bluuebunny/crossref_metadata_2025 huggingface dataset:
Efficient acoustic perception for virtual AI agents
Authors: Chemistruck Mike , Allen Andrew , Snyder John , Raghuvanshi Nikunj
Published: September 2021

We model acoustic perception in AI agents efficiently within complex scenes with many sound events. The key idea is to employ perceptual parameters that capture how each sound event propagates through the scene to the agent's location. This naturally conforms virtual perception to human. We propose a simplified auditory masking model that limits localization capability in the presence of distracting sounds. We show that anisotropic reflections as well as the initial sound serve as useful localization cues. Our system is simple, fast, and modular and obtains natural results in our tests, letting agents navigate through passageways and portals by sound alone, and anticipate or track occluded but audible targets. Source code is provided.
A functional skeleton transfer
Authors: Musoni Pietro , Marin Riccardo , Melzi Simone , Castellani Umberto
Published: September 2021

The animation community has spent significant effort trying to ease rigging procedures. This is necessitated because the increasing availability of 3D data makes manual rigging infeasible. However, object animations involve understanding elaborate geometry and dynamics, and such knowledge is hard to infuse even with modern data-driven techniques. Automatic rigging methods do not provide adequate control and cannot generalize in the presence of unseen artifacts. As an alternative, one can design a system for one shape and then transfer it to other objects. In previous work, this has been implemented by solving the dense point-to-point correspondence problem. Such an approach requires a significant amount of supervision, often placing hundreds of landmarks by hand. This paper proposes a functional approach for skeleton transfer that uses limited information and does not require a complete match between the geometries. To do so, we suggest a novel representation for the skeleton properties, namely the functional regressor, which is compact and invariant to different discretizations and poses. We consider our functional regressor a new operator to adopt in intrinsic geometry pipelines for encoding the pose information, paving the way for several new applications. We numerically stress our method on a large set of different shapes and object classes, providing qualitative and numerical evaluations of precision and computational efficiency. Finally, we show a preliminar transfer of the complete rigging scheme, introducing a promising direction for future explorations.
Fast Corotated Elastic SPH Solids with Implicit Zero-Energy Mode Control
Authors: Kugelstadt Tassilo , Bender Jan , Fernández-Fernández José Antonio , Jeske Stefan Rhys , Löschner Fabian , Longva Andreas
Published: September 2021

We develop a new operator splitting formulation for the simulation of corotated linearly elastic solids with Smoothed Particle Hydrodynamics (SPH). Based on the technique of Kugelstadt et al. [2018] originally developed for the Finite Element Method (FEM), we split the elastic energy into two separate terms corresponding to stretching and volume conservation, and based on this principle, we design a splitting scheme compatible with SPH. The operator splitting scheme enables us to treat the two terms separately, and because the stretching forces lead to a stiffness matrix that is constant in time, we are able to prefactor the system matrix for the implicit integration step. Solid-solid contact and fluid-solid interaction is achieved through a unified pressure solve. We demonstrate more than an order of magnitude improvement in computation time compared to a state-of-the-art SPH simulator for elastic solids.

We further improve the stability and reliability of the simulation through several additional contributions. We introduce a new implicit penalty mechanism that suppresses zero-energy modes inherent in the SPH formulation for elastic solids, and present a new, physics-inspired sampling algorithm for generating high-quality particle distributions for the rest shape of an elastic solid. We finally also devise an efficient method for interpolating vertex positions of a high-resolution surface mesh based on the SPH particle positions for use in high-fidelity visualization.
Computational Psychiatry Needs Time and Context
Authors: Hitchcock Peter F. , Fried Eiko I. , Frank Michael J.
Published: September 2021

Why has computational psychiatry yet to influence routine clinical practice? One reason may be that it has neglected context and temporal dynamics in the models of certain mental health problems. We develop three heuristics for estimating whether time and context are important to a mental health problem: Is it characterized by a core neurobiological mechanism? Does it follow a straightforward natural trajectory? And is intentional mental content peripheral to the problem? For many problems the answers are no, suggesting that modeling time and context is critical. We review computational psychiatry advances toward this end, including modeling state variation, using domain-specific stimuli, and interpreting differences in context. We discuss complementary network and complex systems approaches. Novel methods and unification with adjacent fields may inspire a new generation of computational psychiatry.
Demand Analysis of Online Chinese Behavior Expression in Wireless Sensor Network
Authors: Liu Zheng
Published: September 2021

Due to the common progress and interdependence of wireless sensor networks and language, Chinese semantic analysis under wireless sensor networks has become more and more important. Although there are many research results on wireless networks and Chinese semantics, there are few researches on the influence and relationship between them. Wireless sensor networks have strong application relevance, and the key technologies that need to be solved are also different for different application backgrounds. In order to reveal the basic laws and development trends of online Chinese semantic behavior expression in the context of wireless sensor networks, this paper adopts big data analysis methods and semantic model analysis methods and constructs semantic analysis models through PLSA method calculations, so that the λ construction process conforms to this research topic. Research the accuracy and applicability of the semantic analysis model. Through word extraction of 1.05 million word data of 1,103 documents on Baidu Tieba, HowNet, and citeulike websites, the data set was integrated into a data set, and the PLSA model was verified with this data set. In addition, through the construction of the wireless sensor network, the semantic analysis results in the expression of Chinese behavior are obtained. The results show that the accuracy of the data set extracted from 1103 documents increases with the increase of the number of documents. Second, after using the PLSA model to perform semantic analysis on the data set, the accuracy of the data set is improved. Compared with traditional semantic analysis, the model and the big data analysis framework have obvious advantages. With the continuous development of Internet big data, the big data methods used to count Chinese semantics are also constantly updated, and their efficiency is constantly improving. These updated semantic analysis models and statistical methods are constantly eliminating the uncertainty of modern online Chinese. The basic laws and development trends of statistical Chinese semantics also provide new application scenarios for online Chinese behavior. It also laid a ladder for subsequent scholars.
Effect of Pretransplant Use of Calcimimetic on Parathyroid Function after Renal Transplantation
Authors: Bokuda Kanako , Morimoto Satoshi , Seki Yasufumi , Takano Noriyoshi , Ichihara Atsuhiro
Published: September 2021

Objective. Persistence of hyperparathyroidism (HPT) after renal transplantation leads to undesirable outcomes such as increase in cardiovascular events, graft dysfunction, and increased mortality. Options for therapy include medical management with calcimimetic or operative management. The present study was undertaken to evaluate the natural history of HPT after renal transplantation and to determine risk factors for persistent HPT in the era of calcimimetic. Design. The study is a retrospective review of data from 74 consecutive patients who underwent renal transplantation at our institution from April 2011 to November 2019. Methods. The natural history of HPT after renal transplantation and associations between intact parathyroid hormone (PTH) level after transplantation and clinical variables such as age, sex, duration of pretransplant dialysis, and use of calcimimetic before transplantation were evaluated. Results. Intact PTH decreased after renal transplantation in most of the patients without receiving parathyroidectomy. Known risk factors of persistent HPT did not associate with intact PTH level after renal transplantation in patients who had been receiving calcimimetic before transplantation. Conclusion. In conclusion, we have found that HPT after renal transplantation could be managed successfully by medical treatments. When predicting the prognosis of HPT after transplantation, pretransplant use of calcimimetic should be taken into consideration.
Upregulation of ADAR Promotes Breast Cancer Progression and Serves as a Potential Therapeutic Target
Authors: Li Xiao , Sun Guangshun , Wu Liangliang , Sun Guoqiang , Cheng Ye , Tao Jing , Li Zhouxiao , Tang Weiwei , Wang Hanjin
Published: September 2021

Background. Breast cancer (BC) is the most common cause of cancer death worldwide, and its incidence is increasing every year. This study aims to investigate the expression characteristics of ADAR gene in breast cancer and to explore its role in the occurrence and development of BC and its possible mechanism. Methods. TCGA portal was used to detect the expression of ADAR in cancer including BC, and its correlation with clinicopathological data as well as other genes was analyzed via UALCAN database. The TISCH database evaluated the expression of ADAR in different types of cell populations in BC at the single-cell level. The Kaplan–Meier plotter database was used to predict the correlation between ADAR expression and BC patient prognosis. The Human Protein Atlas was used to detect the expression of ADAR in tissues and location of ADAR mRNA in cells. Moreover, the relationships between immune response and ADAR expression in BC were assessed with the use of the TISIDB. Metascape and STRING were applied to predict ADAR with other protein interactions. Finally, the effect generated by ADAR expression on cell proliferating, invading, and migrating processes was assessed in vitro with knockdown and overexpression strategies. Results. ADAR was significantly upregulated in BC tissues compared to paracancerous tissues. Single-cell RNA analysis showed that ADAR was specifically upregulated in cancer cell clusters and was also expressed in stromal and immune cell clusters. The upregulation of ADAR was positively correlated with clinicopathological stage and negatively correlated with BC prognosis. Experimental processes in vitro revealed ADAR knockdown hindered, proliferated, invaded, and migrated levels of BC cells, whereas over expression of ADAR played the opposite effect. ADAR protein, which may interact with OASL, STAT2, and IFIT3, was mainly located in the nucleoli in cells and primarily involved DNA modification and apoptotic signaling pathway. Immune factors may interact with ADAR in BC, and ADAR was found noticeably linked with immunosuppressor such as IL10, CD274, and IDO1. Conclusion. ADAR is significantly upregulated in breast cancer tissues, which may promote the progression of BC through the interaction of cancer cells, stromal cells, and immune cells. Targeting ADAR may offer new hope in treating breast cancer.
Convective Cold Pool Associated with Offshore Propagation of Convection System over the East Coast of Southern Sumatra, Indonesia
Authors: Yulihastin Erma , Fathrio Ibnu , Trismidianto  , Nauval Fadli , Saufina Elfira , Harjupa Wendi , Satiadi Didi , Nuryanto Danang Eko
Published: September 2021

The cold pool outflow has been previously shown to be generated by decaying Mesoscale Convective Complexes (MCCs) in the Maritime Continent. The cold pool also has a main role in the development processes of oceanic convective systems inducing heavy rainfall. This study investigated a cold pool event (January 1-2, 2021) related to a heavy rainfall system over the coastal region of Lampung, Southern Sumatra, within a high-resolution model simulation using a regional numerical weather prediction of the Weather Research and Forecasting (WRF) with convection permitting of 1 km spatial resolution, which was validated by satellite and radar data observations. It is important to note that the intensity, duration, timing, and structure of heavy rainfall simulated were in good agreement with satellite-observed rainfall. The results also showed that a cold pool (CP) plays an important role in inducing Mesoscale Convective Complex (MCC) and was responsible for the development of an offshore propagation of land-based convective systems due to the late afternoon rainfall over inland. This study also suggests that the propagation speed of the CP 8.8 m·s−1 occurring over the seaside of the coastal region, the so-called CP-coastal, is a plausible mechanism for the speed of the offshore-propagating convection, which is dependent on both the background prevailing wind and outflow. These conditions help to maintain the near-surface low temperatures and inhibit cold pool dissipation, which has implications for the development of consecutive convection.
Application of Jianpi Xiaoai Recipe Combined with Cisplatin and Adriamycin in the Treatment of Endometrial Cancer and Its Effect on Disease Control Rate
Authors: Ding Li , Li Hongyu , Wang Yuping
Published: September 2021

Objective. To explore the application of Jianpi Xiaoai recipe combined with cisplatin and Adriamycin in the treatment of endometrial cancer (EC) and its effect on the disease control rate (DCR). Methods. The data of 120 EC patients treated in People’s Hospital of Rizhao from February 2019 to February 2020 were retrospectively analyzed. They were equally split into experimental group and control group according to the order of admission. All patients were treated with neoadjuvant intra-arterial chemotherapy (continuous infusion of the uterine artery for 5 days before surgery, with 20 mg of cisplatin mixed with 2000 mg of normal saline and 10 mg of Adriamycin mixed with 500 ml of normal saline daily), while the experimental group was treated with Jianpi Xiaoai recipe at the same time to compare the short-term efficacy, immune function indexes, incidence of adverse reactions, and HEC-1-B (human endometrial adenocarcinoma cells) cell inhibition rates between the two groups. Results. The DCR and objective remission rate (ORR) in the experimental group were markedly higher compared with the control group (

P

 < 0.05). The immune function indexes after treatment were remarkably better in the experimental group than in the control group (

P

 < 0.05). Compared with the control group, the incidence of adverse reactions in the experimental group was notably lower (

P

 < 0.05), while the HEC-1-B inhibition rates after treatment were obviously higher (

P

 < 0.05). Conclusion. Jianpi Xiaoai recipe combined with cisplatin and Adriamycin can increase the HEC-1-B cell inhibition rate in EC patients, improve their immune function, reduce the possibility of adverse reactions, and enhance the therapeutic effect, which is worthy of clinical application and popularization.
A Provably Secure Authentication and Key Agreement Protocol in Cloud-Based Smart Healthcare Environments
Authors: Wu Tsu-Yang , Yang Lei , Luo Jia-Ning , Ming-Tai Wu Jimmy
Published: September 2021

The wide applications of the Internet of Things and cloud computing technologies have driven the development of many industries. With the improvement of living standards, health has become the top priority of people’s attention. The emergence of the wireless body area network (WBAN) enables people to master their physical condition all the time and make it more convenient for patients and doctors to communicate with each other. Doctors can provide real-time online treatment with cloud-based smart healthcare environments for patients. In this process, patients, health records, and doctors need to maintain security and privacy. Recently, Kumari et al. proposed a secure framework for the smart medical system. However, we found that their framework cannot provide the anonymity of patients and doctors, data confidentiality, and patient unlinkability and also is subject to impersonation attacks and desynchronization attacks. In order to ensure the security and privacy of patients and doctors, we propose an authentication and key exchange protocol in cloud-based smart healthcare environments. Formal and informal security analyses, as well as performance analysis, demonstrated that our protocol is suitable for these environments.
Highly Porous 3D Printed Tantalum Scaffolds Have Better Biomechanical and Microstructural Properties than Titanium Scaffolds
Authors: Fan Huaquan , Deng Shu , Tang Wentao , Muheremu Aikeremujiang , Wu Xianzhe , He Peng , Tan Caihua , Wang Guohua , Tang Jianzhong , Guo Kaixuan , Yang Liu , Wang Fuyou
Published: September 2021

Objective. To test the biomechanical properties of 3D printed tantalum and titanium porous scaffolds. Methods. Four types of tantalum and titanium scaffolds with four alternative pore diameters, #1 (1000‐700 μm), #2 (700‐1000 μm), #3 (500‐800 μm), and #4 (800‐500 μm), were molded by selective laser melting technique, and the scaffolds were tested by scanning electronic microscope, uniaxial‐compression tests, and Young’s modulus tests; they were compared with same size pig femoral bone scaffolds. Results. Under uniaxial‐compression tests, equivalent stress of tantalum scaffold was 411 ± 1.43 MPa, which was significantly larger than the titanium scaffolds (P < 0.05). Young’s modulus of tantalum scaffold was 2.61 ± 0.02 GPa, which was only half of that of titanium scaffold. The stress‐strain curves of tantalum scaffolds were more similar to pig bone scaffolds than titanium scaffolds. Conclusion. 3D printed tantalum scaffolds with varying pore diameters are more similar to actual bone scaffolds compared with titanium scaffolds in biomechanical properties.
[Retracted] Image Recognition of Badminton Swing Motion Based on Single Inertial Sensor
Authors: Chu Zhesen , Li Min
Published: September 2021

This article analyzes the method of reading data from inertial sensors. We introduce how to create a 3D scene and a 3D human body model and use inertial sensors to drive the 3D human body model. We capture the movement of the lower limbs of the human body when a small number of inertial sensor nodes are used. This paper introduces the idea of residual error into the deep LSTM network to solve the problem of gradient disappearance and gradient explosion. The main problem to be solved by wearable inertial sensor continuous human motion recognition is the modeling of time series. This paper chooses the LSTM network which can handle time series as well as the main frame. In order to reduce the gradient disappearance and gradient explosion problems in the deep LSTM network, the structure of the deep LSTM network is adjusted based on the residual learning idea. In this paper, a data acquisition method using a single inertial sensor fixed on the bottom of a badminton racket is proposed, and a window segmentation method based on the combination of sliding window and action window in real‐time motion data stream is proposed. We performed feature extraction on the intercepted motion data and performed dimensionality reduction. An improved Deep Residual LSTM model is designed to identify six common swing movements. The first‐level recognition algorithm uses the C4.5 decision tree algorithm to recognize the athlete’s gripping style, and the second‐level recognition algorithm uses the random forest algorithm to recognize the swing movement. Simulation experiments confirmed that the proposed improved Deep Residual LSTM algorithm has an accuracy of over 90.0% for the recognition of six common swing movements.
The Application Value of MRI in the Training and Physical Rehabilitation of Gastrocnemius with Acute Sports Injury
Authors: Zhang Xingwei , Liu Yunfa
Published: September 2021

The purpose of this study was to analyze the magnetic resonance imaging (MRI) signs of acute gastrocnemius injury and to provide the basis for clinical diagnosis. Thirty-two patients with acute gastrocnemius muscle injury (19 males and 13 females, aged 18–63 years, mean 46 years) were recruited, and MRI scans were performed. The results showed that all the lesions of calf gastrocnemius were detected in 32 patients, including first-degree injury in 15 cases, second-degree injury in 15 cases, and third-degree injury in 2 cases. MRI can display the location and pathological changes of acute gastrocnemius injury, providing good imaging data for clinical diagnosis and treatment.
SqueezeFace: Integrative Face Recognition Methods with LiDAR Sensors
Authors: Ko Kyoungmin , Gwak Hyunmin , Thoummala Nalinh , Kwon Hyun , Kim SungHwan
Published: September 2021

In this paper, we propose a robust and reliable face recognition model that incorporates depth information such as data from point clouds and depth maps into RGB image data to avoid false facial verification caused by face spoofing attacks while increasing the model’s performance. The proposed model is driven by the spatially adaptive convolution (SAC) block of SqueezeSegv3; this is the attention block that enables the model to weight features according to their importance of spatial location. We also utilize large‐margin loss instead of softmax loss as a supervision signal for the proposed method, to enforce high discriminatory power. In the experiment, the proposed model, which incorporates depth information, had 99.88% accuracy and an F1 score of 93.45%, outperforming the baseline models, which used RGB data alone.
Application of Cloud Computing in the Prediction of Exercise Improvement of Cardiovascular and Digestive Systems in Obese Patients
Authors: Du ZhenZhong , Hu Xin , Wu Jin
Published: September 2021

Based on the cardiovascular and digestive problems of obese patients, this paper adopted the cloud computing method and selected 100 subjects with big data (23 normal weight subjects, 3740 overweight patients, and 40 obese patients) as the research objects, studying the heart configuration and their digestive system of obese people. Results show that BMI = L (24 ≥ BMI > 27.9) and BMI = XL (BMI > 27.9) were identified as target correlation projects in this experiment, associated with each cardiac structural parameters, respectively. Cloud computing facilitates early detection, early prevention, and early intervention in heart configuration changes in overweight and obese patients.
CKAP2L, as an Independent Risk Factor, Closely Related to the Prognosis of Glioma
Authors: Zhu Li , Zheng Yanlei , Hu Ronghua , Hu Chenchen
Published: September 2021

Recent studies have found that cytoskeleton‐associated protein 2 like (CKAP2L), an important oncogene, is involved in the biological behavior of many malignant tumors, but its function in the malignant course of glioma has not been confirmed. The main purpose of this study was to clarify the relationship between prognostic clinical characteristics of glioma patients and CKAP2L expression using data collected from the GEPIA, HPA, CGGA, TCGA, and GEO databases. CKAP2L expression was significantly increased in glioma. Further, Kaplan‐Meier plots revealed that increased expression of CKAP2L was associated with shorter survival time of glioma patients in datasets retrieved from multiple databases. Cox regression analysis indicated that CKAP2L can serve as an independent risk factor but also has relatively reliable diagnostic value for the prognosis of glioma patients. The results of gene set enrichment analysis suggested that CKAP2L may play a regulatory role through the cell cycle, homologous recombination, and N‐glycan biosynthesis cell signaling pathways. Several drugs with potential inhibitory effects on CKAP2L were identified in the CMap database that may have therapeutic effects on glioma. Finally, knockdown of CKAP2L inhibited the proliferation and invasion of cells by reducing the expression level of cell cycle‐related proteins. This is the first study to demonstrate that high CKAP2L expression leads to poor prognosis in glioma patients, providing a novel target for diagnosis and treatment of glioma.
Association of Preoperative Serum Levels of CEA and CA15-3 with Molecular Subtypes of Breast Cancer
Authors: Zhao Wenjing , Li Xiaoyan , Wang Wenqing , Chen Bing , Wang Lijuan , Zhang Ning , Wang Zhe , Yang Qifeng
Published: September 2021

Objectives. Molecular subtypes are employed as a guide for targeted treatment and important prognostic factors. This study focused on investigating the association of serum levels of CEA, CA15-3, and CA125 with clinicopathological characteristics of breast cancer to find prognostic markers for breast cancer and provide precise targeted therapy. Materials and Methods. In this study, 961 breast cancer patients with preoperative serum levels of CEA, CA15-3, and CA125 and molecular subtypes were analyzed. Cut-off values of 5 ng/ml, 25 U/ml, and 35 U/ml were used for CEA, CA15-3, and CA125, respectively. The

χ

2 test and Fisher exact test along with logistic multivariate regression analysis were performed for investigating the correlation of CEA, CA15-3, and CA125 serum levels with molecular subtypes and associated factors. Results. An increase in the serum concentrations of CEA, CA15-3, and CA125 was discovered in 48 (4.99%), 54 (5.62%), and 55 (5.72%) breast cancer patients, respectively. Univariate analysis demonstrated that the levels of CEA (

p
<
0.01

) and CA15-3 (

p
<
0.05

) were significantly linked with molecular types of breast cancer. Moreover, patients having larger tumor size (

p
<
0.01

,

p
<
0.0001

, and

p
<
0.05

, respectively) along with nodal metastasis (

p
<
0.05

,

p
=
0.0001

, and

p
<
0.05

, respectively) exhibited higher rates of elevated CEA, CA15-3, and CA125 levels. Status of Her-2 positive (

p
<
0.01

) had a significant connection with elevated CEA levels. Multivariate analysis further indicated that molecular subtypes were independent factors associated with CEA and CA15-3 levels. Also, Her-2 status was significantly and independently related to CEA levels. Conclusion. Preoperative serum levels of CEA and CA15-3 were independently associated with molecular subtypes of breast cancer. CEA and CA15-3 might improve the prognostic prediction for patients with breast cancer and inform the selection of specific therapies. A further biological analysis is needed for investigating the relationship between Her-2 expression and CEA levels.
Omega Index of Line and Total Graphs
Authors: Demirci Musa , Delen Sadik , Cevik Ahmet Sinan , Cangul Ismail Naci
Published: September 2021

A derived graph is a graph obtained from a given graph according to some predetermined rules. Two of the most frequently used derived graphs are the line graph and the total graph. Calculating some properties of a derived graph helps to calculate the same properties of the original graph. For this reason, the relations between a graph and its derived graphs are always welcomed. A recently introduced graph index which also acts as a graph invariant called omega is used to obtain such relations for line and total graphs. As an illustrative exercise, omega values and the number of faces of the line and total graphs of some frequently used graph classes are calculated.
An Attention‐Based Model for Travel Energy Consumption of Electric Vehicle with Traffic Information
Authors: Li Shen , Zhang Hailong , Tan Huachun , Zhong Zhiyu , Jiang Zhuxi
Published: September 2021

Mileage anxiety is one of the most important factors that affect the driving experience due to the limitation of battery capacity. Robust and accurate prediction of the energy consumption of the journey of the electric vehicle can guide the driver to allocate the power rationally and relieve the anxiety of the mileage. Since vehicle sharing is the biggest application scenario of electric vehicles, it is a critical challenge in share mobility research area. In this paper, a travel energy consumption prediction model of electric vehicles is proposed in order to improve the mobility of shared cars and reduce the anxiety of drivers because they are worried about insufficient power. A recurrent neural network with attention mechanism and deep neural network is used to build the model. To validate the proposed model, a simulation is demonstrated based on both traffic and vehicle information. After the simulation, experimental results show that the proposed model has high prediction accuracy, and we also show through visualization how the model finds high relevant road segments of the road network while dealing with corresponding traffic state input.
An Estimation Model for Hydraulic Conductivity of Low-Permeability and Unfilled Fractured Granite in Underground Water-Sealed Storage Caverns
Authors: Cao Yangbing , Gong Weiguo , Zhang Xiangxiang , Chen Junxi , Huang Zhenping
Published: September 2021

The permeability of rock mass is closely related to the stability and safety of underground structure, especially in underground water-sealed storage caverns. With regard to the estimation approaches in predicting the hydraulic conductivity of fractured granite in water-sealed storage caverns, there are some limitations of parameter selection leading to poor applicability. Focusing on the contribution of the water conduction fractures (WCF) to the hydraulic conductivity, we attempted to propose a novel model, the CA model, for estimating its hydraulic conductivity based on the fracture orientation index and the normal stress index by analyzing the borehole wall imaging results and borehole water-pressure test results in the site of underground water-sealed storage caverns. The results indicated that the proposed model is suitable for low-permeability and unfilled fractured granite, exhibiting good effectiveness by clarifying the relation between geomechanical parameters and hydraulic behavior. Further, the parameters upon which the proposed model is based are representative and easy to obtain, which has certain guiding significance and reference value for analyzing the permeability characteristics of similar rock masses.
Research on Low Frame Rate Video Compression Algorithm in the Context of New Media
Authors: Yang Yixin , Xiang Zhiqang , Li Jianbo
Published: September 2021

When using the current method to compress the low frame rate video animation video, there is no frame rate compensation for the video image, which cannot eliminate the artifacts generated in the compression process, resulting in low definition, poor quality, and low compression efficiency of the compressed low frame rate video animation video. In the context of new media, the linear function model is introduced to study the frame rate video animation video compression algorithm. In this paper, an adaptive detachable convolutional network is used to estimate the offset of low frame rate video animation using local convolution. According to the estimation results, the video frames are compensated to eliminate the artifacts of low frame rate video animation. After the frame rate compensation, the low frame rate video animation video is divided into blocks, the CS value of the image block is measured, the linear estimation of the image block is carried out by using the linear function model, and the compression of the low frame rate video animation video is completed according to the best linear estimation result. The experimental results show that the low frame rate video and animation video compressed by the proposed algorithm have high definition, high compression quality under different compression ratios, and high compression efficiency under different compression ratios.
Quantitative Analysis and Prediction of Global Terrorist Attacks Based on Machine Learning
Authors: Pan Xiaohui
Published: September 2021

Terrorist attacks pose a great threat to global security, and their analysis and prediction are imperative. Considering the high frequency of terrorist attacks and the inherent difficulty in finding related terrorist organizations, we propose a classification framework based on ensemble learning for classifying and predicting terrorist organizations. The framework includes data preprocessing, data splitting, five classifier prediction models, and model evaluation. Based on a quantitative statistical analysis of terrorist organization activities in GTD from 1970 to 2017 and feature selection using the SelectKBest method in scikit learn, we constructed five classification and prediction models of terrorist organizations, namely, decision tree, bagging, random forest, extra tree, and XGBoost, and utilized a 10-fold cross-validation method to verify the performance and stability of the proposed model. Experimental results showed that the five models achieved excellent performance. The XGBoost and random forest models achieved the best accuracies (97.16% and 96.82%, respectively) of predicting 32 terrorist organizations with the highest attack frequencies. The proposed classifier framework is useful for the accurate and efficient prediction of terrorist organizations responsible for attacks and can be extended to predict all terrorist organizations.
Study on the Technology of Enhancing Permeability by Millisecond Blasting in Sanyuan Coal Mine
Authors: Zhao Dan , Wang Mingyu , Gao Xinhao
Published: September 2021

To reduce gas disasters in low permeability and high-gas coal seams and improve gas predrainage efficiency, conventional deep-hole presplitting blasting permeability increasing technology was refined and perfected. The numerical calculation model of presplitting blasting was established by using ANSYS/LS-DYNA numerical simulation software. The damage degree of coal and rock blasting was quantitatively evaluated by using the value range of the damage variable D. According to the actual field test parameters of coal seam #3 in the Sanyuan coal mine, Dlim = 0.81–1.0 was the coal rock crushing area, Dlim = 0.19–0.81 was the coal rock crack area, and Dlim = 0–0.19 was the coal rock disturbance area. By comparing and analysing the damage distribution nephogram of coal and rock mass under the influence of different millisecond blasting time interval and the blasting effect of simulation model, the optimal layout parameters of multilayer through cracks were obtained theoretically. And, the determined parameters were tested on the working face of the 1312 transportation roadway in coal seam #3 of the Sanyuan coal mine. The permeability effect was compared and analysed through the analysis of the gas concentration, gas purity, and mixing volume before and after the implementation of deep-hole presplitting blasting antireflection technology, as well as the change of gas pressure, attenuation coefficient, permeability coefficient, and other parameters between blasting coal seams. The positive role of millisecond blasting in reducing pressure and increasing permeability in low permeability and high-gas coal seam were determined.
Finite Element Analysis of Temperature Distribution and Stress Behavior of Squeeze Pressure Composites
Authors: Gurusamy P. , Sathish T. , Mohanavel V. , Karthick Alagar , Ravichandran M. , Nasif Omaima , Alfarraj Saleh , Manikandan Velu , Prasath S.
Published: September 2021

Aluminium‐reinforced composites play a vital role in the engineering industry because of their better strength and stiffness. The properties are directly related to the solidification phenomenon of the cast alloy. The design engineer should understand the importance of the solidification behavior of base alloy and its reinforcement. Composites’ solidification study is rare, and the reviews are limited. The solidification process is analyzed using the finite element method (FEM), and this would fetch a lot of information about the cooling rate of the composites and also helps to reduce the time in experimentation. This paper reports and plots the cooling curves of Al/SiCp composites using simulation software. Cylindrical‐shaped composites were developed using the squeeze casting method, and the experimental cooling curves were plotted using a K‐type thermocouple. Composites samples were prepared at the following squeeze pressures: 0, 30, 50, 70, 100, and 130 MPa; melt and die temperature was kept constant at 800 and 400°C, respectively. The experimental and FEA cooling curves were compared, and it was agreed that the increase in the squeeze pressure increases the cooling rate of the developed composite. Furthermore, the effect of temperature distribution from the inner region of the melt and die material which causes the radial and tangential stress of components has also been examined.
Aquaporin-4 Autoantibody Detection by ELISA: A Retrospective Characterization of a Commonly Used Assay
Authors: Williams Jon P. , Abbatemarco Justin R. , Galli Jonathan J. , Rodenbeck Stefanie J. , Peterson Lisa K. , Haven Thomas R. , Street Meagan , Rose John W. , Greenlee John E. , Soldan M. Mateo Paz , Clardy Stacey L.
Published: September 2021

Objective. Aquaporin-4 (AQP4) serum autoantibodies are detected by a variety of methods. The highest sensitivity is achieved with cell-based assays, but the enzyme-linked immunosorbent assay (ELISA) is still commonly utilized by clinicians worldwide. Methods. We performed a retrospective review to identify all patients at the University of Utah who had AQP4 ELISA testing at ARUP Laboratories from 2010 to 2017. We then reviewed their diagnostic evaluation and final diagnosis based on the ELISA titer result. Results. A total of 750 tests for the AQP4 ELISA were analyzed, and 47 unique patients with positive titers were identified. Less than half of these patients (49%) met the clinical criteria for neuromyelitis optica spectrum disorder (NMOSD). In cases of low positive titers (3.0–7.9 U/mL,

n
=
19

), the most common final diagnosis was multiple sclerosis (52.6%). In the moderate positive cohort (8.0–79.9 U/mL,

n
=
14

), only a little more than half the cohort (64.3%) had NMOSD. In cases with high positives (80–160 U/mL,

n
=
14

), 100% of patients met clinical criteria for NMOSD. Conclusions. Our data illustrates diagnostic uncertainty associated with the AQP4 ELISA, an assay that is still commonly ordered by clinicians despite the availability of more sensitive and specific tests to detect AQP4 autoantibodies in patients suspected of having NMOSD. In particular, low positive titer AQP4 ELISA results are particularly nonspecific for the diagnosis of NMOSD. The importance of accessibility to both sensitive and specific AQP4 testing cannot be overemphasized in clinical practice.
Bibliometric Analysis on Geriatric Nursing Research in Web of Science (1900–2020)
Authors: Ghamgosar Arezoo , Zarghani Maryam , Nemati-Anaraki Leila
Published: September 2021

Objective. Aging is a growing public health concern for people, organizations, and governments. The current study was undertaken to provide insights into the global research output on geriatric nursing. Methods. A bibliometric study was implemented using the WoS database for the period from 1900 to 2020. Various tools and measures were used to analyze and visualized. Results. The search strategy found 4923 papers. The oldest paper was written by Beverly C. Andre in 1953. As team size increases, so does the number of citations. The USA was the active country and the highest number of coauthors. New York University was an active institution. Stig Karlsson was the most active author in Geriatric Nursing with 28 articles from Sweden, followed by Koen Milisen and Sandman, with 26 articles each from Sweden and Belgium. The most frequent words in this field were depression, malnutrition, education, Alzheimer’s disease, and dementia. The latest research themes in this field were COVID‐19, interprofessional locomotive syndrome, emergency nursing, and public health. The most influential papers were specified. Journal of the American Geriatrics Society was the most active journal. Conclusions. Geriatric nursing is a rooted field and has received special attention in the last decade. Policymakers, especially in developing countries, should pay attention to geriatric nursing as a specialty of nursing to solve aging issues they would face considering the increasing elderly population.
Analysis of Threshold Changes of Tumor Mutation Burden of Gastric Cancer and Its Relationship with Patients’ Prognosis
Authors: Zhang Nan , Li Peiyu , Wu Xin , Xia Shaoyou , Zhao Xudong , Chen Lin
Published: September 2021

Objective. Gastric cancer is a malignant tumor originating from gastric mucosal epithelium. Here, we aimed to investigate the analysis of the threshold change of gastric cancer tumor mutation burden (TMB) and its relationship with the prognosis of patients. Methods. 256 patients with gastric cancer were selected as subjects. All patients were in the advanced stage and received surgical resection of D2 lymph node dissection. After the operation, a follow-up was performed for 24 months, and the disease-free survival and overall survival of patients were counted. The NGS molecular biological was detected to obtain gastric cancer tumor mutation burden (TMB) data. Pearson correlation analysis software was used to analyze the correlation between TMB threshold and disease-free survival or overall survival of patients with gastric cancer, and the multivariate logistic analysis was performed as well. Results. The disease-free survival period and the overall survival period of patients in the low-to-medium TMB group were both longer than those in the high TMB group. Pearson correlation analysis results showed that the TMB threshold was negatively correlated with the disease-free survival and overall survival of gastric cancer patients. Results from multivariate logistic analysis showed that high TMB thresholds have a greater impact on disease-free survival and overall survival of patients, but the impact of medium and low TMB thresholds on disease-free survival and overall survival of patients is weakened. Conclusions. The TMB threshold level has a predictive effect on the effect of surgical resection of D2 lymph node dissection, and high levels of TMB can significantly affect disease-free survival and overall survival of patients with advanced gastric cancer.
[Retracted] Development of Novel Nano‐Silver‐Based Antenna for Green Agriculture
Authors: D. Subitha , R. Vani , A. Raja , S. Balasubramani , A. Manjunathan , Sahile Kibebe
Published: September 2021

The designed antenna is a monopole Z‐shaped antenna operating in an unlicensed band of 2.4 GHz fabricated using low cost inkjet printing technology. The proposed inkjet printing technology is eco‐friendly since the material used here is an ordinary “paper” that is suitable for the green technology. The conducting patch of silver nanoparticle (AgNP) ink has very high conductivity 35,700,000 s/m and instant curing property which helps in fabrication process without UV curing or oven heating. The printer used also is the cheap home printer HP DJ 2130 rather than the expensive Brother and Epson printers used in previous works. The printed antenna will be helpful in conditions to ascertain its performance in green agriculture in the form of RF‐ID sensors, soil pH value sensor, and moisture sensor. The proposed antenna attains the gain of 2.5 dBi at the ISM band of 2.4 GHz with optimal VSWR value between 1 and 2 over the desired frequency band. The directivity and radiation efficiency of the proposed antenna are 2 dBi and 80%, respectively. The overall cost of the proposed antenna is much lesser in the order of 10 times than the recent low cost design.
Crowd Sensing Urban Healthy Street Monitoring Based on Mobile Positioning System
Authors: Jiang Fang , Sun Yunjuan , Sha Jianfeng
Published: September 2021

With the rapid development of social economy, the importance of ecological civilization is increasing day by day. The level of environmental monitoring will directly affect the control of total pollution sources and the evaluation of environmental quality. Crowd sensing is to use the group computing power of users with smart devices to quickly collect surrounding multidimensional data, analyze and calculate based on these massive perception data, and then dig out group behavior patterns and other regular information. In order to meet the basic requirements of environmental protection work put forward by the government in the new era, this paper proposes mobile positioning and crowd sensing technologies, explains the distribution map method and adaptive weighting data fusion related algorithms, and designs and develops a mobile positioning system based on mobile positioning. The system of crowd sensing of urban healthy street monitoring is designed, and then we use this PM2.5 monitoring system to monitor the relevant environmental data of a street in Shanghai and use normalization to process the experimental data. The experimental data and statistical results show that this performance of the system is good, and the accuracy of image PM2.5 monitoring reaches 90%–95%. More than 90% of street residents are also very satisfied with this system, believing that it can play a role in supervising urban healthy street.
Exploring Security Vulnerabilities of Deep Learning Models by Adversarial Attacks
Authors: Fu Xiaopeng , Gu Zhaoquan , Han Weihong , Qian Yaguan , Wang Bin
Published: September 2021

Nowadays, deep learning models play an important role in a variety of scenarios, such as image classification, natural language processing, and speech recognition. However, deep learning models are shown to be vulnerable; a small change to the original data may affect the output of the model, which may incur severe consequences such as misrecognition and privacy leakage. The intentionally modified data is referred to as adversarial examples. In this paper, we explore the security vulnerabilities of deep learning models designed for textual analysis. Specifically, we propose a visual similar word replacement (VSWR) algorithm to generate adversarial examples against textual analysis models. By using adversarial examples as the input of deep learning models, we verified that deep learning models are vulnerable to such adversarial attacks. We have conducted experiments on several sentiment analysis deep learning models to evaluate the performance. The results also confirmed that the generated adversarial examples could successfully attack deep learning models. As the number of modified words increases, the model prediction accuracy becomes lower. This kind of adversarial attack implies security vulnerabilities of deep learning models.