bluuebunny/crossref_metadata_2025 huggingface dataset:
Multiple slip effects of MHD boundary-layer motion of a Casson nanoliquid over a penetrable linearly stretching sheet embedded in non-Darcian porous medium
Authors: Priyanka Patil , Abdelmohsen Shaimaa A. M. , Tawade Jagadish V. , Ashraf Abdelbacki M. M. , Kumar Raman , Biradar Mahadev M.
Published: September 2022

The particular exploration includes the two-dimensional Casson nanoliquid motion predicaments, adjacent to the boundary level over the stretching sheet via non-Darcian porous medium. A particular computational description is given for the governing boundary value problem pertaining to nonlinear partial differential equations relating to momentum, thermal and mass transfer which are computed by fourth-order Runge–Kutta and shooting approaches. The brunt of several parameters like viscous dissipations, magnetic field, porous parameter, thermal diffusivity, Brownian diffusion coefficient and Casson fluid parameter on momentum, heat as well as mass transfer is explored. Moreover, it was noticed that liquid motion is repelled by the Forchheimerr parametric quantity resulting in a reduction in velocity magnitude, within the boundary layer, whereas in the thermic boundary layer, there is an increase in the thermal profile. Furthermore, when the value of the Brownian motion parameter increases, the axial velocity drops, but the thermophoresis parameter is said to have the opposite effect.
Room-temperature superconductivity — or not? Comment on Nature 586, 373 (2020) by E. Snider et al.
Authors: Marel D. van der , Hirsch J. E.
Published: September 2022

Recently, the discovery of room-temperature superconductivity was announced for a carbonaceous sulfur hydride (CSH) under high pressure [E. Snider et al., Nature 586, 373 (2020)]. The evidence for superconductivity was based on resistance and magnetic susceptibility measurements. In the figures showing the susceptibility it was stated that “the background signal, determined from a nonsuperconducting CSH sample at 108 GPa, has been subtracted from the data”. From a thorough data analysis we show that the data are incompatible with the notion that the susceptibility data are obtained from the “measured voltage” using a background correction. On the other hand, the data are compatible with the reverse procedure, namely the “measured voltage” is obtained by adding a “background signal” containing noise to what was reported as the background-corrected susceptibility. For all six of the reported pressures our analysis leads to the conclusion that: (i) the reported background-corrected susceptibility data are pathological, (ii) they were not obtained by the method described in this paper nor by any one of the alternative three methods that were subsequently provided by the authors and (iii) the “measured voltage” data are not raw data.
Fraud Detection Using Large-scale Imbalance Dataset
Authors: Rubaidi Zainab Saad , Ammar Boulbaba Ben , Aouicha Mohamed Ben
Published: September 2022

In the context of machine learning, an imbalanced classification problem states to a dataset in which the classes are not evenly distributed. This problem commonly occurs when attempting to classify data in which the distribution of labels or classes is not uniform. Using resampling methods to accumulate samples or entries from the minority class or to drop those from the majority class can be considered the best solution to this problem. The focus of this study is to propose a framework pattern to handle any imbalance dataset for fraud detection. For this purpose, Undersampling (Random and NearMiss) and oversampling (Random, SMOTE, BorderLine SMOTE) were used as resampling techniques for the concentration of our experiments for balancing an evaluated dataset. For the first time, a large-scale unbalanced dataset collected from the Kaggle website was used to test both methods for detecting fraud in the Tunisian company for electricity and gas consumption. It was also evaluated with four machine learning classifiers: Logistic Regression (LR), Naïve Bayes (NB), Random Forest, and XGBoost. Standard evaluation metrics like precision, recall, F1-score, and accuracy have been used to assess the findings. The experimental results clearly revealed that the RF model provided the best performance and outperformed all other matched classifiers with attained a classification accuracy of 89% using NearMiss undersampling and 99% using Random oversampling.
Geochemical and mineralogical soil survey of the conterminous USA: a project retrospective
Authors: Smith David B.
Published: September 2022

From 2007 to 2010, the United States Geological Survey (USGS) conducted a low-density (one site per 1600 km
2
), geochemical and mineralogical soil survey of the conterminous USA (
c
. 8 × 10
6
 km
2
). This project was initiated to address the lack of a national soil geochemical database that was a critical need for state and federal environmental agencies, public health specialists and those engaged in risk assessment of contaminated land. Sampling and analytical protocols were developed in consultation with stakeholders at a 2003 workshop, and pilot studies were carried out from 2004 to 2007. Sampling began in 2007 and concluded in 2010. Chemical and mineralogical analyses were completed in 2013, and the datasets were released to the public that same year. Geochemical and mineralogical maps were published in 2014, and an interactive website was released in 2019. The author was Project Chief for this effort throughout the lifetime of the study. The evolution of the project is discussed from its inception through to the publication of results and its impact. The lessons learned during the project are reviewed in the hope that applied geochemists who undertake such broad-scale geochemical mapping projects in the future will find them useful.

Thematic collection:
This article is part of the Continental-scale geochemical mapping collection available at:
https://www.lyellcollection.org/topic/collections/continental-scale-geochemical-mapping
GRASP: Scalable Graph Alignment by Spectral Corresponding Functions
Authors: Hermanns Judith , Skitsas Konstantinos , Tsitsulin Anton , Munkhoeva Marina , Kyster Alexander , Nielsen Simon , Bronstein Alexander M. , Mottin Davide , Karras Panagiotis
Published: September 2022

What is the best way to match the nodes of two graphs? This
graph alignment
problem generalizes graph isomorphism and arises in applications from social network analysis to bioinformatics. Some solutions assume that auxiliary information on known matches or node or edge attributes is available, or utilize arbitrary graph features. Such methods fare poorly in the pure form of the problem, in which only graph structures are given. Other proposals translate the problem to one of aligning node embeddings, yet, by doing so, provide only a single-scale view of the graph.

In this article, we transfer the shape-analysis concept of functional maps from the continuous to the discrete case, and treat the graph alignment problem as a special case of the problem of finding a mapping between functions on graphs. We present GRASP, a method that first establishes a correspondence between functions derived from Laplacian matrix eigenvectors, which capture multiscale structural characteristics, and then exploits this correspondence to align nodes. We enhance the basic form of GRASP by altering two of its components, namely the embedding method and the assignment procedure it employs, leveraging its modular, hence adaptable design. Our experimental study, featuring noise levels higher than anything used in previous studies, shows that the enhanced form of GRASP outperforms scalable state-of-the-art methods for graph alignment across noise levels and graph types, and performs competitively with respect to the best non-scalable ones. We include in our study another modular graph alignment algorithm, CONE, which is also adaptable thanks to its modular nature, and show it can manage graphs with skewed power-law degree distributions.
A Contrastive Plan Explanation Framework for Hybrid System Models
Authors: Sarwar Mir , Ray Rajarshi , Banerjee Ansuman
Published: September 2022

In artificial intelligence planning, having an explanation of a plan given by a planner is often desirable. The ability to explain various aspects of a synthesized plan to an end user not only brings in trust on the planner but also reveals insights of the planning domain and the planning process. Contrastive questions such as “Why action A instead of action B?” can be answered with a contrastive explanation that compares properties of the original plan containing A against the contrastive plan containing B. In this article, we explore a set of contrastive questions that a user of a planning tool may raise and propose a
re-model and re-plan
framework to provide explanations to such questions. Earlier work has reported this framework on planning instances for discrete problem domains described in the Planning Domain Definition Language (PDDL) and its variants. In this article, we propose an extension for planning instances described by PDDL+ for hybrid systems that portray a mix of discrete-continuous dynamics. Specifically, given a mixed discrete-continuous system model in PDDL+ and a plan describing the set of desirable actions on the same to achieve a destined goal, we present a framework that can integrate contrastive questions in PDDL+ and synthesize alternate plans. We present a detailed case study on our approach and propose a comparison metric to compare the original plan with the alternate ones.
Video Game Bad Smells: What They Are and How Developers Perceive Them
Authors: Nardone Vittoria , Muse Biruk , Abidi Mouna , Khomh Foutse , Di Penta Massimiliano
Published: September 2022

Video games represent a substantial and increasing share of the software market. However, their development is particularly challenging as it requires multi-faceted knowledge, which is not consolidated in computer science education yet. This article aims at defining a catalog of bad smells related to video game development. To achieve this goal, we mined discussions on general-purpose and video game-specific forums. After querying such a forum, we adopted an open coding strategy on a statistically significant sample of 572 discussions, stratified over different forums. As a result, we obtained a catalog of 28 bad smells, organized into five categories, covering problems related to game design and logic, physics, animation, rendering, or multiplayer. Then, we assessed the perceived relevance of such bad smells by surveying 76 game development professionals. The survey respondents agreed with the identified bad smells but also provided us with further insights about the discussed smells. Upon reporting results, we discuss bad smell examples, their consequences, as well as possible mitigation/fixing strategies and trade-offs to be pursued by developers. The catalog can be used not only as a guideline for developers and educators but also can pave the way toward better automated tool support for video game developers.
Automatic Creation of High-bandwidth Memory Architectures from Domain-specific Languages: The Case of Computational Fluid Dynamics
Authors: Soldavini Stephanie , Friebel Karl , Tibaldi Mattia , Hempel Gerald , Castrillon Jeronimo , Pilato Christian
Published: September 2022

Numerical simulations can help solve complex problems. Most of these algorithms are massively parallel and thus good candidates for FPGA acceleration thanks to spatial parallelism. Modern FPGA devices can leverage high-bandwidth memory technologies, but when applications are memory-bound designers must craft advanced communication and memory architectures for efficient data movement and on-chip storage. This development process requires hardware design skills that are uncommon in domain-specific experts. In this article, we propose an automated tool flow from a domain-specific language for tensor expressions to generate massively parallel accelerators on high-bandwidth-memory-equipped FPGAs. Designers can use this flow to integrate and evaluate various compiler or hardware optimizations. We use
computational fluid dynamics (CFD)
as a paradigmatic example. Our flow starts from the high-level specification of tensor operations and combines a multi-level intermediate representation–based compiler with an in-house hardware generation flow to generate systems with parallel accelerators and a specialized memory architecture that moves data efficiently, aiming at fully exploiting the available CPU-FPGA bandwidth. We simulated applications with millions of elements, achieving up to 103 GFLOPS with one compute unit and custom precision when targeting a Xilinx Alveo U280. Our FPGA implementation is up to 25× more energy efficient than expert-crafted Intel CPU implementations.
Tamera: Contactless Commodity Tracking, Material and Shopping Behavior Recognition Using COTS RFIDs
Authors: Shang Fei , Yang Panlong , Xiong Jie , Feng Yuanhao , Li Xiangyang
Published: September 2022

RFID technology has recently been exploited for not only identification but also fine-grained trajectory tracking and gesture recognition. While contact-based (a tag is attached to the target of interest) sensing has achieved promising results, contactless sensing still faces severe challenges such as low accuracy and inability to sense multiple targets simultaneously in proximity, restricting its applicability in real-world deployment. In this work, we present
Tamera
, a contactless RFID-based sensing system, which significantly improves the tracking accuracy, enables multi-commodity tracking, and even material and shopping behavior recognition. We successfully address multiple technical challenges, and design and implement our prototype on commodity RFID devices. We test the positioning accuracy of
Tamera
in a 5 m × 6 m laboratory.
Tamera
achieves a median error of 1.3 cm and 2.7 cm for contactless single- and multi-commodity tracking, respectively. In our laboratory, two shelves commonly found in the supermarket are arranged and the goods are placed on them.
Tamera
successfully localizes and identifies the material type (metal, plastic, paper, and glass) of the commodities on the shelf with an accuracy higher than 95%.
Tamera
successfully recognizes four shopping behaviors (taking commodity, replacing commodity, buying commodity, and invoking commodity) with an accuracy higher than 93%.
Vision Impairment and On-Road Driving
Authors: Wood Joanne M.
Published: September 2022

Good vision is important for safe driving. The impact of vision impairment associated with common eye diseases on driving performance, and the association between vision measures and driving performance, are discussed. Studies include those where participants drove a real vehicle on a closed road or on public roads. Closed-road studies include evaluation of both simulated and true vision impairment and day- and night-time driving. Collectively, the findings provide important insights into the impact of refractive conditions, cataracts, glaucoma, age-related macular degeneration, and hemianopic field loss on driving; however, study results show varying impacts on driving performance and are often limited by small sample sizes. Vision measures including motion sensitivity, contrast sensitivity, and useful field of view have stronger associations with driving performance than do visual acuity or visual fields, with studies suggesting that some drivers with field loss can compensate for their field defects through increased eye and head movements.
A foundational knowledge assessment tool to predict academic performance of medical students in first-year anatomy and physiology
Authors: Abrahams Amaal , Pienaar Lunelle , Bugarith Kishor , Gunston Geney , Badenhorst Elmi
Published: September 2022

The development of a foundational knowledge assessment tool to predict academic performance of medical students in first-year anatomy and physiology.
Regulation of glomerulotubular balance. IV. Implication of aquaporin 1 in flow-dependent proximal tubule transport and cell volume
Authors: Du Zhaopeng , Yan Qingshang , Shen Emma , Weinstein Alan M. , Wang Tong
Published: September 2022

We found an absence of flow-dependent modulation of fluid absorption but no effect on either proximal tubule (PT) [Formula: see text] absorption or acid-base parameters in the aquaporin 1 (AQP1) knockout mouse. We affirmed the dominance of the water channel AQP1 in mediating isotonic water reabsorption by the mouse PT and demonstrated that flow-stimulated [Formula: see text] reabsorption is independent of AQP1. With reference to the model, the findings also suggest that tight junctional water flux in the PT is less prominent in the mouse than rat kidney.
Aerobic exercise training reduces cerebrovascular impedance in older adults: a 1-year randomized controlled trial
Authors: Sugawara Jun , Tarumi Takashi , Xing Changyang , Liu Jie , Tomoto Tsubasa , Pasha Evan P. , Zhang Rong
Published: September 2022

Estimation of cerebrovascular impedance is essential for understanding dynamic cerebral blood flow regulation. This randomized controlled trial demonstrated that aerobic exercise training reduced cerebrovascular impedance in older adults, which may benefit brain perfusion.
Critical power and work-prime account for variability in endurance training adaptations not captured by V̇o2max
Authors: Collins Jessica , Leach Olivia , Dorff Abigail , Linde Jessica , Kofoed Jason , Sherman Megan , Proffit Meagan , Gifford Jayson R.
Published: September 2022

As the highest percentage of V̇O2max at which steady state conditions can be achieved, a person’s critical power (PCRIT) strongly influences the metabolic strain of a given exercise. In this study we demonstrate that training-induced changes in endurance are more strongly related to the intensity of an exercise training program, relative to PCRIT than relative to V̇o2max. Thus, exercise may be more homogenously and effectively prescribed in relation to PCRIT than traditional factors like V̇o2max.
A systematic review of the biological mediators of fat taste and smell
Authors: Jaime-Lara Rosario B. , Brooks Brianna E. , Vizioli Carlotta , Chiles Mari , Nawal Nafisa , Ortiz-Figueroa Rodrigo S. E. , Livinski Alicia A. , Agarwal Khushbu , Colina-Prisco Claudia , Iannarino Natalia , Hilmi Aliya , Tejeda Hugo A. , Joseph Paule V.
Published: September 2022

Taste and smell play a key role in our ability to perceive foods. Overconsumption of highly palatable energy-dense foods can lead to increased caloric intake and obesity. Thus there is growing interest in the study of the biological mediators of fat taste and associated olfaction as potential targets for pharmacologic and nutritional interventions in the context of obesity and health. The number of studies examining mechanisms underlying fat taste and smell has grown rapidly in the last 5 years. Therefore, the purpose of this systematic review is to summarize emerging evidence examining the biological mechanisms of fat taste and smell. A literature search was conducted of studies published in English between 2014 and 2021 in adult humans and animal models. Database searches were conducted using PubMed, EMBASE, Scopus, and Web of Science for key terms including fat/lipid, taste, and olfaction. Initially, 4,062 articles were identified through database searches, and a total of 84 relevant articles met inclusion and exclusion criteria and are included in this review. Existing literature suggests that there are several proteins integral to fat chemosensation, including cluster of differentiation 36 (CD36) and G protein-coupled receptor 120 (GPR120). This systematic review will discuss these proteins and the signal transduction pathways involved in fat detection. We also review neural circuits, key brain regions, ingestive cues, postingestive signals, and genetic polymorphism that play a role in fat perception and consumption. Finally, we discuss the role of fat taste and smell in the context of eating behavior and obesity.
Fabrication of Hollow Needle Tips with Nanoholes by Plasma Maskless Processing and Calculation and Detection of Complex Surface Edges
Authors: Dai Zuocai , Zhou Sha , Yang Tongguang , Wang Yanpeng
Published: September 2022

The plasma maskless processing method combines the advantages of plasma etching and scanning probe processing, such as high etching efficiency, wide range of applicable materials, and high resolution. This method is based on the probe driving and detection of piezoelectric thin films, which enables all probes in the array to be processed completely independently, greatly improving the overall processing efficiency, and can effectively etch specific nano-scale regions of various materials. And it provides an effective solution for efficiently processing small batches of multi-variety micro-nano devices. In this study, a hollow needle tip with nanoholes was fabricated by plasma maskless processing, and use edge computing to calculate the complex surface shape of the needle tip. By establishing a plasma reactor, using the calculation method of the complex surface edge of the needle tip, and testing the stability of the processed nanotip by means of filling air pressure and power excitation in the experiment. The results show that when the breakdown voltage SF6 discharges, with the increase of air pressure, the breakdown voltage of the arrester first decreases and then increases gradually, and it is the smallest at 5 kPa and reaches the optimal 490 V, (PD) m = 0.5 Pa·m. And the Ar reaction ionization potential (15.76 eV) and excitation potential (11.53 eV, 11.72 eV) are lower than the splitting potential (≥16 eV) of SF6. As time goes on, high-energy particles are neutralized with the vessel wall, so the ion concentration is gradually smaller, sparse between 200 and 250, and increases from 100 to 300, which is close to the peak value, according to the gradual increase of the discharge voltage. The data obtained from the experimental test proves the reliability of the nanotips prepared in this study, and the properties remain stable under different breakdown voltages. Therefore, the nanohole hollow tip processed by maskless scanning plasma in this study has reached the standard of process use and has important application significance.
Mitochondrial Transplantation Promotes Remyelination and Long-Term Locomotion Recovery following Cerebral Ischemia
Authors: Chen Tao , Zhu Yuanyuan , Jia Jia , Meng Han , Xu Chao , Xian Panpan , Li Zijie , Tang Zhengang , Wu Yin , Liu Yan
Published: September 2022

Cerebral ischemia usually leads to axonal degeneration and demyelination in the adjacent white matter. Promoting remyelination still remains a challenging issue in the field. Considering that ischemia deprives energy supply to neural cells and high metabolic activities are required by oligodendrocyte progenitor cells (OPCs) for myelin formation, we assessed the effects of transplanting exogenous healthy mitochondria on the degenerating process of oligodendrocytes following focal cerebral ischemia in the present study. Our results showed that exogenous mitochondria could efficiently restore the overall mitochondrial function and be effectively internalized by OPCs in the ischemic cortex. In comparison with control cortex, there were significantly less apoptotic and more proliferative OPCs in mitochondria-treated cortex. More importantly, higher levels of myelin basic protein (MBP) and more morphologically normal myelin-wrapped axons were observed in mitochondria-treated cortex at 21 days postinjury, as revealed by light and electron microscope. Behavior assay showed better locomotion recovery in mitochondria-treated mice. Further analysis showed that olig2 and lipid synthesis signaling were significantly increased in mitochondria-treated cortex. In together, our data illustrated an antidegenerating and myelination-promoting effect of exogenous mitochondria, indicating mitochondria transplantation as a potentially valuable treatment for ischemic stroke.
Using Social Media Data to Explore Urban Land Value and Sentiment Inequality: A Case Study of Xiamen, China
Authors: Gai Zhenyu , Fan Chenjing , Shen Shiguang , Ge Yanling , Shi Zhan , Li Shiqi , Zhang Yiyang , Cao Yirui
Published: September 2022

Differences in urban land values affect residents’ living experiences and may contribute to sentiment inequality. Due to the popularity of smart mobile devices and social media platforms, online tweets with location information can be used as objective information to reflect sentiment differences of urban residents in different locations, overcoming the limitations of previous studies with small sample sizes or a lack of spatial information. Sentiment quantification based on deep learning enables the identification of spatial patterns of urban residents’ sentiments. It also provides a new approach for analyzing data from big data platforms using an intelligent computing platform. This paper quantitatively analyzes the sentiment contained in social media tweets using a deep learning sentiment analysis algorithm to reveal inequalities between urban residents’ sentiments and land values. The Baidu Intelligent Cloud sentiment analysis platform is used to identify 460,000 Weibo tweets in Xiamen, China, in 2020. We quantitatively analyze the positive and negative sentiments of residents and create a spatial distribution map. The concentration curve indicates sentiment inequality and the impact of high land values on residents’ sentiments. The positive sentiment concentration index (CI) and correlation analysis show that the CI value is 0.07, and significant sentiment inequality exists due to the high land value. The use of social media tweet data to analyze sentiment inequality provides a reference for future interdisciplinary research in psychology, urban planning, geography, and sociology. The proposed approach of analyzing social media data using an intelligent computing platform provides new insights into multiplatform data interaction in the context of the Internet of Everything.
Active Earth Pressure for Sloping Finite Soil While Accounting for Shear Stress and Curved Slip Surface
Authors: Wang Weiwei , Liu Xinxi , Hu Weidong , Luo Hua , Wang Guanghui
Published: September 2022

Calculating the earth pressure of the sloping soil having finite width behind the retaining wall is difficult for stability calculation. Thus, a novel method to calculate the active pressure of cohesionless sloping finite soil behind a retaining wall was developed to investigate. Taking cohesionless soil as the research object and considering the principal stress rotation of soil, the resultant force for active earth pressure, action point position, and earth pressure distribution of sloping finite soil was obtained based on assumptions of the translational mode of the rigid retaining wall and cycloidal slip surface. The accuracy of the proposed method was verified by model tests. The influence of the slope height ratio

l
/
H

and slope angle

α

on earth pressure was analyzed in this study. The result revealed that the horizontal component of the active earth pressure distribution curve for sloping finite soil was linear in area I and nonlinear with a drum shape in area II. There was a noticeable change at the junction of the two areas. The resultant force of earth pressure and the height of action point of resultant force increased and tended to reach a certain value as the aspect ratio

l
/
H

increased. When

l
/
H
≥
0.4

, the height of the action point of resultant force tended to be two-fifths the height of the wall. The resultant force and the height of the action point decreased linearly as the slope bottom angle increased.
Innovation Network Reconfiguration Makes Infrastructure Megaprojects More Resilient
Authors: Sun Ruijiao , Liu Yisheng , Zhao Jianghu
Published: September 2022

Innovation management of infrastructure megaprojects is a challenging task. There are many risks in the process of innovation in engineering technology, such as shortage of funds, policy fluctuations, and difficulties in the transformation of achievements. Meanwhile, innovation organizations involve multiple participants, which makes cooperation complicated. Therefore, resilient innovation is proposed and considered as a tool that can optimize innovation management. The resilience of innovation depends largely on partnerships at the organizational level, which is rarely explored in current studies. This research aims to examine the relationship between organizational resilience and innovation network characteristics. Based on a survey of 164 participants in infrastructure innovation projects, the structural equation model (SEM) is used to explore the factors that influence organizational resilience. The findings show that there is a positive correlation between network characteristics and organizational resilience. Furthermore, the strength of network connections has a direct impact on the preventive and resistance ability of resilience. Network heterogeneity has an impact on the dual ability of resilience. Finally, a case study of the Qinghai-Tibet Railway innovation network shows that based on the above influence paths, we can find a strategy to reconstruct the network to improve resilience.
Research on the Financial Support Performance Evaluation of Big Data Industry Development
Authors: Fu Wenyu , Zhao Jingfeng , Wang Changming , Zhou Xiaobin
Published: September 2022

In order to solve the problem that the traditional information systems have such as slow running responses and cannot meet the expected requirements of users, a performance evaluation method of financial support for the development of the big data industry is proposed. According to the characteristics of financial support, this paper uses the analytic hierarchy process (AHP) and the fuzzy comprehensive evaluation principle to establish a specific evaluation index system for the performance evaluation of financial support in the big data industry and select a suitable index group. In order to reflect the financial support performance, the analytic hierarchy process (AHP) is used to calculate the weight of various performance evaluation indicators, and the fuzzy comprehensive evaluation is used to combine qualitative analysis and quantitative analysis to evaluate and calculate the big data industry scientifically, objectively, fairly, and accurately. In financial support performance, experiments show that the proposed method not only has a fast response time but also can ensure that the actual results after the system runs are in line with the expectations of the system users.
[Retracted] Clinical Comparison of Preterm Birth and Spontaneous Preterm Birth in Severe Preeclampsia
Authors: Wen Yanqing , Yang Xiaoyan
Published: September 2022

Severe preeclampsia is accompanied by many complications, which is extremely harmful to pregnant women and fetuses. However, in the classification of preterm birth, it is generally divided into spontaneous preterm birth and therapeutic preterm birth, and insufficient attention has been paid to preterm birth in severe preeclampsia. This article aims to explore the clinical difference between preterm birth in severe preeclampsia and spontaneous preterm birth. In the experiment, this paper selected pregnant women who delivered and were treated in a hospital from April 2010 to April 2020 as cases. In terms of grouping, not only are they divided into severe eclampsia group (observation group 1), spontaneous preterm birth group (observation group 2), and general delivery group (control group) according to the cause of premature birth, but also according to the gestational age of severe eclampsia onset, preterm weeks, and other groups. Not only the clinical difference between severe preeclampsia preterm birth and spontaneous preterm birth was compared horizontally, but also the factors affecting the complications of preterm pregnant women, perinatal asphyxia rate, and mortality were longitudinally analyzed. The experimental results in this paper showed that there were significant differences in maternal complications and neonatal mortality between the severe preeclampsia preterm group and the spontaneous preterm group (P < 0.05). In addition, the severe preeclampsia preterm birth group was more harmful than the spontaneous preterm birth group. The complication rate of the severe preeclampsia preterm birth group was 10% higher than that of the spontaneous preterm birth group, and the neonatal mortality rate was 2% higher.
Ethnobotanical Study of Medicinal Plants Used against COVID-19
Authors: Chebaibi Mohamed , Bousta Dalila , Bourhia Mohammed , Baammi Soukayna , Salamatullah Ahmad Mohammad , Nafidi Hiba-Allah , Hoummani Hasnae , Achour Sanae
Published: September 2022

During the COVID-19 pandemic, the Moroccan population, like the entire population of the world, used medicinal plants to treat or cure symptoms of SARS-CoV-2. The present work was designed to identify the medicinal plants used by the Moroccan population in the prevention or treatment of COVID-19. To achieve this goal, a survey was conducted to collect data on plants along with the sociodemographic parameters of users. The outcome of this work showed that 1,263 people were interviewed with 63.5% male, aged between 18 and 82 years. Most plant users were between 20 and 40 years, which constituted 80.1% of the study population. The level of education of participants was 70.9% university and 27.6% secondary. The most useful plants were eucalyptus, cloves, lemon, and garlic. Notably, 61.9% of interviewed people used plants for preventing or treating COVID-19: 30.6% of them declared one-time use from the beginning of the pandemic, and 47.8% declared frequent daily use until recovery, while 17.4% declared single daily use. Five out of twenty-one plants used in the treatment are known for their potential toxicity, including Artemisia herba-alba and oleander (Nerium oleander). The findings of the present work could serve society by providing potential medicinal plants to control COVID-19.
An Optimized Systematic Approach to Identify Bugs in Cloud-Based Software
Authors: Marappan Shanmugasundaram , Kollu Archana , Keshta Ismail , Beram Shehab Mohamed , Bhende Sahil , Kaliyaperumal Karthikeyan
Published: September 2022

The resolution of a software bug depends on the severity of the defect report. Open-source software defect tracking solutions have taken over as the principal means of processing enormous amounts of defect information data due to the ongoing increase in software scale. Dealing with software faults requires analyzing the implications of defect report severity in the data warehouse. Thus, the authors have proposed an optimized systematic approach through the research and analysis of Bugzilla defect tracking system data in this study, where it is found that the attribute characteristics of different projects are quite different and the statistical features of the repair rate, resolution time, developers, components, and other attributes are consistent. This technique, therefore, assumes that a rise in the severity of software defect reports will result in a rise in the defect repair rate and that the severity is normally based on the severity distribution of various components and projects. According to the study’s findings, developers hold the most defects when the repair rate is low and the defect resolution time is shortest.
Identification of the Diagnostic Biomarker VIPR1 in Hepatocellular Carcinoma Based on Machine Learning Algorithm
Authors: Ge Song , Xu Chen-rui , Li Yan-ming , Zhang Yu-lin , Li Na , Wang Fei-tong , Ding Liang , Niu Jian
Published: September 2022

The purpose of this study was to identify the potential diagnostic biomarkers in hepatocellular carcinoma (HCC) by machine learning (ML) and to explore the significance of immune cell infiltration in HCC. From GEO datasets, the microarray datasets of HCC patients were obtained and downloaded. Differentially expressed genes (DEGs) were screened from five datasets of GSE57957, GSE84402, GSE112790, GSE113996, and GSE121248, totalling 125 normal liver tissues and 326 HCC tissues. In order to find the diagnostic indicators of HCC, the LASSO regression and the SVM-RFE algorithms were utilized. The prognostic value of VIPR1 was analyzed. Finally, the difference of immune cell infiltration between HCC tissues and normal liver tissues was evaluated by CIBERSORT algorithm. In this study, a total of 232 DEGs were identified in 125 normal liver tissues and 326 HCC tissues. 11 diagnostic markers were identified by LASSO regression and SVM-RFE algorithms. FCN2, ECM1, VIRP1, IGFALS, and ASPG genes with AUC>0.85 were regarded as candidate biomarkers with high diagnostic value, and the above results were verified in GSE36376. Survival analyses showed that VIPR1 and IGFALS were significantly correlated with the OS, while ASPG, ECM1, and FCN2 had no statistical significance with the OS. Multivariate assays indicated that VIPR1 gene could be used as an independent prognostic factor for HCC, while FCN2, ECM1, IGFALS, and ASPG could not be used as independent prognostic factors for HCC. Immune cell infiltration analyses showed that the expression of VIPR1 in HCC was positively correlated with the levels of several immune cells. Overall, VIPR1 gene can be used as a diagnostic feature marker of HCC and may be a potential target for the diagnosis and treatment of liver cancer in the future.
Integration, Game, and Sustainable Development of Shenzhen Port Based on Governmental Regulations
Authors: Wang Xuanfei , Liang Shan , Liao Zhenjie
Published: September 2022

The internal integration, game, and sustainable development of Shenzhen Port play key roles in developing a synergy mechanism among Guangdong‐Hong Kong‐Macau Greater Bay Area (GBA) ports. Using a logistics model, a parameter estimation and evolutionary analysis of Shenzhen Port were performed, revealing that it requires a new impetus to promote growth in its container throughput. Based on the population ecology theory, a two‐population Lotka‐Volterra model was used to discuss the activity and mutual effect between the western and eastern port areas of Shenzhen Port, quantitatively measuring the varying degrees of the hindering effects between them. To reduce competition and strengthen cooperation, a port game matrix with a punishment mechanism of governmental regulations was created to discuss the internal integration of Shenzhen Port. The results show that, along with the promoting effect of port cooperation on the increase in container throughput, there is a gradual increase in the punishment of speculative behaviours. Government‐dominated port cooperation prevents the prisoner’s dilemma in the port activity process. The findings of this study provide quantitative data support and decision‐making references for achieving resource complementarity, developing synergy mechanisms, and attaining sustainable development in Shenzhen Port.
Anaerobic Co-Digestion of Human Feces with Rice Straw for Biogas Production: A Case Study in Sunyani
Authors: Adjama Irédon , Derkyi Nana Sarfo Agyemang , Uba Felix , Akolgo Gilbert Ayine , Opuko Richard
Published: September 2022

The choice of feedstock for biogas production should not only be limited to organic waste like agricultural products, food, and animal waste. Human feces could also be considered a source of biogas production. The ever-increasing cost of fossil fuels and environmental pollution threats are forcing the search for alternative energy sources. Several types of research have to unlock the mysteries behind the difficulties of producing biogas from human feces, especially the production of more HN3, which is a greenhouse gas because of its low C:N ratio. This research experimentally investigated how to reduce their amount using rice straw with a high C:N ratio. Several combinations were made between the human waste and the rice straw at different ratios during the experiment. The result shows that the optimal outcome for methane production fell on the 50% HF and 50% RS combination due to the actions of both aerobic and anaerobic processes.
Development Model of College English Education Service Talents Based on Nonlinear Random Matrix
Authors: Gao Xiaoyan
Published: September 2022

The development of artificial intelligence makes people’s life and work easier and more effective, and computer-based online exams and marking not only improve students’ learning efficiency but also reduce the pressure of teachers’ marking work. For objective questions, marking has gone from manual marking to cursor reader marking to computerized character matching, and the correct rate of marking has soared to 100%; for subjective questions, foreign systems such as PEG and E-rater have been used, and domestic systems such as those using English large corpus similarity matching and those based on natural language understanding using intelligent algorithms have been used for marking. Most of these systems are based on some shallow linguistic features such as rules and LSAs for marking, and there is no deep perception of English language sense. Although the current intelligent marking systems have made a lot of achievements, they do not fundamentally solve the problem of the rationality of intelligent marking of subjective questions. In this article, we propose a regularized discriminant analysis algorithm with good estimation of the mean, and a dimensionality reduction algorithm for high-dimensional missing data by using the relevant research results of random matrix theory to address the problems of traditional machine learning methods in high-dimensional data analysis. Although the linear discriminant analysis algorithm performs well in solving many practical problems, it works poorly in dealing with high-dimensional data. The specific analysis is as follows: in terms of age characteristics, the mobile population under the age of 35 has a significant preference for urban consumer comfort, and it increases with age, peaking at the stage of 30–35 years old and then decreasing rapidly. For this reason, a regularized discriminant analysis algorithm based on random matrix theory is proposed. First, a good estimate of the high-dimensional covariance matrix is made by the nonlinear shrinkage method or the eigenvalue interception method, respectively; then, the estimated high-dimensional covariance matrix is used to calculate the discriminant function values and perform the classification. The classification experiments conducted on simulated and real datasets show that the proposed algorithm is not only more widely applicable but also has a high correct classification rate.
[Retracted] Observation of the Effect of Nursing BPR on Thrombolytic Efficacy and Prognosis of Patients with Cerebral Infarction Based on CT Images
Authors: Wang Shaomin , Miao Xiao , Zhang Guanghui , Li Dandan , Xu Li
Published: September 2022

Cerebral infarction has become the main cause of death among Chinese residents, especially ischemic cerebral infarction. The existing CT technology is not very effective for the detection of cerebral infarction, and some angiography has problems such as blurring and shadowing. In order to understand the treatment methods and effects of patients with cerebral infarction, this article observes the effect of nursing BPR on thrombolytic efficacy and prognosis of patients with cerebral infarction based on CT images. The patients were divided into thrombolytic group and nonthrombolytic group, and a simple rating scale was used to assess the motor function of the patients' limbs, and the stroke scale was used to assess the patient’s neurological function. Compare the baseline data, the time of admission, 24 hours and 7 days, the scores before and after treatment, and the ratio between the two groups. According to the monitoring, record each time point. The analysis of the occurrence of primary endpoint was events and secondary endpoint events and risk factors affecting limb motor function. The results of the study found that, based on the computer scanning observation of nursing BPR, compared with the traditional model, the patient's bleeding was significantly reduced, and the time required for nursing was also reduced by more than 50% compared with the traditional model. Compared with the traditional nursing model, the satisfaction of patients with the BPR nursing model is nearly 40% higher than that of the traditional nursing model. This shows that the observation of thrombolytic effect in patients with cerebral infarction based on computed tomography and BPR nursing can produce good therapeutic effects.
Dual Antiplatelet Therapy in Patients Aged 75 Years and Older with Coronary Artery Disease: A Meta-Analysis and Systematic Review
Authors: Saint Croix Garly , Lacy Spencer C. , Gazzhal Amre , Ibrahim Michel , Gjergjindreaj Medeona , Perez Jorge , Shehadeh Malik , Vedantam Karthik , Torres Christian , Beohar Nirat , Escolar Esteban
Published: September 2022

Objectives. This systematic review and meta-analysis evaluates the safety and efficacy of dual antiplatelet therapy (DAPT) in elderly patients with acute coronary syndrome (ACS). Background. The safety and efficacy of DAPT in elderly patients with ACS is not well characterized. Methods. We performed a systematic literature review to identify clinical studies that reported safety and efficacy outcomes after DAPT for ACS in elderly patients. The primary outcomes of primary efficacy endpoint rates and bleeding event rates were reported as random effects risk ratio (RR) with 95% confidence interval. No prior ethical approval was required since all data are public. Results. Our search yielded 660 potential studies. We included 8 studies reporting on 29,217 patients. There was a higher risk of bleeding event rates in elderly patients treated with prasugrel or ticagrelor when compared to clopidogrel with a risk ratio of 1.17 (95% CI 1.08 to 1.27,

p
<
0.05

). There was no difference in primary efficacy endpoint rates between elderly patients treated with prasugrel or ticagrelor when compared to clopidogrel with a risk ratio of 0.85 (95% CI 0.68 to 1.07,

p
=
0.17

). Conclusions. This systematic review and meta-analysis suggests that DAPT with prasugrel or ticagrelor compared to clopidogrel is associated with a higher risk of bleeding events in elderly patients with ACS. There was no difference in the primary efficacy endpoints between the two treatment groups.
Position Control of a Single-Rod Electro-Hydrostatic Actuator Experiencing a Leaky Piston Seal
Authors: Ren Guangan , Mou Xiangwei , Wen Xiaohao , Chen Lintao
Published: September 2022

Single-rod cylinders are generally employed in electro-hydrostatic actuators (EHAs). A condition that is difficult to detect and could degrade the performance in single-rod EHAs, is the faulty cylinder piston seal. It causes internal leakage from one chamber of the actuator to another. In this work, a position controller with tolerance to actuator internal leakage is synthesized for a single-rod EHA using quantitative feedback theory (QFT). The controller is also robust to different loading and environmental stiffnesses. The ability of the controller is compared with another QFT controller that is synthesized without considering leakage fault. The simulation results show that the QFT fault-tolerant controller can meet prescribed specifications despite internal leakage up to 8.6 L/min.