bluuebunny/crossref_metadata_2025 huggingface dataset:
Pulsatile blood flow in healthy aorta: An application of nonlinear evolution equation
Authors: Jadaun Vishakha
Published: May 2022

Fluid flow dynamics in nature and its applications including hemodynamics are subjected to periodic velocity modulations. The nonlinear evolution equations are perused to understand such fundamental dynamical challenges in hemodynamics. Assuming cardiovascular hemodynamic system as a finite dissipative system and blood as an incompressible Newtonian fluid, a nonlinear evolution equation for pulsatile blood flow in the aorta during the cardiac cycle is modeled. The main results for generalized [Formula: see text]-dimensional nonlinear evolution equation, using the Lie group of transformations method are introduced. The implications of traveling wave solutions to describe the pulsatile blood flow in the aorta are discussed. It is found that the self-interactions among solitary waves along with generation of relatively small-scale and unstable wave fields contribute to turbulence.
Does Institutional Similarity Necessarily Lead to Increased Bilateral Trade?
Authors: Van Vu Hong , Huyen Nguyen Thi Thanh , Doanh Nguyen Khanh
Published: May 2022

To shed light on the question of whether or not institutional similarity always increases bilateral trade, we divide institutional quality into the low level (one standard deviation below the mean), medium level (in between minus one and one standard deviation from the mean), and high level (one standard deviation above the mean). By applying the system-GMM estimator, we found that the institutional similarity between the two countries has a trade-boosting effect, except in cases where one or both countries have low-institutional quality. These imply that measures to enhance institutional similarity must be accompanied by policies to promote institutional adaptability.
The Economics of Irrigation Water: Empirical Evidence from the Awash River Basin, Ethiopia
Authors: Tessema Yohannis Mulu , Alamirew Tena , Charles Katrina , Zeleke Gete
Published: May 2022

Using primary data collected from Ethiopia’s Awash River basin, we study the economics of irrigation water for smallholder farmers. We estimate the impact of irrigation water on gross margin and chemical fertilizer uptake. Our results show that irrigation water has a statistically significant and positive effect on the profitability of crops and chemical fertilizer uptake. We also generate qualitative data on irrigation water returns by producing various crops in the different parts of the basin. In addition, we present the intensity of irrigation water used for various crops in the different parts of the basin as reported by farmers. In doing so, we offer some qualitative evidence on the economic trade-offs involved in irrigation water use at farm and basin levels. The policy implications of the findings are discussed.
Eye Tracking-Based Stress Classification of Athletes in Virtual Reality
Authors: Stoeve Maike , Wirth Markus , Farlock Rosanna , Antunovic André , Müller Victoria , Eskofier Bjoern M.
Published: May 2022

Monitoring stress is relevant in many areas, including sports science. In that scope, various studies showed the feasibility of stress classification using eye tracking data. In most cases, the screen-based experimental design restricted the motion of participants. Consequently, the transferability of results to dynamic sports applications remains unclear. To address this research gap, we conducted a virtual reality-based stress test consisting of a football goalkeeping scenario. We contribute by proposing a stress classification pipeline solely relying on gaze behaviour and pupil diameter metrics extracted from the recorded data. To optimize the analysis pipeline, we applied feature selection and compared the performance of different classification methods. Results show that the Random Forest classifier achieves the best performance with 87.3% accuracy, comparable to state-of-the-art approaches fusing eye tracking data and additional biosignals. Moreover, our approach outperforms existing methods exclusively relying on eye measures.
Dynamic Graph Convolutional Recurrent Network for Traffic Prediction: Benchmark and Solution
Authors: Li Fuxian , Feng Jie , Yan Huan , Jin Guangyin , Yang Fan , Sun Funing , Jin Depeng , Li Yong
Published: May 2022

Traffic prediction is the cornerstone of intelligent transportation system. Accurate traffic forecasting is essential for the applications of smart cities, i.e., intelligent traffic management and urban planning. Although various methods are proposed for spatio-temporal modeling, they ignore the dynamic characteristics of correlations among locations on road network. Meanwhile, most Recurrent Neural Network based works are not efficient enough due to their recurrent operations. Additionally, there is a severe lack of fair comparison among different methods on the same datasets. To address the above challenges, in this article, we propose a novel traffic prediction framework, named Dynamic Graph Convolutional Recurrent Network (DGCRN). In DGCRN, hyper-networks are designed to leverage and extract dynamic characteristics from node attributes, while the parameters of dynamic filters are generated at each time step. We filter the node embeddings and then use them to generate dynamic graph, which is integrated with pre-defined static graph. As far as we know, we are first to employ a generation method to model fine topology of dynamic graph at each time step. Furthermore, to enhance efficiency and performance, we employ a training strategy for DGCRN by restricting the iteration number of decoder during forward and backward propagation. Finally, a reproducible standardized benchmark and a brand new representative traffic dataset are opened for fair comparison and further research. Extensive experiments on three datasets demonstrate that our model outperforms 15 baselines consistently. Source codes are available at
https://github.com/tsinghua-fib-lab/Traffic-Benchmark
.
Read the News, Not the Books: Forecasting Firms’ Long-term Financial Performance via Deep Text Mining
Authors: Zhai Shuang (Sophie) , Zhang Zhu (Drew)
Published: May 2022

In this paper, we show textual data from firm-related events in news articles can effectively predict various firm financial ratios, with or without historical financial ratios. We exploit state-of-the-art neural architectures, including pseudo-event embeddings, Long Short-Term Memory Networks, and attention mechanisms. Our news-powered deep learning models are shown to outperform standard econometric models operating on precise accounting historical data. We also observe forecasting quality improvement when integrating textual and numerical data streams. In addition, we provide in-depth case studies for model explainability and transparency. Our forecasting models, model attention maps, and firm embeddings benefit various stakeholders with quality predictions and explainable insights. Our proposed models can be applied both when numerically historical data is or is not available.
Frequency Analysis of Water Electrolysis Current Fluctuations in a PEM Flow Cell: Insights into Bubble Nucleation and Detachment
Authors: Kwan Jason Tai Hong , Nouri-Khorasani Amin , Bonakdarpour Arman , McClement Daniel G. , Afonso Greg , Wilkinson David P.
Published: May 2022

A cross-shaped transparent electrolysis flow cell was designed to simultaneously perform polarization experiments to about 2 A cm−2while monitoring the current response due to bubble formation and detachment. The flow cell was designed in order to accommodate a large electrolyte flow and have flow conditions similar to a PEM electrolyzer. Ir foil experiments using rapid electrochemical cycling techniques were performed first as a quality control measure for cell functionality. Literature quality cyclic voltammograms were obtained, with the anodically formed iridium oxide film growth plateauing after 1000 cycles. The performance of four different Ti current collector meshes were evaluated using a commercial IrO2CCM using 3.5 M H2SO4at 21 ± 0.5°C. The best performing current collector mesh had the largest triple-phase boundary (TPB) length of 56 mm and a porosity of 52%. Using Fourier Transform analysis, the bubble ratios for different current collector and frequency regimes were obtained. The lowest frequency bubble analysis (< 1 Hz) showed the best correlation with the performance of the different meshes.
Effect of Coordinated Solvent Molecules in Cu-MOF on Enzyme Free Sensing of Glucose and Lactate in Physiological pH
Authors: Ghosh Anamika , Sana Fathima T. K. , Ramaprabhu Sundara
Published: May 2022

Developing a nonenzymatic sweat sensor for selective determination of glucose and lactate holds great significance in clinical diagnostics. Among various catalysts, transition metal-based metal-organic frameworks (MOF) have recently drawn more attention due to their tunable porosity and enhanced electrocatalytic activity. The presence of the solvent molecule in the framework can influence both structural and electrochemical properties. In the present work, Copper-terephthalate (CuBDC) MOF was synthesized in a solvothermal method with different time durations. The effect of the coordinated solvent molecules on the metal centre on structural and electrocatalytic properties was systematically investigated using various characterization techniques. As most of the copper-based composites reported so far showed performance in an alkaline medium, we demonstrate the detection of glucose and lactate in a neutral medium that enables direct analyte measurement from the body fluid. Electrochemical studies indicate that the solvated structure shows superior sensitivity to the desolvated MOF for glucose and lactate. The high sensitivity of the solvated MOF is attributed to the favourable solvent exchange mechanism and ion diffusion through the channels of the MOF. Furthermore, CuBDC12E and CuBDC48E show negligible interference toward competing analytes. The proposed sensor also exhibits good sensing performance in artificial sweat, making it suitable for a non-invasive, practical sweat sensor.
Overexpression of TP53INP2 Promotes Apoptosis in Clear Cell Renal Cell Cancer via Caspase-8/TRAF6 Signaling Pathway
Authors: Li Xunjia , Hu Daixing , Li Ying , Luo Yan , Liang Bing , Yu Kexiao , Xiong Weijian , Zuo Deyu
Published: May 2022

Clear cell renal cell cancer (ccRCC) is a tumor of high malignancy, which can escape apoptosis. The tumor protein p53-inducible nuclear protein 2 (TP53INP2), known as an autophagy protein, is the essential part for autophagosome formation and sensitizes cells to apoptosis. Our study is aimed at exploring the role of TP53INP2 in ccRCC. We have identified the autophagy-related genes (ARGs) of differential expression in ccRCC patients with the help of the TCGA database by bioinformatics analysis. Our assays of quantitative real-time polymerase chain reaction (qRT-PCR) and western blot were for the determination on the both levels of mRNA and protein. Overexpression of TP53INP2 on cellular proliferation, migration, and apoptosis of ccRCC was verified in the ways of performing CCK-8, wound scrape, transwell and flow cytometry assays in vitro, and a mice tumor model in vivo. Transmission electron microscopy was used to measure autophagy formation. The underlying mechanisms of TP53INP2 on ccRCC were determined via coimmunoprecipitation. TP53INP2 was found highly associated with an outcome of worse overall survival (OS) in Kaplan-Meier curves, and this parameter in ccRCC tissues was also lower than the normal tissues. Overexpression of TP53INP2 inhibited ccRCC cellular proliferation, migration, and invasion, as well as the tumor growth of mice. Those cells treated with autophagy inhibitor chloroquine (CQ) or TP53INP2 increased the apoptosis rate. TP53INP2 promoted autophagy formation and elevated the ratio of LC3 II/LC3 I. However, TP53INP2 did not significantly decrease the p-mTOR level. In addition, TP53INP2 activates the expressions of caspase-3, caspase-8, and PARP. Caspase-8 and TNF receptor associated factor 6 (TRAF6) were found to bind to each other in the presence of TP53INP2. TP53INP2 induces apoptosis in ccRCC cells through caspase-8/TRAF6 pathway, rather than the autophagy-dependent pathway.
A Statistical Prediction Model for Healthcare and Landslide Sensitivity Evaluation in Coal Mining Subsidence Area
Authors: Ge Ruoxin , Lv Yiqing , Tao Weiheng
Published: May 2022

The purpose of this study is to compare the results of the frequency ratio (FR) model with the weight of evidence (WOE) and the logical regression (LR) methods when applied to the landslide susceptibility evaluation in coal mining subsidence areas. Key geological disaster prevention and control areas are taken as the research areas. Field investigation is carried out according to the recorded landslide disaster points in the past five years, and 86 landslide disaster points are determined from the remote sensing satellite images. Furthermore, 12 factors affecting the occurrence of landslide are selected as landslide sensitivity evaluation factors. Among them, slope degree, curvature, elevation, and slope aspect are derived using the digital elevation model (DEM) through 30 m × 30 m resolution. The DEM datasets are derived from the geospatial data cloud, lithology datasets are derived from the geological lithology maps, and land use type map is derived from the current situation of national land use. The distances between roads and coal mining subsidence areas are calculated according to field investigation and remote sensing image interpretation results. In addition, the evaluation model includes an annual rainfall distribution map. Finally, the accuracy of three models is compared by ROC curve analysis. The elevation results demonstrate that the frequency ratio-logic regression (FR-LR) model takes the maximum accurateness of 0.913, subsequent to the FR model and the frequency ratio-weight of evidence (FR-WOE) model, respectively. Thus, using LR method based on the FR model has guiding significance for predicting the landslide sensitivity in coal mining. This reduces probable risks and disasters that affect human health. Subsequently, this ensures higher safety from the healthcare perspective in the mining fields.
A Metaheuristic Approach to Map Driving Pattern for Analyzing Driver Behavior Using Big Data Analysis
Authors: Malik Meenakshi , Nandal Rainu , Singh Yudhvir , Barak Dheer Dhwaj , Kumar Yekula Prasanna
Published: May 2022

The modern-day influx of vehicular traffic along with rapid expansion of roadways has made the selection of the best driver based on driving best practices an imperative, thus optimizing cost and ensuring safe arrival at the destination. A key factor in this is the analysis of driver behavior based on driver activities by monitoring adherence to the features comprising the established driving principles. In general, indiscriminate use of features to predict driver performance can increase process complexity due to inclusion of redundant features. An effective knowledge-based approach with a reduced set of features can help attune the driver behavior and improve driving patterns. Hence, a Deep Mutual Invariance Feature Classification (DMIFC) model has been proposed in this study for predicting driver performance to recommend the best driver. To achieve this, first, the driver behavior is broken down into various features corresponding to a simulated driving dataset and subjected to preprocessing to reduce the noise and form a redundant dataset. Thereafter, a Mutual Invariance Scale Feature Selection (MISFS) filter is used to select the relational features by calculating the spectral variance weight between mutual features. The observed mutual features are promoted to create a dominant pattern to estimate the Max feature-pattern generation using Driver Activity Intense Rate (DAIR). The features are then selected for classification based on the DAIR weightage. Additionally, the Interclass-ReLU (Rectified Linear Unit) is used to generate activation functions to produce logical neurons. The logical neurons are further optimized with Multiperceptron Radial Basis Function Networks (MP-RBFNs) to enable better classification of driver features for best prediction results. The proposed system was found to improve the driver pattern prediction accuracy and enable optimal recommendations of driving principles to the driver.
Study on the Influence of Water Immersion in Loess Stratum on the Bearing Capacity of Foundation Bearing Layer of Pipe Gallery
Authors: Pan Xiaoting , Ma Huabing , Zhou Haicheng , Zhu Li , Shan Hongwei
Published: May 2022

Aiming at the influence of water immersion in the loess stratum on the bearing capacity of the pipe gallery foundation, taking the prefabricated pipe gallery demonstration project in Xiongan New District as the research object, the static penetration test and indoor triaxial test of the foundation bearing layer of the pipe gallery before and after water immersion were carried out. In the experiment, the loss law of loess foundation bearing capacity before and after water immersion was analyzed by using the obtained soil physical index changes. The research results show that the foundation bearing capacity calculated using the static penetration test data before and after immersion are in good agreement with the field plate load test results, and the eigenvalues of the foundation bearing capacity calculated using the shear strength index are higher than the field measured results. According to the results of the static penetration test, the bearing capacity of the foundation after immersion will decrease, but the reduction range is within 4%, and there is a certain loss; the bearing capacity of the foundation before and after immersion calculated according to the shear strength index is reduced by 2.5%. It shows that the bearing capacity of the foundation will be lost to a certain extent after immersion in water, but the loss is small. It is recommended to strengthen drainage measures in the later stage to prevent the foundation from being soaked by rainwater.
[Retracted] 3D Video Analysis and Its Application in Developmental and Educational Psychology Teaching
Authors: Chen Zhicheng , Ding Lulu
Published: May 2022

With the rapid development of information technology and network technology and the new requirements of education and teaching in the new era, 3D video technology has been more and more widely used in the field of education and teaching. Educational psychology plays a positive role in the field of education. Applying its relevant theories to classroom teaching is not only conducive to the establishment of a good teacher‐student relationship but also helps teachers to understand the psychological characteristics and learning process of students, so as to greatly improve the quality of teaching. Therefore, this article is based on 3D video analysis technology, through the observation of the teaching video and coding, the verbal interaction between teachers and students’ lines of code and data analysis, mainly to its frequency, specific length and single length data used for analysis, explores the lesson for the current situation of the teaching video case teacher’s teaching behavior, commonness, and difference. From the perspective of learner psychology, the model of teacher’s teaching behavior in MOOCs teaching video is constructed. On the other hand, from the perspective of coding system and learner psychology, this paper proposes the improvement strategies of teachers’ teaching behavior in MOOCs teaching videos in China. The results show that the application of 3D video analysis method and educational psychology in student education can improve student work efficiency.
Modeling of Smart Watch and System Construction Method for the Elderly Based on Big Data
Authors: Zhu Zhu , Ren Yingying , Duan Pei
Published: May 2022

Under the development trend of artificial intelligence and smart city, efforts should be made to improve the connection between the elderly and social development and realize the application of intelligence to travel for the elderly so that the elderly can also enjoy an intelligent and convenient life. With the deepening of China’s aging population, a systematic re-innovative design of smart watches was carried out based on the physiological needs of the elderly living alone in cities. In order to solve the problem that intelligent products for the elderly on the market cannot meet the special needs of the elderly and improve the happiness of the elderly, in this paper, the design method of interaction design is used to analyze the basic needs of the elderly living alone in the city for new technologies. Then, big data, artificial intelligence, sophisticated electronic devices, and GPs-based positioning technologies are used to provide research ideas for the design of smart watches for the elderly living alone in the city. The intelligence can watch for the elderly’s daily movement and posture and also rate and other physiological signals for real-time monitoring, assessment of elderly’s physical health. The smart watches can realize the automatic alarm function of intelligence accidental falls, and they can also work with remote control and have a management platform, which has important social significance.
Short-Term Power Load Forecasting Based on SAPSO-CNN-LSTM Model considering Autocorrelated Errors
Authors: Zhang Yilin
Published: May 2022

Accurate power load forecasting is essential for power grid operation and dispatching. To further improve the accuracy of power load forecasting, this study proposes a new power load forecasting method. Firstly, correlation coefficients of influential variables are calculated for feature selection. Secondly, the form of input data is changed to adjust for autocorrelated errors. Thirdly, data features are extracted by convolutional neural networks (CNN) to construct feature vectors. Finally, the feature vectors are input into long short-term memory (LSTM) network for training to obtain prediction results. Moreover, for solving the problem that network hyperparameters are difficult to set, the simulated annealing particle swarm optimization (SAPSO) algorithm is used to optimize the hyperparameters. Experiments show that the prediction accuracy of the proposed model is higher compared with LSTM, CNN-LSTM, and other models.
A Whale Optimization Algorithm with Convergence and Exploitability Enhancement and Its Application
Authors: Liang Xiaodan , Zhang Zijian
Published: May 2022

The whale optimization algorithm (WOA) is inspired by humpback whale social behavior and WOA is a popular swarm intelligence algorithm. Yet, the WOA does have certain flaws, such as a restricted global search capability and an inconsistent convergence speed, and when dealing with complex optimization problems, WOA is easy to fall into local optimum. To address the WOA’s shortcomings, a modified whale optimization algorithm with convergence and exploitability enhancement called MWOA-CEE is proposed. Three operators, opposition-based learning, exponentially decreasing function, and elite-guided Cauchy mutation, are integrated into the WOA to enhance the exploration performance and avoid local optimum. First, the opposition-based learning used the opposition-based concept to generate the opposite position of the candidate solution, which can enhance the convergence ability and quality of the solution. Then, the exponentially decreasing function balances the exploitation and exploration phases and boosts the exploitation capability. In the end, the elite-guided Cauchy mutation generated a wide disturbance near the current candidate solution, which improves solutions diversity and increases the algorithm’s global search capability. MWOA-CEE is validated on 19 optimization benchmark functions and 14 CEC2014 benchmark functions and used to identify IIR systems. It is compared to seven of the commonly used algorithms. The results prove that MWOA-CEE outperforms the other algorithms in terms of final solution quality and convergence rate.
Recognition of Unknown Entities in Specific Financial Field Based on ERNIE-Doc-BiLSTM-CRF
Authors: Xin Li , Xiaoyan Hao
Published: May 2022

The Internet is rich in information related to the financial field. The financial entity information text containing new internet vocabulary has a certain impact on the results of existing recognition algorithms. How to solve the problems of new vocabulary and polysemy is a problem to be solved in the current field. This paper proposes an ERNIE-Doc-BiLSTM-CRF named entity recognition model based on the pretrained language model. Compared with the traditional model, the ERNIE-Doc pretrained language model constructs a unique word vector from the word vector and combines the location coding, which solves polysemy problem well. The intensive skimming mechanism realizes the long text processing well and captures the context information effectively. The experimental results show that the accuracy of this model is 86.72%, the recall rate is 83.39%, and the F1 value is 85.02%, which is 13.36% higher than other models; the recall rate is increased by 13.05%, and the F1 value is increased by 13.21%.
Proposing an Adaptive Neuro-Fuzzy System-Based Swarm Concept Method for Predicting the Physical Properties of Nanofluids
Authors: Han Gong , Seraj Amir
Published: May 2022

This paper employs dispersed nanoparticles (NPs) to build an adaptive neuro-fuzzy system (ANFIS) for predicting their thermal conductivity (TC) and viscosity according to the most important input data including concentration, size, the thickness of the interfacial layer, and intensive properties of NPs. In this regard, we gather an extensive and comprehensive data set from different sources. Here, the ANFIS model factors are optimized by using the particle swarm optimization (PSO) technique. Afterward, the obtained results are compared with previously published models which did a better job in predicting target values. In the following, to investigate the validity of our proposed model, statistical and graphical techniques are employed and it was proved that this model is efficient to evaluate the output values. Amounts of results obtained from the PSO-ANFIS model evaluation are 0.988 and 0.985 for the R2 and 0.0156 and 0.0876 for root mean squared error (RMSE) of TC ratio and viscosity ratio values, respectively, letting out a valid forecast of targets. Finally, by performing various statistical analyzes, it can be said that this model shows a high ability to predict target values and can be considered a good alternative to previous models.
Systematic Review of Literature Examining Bacterial Urinary Tract Infections in Diabetes
Authors: Paudel Santosh , John Preeti P. , Poorbaghi Seyedeh Leila , Randis Tara M. , Kulkarni Ritwij
Published: May 2022

This systematic review addresses the central research question, “what is known from the published, peer-reviewed literature about the impact of diabetes on the risk of bacterial urinary tract infections (UTI)?” We examine the results from laboratory studies where researchers have successfully adapted mouse models of diabetes to study the pathophysiology of ascending UTI. These studies have identified molecular and cellular effectors shaping immune defenses against infection of the diabetic urinary tract. In addition, we present evidence from clinical studies that in addition to diabetes, female gender, increased age, and diabetes-associated hyperglycemia, glycosuria, and immune impairment are important risk factors which further increase the risk of UTI in diabetic individuals. Clinical studies also show that the uropathogenic genera causing UTI are largely similar between diabetic and nondiabetic individuals, although diabetes significantly increases risk of UTI by drug-resistant uropathogenic bacteria.
Influence of Wet or Dry Conditions on the Fatigue Life of Grout Cementitious Materials under Cyclic Loading
Authors: Zhou Ruirong , Qin Xiaochuan , Sha Jianfang , Guo Fei , Qi Wu
Published: May 2022

Grouting cementing material is a kind of sealing cementing material widely used in underground space. However, the influence of the fatigue life after cementation hardening is often ignored when subjected to, for example, cyclic traffic loads. Therefore, it also often causes engineering problems such as joint cracking, leakage, and plate bottom emptiness. The prediction of fatigue life of grouting cementation material is of great significance to the stability of underground tunnel structure. In this paper, the fatigue characteristics of hardened grout specimens under different fatigue loads and cycle frequencies are studied. Test the cyclic compression load repeatedly in different environments (dry or wet). The results show that the fatigue properties of grout materials are reduced in both air and water and significantly reduced when the stress level is 65% or above of static compressive strength in wet environment, especially when the loading frequency in water drops from 10 to 35 Hz. The main mechanism of fatigue life decline is splitting effect caused by water extrusion in the sample under cyclic loading. Water washes away abrasive action in the cracks. The dry-wet fatigue aging characteristics of superhigh strength grout cement materials lay a foundation for the evolution simulation of underground foundation structures under cyclic loading.
Prediction and Optimization of Stability Parameters of Borehole Sensor for Deep Water Drilling Based on Genetic Algorithm
Authors: Huang Jing , Xia Qian , Cai Rao , Zhou Kunling , Ren Zhiquan , Yan Huachi , Asif Mohammad
Published: May 2022

In order to study the prediction and optimization of borehole stability parameters in deepwater drilling based on genetic algorithm. First, a genetic hybrid algorithm based on pattern search is proposed. Then, based on the adaptive genetic algorithm, the evolutionary population is searched for patterns, which makes the hybrid algorithm not only has a strong global search ability but also improves the local optimization accuracy. Finally, the unit footage cost in the drilling process is taken as the objective function, and the algorithm is verified by taking the drilling in Karamay area as an example. The calculation results show that if the bit wear reaches 0.8-0.9 and then the bit is pulled out, the utilization rate of the bit can be increased, the design efficiency and accuracy can be improved, and the drilling cost can be reduced. The wear amount of the optimized bit is higher than that of the actual bit. Increasing the utilization rate of the bit can reduce the cost of drilling meters to a certain extent and improve the economic benefits of drilling. The objective function and constraint conditions for the optimization of drilling parameters are determined, and the algorithm is verified with the drilling data of Karamay Oilfield. The results show that the algorithm improves the stability and speed of iterative convergence and improves the reliability of data analysis results. Based on the regional three-dimensional formation rock parameter data volume, the optimization method can be used to optimize the drilling parameters before drilling and provide a basis for formulating the drilling design scheme.
Biological and Immune Responses to Current Anti-SARS-CoV-2 mRNA Vaccines beyond Anti-Spike Antibody Production
Authors: Federico Maurizio
Published: May 2022

Several vaccine strategies are now available to fight the current SARS-CoV-2 pandemic. Those based on the administration of lipid-complexed messenger(m)RNA molecules represent the last frontiers in terms of technology innovation. mRNA molecules coding for the SARS-CoV-2 Spike protein are intramuscularly injected, thereby entering cells by virtue of their encapsulation into synthetic lipid nanovesicles. mRNA-targeted cells express the Spike protein on their plasma membrane in a way that it can be sensed by the immune system, which reacts generating anti-Spike antibodies. Although this class of vaccines appears as the most effective against SARS-CoV-2 infection and disease, their safety and efficiency are challenged by several factors included, but not limited to the following: emergence of viral variants, lack of adequate pharmacokinetics/pharmacodynamics studies, inability to protect oral mucosa from infection, and antibody waning. Emergence of viral variants can be a consequence of mass vaccination carried out in a pandemic time using suboptimal vaccines against an RNA virus. On the other hand, understanding the remainder flaws could be of some help in designing next generation anti-SARS-CoV-2 vaccines. In this commentary, issues regarding the fate of injected mRNA, the tissue distribution of the induced antiviral antibodies, and the generation of memory B cells are discussed. Careful evaluation of both experimental and clinical observations on these key aspects should be taken into account before planning booster administration, vaccination to non-at-risk population, and social restrictions.
The Overexpression of SLC25A13 Predicts Poor Prognosis and Is Correlated with Immune Cell Infiltration in Patients with Skin Cutaneous Melanoma
Authors: lv Yue , Yuan Chun-hui , Han Lu-yao , Huang Gao-ru , Ju Ling-ce , Chen Ling-hui , Han Hai-ying , Zhang Chong , Zeng Ling-hui
Published: May 2022

Purpose. Skin cutaneous melanoma (SKCM) is one of the most malignant and aggressive cancers with poor prognosis due to its rapid progression towards metastasis. Thus, finding clinically relevant biomarkers for early diagnosis, prognosis, and therapy prediction is essential. This study focused on the identification of SLC25A13 as a novel biomarker for SKCM and is aimed at investigating the biological functions of solute carrier family 25 member 13 (SLC25A13) in the development of SKCM. Methods. GEPIA was used to analyze the diagnostic and prognostic values of SLC25A13 in SKCM using the TCGA dataset. PrognoScan was used to validate the prognostic value of SLC25A13 and its coexpressed genes in SKCM. TISIDB was established to reveal the relationship between the expression of SLC25A13 and immune infiltration in SKCM. The protein expression of SLC25A13 in SKCM was evaluated by the Human Protein Atlas. The signaling pathways and biological functions of SLC25A13 in SKCM were analyzed by LinkOmics. Metascape was applied to analyze the functional enrichment analysis of SLC25A13. Protein-protein interaction analysis of SLC25A13 was performed by GeneMANIA. Results. The mRNA and protein levels of SLC25A13 in the SKCM were much higher than those in the normal tissue. Furthermore, the overexpression of SLC25A13 predicts worse outcomes of SKCM patients. Moreover, the SLC25A13 expression was negatively correlated with the immune infiltration level of SKCM. The overexpression of SLC25A13 coexpressed genes, such as ACLY and AFG3L2, and SCL25A13 interacting genes also predicted the unfavorable prognosis of SKCM patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of SLC25A13 coexpressed genes showed that these genes are enriched in ATPase activity, cell cycle, mTOR, and VEGFA-VEGFR2 signaling pathways, which were relevant to tumor development and angiogenesis. Gene set enrichment analysis (GSEA) demonstrated that the SLC25A13 expression was related to infiltrating immune cells in SKCM. Conclusion. Our findings revealed that SLC25A13 might be a potential prognostic and therapeutic biomarker for SKCM.
B|Orders in Motion in the Video Game Industry: An Analysis Based on Animal Crossing: New Horizons
Authors: Benti Behailu Shiferaw , Stadtmann Georg
Published: May 2022

In 2020, the video game Animal Crossing: New Horizons was listed among the top ten in terms of revenue. This success can be highlighted by looking at interconnectedness between the game—existing in the virtual world—and various aspects of life existing in the real world. To do so, we analyze the game by relying on an interdisciplinary framework of border studies. This framework expresses that the three states of borders (durability, permeability, and liminality) can be interpreted not only in a geographical sense but also in terms of a temporal dimension as well as a cultural dimension. Considering cultural (fashion and museums), political, and economical aspects of life, we highlight how this game blurs the borders between the real and virtual world. Furthermore, our findings assert that not only borders but also orders can change over time (B|Orders are in motion).
Morphological Edge Detection Algorithm of Colon Pathological Sections Based on Shearlet
Authors: Li Shasha , Deng Caixia , Wang Tong , Zhang Zhaoru
Published: May 2022

This paper proposes an idea of combining the Meyer Shearlet and mathematical morphology to produce the edge detection of pathological sections of the colon. First, the method of constructing a class of sufficiently smooth sigmoid functions along with its relative scale function and Meyer wavelet function is provided in this paper. Based on those, in order to get the new Meyer wavelet function, we use the sigmoid function to construct more general scale functions. Next, taking sufficiently smooth sigmoid functions as examples, combining the relative Meyer wavelet and Shearlet to denoise some pathological sections of the colon leads a decent feedback. At last, this paper provides an improved algorithm for the edge detection of mathematical morphology with the background of multiscale and multistructure. This algorithm is used to carry out the edge detection of images after denoising yields a new edge detection algorithm that fuses the Meyer Shearlet denoising and mathematical morphology. According to the simulation results, the new algorithm is more beneficial for the observation and diagnosis of doctors since the edge noise of the colon pathological image detected by the new algorithm is smaller and provides more continuous and clear lines. Therefore, the fusion algorithm provided in this paper is an effective way to carry out the edge detection of an image.
A Family of
A
-Stable Optimized Hybrid Block Methods for Integrating Stiff Differential Systems
Authors: Singla Rajat , Singh Gurjinder , Ramos Higinio , Kanwar V.
Published: May 2022

In this article, a family of one-step hybrid block methods having two intrastep points is developed for solving first-order initial value stiff differential systems that occur frequently in science and engineering. In each method of the family, an intrastep point controls the order of the main method and a second one has a control over the stability features of the method. The approach used to develop the class of

A

-stable methods is based on interpolation and collocation procedures. The methods exhibit hybrid nature and produce numerical solutions at several points simultaneously. These methods can also be formulated as Runge-Kutta (RK) methods. Comparisons between the RK and block formulations of the proposed methods reveal a better performance of the block formulation in terms of computational efficiency. Furthermore, the efficiency of the methods is improved when they are formulated as adaptive step-size solvers using an error-control approach. Some methods of the proposed class have been tested to solve some well-known stiff differential systems. The numerical experiments show that the proposed family of methods performs well in comparison with some of the existing methods in the scientific literature.
Predicting the Kidney Graft Survival Using Optimized African Buffalo-Based Artificial Neural Network
Authors: Chawla Riddhi , Balaji S. , Alabdali Raed N. , Naguib Ibrahim A. , Hamed Nadir O. , Zahran Heba Y.
Published: May 2022

A variety of receptor and donor characteristics influence long-and short-term kidney graft survival. It is critical to predict the effectiveness of kidney transplantation to optimise organ allocation. This would allow patients to choose the best accessible kidney donor and the optimal immunosuppressive medication. Several studies have attempted to identify factors that predispose to graft rejection, but the results have been contradictory. As a result, the goal of this paper is to use the African buffalo-based artificial neural network (AB-ANN) approach to uncover predictive risk variables related to kidney graft. These two feature selection approaches combine to provide a novel hybrid feature selection technique that could select the most important elements to improve prediction accuracy. The feature analysis revealed that clinical features have varied effects on transplant survival. The collected data is processed in both training and testing methods. The prediction model's performance, in terms of accuracy, precision, recall, and F-measure, was examined, and the results were compared with those of other existing systems, including naive Bayesian, random forest, and J48 classifier. The results suggest that the proposed approach can forecast graft survival in kidney recipients' next visits in a creative manner and with more accuracy compared with other classifiers. This proposed method is more efficient for predicting kidney graft survival. Incorporating those clinical tools into outpatient clinics’ everyday workflows could help physicians make better and more personalised decisions.
Associations between Autoimmunity and Depression: Serum IL-6 and IL-17 Have Directly Impact on the HAMD Scores in Patients with First-Episode Depressive Disorder
Authors: Mao Lijuan , Ren Xiajin , Wang Xin , Tian Feng
Published: May 2022

Objective. The study is aimed at evaluating the immune-activation state before and after treatment in patients with first-episode depressive disorder (FDD) with evaluating the ILs and CRP levels and further clarifying the association between autoimmunity and the etiology and pathogenesis of FDD. Methods. We designed a case-control study. FDD patients and healthy subjects were enrolled in the FDD group and control group. Serum IL-6, IL-17, and CRP were measured before and after selective serotonin reuptake inhibitor (SSRI) therapy, as well as Hamilton rating scale for depression (HAMD) and life event scale (LES) scores. The correlations between IL-6 and IL-17 and HAMD and LES scores were analysed, and multiple linear regression analysis was performed for HAMD score. Results. 40 FDD patients and 40 healthy subjects were included in the FDD and control group from October 2009 to September 2012. Before treatment, the IL-6 (

28.99
±
5.51

,

P
<
0.001

) and IL-17 (

41.15
±
4.80

,

P
<
0.001

) in the FDD group were significantly higher than the control group (

16.84
±
3.78

and

21.68
±
3.72

, respectively). The C-reactive protein (CRP) level in two groups was comparable (

P
=
0.879

). After treatment, the IL-6 (

18.69
±
5.07

,

P
<
0.001

) and IL-17 (

30.67
±
3.47

,

P
<
0.001

) levels and HAMD scores (

6.73
±
4.15

) in the FDD group were significantly decreased than before treatment (

P
<
0.001

, respectively). CRP level was slightly increased after treatment without statistically significant (

P
=
0.239

). The HAMD score correlated with IL-6 (

r
=
0.638

,

P
<
0.001

) and IL-17 (

r
=
0.927

,

P
<
0.001

); the total LES and negative LES also correlated with IL-6 (

r
=
0.226

,

P
<
0.05

) (

r
=
0.366

, P <0.001) and IL-17 (

r
=
0.348

,

P
<
0.001

) (

r
=
0.493

,

P
<
0.001

). Multiple linear regression analysis showed that both of the IL-6 and IL-17 had direct impact on HAMD score. Conclusion. The autoimmunity status was overactivated in FDD patients, and serum IL-6 and IL-17 levels had direct impact on the HAMD score. Patients who experienced more negative life events had higher activation level of autoimmunity status and HAMD scores, and serum IL-6 and IL-17 levels can be decreased by SSRI treatment.
A Massive Ewing Sarcoma of the Rib: A Case Report with Literature Review
Authors: Fahad Naseem , Hamideh Dima , Zareef Rana , Akel Samir
Published: May 2022

A 9-year-old boy with a 16 cm chest wall mass, presenting with progressive cough and exertional dyspnea, was finally diagnosed with Ewing sarcoma of the rib. Such massive tumors usually present with metastasis and carry a bad prognosis. Fortunately, we present here a successful treatment approach for Ewing sarcoma of the ribs, defeating the overwhelming obstacles commonly faced in chest wall tumors. Delays in diagnosis, misdiagnosis, difficulty with general anesthesia, opportunistic infections, disruptions in chemotherapy delivery, and debilitating chest wall deformities are all potential challenges that could complicate the course of treatment.
Diagnostic and Prognostic Significance of microRNA-208a in Acute Myocardial Infarction
Authors: Chen Sheng , Hong Xin , Wu Yong , Chen Ziguo
Published: May 2022

Objective. To determine the prognostic and diagnostic significance of microRNA-208a (miR-208a) in acute myocardial infarction (AMI). Methods. Totally, 84 AMI patients hospitalized in our hospital between Jan. 2019 and Feb. 2021 were enrolled as the patient group (Pat group), and 50 healthy individuals over same time span as the control group (Con group). qRT-PCR assay was carried out to quantify serum miR-208a in the patients and receiver-operating characteristic (ROC) curves for analysing its diagnostic value in AMI patients and its predictive value in clinical efficacy and adverse events in the patients after therapy. The changes of miR-208a and clinical indexes ((lactate dehydrogenase (LDH), creatine kinase (CK) as well as Creatine kinase-MB (CK-MB)) in the patients before and after therapy were evaluated. Pearson’s test was adopted to analyse the associations of miR-208a with clinical indexes. Additionally, the target genes of miR-208a were forecasted. Results. The patient group showed a higher miR-208a level than the control group (

p
<
0.05

), and the area under the curve (AUC) of miR-208a in diagnosing AMI was >0.9. After therapy, patients presented notable decreases in serum miR-208a, LDH, CK, and CK-MB (all

p
<
0.05

). Serum miR-208a presented positive associations with LDH, CK, as well as CK-MB both before and after therapy (all

p
<
0.05

). Before therapy, the ineffective group presented a higher miR-208a level than the effective group (

p
<
0.05

), and miR-208a had an AUC of 0.784 in forecasting efficacy. Additionally, the group with adverse events presented a higher miR-208a level than the group without them before therapy (

p
<
0.05

), and miR-208a had an AUC of 0.713 in forecasting adverse events. According to enrichment analysis, the target genes of miR-208a were bound up with signal pathways of cellular senescence, MTOR and Wnt. Conclusion. With high expression in AMI cases, miR-208a is a promising potential biomarker for diagnosis and prognosis forecasting of AMI.
Numerical Simulation of Coupling Support for High‐Stress Fractured Soft Rock Roadway in Deep Mine
Authors: Yuan Wenhua , Hong Ke , Liu Run , Ji Lianjie , Meng Long
Published: May 2022

Aiming at the difficulties during the support of high‐stress fractured soft rock roadway in deep mine, a comprehensive surrounding rock management method of bolt‐net‐cable‐grout coupling support is proposed and the mechanism of interaction between coupling support and surrounding rock is analyzed by numerical simulation. The effectiveness of the coupling support is proved by an application in the east wing return‐air roadway in the Qingdong Coal Mine of Huaibei Mining Group. The results show that the surrounding rock plastic zone near the sidewall and floor of high‐stress fractured soft rock roadway is larger than that near the roadway roof, and its distribution range can be reduced by using the coupling support. And, the coupling support can improve the reliability of roadway support and the stability of surrounding rock by reducing the axial stress of anchor bolts, the stress concentration of surrounding rock caused by anchor bolt, the roadway surface displacement, and deep displacement of surrounding rock.