bluuebunny/crossref_metadata_2025 huggingface dataset:
Detrital chromian spinels in the Cretaceous Sindong Group, SE Korea: implications for tectonic emplacement of hydrated mantle peridotites
Authors: Lee Yong Il , Lim Hyoun Soo
Published: May 2022

The Sindong Group (Aptian–Albian), distributed in southeastern Korea, is a fluvio-lacustrine sequence deposited in the NE–SW-trending Nakdong Trough formed in an active continental margin. Sediments were derived from the Yeongnam Massif located west and NW of the Nakdong Trough, where Early Jurassic arc magmatism began in the southern part of the Korean Peninsula. We report mineral chemistry of detrital chromian spinels of first-cycle origin occurring as an accessory mineral in arkose to feldspathic litharenites of the Sindong Group. Chromian spinel grains have Cr/(Cr + Al) atomic ratio between 0.36 and 0.94, Mg/(Mg + Fe
2+
) atomic ratio between 0.12 and 0.74, Fe
3+
/(Fe
3+
 + Al + Cr) value mostly <0.1, and <0.2 wt% TiO
2
; these values are consistent with spinel composition of forearc mantle peridotites, but are different from spinel composition of ultramafic rocks exposed in the southern Korean Peninsula. The results of this study indicate that forearc mantle peridotite or serpentinite was present in the Yeongnam Massif, and its tectonic emplacement is interpreted to have occurred by protrusion through the NE–SW-trending strike-slip fault zone developed in Middle Jurassic–Early Cretaceous time in the Yeongnam Massif and along the border with the adjacent tectonic province to the NW as a result of oblique convergence.

Supplementary material
: Locations of sandstone samples and compositions of spinels are available at
https://doi.org/10.6084/m9.figshare.c.6004354
Marine minerals’ role in future holistic mineral resource management
Authors: Ellefmo Steinar Løve , Aberle Nicole , Hagspiel Verena , Ingulstad Mats , Aasly Kurt
Published: May 2022

Deep marine mineral deposits are mineral deposits that have been formed outside the continental slope. Currently three main types of deposits are generally recognized: polymetallic manganese nodules, massive seafloor sulfides and cobalt-rich crusts. The authors argue that marine mineral resource management must be holistic. Holistic marine mineral management requires a clear understanding of the objectives to be achieved through mining and to assess and ensure a proper balance between costs, risks, potential gains and losses. For decades there have been substantial uncertainties regarding the short- and long-term impact of deep-sea mining on international society and the economy and, more recently, environmental issues have become central to the debate over mining the deep seabed. If deep-sea mining is to play a constructive role in the green transition towards more environmentally robust energy production and e-mobility, more ambitious interdisciplinary research is needed to provide the knowledge needed to devise a holistic approach to management of marine minerals. This includes completing thorough baseline studies in conjunction with geological exploration and devising new means of handling financial and technological uncertainties when making investment decisions and when developing regulatory frameworks.
Convening a Minipublic During a Pandemic: A Case Study of the Oregon Citizens’ Assembly Pilot on COVID-19 Recovery
Authors: Gastil John , Anderson Chris , Black Laura , Burkhalter Stephanie , Han Soo-Hye , Reedy Justin , Richards Robert , Rountree John
Published: May 2022

From July–August, 2020, the nonprofit organization Healthy Democracy convened a seven-week pilot test of an online Citizen Assembly on the state of Oregon's response to the COVID-19 pandemic. This pilot project presented a unique research opportunity, because its organizers had ten years of experience running the Citizens’ Initiative Review, a face-to-face minipublic authorized by the State of Oregon to write voting guides for the wider electorate on ballot measures. This case study compares survey data from the Citizen Assembly pilot with the prior Citizens’ Initiative Reviews and provides analysis and recommendations that could improve the design and execution of future online assemblies.
Computing Coordinated Motion Plans for Robot Swarms: The CG:SHOP Challenge 2021
Authors: Fekete Sándor P. , Keldenich Phillip , Krupke Dominik , Mitchell Joseph S. B.
Published: May 2022

We give an overview of the 2021 Computational Geometry Challenge, which targeted the problem of optimally coordinating a set of robots by computing a family of collision-free trajectories for a set
S
of
n
pixel-shaped objects from a given start configuration to a desired target configuration.
SpotSpam: Intention Analysis–driven SMS Spam Detection Using BERT Embeddings
Authors: Oswald C. , Simon Sona Elza , Bhattacharya Arnab
Published: May 2022

Short Message Service (SMS) is one of the widely used mobile applications for global communication for personal and business purposes. Its widespread use for customer interaction, business updates, and reminders has made it a billion-dollar industry in “Text Marketing.” Along with valid SMS, a tsunami of spam messages also pop up that serve various purposes for the sender and the majority of them are fraudulent. Filtering spam SMS in an accurate manner is a crucial and challenging task that will benefit human lives both mentally and economically. Some of the challenges in the filtering of spam SMS include less number of characters, texts in informal languages, lack of public SMS spam corpus, and so on. Focusing solely on the textual features of the SMS is a major handicap of the existing methods, as it lacks in dynamically adapting to the increasing number of new keywords and jargon. In this article, we develop an intention-based approach of SMS spam filtering that efficiently handles dynamic keywords by focusing on the semantics of the words. We capture both semantic and textual features of the short-text messages based on 13 pre-defined intention labels. Moreover, the contextual embeddings of the texts are generated using various pre-trained NLP (Natural Language Processing) models. Finally, intention scores are computed for the pre-defined labels and a bunch of supervised learning classifiers are employed for filtering as spam or ham. Our approaches are evaluated on the SMS Spam Collection [
24
] benchmark dataset, and extensive experimentation shows interesting results. Our model did remarkably well with an accuracy of 98.07%, Precision and Recall of ∼ 0.97, which is better than few of the existing state-of-the-art alternatives. Though the accuracy of our approach is not the best among other existing approaches, the model is highly stable due to its emphasis on extracting the contextual features from the text through intention labels.
A Novel Graph Indexing Approach for Uncovering Potential COVID-19 Transmission Clusters
Authors: Zhu Xuliang , Huang Xin , Sun Longxu , Liu Jiming
Published: May 2022

The COVID-19 pandemic has caused the society lockdowns and a large number of deaths in many countries. Potential transmission cluster discovery is to find all suspected users with infections, which is greatly needed to fast discover virus transmission chains so as to prevent an outbreak of COVID-19 as early as possible. In this article, we study the problem of potential transmission cluster discovery based on the spatio-temporal logs. Given a query of patient user
q
and a timestamp of confirmed infection

t
q

, the problem is to find all potential infected users who have close social contacts to user
q
before time

t
q

. We motivate and formulate the potential transmission cluster model, equipped with a detailed analysis of transmission cluster property and particular model usability. To identify potential clusters, one straightforward method is to compute all close contacts on-the-fly, which is simple but inefficient caused by scanning spatio-temporal logs many times. To accelerate the efficiency, we propose two indexing algorithms by constructing a multigraph index and an advanced BCG-index. Leveraging two well-designed techniques of spatio-temporal compression and graph partition on bipartite contact graphs, our BCG-index approach achieves a good balance of index construction and online query processing to fast discover potential transmission cluster. We theoretically analyze and compare the algorithm complexity of three proposed approaches. Extensive experiments on real-world check-in datasets and COVID-19 confirmed cases in the United States validate the effectiveness and efficiency of our potential transmission cluster model and algorithms.
Performance Health Index for Complex Cyber Infrastructures
Authors: Sondur Sanjeev , Kant Krishna
Published: May 2022

Most IT systems depend on a set of
configuration variables (CVs)
, expressed as a name/value pair that collectively defines the resource allocation for the system. While the ill effects of misconfiguration or improper resource allocation are well-known, there are no effective
a priori
metrics to quantify the impact of the configuration on the desired system attributes such as performance, availability, etc. In this paper, we propose a

Configuration Health Index (CHI)

framework specifically attuned to the performance attribute to capture the influence of CVs on the performance aspects of the system. We show how
CHI
, which is defined as a configuration scoring system, can take advantage of the domain knowledge and the available (but rather limited) performance data to produce important insights into the configuration settings. We compare the
CHI
with both well-advertised segmented non-linear models and state-of-the-art data-driven models, and show that the
CHI
not only consistently provides better results but also avoids the dangers of a pure data drive approach which may predict incorrect behavior or eliminate some essential configuration variables from consideration.
Personalized Visualization Recommendation
Authors: Qian Xin , Rossi Ryan A. , Du Fan , Kim Sungchul , Koh Eunyee , Malik Sana , Lee Tak Yeon , Ahmed Nesreen K.
Published: May 2022

Visualization recommendation work has focused solely on scoring visualizations based on the underlying dataset, and not the actualuserand their past visualization feedback. These systems recommend the same visualizations for every user, despite that the underlying user interests, intent, and visualization preferences are likely to be fundamentally different, yet vitally important. In this work, we formally introduce the problem ofpersonalized visualization recommendationand present a generic learning framework for solving it. In particular, we focus on recommending visualizations personalized for each individual user based on their past visualization interactions (e.g., viewed, clicked, manually created) along with the data from those visualizations. More importantly, the framework can learn from visualizations relevant to other users, even if the visualizations are generated from completely different datasets. Experiments demonstrate the effectiveness of the approach as it leads to higher quality visualization recommendations tailored to the specific user intent and preferences. To support research on this new problem, we release our user-centric visualization corpus consisting of 17.4k users exploring 94k datasets with 2.3 million attributes and 32k user-generated visualizations.
Degree-Based Graph Invariants of some Chemical Structures for the Treatment of Corona Virus
Authors: Amin Shahid , Ali Haidar , Rehman M. A. , Mousa Abd Allah A. , Andualem Mulugeta
Published: May 2022

Coronavirus is a family of viruses that cause upper respiratory infections in humans. It has several variants, e.g., SARS and MERS, and now, a new variant has been identified in 2019 which is the novel coronavirus disease 2019. The novel coronavirus disease 2019 (COVID-19) first appeared in Wuhan, China, and quickly spread throughout the world. Their incident rate is high in winter or in moderate temperature. Clinically, we identified the virus presence by PCR-based test. Preventive measures and vaccination are the only treatment against coronavirus. Some of them are remdesivir (GS-5734), chloroquine, hydroxychloroquine, theaflavin, ritonavir, and arbidol. A topological index (TI) is a mathematical function that assigns a numerical value to a (molecular) graph and predicts many physical, chemical, biological, thermodynamical, and structural features of that network. In this work, we will calculate a new topological index namely, atom-bond connectivity (ABC) index, Geometrically Arithmetic (GA) index, Sombor index, Multiplicative Sambor index, and its reduced version for the molecular graph of remdesivir (GS-5734), chloroquine, hydroxychloroquine, theaflavin, ritonavir, and arbidol. We also plot our computed results to examine how they were affected by the parameters involved. The results obtained can aid in the design of new medicine for the treatment of COVID-19.
A Case Report of Iatrogenic Bronchial Rupture following Endobronchial Blocker Placement
Authors: Alshoubi Abdalhai , Khan Asma , DeJesus Valerie , Hauck Ellen
Published: May 2022

Physiologists Eduard Pfluger and Claude Bernard first introduced one lung ventilation (OLV) in 1871. Today, it is now a frequently used technique in open or minimally invasive cardiothoracic surgeries. One key benefit of the use of OLV is improved surgical exposure. Historically, lung isolation catheters used under fluoroscopic guidance or a Fogarty catheter were used to achieve OLV. In present times, endobronchial blockers (EBBs) in conjunction with single lumen endotracheal tubes and double lumen endotracheal tubes (DLTs) are used to achieve intraoperative OLV. Some complications of EBBs include mucosal injury, bleeding, bronchial rupture, pneumothorax, malpositioning-induced respiratory arrest, severe hypoxemia, and dislodgement. The incidence of iatrogenic tracheal rupture with single lumen endotracheal intubation is reported to be approximately 0.005%, and with double lumen ETT, the incidence may be between 0.05 and 0.19%. Mortality associated with tracheal rupture with DLTs is approximately 8.8%. Data on airway injury with endobronchial blockers is limited, and reported cases of bronchial perforations with use of EBBs are rare suggesting that EBBs may be the safer option for OLV. In this case report, we will be discussing a case of iatrogenic endobronchial rupture following endobronchial blocker placement.
Computer User Behavior Anomaly Detection Based on K-Means Algorithm
Authors: Yang Yang , Hao Juan , Zhao Jianguang , Chen Cihang , Sun Haoyue
Published: May 2022

As an effective network protection method, computer user behavior anomaly detection can detect unknown attack behaviors. In order to detect user behavior anomalies more efficiently, the authors propose a computer user behavior anomaly detection model based on the K-means algorithm. According to the actual characteristics of single-user behavior, the algorithm uses sliding time window to define transactions and uses the first location strategy to mine behavior patterns. On this basis, the fault-tolerant mode is adopted to compare the current behavior mode with the normal behavior mode, and the anomaly detection results are obtained. Experiments show that using data mining technology, the association rules of user commands, and the mining of sequence patterns, it can effectively discover the user’s behavior pattern, and using sequence matching algorithms such as recursive correlation functions and calculating the similarity between the user’s historical pattern and the current pattern, it provides the possibility to accurately judge user behavior. The following conclusions are obtained through experiments: the model training time is short, the accuracy is high, and it has certain robustness.
Data Analysis of Related Factors of Adolescent Physical Exercise Behavior Based upon Artificial Neural Network Model
Authors: Chen Zhiling , Dai Xinghong , Ren Lili
Published: May 2022

Physical exercise behavior is to protect physical health through scientific physical activity (certain frequency, time, and intensity) in leisure time. The purpose of this paper is to study how to analyze and study the related factors of adolescent physical exercise behavior based on an artificial neural network model and describe the BP learning algorithm. This paper raises the question of the influencing factors of adolescents’ physical exercise behavior. This problem is based on an artificial neural network, so the paper expounds around the concept of artificial neural network and related algorithms and designs and analyzes relevant factors. The experimental results show that among the 4-17-year-old respondents, 45 students have the habit of physical exercise, accounting for 37.5% of the respondents in this age group. Among the 59 people with the habit of physical exercise in this survey, 41 people, accounting for 69.5%, believed that their interest in sports was greatly influenced by their surrounding classmates. There are 40 parents who have one parent who is college or above, accounting for 67.8% and other data. All of them show that the influencing factors of adolescents’ physical exercise behavior come from many aspects.
MAP9 Exhibits Protumor Activities and Immune Escape toward Bladder Cancer by Mediating TGF-β1 Pathway
Authors: Zhang Chong , Han Bing , Guo Yuanyuan , Guan Han , Chen Zhijun , Liu Beibei , Sun Wenyan , Li Wenyong , Sun Wei , Wang Sheng
Published: May 2022

To investigate more potential targets for the treatment of human bladder cancer, quantitative reverse transcription polymerase chain reaction (qRT-PCR) and high-content screening (HCS) analysis were performed, and microtubule-associated protein 9 (MAP9), which had the strongest proliferation inhibition from 809 downregulated genes, has been selected. MAP9 is responsible for bipolar spindle assembly and is involved in the progression of many types of tumors; however, its role in bladder cancer (BC) remains unknown. Expressive levels of MAP9 in BC tissues were determined through immunohistochemistry, and the clinical significance of MAP9 in BC was analyzed. Short hairpin ribonucleic acid- (ShRNA-) MAP9 was used to construct stable MAP9 knockdown BC cell lines. The proliferative abilities of MAP9 were measured through assays in vivo and in vitro, and the migrated and invasive abilities of MAP9 were analyzed via in vitro experiments. Quantitative reverse transcription PCR, western blotting, coimmunoprecipitation (Co-IP), and rescue assays were used to identify downstream targets of MAP9. MAP9 expression increased in the tumor tissues, and its increased level was negatively correlated with prognosis. Further, the loss of MAP9 caused decreased BC cell proliferation via inducing the growth 1/synthesis (G1/S) cell cycle arrest in vitro and slowed tumor growth in vivo. In addition, MAP9 silencing attenuated BC cell migration and invasion. Moreover, we found that the growth 1/synthesis (G1/S) cell cycle-related genes and the epithelial mesenchymal transition (EMT) marker levels decreased after silencing MAP9. Finally, we found that the transforming growth factor beta 1 (TGF-β1) pathway is activated as a mediator for MAP9 to regulate genes related to the G1/S cell cycle and EMT. MAP9 promotes BC progression and immune escape activity through the TGF-β1 pathway and is a potential novel target for therapies of BC.
Learning Representations Using RNN Encoder-Decoder for Edge Security Control
Authors: Guo Wei , Chen Hexiong , Hang Feilu , He Yingjun , Zhang Jun
Published: May 2022

Whitelisting is a widely used method in the security field. However, due to the rapid development of the Internet, the traditional whitelisting method cannot promote the security of increasing Internet access. In recent years, with the success of machine learning in different areas, many researchers focus on the security of Internet access through machine learning methods. The most common form of machine learning is supervised learning. Supervised learning requires a large number of labeled samples, but it is difficult to obtain labeled samples in practical applications. This paper introduced an unsupervised deep learning algorithm based on seq2seq, which combined with the recurrent neural network and the autoencoder structure to realize an intelligent boundary security control mechanism. The main methods proposed in this paper are divided into two parts: data processing and modeling. In the phase of data processing, the access text table was coded with dicts, and all sequences were padded to the maximum. In the modeling phase, the network was optimized according to the principle of minimizing the reconstruction error. From the comparative experiments, the proposed method’s AUC on the public data set reached 0.99, and its performance is better than several classical supervised learning algorithms, proving that the proposed method has an efficient defense against abnormal network access.
Multimedia Automation Access Control of Big Data Open Resources Based on Blockchain
Authors: Zhao Nan , Su Hui , Han QingSheng , Zhao Yan
Published: May 2022

In order to better mine the value of data, the author proposes a research on the automatic access control of big data open resources multimedia based on blockchain and introduces big data access control BBAC-BD (blockchain-based access control mechanism for big data environment). The author designed a strategy management contract based on the Bloom filter, as a probabilistic data structure with extremely high space utilization efficiency and proposed the strategic management contract (PAP CONTRACT) and the strategic decision contract (PDP CONTRACT). In this way, the nontampering, auditability, and verifiability of the access control information are guaranteed; then, the access control method based on smart contracts is adopted to realize the user-driven, whole-process transparent, and dynamic and automatic access control of big data resources. The simulation results show that the greater the ratio of n/k, the better the optimization effect, and the greater the ratio, the lower the corresponding misjudgment rate, but it will also take up more space costs. At the same time, the true value of the false positive rate is generally less than the theoretical value of the false positive rate. When the performance of Hash (strategy to retrieve) is better, the result of Hash distribution is more uniform. Under the condition of m = 3, the misjudgment rate acceptable for the expected use can be achieved, and the increase in the number of Hashes will not bring a significant increase in revenue. Freed from the traditional model of providing access control services based on third parties, solve the problem of transparency of authority judgments; at the same time, through smart contracts, based on the strategy published by the resource owner on the blockchain, realize automatic access control to big data resources; and make the judicial process more flexible and the judgment result more credible. The BBAC-BD mechanism realizes a safe, reliable, and transparent new access control architecture, and it can effectively promote the safe circulation and sharing of big data.
[Retracted] Exploration of ACE‐Inhibiting Peptides Encrypted in Artemisia annua Using In Silico Approach
Authors: Shahid Muhammad Naveed , Zawar Maryam , Jamal Adil , Mohamed Bahaeldeen Babiker , Khalid Sana , Bahwerth Fayez Saeed
Published: May 2022

The renin‐angiotensin system (RAS) is involved in body fluid regulation, but one of its enzymes, angiotensin‐converting enzyme (ACE), indirectly causes hypertension by constricting blood vessels. Autoimmune illness is linked to the increased risk of hypertension and cardiovascular disease. In this study, ACE‐inhibiting peptides were studied from Artemisia annua proteins. In silico hydrolysis of proteins was performed by BIOPEP‐UWM using proteolytic enzymes from plant, microbial, and digestive sources. The physicochemical properties of 1160 peptides were determined using the peptide package of R studio. Di‐ and tripeptides were mostly released with a molecular weight of 170 to 350 Da. PeptideRanker was used to select 16 peptides from a pool of 1160 peptides based on their likelihood of being bioactive. Molecular docking was performed by DS 2020 and AutoDock Vina, which revealed that the stability of the ligand‐receptor complex is due to hydrogen bonding and electrostatic and hydrophobic interactions. Their binding energies ranged from ‐31.81 to ‐20.09 kJ/mol. For drug‐likeness evaluation, an online tool SwissADME was used that follows the ADME rule (absorption, distribution, metabolism, and excretion) to check the pharmacokinetics and drug‐likeness of the compound. In the future, the released peptides can be used to make functional nutraceutical foods against hypertension.
Study on the Spectrum-Effect Correlation of Anti-Inflammatory Active Extract of Sauropus spatulifolius Beille
Authors: Qiu Qin , Jiang Lujuan , Huang Chunying , Yu Lifeng , Zhen Dandan , Ye Meifang , Liu Yuanyuan , Shi Junhao , Liu Xiaofang , Gu Baojun , Zhen Hanshen
Published: May 2022

Sauropus spatulifolius Beille (S. spatulifolius) is a commonly used medicine of the Bourau and Yao nationalities. However, the composition of S. spatulifolius is complex, and simple chemical fingerprints cannot accurately evaluate the relationship between its composition and efficacy. In this study, high-performance liquid chromatography (HPLC) method was used to establish the fingerprint of the ethyl acetate extract of S. spatulifolius. Based on the evaluation of the similarity of chromatographic fingerprints of traditional Chinese medicine, combined with cluster analysis and principal component analysis (PCA), the common peaks of fingerprints were evaluated. The anti-inflammatory effect data were extracted through the dimethylbenzene-induced ear-swelling model in mice. The gray relational analysis (GRA) combined with partial least squares regression (PLSR) was used to study the spectrum-effect correlation of S. spatulifolius. As a result, the HPLC fingerprint of the ethyl acetate extract of S. spatulifolius was established, and 18 common peaks were identified. Except for S6, the other similarities are all above 0.915. The reference substance control method was used to identify two absorption peaks, namely, protocatechuic acid and caffeic acid. The cluster analysis results showed that 10 samples from different origins were grouped into four categories, which was consistent with the PCA results. Ethyl acetate extract of 10 batches of S. spatulifolius could significantly inhibit the ear swelling of mice (

P
<
0.01

). Through GRA, the order of the contribution of each chemical component to the anti-inflammatory efficacy was obtained. The results of PLSR showed that the VIP values of peaks 3, 4, and 12 were greater than 1 and were positively correlated with anti-inflammatory activity. In this study, the HPLC fingerprint of the ethyl acetate extract of S. spatulifolius was established. Through the study of the spectrum-effect correlation, the anti-inflammatory active substance of the ethyl acetate extract of S. spatulifolius was obtained. The anti-inflammatory effect of S. spatulifolius was the result of the joint action of multiple ingredients. This research helps to quickly and accurately discover the active ingredient groups of traditional Chinese medicine and provides new ideas and methods for studying the effective substances of traditional Chinese medicine.
A Dynamic Source Tracing Method for Food Supply Chain Quality and Safety Based on Big Data
Authors: Song Jun , Huo Hong , Li Teng , Chu Lingyun
Published: May 2022

The data of food quality tracing information have a few features, such as wide coverage range, many circulation links, complex data sources, low authenticity, and difficult information sharing. The continuous development of big data technology provides infinite possibilities for the construction of food quality source tracing systems. Currently, there are many studies on the application of food quality source tracing systems; however, most of them are in the field of food quality databases, and few have concerned about its application in the field of big data. Therefore, to fill in this research gap, this paper aimed to study a dynamic source tracing method for food supply chain quality and safety based on big data. At first, this paper summarized the variables of food supply chain quality and safety, constructed a Petri net model and a Bayesian network model for food quality prediction and source tracing, and realized the prediction of food quality features. Then, this paper applied two data analysis and processing methods—the density‐based clustering algorithm and the cosine similarity algorithm—to preliminarily process the collected quality tracing information of each link in the food supply chain and analyzed the influencing factors of food quality. Finally, experimental results proved the effectiveness of the constructed model. Relying on the real‐timeliness and authenticity of big data, this paper guarantees the credibility of the traceable information in the tracking process and improves the accuracy through real‐time stream processing of the updated data, providing unlimited possibilities for the comprehensive tracking of food sources.
International Chinese Postgraduate Students’ Adaptation Strategies for Oral English Communication Practices in Malaysian Higher Education Institutions
Authors: Zhai Xiuwen , Razali Abu Bakar
Published: May 2022

Since establishing the Silk Road initiatives, increasing numbers of Chinese students are going to Malaysia to pursue their postgraduate degrees. As the largest group of international students, numerous research studies have reported that Chinese international postgraduate students face language obstacles in English language communication during their overseas studies in English-medium instruction (EMI) campuses. This qualitative case study supplies in-depth insight into different adaptation strategies utilized by international Chinese postgraduate students to overcome challenges faced in academic communication practices. Qualitative data were collected from three sessions of semistructured in-depth interviews. Informants were six international Chinese mainland postgraduate students (i.e., three masters’ students, three Ph.D. students) from diverse disciplines. The interviews allowed the participants to share their own experiences of utilizing effective adaptation strategies to overcome challenges faced in academic English communication practices in their studies. Their adaptation strategies include enhancing their oral English language communication competence and academic achievement. Thus, this study contributes by providing suggestions to Malaysian higher education institutions seeking measures to improve international service quality.
Role of Traditional Cardiovascular Risk Factors after Initiation of Statin Therapy: A PharmLines Inception Cohort Study
Authors: Steenhuis Dennis , de Vos Stijn , Bos Jens , Hak Eelko
Published: May 2022

Background. Multiple studies and meta-analyses examined the role of traditional risk factors for cardiovascular events in statin treatment-naive patients. Nowadays, millions receive such therapy for the primary prevention of cardiovascular events (CVE). Objective. CVEs still occur in patients on primary preventive statin therapy. Therefore, further risk stratification within these patients is urgently needed. Methods. Using the unique linkage between biomedical data and prescription data from the PharmLines Initiative, we assessed the role of several risk factors used in cardiovascular risk models, using a time-dependent Cox PH model, in the occurrence of drug treatment of CVEs after initiation of statin therapy. Results. Among 602 statin therapy starters, 11% received drug treatment for CVE within an average follow-up period of 832 days. After multivariable modelling, cholesterol levels and blood pressure at baseline were no longer associated, whereas self-reported diabetes and increasing age were highly associated with the outcome when on statin therapy (hazard ratio (HR): 3.01, 95% confidence interval (95% CI): 1.48-6.12 and 1.04; 95% CI: 1.01-1.07, respectively). Males, smokers, and nonadherent patients had increased risks (HR 1.6, 1.12, and 1.18, resp.), though not statistically significant. Conclusion. Drug treatment for CVEs after statin initiation is increased in patients with diabetes type 2, in aged patients, males, smokers, and those with poor adherence, while there was no association with baseline cholesterol levels and blood pressure. These factors should be taken into account during the monitoring of statin therapy and may lead to changes in statin treatment or risk-related lifestyle factors.
Spatial-Temporal Change Trend Analysis of Second-Hand House Price in Hefei Based on Spatial Network
Authors: Yin Zheng , Sun Rui , Bi Yuqing
Published: May 2022

Spatial Markov chain can effectively explore the spatial evolution trend of housing price under the influence of lag factor. This paper uses spatial autocorrelation and spatial Markov to study 353 second-hand houses in Hefei. The results show that (1) the housing price of Hefei urban area presents a situation of “two points and one side,” the high housing price is concentrated in the south and southwest of the urban area, and the price level gradually weakens from south to north, and the housing development shows a north-south differentiation. (2) There is a significant spatial autocorrelation between second-hand housing prices in Hefei. The “high-high” residential price clusters are mainly distributed in Shushan District and Binhu New Area, while the “low-low” residential price clusters are mostly in Yaohai district and its surrounding areas. The number of “low-high” agglomeration and “high-low” agglomeration is small, and the degree of change is not big. (3) Under the influence of different neighborhood environments, the housing prices in urban Area of Hefei show club convergence overall. At the same time, under the short-term influence of the policy, the housing prices of low level and middle and low level are promoted in the same neighborhood environment, while the housing prices of high level and middle and high level are negatively affected.
Optimization and Design of General Machinery Production Line Management Process Based on Intelligent Computing Model
Authors: Li Zhenzu , Yu Yuli , Zeng Junjie , Xiang Liyu , Liu Jiahao , Zeng Haibin
Published: May 2022

Machinery is the most important basic tool for modern times, and it is also an indispensable sharp weapon for creating big country projects. The production line management of general machinery is particularly important. Traditional production line management causes a lot of labor waste, time waste, and low production efficiency, so the efficiency of production line management also determines the quality and output of general machinery. Therefore, it has become an industry consensus to realize intelligent management in the manufacturing process of general machinery. General machinery is not only a complex industrial structure but also has a series of preconditions that are not conducive to production, such as the diversity of parts and the low precision of preparation in the early stage. Therefore, to realize automation in the field of construction machinery manufacturing, it usually faces more challenges. Whether it is traditional production process or automatic production process, the realization of intelligent production line is the primary problem, because both production efficiency and product quality are determined by the efficient production line management efficiency and exquisite process. The workload of production line management is heavy, which is time-consuming, labor-consuming, and expensive. Adhere to the goal of quality first, and how to improve the efficiency of the production line has become the biggest problem at present. Based on the above problems, this paper adopts intelligent computing model to improve the efficiency of production line management and optimize the process. A series of tedious processes from product adoption to final shipment of production lines need intelligent technology to simplify the process, which can effectively improve the production efficiency of general machinery, reduce production costs, and improve the production quality of machinery. Supply chain management theory is used to manage suppliers’ production behavior, so as to reduce costs and improve quality and service, thus improving the competitiveness of batch production lines and enterprises. Advanced manufacturing technology is used to realize automation and flexible production of batch production lines, thus improving the rapid response ability of production lines to market demand.
Research on Skiing Sports Planning and Physical Training Optimization Method Based on Computational Intelligence
Authors: Yang Xingdong , Liu Yayuan , Li Fengqing
Published: May 2022

Skiing tourism is a kind of sports tourism product with high participation and high stimulation. With the construction of ski resorts, more and more people understand and participate in skiing tourism, and skiing tourism has gradually become one of the most popular leisure sports and tourism projects in winter. Most of the researches on skiing at home and abroad are based on experimental tests, and few of them involve theoretical modeling. At present, there is still a lack of a platform to comprehensively analyze and deal with skiing. To solve the above problems, this paper takes skiing as the research object. Starting with the related mechanical problems of skiing, the biomechanical model of skiing is established at the same time, to analyze the problems of various technical indexes in the process of skiing by mechanical calculation. The research involves the fields of multibody system dynamics, numerical optimization, mechanical modeling, skiing technical indexes, and so on. In the second part of this paper, based on the skiing motion mechanics model, physical mechanics formula, and load distribution function calculation of multirigid-body system, the sports problems in skiing process are studied.
Behaviour Detection and Recognition of College Basketball Players Based on Multimodal Sequence Matching and Deep Neural Networks
Authors: Zhang Long
Published: May 2022

This study fuses multimodal sequence matching with a deep neural network algorithm for college basketball player behavior detection and recognition to conduct in-depth research and analysis, analyzing the basic components of basketball technical action videos by studying the practical application of technical actions in professional games and teaching videos from self-published authors of short video platforms. The characteristics of the dataset are also analyzed through literature research related to the basketball action dataset. On the established basketball technical action dataset, combined with the SSD target detection algorithm, the video images with cropping of human motion regions to reduce the size of image frames, generating a basketball technical action dataset based on cropped frames, reduces the amount of network training and improves the efficiency of subsequent action recognition training. In this study, by analyzing the characteristics of basic camera motion, a univariate global motion model is proposed to introduce a quadratic term to accurately express the shaking transformation, while the horizontal and vertical motion are independently represented to reduce the model complexity. Comparative experimental results show that the proposed model achieves a good balance between complexity and accuracy of global motion representation. It is suitable for global motion modeling in behavior recognition applications, laying the foundation for global and local motion estimation. On this basis, the visual feature change pattern of the key area of the scene (basketball area) is combined with the group behavior recognition based on motion patterns and the success-failure classification based on key visual information to achieve basketball semantic event recognition. The experimental results at NCAA show that the fusion of global and local motion patterns can effectively improve group behavior recognition performance. The semantic event recognition algorithm combining motion patterns and video key visual information achieves the best performance.
Analysis of Dynamic Influence Mechanism of Network Public Opinion Based on Simulation Feature Extraction
Authors: Zhang Yang
Published: May 2022

In order to improve the effect of network public opinion management, this paper constructs an intelligent computer network simulation model to analyze the dynamic influence mechanism of network public opinion. This paper converts semistructured text information into a form that is easy for computer analysis and recognition, which facilitates opinion extraction and sentiment analysis. Moreover, by analyzing the text content to be mined, this paper uses a specific algorithm to select different words that best represent the text to be tested as the characteristic words of network public opinion. These network public opinion feature words can classify texts, thereby reducing the complexity of text classification. The experimental study shows that the dynamic influence mechanism analysis model of network public opinion based on intelligent computer network simulation constructed in this paper has a good effect.
Reversible Data Hiding in Encrypted Images Based on Adaptive Gradient Prediction
Authors: Qin Jiaohua , He Zhibin , Xiang Xuyu , Tan Yun
Published: May 2022

The existing reversible data hiding (RDH) technology solves the problem that the ciphertext data and carrier are easily damaged in the traditional scheme, which has become a research hotspot in information hiding. However, because of the small coverage area of the predictor and poor prediction ability of the existing reversible data hiding in encrypted images (RDH-EI) technology based on pixel predictor, the embedding performance of the algorithm is limited. Therefore, this paper proposes an adaptive gradient prediction (AGP) scheme. The AGP employs a comprehensive and efficient local complexity measurement strategy to make predictions based on pixel changes around the predicted pixel, including horizontal, vertical, and diagonal directions. The experimental results show that the AGP-RDHEI scheme has apparent advantages in embedding rate.
Effect of Phosphogypsum Amendment on Chemical Properties of Sodic Soils at Different Incubation Periods
Authors: Hasana Haile , Beyene Sheleme , Kifilu Alemayehu , Kidanu Selamyihun
Published: May 2022

The application of phosphogypsum (PG) on sodic soils provides nutrients to the soil, reduces the toxic effect of Na+, and improves soil properties. Laboratory experiments were performed to evaluate the effects of PG on the chemical properties of sodic soils. The treatments were arranged in a completely randomized design with five replications. The treatments included 0% GR (control), 50% GR (28.18 g·kg−1), 100% GR (56.37 g·kg−1), 150% GR (84.50 g·kg−1), and 200% GR (112.74 g·kg−1) rates that were thoroughly mixed with soil under incubation, whereas PG was mixed with topsoil before leaching at the same application rates under the leaching experiment. Soil and leachate samples from each pot were collected in 7, 14, 21, 28, and 35 days and subjected to spectrometric analysis. Results indicated that there was a highly significant (

p
<
0.001

) effect on soil pH, exchangeable sodium percentage (ESP), available P, exchangeable Na+, and Ca+2 under 35-day incubation compared with control. In a closed incubation system, most of the nutrients were released after 7 days of incubation and inconstantly released after 14 days of incubation. Furthermore, the removal of Na+ and SAR increased in initial leachate collection and decreased with the subsequent application of irrigation water (PV). Because of the high contents of Ca+2 released from PG and the residual effect of H2SO4, soil pH and ESP were rapidly reduced compared with control. Post leachate analysis also revealed that available P and extractable S-SO4−2 were significantly (

p
<
0.001

) increased in soil solutions. However, available P was decreased during incubations compared with the value of post leachate analysis. During a closed incubation, displaced Na+ replaces Ca+2 on exchange sites, resulting in increased Ca-P precipitation. Thus, the combined application of PG and irrigation water in 7 to 14 days would allow chemical reaction with the soils and reduce sodicity problems to crop planting.
Research on the Design Safety Factor of the Supporting Falsework Considering the Installation Gaps
Authors: Wang Yongquan , Li Tianfu , Dong Kaifa , Guo Zhengxing , Fu Jing
Published: May 2022

In order to fully consider the influence of the top installation gaps on the axial forces of the vertical poles during the erection of the supporting falsework, a finite element model of the 25 vertical poles considering the top gap element was established based on a large number of field measured data of the top installation gaps. The size of installation gap in this model considered two kinds of distribution: uniform distribution and random distribution. The uniform distribution value of the gap was 1.4mm and the value interval of the random distribution was [0, 0.7] and [0, 1.4]. The numerical simulation results showed that the axial forces of the vertical poles were uniformly distributed without considering the top installation gaps of the vertical poles. When considering the top gaps of the vertical poles, the calculation results of the two distribution types showed that the axial forces were non‐uniform distribution and the maximum axial force of the pole increased by 2.3 times. Besides, according to the measured test data of the axial forces of a certain engineering project, the distribution law of the vertical pole’s axial forces in the actual project was also analyzed and the results showed that the maximum value of the axial force was about twice the minimum value. Since it is impossible to ensure that the elevations of the top of the vertical poles are exactly the same during the actual erection of the falsework, the top installation gaps are inevitable. When designing the bearing capacity of the falsework, the influence of the top installation gaps on the axial forces of the vertical poles should be fully considered. The recommended safety factor range of the bearing capacity of vertical pole is 1.7‐2.1.
New Function Solutions of Ablowitz-Kaup-Newell-Segur Water Wave Equation via Power Index Method
Authors: Ahmad Khalil , Bibi Khudija
Published: May 2022

In this article, the applications of the Power Index Method (PIM) are presented to ascertain function solutions of the AKNS equation. We find out adequate explicit general solutions for the AKNS in the form of exponential, trigonometric, and logarithmic functions which have their importance in the physical sciences. We have used function transformations in which indexes of independent variables are nonrestricted parameters. During our investigation, we have seen that the nonlinear AKNS can be reduced to linear ODE by using PIM. All the solutions contain independent variables with parameters, and they are graphically represented by choosing suitable values of parameters.
Analysis of Drilling of Coir Fiber-Reinforced Polyester Composites Using Multifaceted Drill Bit
Authors: Rajamurugan T. V. , Rajaganapathy C. , Jani S. P. , Gurram Claris Snigdha , Allasi Haiter Lenin , Damtew Samson Zerihun
Published: May 2022

Composite materials are continuously replacing the conventional materials and alloys owing to their weight reduction. Biodegradable fiber-reinforced composite materials are one of the prime attractions to the researcher due to their easy availability and low cost. During drilling of these Natural Fiber Reinforced Plastic composites (NFRP), delamination and surface roughness are the problems encountered, which are to be minimized to get better output by adopting different cutting conditions and tools. This work aims at the drilling of naturally available coir fiber-reinforced composite materials by using a multifaceted drill bit. Material thickness, spindle speed, feed rate, and multifaceted bit diameter are input against the output delamination. After modeling of the output result, a sensitivity analysis tool is introduced to rate the input factor to minimize delamination. SEM images are used to analyze the fracture morphology.