accurately characterize the hysteresis, dynamics, and creep properties of the stage. Also, the results show that the tracking error is less than 0.8% at low frequency and mixed frequency.
Breast MRI Tumor Automatic Segmentation and Triple-Negative Breast Cancer Discrimination Algorithm Based on Deep Learning
Authors: Guo Ying-Ying , Huang Yin-Hui , Wang Yi , Huang Jing , Lai Qing-Quan , Li Yuan-Zhe
Published: September 2022

Background. Breast cancer is a kind of cancer that starts in the epithelial tissue of the breast. Breast cancer has been on the rise in recent years, with a younger generation developing the disease. Magnetic resonance imaging (MRI) plays an important role in breast tumor detection and treatment planning in today’s clinical practice. As manual segmentation grows more time-consuming and the observed topic becomes more diversified, automated segmentation becomes more appealing. Methodology. For MRI breast tumor segmentation, we propose a CNN-SVM network. The labels from the trained convolutional neural network are output using a support vector machine in this technique. During the testing phase, the convolutional neural network’s labeled output, as well as the test grayscale picture, is passed to the SVM classifier for accurate segmentation. Results. We tested on the collected breast tumor dataset and found that our proposed combined CNN-SVM network achieved 0.93, 0.95, and 0.92 on DSC coefficient, PPV, and sensitivity index, respectively. We also compare with the segmentation frameworks of other papers, and the comparison results prove that our CNN-SVM network performs better and can accurately segment breast tumors. Conclusion. Our proposed CNN-SVM combined network achieves good segmentation results on the breast tumor dataset. The method can adapt to the differences in breast tumors and segment breast tumors accurately and efficiently. It is of great significance for identifying triple-negative breast cancer in the future.
Software Security Testing through Coverage in Deep Neural Networks
Authors: Fu Weiyu , Wang Lixia
Published: September 2022

With the continuous progress of society, computer technology and information technology are also experiencing rapid development. Especially in recent years, the application of computer technology has rapidly entered into people's daily life. As people’s lives become richer, these applications have become particularly complex. For some large software, tens of thousands of function points or millions of lines of source code may be triggered to support it when performing related tasks. As a result, the security of such a complicated and excellent software becomes quite essential. The most effective way to ensure software security is to test the security of software products during the development process. A precise and effective security testing process is the basis for ensuring that software is tested for security. Without a detailed scientific software security testing model to guide software development for security testing, software security testing will become very difficult. This not only wastes more time and money but also does not guarantee the security of the software. A great security testing methodology should be able to find security problems that may be hidden deep within the software. In addition, a scientific process management can greatly facilitate the implementation of software security testing. As a result, it is relatively meaningful to establish a complete software security testing process model, generate excellent security test cases, and develop security process management tools for software security testing. At the same time, in recent years, deep learning has gradually entered more and more people's lives. However, the widespread application of deep learning systems can bring convenience to human life but also bring some hidden dangers. Hence, deep neural networks must be adequately tested to eliminate as many security risks as possible in some safety-critical software that involves personal and property safety. As the foundation of deep learning systems, deep neural networks should be adequately tested for security. However, deep learning systems are fundamentally different from traditional software testing, so traditional software testing techniques cannot be directly applied to deep neural network testing. In recent years, many scholars in related fields have proposed coverage guidelines based on deep learning testing, but the usefulness of these guidelines is still debatable. Based on the complexity of the large software development process and the fact that the interrelationship between nodes often constitutes a complex network of collaborative relationships, this study applies coverage-based testing in deep neural networks to test the security of software. To be specific, this research applies metrics such as peak coverage, speed to peak, and computational speed to evaluate coverage criteria and to investigate the feasibility of using coverage to guide test case selection to select solutions for security testing.
Recognition of Human Motion Intentions Based on Bayesian-Optimized XGBOOST Algorithm
Authors: Gao Jingwei , Ma Chao , Wu Da , Xu Xiaoli , Wang Shaohong , Yao Jie
Published: September 2022

In order to improve the recognition rate for lower extremity motion patterns, this study designs a recognition method for such patterns, which integrates electromyography (EMG) and inertial measurement unit (IMU) signals in three posture modes, including walking on the ground, squatting, and extending seated legs, to address the difficulty with obtaining high signal-to-noise ratio EMG and IMU signals synchronously. Besides, this study proposes a synchronous analysis method for EMG and IMU dual-mode information to correct antipower frequency interference accelerometer signals. The collected signals are preprocessed to extract eigenvalues. And by using the kernel principal component analysis (KPCA), the information on these eigenvalues is fused. Finally, according to the characteristics of the data, a Bayesian-optimized XGBOOST algorithm is designed. Lower-limb movement patterns are classified with the feature vector put into the optimization algorithm. Multiperson experimental results show that the average recognition accuracy for different poses can reach 94.42%, the average

F

1 value 95.33%, and the average return value 95.68%, proving that the model proposed can be used to identify human motion intentions and its generalization ability can detect individual differences in human bodies.
Application of Fuzzy Clustering in Higher Education General Management Based on Internet Environment
Authors: Wang Qiangxin , Liu Lei , Liu Zhengtao , Qiao Weichuan , Liu Fengbin
Published: September 2022

General education is an important part of university education aiming at cultivating students’ comprehensive quality and sense of responsibility. As professional sports colleges and universities, higher sports colleges implement the curriculum reform of general education relatively late, and there is a problem of curriculum system construction in the process of implementing general education. The purpose of this paper is to research and discuss the application of general education management in higher education based on fuzzy cluster analysis in the Internet environment. This paper first discusses the teaching mode in the Internet environment and then analyzes the application of fuzzy cluster analysis in general education. Through fuzzy cluster analysis, the students’ understanding of general education before and after learning general education courses is compared, which paves the way for the promotion of general education in colleges and universities. Finally, this paper takes the general education curriculum system of higher physical education colleges as the research object and conducts fuzzy cluster analysis on it. The experimental results show that before studying general courses, 66.7% of the students hope to improve their language communication and expression skills, and 52.8% and 46% of the students hope to improve their practical ability and logical thinking ability. After receiving general education, only 23.5% of students have improved logical thinking ability, and only 13.7% of students have improved language communication and expression skills. The results show that general education does not meet the expectations of students, general education in higher physical education colleges is not effective, and the realization of general education needs to be further improved. To this end, schools can purchase network information resources, cooperate with well-known universities at home and abroad, develop general education courses with a global perspective, train students’ spirit of independent thinking, and master correct thinking methods.
[Retracted] Design of Psychological Well‐Being Education Environment Scheme Based on Deep Learning Theory
Authors: Guo Lingxia
Published: September 2022

This paper discusses the structure of psychological well‐being education programmes in higher education institutions based on an analysis of the connotation and characteristics of deep learning theory, as well as the background of today’s talent training requirements, the psychological traits of contemporary students, and the practical requirements of the teaching reform of psychological well‐being education courses in higher education institutions. A model for evaluating the psychological well‐being of college students based on BPNN is presented in this paper, which also addresses the current severe shortage of full‐time psychological counsellors. Additionally, the traditional BPNN is optimised by GA, and the resulting NN can better achieve the desired effect, demonstrating the viability of BPNN. It enables the psychological well‐being of college students to be self‐diagnosed online and significantly lessens the workload of psychological counselling institutions in higher education. According to the experimental findings, the optimised algorithm’s accuracy can reach 92.47 percent, and it is considered to be reliable. This study not only offers a novel approach to nonlinear data processing, but also paves the way for variable screening in the presence of an ambiguous structure. Additionally, in a limited sense, it offers insightful research for psychological education in higher education institutions.
Determination of Mycotoxin Contamination Levels in Rice and Dietary Exposure Assessment
Authors: Troestch Jose , Reyes Stephany , Vega Aracelly
Published: September 2022

The contamination by aflatoxins, ochratoxin A, and zearalenone of samples of paddy and polished rice stored in silos located in Chiriquí, Panama, was evaluated. A total of 23 samples were extracted using immunoaffinity columns and analyzed by high-performance liquid chromatography (HPLC) with a fluorescence detector (FLD) and post-column photochemical derivatization. For the method used, the detection limits were lower than 0.25 μg/Kg for aflatoxins (AFB1, AFB2, AFG1, AFG2) and ochratoxin A and 9.35 μg/Kg for zearalenone; the limits of quantification were between 0.25 and 18.75 μg/Kg, respectively. Of the samples analyzed, all of the paddy rice samples were positive for at least one of the mycotoxins studied, zearalenone being the one found with the highest incidence (90.91%); for the polished rice samples, the mycotoxin with the highest incidence was zearalenone (50%), although in concentrations lower than those established in European legislation (100 μg/Kg). The estimate of the daily zearalenone intake according to the concentrations found was always less than 0.07 μg/Kg/bw. This is the first report on the determination of 6 mycotoxins in rice grains from Panama by the HPLC-FLD methodology. Considering the high incidence of mycotoxins in the analyzed rice samples, regular control in the production process is recommended to improve quality and ascertain its safety.
The Climate Changes and the Simulation of the Runoff in the Last 50 years (1961–2010) in the Upper Tarim River Basin of Southern Xinjiang, China
Authors: Lu Tianjian , Xiao Qingquan , Ding Leiding , Lu Hanyu , Huang Ziyue , Yuan Yongyi
Published: September 2022

The upper Tarim River basin is supporting approximately 50 million people by melting the glaciers and snow, which are highly vulnerable and sensitive to climate change. Therefore, assessing the relative effects of climate change on the runoff of this region is essential not only for understanding the mechanism of hydrological response over the mountainous areas in Southern Xinjiang but also for local water resource management. This study quantitatively investigated the climate change in the mountainous area of the upper Tarim River basin, using the up-to-date “ground-truth” precipitation and temperature data, the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE, 1961–2010, 0.25°) data; analyzed the potential connections between runoff data, observed at Alar station, and the key climatological variables; and discussed the regression models on simulating the runoff based on precipitation and temperature data. The main findings of this study were as follows—(1) both annual precipitation and temperature generally increase at rates of 0.85 mm/year and 0.25 °C/10a, respectively, while the runoff data measured at the Alar station shows fluctuating decreasing trends. (2) There are significant spatial differences in the temporal trends of precipitation; for example, the larger increasing rates of precipitation occur in the Karakoram mountains, while the larger decreasing rates happen in the northwestern Kashgar county. (3) The decreasing trends of temperature mainly occur in Kashgar county and its surrounding areas in summer. (4) Seasonal correlations in precipitation and temperature trends are more significant than those on a monthly and annual scale. (5) The regression model in simulating the runoff in the upper Tarim River basin based on radial basis function (RBF) is better than that based on the least-squares method, with the predictive values based on RBF models significantly better (correlation coefficient, CC ∼ 0.85) than those by least-squares models (CC ∼ 0.75). These findings will provide valuable information to inform environmental scientists and planners on the climate change issues in the upper Tarim River basin of Southern Xinjiang, China, under a semiarid-arid climate.
Comparison of Transcriptomic Signatures between Monkeypox-Infected Monkey and Human Cell Lines
Authors: Xuan Do Thi Minh , Yeh I-Jeng , Wu Chung-Che , Su Che-Yu , Liu Hsin-Liang , Chiao Chung-Chieh , Ku Su-Chi , Jiang Jia-Zhen , Sun Zhengda , Ta Hoang Dang Khoa , Anuraga Gangga , Wang Chih-Yang , Yen Meng-Chi
Published: September 2022

Monkeypox virus (MPV) is a smallpox-like virus belonging to the genus Orthopoxvirus of the family Poxviridae. Unlike smallpox with no animal reservoir identified and patients suffering from milder symptoms with less mortality, several animals were confirmed to serve as natural hosts of MPV. The reemergence of a recently reported monkeypox epidemic outbreak in nonendemic countries has raised concerns about a global outburst. Since the underlying mechanism of animal-to-human transmission remains largely unknown, comprehensive analyses to discover principal differences in gene signatures during disease progression have become ever more critical. In this study, two MPV-infected in vitro models, including human immortal epithelial cancer (HeLa) cells and rhesus monkey (Macaca mulatta) kidney epithelial (MK2) cells, were chosen as the two subjects to identify alterations in gene expression profiles, together with co-regulated genes and pathways that are affected during monkeypox disease progression. Using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and MetaCore analyses, we discovered that elevated expression of genes associated with interleukins (ILs), G protein-coupled receptors (GPCRs), heat shock proteins (HSPs), Toll-like receptors (TLRs), and metabolic-related pathways play major roles in disease progression of both monkeypox-infected monkey MK2 and human HeLa cell lines. Interestingly, our analytical results also revealed that a cluster of differentiation 40 (CD40), plasmin, and histamine served as major regulators in the monkeypox-infected monkey MK2 cell line model, while interferons (IFNs), macrophages, and neutrophil-related signaling pathways dominated the monkeypox-infected human HeLa cell line model. Among immune pathways of interest, apart from traditional monkeypox-regulated signaling pathways such as nuclear factor- (NF-κB), mitogen-activated protein kinases (MAPKs), and tumor necrosis factors (TNFs), we also identified highly significantly expressed genes in both monkey and human models that played pivotal roles during the progression of monkeypox infection, including CXCL1, TNFAIP3, BIRC3, IL6, CCL2, ZC3H12A, IL11, CSF2, LIF, PTX3, IER3, EGR1, ADORA2A, and DUOX1, together with several epigenetic regulators, such as histone cluster family gene members, HIST1H3D, HIST1H2BJ, etc. These findings might contribute to specific underlying mechanisms related to the pathophysiology and provide suggestions regarding modes of transmission, post-infectious sequelae, and vaccine development for monkeypox in the future.