bluuebunny/crossref_metadata_2025 huggingface dataset:
An ANN-based data-predictive approach for comparative study between CFD finite difference and finite volume method
Authors: Talele Virendra , Vadaje Yuvraj
Published: April 2022

In computational fluid dynamics (CFD), there is a transformation of methods over the years for building commercially coded software. Each method has predicted its own set of importance, but the exportation and prediction of data are some of the crucial elements for post-processing and validating results. In the present investigation, a detailed comparative analysis is performed over finite difference method (FDM) and finite volume method (FVM) method for the 1D steady-state heat conduction problem over a 1-m-long plate. The comparison was made between solution creation and validation between FDM and FVM for the analytical and computational scheme. The convergence-dependent study is performed as multi-objective optimization to predict how artificial neural network (ANN) can be used to verify and validate the solution of CFD.
Canard Cycles and Relaxation Oscillations in a Singularly Perturbed Leslie–Gower Predator–Prey Model with Allee Effect
Authors: Zhu Zirui , Liu Xingbo
Published: April 2022

In this paper, we investigate the dynamics of a modified Leslie–Gower predator–prey model with Allee effect on prey. And the Holling type II functional response is considered in this model. When prey reproduces much faster than predator, by combining the normal form theory of slow–fast systems and the geometric singular perturbation theory, we observe much richer new dynamical phenomena than the existing ones. In the case of strong Allee effect, we prove the existence of canard cycles, homoclinic orbits and heteroclinic orbits. Furthermore, we focus on the case of weak Allee effect. In addition to the similar dynamics of that exhibited by the system with strong Allee effect, we also demonstrate the occurrence of relaxation oscillations created by entry-exit function. Moreover, the existence of canard explosion is further explained analytically and numerically with the help of sophisticated slow–fast techniques.
INVESTIGATION OF MACHINABILITY PERFORMANCE IN TURNING OF Ti–6Al–4V ELI ALLOY USING FIREFLY ALGORITHM AND GRNN APPROACHES
Authors: KUMAR RAMANUJ , PANDEY ANISH , SAHOO ASHOK KUMAR , RAFIGHI MOHAMMAD
Published: April 2022

Ti–6Al–4V ELI alloy is one of the most familiar materials for orthopedic implants, aeronautical parts, marine components, oil and gas production equipment, and cryogenic vessel applications. Therefore, its appropriate quality of finishing is highly essential for these applications. But the characteristics like lower modulus of elasticity, lesser thermal conductivity, and high chemical sensitivity placed it in the categories of difficult-to-cut metal alloys. Also, tooling cost is one of the prime issues in the machining of this alloy. Therefore, this research is more inclined to use a low-budget uncoated carbide tool in turning the Ti–6Al–4V ELI alloy. Also, the selection of suitable levels of machining parameters is highly indispensable to get the appropriate surface finish with a low tooling cost. So, the [Formula: see text] experimental design is utilized to check the performances of the uncoated carbide tool in the turning tests. The performance indexes like surface roughness (Ra), flank wear of tool (VBc), and material removal rate (MRR) are measured and studied with the help of surface plots and interaction plots. Further, the Firefly Algorithm optimization is employed to find the optimal cutting parameters and cutting response values. The local optimal values of the input parameters a, f, and Vc are estimated as 0.3241[Formula: see text]mm, 0.0893[Formula: see text]mm/rev, and 82.41[Formula: see text]m/min, respectively. Similarly, the global optimal values for the responses Ra, VBc, and MRR are reported as 0.6321[Formula: see text]μm, 0.09253[Formula: see text]mm, and 24.61[Formula: see text]g/min, individually. Additionally, to predict the responses, Generalized Regression Neural Network (GRNN) modeling is employed and the average absolute error for each response is noticed to be less than 1%. Therefore, the GRNN modeling tool is strongly recommended for various machining applications.
Almost cosymplectic 3-manifolds with pseudo-parallel characteristic Jacobi operator
Authors: Inoguchi Jun-ichi , Lee Ji-Eun
Published: April 2022

In this paper, we classify almost cosymplectic 3-manifolds with pseudo-parallel characteristic Jacobi operator. The only simply connected and complete non-cosymplectic almost cosymplectic 3-manifold with pseudo parallel characteristic Jacobi operator is the Minkowski motion group.
On a congruence involving harmonic series and Bernoulli numbers
Authors: Chern Shane
Published: April 2022

In 2003, Zhao discovered a curious congruence involving harmonic series and Bernoulli numbers: for any odd prime p [Formula: see text] where [Formula: see text] is the nth Bernoulli number. This congruence was generalized by Wang and Cai in 2014, and Cai, Shen and Jia in 2017 by replacing the odd prime p in the summation and modulus with an odd prime power, and a product of two odd prime powers, respectively. In particular, Cai, Shen and Jia proposed a conjectural congruence: for any positive integer n with an odd prime factor p such that [Formula: see text] where [Formula: see text] [Formula: see text] In this paper, we establish the following generalization of their conjecture: for any positive integer n with an odd prime factor [Formula: see text] such that [Formula: see text] where [Formula: see text] [Formula: see text] [Formula: see text] where [Formula: see text], [Formula: see text] and [Formula: see text] are positive integers coprime to [Formula: see text], and [Formula: see text] is a positive common multiple of [Formula: see text], [Formula: see text] and [Formula: see text]. Also, [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text].
DEVS and MBSE: A review
Authors: Zeigler Bernard P.
Published: April 2022

We review Discrete-Event system Specification (DEVS) in the context of Model-based Systems Engineering (MBSE) and discuss an application of DEVS methodology to MBSE. We outline support for an envisioned MBSE development cycle of DEVS top-to-bottom MBSE capability and offer an example of mapping UML activity diagrams into executable activity-based DEVS models. We close with conclusions and future research directions.
A model validation method based on the orthogonal polynomial transformation and area metric
Authors: Zhang Huan , Li Wei , Ma Ping , Yang Ming
Published: April 2022

Modeling and Simulation Technology has become an important means to study various complex systems with its extensive application. Thus, the accuracy of the simulation models becomes a critical problem and needs to be assessed by employing an appropriate model validation method. The simulation models often have multivariate dynamic responses with uncertainty, while most of the existing validation methods concentrate on the validation of the static responses. Hence, a new validation method is proposed in this paper to validate the dynamic responses of the simulation models over the time domain at a single validation site and multiple validation sites through introducing the discrete Chebyshev polynomials and area metric. For each time series, the orthogonal expansion coefficients are extracted primarily by representing the time series with the discrete orthogonal polynomials. Then, the area metric and the [Formula: see text]-pooling metric are employed to validate all the uncorrelated coefficients at a single validation site and multiple validation sites, respectively, and the final validation result is obtained by summarizing the metric values. The feasibility and effectiveness of the proposed model validation method are illustrated through the example of the terminal guidance stage of the flight vehicle.
Emerging Paradigms in Type 2 Immunity
Authors: Hammad Hamida , Debeuf Nincy , Aegerter Helena , Brown Andrew S. , Lambrecht Bart N.
Published: April 2022

A principal purpose of type 2 immunity was thought to be defense against large parasites, but it also functions in the restoration of homeostasis, such as toxin clearance following snake bites. In other cases, like allergy, the type 2 T helper (Th2) cytokines and cells present in the environment are detrimental and cause diseases. In recent years, the recognition of cell heterogeneity within Th2-associated cell populations has revealed specific functions of cells with a particular phenotype or gene signature. In addition, here we discuss the recent data regarding heterogeneity of type 2 immunity–related cells, as well as their newly identified role in a variety of processes ranging from involvement in respiratory viral infections [especially in the context of the recent COVID-19 (coronavirus disease 2019) pandemic] to control of cancer development or of metabolic homeostasis.
Group Incentives for Environmental Protection and Natural Resource Management
Authors: Segerson Kathleen
Published: April 2022

Group incentives can and have been used to address a range of environmental and resource problems. These schemes base individual penalties and/or rewards on the performance of a group of individuals or firms who contribute to the environmental or resource problem. The economics literature on team incentives and public goods, as well as the literature specifically on environmental and natural resource management, provides insights into the design of group incentives. This article reviews the literature on group incentives in the context of environmental protection and natural resource policy. This literature suggests that group incentives can be effective and even efficient as environmental policy tools. However, the outcomes under group incentives will likely depend on a combination of the policy design and the nature of the internal group interactions. Within-group interactions are likely to be particularly important when policies involve thresholds so that coordination is needed to reach a cooperative equilibrium.
Novel Optical Soliton Waves in Metamaterials with Parabolic Law of Nonlinearity via the IEFM and ISEM
Authors: Li Xiaoyan , Manafian Jalil , Abotaleb Mostafa , Ilhan Onur Alp , Oudah Atheer Y. , Prakaash A. S.
Published: April 2022

Here, the miscellaneous soliton solutions of the generalized nonlinear Schrödinger equation are considered that describe the model of few-cycle pulse propagation in metamaterials with parabolic law of nonlinearity. The novel analytical wave solutions to the mentioned nonlinear equation in the sense of the nonlinear ordinary differential transform equation are obtained. The techniques are the improved

exp

−
Γ

ϖ

function method and the improved simple equation method. The nonlinear ordinary transform to concern the generalized Schrodinger equation to convert it for a solvable integer-order differential equation is used. After the successful implementation of the presented methods, the exact solitary wave solutions in the form of trigonometric, rational, and hyperbolic functions are obtained. Hence, the presented methods are relatable and efficient to solve nonlinear problems in mathematical physics.
Evaluation of Endocrine and Metabolic Changes in Polycystic Ovary Syndrome by Ultrasonic Imaging Features under an Intelligent Algorithm
Authors: Wei Li , Wu Feng , Zhang Jianjun , Li Jing , Yang Di , Wen Guoying
Published: April 2022

To explore the application of intelligent algorithm-based ultrasound in the evaluation of polycystic ovary syndrome (PCOS) and the endocrine and metabolic changes of PCOS, 44 patients diagnosed with PCOS were recruited and rolled into three groups regarding detection methods. Backpropagation algorithm-based ultrasonic detection was adopted for the patients in the experimental group. The patients in the control group were tested by conventional ultrasound. In addition, 18 healthy volunteers were selected as the normal group. The results showed that the images processed by the backpropagation algorithm were substantially better than the traditional ultrasound images (

P
<
0.05

), and the image display was clearer. S, A, and S/A ratios measured by two different detection methods were 7.8 mm2, 3.5 mm2, and 0.449 in the experimental group, respectively, which were significantly different from 6.3 mm2, 2.6 mm2, and 0.413 in the control group (

P
<
0.05

). The PI and RI values of the interstitial ovarian artery in the experimental group were lower than those in the control group, and the systolic peak velocity (PSV) and end diastolic velocity (EDV) values were higher than those in the control group (

P
<
0.05

). Compared with the control group, the ovarian volume, interstitial vascularization-flow index (VFI), and flow index (FI) in the experimental group were substantially increased, and the total number of detected follicles was more (

P
<
0.05

). The level of follicle-stimulating hormone (FSH) in PCOS patients was substantially lower than that in normal controls (

P
<
0.05

). The LH, E2, P, and T of PCOS patients were substantially higher than those of normal controls (

P
<
0.05

). Ultrasound on account of the backpropagation algorithm can directly display the three-dimensional structure of the ovary and follicle and accurately measure the ovarian volume and follicle number. Endocrine and metabolic indicators can provide objective information for the clinical diagnosis of PCOS and can be used as a way of clinical evaluation of PCOS.
Differentially Expressed Genes in Nasopharyngeal Carcinoma Tissues and Their Correlation with Recurrence and Metastasis of Nasopharyngeal Carcinoma
Authors: Yue Haiying , Zhu Huijun , Luo Danjing , Du Qinghua , Xie Yiting , Huang Sijie , Liu Wenqi
Published: April 2022

In this study, bioinformatics tools were used to identify key genes to study the molecular mechanism of nasopharyngeal carcinoma (NPC) development and to explore the correlation of these key genes with the recurrence and metastasis of NPC. The GSE61218 microarray dataset obtained from the Gene Expression Omnibus Database (GEO) was used. The limma R package was used to screen differentially expressed genes (DEGs) between NPC and normal nasopharyngeal (NP) tissues. KEGG functional enrichment was performed on these selected DEGs. Protein-protein interaction (PPI) networks were constructed using Cytoscape software to identify key node proteins. The NPC-metastasis microarray dataset GSE103611 was obtained from GEO to analyze the expression of DEGs in NPC metastasis. A total of 239 DEGs were identified. DEGs were mainly enriched in oocyte maturation-related pathways, cytokine-related pathways, cell cycle-related pathways, cancer-related pathways, and homologous recombination-related pathways. In addition, the top 10 nodes with the higher degree in the DEG PPI network were as follows: CDK1, CCNB2, BUB1, CCNA2, AURKB, BUB1B, MAD2L1, NDC80, BIRC5, and CENPF. The results indicated that DEGs may be involved in the pathogenesis of NPC by regulating cell cycle and mitosis, which can be used as molecular biomarkers for the diagnosis of NPC. In addition, we identified 87 DEGs with

FC
>
2

and

P
<
0.01

from the metastasis spectrum of NPC. The intersection gene between DEGs of NPC and normal NP tissue samples and those of the metastatic spectrum of NPC was identified to be VRK2. The expression of VRK2 in NPC samples was significantly higher than that in normal NP tissue, and similarly, VRK2 expression was significantly upregulated in metastatic samples compared with nonmetastatic samples (

P
<
0.05

). Therefore, VRK2 may be a biomarker for predicting the metastasis of NPC patients after treatment.
Pathological Clinical Analysis and Imaging Manifestations for Spinal Bone Tumors Based on Cement Injection
Authors: Li Jie , Wang Xu , Li Yongmin , Cao Qinhui , Bu Yi , Cao Hengcong , Wang Xiaoqiang
Published: April 2022

In order to explore the imaging manifestations and pathological characteristics of spine tumors, this article explores the clinical diagnosis and treatment methods through multi-sample case analysis with the support of imaging, and proposes a targeted treatment method that uses a special PVP needle with a beveled puncture surface for puncture. Moreover, this article uses the supporting PVP syringe for bone cement injection, develops a health status questionnaire, and adopts a scoring method for comprehensive assessment. The purpose of this article is to show that through the combination of preoperative radiotherapy and postoperative bracing, bone cement injection to treat vertebral tumors can immediately obtain satisfactory pain relief. Finally, through case analysis and image performance, we can see that the method proposed in this article has a certain effect.
Myocardial Infarction in a 7-Year-Old Girl with Polyarteritis Nodosa
Authors: Bayazeed Lina , Felimban Alaa , Alsaiad Abdulsalam , Alsufiani Fahd , Alqanatish Jubran
Published: April 2022

Polyarteritis nodosa (PAN) is a rare systemic vasculitis that affects small to medium-sized arteries. It could affect any organ including the heart. However coronary artery involvements are very rare. We describe a young girl who presented following a histopathological diagnosis of PAN with acute chest pain, high serum troponin, and progressive ischemic changes in the electrocardiogram (ECG). Induction of remission of her disease was done with six-moths Cyclophosphamide infusions and pulse corticosteroids. In addition to anticoagulation and dual antithrombotic therapy, the disease remission was maintained with mycophenolate mofetil which helps in the recovery of coronary disease. Our case illustrates the serious cardiac involvement of PAN in a child that responded to intensive management.
DDAH1/ADMA Regulates Adiponectin Resistance in Cerebral Ischemia via the ROS/FOXO1/APR1 Pathway
Authors: Zhao Yichen , Zhang Minjie , Dou Yunxiao , Du Kangshuai , Liu Xueyuan , Zhao Yanxin
Published: April 2022

Dimethylarginine dimethylaminohydrolase 1 (DDAH1) protects against cerebral ischemia injury via regulating the level of asymmetric dimethylarginine (ADMA). This study is aimed at exploring the effect of adiponectin resistance on ADMA-induced neuronal loss in ischemic stroke (IS) and the underlying mechanism. DDAH1 knockout (DDAH1-/-) and wild-type (DDAH1+/+) rats underwent middle cerebral artery occlusion/reperfusion (MCAO/R). Plasma and brain adiponectin levels and the expressions of adiponectin receptor 1 (APR1), adaptor protein, phosphotyrosine interacting with PH domain and leucine zipper 1 (APPL1), adenosine monophosphate-activated protein kinase (AMPK), and phosphorylated AMPK were determined after 24 h, 3 days, and 7 days. Neurological behavior, infarct volume, and adiponectin signaling were evaluated using adiponectin peptide or AdipoRon. The levels of reactive oxygen species (ROS) and Forkhead box O1 (FOXO1) (a transcription factor for APR1) were also assessed. An oxygen-glucose deprivation/reoxygenation (OGD/R) model was established in primary neurons. DDAH1 was overexpressed in neurons, after which FOXO1 expression, ROS production, adiponectin resistance, and cell viability were detected. DDAH1-/- rats showed no significant difference in adiponectin level in either plasma or brain after MCAO/R in DDAH1+/+ rats, but downregulated APR1 expression and suppressed adiponectin signaling were observed. AdipoRon, but not adiponectin peptide, attenuated the neurological deficits and adiponectin resistance in DDAH1-/- rats. ROS accumulation and phosphorylated FOXO1 expression also increased with DDAH1 depletion. Following DDAH1 overexpression, decreased cell viability and inhibited adiponectin signaling induced by OGD/R were alleviated in primary neurons, accompanied by reduced ROS production and phosphorylated FOXO1 expression. Our study elucidated that in IS, DDAH1 protected against adiponectin resistance in IS via the ROS/FOXO1/APR1 pathway.
An Integrated Development Model of Cultural and Creative Industry and Rural Tourism Industry Based on Data Mining
Authors: Chang Weifeng
Published: April 2022

In the later stage of industrialization or postindustrialization era, the driving force of human social and economic development mainly comes from technological innovation, cultural creativity, and the development of creative industries, high-tech industries, and service industries. The cultural and creative industries meet the needs of the postindustrial era and the era of knowledge economy. This shows the importance of culture, knowledge, and technology in the rapid economic development. This paper introduces data mining technology and obtains a data mining method system suitable for this study. It mines and analyzes data related to cultural and creative industries and rural tourism industries. It finally obtained that the degree of integration of the two industries in the past six years was 0.498, 0.496, 0.487, 0.498, 0.499, and 0.5, respectively.
Age-Adjusted Schedules of Venetoclax and Hypomethylating Agents to Treat Extremely Elderly Patients with Acute Myeloid Leukemia
Authors: Lee Aaron M. , Goodman Aaron M. , Mangan James K.
Published: April 2022

Acute myeloid leukemia (AML) is associated with particularly poor outcomes in the elderly population, in whom the disease is most prevalent. BCL-2 has been identified as an antiapoptotic protein and promotes survival of leukemia stem cells. Recently, the United States FDA has approved venetoclax, a selective oral BCL-2 inhibitor, for use in conjunction with hypomethylating agents (azacitidine or decitabine) or low-dose cytarabine as a first-line treatment option for those AML patients ineligible for standard induction chemotherapy. However, there are nuances and challenges when using this regimen in the extremely elderly AML patients. Given the widespread adoption of this regimen and increasing prevalence of patients who are well into their 80 s, it is important to evaluate and understand how to safely use this regimen in this so-called “extremely elderly” population. We present here 3 case studies involving AML patients >85 years of age who were treated with venetoclax plus HMA and provide clinical knowledge on how this population should be appropriately managed.
A Case Study on Annoyance Noise Caused by Metro Railway at a TOD Developed Depot
Authors: Liu Qiang , Kang Zhongxu , Zhang Hougui , Song Ruixiang , Li Ge
Published: April 2022

As an importance strategy to finance the transport infrastructure investment, transit‐oriented development (TOD) for railway depot is rapidly growing in China. However, railway‐induced noise emerged as the leading environmental nuisance triggering one of the most common public complaints. In order to discuss the influence of railway‐induced noise on the neighbourhood residents in a TOD developed depot, a special case study is provided in this study. Measurement campaigns were conducted for responding to the complaints, and noise map was calculated for finding out a noise control solution. Based on the recommendation awakening threshold value of 42 dB (A) summarized from the Night Noise Guidelines for Europe (WHO, 2009), the measurement results confirmed that the neighbourhood residents were indeed suffering from the instantaneous railway noise, and the sleep disturbance correlated indicator LAmax was more than 56 dB (A) at outside of buildings. Furthermore, calculation results indicated that the lower office buildings became mirrors which reflected the noise across over the cover structure to the higher stories of residents’ buildings. At the ends, a kind of dissipative splitter muffler was proposed as a potential solution, and its performance was also simulated.
New Optical Solitons with Variational Principle and Collective Variable Strategy for Cold Bosons in Zig‐Zag Optical Lattices
Authors: Alhussain Ziyad A. , Raza Nauman
Published: April 2022

The theme of this piece of research is to investigate the collective variable (CV) as well as semi‐inverse techniques to explore a significant model of cold bosonic atoms in a zig‐zag optical lattice. The system is reduced to an important equation by utilizing the continuum approximation and explains the soliton’s dynamics in sense of pulse variables. These parameters are amplitude, temporal position, chirp, width, frequency, and phase which are termed as collective variables (CVs). The proposed methods are more straightforward, succinct, accurate, and simple to calculate. Furthermore, to employ the computational counterfeit on the system of six ordinary differential equations that denote all the CVs incorporated in the supposed ansatz, a well‐established computational method which is the Runge–Kutta scheme of order four is applied. The CV method is exerted to resolve the evolution of pulse parameters with the propagation distance and graphical illustrations which are also given. Moreover, figures reveal the fascinating periodic oscillations of frequency, width, amplitude, and chirp of soliton. Solitons and their numerical behavior to interpret fluctuations in CVs are presented for several values of super‐Gaussian pulse parameters. Also, the results for the semi‐inverse method which are bright solitons are provided in the form of 2D, 3D, and density plots for the distinct values of the fractal parameter to understand their physical significance. This scheme is effective in finding variational principles of various nonlinear evolution equations. Some compelling characteristics pertaining to the current scrutiny are also deduced.
Predictive Model of Cerebral Vasospasm in Subarachnoid Hemorrhage Based on Regression Equation
Authors: Li Jianzhong , Zhou Kaiguo , Wang Lei , Cao Qiumei
Published: April 2022

In order to explore the regression equation for the prediction model of subarachnoid hemorrhage and cerebral vasospasm, the nomogram prediction model of SCVS occurrence was established. This study is a retrospective analysis of 125 cases of aSAH admitted to a hospital; the patients were divided into SCVS group and non-SCVS group. Select SIRI as a simple and reliable marker of inflammation, analyze its correlation with SCVS and its predictive value, and analyze the predictive value of SIRI to SCVS through ROC curve. Based on the SIRI inflammation level and other related risk factors, a nomogram prediction model for the occurrence of SCVS was built. The experimental results show that the SIRI level of patients in the SCVS group was significantly higher than that of the non-SCVS group, and logistic regression analysis found that SIRI is an independent risk factor for SCVS.

S
I
R
I
=
3.63
×

10

9

/
L

is the best cutoff value for diagnosing the occurrence of SCVS. When

TC
=
2.24

 mmol/L and

SIRI
=
3.63
×
10

%/L, its Youden Index is the largest (0.312, 0.296) and is the best cutoff value for predicting the occurrence of SCVS; at the same time, its prediction accuracy (area under the ROC curve (AUC)), sensitivity, specificity, the positive predictive value, and negative predictive value are 0.743, 72.70%, 80.10%, 77.53%, and 94.24% and 0.725, 70.60%, 76.90%, 73.49%, and 93.59%. Nomogram prediction model establishment and evaluation combined with the results of multifactor analysis are used to build an individual nomogram prediction model. The model has good prediction consistency (C-

index
=
0.685

,

P
<
0.01

). ROC analysis results showed that the model that combined SIRI and other standard variables (

AUC
=
0.896

, 95% CI was 0.803-0.929,

P
<
0.001

) was better than the model that did not combine SIRI (

AUC
=
0.859

, 95% CI was 0.759-0.912,

P
<
0.001

) and the model based only on SIRI (

AUC
=
0.725

, 95% CI was 0.586-0.793,

P
=
0.001

) has better predictive value for SCVS. Joint SIRI will optimize the prediction performance of the nomogram model and improve the early recognition and screening capabilities of SCVS.
Topological Indices of Families of Bistar and Corona Product of Graphs
Authors: Khalid A. , Kausar N. , Munir M. , Gulistan M. , Al-Shamiri M. M. , Lamoudan T.
Published: April 2022

Topological indices are graph invariants that are used to correlate the physicochemical properties of a chemical compound with its (molecular) graph. In this study, we study certain degree‐based topological indices such as Randić index, Zagreb indices, multiplicative Zagreb indices, Narumi–Katayama index, atom‐bond connectivity index, augmented Zagreb index, geometric‐arithmetic index, harmonic index, and sum‐connectivity index for the bistar graphs and the corona product , where represents the complement of complete graph Kn.
Dynamic and Static Reserve Recharge Characteristics of a Dewatering Well Seepage from an Aquifer Bottom by Sand Tank Seepage Experiment
Authors: Wang Wenxue , Xue Jingyuan , Qingyang Hao , Jiang Tong , Dong Jinyu
Published: April 2022

Ground water inflow from an overlying aquifer happens frequently in underground engineering; for properly controlling ground water during construction, the most important tasks are to assess the possibility and the probable water inflow, which depends on the ground water recharge characteristics. However, less work has been carried out on this topic. In order to study the water inflow variation characteristics including dynamic replenishment and static reserve release from a submersible aquifer, sand-tank seepage experiments on a dewatering well seepage from an aquifer bottom under the conditions of different water pressure heads insides the dewatering well (

P

) were designed and performed. The results show that (1) when the dewatering beginning, the water inflow increases to a peak value rapidly and then decreases following a first-order exponential decay function. In the initial phase, the water inflow is mainly composed of static reserve release, which decreases quickly with time also following a first-order exponential decay function. Meanwhile, the recharge of dynamic replenishment quickly increases with time following a first-order exponential growth function, which mainly forms the water inflow in the quasi-steady state. (2) The equal time of dynamic and static reserve release and the quasi-steady state time both present a first-order exponential decay function with the decrease of

P

. (3) The peak water inflow should perform at the beginning of dewatering which presents a little late after the beginning of dewatering and increases with the decreasing of

P

following a linear function. The results will play important references for water inflow prediction and calculation from overlying aquifers for underground projects.
Cerebral Arterial Stenosis Detection Based on a Retained Two‐Stage Detection Algorithm
Authors: Liu Hanqing , Li Xiaojun , Wei Jin , Kang Xiaodong
Published: April 2022

Stroke is one of the fatal diseases worldwide, and its primary mechanism is produced by cerebrovascular stenosis, blockages, or embolisms. Computer‐aided diagnosis can assist clinical practitioners in identifying cerebrovascular anomalies, elucidating the precise lesions’ location in the patients, and providing guidance for clinical therapy. Due to different portions of the cerebrovascular possessing diverse morphological properties and the limited narrow area, the detection effect is unsatisfactory. A retrained two‐stage algorithm for detecting cerebral arterial stenosis in CTA images is proposed to solve these problems by further fusing image features and improving the quality of regions of interest. In Faster R‐CNN and Libra R‐CNN, the backbone network was Resnet50, with deformable convolutional and nonlocal neural networks introduced in the third, fourth, and fifth stages of the backbone network. Deformable convolutional networks learned offsets to extract morphological features of blood vessels in different tomographic planes. Nonlocal neural networks fused global information and extracted global features from location information of feature maps. A cascade detector refined object classification and bounding box regression before prediction. The experimental results show that the retained algorithm increases mAP by 7.3% and 7.5%, respectively, compared with Faster R‐CNN and Libra R‐CNN. Deformable convolutional networks, nonlocal neural networks, and cascade detectors are incorporated into further feature fusion; thus, semantic information about the cerebrovascular structure is learned, demonstrating more accurate stenotic region detection and demonstrating generalizability across different two‐stage algorithms.
Investigation on Physical and Mechanical Characteristics of Date Palm Fiber Reinforced Aliphatic Epoxy Hybrid Composites
Authors: Refaai Mohamad Reda A. , Reddy R. Meenakshi , Reddy M. Indra , Khan B. Shahul Hamid , Nagaraju V. , Kumar S. Praveen
Published: April 2022

Automotive industry attention in using date palm fiber as an internal material has been sparked by its use as a polymer reinforced composite. Date palm fiber-reinforced aliphatic epoxy composites for semistructural applications are the key goals of this work. To make the various composites, they used a combination of manual lay-up and adhesive bonding. Date palm fiber/bamboo hybrid composite and uncontaminated composites were tested through density, tensile, flexural, and impact tests and also studied the effects of swelling, water absorption, and physical performance in greater depth. According to studies, hybrid composites constructed from date palm fiber and bamboo had the best mechanical properties. The date palm/bamboo hybrid composite was created to impact the toughness of 12.72 J/m in tensile, flexural strength, and impact toughness measurements. The reduced swelling and water absorption were 27.66 percent and 15.37 percent, respectively, when testing a date palm fiber/bamboo hybrid composite. Density ranged from 1.15 g/cm3 to 1.25 g/cm3 for bamboo fiber composite material and from 1.23 to 1.27 g/cm3 for date palm fiber/bamboo composite material. High flexural strength is achieved by the bamboo composite specimen (bamboo: 6.18 MPa), followed by (PDF-A/B: 61.12 MPa, date palm fiber-AA/B: 61.08 MPa, date palm fiber-L/B: 60.82 MPa, and date palm fiber-G/B: 61.47 MPa), and the PDF composite specimens (date palm fiber-A/B: 61.112). Hybridized materials (date palm fiber/bamboo fiber) with a 50 : 50 ratio had higher impact strength.
Towards Understanding the Analysis, Models, and Future Directions of Sports Social Networks
Authors: Bai Zhongbo , Bai Xiaomei
Published: April 2022

With the rapid growth of information technology and sports, a large amount of sports social network data has emerged. Sports social network data contains rich entity information about athletes, coaches, sports teams, football, basketball, and other sports. Understanding the interaction among these entities is meaningful and challenging. To this end, we first introduce the background of sports social networks. Secondly, we review and categorize the recent research efforts in sports social networks and sports social network analysis based on passing networks, from the centrality and its variants to entropy, and several other metrics. Thirdly, we present and compare different sports social network models that have been used for sports social network analysis, modeling, and prediction. Finally, we present promising research directions in the rapidly growing field, including mining the genes of sports team success with multiview learning, evaluating the impact of sports team collaboration with motif‐based graph networks, finding the best collaborative partners in a sports team with attention‐aware graph networks, and finding the rising star for a sports team with attribute‐based convolutional neural networks. This paper aims to provide the researchers with a broader understanding of the sports social networks, especially valuable as a concise introduction for budding researchers interested in this field.
Forecasting Method of Stock Market Volatility Based on Multidimensional Data Fusion
Authors: Zhang Xiaoyong , Zhang Li
Published: April 2022

The volatility of the stock market is related to the vital interests of stockholders and is essential for maintaining a stable financial environment. Through the analysis of data changes, excellent professional traders can extract information about the direction of stock changes, whether it is worth investing, and long-term or short-term trading. This article aims to study the forecasting methods of stock market volatility, by integrating multiparty data, in-depth analysis of the direction of data changes, predicting the price changes of the stock market, and better guiding stockholders’ investment. This paper proposes a multisource data fusion method to analyze the stock market price changes and find the best risk prediction method. The experimental results in this paper show that multisource data fusion can better help the stock market predict stock changes and reduce financial investment risks by 20%. Comparing the obtained prediction results with the real data, the MSE predicted by the ARIMA model is calculated to be 2.35. It provides a new idea for effectively analyzing nonstationary time series data with complex trend fusion characteristics by rationally screening feature signals and trend signals and modeling probability distribution.
Memory-Augmented Insider Threat Detection with Temporal-Spatial Fusion
Authors: Li Dongyang , Yang Lin , Zhang Hongguang , Wang Xiaolei , Ma Linru
Published: April 2022

Insider threat detection is important for the smooth operation and security protection of an organizational system. Most existing detection models establish historical baseline by reconstructing single-day and individual user behaviors, and then treat any outlier of the baseline as a threat. However, such methods ignore the temporal and spatial correlations between different activities, which result in an unsatisfying performance. To address such an issue, we propose a novel insider threat detection method, namely, Memory-Augmented Insider Threat Detection (MAITD), in this paper. Such an idea is motivated by the observation that the combination of individual model that focuses on historical baseline and group model that represents peer baseline can effectively identify the low-signal yet long-lasting insider threats, and reduce the possibility of false positives. To illustrate, our MAITD captures the temporal and spatial correlation of user behaviors by constructing compound behavioral matrix and common group model, and combines specific application scenarios to integrate the detection results. Moreover, it introduces the memory-augmented network into autoencoder to enlarge the reconstruction error of abnormal samples, thereby reducing the false negative rate. The experimental results on CERT dataset show that the instance-based and user-based AUCs of MAITD reach up to 87.54% and 94.56%, respectively, which significantly outperform previous works.
Sustainable Technical Debt-Aware Computing Model for Virtual Machine Migration (TD4VM) in IaaS Cloud
Authors: Vashistha Avneesh , Sharma Chandra Mani , Mahapatra Rajendra Prasad , Chariar Vijayaraghavan M. , Sharma Navel
Published: April 2022

In the cloud, optimal CPU and memory utilization can lead to low energy consumption, which is an important aspect of green computing. However, constantly changing workloads may contribute to resource over- or underutilization. The former violates the service level agreement’s quality of service constraints. The latter indicates that as workload decreases, virtual machine resource utilization decreases. They introduce difficult decision-making tasks when dynamically adapting (e.g., migrating) a virtual machine in order to maximize its resource utilization over time. To address these challenges, we propose a newer mathematical model called the technical debt-aware computing model for virtual machine migration (TD4VM). The model promotes a holistic approach to dynamic virtual machine adaptation for cloud service providers and addresses existing issues regarding logical aspects of virtual machine adaptation in a highly dynamic cloud environment, which includes a measurement mechanism and estimation guidelines for estimating future debt and utility. Technical debt-aware models make decisions based on VM operating costs, quality, minimizing SLA violations, and incurring technical debt. This approach connects decisions about virtual machine migration that affect overall utility over time. Our method can determine whether a virtual machine should be moved when it is over or underutilized based on its technical debt. The experimental results on a dataset obtained from the Materna-trace-1 demonstrate that the proposed approach outperforms other state-of-the-art methods on a variety of performance metrics. A numerical comparison shows that TD4VM outperforms the other approaches, with VM resource economies of 171.84%, 91.33%, 97.85%, and 93.89% for TD4VM, LRMMT, IQRMC, and IQRMMT, respectively. Additionally, we quantify the debt amassed using TD4VM and state-of-the-art techniques. When compared to LRMMT, IQRMC, and IQRMMT, which cost (in $) 0.77, 0.73, and 0.76, respectively, TD4VM accumulates the minimum debt of 0.17.
Experimental Study of 3D Micro-CT on Meso-Structure Evolution of Coal Samples with Different Coal Grades under the Action of Temperature
Authors: Xie Jianlin , Zhao Dong , Li Pengwei
Published: April 2022

In the three-dimensional micro-CT experiment system, the room temperature is set to 300°C when different coal samples (lignite, anthracite, lean coal and gas coal) are observed for mesoscopic observation. The evolution regularity of mesoscopic structure is analyzed according to the CT scan of coal samples under different temperatures and three sections of scanning images, and by ImageJ image processing software, image processing, and analysis of the characteristics of the profile, the following conclusions are obtained: (1) Coal specimen will have an overall expansion deformation along with the rise of temperature. The sample expansion can be divided into two types: outward expansion and inward expansion. Outward expansion means that the expansion of the skeleton extends outward from the adjacent pores, while inward expansion means that the solid skeleton intrudes into the adjacent pores. When the temperature rises, the outward expansion and inward expansion occur simultaneously. The dominant expansion mode is influenced by the type of coal sample and the temperature value. (2) With the increase of temperature, coal and anthracite coal specimen pore fissure structure shows an expansion tendency before contraction, while gas coal and lean coal show reverse patterns; in addition to the above the reason of the difference vitrinite differences, one must also consider selected specimen original porosity and mechanical physical properties, such as a combination of other factors. (3) In the temperature range of 100–200°C, when the temperature increases at the same rate, lignite porosity increases the most, followed by gas coal, lean coal, and anthracite. (4) There are certain differences in the variation trends of the pixel proportions of the three sections of the coal specimen, and the temperature values of the three curves at the maximum pixel point are also different, which indicates that the expansion of each point in the coal specimen with the change of temperature is not completely synchronous, and the physical and mechanical properties of the sample are heterogeneous.
Characteristics of Carboniferous Volcanic Reservoirs in Beisantai Oilfield, Junggar Basin
Authors: Cheng Hanlie , Ma Peng , Dong Guofeng , Zhang Shun , Wei Jianfei , Qin Qiang
Published: April 2022

In recent years, petroleum exploration in the Carboniferous volcanic rock reservoirs in the Junggar Basin has been the focus of important petroleum energy development in western China. The lithologic identification of volcanic rock reservoirs seriously restricts the accuracy of reservoir prediction and affects the success rate of oil exploration. Different types of volcanic rocks have different petrological characteristics and mineral assemblages, especially affected by the depositional environment. The volcanic rocks in different regions have their own uniqueness. This paper takes the Carboniferous volcanic reservoirs in Xiquan block, Beisantai Oilfield, Junggar Basin as the research target. Through a large number of core observations, casting slices, scanning electron microscopy, and X-ray diffraction methods, the Carboniferous volcanic rocks are analyzed. The petrology, pore characteristics, physical properties, and diagenetic evolution history of the reservoir are analyzed. The study shows that the volcanic facies in the Xiquan block can be divided into explosive facies, overflow facies, and volcanic sedimentary facies, among which the explosive facies is subdivided into empty subfacies (volcanic breccia-breccia tuff combination) and thermal base wave subfacies (tuff). The lithology of the reservoir is pyroclastic rock and volcanic lava, belonging to medium-porous and ultralow permeability reservoirs, and the storage space can be divided into three types: primary pores, secondary pores, and fractures. The lithology of key exploration is breccia tuff, followed by breccia tuff and volcanic breccia.
Dynamic Emergency Vehicle Path Planning and Traffic Evacuation Based on Salp Swarm Algorithm
Authors: Duan X. H. , Wu J. X. , Xiong Y. L.
Published: April 2022

In view of the rescue delay due to traffic congestion in the urban road network, this paper implemented real-time traffic control with congestion index constraints in emergency vehicle dispatching and proposed a two-stage optimization model and algorithm. In the first stage, salp swarm algorithm (SSA) was combined with Dijkstra algorithm, and a novel hybrid algorithm with new updating rules was designed to get the multiple alternative paths. In the second stage, an improved salp swarm algorithm (ISSA) with a population grouping strategy was proposed to obtain the best evacuation schemes and the optimal rescue paths of emergency vehicles. Results of the illustrative examples show that, after evacuation, the average travel time of all alternative paths is reduced by 24.22%, while traffic congestion indexes of the adjacent road sections almost unchanged. The computation time of the hybrid algorithm for obtaining the set number of alternative paths is 56.62% and 50.47% shorter than that of bat algorithm (BA) and SSA. For the solution of the evacuation model, the computation time of the ISSA is 33.51%, 30.15%, and 30.60% shorter than that of particle swarm optimization (PSO), BA, and SSA, and the optimal solution of the ISSA is 25.92%, 10.06%, and 0.97% better than that of PSO, BA, and SSA. That is, we shorten the emergency response time and control the adverse impact of traffic evacuation on background traffic. The improved algorithm has excellent performance. This study provides a new idea and method for emergency rescue of traffic accidents.
Heterogeneous Graph Convolutional Network‐Based Dynamic Rumor Detection on Social Media
Authors: Yu Dingguo , Zhou Yijie , Zhang Suiyu , Liu Chang
Published: April 2022

The development of social media has provided open and convenient platforms for people to express their opinions, which leads to rumors being circulated. Therefore, detecting rumors from massive information becomes particularly essential. Previous methods for rumor detection focused on mining features from content and propagation patterns but neglected the dynamic features with joint content and propagation pattern. In this paper, we propose a novel heterogeneous GCN‐based method for dynamic rumor detection (HDGCN), mainly composed of a joint content and propagation module and an ODE‐based dynamic module. The joint content and propagation module constructs a content‐propagation heterogeneous graph to obtain rumor representations by mining and discovering the interaction between post content and propagation structures in the rumor propagation process. The ODE‐based dynamic module leverages a GCN integrated with an ordinary differential system to explore dynamic features of heterogeneous graphs. To evaluate the performance of our proposed HDGCN model, we have conducted extensive experiments on two real‐world datasets from Twitter. The results of our proposed model have outperformed the mainstream model.