bluuebunny/crossref_metadata_2025 huggingface dataset:
The truncate soft-shell clam,Mya truncata, as a biomonitor of municipal wastewater exposure and historical anthropogenic impacts in the Canadian Arctic
Authors: Schaefer Christina M. , Deslauriers David , Jeffries Ken M.
Published: July 2021

Municipal wastewater is a large source of pollution to Canadian waters, yet its effects on Arctic marine ecosystems remains relatively unknown. We characterized the impacts of municipal wastewater from a growing northern community, Iqaluit, Nunavut, on the Arctic truncate soft-shell clam, Mya truncata. Clams were sampled from six locations that varied in proximity to the wastewater treatment plant, and shell biogeochemical analysis revealed that clams nearest the wastewater treatment plant had slower growth rates, lower carbon and oxygen stable isotope ratios, and elevated concentrations of copper and lead. A parallel analysis on mRNA expression profiles characterized M. truncata’s physiological response to wastewater effluent. Clams nearest the wastewater treatment plant had significantly lower mRNA expression of genes associated with metabolism, antioxidants, molecular chaperones, and phase I and II detoxification, but had heightened mRNA expression in genes coding for enzymes that bind and remove contaminants. These results demonstrated a biological response to Iqaluit’s wastewater effluent and highlight M. truncata’s potential to act as a biomonitor of municipal wastewater along Arctic coastlines in Canada.
Qualifying ringdown and shadow of black holes under general parametrized metrics with photon orbits
Authors: Li Song , Abdujabbarov Ahmadjon A. , Han Wen-Biao
Published: July 2021

The motion of photons around black holes determines the shape of shadow and match the ringdown properties of a perturbed black hole. Observations of shadows and ringdown waveforms will reveal the nature of black holes. In this paper, we study the motion of photons in a general parametrized metric beyond the Kerr hypothesis. We investigated the radius and frequency of the photon circular orbits on the equatorial plane and obtained fitted formula with varied parameters. The Lyapunov exponent which connects to the decay rate of the ringdown amplitude is also calculated. We also analyzed the shape of shadow with full parameters of the generally axisymmetric metric. Our results imply the potential constraint on black hole parameters by combining the Event Horizon Telescope and gravitational wave observations in the future.
Irradiation-driven molecular dynamics: a review
Authors: Verkhovtsev Alexey V. , Solov’yov Ilia A. , Solov’yov Andrey V.
Published: July 2021

This paper reviews Irradiation-Driven Molecular Dynamics (IDMD)—a novel computational methodology for atomistic simulations of the irradiation-driven transformations of complex molecular systems implemented in the MBN Explorer software package. Within the IDMD framework, various quantum processes occurring in irradiated systems are treated as random, fast and local transformations incorporated into the classical MD framework in a stochastic manner with the probabilities elaborated on the basis of quantum mechanics. Major transformations of irradiated molecular systems (such as topological changes, redistribution of atomic partial charges, alteration of interatomic interactions) and possible paths of their further reactive transformations can be simulated by means of MD with reactive force fields, in particular with the reactive CHARMM (rCHARMM) force field implemented in MBN Explorer. This paper reviews the general concept of the IDMD methodology and the rCHARMM force field and provides several exemplary case studies illustrating the utilization of these methods.

Graphic abstract
Generation of an isolated attosecond pulse from aligned molecules via laser synthesis
Authors: Min Cong , Zhang Yi , Zhang Chaojin , Liu Chengpu
Published: July 2021

A laser synthesis scheme via the superposition of single-color and hyper-Gaussian pulses is proposed, and the synthesized laser pulse is adopted to interact with pre-aligned molecules for the production of an isolated attosecond pulse (IAP). The numerical results based on strong-field approximation disclose that the proposed scheme is efficient for the generation of an IAP under suitable hyper-Gaussian parameters, and the intensity of an IAP is sensitive to molecular pre-alignment angles, and thus an optimal set of hyper-Gaussian parameters and pre-alignment angles can support the strongest IAP.
Thermally conductive microcapsule/high-density polyethylene composite for energy saving and storage
Authors: Shih Yeng-Fong , Chen Pei Tian , Lau Edwin M. , Hsu Liang Rong
Published: July 2021

Phase change material (PCM) is useful for the storage and release of latent heat. However, its ability to conduct has hindered its engineering application. This study prepares a novel microencapsulated phase change material (MEPCM) by suspension polymerization. To improve the adhesion between the shell and the inorganic additive, triethoxyvinylsilane was incorporated copolymerizing with methyl methacrylate. Thermally conductive nanographite particle was added. This MEPCM was then incorporated into high-density polyethylene (HDPE) to form a series of thermally conductive PCM microcapsules that approached sphere shapes with diameters less than 2 [Formula: see text]m. Thermal analysis showed that the thermal stability and heat resistance of the microcapsule were improved. The thermal conductivity of HDPE was increased by 39% to 0.6358 W/m[Formula: see text]K, and the surface resistivity was lowered to [Formula: see text]/sq after the addition of MEPCM. The temperature on the top of the composite tested was lower than pristine HDPE. This was close to the onset melting temperature of the MEPCM (38.5[Formula: see text]C), [Formula: see text] lower than pure HDPE. The reduction is a significant improvement in temperature regulation. This enables MEPCMs to store and release heat much more effectively, and can thus be applied to medical construction materials to meet the temperature requirements of COVID-19 patients.
Note on strongly quasi-primary ideals
Authors: Alshammari Ibtesam , Kammoun Rania , Mamouni Abdellah , Tamekkante Mohammed
Published: July 2021

Let [Formula: see text] be a commutative ring with [Formula: see text]. A proper ideal [Formula: see text] of [Formula: see text] is said to be a strongly quasi-primary ideal if, whenever [Formula: see text] with [Formula: see text], then either [Formula: see text] or [Formula: see text]. In this paper, we characterize Noetherian and reduced rings over which every (respectively, nonzero) proper ideal is strongly quasi-primary. We also characterize ring over which every strongly quasi primary ideal of [Formula: see text] is prime. Many examples are given to illustrate the obtained results.
The existence of left eigenvalues for quaternionic matrix
Authors: Yang Yan , Kou Kit Ian
Published: July 2021

In this work, an algebraic method to prove the existence of left eigenvalues for the quaternionic matrix is investigated. The left eigenvalues of a [Formula: see text] quaternionic matrix can be derived by solving the zeros of a general quaternionic polynomial of degree [Formula: see text]. Using the Study’s determinant, it can be found by solving the zeros of quaternionic polynomials of degree at most [Formula: see text] or of rational functions.
iDNA6mA-Rice-DL: A local web server for identifying DNA N6-methyladenine sites in rice genome by deep learning method
Authors: He Shiqian , Kong Liang , Chen Jing
Published: July 2021

Accurate detection of N6-methyladenine (6mA) sites by biochemical experiments will help to reveal their biological functions, still, these wet experiments are laborious and expensive. Therefore, it is necessary to introduce a powerful computational model to identify the 6mA sites on a genomic scale, especially for plant genomes. In view of this, we proposed a model called iDNA6mA-Rice-DL for the effective identification of 6mA sites in rice genome, which is an intelligent computing model based on deep learning method. Traditional machine learning methods assume the preparation of the features for analysis. However, our proposed model automatically encodes and extracts key DNA features through an embedded layer and several groups of dense layers. We use an independent dataset to evaluate the generalization ability of our model. An area under the receiver operating characteristic curve (auROC) of 0.98 with an accuracy of 95.96% was obtained. The experiment results demonstrate that our model had good performance in predicting 6mA sites in the rice genome. A user-friendly local web server has been established. The Docker image of the local web server can be freely downloaded at https://hub.docker.com/r/his1server/idna6ma-rice-dl .
2-Variable Fubini-degenerate Apostol-type polynomials
Authors: Nahid Tabinda , Ryoo Cheon Seoung
Published: July 2021

This work deals with the mathematical inspection of a hybrid family of the degenerate polynomials of the Apostol-type. The inclusion of the derivation of few series expansion formulas, explicit representations and difference equations for this hybrid family brings a novelty to the existing literature. Moreover, certain connection formulas and several novel identities for these polynomials are established and investigated. The graphical representations of certain degenerate polynomials are explored and several new interesting pattern of the zeros are observed.
Merging Grid Maps in Diverse Resolutions by the Context-based Descriptor
Authors: Lin Zhiyang , Zhu Jihua , Jiang Zutao , Li Yujie , Li Yaochen , Li Zhongyu
Published: July 2021

Building an accurate map is essential for autonomous robot navigation in the environment without GPS. Compared with single-robot, the multiple-robot system has much better performance in terms of accuracy, efficiency and robustness for the simultaneous localization and mapping (SLAM). As a critical component of multiple-robot SLAM, the problem of map merging still remains a challenge. To this end, this article casts it into point set registration problem and proposes an effective map merging method based on the context-based descriptors and correspondence expansion. It first extracts interest points from grid maps by the Harris corner detector. By exploiting neighborhood information of interest points, it automatically calculates the maximum response radius as scale information to compute the context-based descriptor, which includes eigenvalues and normals computed from local structures of each interest point. Then, it effectively establishes origin matches with low precision by applying the nearest neighbor search on the context-based descriptor. Further, it designs a scale-based corresponding expansion strategy to expand each origin match into a set of feature matches, where one similarity transformation between two grid maps can be estimated by the Random Sample Consensus algorithm. Subsequently, a measure function formulated from the trimmed mean square error is utilized to confirm the best similarity transformation and accomplish the coarse map merging. Finally, it utilizes the scaling trimmed iterative closest point algorithm to refine initial similarity transformation so as to achieve accurate merging. As the proposed method considers scale information in the context-based descriptor, it is able to merge grid maps in diverse resolutions. Experimental results on real robot datasets demonstrate its superior performance over other related methods on accuracy and robustness.
Random Variate Generation for Exponential and Gamma Tilted Stable Distributions
Authors: Qu Yan , Dassios Angelos , Zhao Hongbiao
Published: July 2021

We develop a new efficient simulation scheme for sampling two families of tilted stable distributions: exponential tilted stable (ETS) and gamma tilted stable (GTS) distributions. Our scheme is based on two-dimensional single rejection. For the ETS family, its complexity is uniformly bounded over all ranges of parameters. This new algorithm outperforms all existing schemes. In particular, it is more efficient than the well-known double rejection scheme, which is the only algorithm with uniformly bounded complexity that we can find in the current literature. Beside the ETS family, our scheme is also flexible to be further extended for generating the GTS family, which cannot easily be done by extending the double rejection scheme. Our algorithms are straightforward to implement, and numerical experiments and tests are conducted to demonstrate the accuracy and efficiency.
Designing for Youth-Centered Moderation and Community Governance in Minecraft
Authors: Tekinbaş Katie Salen , Jagannath Krithika , Lyngs Ulrik , Slovák Petr
Published: July 2021

Online settings have been suggested as viable sites for youth to develop social, emotional, and technical skills that can positively shape their behavior online. However, little work has been done to understand how online governance structures might support (or hinder) such learning. Using mixed-methods research, we report findings from a 2-year, in-the-wild study of 8–13 year olds on a custom multiplayer Minecraft server. The two-part study focuses on the design of youth-centered models of community governance drawn from evidence-based offline practices in the prevention and learning sciences. Preliminary results point to a set of socio-technical design approaches shaping player behavior while also supporting youth interest in Minecraft-like online environments. More broadly, the findings suggest an alternative vision of youth’s capacity for ownership and control of mechanisms shaping the culture and climate of their online communities: managing player behavior while challenging current norms around adult control and surveillance of youth activity.
The graph neural networking challenge
Authors: Suárez-Varela José , Ferriol-Galmés Miquel , López Albert , Almasan Paul , Bernárdez Guillermo , Pujol-Perich David , Rusek Krzysztof , Bonniot Loïck , Neumann Christoph , Schnitzler François , Taïani François , Happ Martin , Maier Christian , Du Jia Lei , Herlich Matthias , Dorfinger Peter , Hainke Nick Vincent , Venz Stefan , Wegener Johannes , Wissing Henrike , Wu Bo , Xiao Shihan , Barlet-Ros Pere , Cabellos-Aparicio Albert
Published: July 2021

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020". We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.
P4Pi
Authors: Laki Sándor , Stoyanov Radostin , Kis Dávid , Soulé Robert , Vörös Péter , Zilberman Noa
Published: July 2021

High level, network programming languages, like P4, enable students to gain hands-on experience in the structure of a switch or router. Students can implement the packet processing pipeline themselves, without prior knowledge of circuit design. However, when choosing a P4 programmable target for use in the classroom, instructors face a lack of options. On the one hand, software solutions, such as the behavioral model (BMv2) switch, are overly simplified and offer low performance. On the other hand, existing hardware solutions are closed source and expensive.

In this paper, we present P4Pi, a new, low-cost, open-source hardware platform intended for networking education. P4Pi allows students to design and deploy P4-based network devices using the Raspberry Pi board, which has a price tag of less than many academic textbooks. We describe the high-level design of the P4Pi platform, offer some suggestions for how P4Pi could be used in the classroom, and present some additional use-cases for applications and functionality that could be developed using P4Pi.
A square law revisited
Authors: Carpenter Brian E.
Published: July 2021

An earlier study observed that until 2008, the size of the BGP4 system for IPv4 appeared to have grown approximately in proportion to the square root of the host count of the globally addressable Internet. This article revisits this study by including IPv4 data until 2020 and adding IPv6 data. The results indicate that BGP4 for IPv4 is continuing to scale steadily even as IPv4 approaches its end of life, and that it is working as it should for IPv6, except for a slight concern that the number of announced routes is trending upwards faster as time goes on.
Constructed Languages
Authors: Schreyer Christine
Published: July 2021

Constructed languages, also known as conlangs, are languages that have been purposefully created for either real-world or fictional speakers. Within this article, I provide a summary of the language creation process and how the community of conlangers, people who make languages, come to know each other's work, as well as how language creation assignments are being adopted within university classrooms. I also explore the role of the language creator in bringing a community of speakers into existence through the invention of a language. I discuss whether speakers of a constructed language are part of a community of practice or a speech community and the implications for this distinction within anthropology. I also describe conscripts, or constructed orthographies, as well as the relationship between endangered languages and constructed languages, how invented worlds can create real-world shifts in worldview, and suggestions for new directions in research linking anthropology and constructed languages.
Blunted peripheral but not cerebral vasodilator function in young otherwise healthy adults with persistent symptoms following COVID-19
Authors: Nandadeva Damsara , Young Benjamin E. , Stephens Brandi Y. , Grotle Ann-Katrin , Skow Rachel J. , Middleton Angela J. , Haseltine Florence P. , Fadel Paul J.
Published: July 2021

This study was the first to investigate the persistent effects of COVID-19 on vascular function in otherwise healthy young adults. We demonstrated that peripheral macrovascular and microvascular vasodilation was significantly blunted in young adults still symptomatic from COVID-19 beyond the acute phase (>4 wk from diagnosis), whereas those who become asymptomatic have similar vascular function compared with controls who never had COVID-19. In contrast, cerebral vascular function and central arterial stiffness were unaffected irrespective of COVID-19 symptomology.
Rapid Quality Identification of Decoction Pieces of Crude and Processed Corydalis Rhizoma by Near-Infrared Spectroscopy Coupled with Chemometrics
Authors: Zhu Weihao , Hong Hao , Hong Zhihui , Kang Xianjie , Du Weifeng , Ge Weihong , Li Changyu
Published: July 2021

In order to identify the quality of crude and processed Corydalis Rhizoma decoction pieces, the research established a simple, fast, reliable, and validated near-infrared qualitative and quantitative model combined with chemometrics. 51 batches of crude and 40 batches of processed Corydalis Rhizoma from the Zhejiang and Jiangsu provinces of China were collected and analyzed. Crude and processed Corydalis Rhizoma samples were crushed to obtain NIR spectra. The content of seven alkaloids in crude and processed Corydalis Rhizoma was determined by high-performance liquid chromatography (HPLC). Pretreatment methods were screened such as normalization methods, offset filtering methods, and smoothing. Combined with partial least squares-discriminant analysis (PLS-DA) and partial least squares (PLS), the qualitative and quantitative models of crude and processed Corydalis Rhizoma were established, and the correlation coefficient (R2), root mean square error of calibration (RMSEC), and root mean square error of prediction (RMSEP) were used as evaluation indexes. Tetrahydropalmatine was used as an example for screening pretreatment methods; the results showed that MSC combined with the second derivative and no smoothing and the model with the wavelength range of 10000–5000 cm−1 had the best predictive ability and applied to all seven alkaloid components. Among them, the correlation coefficients were all higher than 0.99, and RMSEC and RMSEP were all less than 1%. The qualitative and quantitative model of the seven alkaloids in Corydalis Rhizoma can effectively identify the crude and processed Corydalis Rhizoma and determine the content of the seven alkaloids. By studying the NIR qualitative and quantitative models of crude and processed Corydalis Rhizoma, we can achieve rapid discrimination and quantitative prediction of crude and processed Corydalis Rhizoma. These methods can greatly improve the efficiency of traditional Chinese medicine analysis and provide a strong scientific basis for the quality identification and control of traditional Chinese medicine.
Gesture Recognition Algorithm of Human Motion Target Based on Deep Neural Network
Authors: Xia Zhonghua , Xing Jinming , Wang Changzai , Li Xiaofeng
Published: July 2021

There are some problems in the current human motion target gesture recognition algorithms, such as classification accuracy, overlap ratio, low recognition accuracy and recall, and long recognition time. A gesture recognition algorithm of human motion based on deep neural network was proposed. First, Kinect interface equipment was used to collect the coordinate information of human skeleton joints, extract the characteristics of motion gesture nodes, and construct the whole structure of key node network by using deep neural network. Second, the local recognition region was introduced to generate high-dimensional feature map, and the sampling kernel function was defined. The minimum space-time domain of node structure map was located by sampling in the space-time domain. Finally, the deep neural network classifier was constructed to integrate and classify the human motion target gesture data features to realize the recognition of human motion target. The results show that the proposed algorithm has high classification accuracy and overlap ratio of human motion target gesture, the recognition accuracy is as high as 93%, the recall rate is as high as 88%, and the recognition time is 17.8 s, which can effectively improve the human motion target attitude recognition effect.
Modeling the Incentive Mechanism of Information Sharing in a Dual-Channel Supply Chain
Authors: Wang Lixin , Jiao Xiaolong , Hao Qian
Published: July 2021

Information sharing is the premise of the stable operation and win-win cooperation of the dual-channel agricultural supply chain. To arouse the enthusiasm of information sharing, this paper studies the incentive mechanism of information sharing of dual-channel agricultural product supply chain. The invention problem-solving theory (TRIZ) is adopted to analyze the functional model, the key problems, and the available resources of a dual-channel agricultural supply chain information sharing system. By using the invention principles of the conflict resolution theory, such as separation, precompensation, and feedback, the point bank reward mechanism and the risk evaluation and control mechanism of information sharing are proposed. TRIZ theory provides a new research method and incentive measure for effectively mobilizing the enthusiasm of information sharing.
Changes in Corneal Parameters after DMEK Surgery: A Swept-Source Imaging Analysis at 12-Month Follow-Up Time
Authors: Machalińska Anna , Kuligowska Agnieszka , Kaleta Karolina , Kuśmierz-Wojtasik Monika , Safranow Krzysztof
Published: July 2021

Purpose. To assess the time course changes in corneal topographic parameters during the one-year follow-up after Descemet membrane endothelial keratoplasty (DMEK) surgery. Materials and Methods. Twenty-one patients (24 eyes) who underwent DMEK surgery were evaluated. Best corrected visual acuity (BCVA), endothelial cell count (ECC), central corneal thickness (CCT), mean keratometry (MK), mean astigmatism (MA), astigmatism asymmetry (AA), and higher-order aberration (HOA) were assessed at baseline and 1, 3, 6 and 12 months after the surgery using CASIA2 anterior segment swept-source OCT (Tomey, Japan). Results. In patients who underwent DMEK surgery, BCVA improved gradually at the subsequent visits during the 12-month follow-up. A significant reduction in ECC and CCT at the 1st month was noted, which remained stable until the 6th month postoperatively. Anterior and total MK values remained unchanged, whereas changes in posterior keratometry were noticeable until the 6th month after surgery. A significant reduction in the anterior, posterior, and total astigmatism magnitude as well as astigmatism asymmetry was observed during the first 6 months after surgery. A gradual anterior, posterior, and total HOA decrease was documented until the 12th month after surgery. Negative correlations between baseline values of CCT, MK, MA, AA, and HOA and postoperative variations in those parameters at consecutive follow-up time points were observed. Accordingly, negative correlations between baseline CCT and postoperative changes in corneal topographic parameters after surgery were found. Conclusion. The stabilization of most corneal topographic parameters takes place within 6 months after the procedure, whereas HOA reduction and BCVA improvement gradually occur during the first year after surgery. Preoperative values of corneal topographic parameters strongly determine their changes detected after DMEK surgery, which may suggest that early therapeutic intervention results in better visual outcomes.
Analysis of Image Features and TCM Syndrome Types of Lobar Pneumonia in Children Based on Mean Square Deviation Lung CT Image Registration Algorithm
Authors: Wang Shuang , Zhang Yi , Liu Yongkun
Published: July 2021

The aim was to analyze the application of computed tomography (CT) images in the diagnosis of lesions, and the composition of traditional Chinese medicine (TCM) syndromes in children with lobar pneumonia. Lung CT image registration algorithm was constructed based on optimized mean square deviation (OMSD) algorithm, which was applied to CT images of 188 children patients with lobar pneumonia before and after treatment. Besides, free-form deformation (FFD) algorithm and mean square deviation (MSD) algorithm were introduced for comparison with OMSD. Results showed that sum of squared differences (SSD) of OMSD was significantly lower than that of MSD and FFD (

P
<
0.05

). The mutual information (MI), relative overlap rate (ROR), and rcc of OMSD were markedly higher than those of MSD and FFD (

P
<
0.05

). After treatment, the number of pulmonary interstitial thickening, lobular interstitial thickening, ground-glass shadow, patchy shadow, consolidation shadow, pleural thickening, pleural effusion, lymphadenopathy, pneumothorax, and mediastinal emphysema decreased sharply in contrast to before treatment (

P
<
0.05

). Among the selected children, there were 184 children patients with empirical TCM syndrome, accounting for 97.85%, and its dominant syndrome was wind-heat closed lung syndrome (52.75%) and phlegm-heat closed lung syndrome (46.11%). The main symptoms of wind-heat closed and phlegm-heat closed lung syndrome were fever, cough, and pulmonary rales. In conclusion, OMSD was superior to MSD and FFD in lung CT image registration. CT registration image based on OMSD could clearly display the intrapulmonary and extrapulmonary manifestations of children patients, so as to enhance the clinical diagnosis effect. Besides, wind-heat closed lung syndrome and phlegm-heat closed lung syndrome were the common types of TCM syndrome of children pneumonia with the common symptoms of fever and cough.
Prescribed Performance Synchronization Control of Chaotic Systems with Unknown Control Gain Signs
Authors: Ma Xin , Zhu Fang
Published: July 2021

For a class of uncertain nonlinear chaotic systems with unknown control gain signs and saturated input, by means of Nussbaum function, a scheme of finite-time prescribed performance synchronization control is proposed. Here, Nussbaum function is used to eliminate the influence of unknown control gain signs, and fuzzy logic systems are used to estimate unknown functions. Lyapunov theory is used to prove that all synchronization errors converge to a predefined small performance range under the designed control method. Finally, simulation results are provided to illustrate the feasibility of the proposed method.
Compressed Sensing‐Based Range‐Doppler Processing Method for Passive Radar
Authors: Bai Xia , Guo Hejing , Zhao Juan , Shan Tao
Published: July 2021

Passive radar (PR) systems use the existing transmitters of opportunity in the environment to perform tasks such as detection, tracking, and imaging. The classical cross‐correlation based methods to obtain the range‐Doppler map have the problems of high sidelobe and limited resolution due to the influence of signal bandwidth. In this paper, we propose a novel range‐Doppler processing method based on compressed sensing (CS), which performs sparse reconstruction in range and Doppler dimensions to achieve high resolution and reduces sidelobe without excessive computational burden. Results from numerical simulations and experimental measurements recorded with the Chinese standard digital television terrestrial broadcasting (DTTB) based PR show that the proposed method successfully handles the range‐Doppler map formatting problem for PR and outperforms the existing CS‐based PR processing methods.
Effective Factors in Critical Thinking Disposition in Nursing Students
Authors: Mousazadeh Noushin , Momennasab Marzieh , Sharif Nia Hamid , Nazari Roghieh , Hajihosseini Fatemeh
Published: July 2021

Introduction. Given the vital role of critical thinking and possibility of developing this meta-cognitive skill through training programs, the present study was conducted to determine the tendency and effective factors in critical thinking in nursing students. Materials and Methods. The present study is a cross-sectional study that was conducted in 2020 in a school of nursing in northern Iran. Participants in the study were all second to eighth semester nursing students studying in the faculty who entered the study by the census method. Inclusion criteria included employment in the field of nursing in the second semester and above, and exclusion criteria were employment and incomplete completion of the questionnaire questions. The required information was prepared in two parts: the first part of recording demographic information including age, gender, marital status, semester, average score, previous semester average score, and level of interest in the major and the second part was the adopted Ricketts’ critical thinking disposition questionnaire. SPSS 26 software and descriptive and analytical statistics were used to analyze the data. Results. The total average score of critical thinking disposition obtained as 120.20 ± 14.05 35 was found to positively and significantly relate to age (r = 0.150,

p
=
0.04

, d = 0.30), academic 36 semester (

p
=
0.001

, d = 0.25), and interest in the major (

p
=
0.003

, d = 0.38). Dimensions of 37 innovation and maturity were also significantly associated with academic semester and 38 interested in the major. Also, the total score of critical thinking and its dimensions in married people was higher than single, but this difference was not statistically significant. The results of analysis of variance showed that there is a positive and significant relationship between the mean score of tendency to critical thinking and different semesters. Conclusion. Identifying the effective factors in critical thinking disposition can assist teachers in developing lesson plans and academic counseling, and at the macro level, can help with educational planning and revision of curricula.
An Evaluation Method of Rock Brittleness Based on the Prepeak Crack Initiation and Postpeak Stress Drop Characteristics
Authors: Gao Meiben , Li Tianbin , Meng Lubo
Published: July 2021

Recent research shows that the brittleness of rock is closely related to the initiation and propagation of internal microcracks, but there are few brittleness evaluation indices considering the characteristics of rock initiation. Based on the theoretical analysis of brittleness and the characteristics of rock initiation, this study proposes an evaluation method of rock brittleness based on the prepeak crack initiation and postpeak stress drop characteristics. First, based on the description and definition of brittleness by George Tarasov and Potvin et al., the feasibility of an evaluation method based on the prepeak crack initiation and postpeak stress drop is theoretically analyzed. Second, the component Bi representing the prepeak brittleness of rock and component Bii representing the prepeak brittleness of rock are constructed, and the product of the two is the brittleness index BI, representing the prepeak crack initiation and postpeak stress drop. Finally, experimental tests of granite and marble were conducted to evaluate the new index, and the brittleness indices of different methods are calculated and compared. The results show that, like other brittleness indices (B1∼B5), the brittleness index BI can effectively reflect the effects of different confining pressures and loading modes on rock brittleness. The brittleness of marble decreases with increasing confining pressure from 5 MPa to 35 MPa. At a confining pressure of 5 MPa, the brittleness of granite during a triaxial unloading test is greater than that during a triaxial compression test. The calculated results are consistent with the experimental results. By tests and comparison results, the reliability of this evaluation method was verified, which provides a way to evaluate rock brittleness from the perspective of crack initiation and is helpful to enrich the analysis and evaluation of rock brittleness in the laboratory.
Antitumor Effect of Lenvatinib Combined with Alisertib in Hepatocellular Carcinoma by Targeting the DNA Damage Pathway
Authors: Hao Jianwen , Peng Qizhen , Wang Keruo , Yu Ge , Pan Yi , Du Xiaoling , Hu Na , Zhang Xuening , Qin Yu , Li Huikai
Published: July 2021

Background and Aim. Although a strong antitumor effect of lenvatinib has been noted for patients with unresectable hepatocellular carcinoma (HCC), its efficacy requires improvement. It is imperative to seek therapeutic strategies that combine Lenvatinib with other anticancer agents. In this study, we investigated the anticancer effect of combining lenvatinib with alisertib, aurora A kinase (AURKA) target drug, against HCC in vitro and in vivo. Methods. Immunohistochemical staining, sequencing, and genetic analysis of liver cancer tissues were performed. The antitumor efficacy of single‐agent or combination treatment was measured by cell counting kit‐8 assay and colony formation assays. Their antiproliferative and apoptosis activity is evaluated by cell cycle analyses and wound healing assays. The DNA‐related proteins were also measured by Western blotting and immunohistochemical staining. The HepG2 xenograft model was used to detect the effects of lenvatinib‐alisertib on the antitumor activity. Results. AURKA was found to be upregulated in HCC tissues (77.3%, 17/22). Combined alisertib and lenvatinib treatment significantly enhanced the inhibition of proliferation and migration in HepG2 and Hep3B cell lines compared to single‐agent treatments (all Ps < 0.01). Alisertib alone or in combination with lenvatinib demonstrated a significant increase in the percentage of super‐G2 cells (lenvatinib 1 μM vs. lenvatinib 1 μM + alisertib 0.1 μM 8.84 ± 0.84 vs. 34.0 ± 1.54, P < 0.001). Discontinuous spindles and missegregated chromosomes in HCC cells treated with alisertib in combination with lenvatinib were observed. We further revealed that combined treatment inhibited the expression of DNA damage pathway proteins compared to those of single‐agent treatments. In nude mice, combined administration of alisertib combined with lenvatinib significantly enhanced the suppression of tumor growth and induced apoptosis (all Ps < 0.01). Conclusions. Our findings provide evidence for the possible use of alisertib in combination with lenvatinib in the treatment of HCC for better therapeutic outcomes.
Long‐Term Efficacy of Ultrasound‐Guided Percutaneous Laser Ablation for Low‐Risk Papillary Thyroid Microcarcinoma: A 5‐Year Follow‐Up Study
Authors: Peng Kai , Zhou Ping , Liu Wengang
Published: July 2021

Objective. To evaluate the long‐term efficacy and safety of ultrasound‐guided percutaneous laser ablation (PLA) for the treatment of low‐risk papillary thyroid microcarcinoma (PTMC). Methods. From June 2012 to May 2015, 105 patients with solitary, pathologically confirmed PTMC lesions were treated with ultrasound‐guided PLA. Nodule location, nodule volume, thyroid function, and clinical symptoms were evaluated before ablation. Contrast‐enhanced ultrasound (CEUS) was performed 1 h after treatment to evaluate whether the ablation was complete. Ultrasound examination was performed at 1, 3, 6, and 12 months after ablation and every 6 months thereafter to determine the size of the ablation area and search for recurrence in the thyroid parenchyma and lymph node metastasis. Thyroid function was examined before and 1 month after ablation. Fine needle aspiration biopsy was performed for any suspicious metastatic lymph nodes and recurrent lesions in the thyroid. Results. All 105 lesions were completely inactivated after one ablation, making the success rate for single ablation 100%. The average ablation time was 2.78 ± 1.05 min, and the average ablation energy was 505 ± 185 J. All patients could tolerate and complete the ablation. No serious complications occurred during the treatment; only minor side effects such as pain and local discomfort were reported. The volume reduction rates were −781.14 ± 653.29% at 1 h posttreatment and −268.65 ± 179.57%, −98.39 ± 76.58%, 36.78 ± 30.32%, 75.55 ± 21.81%, 96.79 ± 10.57%, and 100% at 1, 3, 6, 12, 18, and 24 months after ablation, respectively. This rate remained 100% at the later follow‐up times. Overall, 28 (26.67%), 74 (70.48%), 96 (91.43%), and 103 (100%) were completely absorbed by 6, 12, 18, and 24 months after PLA. One patient developed another lesion 12 months after ablation, and two patients had central cervical lymph node metastasis 24 months after ablation. Conclusion. PLA is a safe and effective alternative clinical treatment for low‐risk PTMC.
Bioinformatics Analysis: The Regulatory Network of hsa_circ_0007843 and hsa_circ_0007331 in Colon Cancer
Authors: Han Zeping , Chen Huafang , Guo Zhonghui , Zhu Jianxia , Xie Xingyi , Li Yuguang , He Jinhua
Published: July 2021

Objective. To analyze the molecular regulation network of circular RNA (circRNA) in colon cancer (CC) by bioinformatics method. Methods. hsa_circ_0007843 and hsa_circ_0007331 proved to be associated with CC in previous studies were chosen as the research object. ConSite database was used to predict the transcription factors associated with circRNA, and the CC‐associated transcription factors were screened out after intersection. The CircInteractome database was used to predict the RNA‐binding proteins (RBPs) interacting with circRNAs and screen out the CC‐associated RBPs after an intersection. Furthermore, the CircInteractome database was used to predict the miRNAs interrelated with circRNAs, and the HMDD v3.2 database was used to search for miRNAs associated with CC. The target mRNAs of miRNA were predicted by the miRWalk v3.0 database. CC‐associated target genes were screened out from the GeneCards database, and the upregulated genes were enriched and analyzed by the FunRich 3.1.3 software. Finally, the molecular regulatory network diagram of circRNA in CC was plotted. Results. The ConSite database predicted a total of 14 common transcription factors of hsa_circ_0007843 and hsa_circ_0007331, among which Snail, SOX17, HNF3, C‐FOS, and RORα‐1 were related to CC. The CircInteractome database predicted that the RBPs interacting with these two circRNAs were AGO2 and EIF4A3, and both of them were related to CC. A total of 17 miRNAs interacting with hsa_circ_0007843 and hsa_circ_0007331 were predicted by CircInteractome database. miR‐145‐5p, miR‐21, miR‐330‐5p, miR‐326, and miR‐766 were associated with CC according to the HMDDv3.2 database. miR‐145‐5p and miR‐330‐5p, lowly expressed in CC, were analyzed in the follow‐up study. A total of 676 common target genes of these two miRNAs were predicted by the miRWalk3.0 database. And 57 target genes were involved in the occurrence and development of CC from the GeneCards database, with 23 genes downregulated and 34 genes upregulated. Additionally, GO analysis showed that the 34 upregulated genes were mainly enriched in biological processes such as signal transduction and cell communication. KEGG pathway analysis showed that the upregulated genes were closely related to integrin, ErbB receptor, and ALK1 signal pathways. Finally, a complete regulatory network of hsa_circ_0007843 and hsa_circ_0007331 in CC was proposed, whereby each one of the participants was either directly or indirectly associated and whose deregulation may result in CC progression. Conclusion. Predicting the molecular regulatory network of circRNAs by bioinformatics provides a new theoretical basis for further occurrence and development pathogenesis of CC and good guidance for future experimental research.
Revisiting the Benefits of Combining Data of a Different Nature: Strategic Forecasting of New Mode Alternatives
Authors: Guzman Luis A. , Arellana Julian , Cantillo-García Victor , Ortúzar Juan de Dios
Published: July 2021

We revisit the practice of combining revealed (RP) and stated preference (SP) data (i.e., the data enrichment, DE, paradigm) in discrete choice models using secondary data obtained from emerging sources; these facilitate access to massive information about travel choices and can be used to improve transport models. Even though the benefits of the DE paradigm have been known for years, there is a large gap between the state of practice and the state of the art, particularly in Global South countries (but also in many industrialized nations). We use a SP dataset considering two new transport alternatives (train and metro) and a RP dataset based on a large mobility survey in Bogotá, Colombia, complemented with fairly precise level-of-service data obtained using GIS utilities and the Distance Matrix API by Google. Our results allow us to discuss good practice, identify barriers and challenges to the paradigm’s application, and draw recommendations for forecasting the demand for new alternatives using joint RP and SP data.
A Practical Approach to Identifying Processed White Meat of Guinea Fowl, Rabbit, and Selected Fish Species Using End-Point PCR
Authors: Spychaj Anita , Goderska Kamila , Fornal Emilia , Montowska Magdalena
Published: July 2021

Among the foodstuff, most often adulterated are white meat and meat products as well as fish and fish products. For this reason, we evaluated in practice the possibilities of identifying selected species of white meat, i.e., guinea fowl and rabbit as well as four fish species, namely, pollock, hake, sole, and panga, in thermally treated samples. The aim was to check whether the previously published in the scientific literature species-specific primers allows for the identification of processed meat using the end-point PCR technique. To identify the six species, the short sequence fragments (from 130 to 255 bp) of 12S rRNA, COX3, mitochondrial ATP synthase Fo subunit 6 (ATP6) gene, pantophysin (Pan I) gene, 5S rRNA gene, and microsatellite markers (locus: Phy01-KUL) were selected. Stability and specificity of the six pair primers were evaluated on cooked and autoclaved meat, and commercially processed food samples such as rabbit and guinea pâtés, ready-made baby food, and breaded, fried, and deep-frozen fish products. The method proved to be useful for the authentication of severely processed food products against fraudulent species substitution and mislabelling and this approach may be an alternative to more advanced and more expensive PCR techniques.
[Retracted] Quantitative Evaluation of Plant and Modern Urban Landscape Spatial Scale Based on Multiscale Convolutional Neural Network
Authors: Wang Yang , Li Moyang
Published: July 2021

Modern urban landscape is a simple ecosystem, which is of great significance to the sustainable development of the city. This study proposes a landscape information extraction model based on deep convolutional neural network, studies the multiscale landscape convolutional neural network classification method, constructs a landscape information extraction model based on multiscale CNN, and finally analyzes the quantitative effect of deep convolutional neural network. The results show that the overall kappa coefficient is 0.91 and the classification accuracy is 93% by calculating the confusion matrix, production accuracy, and user accuracy. The method proposed in this study can identify more than 90% of water targets, the user accuracy and production accuracy are 99.78% and 91.94%, respectively, and the overall accuracy is 93.33%. The method proposed in this study is obviously better than other methods, and the kappa coefficient and overall accuracy are the best. This study provides a certain reference value for the quantitative evaluation of modern urban landscape spatial scale.
An Empirical Study on Application of Machine Learning and Neural Network in English Learning
Authors: Dong He , Tsai Sang-Bing
Published: July 2021

With the continuous development of neural network theory itself and related theories and related technologies, neural network is one of the main branches of intelligent control technology. Artificial neural network is a nonlinear and adaptive information processing composed of a large number of processing units. In this paper, an adaptive fuzzy neural network (FNN) is used to construct an intelligent system architecture for English learning, and activation function is used to apply the knowledge of computer science and linguistics to English learning. The network neural structure diagram is presented. English machine learning model framework is established based on recursive neural network. On this basis, feature vector extraction and normalization algorithm are used to meet the needs of neural network model. After acquiring the feature vectors of users’ learning styles, the clustering algorithm is used to effectively form a variety of learning styles. The validity of the English learning model was verified by designing the functional flow based on tests. Accurate mastery can activate the corresponding brain regions not only to improve the efficiency of learning, but also to better facilitate language learning.