bluuebunny/crossref_metadata_2025 huggingface dataset:
Formal Methods in Railways: A Systematic Mapping Study
Authors: Ferrari Alessio , Beek Maurice H. Ter
Published: March 2022

Formal methods are mathematically based techniques for the rigorous development of software-intensive systems. The railway signaling domain is a field in which formal methods have traditionally been applied, with several success stories. This article reports on a mapping study that surveys the landscape of research on applications of formal methods to the development of railway systems. Following the guidelines of systematic reviews, we identify 328 relevant primary studies, and extract information about their demographics, the characteristics of formal methods used and railway-specific aspects. Our main results are as follows: (i) we identify a total of 328 primary studies relevant to our scope published between 1989 and 2020, of which 44% published during the last 5 years and 24% involved industry; (ii) the majority of studies are evaluated through Examples (41%) and Experience Reports (38%), while full-fledged Case Studies are limited (1.5%); (iii) Model checking is the most commonly adopted technique (47%), followed by simulation (27%) and theorem proving (19.5%); (iv) the dominant languages are UML (18%) and B (15%), while frequently used tools are ProB (9%), NuSMV (8%), and UPPAAL (7%); however, a diverse landscape of languages and tools is employed; (v) the majority of systems are interlocking products (40%), followed by models of high-level control logic (27%); and (vi) most of the studies focus on the Architecture (66%) and Detailed Design (45%) development phases. Based on these findings, we highlight current research gaps and expected actions. In particular, the need to focus on more empirically sound research methods, such as Case Studies and Controlled Experiments, and to lower the degree of abstraction, by applying formal methods and tools to development phases that are closer to software development. Our study contributes with an empirically based perspective on the future of research and practice in formal methods applications for railways. It can be used by formal methods researchers to better focus their scientific inquiries, and by railway practitioners for an improved understanding of the interplay between formal methods and their specific application domain.
Methods—Unexpected Effects in Galvanostatic EIS of Randles’ Cells: Initial Transients and Harmonics Generated
Authors: Katırcı Gökberk , Zabara Mohammed Ahmed , Ülgüt Burak
Published: March 2022

Non-linear EIS analysis is gaining wider attention and interest due to the deeper understanding that is provided especially when combined with linear EIS. The nonlinear part of the data can get corrupted due to a number of effects. One of these is the initial transient, which is the response right after excitation signal is applied before a steady-state is reached. In this study, we demonstrate this phenomenon through analyzing simplified Randles’ cells via Kirchoff’s laws. To get rid of the effects of the initial transient, instrument manufacturers typically discard some fraction of the response, the effectiveness of which, as demonstrated here has to be checked.
Photoinduced Charge Transport in Films of Near-Infrared Dye Assisted by Nanoscale Inhomogeneity of Dye Aggregates
Authors: Dimitriev Oleg , Smertenko Petro , Slominskii Yuri
Published: March 2022

To clarify the mechanism of a photoinduced charge transport in films of relatively small organic molecules, i.e., whether it is stimulated by energetic and/or structural disorder, three types of films composed of a specific tricarbocyanine near-infrared dye were prepared, i.e., a polycrystalline film, an amorphous film composed of a mixture of monomers and J- and H-aggregates, and a composite dye film in a polymer matrix composed predominantly of dye J-aggregates. It was found that in most cases the charge transport is governed by trap-limited conduction; however, the suggested driving force for the photoinduced transport of charge carriers can be interpreted to be due to the structural inhomogeneity (i.e., different aggregate types) and energetic disorder in the dye film, where Gaussian and discrete, rather than exponential, trap distributions play a decisive role. It is concluded that intermolecular interactions in J- and H-aggregates of π-conjugated molecules provide fascinating incentives for charge transfer pathways.
Deep Learning Algorithm‐Based MRI Image in the Diagnosis of Diabetic Macular Edema
Authors: Han Xiuping , Tan Juan , He Yumei
Published: March 2022

This study investigates the value of magnetic resonance imaging (MRI) based on a deep learning algorithm in the diagnosis of diabetic macular edema (DME) patients. A total of 96 patients with DME were randomly divided into the experimental group (N  = 48) and the control group (N  = 48). A deep learning 3D convolutional neural network (3D‐CNN) algorithm for MRI images of patients with DME was designed. The application value of this algorithm was comprehensively evaluated by MRI image segmentation Dice value, sensitivity, specificity, and other indicators and diagnostic accuracy. The results showed that the quality of MRI images processed by the 3D‐CNN algorithm based on deep learning was significantly improved, and the Dice value, sensitivity, and specificity index data were significantly better than those of the traditional CNN algorithm (P < 0.05). In addition, the diagnostic accuracy of MRI images processed by this algorithm was 93.78 ± 5.32%, which was significantly better than the diagnostic accuracy of 64.25 ± 10.24% of traditional MRI images in the control group (P < 0.05). In summary, the 3D‐CNN algorithm based on deep learning can significantly improve the accuracy and sensitivity of MRI image recognition and segmentation in patients with DME, can significantly improve the diagnostic accuracy of MRI in patients with DME, and has a good clinical application value.
Effect of Ground Motion Orientation on Seismic Responses of an Asymmetric Stress Ribbon Pedestrian Bridge
Authors: Zhang Yi , Pu Wuchuan , Zhang Qishuo , Liu Kaiwen , Dong Hua
Published: March 2022

The stress ribbon bridge uses ribbon in high tension to transfer loads and exhibits geometric nonlinearity under dynamic earthquake excitations. A typical double‐span asymmetric stress ribbon pedestrian bridge was introduced as a prototype, and nonlinear time history analysis was performed to investigate the effect of ground motion orientation on the structural responses. Non‐pulse‐type and pulse‐type ground motions were considered, and the influence of vertical ground motions was investigated. Numerical results showed that the stress ribbon bridge’s vertical and transverse displacements were sensitive to the excitation of the vertical ground motion. No single orientation led to the most critical values in all response indexes, and the critical orientation was approximately independent of the vertical ground motion. The negative bending moment of the ribbons, the pier top displacement, and the pier base moment at the transverse direction were sensitive to the ground motion orientation. Checking the responses resulting from different directions is necessary for a comprehensive estimation of the seismic performance of the stress ribbon bridge.
Human Umbilical Cord Mesenchymal Stem Cells Prevent Bacterial Biofilm Formation
Authors: Yang Haoming , Xu Fang , Zheng Xuaner , Yang Shumei , Ren Zhuxiao , Yang Jie
Published: March 2022

Biofilm formation is easily found in patients suffered from ventilator‐associated pneumonia (VAP) in neonatal intensive care unit (NICU) and makes the VAP infections not only harder to be treated but easier to relapse. In order to find some novel ways to inhibit biofilm formation, this study describe a previously unrecognized role for the human umbilical cord mesenchymal stem cells (hUCMSCs). In addition to multiple differentiation, hUCMSCs have the ability to prevent the biofilms formation in vitro by secreting antibacterial peptides (LL‐37 and hBD‐2). This occurred while P. aeruginosa PA27853 and hUCMSCs were cocultured, and the filtrated medium, which was the supernatant containing antibacterial peptides (5.9 ng/ml of LL‐37, 1.77 ng/ml of hBD‐2), and inhibited the growth of the bacterial biofilm on the surface of tracheal tube (2.5#, for preterm infant). Using microarrays, we were able to demonstrate that the antibacterial peptides from hUCMSC affected biofilm formation by downregulating the gene‐encoded polysaccharide biosynthesis protein. In addition, in order to find out the most suitable concentration of hUCMSCs, P. aeruginosa was cocultured with eight‐level concentrations of hUCMSCs, and we found that the concentration of LL‐37 was positively correlated with the concentration of hUCMSCs. Meanwhile, the concentration of LL‐37 became stable while the hUCMSC concentration reaches higher than 5 × 106 cells/ml. But the concentration of hBD‐2 had no significant correlation with hUCMSCs. The collection of these stem cells is not only limited by ethics but also reduces host rejection. This makes it possible to use autologous hUCMSCs to treat neonatal VAP.
Meloxicam Alleviates Sepsis-Induced Kidney Injury by Suppression of Inflammation and Apoptosis via Upregulating GPNMB
Authors: Zhao Shilei , Cui Lei , Zheng Xiufeng , Ji Ying , Yu Chengyuan
Published: March 2022

Objective. At present, renal injury caused by sepsis seriously endangers the health of patients. Our paper proposed to study the protective effects of meloxicam (Mel) in sepsis-induced acute kidney injury (SAKI) and the underlying mechanisms. Methods. The in vitro and in vivo models of SAKI were established using lipopolysaccharide (LPS). Mel was injected intraperitoneally at 60 mg/kg into male C57BL/6 mice 4 hours before LPS injection (10 mg/kg). The HK-2 cells were treated with LPS (1 μg/mL) and Mel (40 μM). The renal function and renal pathological changes as well as renal inflammation and apoptosis were detected in SAKI mice. The inflammation and apoptosis of HK-2 cells induced by LPS were also detected. Results. The treatment of Mel significantly decreased the elevated levels of serum creatinine (Scr) and blood urea nitrogen (BUN) in SAKI mice. In addition, the results of HE staining suggested that Mel significantly reduced kidney damage in SAKI mice. Consistently, Mel reduced the expression of LPS-induced kidney injury markers (NGAL and KIM-1). Moreover, LPS induced the expression of inflammatory cytokines (IL-1β, IL-6, and TNF-α) in the kidney, which can be reduced by Mel. Furthermore, Mel effectively reduced the number of apoptotic cells and inhibited the expression of proapoptotic-related proteins (cleaved Caspase-3 and Bax) but increased the antiapoptotic-related protein (Bcl-2) in the kidneys of SAKI mice. Mechanistically, Mel inhibited the phosphorylation of P65 but induced the phosphorylation of AKT and the expression of glycoprotein B of nonmetastatic melanoma (GPNMB). However, knocking down GPNMB can eliminate the anti-inflammatory and antiapoptotic effects of Mel. Conclusion. Mel alleviated sepsis-induced kidney injury by inhibiting kidney inflammation and apoptosis via upregulating GPNMB.
Research on the Impact of Embedded Intelligent Robots on English News Dissemination
Authors: Xu Yushu , Gu Weixing
Published: March 2022

With the rapid development of the Internet, although users can obtain information conveniently and quickly, they still cannot accurately obtain the target information they need in a short period of time in the face of massive information content. The use of embedded intelligent robots in the English news industry promotes the “Internet +” strategy, the goal of which is to connect everything. This article is aimed at studying the impact of embedded intelligent robots on the dissemination of English news. This article first analyzes embedded intelligent robots and news dissemination and then analyzes the collaborative filtering algorithm and text analysis technology of the recommendation system. Then, this article discusses the impact of embedded intelligent robot writing on English news dissemination and news media in the context of artificial intelligence. Direction of Development. Finally, in the experimental part, this paper implements a news recommendation system based on item recommendation algorithm and text analysis technology. Experiments have proved that the news recommendation algorithm of this system has a high hit rate and has great practical application value. This article sets each recommended page to 15 news, then randomly selects 5 users, and counts the number of news they visit, and the average number of news visited is 9.22. The results show that the embedded robot is helpful for English news dissemination, increasing the dissemination breadth by 20% and the dissemination accuracy by 30%.
Pseudospectral Method Based on Müntz–Legendre Wavelets for Solving the Abel Integral Equation
Authors: Dassios Ioannis , Tchier Fairouz , Tawfiq F. M. O.
Published: March 2022

This paper deals with the numerical solution of the Abel integral equation based on Müntz–Legendre wavelets. To this end, the Abel integral operator is represented by Müntz–Legendre wavelets as an operational matrix. To find this matrix, we use the similarity between the Abel integral operator and the fractional integral operator. The proposed method can be easily used to solve weakly singular Volterra integral equations. We have proved the convergence of the proposed method. To demonstrate the ability and accuracy of the method, some numerical examples are presented.
A Real-Time Framework for Human Face Detection and Recognition in CCTV Images
Authors: Ullah Rehmat , Hayat Hassan , Siddiqui Afsah Abid , Siddiqui Uzma Abid , Khan Jebran , Ullah Farman , Hassan Shoaib , Hasan Laiq , Albattah Waleed , Islam Muhammad , Karami Ghulam Mohammad
Published: March 2022

This paper aims to develop a machine learning and deep learning-based real-time framework for detecting and recognizing human faces in closed-circuit television (CCTV) images. The traditional CCTV system needs a human for 24/7 monitoring, which is costly and insufficient. The automatic recognition system of faces in CCTV images with minimum human intervention and reduced cost can help many organizations, such as law enforcement, identifying the suspects, missing people, and people entering a restricted territory. However, image-based recognition has many issues, such as scaling, rotation, cluttered backgrounds, and variation in light intensity. This paper aims to develop a CCTV image-based human face recognition system using different techniques for feature extraction and face recognition. The proposed system includes image acquisition from CCTV, image preprocessing, face detection, localization, extraction from the acquired images, and recognition. We use two feature extraction algorithms, principal component analysis (PCA) and convolutional neural network (CNN). We use and compare the performance of the algorithms K-nearest neighbor (KNN), decision tree, random forest, and CNN. The recognition is done by applying these techniques to the dataset with more than 40K acquired real-time images at different settings such as light level, rotation, and scaling for simulation and performance evaluation. Finally, we recognized faces with a minimum computing time and an accuracy of more than 90%.
Research on Financial Risk Prediction Based on Improved Random Subspace
Authors: Li Yinghui
Published: March 2022

In order to provide timely and effective information and decision support for financial market entities, combined with random subspace and weight fused Lasso, this paper constructs a financial risk prediction model based on the improved random subspace method. Firstly, the basic principles of random subspace and SVM algorithm are introduced. Then, WFL and Al methods are introduced to improve random subspace, so as to reduce the dimension of multisource heterogeneous data and realize the adaptive fusion of features. Then, a financial risk prediction model based on weighted fusion adaptive random subspace is constructed, in which SVM is used as the basic classifier and the output strategy of result integration is introduced. Finally, based on the data of some listed companies, the improved random subspace method is compared with other methods. The results show that the improved random subspace method has a higher prediction value, which indicates that the method is reasonable and effective in financial risk prediction. In the improved random subspace method, combined feature F1 + F2 + F3 is better than other methods in T − 3, T − 4, and T − 5, and the prediction value is more than 95%, which fully demonstrates the rationality of the improved random subspace method in financial risk prediction. The area under the ROC curve (AUC) predicted by weight fused adaptive integration-based random subspace (FAIB_RS) method is about 95% in T − 3, 93% in T − 4, and 95.5% in T − 5, which is obviously higher than that of the other eight methods.
A Metaheuristic Autoencoder Deep Learning Model for Intrusion Detector System
Authors: Pandey Jay Kumar , Kumar Sumit , Lamin Madonna , Gupta Suneet , Dubey Rajesh Kumar , Sammy F.
Published: March 2022

A multichannel autoencoder deep learning approach is developed to address the present intrusion detection systems’ detection accuracy and false alarm rate. First, two separate autoencoders are trained with average traffic and assault traffic. The original samples and the two additional feature vectors comprise a multichannel feature vector. Next, a one-dimensional convolution neural network (CNN) learns probable relationships across channels to better discriminate between ordinary and attack traffic. Unaided multichannel characteristic learning and supervised cross-channel characteristic dependency are used to develop an effective intrusion detection model. The scope of this research is that the method described in this study may significantly minimize false positives while also improving the detection accuracy of unknown attacks, which is the focus of this paper. This research was done in order to improve intrusion detection prediction performance. The autoencoder can successfully reduce the number of features while also allowing for easy integration with different neural networks; it can reduce the time it takes to train a model while also improving its detection accuracy. An evolutionary algorithm is utilized to discover the ideal topology set of the CNN model to maximize the hyperparameters and improve the network’s capacity to recognize interchannel dependencies. This paper is based on the multichannel autoencoder’s effectiveness; the fourth experiment is a comparative analysis, which proves the benefits of the approach in this article by correlating it to the findings of various different intrusion detection methods. This technique outperforms previous intrusion detection algorithms in several datasets and has superior forecast accuracy.
Artificial Intelligence Algorithm‐Based Intraoperative Magnetic Resonance Navigation for Glioma Resection
Authors: Wei Jianqiang , Zhang Chunman , Ma Liujia , Zhang Chunrui
Published: March 2022

The study aimed to analyze the application value of artificial intelligence algorithm‐based intraoperative magnetic resonance imaging (iMRI) in neurosurgical glioma resection. 108 patients with glioma in a hospital were selected and divided into the experimental group (intraoperative magnetic resonance assisted glioma resection) and the control group (conventional surgical experience resection), with 54 patients in each group. After the resection, the tumor resection rate, NIHSS (National Institute of Health Stroke Scale) score, Karnofsky score, and postoperative intracranial infection were calculated in the two groups. The results revealed that the average tumor resection rate in the experimental group was significantly higher than that in the control group (P < 0.05). There was no significant difference in Karnofsky score before and after the operation in the experimental group (P > 0.05). There was no significant difference in NIHSS score between the experimental group and the control group after resection (P > 0.05). The number of patients with postoperative neurological deficits in the experimental group was smaller than that in the control group. In addition, there was no significant difference in infection rates between the two groups after glioma resection (P > 0.05). In summary, intraoperative magnetic resonance navigation on the basis of a segmentation dictionary learning algorithm has great clinical value in neurosurgical glioma resection. It can maximize the removal of tumors and ensure the integrity of neurological function while avoiding an increased risk of postoperative infection, which is of great significance for the treatment of glioma.
Probiotic Foods Are Effective on Weight Loss, Biochemical Parameters, and Intestinal Microbiota in Wistar Albino Rats with Obese Microbiota
Authors: Tarakci Nadide Gizem , Erdem Nihal Zekiye , Dumen Emek
Published: March 2022

The positive effects of various probiotic foods on weight control, intestinal microbiota, and biochemical markers have been proven by various studies. However, there is no study on such effects of tarhana and kefir + tarhana consumption, a type of Turkish food rich in Lactobacillus spp., Pediococcus pentosaceus, Pediococcus acidilactici, and Saccharomyces cerevisiae. This study aimed to determine the changes caused by regular consumption of kefir and/or tarhana for 6 months on weight gain, intestinal microbiota, and biochemical parameters in Wistar albino rats with obese microbiota. Therefore, thirty-five rats were fed with five different methods of oral gavage (n = 7 per group): Normal Diet Control (NDC), High Fat Diet Control (HFDC), 6 mL/kg Kefir + High Fat Diet (Kefir + HFD), 0.2 g/kg Tarhana + High Fat Diet (Tarhana + HFD), and 6 mL/kg Kefir + 0.2 g/kg Tarhana + High Fat Diet (Kefir + Tarhana + HFD). Normality tests were evaluated using the One-Sample Kolmogorov test and Histogram graph. Multiple group comparisons were performed using one-way ANOVA and Tukey’s HSD post hoc test, and the statistical significances were indicated by different letters (

p
<
0.05

). Comparisons by gender were performed using the independent samples t-test. Kefir consumption was more effective on decreasing weight gain. Obese microbiota significantly increased blood glucose level and decreased red blood cell (RBC), hematocrit (HCT), hemoglobin, mean corpuscular hemoglobin (MCH), mean corpuscular hemoglobin concentration (MCHC), platelets (PLT), and white blood cells. RBC and HCT values in Kefir + HFD, PLT value in Tarhana + HFD, and mean corpuscular volume (MCV), MCH, and MCHC values in Kefir + Tarhana + HFD were higher than those of other groups (

p
<
0.05

). Kefir + tarhana consumption significantly showed an increase in blood glucose. Kefir and/or tarhana induced the abundance of Lactobacillus and blocked the abundances of total coliform bacteria and Escherichia coli (

p
<
0.05

). We demonstrated that kefir was effective in decreasing weight gain, and all dietary interventions induced positive alterations on biochemical findings and intestinal microbiota.
Research on IQ Imbalance Error of Orthogonal Alternating Sampling
Authors: Luo Junyi , Liu Tao
Published: March 2022

In the signal acquisition of complete orthogonal frequency alternating acquisition system, we need to transform the broadband input signal to baseband through analogue orthogonal demodulation. Thus, the IQ imbalance errors will inevitably exist, including amplitude imbalance error and phase imbalance error, resulting in distortion deterioration of reconstruction signals. This paper makes an in‐depth study on such IQ imbalance errors and mathematically deduces the mathematical expressions of imbalance errors and the spurious frequency distribution caused by them, which shows that the positions where the spurious signals appear are closely related to the frequency of input signal and the tuning frequency of analogue quadrature demodulation. Meanwhile, the amplitude and phase imbalance parameters of the channels I and Q are also calculated. Finally, the spurious effects caused by imbalance errors are verified by simulation experiment, and the verification results are consistent with the theoretical derivation results.
Evaluation of the Threshold for an Improved Surface Water Extraction Index Using Optical Remote Sensing Data
Authors: Yulianto Fajar , Kushardono Dony , Budhiman Syarif , Nugroho Gatot , Chulafak Galdita Aruba , Dewi Esthi Kurnia , Pambudi Anjar Ilham
Published: March 2022

In this study, we proposed an automatic water extraction index (AWEI) threshold improvement model that can be used to detect lake surface water based on optical remote sensing data. An annual Landsat 8 mosaic was created using the Google Earth Engine (GEE) platform to obtain cloud-free satellite image data. The challenge of this study was to determine the threshold value, which is essential to show the boundary between water and nonwater. The AWEI was selected for the study to address this challenge. The AWEI approach was developed by adding a threshold water value based on the split-based approach (SBA) calculation analysis for Landsat 8 satellite images. The SBA was used to determine local threshold variations in data scenes that were used to classify water and nonwater. The class threshold between water and nonwater in each selected subscene image can be determined based on the calculation of class intervals generated by geostatistical analysis, initially referred to as smart quantiles. It was used to determine the class separation between water and nonwater in the resulting subscene images. The objectives of this study were (a) to increase the accuracy of automatic lake surface water detection by improvising the determination of threshold values based on analysis and calculations using the SBA and (b) to conduct a test case study of AWEI threshold improvement on several lakes’ surface water, which has a variety of different or heterogeneous characteristics. The results show that the threshold value obtained based on the smart quantile calculation from the natural break approach (AWEI ≥ −0.23) gave an overall accuracy of close to 100%. Those results were better than the normal threshold (AWEI ≥ 0.00), with an overall accuracy of 98%. It shows that there has been an increase of 2% in the accuracy based on the confusion matrix calculation. In addition to that, the results obtained when classifying water and nonwater classes for the different national priority lakes in Indonesia vary in overall accuracy from 94% to 100%.
BMP-2 Enhances Osteogenic Differentiation of Human Adipose-Derived and Dental Pulp Stem Cells in 2D and 3D In Vitro Models
Authors: Martin-Iglesias Sara , Milian Lara , Sancho-Tello María , Salvador-Clavell Rubén , Martín de Llano José Javier , Carda Carmen , Mata Manuel
Published: March 2022

Bone tissue provides support and protection to different organs and tissues. Aging and different diseases can cause a decrease in the rate of bone regeneration or incomplete healing; thus, tissue-engineered substitutes can be an acceptable alternative to traditional therapies. In the present work, we have developed an in vitro osteogenic differentiation model based on mesenchymal stem cells (MSCs), to first analyse the influence of the culture media and the origin of the cells on the efficiency of this process and secondly to extrapolate it to a 3D environment to evaluate its possible application in bone regeneration therapies. Two osteogenic culture media were used (one commercial from Stemcell Technologies and a second supplemented with dexamethasone, ascorbic acid, glycerol-2-phosphate, and BMP-2), with human cells of a mesenchymal phenotype from two different origins: adipose tissue (hADSCs) and dental pulp (hDPSCs). The expression of osteogenic markers in 2D cultures was evaluated in several culture periods by means of the immunofluorescence technique and real-time gene expression analysis, taking as reference MG-63 cells of osteogenic origin. The same strategy was extrapolated to a 3D environment of polylactic acid (PLA), with a 3% alginate hydrogel. The expression of osteogenic markers was detected in both hADSCs and hDPSCs, cultured in either 2D or 3D environments. However, the osteogenic differentiation of MSCs was obtained based on the culture medium and the cell origin used, since higher osteogenic marker levels were found when hADSCs were cultured with medium supplemented with BMP-2. Furthermore, the 3D culture used was suitable for cell survival and osteogenic induction.
Preparation Method of High Resilience Nonslip Basketball Sole Composite Material
Authors: Wang Zheng , Liu Yihe , Zhang Shuang
Published: March 2022

Today, with the gradual improvement of material living standards, basketball is becoming more and more popular, and the pursuit of sports protection and shoes performance is also getting higher and higher. A good pair of basketball shoe soles can help athletes solve these problems. This article is aimed at studying the preparation of basketball shoe soles made of high resilience and nonslip composite materials. Under this research topic, this paper proposes a method for the design of shoe sole antislip pattern and the measurement method of composite material’s resilience performance and a 3D printing method based on FDM technology to print the shoe sole. At the same time, an experiment was designed to explore the high resilience and antislip performance of the sole. And the stability of printing technology is analyzed to ensure the quality of sneaker printing. The experimental results in this article show that the antislip performance of the sole printed by the above method design has been improved by 31%, and the rebound capacity has been improved by 44%. At the same time, the rebound time is significantly shorter than that of several common basketball shoes on the market.
System Simulation of Driving Mechanism of Rural Tourism Development Based on Data Mining Analysis and Cloud Computing
Authors: Li Mingtong
Published: March 2022

Rural tourism (RT) and cloud computing are important forces in implementing the rural revitalization strategy, and they play a key role in accelerating agriculture and rural area modernization, urban-rural integration, poverty alleviation in impoverished areas, and so on. RT’s development is influenced by a variety of factors due to its high industrial relevance and strong driving function. The development of RT is examined from the perspectives of data mining and cloud computing in this paper, and a data mining model of the RT development’s driving mechanism is built. Five driving modes of RT development are simulated by adjusting the parameters of industrial economy, resources, environment, and social culture in H city as an example. The simulation results are compared and analyzed in various modes. Finally, the coordinated development model of industrial economy, social culture, resources, and environment is found to be the best model of RT development in H city, and countermeasures and suggestions for RT development in H city are proposed based on the analysis results.
Upgradation of Iron Ore Fines and Slime by Selective Flocculation Using Surface-Active Agents, Settling Study, and Characterization of the Beneficiation Waste for Value Addition
Authors: Sarma Jyotirmoy , Rajkhowa Sanchayita , Mahiuddin Sekh
Published: March 2022

Washing of iron ore fines and slime (10% and 25% w/v, slurry concentrations) with two types of surface-active agents (sodium humate (synthesized) and AD 200 (commercial)) at varying concentrations at pH 8 was conducted for ascertaining the efficacy of dispersants in beneficiating the low-grade iron ores. The beneficiation process follows the “selective dispersion-cum-settling technique.” The process results in the formation of a dispersed phase rich in gangue minerals and a settled phase of predominantly active hematite mineral. The stability of dispersed phase (DP) was evaluated by determination of the percentage solid content in the DP. Settling tests were performed. First-order kinetic models have been applied to the dispersion-cum-settling behaviour of both the samples, and evaluated kinetic parameters were found to have good agreement with experimental data. Removal of gangue minerals from iron ore depends on the pH of the slurry, concentration of the slurry, and concentration of the surface-active agent used. The surface-active agents at pH 8 produce ∼1.2–1.5 times more stable suspension in the case of iron ore fines and slimes than that of without surface-active agent. They significantly remove gangue minerals and increase the iron value ∼2–7% with ∼58–74% recovery depending on the experimental conditions. The concentrates collected satisfy the required specifications (Al203/Fe < 0.05 and Al203/Si02 < 1). The gangues in the dispersed phase as characterised by “SEM-EDXA” are mostly clay-bearing minerals like kaolinite, goethite, chlorite, and alumina-silicate minerals. Heat treatment causes distortion of clay minerals present in the dispersed phase and also indicates the complex nature of the gangue minerals.
Research on the Coordination Model of Passenger Transportation Mode in the Intercity Comprehensive Transportation Corridor
Authors: Liao Yong
Published: March 2022

At present, the study of comprehensive transportation corridors primarily focuses on the planning and construction of transportation corridors. There are few studies on the coordinated operation of all modes of transportation after the construction of the transportation corridor is completed. Comprehensive transportation corridor takes on the characteristics that the volume of transport demand and supply is large, the number of transportation modes is not only one, and competition among different transportation modes is fierce. In order to prevent unhealthy competition among different transportation modes from disrupting the transportation market, this paper takes the passenger transportation modes within the intercity comprehensive transportation corridor as the studied object and establishes a coordination model for them. The model introduces the disaggregate model as the research theory, takes the quantizable attributes of transportation modes as the decision variables, takes the reasonable distribution of passenger flow among different transportation modes as the objective function, and takes the supply capacity of different transportation modes and the reasonable value range of decision variables as the constraints. This model is a multiobjective nonlinear programming problem. The multiobjective genetic algorithm is designed to solve the model. The real number encoding method is adopted to encode the decision variables; the penalty function method is used to eliminate the solutions that do not meet the nonlinear constraints after crossover and mutation; the repairment algorithm is used to convert the solutions that do not satisfy the linear constraints and bound constraints after crossover and mutation into the feasible region. Pareto optimal solution set is obtained through continuous selection, crossover, and mutation. Finally, a numeric example is made to demonstrate that the method proposed in this paper is effective and feasible.
Classification and Treatment for Cervical Spine Fracture with Ankylosing Spondylitis: A Clinical Nomogram Prediction Study
Authors: Shen Nana , Wu Xiaolin , Guo Zhu , Yang Shuai , Liu Chang , Guo Zhaoyang , Yang Shang-You , Xing Dongming , Chen Bohua , Xiang Hongfei
Published: March 2022

Objective. Through the follow-up analysis of cervical spine fracture cases with ankylosing spondylitis (AS), a treatment-oriented fracture classification method is introduced to evaluate the clinical efficacy guided by this classification method. Method. A retrospective analysis was performed on 128 AS patients who underwent comprehensive treatment in the Spine Surgery Department of Qingdao University Hospital from January 2009 to May 2018. Statistics of patient demographic data, distribution of different fractures corresponding to surgical methods, 3-year follow-up outcomes, and summary of objective fracture classification methods were analyzed. A prospective 5-year follow-up study of 90 patients with AS cervical spine fractures from June 2015 to August 2020 was also included. Statistical differences on the distribution of factors such as case information, cervical spine sagittal sequence parameters, and fracture classification were assessed. Correlations between surgical information, American Spinal Injuries Association grade (ASIA), modified Japanese Orthopaedic Association scores (mJOA), and other factors were analyzed to establish a nomogram predictive model for curative effect outcomes. Overall, three major types and the four subtypes of AS cervical spine fractures were evaluated based on the clinical efficacy of the classification and the selection of surgical treatment methods. Result. The most common type of fracture was type II (30 cases, 33.33%), most of the subtypes were A (37 cases), followed by B (36 cases) and C (17 cases). Twenty-four of 28 patients with type I underwent anterior surgery, and 47 of 62 patients with type II and III underwent posterior surgery. The average follow-up time was 25.76 ± 11.80 months. The results of predicting clinical variables are different but include factors such as fracture type and subtype, type of operation, and age. The predictor variables include the above-mentioned similar variables, but survival is more affected by the fracture type of the patient. Conclusion. This predictive model based on follow-up information delineation points out the impact of ankylosing spondylitis cervical spine fracture classification on surgical selection and clinical efficacy.
Mechanism Design and Motion Analysis of a Flapping-Wing Air Vehicle
Authors: Shi Yunde , He Wang , Guo Mingqiu , Xia Dan , Luo Xiang , Ji Xiaoqiang
Published: March 2022

Micro flapping-wing air vehicles (FWAV) are highly appreciated by scientists and scholars around the world due to their small size, high flexibility, and better manoeuvrability. There are huge potential applications of flapping-wing robots in rescue, survey, and search, as they can enter more dangerous and narrow spaces to accomplish tasks that were previously impossible for people. In this paper, we first analyze the flapping motion of birds by reviewing a large amount of information and literature and identifying the sources of lift and thrust during flight. Then, the double crank and double rocker mechanisms are optimized as the flapping mechanism. Proper flapping-wing size is further determined by analyzing the relative position between the driving mechanism and the flapping mechanism. Moreover, a pitch mechanism consisting of ball hinges is designed to realize the wing’s pitching motion for additional thrust. The fuselage and landing gear of the flapping-wing robot are designed for low air resistance and lightweight, and the tail mechanism are designed to have both roll and pitch freedoms, offering excellent manoeuvrability during flight. Simulation results show positive lift force is generated based on the novel pitching design for the small wing. The research in this paper is an important resource for novice researchers to design and study the advanced and complex problems of flapping-wing air vehicles.
Disease Identification of Lentinus Edodes Sticks Based on Deep Learning Model
Authors: Zu Dawei , Zhang Feng , Wu Qiulan , Wang Wenyan , Yang Zimeng , Hu Zhengpeng
Published: March 2022

Lentinus edodes sticks are susceptible to mold infection during the culture process, and manual identification of infected sticks is heavy, untimely, and inaccurate. Aiming to solve this problem, this paper proposes a method for identifying infected Lentinus edodes sticks based on improved ResNeXt‐50(32 × 4d) deep transfer learning. First, a dataset of Lentinus edodes stick diseases was constructed. Second, based on the ResNeXt‐50(32 × 4d) model and the pretraining weight of the ImageNet dataset, the influence of pretraining weight parameters on recognition accuracy was studied. Finally, six fine‐tuning strategies of the fully connected layer were designed to modify the fully connected layer of ResNeXt‐50(32 × 4d). The experimental results show that the recognition accuracy of the method proposed in this paper can reach 94.27%, which is higher than the Vgg16, GoogLeNet, ResNet50, and MobileNet v2 models by 8.47%, 6.49%, 4.68%, and 9.38%, respectively, and the F1‐score can reach 0.9422. The improved method proposed in this paper can reduce the calculation pressure and overfitting problem of the model, improve the accuracy of the model in the identification of Lentinus edodes stick mold diseases, and provide an effective solution for the selection of diseased sticks.
Obesity in Older Adults and Associations with Cardiovascular Structure and Function
Authors: Tan Yen How , Lim Jun Pei , Lim Wee Shiong , Gao Fei , Teo Louis L.Y. , Ewe See Hooi , Keng Bryan M.H. , Tan Ru San , Koh Woon-Puay , Koh Angela S.
Published: March 2022

Introduction: Body mass index (BMI), despite being widely used as a marker of obesity, fails to fully capture cardiovascular risks as it is an insufficient biomarker of abdominal adiposity, unlike waist circumference (WC). We aimed to characterize associations between BMI and WC with cardiovascular structure and function in older adults. Methods: Among an observational cohort study of a community of older adults, transthoracic echocardiography determined cardiovascular structure and function, while aerobic capacity was determined by peak oxygen uptake (VO2) metrics. The cut-offs for obesity were 27.5 kg/m2 for BMI, and >90 cm for males and >80 cm for females for WC. Results: Of 970 older adults without cardiovascular disease (mean age 73 ± 4 years, 432 [44%] males), 124 (12.8%) were obese by BMI definition while 347 (35.7%) were obese by WC definition. Inter-definitional agreement was fair (Cohen’s κ = 0.345). Unlike the BMI definition, participants defined as obese by WC were more likely to be women (65% vs. 50%, p < 0.001), older (65 ± 11 vs. 63 ± 14 years, p = 0.007), and had lower handgrip strength (24 ± 0.6 vs. 26 ± 0.4 kg, p = 0.022). Across BMI categories, high WC was associated with more impaired myocardial relaxation (E/A), and VO2 measurements (all p < 0.05). Among those with low BMI, high WC was associated with larger left atrial (LA) volumes (p = 0.003). WC, but not BMI, was independently associated with E/A (β = −0.114, SE −0.114 ± 0.024, p < 0.001) in regression analysis. Conclusion: WC identified a higher prevalence of obesity, possibly related to central adiposity. Across BMI categories, WC identified more adverse measurements in E/A, aerobic capacity, and LA structure. Trial Registration: ClinicalTrials.gov Identifier: NCT02791139.
Adherence to Life’s Simple 7 and Cognitive Function Among Older Adults: The National Health and Nutrition Examination Survey 2011 to 2014
Authors: Wei Jingkai , Wang Liang , Kulshreshtha Ambar , Xu Hanzhang
Published: March 2022

Background

The American Heart Association proposed the Life’s Simple 7 (LS7; including diet, physical activity, smoking, body mass index, blood pressure, plasma fasting glucose, total cholesterol) to promote cardiovascular health. Adherence to LS7 has been found to be associated with better cognitive health as well, but the generalizability of previous studies is limited. We aimed to examine the associations of adherence to LS7 and cognitive function among older adults in a nationally representative sample of population.

Methods and Results

A total of 2585 older adults (≥60 years, 54% female, 80% non‐Hispanic White) in the National Health and Nutrition Examination Survey 2011 to 2014 were included for analysis. Components of LS7 were measured, and adherence to LS7 was calculated on the basis of established cutoff points of individual components. Cognitive function was examined using the Consortium to Establish a Registry for Alzheimer’s Disease Word List Memory Task (immediate and delayed memory), Digit Symbol Substitution Test (DSST), and Animal Fluency Test. Test‐specific and global cognition
Z
scores were created. Multivariable linear regression models were conducted on the associations of adherence to LS7 with domain‐specific and global cognition
Z
scores. Each incremental point in adherence to LS7 was associated with higher
Z
scores for global cognition (β=0.05; 95% CI, 0.02–0.07), Digit Symbol Substitution Test (β=0.05; 95% CI, 0.03–0.07), Consortium to Establish a Registry for Alzheimer’s Disease Word Learning subtest immediate memory (β=0.03; 95% CI, 0.004–0.05), and animal fluency test (β=0.05; 95% CI, 0.02–0.07).

Conclusions

Greater adherence to LS7 metrics is associated with better cognitive function among older US adults in a nationally representative sample of population.
The Many Tasks of the Marxist Translator
Authors: Arnall Gavin
Published: March 2022

This article examines numerous conceptions of translation within the Marxist tradition. It begins with Antonio Gramsci’s theorisation of the concept before turning to the problem of Marxism in Latin America and how the Zapatistas have dealt with this problem. The aim is to shed light on a critical school of Marxist thinking, which requires that Marxism’s universalist claims be translated in response to changing historical conditions so that they may become concrete formulations capable of speaking to and intervening in concrete situations. Yet the Zapatistas go further by maintaining that translation must also occur between the universalist claims of Marxist theory and the competing universalist claims of indigenous Maya cosmology. The article thus underscores what can be gleaned from different modes of translation in terms of Marxism’s future and its capacity to enter into mutually beneficial alliances with distinct worldviews and ongoing social movements.
From Religion to Spirituality: Lessons for Values Education
Authors: Maharajh Lokesh Ramnath
Published: March 2022

This paper discusses the concepts of religion and spirituality in the context of a study on the perceptions and possibilities of the Bhagavad Gita for values education in Durban, South Africa. The ideas are explained through a model of spirituality that has been adopted from the 3H (head, heart, hands) and BMSEST (body, mind, spirit, environment, social, transcendent) models of spirituality in multicultural whole-person medicine (Anandarajah 2008). I argue for a broad and open understanding of spirituality, which I feel has the potential to extend the youth beyond religion. However, I also maintain that young people are easily influenced by material surroundings and are seen as a ‘generation of suspects’ (Giroux 2006, 149). The model that I present indicates that religious scriptures (in the case of this paper, the Bhagavad Gita) have a perennially valid set of values, which has the potential to contribute to a programme of values education among the youth. However, the youth are not abstracting and extracting these values. For the youth these values remain elusive and even ethereal.
‘Let No Black Cat Cross Our Path’: An Introduction to Ga Rituals of Affliction
Authors: Adu Gyau Kumi
Published: March 2022

Although much has been said about Ga rituals by earlier scholars such as Ammah, Kilson, and Field, no direct and comprehensive literature exists that deals specifically with Ga rituals of affliction. Rituals of affliction are measures by which cultures attempt to deal with the problem of ‘affliction’. All cultures have a different way in which affliction is explained and dealt with. This article explores Ga rituals of affliction based on an analysis of one text line in Ga libation prayers, ‘Let no black cat cross our path’ (alͻnte diŋ ko akafo wͻteŋ).