bluuebunny/crossref_metadata_2025 huggingface dataset:
Context-Aware Code Change Embedding for Better Patch Correctness Assessment
Authors: Lin Bo , Wang Shangwen , Wen Ming , Mao Xiaoguang
Published: May 2022

Despite the capability in successfully fixing more and more real-world bugs, existing
Automated Program Repair (APR)
techniques are still challenged by the long-standing overfitting problem (i.e., a generated patch that passes all tests is actually incorrect). Plenty of approaches have been proposed for
automated patch correctness assessment (APCA
). Nonetheless, dynamic ones (i.e., those that needed to execute tests) are time-consuming while static ones (i.e., those built on top of static code features) are less precise. Therefore, embedding techniques have been proposed recently, which assess patch correctness via embedding token sequences extracted from the changed code of a generated patch. However, existing techniques rarely considered the context information and program structures of a generated patch, which are crucial for patch correctness assessment as revealed by existing studies. In this study, we explore the idea of context-aware code change embedding considering program structures for patch correctness assessment. Specifically, given a patch, we not only focus on the changed code but also take the correlated unchanged part into consideration, through which the context information can be extracted and leveraged. We then utilize the
AST path
technique for representation where the structure information from AST node can be captured. Finally, based on several pre-defined heuristics, we build a deep learning based classifier to predict the correctness of the patch. We implemented this idea as
Cache
and performed extensive experiments to assess its effectiveness. Our results demonstrate that
Cache
can (1) perform better than previous representation learning based techniques (e.g.,
Cache
relatively outperforms existing techniques by

\( \approx \)

6%,

\( \approx \)

3%, and

\( \approx \)

16%, respectively under three diverse experiment settings), and (2) achieve overall higher performance than existing APCA techniques while even being more precise than certain dynamic ones including PATCH-SIM (92.9% vs. 83.0%). Further results reveal that the context information and program structures leveraged by
Cache
contributed significantly to its outstanding performance.
Redesigning Bitcoin’s Fee Market
Authors: Lavi Ron , Sattath Or , Zohar Aviv
Published: May 2022

The Bitcoin payment system involves two agent types: users that transact with the currency and pay fees and miners in charge of authorizing transactions and securing the system in return for these fees. Two of Bitcoin’s challenges are (i) securing sufficient miner revenues as block rewards decrease, and (ii) alleviating the throughput limitation due to a small maximal block size cap. These issues are strongly related as increasing the maximal block size may decrease revenue due to Bitcoin’s pay-your-bid approach. To decouple them, we analyze the “monopolistic auction” [
16
], showing (i) its revenue does not decrease as the maximal block size increases, (ii) it is resilient to an untrusted auctioneer (the miner), and (iii) simplicity for transaction issuers (bidders), as the average gain from strategic bid shading (relative to bidding one’s value) diminishes as the number of bids increases.
Perspective–The Elusive Quantum Anomalous Hall Effect in MnBi2Te4: Materials
Authors: Yan J.-Q.
Published: May 2022

Observation of the quantum anomalous Hall effect (QAHE) in MnBi2Te4 flakes is one of the most exciting results in the study of the intrinsic magnetic topological insulator MnBi2Te4 and related compounds. Reproducing this fascinating result has been found to be challenging. The quality of starting MnBi2Te4 single crystals is believed to be the key factor. An interesting and important question to address is what is the right quality to enable the QAHE. In this perspective, we present possible approaches to tuning the magnetic and topological properties of MnBi2Te4 by using lattice imperfections, strain, stacking sequence, and interactions between the substrate and flakes/films. It is of critical importance to eventually identify the factor(s) responsible for the realization of QAHE. This paper is part of the JES/JSS Joint Focus Issue In Honor of John Goodenough: A Centenarian Milestone
The senescence-associated secretory phenotype in ovarian cancer dissemination
Authors: Veenstra Jacob P. , Bittencourt Lucas Felipe Fernandes , Aird Katherine M.
Published: May 2022

Ovarian cancer is a highly aggressive disease with poor survival rates in part due to diagnosis after dissemination throughout the peritoneal cavity. It is well-known that inflammatory signals affect ovarian cancer dissemination. Inflammation is a hallmark of cellular senescence, a stable cell cycle arrest induced by a variety of stimuli including many of the therapies used to treat patients with ovarian cancer. Indeed, recent work has illustrated that ovarian cancer cells in vitro, mouse models, and patient tumors undergo senescence in response to platinum-based or poly(ADP-ribose) polymerase (PARP) inhibitor therapies, standard-of-care therapies for ovarian cancer. This inflammatory response, termed the senescence-associated secretory phenotype (SASP), is highly dynamic and has pleiotropic roles that can be both beneficial and detrimental in cell-intrinsic and cell-extrinsic ways. Recent data on other cancer types suggest that the SASP promotes metastasis. Here, we outline what is known about the SASP in ovarian cancer and discuss both how the SASP may promote ovarian cancer dissemination and strategies to mitigate the effects of the SASP.
DOCK2 contributes to pulmonary fibrosis by promoting lung fibroblast to myofibroblast transition
Authors: Guo Xia , Adeyanju Oluwaseun , Sunil Christudas , Mandlem Venkatakirankumar , Olajuyin Ayobami , Huang Steven , Chen Shi-You , Idell Steven , Tucker Torry A. , Qian Guoqing
Published: May 2022

Idiopathic pulmonary fibrosis (IPF) is the most common chronic interstitial lung disease and is characterized by progressive scarring of the lung. Transforming growth factor-β (TGF-β) signaling plays an essential role in IPF and drives fibroblast to myofibroblast transition (FMT). Dedicator of cytokinesis 2 (DOCK2) is known to regulate diverse immune functions by activating Rac and has been recently implicated in pleural fibrosis. We now report a novel role of DOCK2 in pulmonary fibrosis development by mediating FMT. In primary normal and IPF human lung fibroblasts (HLFs), TGF-β induced DOCK2 expression concurrent with FMT markers, smooth muscle α-actin (α-SMA), collagen-1, and fibronectin. Knockdown of DOCK2 significantly attenuated TGF-β-induced expression of these FMT markers. In addition, we found that the upregulation of DOCK2 by TGF-β is dependent on both Smad3 and ERK pathways as their respective inhibitors blocked TGF-β-mediated induction. TGF-β also stabilized DOCK2 protein, which contributes to increased DOCK2 expression. In addition, DOCK2 was also dramatically induced in the lungs of patients with IPF and in bleomycin, and TGF-β induced pulmonary fibrosis in C57BL/6 mice. Furthermore, increased lung DOCK2 expression colocalized with the FMT marker α-SMA in the bleomycin-induced pulmonary fibrosis model, implicating DOCK2 in the regulation of lung fibroblast phenotypic changes. Importantly, DOCK2 deficiency also attenuated bleomycin-induced pulmonary fibrosis and α-SMA expression. Taken together, our study demonstrates a novel role of DOCK2 in pulmonary fibrosis by modulating FMT and suggests that targeting DOCK2 may present a potential therapeutic strategy for the prevention or treatment of IPF.
Kir7.1 disease mutant T153I within the inner pore affects K+ conduction
Authors: Beverley Katie M. , Shahi Pawan K. , Kabra Meha , Zhao Qianqian , Heyrman Joseph , Steffen Jack , Pattnaik Bikash R.
Published: May 2022

Inward-rectifier potassium channel 7.1 (Kir7.1) is present in the polarized epithelium, including the retinal pigmented epithelium. A single amino acid change at position 153 in the KCNJ13 gene, a substitution of threonine to isoleucine in the Kir7.1 protein, causes blindness. We hypothesized that the disease caused by this single amino acid substitution within the transmembrane protein domain could alter the translation, localization, or ion transport properties. We assessed the effects of amino acid side-chain length, arrangement, and polarity on channel structure and function. We showed that the T153I mutation yielded a full-length protein localized to the cell membrane. Whole cell patch-clamp recordings and chord conductance analyses revealed that the T153I mutant channel had negligible K+ conductance and failed to hyperpolarize the membrane potential. However, the mutant channel exhibited enhanced inward current when rubidium was used as a charge carrier, suggesting that an inner pore had formed and the channel was dysfunctional. Substituting with a polar, nonpolar, or short side-chain amino acid did not affect the localization of the protein. Still, it had an altered channel function due to differences in pore radius. Polar side chains (cysteine and serine) with inner pore radii comparable to wildtype exhibited normal inward K+ conductance. Short side chains (glycine and alanine) produced a channel with wider than expected inner pore size and lacked the biophysical characteristics of the wild-type channel. Leucine substitution produced results similar to the T153I mutant channel. This study provides direct electrophysiological evidence for the structure and function of the Kir7.1 channel’s narrow inner pore in regulating conductance.
Effect of dopamine receptor-related compounds on naïve common marmosets for auditory steady-state response
Authors: Iwamura Yoshihiro , Nakayama Tatsuo , Matsumoto Atsushi , Ogi Yuji , Yamaguchi Masataka , Kobayashi Atsushi , Matsumoto Kenji , Katsura Yasunori , Konoike Naho , Nakamura Katsuki , Ikeda Kazuhito
Published: May 2022

We measured the effects of dopamine receptor-related compounds on ASSR in marmosets. D1 receptor stimulation increased the phase locking factor (PLF) and evoked power (EP), and reduced the induced power (IP). D2 receptor stimulation increased the IP. D1 and D2 receptor blockers reduced the PLF and EP at 30 Hz. Different modes of action of various drugs related to psychiatric disorders were evaluated by administering antipsychotic drugs to naïve marmosets.
Resolving the time course of visual and auditory object categorization
Authors: Iamshchinina Polina , Karapetian Agnessa , Kaiser Daniel , Cichy Radoslaw M.
Published: May 2022

Object categorization operates on inputs from different sensory modalities, such as vision and audition. This process was mainly studied in vision. Here, we explore auditory object categorization. We show that auditory object category representations can be reliably extracted from EEG signals and, similar to vision, auditory representations initially carry information about individual objects, which is followed by a subsequent representation of the objects’ category membership.
Urinary Kallidinogenase plus rt-PA Intravenous Thrombolysis for Acute Ischemic Stroke: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Authors: Wu Jing , Wu Jiang , Wang Le , Liu Jinmin
Published: May 2022

Purpose. This research is aimed at systematically assessing the safety and effectiveness of intravenous thrombolysis (IVT) with rt-PA plus human urinary kallidinogenase (HUK) for acute ischemic stroke (AIS). Methods. The data were obtained through rigorous searching of both domestic and foreign databases from inception to 2021.7.1. Randomized controlled trials (RCTs) were included for the comparison of the efficacy of IVT plus HUK. The Cochrane risk of bias (RoB) tool and Review Manager software 5.3 were responsible for RoB assessment and statistical analyses, respectively. Results. A total of 18 articles were retrieved, including 2676 AIS patients treated with IVT within the time window. The control group used standardized rt-PA IVT, and the test group added HUK. After 14 days of combined application of HUK, the National Institute of Health Stroke Scale (NIHSS) score was significantly better in moderate stroke patients using the combination treatment versus those with IVT alone (

mean
 
difference
 

MD

=
−
3.13

; 95% confidence intervals (CI): -3.40,-2.86;

P
<
0.00001

); the NIHSS score was also statistically in severe stroke patients with combined treatment than in those with IVT alone, but the degree of recovery of patients varied greatly. After 90 days of treatment, the NIHSS (

MD
=
−
1.93

; 95% CI: -2.51,-1.34;

P
<
0.00001

) and Barthel index (BI) scores (

MD
=
22.23

; 95% CI: 18.96, 25.49;

P
<
0.00001

) of patients plus HUK were significantly better than those of patients with IVT alone, with fewer adverse events during treatment (

Relative
 
Risk
 

RR

=
0.66

; 95% CI: 0.47, 0.92;

P
=
0.02

). Conclusions. For AIS patients with IVT within the time window, HUK plus rt-PA IVT could significantly improve the neurological function recovery after 14 days and the quality of life after 90 days and reduce the adverse reactions of IVT. This trial is registered with CRD42021226975.
[Retracted] Integrated Analysis of lncRNA‐miRNA‐mRNA ceRNA Network in Mixed Dry Eye Disease
Authors: Tang Zhongxia , Zhang Yu , Wu Haoyang , Liu Chunhua , Lian Yunyu , Ling Haocong , Li Xiang , Lu Xiaohe
Published: May 2022

In order to investigate the relationship between inflammation and lncRNA in mixed dry eye disease (DED), this study establishes competitive endogenous RNA (ceRNA) network in mixed DED. Microarray analysis of cornea from mixed DED mice is performed to screen for differences in lncRNA and target genes, and miRNA bioinformatics were predicted based on the ceRNA hypothesis. The ceRNA network, which consists of 96 relationship pairs, is constructed using the top 10 upregulated lncRNAs and all upregulated mRNAs and two pairs of lncRNA‐miRNA‐mRNA pairs (NONMMUT047964.2‐miR‐671‐5p‐Egr‐1andNONMMUT054540.2‐miR‐1934‐5p‐Grm2) are selected for RT‐qRCR verification in mouse corneal epithelial cells under high osmotic pressure and the samples for microarray. Meanwhile, mouse corneal epithelial cell lines (MCECs), transfected siRNA of NONMMUT047964.2 under high osmotic pressure, shows a decrease in apoptosis rate and a decrease in expression of IL‐1β and IL‐6. The experimental results show that the NONMMUT047964.2‐miR‐671‐5p‐Egr‐1 axis may regulate the inflammation and promote the apoptosis of corneal epithelial cells under hypertonic condition.
Risk Assessment of Liver Metastasis in Pancreatic Cancer Patients Using Multiple Models Based on Machine Learning: A Large Population-Based Study
Authors: Li Qinggang , Bai Lu , Xing Jiyuan , Liu Xiaorui , Liu Dan , Hu Xiaobo
Published: May 2022

Background. A more accurate prediction of liver metastasis (LM) in pancreatic cancer (PC) would help improve clinical therapeutic effects and follow-up strategies for the management of this disease. This study was to assess various prediction models to evaluate the risk of LM based on machine learning algorithms. Methods. We retrospectively reviewed clinicopathological characteristics of PC patients from the Surveillance, Epidemiology, and End Results database from 2010 to 2018. The logistic regression, extreme gradient boosting, support vector, random forest (RF), and deep neural network machine algorithms were used to establish models to predict the risk of LM in PC patients. Specificity, sensitivity, and receiver operating characteristic (ROC) curves were used to determine the discriminatory capacity of the prediction models. Results. A total of 47,919 PC patients were identified; 15,909 (33.2%) of which developed LM. After iterative filtering, a total of nine features were included to establish the risk model for LM based on machine learning. The RF showed the most promising results in the prediction of complications among the models (ROC 0.871 for training and 0.832 for test sets). In risk stratification analysis, the LM rate and 5-year cancer-specific survival (CSS) in the high-risk group were worse than those in the intermediate- and low-risk groups. Surgery, radiotherapy, and chemotherapy were found to significantly improve the CSS in the high- and intermediate-risk groups. Conclusion. In this study, the RF model constructed could accurately predict the risk of LM in PC patients, which has the potential to provide clinicians with more personalized clinical decision-making recommendations.
Big Data System for Dragon Boat Rowing Action Training Based on Multidimensional Stereo Vision
Authors: Tao Sun
Published: May 2022

With the rapid advancement of artificial intelligence technology and the widespread use of sensing technology in education, human-computer interaction teaching has gradually developed in sports and education. Traditionally, teachers explain and demonstrate the fundamentals of movements first, then organize exercises, and students gradually consolidate technical movements through repetition. This process requires teachers to repeatedly explain, such that students can develop movement concepts, and to assist students in correcting their movements through practice. Eventually, students can master the dragon boat’s paddling movements. Teachers frequently struggle to observe all of their students’ movements and are therefore unable to correct them in a timely and effective manner. To address the aforementioned issues, this paper proposes a big data system for multidimensional stereo vision training in dragon boat paddling action. The stock price action recognition of dragon boat paddles and the movement of students’ dragon boat paddles are realized through the multidimensional fusion of attention mechanism and spatiotemporal graph convolution. Make judgments to more effectively guide and train students’ paddling movements.
Critical Retrospection of Security Implication in Cloud Computing and Its Forensic Applications
Authors: Raghavendra S. , Srividya Putty , Mohseni Milad , Bhaskar Seelam Ch. Vijaya , Chaudhury Sushovan , Sankaran K. Sakthidasan , Singh Bhupesh Kumar
Published: May 2022

Using cloud computing, businesses can adopt IT without incurring a significant upfront cost. The Internet has numerous benefits, but model security remains a concern, which affects cloud embracing negatively. The security challenge gets too difficult underneath data center, while additional dimensions such as model design, multitenancy, elasticity, and the layers dependency stack have been added to the problem scope. We present a thorough examination of the cloud security challenge in this article. We looked at the issue from the standpoints of network infrastructure: cloud-provided features, cloud consumers, and cloud service delivery methods. Based on this research, we developed an in-depth characterization of the data protection challenge and recommended security solutions that should address the critical aspects of the issue. Cloud computing is an exploding field of research that relies on distributing computing power instead of using dedicated computers or smart devices. Most of the growth in this sector is attributed to the indispensability of electronic and digital gadgets and the shift from a traditional IT subscription model to a unique cloud model. Cloud computing offered a significant danger and difficulty for information system projects, but it also provided them with several possibilities to improve their data processing. It has also been noted that cloud users and consumers do not yet have the necessary forensic skills to detect illegal activity in the cloud. Although the cloud offers potential technological and economic advantages, consumers have been reluctant to adopt it primarily due to security concerns and the difficulty of conducting an appropriate investigation into the cloud. Some study has been done in this area, and strategies for conducting forensic investigations have been proposed. In this research paper, we begin by analyzing the intrusion detection progress made by other academics, and then we analyze and evaluate our conclusions in order to assess the potential difficulties that cloud forensics face based on these findings.
Analysis of Smart Grid Using Multimedia Sensor Networks with Effective Resource Allocation
Authors: Teekaraman Yuvaraja , Kirpichnikova Irina , Manoharan Hariprasath , Kuppusamy Ramya , Radhakrishnan Arun
Published: May 2022

In recent days, for smart grid network updates, video sensors are used where each node needs to be compressed before transmission. This is very much useful for developing countries as in future the smart grid communication process will play a major role in entire society. Therefore, in this article, minimization of power, energy consumption, and delay for wireless video sensor networks have been computed by providing better quality of service at a similar period. Also, the problem of optimizing transmission and delivery rate has been studied. For solving the aforementioned problems, the intuitive migrant algorithm has been implemented for providing better energy consumption. Additionally, modified larvae optimization tool has been integrated for providing better convergence rate, where all the nodes will be compressed at a better rate with necessary quality of service. The simulation results show that by applying algorithms in two folds, and each video sensor node is compressed by satisfying necessary constraints with fast convergence rate.
Medication Rules in Herbal Medicine for Mild Cognitive Impairment: A Network Pharmacology and Data Mining Study
Authors: Chang Z. , Wang Yu-Chun , Tian Danfeng , Hu Wen-Yue , Wang Zhen-Yi , Liu Gan-Lu , Ma Hua-Ping , Hu Yu-Li , Wu Bin , Han Zhen-Yun
Published: May 2022

Background. Although traditional Chinese medicine (TCM) has good efficacy in the treatment of mild cognitive impairment (MCI), especially memory improvement and safety, its substance basis and intervention mechanism are particularly complex and unknown. Therefore, based on network pharmacology and data mining, this study aims to explore the rules, active ingredients and mechanism of TCM in the treatment of MCI. Methods. By searching the GeneCard, OMIM, DisGeNET and DrugBank databases, we obtained the critical targets associated with MCI. We matched the components and herbs corresponding to the important targets in the TCMSP platform. Using Cytoscape 3.7.2 software, we constructed a target-component-herb network and conducted a network topology analysis to obtain the core components and herbs. Molecular docking was used to preliminarily analyze and predict the binding activities and main binding combinations of the core targets and components. Based on the analysis of the properties, flavor and meridian distribution of herbs, the rules of herbal therapy for MCI were summarized. Results. Twenty-eight critical targets were obtained after the screening. Using the TCMSP platform, 492 components were obtained. After standardization, we obtained 387 herbs. Based on the target-composition-herb network analysis, the core targets were ADRB2, ADRA1B, DPP4, ACHE and ADRA1D. According to the screening, the core ingredients were beta-sitosterol, quercetin, kaempferol, stigmasterol and luteolin. The core herbs were matched to Danshen, Yanhusuo, Gancao, Gouteng and Jiangxiang. It was found that the herbs were mainly warm in nature, pungent in taste and liver and lung in meridian. The molecular docking results showed that most core components exhibited strong binding activity to the target combination regardless of the in or out of network combination. Conclusion. The results of this study indicate that herbs have great potential in the treatment of MCI. This study provides a reference and basis for clinical application, experimental research and new drug development of herbal therapy for MCI.
Detection of Botnet Attacks against Industrial IoT Systems by Multilayer Deep Learning Approaches
Authors: Mudassir Mohammed , Unal Devrim , Hammoudeh Mohammad , Azzedin Farag
Published: May 2022

Industry 4.0 is the next revolution in manufacturing technology that is going to change the production and distribution of goods and services within the following decade. Powered by different enabling technologies that are also being developed simultaneously, it has the potential to create radical changes in our societies such as by giving rise to highly-integrated smart cities. The Industrial Internet of Things (IIoT) is one of the main areas of development for Industry 4.0. These IIoT devices are used in mission-critical sectors such as the manufacturing industry, power generation, and healthcare management. However, smart factories and cities can only function when threats to cyber security, data privacy, and information integrity are properly managed. In this regard, securing IIoT devices and their networks is vital to preserving data and privacy. The use of artificial intelligence is an enabler for more secure IIoT systems. In this study, we propose high-performing deep learning models for the classification of botnet attacks that commonly affect IIoT devices and networks. Evaluation of results shows that deep learning models such as the artificial neural network (ANN), the long short-term memory (LSTM), and the gated recurrent unit (GRU) can successfully be used for classifications of IIoT malware attacks with an accuracy of up to

99
%

.
The Performance Research of the Data Augmentation Method for Image Classification
Authors: Zhang Ruirui , Zhou Bolin , Lu Chang , Ma Manzeng
Published: May 2022

To collect full-labeled data is a challenge problem for learning classifiers. Nowadays, the general tendency of developing a model is becoming larger to be able to obtain more potential capacity to effectively predict unknown instances. However, imbalanced datasets still are not able to meet the needs for training a robustness classifier. A convincing guidance to extract invariance features from images is training in augmented input datasets. However, selecting a proper way to generate synthetic samples from a larger quality of feasible augmentation methods is still a big challenge. In the paper, we use three types of datasets and investigate the merits and demerits of five image transformation methods—color manipulate methods (color and contrast) and traditional affine transformation (shift, rotation, and flip). We found a common experiment result that plausible color transformation methods perform worse against traditional affine transformations in solving the overfitting problem and improve the classification accuracy.
Assessment and Mitigation of Cardiovascular Risk for Prostate Cancer Patients: A Review of the Evidence
Authors: Davey Patrick , Alexandrou Kyriacos
Published: May 2022

Background. Cardiovascular disease (CVD) is a common comorbidity in patients with prostate cancer. In this review, we summarize the published literature on the association of cardiovascular risk with androgen deprivation therapy (ADT) treatment and explore the potential differences between the gonadotropin-releasing hormone (GnRH) agonists and antagonists and the molecular mechanisms that may be involved. We also provide a practical outlook on the identification of underlying CV risk and explore the different stratification tools available. Results. While not definitive, the current evidence suggests that GnRH antagonists may be associated with lower rates of certain CV events vs agonists, particularly in patients with preexisting CVD. Risk reduction strategies such as lifestyle advice, consideration of ADT modality, and comedications may help to reduce CV risk factors and improve outcomes in prostate cancer patients receiving ADT. Conclusions. Given all the data that is currently available, identification of baseline CV risk factors may be key to risk mitigation in patients with prostate cancer receiving ADT.
A New Multiechelon Mathematical Modeling for Pre‐ and Postdisaster Blood Supply Chain: Robust Optimization Approach
Authors: Ghasemi Peiman , Khalili Hossein Amoozad , Chobar Adel Pourghader , Safavi Soroush , Hejri Farkhondeh Mortaz
Published: May 2022

Disaster management is one of the most important actions to protect the property and lives of the victims. Failure to pay attention to logistical decisions of disaster can have irreversible consequences. Therefore, a multiechelon mathematical model for blood supply chain management in disaster situations is proposed in this research. The proposed supply chain includes supplier, central warehouse, reliable distributor, unreliable distributor, distributor, and affected areas. How the proposed model performs is explained as follows: blood is sent from the supplier to warehouses and distribution centers. Also, the capacity of suppliers is limited. The main objective of the mathematical model is to minimize supply chain costs while maximizing the level of satisfaction in order to meet the demand of the affected area. Hence, this research seeks to decide whether or not to establish a reliable distributor, unreliable distributor, and central warehouse. The amount of blood sent to the centers will also be calculated. One of the contributions of the proposed model is to consider the pre‐ and postdisaster modes simultaneously. Locating and investigating the flow between centers are also the other contributions of this study. Solving the proposed model using a robust optimization approach is another innovation taken into account in this research. The proposed model is solved using robust optimization, and finally, the results indicate the proper performance of the proposed model.
A Huge Mesenteric Lymphangioma Presenting as a Small Bowel Volvulus in a Paediatric Patient: A Case Report
Authors: Thapa Sushma , Sharma Abhinav , Upreti Dipesh , Karki Om Bahadur , Regmi Sudeep , Ghartimagar Dilasma , Ghosh Arnab
Published: May 2022

Lymphangioma is a benign tumor characterized by proliferation of thin-walled lymphatic spaces. Lymphangioma of the small-bowel mesentery is rare, with an incidence of 1 : 250,000, representing less than 1% of all lymphangiomas. The predilection of the tumor is in the head and neck (70%), axillary (20%), and internal organs (10%). They are usually asymptomatic but can cause acute abdominal symptoms due to complications such as volvulus, bleeding, or lymphangioma rupture that require emergent surgery. Here, we report a case of mesenteric lymphangioma (ML) of a small bowel in a paediatric patient who presented with pain abdomen on and off which increased in severity and later had features of subacute intestinal obstruction. He underwent explorative laparotomy, and the mass was excised completely along with the part of small intestine. Pathological analysis of the surgical specimen confirmed the diagnosis of ML of the small intestine. The postoperative recovery was uneventful, and the patient was discharged after ten days of hospital stay. Though benign in nature, ML may cause acute abdominal symptoms that require emergent surgery. Therefore, it has to be kept in differential diagnosis of the acute abdominal condition.
A New Type Iterative Ridge Estimator: Applications and Performance Evaluations
Authors: Karakoca Aydın
Published: May 2022

The usage of the ridge estimators is very common in presence of multicollinearity in multiple linear regression models. The ridge estimators are used as an alternative to ordinary least squares in case of multicollinearity as they have lower mean square error. Choosing the optimal value of the biasing parameter k is vital in ridge regression in terms of bias‐variance trade off. Since the theoretical comparisons among the ridge estimators are not possible, it is general practice to carry out a Monte Carlo study to compare them. When the Monte Carlo designs on the existing ridge estimators are examined, it is seen that the performances of the ridge estimators are only considered for the same level of relationship between the independent variables. However, it is more likely to encounter different levels of relationships between the independent variables in real data sets. In this study, a new type iterative ridge estimator is proposed based on a modified form of the estimated mean square error function. Furthermore, a novel search algorithm is provided to achieve the estimations. The performance of the proposed estimator is compared with that of the ordinary least squares estimator and existing 18 ridge estimators through an extensive Monte Carlo design. In the design of the Monte Carlo, both data generation techniques were taken into account, based on the constant and varying correlation levels between the independent variables. Two illustrative real data examples are presented. The proposed estimator outperforms the existing estimators in the sense of the mean squared error for both data generating types. Moreover, it is also superior with respect to the k‐fold cross‐validation method in the real data examples.
Research on Public Management Application Innovation Based on Spark Big Data Framework
Authors: Liu Zhi
Published: May 2022

Public management service is the key to urban intelligent construction. This paper proposes an analysis method and model based on Spark big data framework and takes resident income, happiness index, urban planning, and ecological environment as the indicators of Spark big data. From the high difficulty of Spark big data cluster analysis of urban public management, we build the index weight by the entropy weight method, optimize the similarity calculation, and achieve the rapid understanding of urban public management. Subsequently, the Spark big data public management platform is applied to the public management of Beijing. The results indicate that the public management platform based on Spark big data framework can improve the public management level of the city and help to build an intelligent city.
Three‐Dimensional Poincaré Plot Analysis for Heart Rate Variability
Authors: Wang Bin , Liu Dewen , Gao Xin , Luo Yunlei
Published: May 2022

For the limitation of Poincaré plot analysis, the three‐dimensional Poincaré plot analysis is proposed to analyze the heart rate variability. Firstly, the Poincaré plot and some classic indicators are briefly discussed. Because the standard analysis method inherently ignores the embedding temporal information of the RR interval time series, the temporal variation of the time series cannot be adequately reflected. Secondly, for the limitation of the Poincaré plot analysis, a three‐dimensional Poincaré plot is presented, which can fully describe the temporal and spatial characteristics of the RR interval time series. Thirdly, we propose the local distribution entropy, which can quantify the temporal and spatial patterns of the scatter points in novel space. Finally, PhysioNet/PhysioBank is applied in this study. The experimental results demonstrate the effectiveness of the three‐dimensional Poincaré plot analysis.
Development Path and Mechanism of Cultural Tourism Integration Based on Deep Learning Mobile Big Data Analysis
Authors: Zhang Renfei , Bai Yang
Published: May 2022

Under the current situation, there are many drawbacks in the integrated development of culture and tourism. Therefore, this paper aims to use big data analysis to study the path and development mechanism of cultural and tourism integration, aiming to explore a road of cultural and tourism integration that is most suitable for domestic development. This paper proposes a big data platform based on deep learning to adapt to domestic cultural data and tourism data. The system can intuitively analyze the domestic cultural tourism industry and display the development of cultural tourism with brief pictures and figures. This article analyzes the performance of the designed system and tests the fusion of cultural tourism data. The experimental results of this paper prove that the stability design of the big data cultural tourism industry analysis platform in this paper meets the requirements, and the platform can be stable at 5 Mb/s for 40–180 G big data transmission. The results of the modeling analysis of the integrated development of cultural tourism can be considered; that is, the cultural tourism industry has made great progress in the process of integration, and the integrated development of these two industries is imperative.
The Design Model of English Graded Teaching Assistant Expert System Based on Improved B/S Three-Tier Structure System
Authors: Tang Jingwei , Deng Yi
Published: May 2022

With the continuous development of Internet technology, the modern education system is becoming increasingly intelligent. The integration of information technology with the traditional curriculum can unlock the potential of modern education, which is essentially a change from the traditional teacher-centered classroom teaching structure. The disadvantage of the traditional education system is that it is difficult to teach students according to their abilities because of the differences in English language standards caused by differences in their knowledge and learning potential. Although tiered teaching is one way of addressing this difference, it can have a negative impact in some ways, and implicit tiered teaching is a great solution to this problem. In addition, artificial intelligence is a science that studies the laws of human intelligence. The application of artificial intelligence technology to develop an expert system for teaching English can effectively solve the issues of insufficient teachers and a single teaching mode in the current education system. This will enable the sharing of teaching resources to effectively improve teaching quality, reduce the burden on teachers, and strengthen student’ ability to learn on their own. This paper introduces the principles of artificial intelligence and the B/S three-tier structure system, analyses the current problems faced by English teaching, and proposes a solution for the implementation of a university English teaching aid system based on the B/S three-tier structure system. This system is based on the teacher English language teaching assistant expert system module, which uses a combination of generative and framework-based knowledge representations of the domain knowledge of English language experts to better enable teachers to understand their students’ mastery of knowledge points. The results of this research show that the system can be used to help teachers understand students’ learning through reasoning and analysis, and to improve students’ interest, learning strategies and learning abilities in English.
Analysis of the Longitudinal-Bending-Torsional Coupled Vibration Mechanism of the Drilling of a Roof Bolter for Mine Support System
Authors: Liu Youyu , Ma Liteng , Li Renjun , Chen Bo , Guo Yongcun
Published: May 2022

The condition of the drilling of a roof bolter for mine support system is complex, and the existing analytical approaches for the vibration of the drilling for oil wells cannot be fully applicable to it. In order to reveal the vibration mechanism of bolt drilling, some models for longitudinal, bending, and torsional coupled vibration were established based on the energy method and finite element method. A global matrix was assembled in the order of units. Taking the drilling condition as the boundary conditions, a reduced-order model was constructed. The simulations for drilling mudstone show that the order of mode shape is positively correlated with the corresponding natural frequency, and the low-order mode shape plays a decisive role in the vibration characteristics of a drill string. The order of causing drill string displacement from large to small is as follows: longitudinal vibration, torsional vibration, and bending vibration. Compared with the bit, the displacements of longitudinal and torsional vibration of the tail of the drill string are decreased by 93.2% and 84.7%, respectively. The perturbed force and reaction torque are the main reasons for inducing the coupled vibration of it. This research provides a theoretical basis for analysing the vibration characteristics of the drilling of roof bolters for mine support systems and developing their absorbers.
11,12-Diacetyl-carnosol Protects SH-SY5Y Cells from Hydrogen Peroxide Damage through the Nrf2/HO-1 Pathway
Authors: Luo Qingyi , Hu Weiyan , Yu Haofei , Zhang Rongping , Chen Xinglong
Published: May 2022

Background. Oxidative stress-induced neurotoxicity plays a key role in Alzheimer’s disease (AD). 11,12-Diacetyl-carnosol (NO.20), an acetylated derivative of carnosol extracted from rosemary, displays a high antioxidative effect in vitro. Purpose. We investigated the neuroprotective effect of NO.20 on H2O2-induced neurotoxicity in human neuroblastoma SH-SY5Y cells and its possible mechanism. Results. We found that NO.20 pretreatment (1 μM for 1 h) had cytoprotective effects and weakened H2O2-induced damage in SH-SY5Y cells by reducing viability loss, apoptotic rate, and reactive oxygen species production. In addition, NO.20 inhibited H2O2-induced mitochondrial dysfunctions: it alleviated mitochondrial membrane potential loss and cytochrome c release, decreased the Bax/Bcl-2 ratio, and reduced caspase-3 expression. NO.20 also downregulated malondialdehyde and upregulated glutathione. Furthermore, NO.20 pretreatment caused the nuclear translocation of the transcription factor NF-E2-related factor 2 (Nrf2), increasing heme oxygenase-1 (HO-1) expression in SH-SY5Y cells. Notably, we found that silencing Nrf2 using small interfering RNA (siRNA) suppressed the NO.20-induced HO-1 expression and abolished the neuroprotective effect of NO.20. Conclusion. These results demonstrate that NO.20 protects SH-SY5Y cells from H2O2-induced neurotoxicity by activating the Nrf2/HO-1 pathway. Thus, the neuroprotective and antioxidative stress effects of NO.20 may make it a promising neuroprotective compound for AD treatment.
A Novel Deep Learning Technique for Higher Education System
Authors: Tang Yali , Liu Lina
Published: May 2022

This study aims to enhance the interest of university classrooms and the learning efficiency of students. From deep learning, gesture recognition algorithms (GRA) and long short-term memory (LSTM) are adopted to build prediction modules. After the system process is completed, a brand-new human-computer interaction (HCI) system is established. The deep learning GRA augments the operation of the recognition algorithm of the pattern matching method created on the previous image segmentation. Its enticements on two merits of gesture estimation are as follows: rapid detection of gesture joint features and convolutional neural network (CNN) image classification, thereby improving GRA. The recognition rates of “take”, “pinch”, and “point” are 98.6%, 99.5%, and 99.4%, respectively. When the data volume of the LSTM network model is less than 10, the prediction accuracy can reach 70%, and the performance is relatively stable. The designed HCI system can better recognize the teacher’s gesture intent in teaching, execute gesture commands, and optimize the interactive teaching method between the teacher and the computer. This system is a derivative form of gamification teaching. While it improves the interest in teaching, it is also of great significance for improving the teaching efficiency of general higher education.
A Regularized Alternating Least-Squares Method for Minimizing a Sum of Squared Euclidean Norms with Rank Constraint
Authors: Soto-Quiros Pablo
Published: May 2022

Minimizing a sum of Euclidean norms (MSEN) is a classic minimization problem widely used in several applications, including the determination of single and multifacility locations. The objective of the MSEN problem is to find a vector

x

such that it minimizes a sum of Euclidean norms of systems of equations. In this paper, we propose a modification of the MSEN problem, which we call the problem of minimizing a sum of squared Euclidean norms with rank constraint, or simply the MSSEN-RC problem. The objective of the MSSEN-RC problem is to obtain a vector

x

and rank-constrained matrices

A

1

,
⋯
,

A

p

such that they minimize a sum of squared Euclidean norms of systems of equations. Additionally, we present an algorithm based on the regularized alternating least-squares (RALS) method for solving the MSSEN-RC problem. We show that given the existence of critical points of the alternating least-squares method, the limit points of the converging sequences of the RALS are the critical points of the objective function. Finally, we show numerical experiments that demonstrate the efficiency of the RALS method.
Preliminary Study on Risk Identification and Assessment Framework for Fusion Radioactive Waste Management
Authors: Guo Dingqing , Wang Jinkai , Chen Chao , Xia Dongqin , Yong Nuo , Ge Daochuan
Published: May 2022

Fusion reactors are expected to be safer, more environmentally friendly, and to have a lower nuclear proliferation risk, compared with other nuclear energy systems. However, it is widely recognized that a large amount of radioactive materials will be produced by a fusion reactor. Therefore, it is important to fully understand the overall radiation risk level of fusion radioactive wastes (radwaste) compared with existing nuclear energy systems. Studies on the treatment of the fusion radwaste have been currently focused on three ultimate options: clearance, recycling, and disposal by activation assessment of radioactive materials from the operation and decommissioning of fusion reactors. However, the radiation risk in the management of fusion radwaste, especially in the final disposal, was seldom studied. Based on the comparative analysis of fusion radioactive waste with ITER and fission reactors (e.g., pressurized water reactor, PWR), this paper tries to discuss how to determine the radiation risk in the process of fusion radwaste management on the premise of the current feasible industrial technology. On this basis, a risk assessment framework for repository disposal under normal degradation and external events is proposed.
Monitoring the Emotional Response to the COVID-19 Pandemic Using Sentiment Analysis: A Case Study in Mexico
Authors: León-Sandoval Edgar , Zareei Mahdi , Barbosa-Santillán Liliana Ibeth , Falcón Morales Luis Eduardo , Pareja Lora Antonio , Ochoa Ruiz Gilberto
Published: May 2022

The world is facing the COVID-19 pandemic, leading to an unprecedented change in the lifestyle routines of millions. Beyond the general physical health, financial, and social repercussions of the pandemic, the adopted mitigation measures also present significant challenges in the population’s mental health and health programs. It is complex for public organizations to measure the population’s mental health in order to incorporate it into their own decision-making process. Traditional survey methods are time-consuming, expensive, and fail to provide the continuous information needed to respond to the rapidly evolving effects of governmental policies on the population’s mental health. A significant portion of the population has turned to social media to express the details of their daily life, rendering this public data a rich field for understanding emotional and mental well-being. This study aims to track and measure the sentiment changes of the Mexican population in response to the COVID-19 pandemic. To this end, we analyzed 760,064,879 public domain tweets collected from a public access repository to examine the collective shifts in the general mood about the pandemic evolution, news cycles, and governmental policies using open sentiment analysis tools. Sentiment analysis polarity scores, which oscillate around -0.15, show a weekly seasonality according to Twitter’s usage and a consistently negative outlook from the population. It also remarks on the increased controversy after the governmental decision to terminate the lockdown and the celebrated holidays, which encouraged the people to incur social gatherings. These findings expose the adverse emotional effects of the ongoing pandemic while showing an increase in social media usage rates of 2.38 times, which users employ as a coping mechanism to mitigate the feelings of isolation related to long-term social distancing. The findings have important implications in the mental health infrastructure for ongoing mitigation efforts and feedback on the perception of policies and other measures. The overall trend of the sentiment polarity is 0.0001110643.