bluuebunny/crossref_metadata_2025 huggingface dataset:
Categorizing Sexism and Misogyny through Neural Approaches
Authors: Parikh Pulkit , Abburi Harika , Chhaya Niyati , Gupta Manish , Varma Vasudeva
Published: June 2021

Sexism, an injustice that subjects women and girls to enormous suffering, manifests in blatant as well as subtle ways. In the wake of growing documentation of experiences of sexism on the web, the automatic categorization of accounts of sexism has the potential to assist social scientists and policymakers in studying and thereby countering sexism. The existing work on sexism classification has certain limitations in terms of the categories of sexism used and/or whether they can co-occur. To the best of our knowledge, this is the first work on the multi-label classification of sexism of any kind(s).
1
We also consider the related task of misogyny classification. While sexism classification is performed on textual accounts describing sexism suffered or observed, misogyny classification is carried out on tweets perpetrating misogyny. We devise a novel neural framework for classifying sexism and misogyny that can combine text representations obtained using models such as Bidirectional Encoder Representations from Transformers with distributional and linguistic word embeddings using a flexible architecture involving recurrent components and optional convolutional ones. Further, we leverage unlabeled accounts of sexism to infuse domain-specific elements into our framework. To evaluate the versatility of our neural approach for tasks pertaining to sexism and misogyny, we experiment with adapting it for misogyny identification. For categorizing sexism, we investigate multiple loss functions and problem transformation techniques to address the multi-label problem formulation. We develop an ensemble approach using a proposed multi-label classification model with potentially overlapping subsets of the category set. Proposed methods outperform several deep-learning as well as traditional machine learning baselines for all three tasks.
Identifying and Evaluating Anomalous Structural Change-based Nodes in Generalized Dynamic Social Networks
Authors: Wang Huan , Qiao Chunming , Guo Xuan , Fang Lei , Sha Ying , Gong Zhiguo
Published: June 2021

Recently, dynamic social network research has attracted a great amount of attention, especially in the area of anomaly analysis that analyzes the anomalous change in the evolution of dynamic social networks. However, most of the current research focused on anomaly analysis of the macro representation of dynamic social networks and failed to analyze the nodes that have anomalous structural changes at a micro level. To identify and evaluate anomalous structural change-based nodes in generalized dynamic social networks that only have limited structural information, this research considers undirected and unweighted graphs and develops a multiple-neighbor superposition similarity method (

), which mainly consists of a multiple-neighbor range algorithm (

) and a superposition similarity fluctuation algorithm (

).

introduces observation nodes, characterizes the structural similarities of nodes within multiple-neighbor ranges, and proposes a new multiple-neighbor similarity index on the basis of extensional similarity indices. Subsequently,

maximally reflects the structural change of each node, using a new superposition similarity fluctuation index from the perspective of diverse multiple-neighbor similarities. As a result, based on

and

,

not only identifies anomalous structural change-based nodes by detecting the anomalous structural changes of nodes but also evaluates their anomalous degrees by quantifying these changes. Results obtained by comparing with state-of-the-art methods via extensive experiments show that

can accurately identify anomalous structural change-based nodes and evaluate their anomalous degrees well.
Equivariant Adversarial Network for Image-to-image Translation
Authors: Zareapoor Masoumeh , Yang Jie
Published: June 2021

Image-to-Image translation aims to learn an image from a source domain to a target domain. However, there are three main challenges, such as lack of paired datasets, multimodality, and diversity, that are associated with these problems and need to be dealt with. Convolutional neural networks (CNNs), despite of having great performance in many computer vision tasks, they fail to detect the hierarchy of spatial relationships between different parts of an object and thus do not form the ideal representative model we look for. This article presents a new variation of generative models that aims to remedy this problem. We use a trainable transformer, which explicitly allows the spatial manipulation of data within training. This differentiable module can be augmented into the convolutional layers in the generative model, and it allows to freely alter the generated distributions for image-to-image translation. To reap the benefits of proposed module into generative model, our architecture incorporates a new loss function to facilitate an effective end-to-end generative learning for image-to-image translation. The proposed model is evaluated through comprehensive experiments on image synthesizing and image-to-image translation, along with comparisons with several state-of-the-art algorithms.
A Practical Index Structure Supporting Fréchet Proximity Queries among Trajectories
Authors: Gudmundsson Joachim , Horton Michael , Pfeifer John , Seybold Martin P.
Published: June 2021

We present a scalable approach for range and
k
nearest neighbor queries under computationally expensive metrics, like the continuous Fréchet distance on trajectory data. Based on clustering for metric indexes, we obtain a dynamic tree structure whose size is linear in the number of trajectories, regardless of the trajectory’s individual sizes or the spatial dimension, which allows one to exploit low “intrinsic dimensionality” of datasets for effective search space pruning.

Since the distance computation is expensive, generic metric indexing methods are rendered impractical. We present strategies that (i) improve on known upper and lower bound computations, (ii) build cluster trees without any or very few distance calls, and (iii) search using bounds for metric pruning, interval orderings for reduction, and randomized pivoting for reporting the final results.

We analyze the efficiency and effectiveness of our methods with extensive experiments on diverse synthetic and real-world datasets. The results show improvement over state-of-the-art methods for exact queries, and even further speedups are achieved for queries that may return approximate results. Surprisingly, the majority of exact nearest-neighbor queries on real datasets are answered
without any
distance computations.
Retrospective analysis of a STEM outreach event reveals positive influences on student attitudes toward STEM careers but not scientific methodology
Authors: Crawford A. J. , Hays Cassandra L. , Schlichte Sarah L. , Greer Sydney E. , Mallard Halle J. , Singh Ryan M. , Clarke Martina A. , Schiller Alicia M.
Published: June 2021

Substantial, involved, and expensive efforts to promote the dissemination of scientific knowledge and career interest in Science, Technology, Engineering, and Mathematics (STEM) are enthusiastically supported by many scientific, federal, and local organizations. The articulated underlying goals for these efforts include an enhanced public understanding of science and science-related policy, an increased diversity in STEM careers, and an increase in the future STEM workforce. This effort is primarily driven by an underperformance of the United States that includes poor test performance and limited number of students pursuing STEM degrees. Despite this investment, attitudes toward STEM have not notably changed. The goal of this project was to determine students’ attitudes toward STEM in response to a previously established scientific outreach event. This event was used to address three common goals in STEM outreach: STEM literacy, diversity and inclusion, and career preparedness. We found there was a notable difference in the attitudes toward scientific activities and interest in pursuing a “Science Career” after participation in this event. Strikingly, interest in hypothesis development, the keystone of all STEM disciplines, was the least liked of all the activities offered during the event. Our data suggest that events designed to enhance interest in pursuing a STEM career may benefit from different elements compared with events designed to increase understanding of STEM literacy concepts, such as hypothesis development.
Exercise-induced benefits on glucose handling in a model of diet-induced obesity are reduced by concurrent nicotinamide mononucleotide
Authors: Yu Josephine , Laybutt David Ross , Kim Lynn-Jee , Quek Lake-Ee , Wu Lindsay E. , Morris Margaret J. , Youngson Neil A.
Published: June 2021

NMN dampened exercise-induced benefits on glucose handling in diet-induced obesity. NMN administration alongside treadmill exercise enhanced the ratio of antioxidants to prooxidants. We suggest that NMN administration may not be beneficial when NAD+ levels are replete.
Skeletal muscle mitochondrial adaptations induced by long-term cigarette smoke exposure
Authors: Decker Stephen T. , Kwon Oh-Sung , Zhao Jia , Hoidal John R. , Heuckstadt Thomas , Richardson Russell S. , Sanders Karl A. , Layec Gwenael
Published: June 2021

It is unclear whether the exercise intolerance and skeletal muscle mitochondrial dysfunction observed in patients with COPD is due to cigarette smoke exposure, per se, or if they are secondary consequences to inactivity. Herein, while long-term exposure to cigarette smoke induces oxidative stress and an altered skeletal muscle phenotype, cigarette smoke does not directly contribute to mitochondrial dysfunction. With this evidence, we demonstrate the critical role of physical inactivity in cigarette smoke-related skeletal muscle dysfunction.
Role of the CX3CL1-CX3CR1 axis in renal disease
Authors: Hamdan Diana , Robinson Lisa A.
Published: June 2021

Excessive infiltration of immune cells into the kidney is a key feature of acute and chronic kidney diseases. The family of chemokines comprises key drivers of this process. Fractalkine [chemokine (C-X3-C motif) ligand 1 (CX3CL1)] is one of two unique chemokines synthesized as a transmembrane protein that undergoes proteolytic cleavage to generate a soluble species. Through interacting with its cognate receptor, chemokine (C-X3-C motif) receptor 1 (CX3CR1), CX3CL1 was originally shown to act as a conventional chemoattractant in the soluble form and as an adhesion molecule in the transmembrane form. Since then, other functions of CX3CL1 beyond leukocyte recruitment have been described, including cell survival, immunosurveillance, and cell-mediated cytotoxicity. This review summarizes diverse roles of CX3CL1 in kidney disease and potential uses as a therapeutic target and novel biomarker. As the CX3CL1-CX3CR1 axis has been shown to contribute to both detrimental and protective effects in various kidney diseases, a thorough understanding of how the expression and function of CX3CL1 are regulated is needed to unlock its therapeutic potential.
The influence of intermittent hypoxia, obesity, and diabetes on male genitourinary anatomy and voiding physiology
Authors: Abler Lisa L. , O’Driscoll Chelsea A. , Colopy Sara A. , Stietz Kimberly P. Keil , Wang Peiqing , Wang Zunyi , Hartmann Faye , Crader-Smith Stephanie M. , Oellete Jonathan N. , Mehta Vatsal , Oakes Steven R. , Grimes Matthew D. , Mitchell Gordon S. , Baan Mieke , Gallagher Shannon J. , Davis Dawn B. , Kimple Michelle E. , Bjorling Dale E. , Watters Jyoti J. , Vezina Chad M.
Published: June 2021

Metabolic syndrome and obstructive sleep apnea are common in aging men, and both have been linked to urinary voiding dysfunction. Here, we show that metabolic syndrome and intermittent hypoxia (a manifestation of sleep apnea) have individual and combined influences on voiding function and urogenital anatomy in male mice.
Hydrodynamic Performance Improvement of Double-Row Floating Breakwaters by Changing the Cross-Sectional Geometry
Authors: Rajabi Meysam , Ghassemi Hassan
Published: June 2021

This paper is presented to develop the hydrodynamic performance of double-row floating breakwater (FBW) by changing cross-sectional geometry in the high wave periods. The ANSYS-AQWA software is employed for the present calculations, which is a potential-based boundary element method (BEM). The rectangular moored pontoons in the single- and double-row types are selected, and the results of the wave transmission coefficient and response amplitude operator (RAO) are presented and compared. The numerical results showed good agreement with experimental data at different wavelengths, wave height, and the distance between double-row FBWs. Then, the performance results of FBWs for five shapes (rectangular, π-shaped, plus-shaped, triangular-shaped, and box-shaped) in the wave transmission coefficient, RAO, and mooring line tension are presented and compared to each other. The results showed that the plus-shaped FBW has a better performance in reducing wave transmission than other shapes. In waves with long periods, the performance of π-shaped, triangular-shaped, and box-shaped FBWs is reduced, and the rectangular FBW loses its efficiency. Overall, the plus-shaped FBW has preferable performance regarding RAO response, mooring tension, and wave transmission.
Genre Analysis of Research Article Abstracts in English for Academic Purposes Journals: Exploring the Possible Variations across the Venues of Research
Authors: Saidi Mavadat , Talebi Said
Published: June 2021

Research article (RA) abstracts play a pivotal role in settling the fate of the academic papers. Despite the abundance of research on investigating the generic structure of this academic genre, the variations in its move patterns between the two venues within a single area of research seem to have remained untouched. The current study attempted to explore the constituent moves and move patterns of RA abstracts published in a local (Iranian Journal of English for Academic Purposes) and an international (The Journal of English for Academic Purposes) journal in the field of applied linguistics. Drawing on Hyland’s (2000) five-move model, the RA abstracts were analyzed. The results indicated that the abstracts published in the two journals included the conventional moves. Furthermore, the most frequent moves were the purpose, method, and product in the two corpora. Moreover, although the results of the chi-square test pointed to no significant difference in terms of the frequency of the moves in the abstracts, the analysis divulged variations in the move patterns across the two sets of articles. The findings carry pedagogical implications for the academic writing course designers, materials developers, and instructors to enhance the novice researchers’ familiarity with the writing conventions to facilitate their accommodation by the scientific communities.
Driver Fatigue Detection Based on Facial Key Points and LSTM
Authors: Chen Long , Xin Guojiang , Liu Yuling , Huang Junwei
Published: June 2021

In recent years, fatigue driving has been a serious threat to the traffic safety, which makes the research of fatigue detection a hotspot field. Research on fatigue recognition has a great significance to improve the traffic safety. However, the existing fatigue detection methods still have room for improvement in detection accuracy and efficiency. In order to detect whether the driver has fatigue driving, this paper proposes a fatigue state recognition algorithm. The method first uses MTCNN (multitask convolutional neural network) to detect human face, and then DLIB (an open-source software library) is used to locate facial key points to extract the fatigue feature vector of each frame. The fatigue feature vectors of multiple frames are spliced into a temporal feature sequence and sent to the LSTM (long short-term memory) network to obtain a final fatigue feature value. Experiments show that compared with other methods, the fatigue state recognition algorithm proposed in this paper has achieved better results in accuracy. The average accuracy of the proposed method in detecting key points of the face is as high as 93%, and the running time is less than half of the ordinary DLIB method.
Clinical Characteristics and Outcomes of Acute Ischemic Stroke in Patients with Type 2 Diabetes: A Single-Center, Retrospective Study in Southern China
Authors: Chen Minrui , Luo Weiliang , Li Jiming , Cao Kaiyi , Li Xiaohui , Huang Huihong , He Yan
Published: June 2021

Objective. To explore the associations between type 2 diabetes mellitus (DM) and stroke by evaluating the clinical risk factors, characteristics, and outcomes of acute ischemic stroke (AIS) patients with and without type 2 DM. Methods. A total of 1,156 AIS patients (including 410 with type 2 DM (AIS-DM group)) and 746 without type 2 DM (AIS-NDM group)) were included. Patients’ demographics, auxiliary examinations, clinical manifestations, and treatment outcomes were recorded and analyzed. Results. Among the included AIS patients, 35.46% had type 2 DM. The AIS-DM group had less males (59.76% versus 70.64%), less smokers (33.90% versus 41.96%), more patients with hypertension (72.93% versus 63.94%;

p
=
0.002

), higher triglyceride levels (42.93% versus 25.08%;

p
≤
0.01

), and lower total cholesterol (147.06 mg/dl versus 175.31 mg/dl) than the AIS-NDM group. The proportion of patients with large artery atherosclerosis (LAA) in the AIS-DM group was lower (77.56% versus 85.92%;

p
<
0.05

) than that in the AIS-NDM group, and the proportion of patients with small arterial occlusions (SAO) in the AIS-DM group was higher (27.07% versus 13.67%;

p
<
0.05

) than that in the AIS-NDM group. The mean National Institutes of Health Stroke Scale (NIHSS) score at admission in the AIS-DM group was lower than that in the AIS-NDM group (4.39 versus 5.00;

p
=
0.008

), but there was no significant difference in the NIHSS score or the modified Rankin Scale score between the two groups at discharge. A total of 85 AIS patients underwent intravenous thrombolysis treatment with recombinant tissue plasminogen activator (rtPA). The door-to-needle time (DNT) did not differ significantly between the groups (49.39 ± 30.40 min versus 44.25 ± 15.24 min;

p
=
0.433

). In addition, there were no significant differences in the baseline NIHSS score, 7-day NIHSS score, and mRS score at discharge between the groups. After intravenous thrombolysis with rtPA, the AIS-NDM group had better recovery (44.30% versus 29.20%;

p
=
0.017

) and a higher ratio of good treatment outcome at discharge (65.60% versus 54.20%;

p
=
0.762

). Conclusions. Type 2 DM is associated with AIS and its risk factors, such as dyslipidemia and hypertension. Patients in the AIS-DM group had less LAA and smaller arterial occlusions, and DM could exacerbate the short-term clinical outcomes in AIS patients.
Achieving Lightweight Privacy-Preserving Image Sharing and Illegal Distributor Detection in Social IoT
Authors: Deng Tianpeng , Li Xuan , Jin Biao , Chen Lei , Lin Jie
Published: June 2021

The applications of social Internet of Things (SIoT) with large numbers of intelligent devices provide a novel way for social behaviors. Intelligent devices share images according to the groups of their specified owners. However, sharing images may cause privacy disclosure when the images are illegally distributed without owners’ permission. To tackle this issue, combining blind watermark with additive secret sharing technique, we propose a lightweight and privacy-preserving image sharing (LPIS) scheme with illegal distributor detection in SIoT. Specifically, the query user’s authentication information is embedded in two shares of the transformed encrypted image by using discrete cosine transform (DCT) and additive secret sharing technique. The robustness against attacks, such as JPEG attack and the least significant bit planes (LSBs) replacement attacks, are improved by modifying 1/8 of coefficients of the transformed image. Moreover, we adopt two edge servers to provide image storage and authentication information embedding services for reducing the operational burden of clients. As a result, the identity of the illegal distributor can be confirmed by the watermark extraction of the suspicious image. Finally, we conduct security analysis and ample experiments. The results show that LPIS is secure and robust to prevent illegal distributors from modifying images and manipulating the embedded information before unlawful sharing.
Kai-Xin-San Attenuates Doxorubicin-Induced Cognitive Impairment by Reducing Inflammation, Oxidative Stress, and Neural Degeneration in 4T1 Breast Cancer Mice
Authors: Lyu Wenjiao , Ouyang Mingzi , Ma Xiaomeng , Han Tiantian , Pi Dajin , Qiu Shijun
Published: June 2021

Objective. This study explored the potential therapeutic effect and possible mechanism of Kai-Xin-San (KXS) on doxorubicin-induced cognitive impairment in 4T1 breast cancer mice. Methods. A model of chemotherapy-induced cognitive impairment (CICI) was established with the injection of doxorubicin (DOX, 5 mg/kg) at a 7-day interval in a 4T1 breast cancer mouse. KXS was given (1 g/kg) daily by gavage over three weeks starting at the first week while giving DOX. The Morris water maze task was performed to measure the CICI-like behaviors. Oxidative stress markers in the hippocampus, inflammatory cytokines in the serum and hippocampus, Nissl staining, immunofluorescence staining, and analysis for Glial fibrillary acidic protein and ionized calcium-binding adapter molecule 1 of the hippocampus were examined to explore the effect and mechanism of KXS on DOX-induced CICI. Meanwhile, tumor growth and survival time were tested in this study. Results. CICI-like behaviors induced by DOX occurred earlier and were severer than the cognitive impairment induced by the tumor, and the effect of KXS on improving the cognitive impairment was obvious. KXS protected against DOX-induced neuroinflammation by decreasing levels of proinflammatory cytokines interleukin-6, interleukin-12p70, and tumor necrosis factor-alpha in both serum and brain and interleukin-1β in the brain, increasing the anti-inflammatory cytokines interleukin-4 in the serum and interleukin-10 in the hippocampus, and inhibiting the astrocytic hyperplasia and microglial polarization in the hippocampus. KXS reduced neural degeneration and protected against DOX-induced oxidative stress according to decreased malondialdehyde level, increased glutathione level, and enhanced activities of superoxide dismutase, catalase, and glutathione peroxidase. Moreover, KXS recovered the lost body weights after DOX administration and prolonged the survival times of mice. Conclusions. KXS may attenuate DOX-induced cognitive impairment by regulating inflammatory responses and reducing oxidative stress and neural degeneration. These findings also presented the role of KXS in improving the quality of life and prolonging survival time in breast cancer mice that received chemotherapy.
A Fusion Method of Local Path Planning for Mobile Robots Based on LSTM Neural Network and Reinforcement Learning
Authors: Guo Na , Li Caihong , Gao Tengteng , Liu Guoming , Li Yongdi , Wang Di
Published: June 2021

Due to the limitation of mobile robots’ understanding of the environment in local path planning tasks, the problems of local deadlock and path redundancy during planning exist in unknown and complex environments. In this paper, a novel algorithm based on the combination of a long short-term memory (LSTM) neural network, fuzzy logic control, and reinforcement learning is proposed, and uses the advantages of each algorithm to overcome the other’s shortcomings. First, a neural network model including LSTM units is designed for local path planning. Second, a low-dimensional input fuzzy logic control (FL) algorithm is used to collect training data, and a network model (LSTM_FT) is pretrained by transferring the learned method to learn the basic ability. Then, reinforcement learning is combined to learn new rules from the environments autonomously to better suit different scenarios. Finally, the fusion algorithm LSTM_FTR is simulated in static and dynamic environments, and compared to FL and LSTM_FT algorithms, respectively. Numerical simulations show that, compared to FL, LSTM_FTR can significantly improve decision-making efficiency, improve the success rate of path planning, and optimize the path length. Compared to the LSTM_FT, LSTM_FTR can improve the success rate and learn new rules.
Experimental and Numerical Studies on Static Aeroelastic Behaviours of a Forward‐Swept Wing Model
Authors: Ouyang Yan , Zeng Kaichun , Kou Xiping , Gu Yingsong , Yang Zhichun
Published: June 2021

The static aeroelastic behaviours of a flat‐plate forward‐swept wing model in the vicinity of static divergence are investigated by numerical simulations and wind tunnel tests. A medium fidelity model based on the vortex lattice method (VLM) and nonlinear structural analysis is proposed to calculate the displacements of the wing structure with large deformation. Follower forces effect and geometric nonlinearity are considered to calculate the deformation of the wing by finite element method (FEM). In the wind tunnel tests, the divergence dynamic pressure is predicted by the Southwell method, and the static aeroelastic displacement is measured by a photogrammetric method. The results obtained by the medium fidelity model calculations show reasonable agreement with wind tunnel test results. A high fidelity model based on coupled computational fluid dynamics (CFD) and computational structural dynamics (CSD) predicts better results of the wing tip displacement when the freestream dynamic pressure is approaching the divergence dynamic pressure.
Comparative Study of Phytochemical, Antioxidant, and Cytotoxic Activities and Phenolic Content of Syzygium aqueum (Burm. f. Alston f.) Extracts Growing in West Sumatera Indonesia
Authors: Itam Afrizal , Wati Mutia Siska , Agustin Vina , Sabri Nursal , Jumanah Rafika Aris , Efdi Mai
Published: June 2021

Syzygium aqueum, consisting of various fruit colors, is one of the plants that have been used as traditional medicine. This study aims to evaluate and compare phytochemical, antioxidant, and cytotoxic activities and total phenolic content of leaves and stem bark extracts of S. aqueum with pink and red fruits, in order to identify the best extract that can be used as a natural antioxidant. Phytochemical constituents were evaluated qualitatively using chemicals, while cytotoxic activities were identified using the brine shrimp lethality test. Total phenolic content was determined via the Folin–Ciocalteu method. Leaves and stem bark of S. aqueum contained flavonoids, phenolics, and triterpenoids, but the stem bark also contained saponins and alkaloids. Methanol and ethyl acetate extracts of leaves and stem bark were categorized as very powerful antioxidants to DPPH (IC50 9.71–38.69 μg/mL) and hydrogen peroxide (IC50 16.44–44.02 μg/mL), while hexane extracts were inactive. Methanol, ethyl acetate, and hexane extracts of leaves and stem bark were categorized as moderately cytotoxic to A. salina larvae (LC50 104.04–440.65 μg/mL). Comparing leaves and stem barks, antioxidant and cytotoxic activities of stem bark extracts were higher than those of leaves extracts. Total phenolic content of leaves extracts was higher than that of stem bark extracts where the order of total phenolic content progressed from methanol extracts > ethyl acetate extracts > hexane extracts. Therefore, the stem bark of S. aqueum was identified as the better source of natural antioxidant compared with the leaves.
Identifying Incident Causal Factors to Improve Aviation Transportation Safety: Proposing a Deep Learning Approach
Authors: Dong Tianxi , Yang Qiwei , Ebadi Nima , Luo Xin Robert , Rad Paul
Published: June 2021

Aviation is a complicated transportation system, and safety is of paramount importance because aircraft failure often involves casualties. Prevention is clearly the best strategy for aviation transportation safety. Learning from past incident data to prevent potential accidents from happening has proved to be a successful approach. To prevent potential safety hazards and make effective prevention plans, aviation safety experts identify primary and contributing factors from incident reports. However, safety experts’ review processes have become prohibitively expensive nowadays. The number of incident reports is increasing rapidly due to the acceleration of advances in information technologies and the growth of the commercial and private aviation transportation industries. Consequently, advanced text mining algorithms should be applied to help aviation safety experts facilitate the process of incident data extraction. This paper focuses on constructing deep-learning-based models to identify causal factors from incident reports. First, we prepare the data sets used for training, validation, and testing with approximately 200,000 qualified incident reports from the Aviation Safety Reporting System (ASRS). Then, we take an open-source natural language model, which is well trained with a large corpus of Wikipedia texts, as the baseline and fine-tune it with the texts in incident reports to make it more suited to our specific research task. Finally, we build and train an attention-based long short-term memory (LSTM) model to identify primary and contributing factors in each incident report. The solution we propose has multilabel capability and is automated and customizable, and it is more accurate and adaptable than traditional machine learning methods in extant research. This novel application of deep learning algorithms to the incident reporting system can efficiently improve aviation safety.
An Efficient Universal Generating Function‐Based Analyzing Approach for Multistate System with Imperfect Coverage Failure
Authors: Li Zheng , Qin Jinlei
Published: June 2021

A system with more than two states is called a multistate system (MSS), and such systems have already become a general trend in the arena of complex industrial products and/or systems. Fault‐tolerant technology often plays a very important role in improving the reliability of an MSS. However, the existence of imperfect coverage failure (ICF) in a work‐sharing group (WSG) decreases the reliability of MSS. A method is proposed to assess the reliability and sensitivity of an MSS with ICF. The components in a WSG can cooperate so as to improve overall efficiency by increasing performance levels. Using the technique of the universal generating function (UGF), a component’s UGF expression with ICF can be incorporated in two steps. During the computation of the system’s UGF, an algorithm based on matrix (ABM) is developed to reduce the computational complexity. Consequently, indices of reliability can be easily calculated based on the UGF expression of an MSS. Sensitivity analysis can help engineers judge which WSG should be eliminated first under various resource limitations. Examples illustrate and validate this method.
LHC Signals of the Next-to-Lightest Scalar Higgs State of the NMSSM in the
4
τ
Decay Channel
Authors: Almarashi M. M.
Published: June 2021

We study the

a

1

a

1

and

Z

a

1

decay channels of the next-to-lightest CP-even Higgs boson

h

2

of the NMSSM at the LHC, where the

h

2

is produced in gluon fusion. It is found that while the

h

2

discovery is impossible through the latter channel, the former one in the

4
τ

final state is a promising channel to discover the

h

2

with masses up to around 250 GeV at the LHC. Such a discovery of the

h

2

is mostly accompanied with a light

a

1

, which is a clear evidence for distinguishing the NMSSM from the MSSM since such a light

a

1

is impossible in the MSSM.
Transcriptome Profile Analysis of Strawberry Leaves Reveals Flowering Regulation under Blue Light Treatment
Authors: Ye Yuntian , Liu Yongqiang , Li Xiaolong , Chen Qing , Zhang Yong , Luo Ya , Liu Zejing , Wang Yan , Lin Yuanxiu , Zhang Yunting , Wang Xiaorong , Tang Haoru
Published: June 2021

Blue light is an important signal that regulates the flowering of strawberry plants. To reveal the mechanism of early flowering under blue light treatment at the transcriptional regulation level, seedlings of cultivated strawberry (Fragaria × ananassa Duch.) “Benihoppe” were subjected to a white light treatment (WL) and blue light treatment (BL) until their flowering. To detect the expression patterns of genes in response to BL, a transcriptome analysis was performed based on RNA-Seq. The results identified a total of 6875 differentially expressed genes (DEGs) that responded to BL, consisting of 3138 (45.64%) downregulated ones and 3737 (54.36%) upregulated ones. These DEGs were significantly enriched into 98 GO terms and 71 KEGG pathways based on gene function annotation. Among the DEGs, the expression levels of genes that might participate in light signaling (PhyB, PIFs, and HY5) and circadian rhythm (FKF1, CCA1, LHY, and CO) in plants were altered under BL. The BBX transcription factors which responded to BL were also identified. The result showed that the FaBBX29, one of strawberry’s BBX family genes, may play an important role in flowering regulation. Our results provide a timely, comprehensive view and a reliable reference data resource for further study of flowering regulation under different light qualities.
Hydroxide Sludge/Hydrochar-Fe Composite Catalysts for Photo-Fenton Degradation of Dyes
Authors: Izghri Zaina , Enaime Ghizlane , Eouarrat Mehdi , Chahid Lhoussaine , El Gaini Layla , Baçaoui Abdelaziz , Yaacoubi Abdelrani
Published: June 2021

The photo-Fenton oxidation process was employed to degrade methylene blue (MB) using a hydroxide sludge/hydrochar-Fe composite as a catalyst prepared by physical activation of raw hydroxide sludge from a drinking water treatment plant and hydrochar-Fe prepared by hydrothermal carbonization from two-phase olive mill waste. The prepared composite was characterized by XRD, SEM, EDS, ICP, and FT-IR. The effect of major parameters, including pH, H2O2 concentration, and a dose of composite on the removal of MB has been studied. The results indicated that the MB decolorization rate increased with the increase of H2O2 concentration and catalyst addition; however, further increase in H2O2 concentration and catalyst dosage could not result in an increase of MB removal efficiency. A high degradation of 95% was achieved within 150 min under UV light irradiation at natural pH (pH = 5), a catalyst loading of 2.5 g/L, a H2O2 dosage of 14.68 mol/L, and MB concentration of 50 mg/L. Recycling studies show a MB decolorization of 92% after three cycles and the use of the composite for the degradation of another dye (methyl orange) shows a degradation of 99%, demonstrating that this composite is a promising heterogeneous photo-Fenton catalyst for long-term removal of dyes from industrial wastewater.
Enhancing Mixed Traffic Flow Safety via Connected and Autonomous Vehicle Trajectory Planning with a Reinforcement Learning Approach
Authors: Cheng Yanqiu , Chen Chenxi , Hu Xianbiao , Chen Kuanmin , Tang Qing , Song Yang
Published: June 2021

The longitudinal trajectory planning of connected and autonomous vehicle (CAV) has been widely studied in the literature to reduce travel time or fuel consumptions. The safety impact of CAV trajectory planning to the mixed traffic flow with both CAV and human-driven vehicle (HDV), however, is not well understood yet. This study presents a reinforcement learning modeling approach, named Monte Carlo tree search-based autonomous vehicle safety algorithm, or MCTS-AVS, to optimize the safety of mixed traffic flow, on a one-lane roadway with signalized intersection control. Crash potential index (CPI) is defined to quantitively measure the safety performance of the mixed traffic flow. The CAV trajectory planning problem is firstly formulated as an optimization model; then, the solution procedure based on reinforcement learning is proposed. The tree-expansion determination module and rollout termination module are developed to identify and reduce the unnecessary tree expansion, so as to train the model more efficiently towards the desired direction. The case study results showed that the proposed algorithm was able to reduce the CPI by 76.56%, when compared with a benchmark model without any intelligence, and 12.08%, when compared with another benchmark model that the team developed earlier. These results demonstrated the satisfactory performance of the proposed algorithm in enhancing the safety of the mixed traffic flow.
miR-101-3p Contributes to α-Synuclein Aggregation in Neural Cells through the miR-101-3p/SKP1/PLK2 Pathway
Authors: Zhang Min , Liu Wei , Zhang Qingan , Hu Hongfeng
Published: June 2021

Parkinson’s disease (PD) is a neurodegenerative disorder characterized by progressive neuronal loss in different brain regions, including the dopaminergic (DA) neurons of the substantia nigra pars compacta (SNc). The aggregation of α-synuclein (α-Syn) plays an essential role in the progression of PD-related neuron toxicity. In this study, bioinformatic analysis was used to confirm differentially expressed genes between patients with PD and healthy donors. Immunofluorescence was used to study the aggregation of α-Syn. Flow cytometry was used to confirm the apoptosis of neurons. Western blot was used to investigate the underlying mechanism. Coimmunoprecipitation (co-IP) was used to verify the interaction between proteins. Luciferase activity assay was used to confirm the target gene of miRNA. In vitro protein ubiquitination assay was used to ascertain the role of S-phase kinase-associated protein 1 (SKP1) on the ubiquitination processes of polo-like kinase 2 (PLK2). The result indicated that miR-101-3p was overexpressed in the substantia nigra of the postmortem brains of patients with PD. The underlying role was investigated in the SH-SY5Y cell line. The overexpression of α-Syn did not result in toxicity or aggregation. However, the co-overexpression of miR-101-3p and α-Syn promoted aggregation and neuron toxicity. Luciferase activity assay indicated that SKP1 is a target gene of miR-101-3p. The co-IP experiment confirmed that SKP1 could directly interact with PLK2. In vitro protein ubiquitination assay confirmed that SKP1 could promote the ubiquitination and subsequent protein degradation of PLK2. We also observed that the cotransfection of short hairpin RNA that targets PLK2 and α-Syn overexpression plasmid results in the endoplasmic reticulum stress of neurons. Our results collectively provide evidence that miR-101-3p contributes to α-Syn aggregation in neurons through the miR-101-3p/SKP1/PLK2 pathway.
Hedyotis diffusa plus Scutellaria barbata Suppress the Growth of Non-Small-Cell Lung Cancer via NLRP3/NF-
κ
B/MAPK Signaling Pathways
Authors: Lv Ya-Xin , Pan Hao-Ran , Song Xin-Ying , Chang Qing-Qi , Zhang Dan-Dan
Published: June 2021

Hedyotis diffusa (HD) plus Scutellaria barbata (SB) have been widely used in antitumor clinical prescribes as one of herb pairs in China. We investigated the effect of aqueous extract from Hedyotis diffusa plus Scutellaria barbata at the equal weight ratio (HDSB11) in inhibiting the growth of murine non-small-cell lung cancer cell (NSCLC) line LLC in vivo and in vitro in this study. Compared with other aqueous extracts, HDSB11 showed the lowest IC50 in inhibiting cell proliferation at 0.43 mg/ml. Besides, HDSB11 effectively suppressed colony formation and induced cell apoptosis. The further assessment of HDSB11 on the murine Lewis-lung-carcinoma-bearing mouse model showed it significantly inhibited tumors’ bioluminescence at the dose of 30 g crude drug/kg. Mechanistically, HDSB11 attenuated the expressions of NLRP3, procaspase-1, caspase-1, PRAP, Bcl-2, and cyclin D1 and downregulated the phosphorylation levels of NF-κB, ERK, JNK, and p38 MAPK. In conclusion, HDSB11 could alleviate cell proliferation and colony formation and induce apoptosis in vitro and tumor growth in vivo, partly via NF-κB and MAPK signaling pathways to suppress NLRP3 expression.
Top‐Tier Maternity and Children Specialist Smart Medical Care and Innovative 5G Outpatient Service Model
Authors: Guo ChaoRong , Zhu Hui , Zhou LiHua , Liu YouQiong
Published: June 2021

Purposes. Under the influence of big data, 5G technology, and the two‐child policy, we are facing a shortage of medical resources, in short supply, and a substantial increase in demand for maternal and child health services. Medical needs improve patient experience, improve patient satisfaction, and apply smart medical care and 5G new model. Methods. Retrospective research methods and a large number of observation methods were used, and patient medical experience was improved through smart medical WeChat services, 5G technology, MDT (Multidisciplinary Team), one‐stop services, and other measures. Research Results. Smart medicine and 5G models have effectively improved patient satisfaction and medical experience. Conclusions. Smart medicine meets the medical needs of women and children. 5G technology makes the medical model efficient and convenient and at the same time enhances the hospital’s core competitiveness.
Study on the Production Mode of Horizontal Well from Water Huff and Puff to Segmented Injection and Production in Tight Reservoir
Authors: Di Shiying , Cheng Shiqing , Wei Cao , Bai Wenpeng , Shang Ruyuan , Cao Nai , Zhang Youpeng
Published: June 2021

Water huff and puff in horizontal wells in tight reservoirs has achieved good results in replenishing formation energy. However, after multiple rounds of treatment, a rapid decrease in formation pressure takes place making it difficult to maintain stable production. To improve the oil recovery rate of tight reservoirs, it is imminent to change the development mode. In this work, the stress distribution characteristics at fracture tips were analyzed based on Irwin theory and elastic theory. A model of propagation and closure length of fractures was established based on the propagation mechanism of water injection-induced natural fracture and the energy balance principle of fracture mechanics. Surfactant imbibition experiments were carried out according to the imbibition principle of surfactant system, and the propagation law of natural fractures was described with numerical simulation to analyze the seepage characteristics of dynamic fracture network. On the basis of the above works, alternating water huff and puff into segmented injection and production was proposed according to the distribution law of dynamic fracture network. The developing process of an actual well case by these two developing modes was simulated to predict 18 years of cumulative recovery, pressure distribution, and recovery rate. Results showed that when stress intensity factor exceeds the fracture toughness, the natural fractures will extend along their original directions and get connected, forming an irregular fracture network. The lengths of fractures after propagation and closure will not bring about water channeling for they are far shorter than well and interval spacing. Surfactant could diminish the resistance of boundary layer by reducing the wetting contact angle, ending up with an improvement in imbibition efficiency. Radial displacement and dynamic imbibition occur simultaneously in a dynamic fracture network during the early stage of water injection, while static imbibition mainly occurs during injection shutdown period and well soaking. According to comparison, the swept area of segmented injection and production was larger, ending up with a continuous increase of simulated recovery rate and cumulative recovery. The findings of this study show alternating water huff and puff after to segmented injection and production in fractured tight reservoir can allow full play of dynamic fracture network’s potential and achieve effective enhancement in oil recovery rate.
Stability Analysis and Control Measures of Large-Span Open-Off Cut with Argillaceous Cemented Sandstone Layered Roof
Authors: Zheng Wenxiang , Guo Yujie , Zhi Guojun , Bao Xiankai , Sun Ming , Ren Ruiping , Duan Zhijun , Gao Zhendong
Published: June 2021

This paper is based on the condition where layered argillaceous cemented sandstone as an engineering background is met by the No. 207 fully mechanized working face open-off cut (Wanli No. 1 Coal Mine). Through mechanical theory analysis and field practice, the engineering safety problem of the large-span argillaceous cemented sandstone layered open-off cut roof supporting structure was analyzed. The roof caving arch height of the open-off cut roadway in 207 working face was obtained based on the mechanical mechanism of instability and caving of the layered surrounding rock mass roof. The anchor cable suspension and bearing stability of the open-off cut roof were analyzed in terms of the layered beam structure model. Meanwhile, combining with conditions, reasonable and effective support countermeasures and key parameters are proposed for such open-off cut roadway and enhance the actual supporting engineering on-site. These research results could provide engineering reference for an open-off cut roadway with composite roof conditions featured to weak cementation and weak interlayer.
Physical and Mechanical Properties of Deep Oceanic Sediments Cored from the Bottom of Challenger Deep, Mariana Trench
Authors: Dai Xu , Xu Tao , Chen Jian
Published: June 2021

The deep oceanic sediments were collected from the Challenger Deep in the southwestern part of the Mariana Trench. Considering the salt in the pore water, a modified method for determining the physical and mechanical properties of the deep-sea sediments was proposed, by which the geological engineering indices were measured and corrected. Through the scanning electronic microscope (SEM), the microstructures of the sediments were found to be composed of flocculation, with a large number of diatom debris and empty shells of organism around it. As a consequence, the porosity and compressibility are high; internal friction angle and cohesion are low. Besides high water content, high porosity, high liquid limit, high plasticity, high consolidation coefficient, low compressive modulus, low shear strength, low density, and low specific gravity, the deep-sea mining machine may slip and subside. This research can improve the understanding of the deep-sea sedimentary environment of the Challenger Deep in the southwestern part of the Mariana Trench and provide an essential reference for the parameter calibration as well as the basis for walking-characteristic study and optimization design of the deep-sea mining vehicle.