bluuebunny/crossref_metadata_2025 huggingface dataset:
The role of strangeness in chiral and $$U(1)_A$$ restoration
Authors: Gómez Nicola A. , Ruiz de Elvira J. , Vioque-Rodríguez A. , Álvarez-Herrero D.
Published: July 2021

We use recently derived Ward identities and lattice data for the light- and strange-quark condensates to reconstruct the scalar and pseudoscalar susceptibilities ($$\chi _S^\kappa $$

χ
S
κ

, $$\chi _P^K$$

χ
P
K

) in the isospin 1/2 channel. We show that $$\chi _S^\kappa $$

χ
S
κ

develops a maximum above the QCD chiral transition, after which it degenerates with $$\chi _P^K$$

χ
P
K

. We also obtain $$\chi _S^\kappa $$

χ
S
κ

within Unitarized Chiral Perturbation Theory (UChPT) at finite temperature, when it is saturated with the $$K_0^*(700)$$

K
0
∗

(
700
)

(or $$\kappa $$
κ
) meson, the dominant lowest-energy state in the isospin 1/2 scalar channel of $$\pi K$$

π
K

scattering. Such UChPT result reproduces the expected peak structure, revealing the importance of thermal interactions, and makes it possible to examine the $$\chi _S^\kappa $$

χ
S
κ

dependence on the light- and strange-quark masses. A consistent picture emerges controlled by the $$m_l/m_s$$

m
l

/

m
s

ratio that allows one studying $$K-\kappa $$

K
-
κ

degeneration in the chiral, two-flavor and SU(3) limits. These results provide an alternative sign for $$O(4)\times U(1)_A$$

O

(
4
)

×
U

(
1
)

A

restoration that can be explored in lattice simulations and highlight the role of strangeness, which regulated by the strange-quark condensate helps to reconcile the current tension among lattice results regarding $$U(1)_A$$

U

(
1
)

A

restoration.
Development of Two Highly-Efficient and Innovative Inspection Schemes for PSC Inspection
Authors: Yan Ran , Zhuge Dan , Wang Shuaian
Published: July 2021

Port state control (PSC) inspection contributes a lot to improving maritime safety and protecting the marine environment. After selecting the ships coming to a port for inspection, one critical challenge faced by the PSC authorities is deciding what deficiency items should be inspected and what the inspection sequence of these items is. To address this problem, two innovative and high-efficient PSC inspection schemes describing specific PSC inspection items and sequence are proposed for the inspectors’ reference when time and resources are limited, especially when there are difficulties in estimating the possible deficiencies in advance. Both schemes take the occurrence probability, inspection cost, and ignoring loss of each deficiency item into account. More specifically, the first inspection scheme is based on the occurrence probabilities of the deficiency items in the whole data set, while the second scheme further considers the correlations among the deficiency items extracted by association rules. The results of numerical experiments show that the efficiency of the two proposed inspection schemes is 1.5 times higher than that of the currently used inspection scheme. In addition, the second inspection scheme performs better than the first inspection scheme, especially with inspecting ships with no less than five deficiency items and limited inspection resources.
ANALYSIS ON WEYL–MARCHAUD FRACTIONAL DERIVATIVE FOR TYPES OF FRACTAL INTERPOLATION FUNCTION WITH FRACTAL DIMENSION
Authors: PRIYANKA T. M. C. , GOWRISANKAR A.
Published: July 2021

In this paper, the Weyl–Marchaud fractional derivative of various fractal interpolation functions (FIFs) like linear FIF, quadratic FIF, hidden variable FIF and [Formula: see text]-FIF is investigated. Further, the fractal dimension of the quadratic FIF is estimated and it is compared with the order of the Weyl–Marchaud fractional derivative. Besides, this paper shows that the Weyl–Marchaud fractional derivative of all FIFs is again FIFs if the order of the fractional derivative meets the necessary condition.
ALGORITHM TO CALCULATE THE FRACTAL DIMENSION AND NUMERICAL INTEGRATION OF FLUCTUATING CONTINUOUS FUNCTIONS
Authors: TABARES-OSPINA HÉCTOR A. , OSORIO MAURICIO
Published: July 2021

For calculating the fractal dimension, the standard method uses a compass that with a specific radius, an arc is drawn to find the points that intercept the curve. An important consideration when making a measurement using the compass or ruler method is to consider the impact of the observer. Most of the rules are read by the user and are therefore very susceptible to misreading or visual errors. The main contribution of this work lies in the development of an algorithm for calculating the fractal dimension of fluctuating continuous functions, over a fractally divided space, from which it is possible to obtain also the integral of the function with an acceptable precision using the trapezoid rule compound.
BLOCKCHAIN CNN DEEP LEARNING EXPERT SYSTEM FOR HEALTHCARE EMERGENCY
Authors: AGUILERA RICARDO CARREÑO , ORTIZ MIGUEL PATIÑO , BANDA ADAN ACOSTA , AGUILERA LUIS ENRIQUE CARREÑO
Published: July 2021

This paper relates to the field of Artificial Intelligence, specifically to image recognition, and provides an innovative method to take advantage of Blockchain Convolutional Neural Networks (BCNNs) in Emotion Recognitions (ERs) using audio–visual emotion patterns to determine a healthcare emergency to be attended. BCNN architectures were used to identify emergency patterns. The results obtained indicate that the proposed method is adequate for the classification and identification of audio–visual patterns using deep learning (DL) with Restricted Boltzmann Machines (RBMs). It is concluded that it is sufficient to consider the audio–visible key features obtained from the patient’s face and voice of the proposed model to recognize a healthcare emergency for immediate action. “Sense of urgency” and “with urgency but with self-control” are the emotion profiles considered for a healthcare emergency, and user personal emotion profiles are stored in the Blockchain ecosystem since they are deemed sensitive data.
The Bounds of Generalized 4-Connectivity of Alternating Group Graphs
Authors: Jin Cheng , Zhang Hai-Yi , Wei Chao
Published: July 2021

Generalized connectivity is a parameter of evaluating the reliability of a network. Let [Formula: see text] be a vertex set of graph [Formula: see text] and [Formula: see text], there is a tree in [Formula: see text] as it connects all vertices of [Formula: see text] which is called a [Formula: see text]-tree. Let [Formula: see text] and [Formula: see text] be two [Formula: see text]-trees in [Formula: see text]. If [Formula: see text] and [Formula: see text] are given, we define that two [Formula: see text]-trees [Formula: see text] and [Formula: see text] are internally disjoint. Then the maximum number of internally disjoint [Formula: see text]-trees in [Formula: see text] is denoted by [Formula: see text]. For an integer [Formula: see text] with [Formula: see text], the generalized [Formula: see text]-connectivity of a graph [Formula: see text] is denoted as [Formula: see text]. In this paper, we study the generalized [Formula: see text]-connectivity of alternating group graphs, denoted by [Formula: see text]. We show the upper bound and the lower bound of [Formula: see text], [Formula: see text] for [Formula: see text], and that [Formula: see text] is given.
Global solutions to the spherically symmetric Einstein-scalar field system with a positive cosmological constant in Bondi coordinates
Authors: Costa João L. , Mena Filipe C.
Published: July 2021

We consider a characteristic initial value problem, with initial data given on a future null cone, for the Einstein (massless) scalar field system with a positive cosmological constant, in Bondi coordinates. We prove that, for small data, this system has a unique global classical solution which is causally geodesically complete to the future and decays polynomially in radius and exponentially in Bondi time, approaching the de Sitter solution.
Construction of excited multi-solitons for the 5D energy-critical wave equation
Authors: Yuan Xu
Published: July 2021

For the 5D energy-critical wave equation, we construct excited [Formula: see text]-solitons with collinear speeds, i.e. solutions [Formula: see text] of the equation such that [Formula: see text] where for [Formula: see text], [Formula: see text] is the Lorentz transform of a non-degenerate and sufficiently decaying excited state, each with different but collinear speeds. The existence proof follows the ideas of Martel–Merle [Construction of multi-solitons for the energy-critical wave equation in dimension 5, Arch. Ration. Mech. Anal. 222(3) (2016) 1113–1160] and Côte–Martel [Multi-travelling waves for the nonlinear Klein–Gordon equation, Trans. Amer. Math. Soc. 370(10) (2018) 7461–7487] developed for the energy-critical wave and nonlinear Klein–Gordon equations. In particular, we rely on an energy method and on a general coercivity property for the linearized operator.
Hybrid Simulation of Turbulent Natural Convection in an Enclosure with Thermally-Conductive Walls
Authors: Nee Alexander , Chamkha Ali J.
Published: July 2021

This paper analyzes the interaction of high Rayleigh number flow with conjugate heat transfer. The two-relaxation time lattice Boltzmann is used as a turbulent buoyancy-driven flow solver whereas the implicit finite difference technique is applied as a heat transfer solver. An in-house numerical code is developed and successfully validated on typical CFD problems. The impact of the Biot number, heat diffusivity ratio and the Rayleigh number on turbulent fluid flow and heat transfer patterns is studied. It is revealed that the thermally-conductive walls of finite thickness reduce the heat transfer rate. The temperature of the cooled wall slightly depends on the value of the buoyancy force. The heat diffusivity ratio has a significant effect on thermal and flow behavior. The Biot number significantly affects the mean Nusselt number at the right solid–fluid interface whereas the mean Nusselt number at the left interface is almost insensible to the Biot number variation.
South Korea’s Strategic Options amid the US–China Strategic Competition Under the Biden Administration
Authors: PARK Hahnkyu , YANG Chun Hee
Published: July 2021

South Korea has so far maintained a “double hedging” strategy—that is, the United States for security, China for economy—in managing its relations with the United States and China. Both Washington and Beijing are recently increasing their pressures on Seoul to join their side, respectively. South Korea needs to re-evaluate its current strategy and adopt a more practical strategy based upon rational calculation of national interests rather than upon political leaders’ ideological beliefs or domestic political considerations.
The FATE Landscape of Sign Language AI Datasets
Authors: Bragg Danielle , Caselli Naomi , Hochgesang Julie A. , Huenerfauth Matt , Katz-Hernandez Leah , Koller Oscar , Kushalnagar Raja , Vogler Christian , Ladner Richard E.
Published: July 2021

Sign language datasets are essential to developing many sign language technologies. In particular, datasets are required for training artificial intelligence (AI) and machine learning (ML) systems. Though the idea of using AI/ML for sign languages is not new, technology has now advanced to a point where developing such sign language technologies is becoming increasingly tractable. This critical juncture provides an opportunity to be thoughtful about an array of Fairness, Accountability, Transparency, and Ethics (FATE) considerations. Sign language datasets typically contain recordings of people signing, which is highly personal. The rights and responsibilities of the parties involved in data collection and storage are also complex and involve individual data contributors, data collectors or owners, and data users who may interact through a variety of exchange and access mechanisms. Deaf community members (and signers, more generally) are also central stakeholders in any end applications of sign language data. The centrality of sign language to deaf culture identity, coupled with a history of oppression, makes usage by technologists particularly sensitive. This piece presents many of these issues that characterize working with sign language AI datasets, based on the authors’ experiences living, working, and studying in this space.
MVGAN: Multi-View Graph Attention Network for Social Event Detection
Authors: Cui Wanqiu , Du Junping , Wang Dawei , Kou Feifei , Xue Zhe
Published: July 2021

Social networks are critical sources for event detection thanks to the characteristics of publicity and dissemination. Unfortunately, the randomness and semantic sparsity of the social network text bring significant challenges to the event detection task. In addition to text, time is another vital element in reflecting events since events are often followed for a while. Therefore, in this article, we propose a novel method named Multi-View Graph Attention Network (MVGAN) for event detection in social networks. It enriches event semantics through both neighbor aggregation and multi-view fusion in a heterogeneous social event graph. Specifically, we first construct a heterogeneous graph by adding the hashtag to associate the isolated short texts and describe events comprehensively. Then, we learn view-specific representations of events through graph convolutional networks from the perspectives of text semantics and time distribution, respectively. Finally, we design a hashtag-based multi-view graph attention mechanism to capture the intrinsic interaction across different views and integrate the feature representations to discover events. Extensive experiments on public benchmark datasets demonstrate that MVGAN performs favorably against many state-of-the-art social network event detection algorithms. It also proves that more meaningful signals can contribute to improving the event detection effect in social networks, such as published time and hashtags.
DataStorm: Coupled, Continuous Simulations for Complex Urban Environments
Authors: Behrens Hans Walter , Candan K. Selçuk , Chen Xilun , Garg Yash , Li Mao-Lin , Li Xinsheng , Liu Sicong , Sapino Maria Luisa , Shadab Md , Turner Dalton , Vijayakumaren Magesh
Published: July 2021

Urban systems are characterized by complexity and dynamicity. Data-driven simulations represent a promising approach in understanding and predicting complex dynamic processes in the presence of shifting demands of urban systems. Yet, today’s silo-based, de-coupled simulation engines fail to provide an end-to-end view of the complex urban system, preventing informed decision-making. In this article, we present
DataStorm
to support integration of existing simulation, analysis and visualization components into integrated workflows.
DataStorm
provides a flow engine,
DataStorm-FE
, for coordinating data and decision flows among multiple actors (each representing a model, analytic operation, or a decision criterion) and enables ensemble planning and optimization across cloud resources.
DataStorm
provides native support for simulation ensemble creation through parameter space sampling to decide which simulations to run, as well as distributed instantiation and parallel execution of simulation instances on cluster resources. Recognizing that simulation ensembles are inherently sparse relative to the potential parameter space, we also present a density-boosting partition-stitch sampling scheme to increase the effective density of the simulation ensemble through a sub-space partitioning scheme, complemented with an efficient stitching mechanism that leverages partial and imperfect knowledge from partial dynamical systems to effectively obtain a global view of the complex urban process being simulated.
A Hybrid CNN-LSTM: A Deep Learning Approach for Consumer Sentiment Analysis Using Qualitative User-Generated Contents
Authors: Jain Praphula Kumar , Saravanan Vijayalakshmi , Pamula Rajendra
Published: July 2021

With the fastest growth of information and communication technology (ICT), the availability of web content on social media platforms is increasing day by day. Sentiment analysis from online reviews drawing researchers’ attention from various organizations such as academics, government, and private industries. Sentiment analysis has been a hot research topic in Machine Learning (ML) and Natural Language Processing (NLP). Currently, Deep Learning (DL) techniques are implemented in sentiment analysis to get excellent results. This study proposed a hybrid convolutional neural network-long short-term memory (CNN-LSTM) model for sentiment analysis. Our proposed model is being applied with dropout, max pooling, and batch normalization to get results. Experimental analysis carried out on Airlinequality and Twitter airline sentiment datasets. We employed the Keras word embedding approach, which converts texts into vectors of numeric values, where similar words have small vector distances between them. We calculated various parameters, such as accuracy, precision, recall, and F1-measure, to measure the model’s performance. These parameters for the proposed model are better than the classical ML models in sentiment analysis. Our results analysis demonstrates that the proposed model outperforms with 91.3% accuracy in sentiment analysis.
Emerging ExG-based NUI Inputs in Extended Realities: A Bottom-up Survey
Authors: Shatilov Kirill A. , Chatzopoulos Dimitris , Lee Lik-Hang , Hui Pan
Published: July 2021

Incremental and quantitative improvements of two-way interactions with e
x
tended realities (XR) are contributing toward a qualitative leap into a state of XR ecosystems being efficient, user-friendly, and widely adopted. However, there are multiple barriers on the way toward the omnipresence of XR; among them are the following: computational and power limitations of portable hardware, social acceptance of novel interaction protocols, and usability and efficiency of interfaces. In this article, we overview and analyse novel natural user interfaces based on sensing electrical bio-signals that can be leveraged to tackle the challenges of XR input interactions. Electroencephalography-based brain-machine interfaces that enable thought-only hands-free interaction, myoelectric input methods that track body gestures employing electromyography, and gaze-tracking electrooculography input interfaces are the examples of electrical bio-signal sensing technologies united under a collective concept of ExG. ExG signal acquisition modalities provide a way to interact with computing systems using natural intuitive actions enriching interactions with XR. This survey will provide a bottom-up overview starting from (i) underlying biological aspects and signal acquisition techniques, (ii) ExG hardware solutions, (iii) ExG-enabled applications, (iv) discussion on social acceptance of such applications and technologies, as well as (v) research challenges, application directions, and open problems; evidencing the benefits that ExG-based Natural User Interfaces inputs can introduce to the area of XR.
Accessible Web Development
Authors: Kearney-Volpe Claire , Hurst Amy
Published: July 2021

There are a growing number of jobs related to web development, yet there is little formal literature about the accessibility of web development with a screen reader. This article describes research to explore (1) web development accessibility issues and their impact on blind learners and programmers; (2) tools and strategies used to address issues; and (3) opportunities for creating inclusive web development curriculum and supportive tools. We conducted a Comprehensive Literature Review (CLR) to formulate accessibility issue categories, then interviewed 12 blind programmers to validate and expand on both issues in education and practice. The CLR yielded five issue categories: (1) visual information without an accessible equivalent, (2) orienting, (3) navigating, (4) lack of support, and (5) knowledge and use of supportive technologies. Our interview findings validated the use of CLR-derived categories and revealed nuances specific to learning and practicing web development. Blind web developers grapple with the inaccessibility of demonstrations and explanations of web design concepts, wireframing software, independent verification of computed Cascading Style Sheets (CSS), and navigating browser-based developer tool interfaces. Tools and strategies include seeking out alternative education materials to learn independently, use of CSS frameworks, collaboration with sighted colleagues, and avoidance of design and front-end development. This work contributes to our understanding of accessibility issues specific to web development and the strategies that blind web developers employ in both educational and applied contexts. We identify areas in which greater awareness and application of accessibility best practices are required in Web education, a need to disseminate existing screen reader strategies and accessible tools, and to develop new tools that support Web design and validation of CSS. Finally, this research signals future directions for the development of accessible web curriculum and supportive tools, including solutions that leverage artificial intelligence, tactile graphics, and supportive-online communities of practice.
Algorithmic Techniques for Necessary and Possible Winners
Authors: Chakraborty Vishal , Delemazure Theo , Kimelfeld Benny , Kolaitis Phokion G. , Relia Kunal , Stoyanovich Julia
Published: July 2021

We investigate the practical aspects of computing the necessary and possible winners in elections over incomplete voter preferences. In the case of the necessary winners, we show how to implement and accelerate the polynomial-time algorithm of Xia and Conitzer. In the case of the possible winners, where the problem is NP-hard, we give a natural reduction to Integer Linear Programming (ILP) for all positional scoring rules and implement it in a leading commercial optimization solver. Further, we devise optimization techniques to minimize the number of ILP executions and, oftentimes, avoid them altogether. We conduct a thorough experimental study that includes the construction of a rich benchmark of election data based on real and synthetic data. Our findings suggest that, the worst-case intractability of the possible winners notwithstanding, the algorithmic techniques presented here scale well and can be used to compute the possible winners in realistic scenarios.
A Synopsis Based Approach for Itemset Frequency Estimation over Massive Multi-Transaction Stream
Authors: Wang Guangtao , Cong Gao , Zhang Ying , Hai Zhen , Ye Jieping
Published: July 2021

The streams where multiple transactions are associated with the same key are prevalent in practice, e.g., a customer has multiple shopping records arriving at different time. Itemset frequency estimation on such streams is very challenging since sampling based methods, such as the popularly used reservoir sampling, cannot be used. In this article, we propose a novel
k
-Minimum Value (KMV) synopsis based method to estimate the frequency of itemsets over multi-transaction streams. First, we extract the KMV synopses for each item from the stream. Then, we propose a novel estimator to estimate the frequency of an itemset over the KMV synopses. Comparing to the existing estimator, our method is not only more accurate and efficient to calculate but also follows the downward-closure property. These properties enable the incorporation of our new estimator with existing frequent itemset mining (FIM) algorithm (e.g., FP-Growth) to mine frequent itemsets over multi-transaction streams. To demonstrate this, we implement a KMV synopsis based FIM algorithm by integrating our estimator into existing FIM algorithms, and we prove it is capable of guaranteeing the accuracy of FIM with a bounded size of KMV synopsis. Experimental results on massive streams show our estimator can significantly improve on the accuracy for both estimating itemset frequency and FIM compared to the existing estimators.
SIGSOFT Outstanding Doctoral Dissertation Award
Authors: Shi August
Published: July 2021

As software becomes more important and ubiquitous, high quality software also becomes crucial. We depend on software developers who write the software to also maintain and improve its quality. When developers make changes to software, they rely on continuous integration [6] and regression testing [15] to check that changes do not break existing functionality. Continuous integration (CI) automates the process of building and testing software after every change. The process of running tests on the code after every change is known as regression testing. The goal of regression testing is to allow developers to detect and fix faults early on, ideally the moment the faults are introduced. Regression testing is widely used in both industry and open source, but regression testing suffers from two main challenges: (1) regression testing is costly, and (2) regression test suites often contain flaky tests.
Finding Software License Violations Through Binary Code Clone Detection - A Retrospective
Authors: Hemel Armijn , Kalleberg Karl Trygve , Vermaas Rob , Dolstra Eelco
Published: July 2021

Ten years ago, we published the article Finding software license violations through binary code clone detection at the MSR 2011 conference. Our paper was motivated by the tendency of em- bedded hardware vendors to only release binary blobs of their rmware, often violating the licensing terms of open-source soft- ware present inside those blobs. The techniques presented in our paper were designed to accurately identify open-source code hid- den inside binary blobs. Here, we give our perspectives on the impact of our work, both industrially and academically, and re- visit the original problem statement to see what has happened in the eld of open-source compliance in the intervening decade.
Retrieval from software libraries for bug localization
Authors: Rao Shivani , Kak Avinash
Published: July 2021

This retrospective on our 2011 MSR publication starts with the research milieu that led to the work reported in our paper. We brie y review the competing ideas of a decade ago that could be applied to solving the problem of identifying the les in a software library related to a query. We were especially interested in nding out if the more complex text retrieval methods of that time would be e ective in the software context. A surprising conclusion of our paper was that the reality was exactly the opposite: the more traditional simpler methods outperformed the complex methods. In addition to this surprising result, our paper was also the rst to report what was considered at that time a large-scale quantitative evaluation of the IR-based approaches to automatic bug localization. Over the years, such quantitative evaluations have become the norm. We believe that these contributions were largely responsible for the popularity of this paper in the research literature.
miR-511-3p promotes hepatic sinusoidal obstruction syndrome by activating hedgehog pathway via targeting Ptch1
Authors: Yang Li , Xu Xiaoping , Chen Zhiyuan , Zhang Yu , Chen Hui , Wang Xiangyang
Published: July 2021

miR-511-3p is upregulated in HSOS in vivo and in vitro models. miR-511-3p activates the Hedgehog pathway by directly targeting Ptch1. Knockdown of miR-511-3p shows a protective effect against LSEC injury and apoptosis via Hedgehog signaling pathway. Inhibition of Ptch1 reserves the effect of miR-511-3p knockdown on LSEC damage and apoptosis.
Effect of intermittent high-frequency stimulation on muscle velocity recovery cycle recordings
Authors: Hochstrasser Annie , Rodriguez Belén , Söll Nicole , Bostock Hugh , Z’Graggen Werner J.
Published: July 2021

A conditioning impulse in human muscle fibers induces a prolonged phase of increased velocity (also called supernormality) with two phases related to an early and late afterpotential. We investigated the effects of intermittent 37-Hz stimulation on muscle fiber supernormality and found that the early and late phases of supernormality changed differently, and that the late phase may reflect the ionic interactions responsible for the counter-regulation of muscle fatigue.
Channel Access‐Based Joint Optimization of AoI and SINR under Attack: Game Theory and Distributed Approach
Authors: Yang Yaoqi , Wei Xianglin , Xu Renhui , Peng Laixian , Cheng Shuai , Ge Lin
Published: July 2021

This paper focuses on the joint optimization of the Age of Information (AoI) and Signal to Interference plus Noise Ratio‐ (SINR‐) oriented channel access problem under attack in the Wireless Sensor Networks (WSNs). Firstly, to overcome the uncertain, dynamic, and incomplete information constrains, an active probability model and a controlling channel model are proposed for the sensors and the receiving end, respectively. Secondly, to ensure the AoI and SINR of the data generated by the sensors when transmitted under attack, one utility function based on average AoI and SINR is defined. Then, considering the distributed feature of the channel access process, the joint optimization problem is formulated under the game theory structure. Then, a distributed learning algorithm is proposed to reach the Nash Equilibrium (NE) of the game. Finally, simulation results have verified the correctness and effectiveness of the proposed method.
Research on the Scheduling of Tractors in the Major Epidemic to Ensure Spring Ploughing
Authors: Cong Chen , Jianping Hu , Qingkai Zhang , Meng Zhang , Yibai Li , Feng Nan , Guangqiao Cao
Published: July 2021

When the outbreak of COVID-19 began, people could not go out. It was not allowed to provide agricultural machinery services in different places across regions to reduce the flow and gathering of people. Improvement of utilization efficiency of agricultural machinery resources is required through scientific scheduling of agricultural machinery. With seizing the farming season and stabilizing production as the goal, this paper studied the scientific scheduling of tractors within the scope of town and established agricultural machinery operation scheduling model with the minimization of total scheduling cost as the optimization objective. Factors such as farmland area, agricultural machinery, and farmland location information and operating time window are considered in this model to improve the accuracy of the agricultural machinery operation scheduling model. The characteristics of multiple scheduling algorithms are analyzed comprehensively. The scheduling requirements of agricultural machinery operation to ensure spring ploughing are combined to design the agricultural machinery scheduling algorithm based on the SA algorithm. With Hushu Street, Jiangning District, Nanjing City, as an example, a comparative experiment is conducted on the simulated annealing algorithm (SA) designed in this paper and the empirical algorithm and genetic algorithm (GA). The results suggest that the total cost of the scheduling scheme generated by the SA algorithm is 19,042.07 yuan lower than that by the empirical scheduling algorithm and 779.19 yuan lower than that by the genetic algorithm on average. Compared with the GA algorithm, the transfer distance, waiting cost, and delay cost of the SA algorithm are reduced by 11.6%, 100%, and 98.1% on average, indicating that the transfer distance of agricultural machinery in the scheduling scheme generated by the SA algorithm is shorter, so is the waiting and delay time. Meanwhile, it can effectively obtain the near-optimal solution that meets the time window constraint, with good convergence, stability, and adaptability.
Minimum Covariance Determinant-Based Quantile Robust Regression-Type Estimators for Mean Parameter
Authors: Shahzad Usman , Al-Noor Nadia H. , Afshan Noureen , Alilah David Anekeya , Hanif Muhammad , Anas Malik Muhammad
Published: July 2021

Robust regression tools are commonly used to develop regression-type ratio estimators with traditional measures of location whenever data are contaminated with outliers. Recently, the researchers extended this idea and developed regression-type ratio estimators through robust minimum covariance determinant (MCD) estimation. In this study, the quantile regression with MCD-based measures of location is utilized and a class of quantile regression-type mean estimators is proposed. The mean squared errors (MSEs) of the proposed estimators are also obtained. The proposed estimators are compared with the reviewed class of estimators through a simulation study. We also incorporated two real-life applications. To assess the presence of outliers in these real-life applications, the Dixon chi-squared test is used. It is found that the quantile regression estimators are performing better as compared to some existing estimators.
Soil Properties as Affected by Soil Conservation Practices and Soil Depths in Uwite Watershed, Hadero Tunto District, Southern Ethiopia
Authors: Hadaro Mulugeta , Ayele Tuma , Parshotam Datt Sharma , Teshome Rahel
Published: July 2021

Soil is a precious natural resource, forming the basis for sustained ecosystem services for the mankind. Its degradation due to the ever-increasing anthropogenic influence is, however, threatening food security and quality of the environment in many regions. The present investigation was, therefore, undertaken with an objective of assessing the effect of soil and water conservation practices of soil bund and Fanya juu terrace compared to no conservation practice on cultivated soils of Uwite watershed in Hadero Tunto District, southern Ethiopia. Soil sampling sites were selected both from the farm fields, where soil conservation structures were raised for the last 10 years, and from fields where there was no conservation practice. A total of eighteen composite soil samples (3 conservation practices × 2 depths, 0–15 and 15–30 cm, ×3 replicates) were collected and analyzed for different physical and chemical soil parameters. The results were subjected to analysis of variance using the General Linear Model of two-way ANOVA by RCBD with factorial arrangement using SAS software. Both practices, statistically similar to each other, indicated significant improvement in soil parameters compared to no conservation practice in higher content of clay, lower bulk density, higher total porosity, higher moisture content, higher pH, higher soil organic matter, higher percent base saturation, higher cation exchange capacity, and higher amounts of macro- (N, P, K, Ca, Mg, and S) and micronutrients (Fe, Zn, and Cu). The surface soil layer (0–15 cm) was significantly superior to the subsurface layer (15–30 cm) in most of these soil parameters. Both practices of soil bund and Fanya juu terrace merit their continuation on the existing lands and upscaling to other degraded areas for sustained land productivity and socioeconomic-environmental stability in the region
Elevated LINC00909 Promotes Tumor Progression of Ovarian Cancer via Regulating the miR‐23b‐3p/MRC2 Axis
Authors: Yang Xu , Wu Guixia , Yang Fan , He Lang , Xie Xiaohui , Li Lan , Yang Lei , Ma Yulan , Zhang Qin , Chen Jiao , Zou SiYing , Han Qian , Wang Yan , Liu Shuai , Li Juan , Han Bin , Liu KaiJiang
Published: July 2021

Ovarian cancer (OC), the third common gynecologic malignancy, contributes to the most cancer‐caused mortality in women. However, 70% of patients with OC are diagnosed at an advanced stage, of which the 5‐year survival is less than 30%. Long noncoding RNAs (long ncRNAs or lncRNA), a type of RNA with exceeding 200 nucleotides in length but no protein‐coding capability, have been demonstrated to involve the pathogenesis of various cancers and show considerable potential in the diagnosis of OC. In this study, we found that the LINC00909 expression in tumor and serum specimens of OC patients was elevated, determined by real‐time quantitative, and droplet digital PCR. In receiver operating characteristic (ROC) analysis, our results revealed that serum LINC00909 distinguished cancers from normal ovarian tissue with 87.8% of sensitivity and 69.6% of specificity (AUC, 81.2%) and distinguished serous ovarian cancer from normal ovarian tissue with 90.0% of sensitivity and 75.9% of specificity (AUC, 84.5%). Furthermore, we observed that the tumor and serum LINC00909 level was positively associated with the International Federation of Gynecology and Obstetrics (FIGO) stage and the Eastern Cooperative Oncology Group (ECOG) score (reflecting patients’ performance status). Also, patients with low serum LINC00909 level showed a longer overall (hazard ratio, HR = 1.874, p = 0.0004) and progression‐free (HR = 1.656, p = 0.0017) survival. Functional assays indicated that the elevation of LINC00909 expression contributes to cell proliferation, migration, and invasion capability of ovarian cancer cells. Besides, we demonstrated that LINC00909 functions as a competing endogenous RNA (ceRNA) of MRC2 mRNA by sponging miR‐23‐3p, and thereby promotes epithelial‐to‐mesenchymal transition (EMT) of ovarian cancer cells. Therefore, we highlight that the LINC00909/miR‐23b‐3p/MRC2 axis is implicated in the pathogenesis of ovarian cancer, and serum LINC00909 may be a promising biomarker for the diagnosis of OC.
The Effectiveness of Cancer Pain Management in a Tertiary Hospital Outpatient Pain Clinic in Thailand: A Prospective Observational Study
Authors: Wangnamthip Suratsawadee , Panchoowong Skaorat , Donado Carolina , Lobo Kimberly , Phankhongsap Pimporn , Sriveerachai Pinyo , Euasobhon Pramote , Rushatamukayanunt Pranee , Mandee Sahatsa , Zinboonyahgoon Nantthasorn , Berde Charles B.
Published: July 2021

Context. In a previous retrospective study, cancer pain management was effective in 47.5% of a cohort assessed after 3 months in a pain clinic at Siriraj Hospital. New guidelines were established, including a multidisciplinary approach, availability of pain interventions, and palliative care referral. Objectives. The objective was to examine the effectiveness of the updated approach. Methods. With IRB approval, outpatients with cancer were enrolled from January to December 2018. Assessments were recorded at baseline and three consecutive visits (BL, FU1, FU2, and FU3), including Numerical Rating Scale (NRS), the Brief Pain Inventory (BPI), the Edmonton Symptom Assessment System (ESAS), side effects, and analgesic use. The primary outcome was a favorable response, defined as an NRS decrease more than 30% or NRS <4. Secondary outcomes included trends over time in BPI, ESAS, side effects, and analgesic use. Pain response predictors at FU3 were analyzed using logistic regression. Results. Among 150 patients, 72 (48%) completed follow-ups. Of these, 61% achieved a favorable response at FU3. Pain interference diminished at all visits relative to baseline (

p
<
0.05

). Median morphine equivalent daily dosage (MEDD) at BL was 20 mg/day, with a statistically significant, but clinically modest increase to 26.4 mg/day at FU3. Radiation therapy during pain care was a predictor of pain responders. Conclusion. The current Siriraj multidisciplinary approach provided effective relief of pain and stabilization of other cancer-related symptoms. Radiation therapy during pain care can be used to predict pain outcomes. Ongoing improvement domains were identified and considered in the context of cultural, economic, and geographic factors.
Engineering Application of an Identification Method to Shock-Induced Vortex Stability in the Transonic Axial Fan Rotor
Authors: Xue Yan , Ge Ning
Published: July 2021

In the present paper, the steady RANS (Reynolds-Averaged Navier-Stokes) simulations based on our independently developed CFD (Computational Fluid Dynamics) solver NUAA-Turbo 2.0, are carried out to investigate the shock wave/tip leakage vortex (SW/TLV) interaction in two representative transonic axial fan rotors, NASA Rotor 67 and NASA Rotor 37. The intent of this study is mainly to verify if an identification method derived from relevant theories is suitable for shock-induced vortex stability in the real engineering environment. As the additional findings, a universal tip vortex model is established and the characteristics of vortex breakdown or not are also summarized under different load levels. To ensure the prediction accuracy of all numerical methods selected in this research, detailed comparisons are made between computational and experimental results before flow analysis. The excellent agreement between the both indicates that the current code is capable of capturing the dominant secondary flow structures and aerodynamic phenomenon, especially the vortex system in tip region and SW/TLV interaction. It is found that three vortical structures such as tip leakage vortex (TLV), shock-induced vortex (SIV), tip separation vortex (TSV) in addition the tip leakage vortex-induced vortex (TLV-IV, which only occurs when the TLV strength increases to a certain extent) frequently exist near the blade tip and then abstracted as a tip vortex model. A stable TLV after passing through the passage shock is commonly characterized by tight rolling-up, slow deceleration and slight expansion. Conversely, the vortex behaves in a breakdown state. The final verification results show that the above two vortex states can be satisfactorily detected by the theoretical discriminant introduced in this work.
Artificial Intelligence Pulse Coupled Neural Network Algorithm in the Diagnosis and Treatment of Severe Sepsis Complicated with Acute Kidney Injury under Ultrasound Image
Authors: Ying Fu , Chen Shuhua , Pan Guojun , He Zemin
Published: July 2021

The objective of this study was to explore the diagnosis of severe sepsis complicated with acute kidney injury (AKI) by ultrasonic image information based on the artificial intelligence pulse coupled neural network (PCNN) algorithm. In this study, an algorithm of ultrasonic image information enhancement based on the artificial intelligence PCNN was constructed and compared with the histogram equalization algorithm and linear transformation algorithm. After that, it was applied to the ultrasonic image diagnosis of 20 cases of severe sepsis combined with AKI in hospital. The condition of each patient was diagnosed by ultrasound image performance, change of renal resistance index (RRI), ultrasound score, and receiver operator characteristic curve (ROC) analysis. It was found that the histogram distribution of this algorithm was relatively uniform, and the information of each gray level was obviously retained and enhanced, which had the best effect in this algorithm; there was a marked individual difference in the values of RRI. Overall, the values of RRI showed a slight upward trend after admission to the intensive care unit (ICU). The RRI was taken as the dependent variable, time as the fixed-effect model, and patients as the random effect; the parameter value of time was between 0.012 and 0.015,

p
=
0.000
<
0.05

. Besides, there was no huge difference in the ultrasonic score among different time measurements (t = 1.348 and

p
=
0.128
>
0.05

). The area under the ROC curve of the RRI for the diagnosis of AKI at the 2nd day, 4th day, and 6th day was 0.758, 0.841, and 0.856, respectively, which was all greater than 0.5 (

p
<
0.05

). In conclusion, the proposed algorithm in this study could significantly enhance the amount of information in ultrasound images. In addition, the change of RRI values measured by ultrasound images based on the artificial intelligence PCNN was associated with AKI.
Health Monitoring System with Hybrid Laser Sensor Networks and Cloud Computing for Subway Tunnels
Authors: Zhu Fu-guang , Xu Dong-sheng , Tan Rui-shan , Peng Bin , Huang He , Liu Zhuo-wen
Published: July 2021

The settlement and deformation monitoring for existing tunnels would encounter the difficulties in sensor installation and health evaluation in real time. Thus, it is necessary to develop effective methods for settlement and deformation measurements in the existing tunnels. This study proposed a more practical tunnel deformation monitoring theory and developed remote wireless tunnel deformation real‐time monitoring and early warning system based on infrared laser ranging technology and cable sensing technology. The system could monitor the settlement and deformation of the tunnel in real time from a long distance and transmit the deformation data wirelessly in real time; then, three‐dimensional space model of tunnel deformation was established, which intuitively and comprehensively reflected the actual deformation of the tunnel and intelligently identified the deformation. Finally, based on the practice of deformation control during tunnel excavation at Xiaohongshu Station of Wuhan Metro Line 8 of China, the measurement results of this new method were compared with the measurement results of particle image velocimetry technology (PIV), the reliability of the new tunnel deformation monitoring method was proved, the settlement error of each monitoring point was found to be less than 1 mm, and the deformation trend was consistent with the actual deformation. The results of this research could provide useful guidance for tunnel deformation monitoring.
Intelligent Recognition Method of Turning Tool Wear State Based on Information Fusion Technology and BP Neural Network
Authors: Xu Yanwei , Gui Lin , Xie Tancheng
Published: July 2021

The multi‐information data acquisition system of tool wear condition of CNC lathe is built by acquiring the acoustic emission and vibration acceleration signals. The data of acoustic emission and vibration acceleration signals during the process of CNC machine tool processing under the conditions of different tool wear degrees and different cutting conditions are acquired and analyzed using the orthogonal experimental method. The optimum characteristic frequency band of acoustic emission and vibration acceleration signals was extracted by the wavelet envelope decomposition method so as to recognize tool wear condition as the characteristic parameters. The characteristic information of acoustic emission and vibration acceleration signals during the process of CNC machine tool processing was fused. In addition, the intelligent recognition of tool wear condition during the process of machine tool processing was researched.