bluuebunny/crossref_metadata_2025 huggingface dataset:
Effect Confirmed, Patient Dead
Authors: Fischer Kerstin
Published: February 2021

This article is a commentary on Hoffman 8 Zhao’s “A Primer for Conducting Experiments in Human-robot Interaction.” I argue that a too-narrow view of HRI methodology fails to address the dynamic systems properties of interaction. Furthermore, the focus on addressing the so-called “replicability crisis” makes field studies next to impossible, inhibits interdisciplinarity and methodological pluralism, and draws our attention and resources away from the fact that contexts, people, cultures, expectations, and interaction itself may influence how social signals are interpreted. Therefore, in spite of its great benefits, the “Primer” may not be taken as an instruction on “how to carry out research in HRI” in general.
VM-NSP
Authors: Wang Wei , Cao Longbing
Published: February 2021

Negative sequential patterns (NSPs) capture more informative and actionable knowledge than classic positive sequential patterns (PSPs) due to the involvement of both occurring and nonoccurring behaviors and events, which can contribute to many relevant applications. However, NSP mining is nontrivial, as it involves fundamental challenges requiring distinct theoretical foundations and is not directly addressable by PSP mining. In the very limited research reported on NSP mining, a negative element constraint (NEC) is incorporated to only consider the NSPs composed of specific forms of elements (containing either positive or negative items), which results in many valuable NSPs being missed. Here, we loosen the NEC (called
loose negative element constraint
(LNEC)) to include partial negative elements containing both positive and negative items, which enables the discovery of more flexible patterns but incorporates significant new learning challenges, such as representing and mining complete NSPs. Accordingly, we formalize the LNEC-based NSP mining problem and propose a novel
vertical NSP mining framework
, VM-NSP, to efficiently mine the complete set of NSPs by a vertical representation (VR) of each sequence. An efficient
bitmap-based vertical NSP mining algorithm
, bM-NSP, introduces a bitmap hash table--based VR and a prefix-based negative sequential candidate generation strategy to optimize the discovery performance. VM-NSP and its implementation bM-NSP form the first VR-based approach for complete NSP mining with LNEC. Theoretical analyses and experiments confirm the performance superiority of bM-NSP on synthetic and real-life datasets w.r.t. diverse data factors, which substantially expands existing NSP mining methods toward flexible NSP discovery.
Comparative Embryogenesis in Angiosperms: Activation and Patterning of Embryonic Cell Lineages
Authors: Dresselhaus Thomas , Jürgens Gerd
Published: February 2021

Following fertilization in flowering plants (angiosperms), egg and sperm cells unite to form the zygote, which generates an entire new organism through a process called embryogenesis. In this review, we provide a comparative perspective on early zygotic embryogenesis in flowering plants by using the Poaceae maize and rice as monocot grass and crop models as well as Arabidopsis as a eudicot model of the Brassicaceae family. Beginning with the activation of the egg cell, we summarize and discuss the process of maternal-to-zygotic transition in plants, also taking recent work on parthenogenesis and haploid induction into consideration. Aspects like imprinting, which is mainly associated with endosperm development and somatic embryogenesis, are not considered. Controversial findings about the timing of zygotic genome activation as well as maternal versus paternal contribution to zygote and early embryo development are highlighted. The establishment of zygotic polarity, asymmetric division, and apical and basal cell lineages represents another chapter in which we also examine and compare the role of major signaling pathways, cell fate genes, and hormones in early embryogenesis. Except for the model Arabidopsis, little is known about embryopatterning and the establishment of the basic body plan in angiosperms. Using available in situ hybridization, RNA-sequencing, and marker data, we try to compare how and when stem cell niches are established. Finally, evolutionary aspects of plant embryo development are discussed.
Mechanisms for Regulating and Organizing Receptor Signaling by Endocytosis
Authors: von Zastrow Mark , Sorkin Alexander
Published: February 2021

Intricate relationships between endocytosis and cellular signaling, first recognized nearly 40 years ago through the study of tyrosine kinase growth factor receptors, are now known to exist for multiple receptor classes and to affect myriad physiological and developmental processes. This review summarizes our present understanding of how endocytosis orchestrates cellular signaling networks, with an emphasis on mechanistic underpinnings and focusing on two receptor classes—tyrosine kinase and G protein–coupled receptors—that have been investigated in particular detail. Together, these examples provide a useful survey of the current consensus, uncertainties, and controversies in this rapidly advancing area of cell biology.
Biomolecular Systems Engineering: Unlocking the Potential of Engineered Allostery via the Lactose Repressor Topology
Authors: Groseclose Thomas M. , Rondon Ronald E. , Hersey Ashley N. , Milner Prasaad T. , Kim Dowan , Zhang Fumin , Realff Matthew J. , Wilson Corey J.
Published: February 2021

Allosteric function is a critical component of many of the parts used to construct gene networks throughout synthetic biology. In this review, we discuss an emerging field of research and education, biomolecular systems engineering, that expands on the synthetic biology edifice—integrating workflows and strategies from protein engineering, chemical engineering, electrical engineering, and computer science principles. We focus on the role of engineered allosteric communication as it relates to transcriptional gene regulators—i.e., transcription factors and corresponding unit operations. In this review, we ( a) explore allosteric communication in the lactose repressor LacI topology, ( b) demonstrate how to leverage this understanding of allostery in the LacI system to engineer non-natural BUFFER and NOT logical operations, ( c) illustrate how engineering workflows can be used to confer alternate allosteric functions in disparate systems that share the LacI topology, and ( d) demonstrate how fundamental unit operations can be directed to form combinational logical operations.
Understanding and Controlling Intersystem Crossing in Molecules
Authors: Marian Christel M.
Published: February 2021

This review article focuses on the understanding of intersystem crossing (ISC) in molecules. It addresses readers who are interested in the phenomenon of intercombination transitions between states of different electron spin multiplicities but are not familiar with relativistic quantum chemistry. Among the spin-dependent interaction terms that enable a crossover between states of different electron spin multiplicities, spin–orbit coupling (SOC) is by far the most important. If SOC is small or vanishes by symmetry, ISC can proceed by electronic spin–spin coupling (SSC) or hyperfine interaction (HFI). Although this review discusses SSC- and HFI-based ISC, the emphasis is on SOC-based ISC. In addition to laying the theoretical foundations for the understanding of ISC, the review elaborates on the qualitative rules for estimating transition probabilities. Research on the mechanisms of ISC has experienced a major revival in recent years owing to its importance in organic light-emitting diodes (OLEDs). Exemplified by challenging case studies, chemical substitution and solvent environment effects are discussed with the aim of helping the reader to understand and thereby get a handle on the factors that steer the efficiency of ISC.
Equidistribution Modulo 1
Authors: Gadri Wiem
Published: February 2021

The generalisation of questions of the classic arithmetic has long been of interest. One line of questioning, introduced by Car in 1995, inspired by the equidistribution of the sequence

n

α

n
∈
N

where

0
<
α
<
1

, is the study of the sequence

K

1
/
l

, where

K

is a polynomial having an l-th root in the field of formal power series. In this paper, we consider the sequence

L

′

1
/
l

, where

L
′

is a polynomial having an l-th root in the field of formal power series and satisfying

L
′
≡
B
 
mod
 
C

. Our main result is to prove the uniform distribution in the Laurent series case. Particularly, we prove the case for irreducible polynomials.
Multi‐Indices Quantification for Left Ventricle via DenseNet and GRU‐Based Encoder‐Decoder with Attention
Authors: Liu Zhi , Lu Yunhua , Zhang Xiaochuan , Wang Sen , Li Shuo , Chen Bo
Published: February 2021

More and more research on left ventricle quantification skips segmentation due to its requirement of large amounts of pixel‐by‐pixel labels. In this study, a framework is developed to directly quantify left ventricle multiple indices without the process of segmentation. At first, DenseNet is utilized to extract spatial features for each cardiac frame. Then, in order to take advantage of the time sequence information, the temporal feature for consecutive frames is encoded using gated recurrent unit (GRU). After that, the attention mechanism is integrated into the decoder to effectively establish the mappings between the input sequence and corresponding output sequence. Simultaneously, a regression layer with the same decoder output is used to predict multi‐indices of the left ventricle. Different weights are set for different types of indices based on experience, and l2‐norm is used to avoid model overfitting. Compared with the state‐of‐the‐art (SOTA), our method can not only produce more competitive results but also be more flexible. This is because the prediction results in our study can be obtained for each frame online while the SOTA only can output results after all frames are analyzed.
The Optimization Model for Reducing RON Loss in Gasoline Refining Process
Authors: Lu Xuefei , Wang Xiaoyan , Yang Yifang , Xue Jianan
Published: February 2021

As gasoline is the main fuel of small vehicles, the exhaust emissions from its combustion will affect air quality. The focus of gasoline cleaning is to reduce the sulfur and olefin content in gasoline while maintaining its RON as much as possible. The reduction of RON will bring great economic losses to enterprises. Therefore, it is very important for petrochemical enterprises to construct a RON loss model in the gasoline refining process. The model construction, which reduces RON loss during gasoline refining, is the main question in this paper. By Python and SPSS software, we got two variable filtering methods: the random forest importance filtering and PCA filtering, and combined with SVR and random forest models, RON of the product and sulfur content were predicted. The filtering order of the original data by Excel and Python is maximum and minimum removal, 3σ criterion removal, deletion of too many sites in incomplete data, and filling of empty values in the mean within two hours. Several RON prediction models were established with the help of Python software, and the variables selected were compared by two filtering methods: one is the SVR model based on Gaussian, linear, polynomial, and Sigmoid kernel functions; the other is the random forest model. The sulfur content and RON prediction model was constructed, which use evaluation functions such as MSE,

R

2

, and RMSE to evaluate and sulfur content as the subject condition. We convert the problem into linear and nonlinear model variable optimization problems: the linear model is the variable selected by the SVR linear kernel function model and random forest; the nonlinear model is the combination of variables selected by the random forest model and random forest. Optimizing for each sample, the optimization method is to find the optimal solution for each variable and use the optimal method for each variable as the local optimal solution for the sample. The two models are evaluated from the perspectives of optimization degree, optimization rate, model running speed, etc.
Detecting and Evaluating Defects in Beams by Correlation Coefficients
Authors: Nguyen Thanh Q. , Nguyen Hoang B.
Published: February 2021

This research proposes a correlation coefficient for detecting and evaluating defects in beams, which brings about a positive outcome in terms of accuracy and efficiency. This parameter surpasses other parameters, such as natural frequency and damping coefficient, thanks to its sensitivity to structural changes. Our results show that although the damping coefficient had more variation than the natural frequency value in the same experiment, its changes were insufficient and unstable at different levels of defects. In addition, the proposed correlation coefficient parameter has a linear characteristic and always changes significantly according to increasing levels of defects. The results outweigh damping coefficient and natural frequency values. Furthermore, this value is always sensitive to measurement channels, which could be an important factor in locating defects in beams. The testing index is statistically evaluated by a normal distribution of the amplitude value of vibration measurement signals. Changes and shifts in this distribution are the basis for evaluating beam defects. Thus, the suggested parameter is a reliable alternative for assessing the defects of a structure.
Ethnobotanical Study of Plants Used for Human Ailments in Yilmana Densa and Quarit Districts of West Gojjam Zone, Amhara Region, Ethiopia
Authors: Alemneh Derebe
Published: February 2021

Like other populations of developing countries, most of the populations of Ethiopia depend greatly on the use of traditional herbal medicines as the primary source of health care. However, these resources have been degraded throughout the country. Therefore, recording and documenting medicinal plants are essential for the future conservation of the species. Thus, the current study is related to this issue. Three hundred ninety‐five informants participated in this study. The main data collection tools were semistructured interviews, discussions, and observation. The data were analyzed using both qualitative and quantitative methods. The quantitative methods (informant consensus factor, fidelity level) were used to analyze the level of homogeneity of the data, the agreements of informants, and the healing potential of medicinal plants. The ranking exercises (preference and direct matrix ranking) were used to quantify the most preferred and multipurpose medicinal plants. A statistical test was conducted using SPSS version 20 to test knowledge differences among the people. Following the analysis, 112 species of medicinal plants with 62 families were recorded. Most of the species were herbs, of which Fabaceae was the dominant family. Blood pressure and asthma were the most commonly reported human health problems. Most of the remedies were prepared from leaves, and most of them were prepared by pounding and were applied orally. Gastrointestinal diseases had the highest informant consensus. Five medicinal plants were recorded with the highest healing potential value for malaria, asthma, tapeworm, cough, and stomach ulcer. Verbascum sinaiticum Benth. was the most preferred medicinal plant in treating blood pressure. Trigonella foenum-graecum L. had the highest use value. The analysis results showed that local people showed significant knowledge differences (p < 0.05). In conclusion, the indigenous people have a wealth of indigenous herbal knowledge. Therefore, to sustain the wealth of indigenous knowledge of the districts, immediate and well‐designed conservation practices of medicinal plants should be conducted.
Hořava-Lifshitz Scalar Field Cosmology: Classical and Quantum Viewpoints
Authors: Tavakoli Fatimah , Vakili Babak , Ardehali Hossein
Published: February 2021

In this paper, we study a projectable Hořava-Lifshitz cosmology without the detailed balance condition minimally coupled to a nonlinear self-coupling scalar field. In the minisuperspace framework, the super-Hamiltonian of the presented model is constructed by means of which some classical solutions for the scale factor and scalar field are obtained. Since these solutions exhibit various types of singularities, we came up with the quantization of the model in the context of the Wheeler-DeWitt approach of quantum cosmology. The resulting quantum wave functions are then used to investigate the possibility of the avoidance of classical singularities due to quantum effects which show themselves important near these singularities.
A Case Report of Complete Resolution of Auricular Mucormycosis in an 18-Month-Old Diabetic Child
Authors: Aljehani Mariam , Alahmadi Hatem , Alshamani Mansour
Published: February 2021

Background. One of the most rare but deadly types of infectious fungal infection is Mucormycosis. All the cases reported with this type of infection are immunocompromised individuals. The challenge of early detection and intervention makes it one of the high mortality rates among other infectious diseases. Case Report. We report an 18-month-old girl with undiagnosed diabetes presented with a very aggressive form of necrotic infection of the ear auricle with facial nerve palsy. Using a series of magnetic resonance imaging, antibiotics, and high clinical suspicion, a diagnosis was established, and the patient was sent to the operation theatre for surgical debridement. Monthly follow-ups showed improvement of the facial palsy, and a plan for artificial auricle is set to occur in the following months before the age of five. Discussion. Mucormycosis is considered a very fatal and aggressive infection that has a very high mortality rate in immunocompromised patients. Early detection of such cases with an array of magnetic resonance imaging (MRI) and computed tomography (CT) is crucial in early treatment. Early aggressive surgical debridement and empirical coverage of bacterial, viral, and fungal infections can also alleviate the chances of preventing any secondary infection to develop in such cases. Conclusion. A combination of antifungal, antibiotic, and antiviral with timely surgical intervention improved the patient with complete resolution of the facial nerve palsy and no further recurrence of the infection.
[Retracted] 3D Printed Guides and Preoperative Planning for Uncemented Stem Anteversion Reconstruction during Hip Arthroplasty: A Pilot Study
Authors: Zhang Yingqi , Rao Zhitao , Zhang Jincheng , Li Shijie , Chang Shimin , Sun Yeqing
Published: February 2021

Objective. To investigate if 3D printed guides and preoperative planning can accurately control femoral stem anteversion. Methods. A prospective comparative study was carried out from 2018 to 2020, including 53 patients who underwent hip arthroplasty for femoral neck fracture. The target rotation center of the femoral head is determined by three‐dimensional planning. In group A, planning was made by 2D templates. In group B, preoperative 3D planning and 3D printed osteotomy/positioning guides were performed. After the operation, 3D model registration was performed to calculate the accuracy of anteversion restoration. Results. We screened 60 patients and randomized a total of 53 to 2 parallel study arms: 30 patients to the group A (traditional operation) and 23 patients to the group B (3D preoperative planning and 3D printed guide). There were no significant differences in demographic or perioperative data between study groups. The restoration accuracy of group A was 5.42° ± 3.65° and of group B was 2.32° ± 1.89°. The number and rate of abnormal cases was 15 (50%) and 2 (8.7%), respectively. Significant statistical differences were found in angle change, restoration accuracy, and number of abnormal cases. Conclusion. Three‐dimensional preoperative planning and 3D printed guides can improve the accuracy of the restoration of femoral anteversion during hip arthroplasty.
Development and Performance Analysis of Pneumatic Soft-Bodied Bionic Actuator
Authors: Zhao Wenchuan , Zhang Yu , Wang Ning
Published: February 2021

The design of a pneumatic soft-bodied bionic actuator derives from the structural characteristics and motion mechanism of biological muscles, combined with the nonlinear hyperelasticity of silica gel, which can improve the mobility and environmental adaptability of soft-bodied bionic robots. Based on Yeoh’s second-order constitutive model of silica gel, the deformation analysis model of the actuator is established, and the rationality of the structure design and motion forms of the actuator and the accuracy of the deformation analysis model are verified by using the numerical simulation algorithm. According to the physical model of the pneumatic soft-bodied bionic actuator, the motion and dynamic characteristics of the actuator are tested and analyzed, the curves of motion and dynamic characteristics of the actuator are obtained, and the empirical formula of the bending angle and driving torque of the actuator is fitted out. The results show that the deformation analysis model and numerical simulation method are accurate, and the pneumatic soft-bodied bionic actuator is feasible and effective, which can provide a design method and reference basis for the research and implementation of soft-bodied bionic robot actuator.
A Parameter-Optimized Variational Mode Decomposition Investigation for Fault Feature Extraction of Rolling Element Bearings
Authors: An Guoping , Tong Qingbin , Zhang Yanan , Liu Ruifang , Li Weili , Cao Junci , Lin Yuyi , Wang Qiang , Zhu Ying , Pu Xiaowen
Published: February 2021

Reliable fault diagnosis of the rolling element bearings highly relies on the correct extraction of fault-related features from vibration signals in time-frequency analysis. However, considering the nonlinear, nonstationary characteristics of vibration signals, the extraction of fault features hidden in the heavy noise has become a challenging task. Variable mode decomposition (VMD) is an adaptive, completely nonrecursive method of mode variation and signal processing. This paper analyzes the advantages of VMD compared with EMD in robustness of against noise, overcoming the end effect and mode aliasing. The signal decomposition performance of VMD algorithm largely depends on the selection of mode number k and bandwidth control parameter α. To realize the adaptability of influence parameters and the improvement of decomposition accuracy, a parameter-optimized VMD method is presented. The random frog leaping algorithm (SFLA) is used to search the optimal combination of influence parameters, and the mode number and bandwidth control parameters are set according to the search results. A multiobjective evaluation function is constructed to select the optimal mode component. The envelope spectrum technique is used to analyze the optimal mode component. The proposed method is evaluated by simulation and practical bearing vibration signals under different conditions. The results show that the proposed method can improve the decomposition accuracy of the signal and the adaptability of the influence parameters and realize the effective extraction of the bearing vibration signal.
Performance Assessment of Shockwaves of Chute Spillways in Large Dams
Authors: Mousavimehr S. M. , Yamini Omid Aminoroayaie , Kavianpour M. R.
Published: February 2021

Spillways are the most important structures of large dams that are responsible for releasing the excessive flood discharge from the reservoir. Although many studies have been performed to determine the flow characteristics over these structures, however, the available information on the shockwaves’ characteristics for spillways’ design is limited. The supercritical flow below the chute piers generates an aerated flow known as shockwaves. Due to the flow interaction with the chute piers, three kinds of standing waves just downstream of the pier, in the middle of the chute, and on the sidewalls are generated. This phenomenon affects the flow domain and its hydraulic characteristics along the chute spillway. The height of the waves increases downstream, where they hit the chute walls and reflect again into the flow to interact together again. The process repeated and intensified downstream in a lozenge shape. The height of these waves can be more than twice the depth flow and thus run over the sidewalls. This is important for the design of chute walls in chute spillways with control gates. In this study, the experimental formation of the shockwaves and their behavior along the chute and their reduction measures are presented. Experiments were conducted on a scaled physical model (1/50) of Kheirabad Dam, Water Research Institute, Iran. It was realized that apart from the geometry of piers and chute spillway, Froude number of flow and gate opening are the main effective parameters on the hydraulic performance of shockwaves’ formation and their development on gated spillways.
Recognition of Disease Genetic Information from Unstructured Text Data Based on BiLSTM-CRF for Molecular Mechanisms
Authors: Gong Lejun , Zhang Xingxing , Chen Tianyin , Zhang Li
Published: February 2021

Disease relevant entities are an important task in mining unstructured text data from the biomedical literature for achieving biomedical knowledge. Autism spectrum disorder (ASD) is a disease related to a neurological and developmental disorder characterized by deficits in communication and social interaction and by repetitive behaviour. However, this kind of disease remains unclear to date. In this study, it identifies entities associated with disease using the machine learning of a computational way from text data collection for molecular mechanisms related to ASD. Entities related to disease are extracted from the biomedical literature related to autism by using deep learning with bidirectional long short-term memory (BiLSTM) and conditional random field (CRF) model. Compared other previous works, the approach is promising for identifying entities related to disease. The proposed approach including five types of molecular entities is evaluated by GENIA corpus to obtain an F-score of 76.81%. The work has extracted 9146 proteins, 145 RNAs, 7680 DNAs, 1058 cell-types, and 981 cell-lines from the autism biomedical literature after removing repeated molecular entities. Finally, we perform GO and KEGG analyses of the test dataset. This study could serve as a reference for further studies on the etiology of disease on the basis of molecular mechanisms and provide a way to explore disease genetic information.
Comparison of 1L Adjuvant Auxiliary Preparations with 2L Solely Polyethylene Glycol plus Ascorbic Acid Regime for Bowel Cleaning: A Meta‐analysis of Randomized, Controlled Trials
Authors: Yuan Xin , Zhang Zhixin , Xie Jiarong , Zhang Yu , Xu Lu , Wang Weihong , Xu Lei
Published: February 2021

The effectiveness of additional usage of adjuvants for bowel preparation is still unclear. This study compared 1L polyethylene glycol plus ascorbic acid with adjuvant drug regimens (1L PEG‐AA, lower volume) with 2L polyethylene glycol plus ascorbic acid (2L PEG‐A, low volume) to evaluate whether the adjuvants can be used to reduce the standard dosage of purgative further. The PubMed/MEDLINE, EMBASE, Cochrane Library, and Web of Science database were searched for randomized controlled trials (RCTs). The primary outcome was the efficacy of bowel preparation, and the secondary outcomes were patients’ tolerability and complication rate. The overall quality of evidence was assessed using the GRADEpro guideline development tool. Five RCTs with a total of 1013 patients from Korea were included. The majority of patients were outpatients from different hospitals. The pooled data showed no significant difference in the adequate bowel preparation rate (89.3% versus 89.4%, RR 1, 95% CI 0.95‐1.05, I2 = 47%) as well as in the complication rate (RR for nausea 1.22, 95% CI 0.89‐1.65, I2 = 49%; RR for bloating 0.96, 95% CI 0.73‐1.28, I2 = 0%; RR for vomiting 0.69, 95% CI 0.32‐1.50, I2 = 33%; RR for abdominal pain 1.01, 95% CI 0.61‐1.69, I2 = 0%). But a significantly higher willingness rate was observed in the lower volume (85.1% versus 67.9%, RR 1.25, 95% CI 1.14‐1.38, I2 = 46%). The quality of primary outcome evidence was moderate. The findings of this meta‐analysis revealed that 1L PEG‐AA may be a viable alternative to 2L PEG‐A, with comparable effectiveness, better patient preference, and no statistically significant adverse event occurrence.
Identifying the Immunological Gene Signatures of Immune Cell Subtypes
Authors: Zhang Yu-Hang , Li Zhandong , Zeng Tao , Lu WenCong , Huang Tao , Cai Yu-Dong
Published: February 2021

The immune system is a complicated defensive system that comprises multiple functional cells and molecules acting against endogenous and exogenous pathogenic factors. Identifying immune cell subtypes and recognizing their unique immunological functions are difficult because of the complicated cellular components and immunological functions of the immune system. With the development of transcriptomics and high‐throughput sequencing, the gene expression profiling of immune cells can provide a new strategy to explore the immune cell subtyping. On the basis of the new profiling data of mouse immune cell gene expression from the Immunological Genome Project (ImmGen), a novel computational pipeline was applied to identify different immune cell subtypes, including αβ T cells, B cells, γδ T cells, and innate lymphocytes. First, the profiling data was analyzed by a powerful feature selection method, Monte‐Carlo Feature Selection, resulting in a feature list and some informative features. For the list, the two‐stage incremental feature selection method, incorporating random forest as the classification algorithm, was applied to extract essential gene signatures and build an efficient classifier. On the other hand, a rule learning scheme was applied on the informative features to construct quantitative expression rules. A group of gene signatures was found as qualitatively related to the biological processes of four immune cell subtypes. The quantitative expression rules can efficiently cluster immune cells. This work provides a novel computational tool for immune cell quantitative subtyping and biomarker recognition.
Conflict and Sensitivity Analysis of Vehicular Stability Using a Two‐State Linear Bicycle Model
Authors: Hassan Mohamed A. , Abdelkareem Mohamed A. A. , Tan Gangfeng , Moheyeldein M. M.
Published: February 2021

Vehicle parameters and operation conditions play a critical role in vehicular handling and stability. This study aimed to evaluate vehicle stability based on cornering tire stiffness integrated with vehicle parameters. A passenger vehicle is considered in which a two‐state linear bicycle model is developed in the Matlab/Simulink. The effect of the vehicle parameters on lateral vehicle stability has been investigated and analyzed. The investigated parameters included CG longitudinal position, wheelbase, and tire cornering stiffness. Furthermore, the effects of load variation and vehicle speed were addressed. Based on a Fishhook steering maneuver, the lateral stability criteria represented in lateral acceleration, yaw rate, vehicle sideslip angle, tire sideslip angles, and the lateral tire force were analyzed. The results demonstrated that the parameters that affect the lateral vehicle stability the most are the cornering stiffness coefficient and the CG longitudinal location. The findings also indicated a positive correlation between vehicle properties and lateral handling and stability.
Predicting Rainfall-Induced Soil Erosion Based on a Hybridization of Adaptive Differential Evolution and Support Vector Machine Classification
Authors: Dinh Tuan Vu , Nguyen Hieu , Tran Xuan-Linh , Hoang Nhat-Duc
Published: February 2021

Soil erosion induced by rainfall is a critical problem in many regions in the world, particularly in tropical areas where the annual rainfall amount often exceeds 2000 mm. Predicting soil erosion is a challenging task, subjecting to variation of soil characteristics, slope, vegetation cover, land management, and weather condition. Conventional models based on the mechanism of soil erosion processes generally provide good results but are time-consuming due to calibration and validation. The goal of this study is to develop a machine learning model based on support vector machine (SVM) for soil erosion prediction. The SVM serves as the main prediction machinery establishing a nonlinear function that maps considered influencing factors to accurate predictions. In addition, in order to improve the accuracy of the model, the history-based adaptive differential evolution with linear population size reduction and population-wide inertia term (L-SHADE-PWI) is employed to find an optimal set of parameters for SVM. Thus, the proposed method, named L-SHADE-PWI-SVM, is an integration of machine learning and metaheuristic optimization. For the purpose of training and testing the method, a dataset consisting of 236 samples of soil erosion in Northwest Vietnam is collected with 10 influencing factors. The training set includes 90% of the original dataset; the rest of the dataset is reserved for assessing the generalization capability of the model. The experimental results indicate that the newly developed L-SHADE-PWI-SVM method is a competitive soil erosion predictor with superior performance statistics. Most importantly, L-SHADE-PWI-SVM can achieve a high classification accuracy rate of 92%, which is much better than that of backpropagation artificial neural network (87%) and radial basis function artificial neural network (78%).
Arsenic Content, Speciation, and Distribution in Wild Cordyceps sinensis
Authors: Xiao Yuancan , Li Cen , Xu Wei , Du Yuzhi , Zhang Ming , Yang Hongxia , Wei Lixin , Bi Hongtao
Published: February 2021

The excessive arsenic content in wild Cordyceps sinensis has caused great concerns on human health. The toxicity of arsenic depends on its concentration, chemical form, and valence. The source studies of arsenic in C. sinensis are essential for safety evolution and quality control. We used ICP-MS and HPLC-ICP-MS methods to determine the total arsenic amount and the arsenic speciation. Synchrotron-based XANES and micro-XRF imaging techniques were used to characterize arsenic valence and distribution. The total arsenic amount range in wild C. sinensis samples was 5.77–13.20 μg/g with an average of 8.85 ± 2.5 μg/g. As(III) and As(V) were the main species in wild C. sinensis samples. The iAs only accounts for 4.47–11.42% of the extracted arsenic. Trivalent and pentavalent forms were the dominant chemical forms of arsenic. Besides, we found that arsenic was accumulated at the digestive tract of the host larva.
Visually Meaningful Image Encryption Scheme Based on DWT and Schur Decomposition
Authors: Dong Youxia , Huang Xiaoling , Ye Guodong
Published: February 2021

A visually meaningful image encryption scheme with an asymmetric structure based on the discrete wavelet transform (DWT) and Schur decomposition is proposed in this study. First, the RSA algorithm is used to generate the initial values for the chaotic system to produce the random sequence. Then, both scrambling and diffusion operations are performed on the plain image to obtain the preencrypted image. Moreover, the Schur decomposition is applied on the preencrypted plain image to obtain the upper triangular and orthogonal matrices. Second, the cover image is scrambled followed by a DWT operation. Four subbands are then formed, namely, LL, HL, LH, and HH. Finally, the former upper triangular matrix and orthogonal matrix are embedded into subbands LH and HH produced by the cover image. After the application of the inverse DWT and inverse scrambling operation, the final visually meaningful cover image embedded with a secret plain image can be obtained. No one can identify any useful information about the plain image from the final embedded cover image, nor can anybody know that there is any hidden secret image. Experimental simulations show that the normalized correlation values between the original cover image and the final visually meaningful cover image are approximately 0.9997. Therefore, the proposed encryption scheme is imperceptible for secret image communications.
Root and Root Canal Morphology Classification Systems
Authors: Karobari Mohmed Isaqali , Parveen Ayesha , Mirza Mubashir Baig , Makandar Saleem D. , Nik Abdul Ghani Nik Rozainah , Noorani Tahir Yusuf , Marya Anand
Published: February 2021

Introduction. While there are many root morphology classification systems with their own distinct advantages, there are many shortcomings that come along with each system. Objectives. The aim of this review was to compare the various root and root canal morphology classifications, their advantages, limitations, and clinical and research implications. Data Sources and Selection. An extensive literature search was conducted on PubMed and Scopus to identify the published data on root and root canal classification systems published until 1 May 2020 using keywords, root canal classification system, classification systems for root canals, and root morphology. The related literature was reviewed and then summarized. Data Synthesis. Several studies have analysed and detailed root and root canal classifications and further added new subsystems, works of Weine et al. (1969) and Vertucci et al. (1974). Besides, Sert and Bayirli (2004) added supplementary types to Vertucci’s classification system. A new classification was most recently introduced by Ahmed et al. (2017) involving the use of codes for tooth numbering, number of roots, and canal configuration. Conclusions. Weine et al. classified only single-rooted teeth, without considering multirooted teeth and complex configurations. Vertucci’s classification included complex configurations, with Sert and Bayirli adding further complex supplemental types. Ahmed et al.’s classification simplifies classifying root and canal morphology while overcoming the limitations of several previous classification systems making it beneficial for implementation in dental schools.
Dynamic Fracture and Energy Evolution Characterization of Naturally Fractured Granite Subjected to Multilevel Cyclic Loads
Authors: Guo Ning , Li Changhong , Liu Hao , Wang Yu
Published: February 2021

Naturally fractured rock mass is susceptible to stress disturbance and could result in the stimulation of natural fractures and even serious geological hazards. In this work, multilevel uniaxial fatigue loading experiments were carried out to reveal the fracture and energy evolution of naturally fractured granite using stress-strain descriptions and energy evolution analysis. Results reveal the influence of natural fracture on mechanical properties of granite, regarding the fatigue lifetime, fatigue deformation characteristics, fatigue damage, energy evolution, and fatigue failure pattern. Volumetric and shear processes caused by the sliding and shearing along the natural fracture control the whole failure process. The energy dissipation and release characteristics are strongly impacted by natural fractures. The elastic energy and dissipated energy both decrease with increasing natural fracture volume, growth of the dissipated energy becomes faster for rock near to failure. It is proved that the dissipated energy is mainly used to activate the preexisting natural fractures.
The Heterogeneity of Neutrophil Recruitment in the Tumor Microenvironment and the Formation of Premetastatic Niches
Authors: Xing Xin , Bai Yongrui , Song Jian
Published: February 2021

The recruitment of neutrophil to the primary cancer has been shown to be steered by neoplastic cells or tumor-educated mesenchymal stromal cells and has a prometastatic effect. However, the neutrophil chemotaxis and their interaction with tumor cells in the distal metastasized tissues remain elusive. In this review, we discussed emerging research on the interaction between neutrophil recruitment and tumor metastasis, which is essential for studying tumor cell invasion and related immunotherapy.
Efficient Concurrent Execution of Smart Contracts in Blockchain Sharding
Authors: Wang Yan , Li Jixin , Liu Wansheng , Tan Aiping
Published: February 2021

Throughput performance is a critical issue in blockchain technology, especially in blockchain sharding systems. Although sharding proposals can improve transaction throughput by parallel processing, the essence of each shard is still a small blockchain. Using serial execution of smart contract transactions, performance has not significantly improved, and there is still room for improvement. A smart contract concurrent execution strategy based on concurrency degree optimization is proposed for performance optimization within a single shard. This strategy is applied to each shard. First, it characterizes the conflicting contract feature information by executing a smart contract, analyzing the factors that affect the concurrent execution of the smart contracts, and clustering the contract transaction. Second, in shards with high transaction frequency, considering the execution time, conflict rate, and available resources of contract transactions, finding a serializable schedule of contract transactions by redundant computation and a Variable Shadow Speculative Concurrency Control (SCC-VS) algorithm for smart contract scheduling is proposed. Finally, experimental results show that the strategy increases the concurrency of smart contract execution by 39% on average and the transaction throughput of the whole system by 21% on average.
A Two-Phase Numerical Model of Well Test Analysis to Characterize Formation Damage in Near-Well Regions of Injection Wells
Authors: Zhang Jia , Cheng Shiqing , Di Shiying , Gao Zhanwu , Yang Rui , Cao Pengliang
Published: February 2021

Formation damage usually occurs in near-well regions for injection wells completed in offshore oilfields under the development of line drive patterns. However, current works on characterizing the damage by well test analysis were basically focused on using single-phase analogy to solve two-phase flow issues, resulting in errors on the diagnosis and interpretation of transient pressure data. In this paper, we developed a two-phase model to simulate the pressure transient behavior of a water injection well in a multiwell system. To solve the model more efficiently, we used the finite volume method to discretize partially differential flow equations in a hybrid grid system, including both Cartesian and radial meshes. The fully implicit Newton-Raphson method was also employed to solve the equations in our model. With this methodology, we compared the resulting solutions with a commercial simulator. Our results keep a good agreement with the solutions from the simulator. We then graphed the solutions on a log-log plot and concluded that the effects of transitional zone and interwell interference can be individually identified by analyzing specific flow regimes on the plot. Further, seven scenarios were raised to understand the parameters which dominate the pressure transient behavior of these flow regimes. Finally, we showed a workflow and verified the applicability of our model by demonstrating a case study in a Chinese offshore oilfield. Our model provides a useful tool to reduce errors in the interpretation of pressure transient data derived from injection wells located in a line drive pattern.
Blockchain-Based Automated System for Identification and Storage of Networks
Authors: Prashar Deepak , Jha Nishant , Shafiq Muhammad , Ahmad Nazir , Rashid Mamoon , Amin Banday Shoeib , Khan Habib Ullah
Published: February 2021

Network topology is one of the major factors in defining the behavior of a network. In the present scenario, the demand for network security has increased due to an increase in the possibility of attacks by malicious users. In this paper, a blockchain-based system is suggested for securely discovering and storing networks. Techniques such as cloud-based storage systems are not efficient and are lacking in trust, privacy, security, and data control. The blockchain-based technique suggested in this paper is capable of resolving these challenges. Experiments were performed using Mininet, Cisco Packet Tracer, and Ethereum blockchain with the network inference algorithm. This algorithm is capable of inferring the network topology even when only partial information regarding the network is available. The results obtained clearly show that the network is resistant to malicious users and various external attacks, making the network robust.
Health State Prediction and Performance Evaluation of Belt Conveyor Based on Dynamic Bayesian Network in Underground Mining
Authors: Li Xiangong , Zhang Yuzhi , Li Yu , Zhan Yujie , Yang Lin
Published: February 2021

To deal with the problem of weak prediction and performance evaluation capabilities of traditional prediction and evaluation methods, a method of health state prediction and performance evaluation of belt conveyor based on Dynamic Bayesian Network (DBN) is proposed. First, the belt conveyor sensor monitoring data are preprocessed to obtain the feature data set with labels. At the same time, qualitative and quantitative analyses and interval discretization are carried out from belt conveyor fault‐causing elements to construct the DBN network. Then, the sample data are applied to the DBN network for training, and the DBN‐based prediction and performance evaluation model is established. Finally, taking the real‐time monitoring data of a belt conveyor in an underground mine as an example, a DBN‐based belt conveyor health prediction and evaluation model is constructed to evaluate and predict the health of the equipment. The results show that the model could identify different operating conditions and failure modes and further improves the prediction accuracy.
A Deep Learning‐Based Power Control and Consensus Performance of Spectrum Sharing in the CR Network
Authors: Aslam Muhammad Muzamil , Du Liping , Ahmed Zahoor , Irshad Muhammad Nauman , Azeem Hassan
Published: February 2021

The cognitive radio network (CRN) is aimed at strengthening the system through learning and adjusting by observing and measuring the available resources. Due to spectrum sensing capability in CRN, it should be feasible and fast. The capability to observe and reconfigure is the key feature of CRN, while current machine learning techniques work great when incorporated with system adaption algorithms. This paper describes the consensus performance and power control of spectrum sharing in CRN. (1) CRN users are considered noncooperative users such that the power control policy of a primary user (PU) is predefined keeping the secondary user (SU) unaware of PU’s power control policy. For a more efficient spectrum sharing performance, a deep learning power control strategy has been developed. This algorithm is based on the received signal strength at CRN nodes. (2) An agent‐based approach is introduced for the CR user’s consensus performance. (3) All agents reached their steady‐state value after nearly 100 seconds. However, the settling time is large. Sensing delay of 0.4 second inside whole operation is identical. The assumed method is enough for the representation of large‐scale sensing delay in the CR network.