bluuebunny/crossref_metadata_2025 huggingface dataset:
Course Certificate Integration Based on Wireless Communication ‘
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’ Intelligent Finance and Taxation
Authors: Li Jing
Published: April 2022

Due to the ever-changing market environment and fierce competition, tax personnel are increasingly pursuing intelligence. Based on the background of smart finance and taxation, this article explains the issues related to the “

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” certificate and analyzes the development ideas and reform directions of the teaching plan and certificate integration teaching mode. This paper presents a study on the integration of academic certificates under the background of “

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” intelligent finance and taxation wireless communication. From 2018 to 2019, the development trend of smart finance and taxation has been rising. In 2018, the lowest was 11%, and the highest was 25%; in 2019, the lowest was 16%, and the highest was 37%. The lowest growth rate in 2019 was 5%, and the highest was 21%; the results show that although many relevant personnel understand smart finance and taxation, they have not planned how to use smart finance and taxation to improve their professional capabilities. The results show that smart fiscal and taxation is becoming more and more important in social development and has been widely used. However, traditional fiscal and taxation teaching can no longer meet the requirements of today’s society for fiscal and taxation personnel, so related fiscal and taxation teaching should be innovated.
Interpatient ECG Arrhythmia Detection by Residual Attention CNN
Authors: Xu Pengyao , Liu Hui , Xie Xiaoyun , Zhou Shuwang , Shu Minglei , Wang Yinglong
Published: April 2022

The precise identification of arrhythmia is critical in electrocardiogram (ECG) research. Many automatic classification methods have been suggested so far. However, efficient and accurate classification is still a challenge due to the limited feature extraction and model generalization ability. We integrate attention mechanism and residual skip connection into the U-Net (RA-UNET); besides, a skip connection between the RA-UNET and a residual block is executed as a residual attention convolutional neural network (RA-CNN) for accurate classification. The model was evaluated using the MIT-BIH arrhythmia database and achieved an accuracy of 98.5% and

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scores for the classes

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and

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of 82.8% and 91.7%, respectively, which is far superior to other approaches.
Protective Prognostic Biomarkers Negatively Correlated with Macrophage M2 Infiltration in Low-Grade Glioma
Authors: Zhu Yunyang , Song Zhaoming , Wang Zhong , Chen Gang
Published: April 2022

Tumor-associated Macrophages (TAMs) play a vital role in the progression of glioma. Macrophage M2 has been confirmed to promote immunosuppression and proliferation of low-grade glioma (LGG). Here, we searched for genes negatively correlated with Macrophages M2 by bioinformatical methods and investigated their protective ability for prognosis. LGG and adjacent normal samples were screened out in TCGA and three GEO datasets. 326 overlapped differentially expressed genes were calculated, and their biological functions were investigated by Go and KEGG analyses. Macrophage M2 accounted for the highest proportion among all 22 immune cells by CIBERSORT deconvolution algorithm. The proportion of Macrophage M2 in LGG was also higher than that in normal tissue according to several deconvolution algorithms. 43 genes in the blue module negatively correlated with Macrophage M2 infiltration were identified by weighted gene coexpression network analysis (WGCNA). Through immune infiltration and correlation analysis, FGFBP3, VAX2, and SHD were selected and they were enriched in G protein-coupled receptors’ signaling regulation and cytokine receptor interaction. They could prolong the overall and disease-free survival time. Univariate and multivariate Cox regression analyses were applied to evaluate prognosis prediction ability. Interestingly, FGFBP3 and AHD were independent prognostic predictors. A nomogram was drawn, and its 1-year, 3-year, and 5-year survival prognostic value was verified by ROC curves and calibration plots. In conclusion, FGFBP3, VAX2, and SHD were protective prognostic biomarkers against Macrophage M2 infiltration in low-grade glioma. The FGFBP3 and SHD were independent factors to effectively predict long-term survival probability.
An Intelligent Collection System of Big Data in Medical and Health Education Based on the Internet of Things
Authors: Wang Zhiling
Published: April 2022

The Internet of Medical Things has developed rapidly as an important direction in the field of Internet of Things, especially through the use of the new generation of information technology, theoretical and applied research on medical IoT intelligent health management that provides “full-service.” It has become a research hotspot of many universities and research institutions. Therefore, conducting research on intelligent health management in the network of medical things is of great engineering importance and theoretical guidance to improve the level of medical information. For health management in the network of medical things to conduct research around the goal of health management “Everything full spectrum for everyone,” analyzed the lack of sharing of health information in current health management, lack of continuous monitoring and management of health indicators, etc., a new “individual-family-community-hospital” four-level intelligent health management service model is proposed, the hardware architecture of intelligent healthcare management and the software maintenance system have been built. Through methods such as real-time multi-source data collection, mobile sensing, cloud computing, multi-network fusion technology, continuous monitoring and intelligent management of health data is realized convenient, fast and efficient. It solves the problems that the existing medical system cannot meet the multi-level health needs, personal data security and privacy protection, etc., it has achieved the goal of real-time interactive health management of regionalization, multi-level and multi-center, and whole-person, whole-process and all-round.
Identification of a Ferroptosis-Related Prognostic Gene PTGS2 Based on Risk Modeling and Immune Microenvironment of Early-Stage Cervical Cancer
Authors: Zou Chang , Xu Fangfang , Shen Jiacheng , Xu Shaohua
Published: April 2022

Background. Cervical cancer (CC) has long been a concern, as a gynecological cancer type of high-risk. At present, there are few studies on the early detection of CC at the genetic level. The breakthrough is to recognize CC patients tending to have a worse prognosis by checking the expression pattern of ferroptosis-related genes, which enjoy a great potential of being applied to cancer treatment. Methods. Data used in this study was obtained from a series of public online databases, integrated with ferroptosis-related gene collection stored from the FerrDb database and GeneCards database. The least absolute shrinkage and selection operator- (LASSO-) penalized analysis was taken for modeling, and before, univariate Cox regression analysis got done to shrink the candidates’ range. Several analyses were made for the evaluation of the efficacy of the new model, based on CC patients’ overall survival (OS). Tumor microenvironment- (TME-) related analyses were conducted by various algorithms on different populations, comprising CIBERSORT, ssGSEA, XCELL, etc. Nonnegative matrix factorization (NMF) clustering got applied to find that ferroptosis-marker genes affect prognosis more than “driver” and “suppressor”. Hub-gene PTGS2 was screened out by protein-protein interaction analysis and real-time qPCR after ferroptosis induction, and ELISA was conducted for further verification on the correlation between ferroptosis and M1 polarization. Results. The twenty-five ferroptosis-related genes model can estimate the prognosis of patients independently of other clinical factors, and the low-risk score group shows higher expression of immune-enhancing cells, noteworthily for M1 macrophages. It is experimentally validated that the M1 marker TNF-α significantly increased after coculturing M1 macrophages and SiHa cells processed with ferroptosis inductor before. The key gene to the model, PTGS2, presented to be a risk factor in cervical cancer, and its low-expression group has stronger immune activity and higher tumor mutation burden, with the significantly highly mutated gene TENM2 in it showing high drug sensitivity and neoantigen for patients with its mutant-type. Meanwhile, it influences macrophage polarization. Conclusion. Prognosis of early-stage cervical cancer patients can be exactly predicted on ferroptosis-related genes. Among model genes, PTGS2 may have a major impact by affecting macrophage polarization and mutation effects.
Model-Driven Approach to Fading-Aware Wireless Network Planning Leveraging Multiobjective Optimization and Deep Learning
Authors: Krstić Dragana , Petrović Nenad , Al-Azzoni Issam
Published: April 2022

Efficient resource planning is recognized as one of the key enablers making the large-scale deployment of next-generation wireless networks available for mass usage. Modelling, planning, and software simulation tools reduce both the time needed and costs of their tuning and realization. In this paper, we propose a model-driven framework for proactive network planning relying on synergy of deep learning and multiobjective optimization. The predictions about service demand and energy consumption are taken into account. Also, the impact of degradations resulting from fading and cochannel interference (CCI) effects is also considered. The optimization task is treated as a component allocation problem (CAP) aiming to find the best possible base station allocation for the considered smart city locations with respect to performance and service demand constraints. The goal is to maximize Quality of Service (QoS) while keeping the costs and energy consumption as low as possible. The adoption of a model-driven approach in combination with model-to-model transformations and automated code generation does not only reduce the complexity, making experimentation more rapid and convenient at the same time, but also increase the overall reusability and expandability of the planning tool. According to the obtained results, the proposed solution seems to be promising not only due to achieved benefits but also regarding the execution time, which is shorter than that achieved in our previous works, especially for larger distances. Further, we adopt model-based representation of handover strategies within the planning tool, enabling examination of the dynamic behavior of user-created plan, which is not exploited in other similar works. The main contributions of the paper are (1) wireless network planning (WNP) metamodel, a modelling notation for network plans; (2) model-to-model transformation for conversion of WNP to generalized CAP metamodel; (3) prediction problem (PP) metamodel, high-level abstraction for representation of prediction-related regression and classification problems; (4) code generator that creates PyTorch neural network from PP representation; (5) service demand and energy consumption prediction modules performing regression; (6) multiobjective optimization model for base station allocation; (7) Handover Strategy (HS) metamodel used for description of dynamic aspects and adaptability relevant to network planning.
Analysis of an Enterprise Human Resource Management Performance Evaluation Model Based on the DEA Method
Authors: Gao Bing , Zhang XiMeng
Published: April 2022

Performance evaluation is an important sand central part of the human resource management system and the process of assessing performance and documenting performance with uniform criteria. Based on the hypothesis of high-performance work systems and HRM effectiveness, this paper proposes a DEA model for evaluating the performance of HRM in enterprises through case studies, field interviews, and questionnaires and tests the validity of the model, by establishing an evaluation index system with HRM in large enterprises as the core, orders and customers as output indicators, and the number of personnel and total costs as input indicators and using the AHP method to optimise the DEA model applied to specific cases for testing. At the same time, the degree of variation between technical level, professional level, and strategic HRM gradually narrows as the HRM performance of the enterprise increases. Finally, targeted solution suggestions are given in relation to the actual situation.
Shear Bond Strength of Lithium Disilicate Bonded with Various Surface-Treated Titanium
Authors: Amornwichitwech Laongdao , Palanuwech Mali
Published: April 2022

Purpose. Retention is one of the most important factors for fixed dental prostheses, especially in implant dentistry. Accordingly, the goal of this study was to evaluate the level of shear bond strength between titanium (Ti) subjected to different surface treatments and lithium disilicate glass-ceramics. Materials and Methods. In this work, 90 titanium alloy specimens were divided into six groups as follows: the control group (CT), 50 μm alumina airborne-particle abrasion group (SB), silica-coated group (CJ), anodization group (AN), anodization followed by alumina 50 μm airborne-particle abrasion group (ANSB), and anodization followed by silica coating group (ANCJ). Titanium specimens were bonded to lithium disilicate specimens with resin cement (Multilink N). The specimens were restored in water at 37°C for 24 h, and then, shear bond strength (SBS) tests were performed using a universal testing machine (Shimadzu, Japan). The SBS values were statistically analyzed. The failure mode of the debonded titanium was classified after viewing the samples under a stereoscope. Results. The results demonstrated that the mean SBSs of CT and AN were significantly lower than those of the other groups (p<0.05). The SB group showed the highest SBS (29.47 ± 2.41 MPa); however, there was no significant difference between SB, ANSB, ANCJ, and CJ. The stereoscopic analysis demonstrated that the failure mode of AN was predominantly adhesive failure; whereas, the other groups showed cohesive and mixed failures. Conclusions. In this study, it was found that the surface treatment with 50 μm alumina airborne-particle abrasion, silica coating with Cojet™ sand, anodization followed by 50 μm alumina airborne-particle abrasion, and anodization followed by silica coating with Cojet™ sand improved the SBS between titanium and lithium disilicate luted with Multilink N resin cement.
Physical, Mechanical, and Adhesive Properties of Novel Self-Adhesive Resin Cement
Authors: Ling Long , Ma Yumeng , Chen Yulin , Malyala Raj
Published: April 2022

Objective. To evaluate a newly developed self-adhesive resin cement on physical, mechanical, and adhesive properties and compare it with other commercial self-adhesive resin cements. Materials and Methods. Experimental self-adhesive resin cement (SARC) was formulated by our proprietary adhesive resin and filler technology. Maxcem Elite, RelyX Unicem 2, SpeedCem Plus, SmartCEM 2, and Calibra Universal 2 were selected for comparison. Working and setting times, film thickness, water sorption and solubility, flexural strength, and modulus were measured in accordance with ISO-4049. Consistency was tested according to modified ISO 4823. Shear bond strengths were conducted according to ISO 29022. The data were analyzed by one-way ANOVA and post hoc Tukey’s tests (p ≤ 0.05). Results. All cements showed about 2–4 min working time and about 3–6 min setting time except that RelyX Unicem 2 has a longer working time (9’58”) and setting time (10’18”). All cements meet ISO standards for film thickness (≤50 µm), water sorption (≤40 µg/mm3) except Maxcem Elite (46.19 µg/mm3), and water solubility (≤7.5 µg/mm3) except SmartCEM 2 (11.35 µg/mm3) and Calibra Universal (9.87 µg/mm3). Experimental SARC showed significantly higher flexural strength and modulus than other cements (p < 0.001). For self-curing, Experimental SARC has statistically higher bond strength than other cements (p < 0.001) except statistically the same as RelyX Unicem 2 (p > 0.05). For light-curing, Experimental SARC showed significantly higher bond strength than other cements (p < 0.001) except statistically the same as Maxcem Elite and RelyX Unicem 2 (p > 0.05). For dual-curing, the bond strength of Experimental SARC is significantly higher than that of other cements (p < 0.001). Conclusion. The newly developed self-adhesive resin cement exhibited favorable bonding capability and physical and mechanical properties compared to other commercial self-adhesive resin cements and is a good option for cementation of indirect restorations with potential long-term clinical success.
Occult Renal Calcifications in Patients with Normocalcemic Primary Hyperparathyroidism and Their Association with the Parathyroid Hormone-Vitamin D Axis
Authors: Victor Fernanda , Pereira Lemos Alyne Layane , de Holanda Ribas Anna Mirella , Bandeira Leonardo , Pimentel José Henrique , de Andrade Damázio Luiz Otávio , Bandeira Francisco
Published: April 2022

Normocalcemic primary hyperparathyroidism (NPHPT) is characterized by elevated serum levels of parathyroid hormone (PTH) with persistently normal serum calcium concentrations after excluding secondary causes of hyperparathyroidism. Urolithiasis and/or nephrocalcinosis may occur in hypercalcemic PHPT, but little is known about these complications in NPHPT. Objectives. To identify occult urolithiasis and nephrocalcinosis in asymptomatic patients with NPHPT and evaluate biochemical markers as risk predictors for the development of renal calcification (RC). Methods. Cross-sectional analysis of 34 patients with no history of urolithiasis and/or nephrocalcinosis. The diagnosis of NPHPT was as follows: elevated serum PTH (reference range: 15–65 pg/mL), normal albumin-corrected serum calcium, normal urinary calcium excretion, serum 25(OH)D >30 ng/mL, eGFR (CKD-EPI) > 60 mL/min/1.73 m2, without intestinal disease, and not on medications such as thiazide diuretics, lithium, bisphosphonates, or denosumab. Patients were categorized according to the presence or absence of RC identified by renal imaging. Their clinical and biochemical characteristics were then compared. Results. The patients had a mean age of 67.97 ± 10.45 years, predominantly postmenopausal women (88.2%); serum PTH, 119.67 ± 64.44 pg/mL; 25(OH)D, 39.00 ± 8.88 ng/dL; 1.25(OH))2D, 74.53 ± 26.37 pg/mL; corrected serum calcium, 9.34 ± 0.62 mg/dL; and 24-hour urinary calcium, 134.87 ± 79.68 mg/day. RC was identified in 26.5% of the patients. There was no difference in anthropometric and clinical parameters, renal function, 25(OH)D, and urinary pH in patients with or without RC. Patients with RC had higher PTH values (176.22 vs. 99.32 pg/mL,

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 = 0.001), 1.25(OH) 2D (96.83 vs. 62.36 pg/mL,

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 = 0.005), and 24-hour urinary calcium (181.9 vs. 117.94 mg/day,

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 = 0.037). Conclusion. Occult renal calcifications are common in NPHPT and are associated with increased serum PTH, 1.25(OH))2D, and 24 h urinary calcium.
A Novel Immune‐Related Gene Signature Predicts Prognosis of Lung Adenocarcinoma
Authors: Ma Chao , Li Feng , Wang Ziming , Luo Huan
Published: April 2022

Background. Lung adenocarcinoma (LUAD) is the most common form of lung cancer, accounting for 30% of all cases and 40% of all non‐small‐cell lung cancer cases. Immune‐related genes play a significant role in predicting the overall survival and monitoring the status of the cancer immune microenvironment. The present study was aimed at finding an immune‐related gene signature for predicting LUAD patient outcomes. Methods. First, we chose the TCGA‐LUAD project in the TCGA database as the training cohort for model training. For model validating, we found the datasets of GSE72094 and GSE68465 in the GEO database and took them as the candidate cohorts. We obtained 1793 immune‐related genes from the ImmPort database and put them into a univariate Cox proportional hazard model to initially look for the genes with potential prognostic ability using the data of the training cohort. These identified genes then entered into a random survival forests‐variable hunting algorithm for the best combination of genes for prognosis. In addition, the LASSO Cox regression model tested whether the gene combination can be further shrinkage, thereby constructing a gene signature. The Kaplan‐Meier, Cox model, and ROC curve were deployed to examine the gene signature’s prognosis in both cohorts. We conducted GSEA analysis to study further the mechanisms and pathways that involved the gene signature. Finally, we performed integrating analyses about the 22 TICs, fully interpreted the relationship between our signature and each TIC, and highlighted some TICs playing vital roles in the signature’s prognostic ability. Results. A nine‐gene signature was produced from the data of the training cohort. The Kaplan‐Meier estimator, Cox proportional hazard model, and ROC curve confirmed the independence and predictive ability of the signature, using the data from the validation cohort. The GSEA analysis results illustrated the gene signature’s mechanism and emphasized the importance of immune‐related pathways for the gene signature. 22 TICs immune infiltration analysis revealed resting mast cells’ key roles in contributing to gene signature’s prognostic ability. Conclusions. This study discovered a novel immune‐related nine‐gene signature (BTK, CCR6, S100A10, SEMA3C, GPI, SCG2, TNFRSF11A, CCL20, and DKK1) that predicts LUAD prognosis precisely and associates with resting mast cells strongly.
Semantic Detection of Vehicle Violation Video Based on Computer 3D Vision
Authors: Dai Yue
Published: April 2022

In order to study the semantic detection accuracy of 3D vehicle accident video, an accident detection method combining 2D image and 3D information was proposed. The 3D semantic bounding box generated by the 3D detection and tracking task of the vehicle is used to extract the motion features of the vehicle, it includes the trajectory of the vehicle and the dimension and orientation of the 3D bounding frame, and the 3D semantic bounding frame is used to establish the evaluation index of the accident detection. The experimental results show that the average loss function of each batch of 1000 images is calculated by the stochastic gradient descent method to update the parameter values. The learning rate was set to 0.001 in the first 30,000 iterations and 0.0001 in the last 10,000 iterations. The MOTA of the CEM algorithm is 78.4%, FP is 1.1%, and FN is 3.5%, and the MOTA of the 3-DCMK algorithm is 88.6%, FP is 0.9%, and FN is 1.9%. The MOTA of this method is 89.3%, FP is 0.9%, and FN is 1.2%. The 3D target semantic detection of vehicle accident video has stability and accuracy.
Research on Integrated Management of Logistics Economy Based on Internet of Things
Authors: Zhu Jiang
Published: April 2022

In order to study the management and control ability of the government affairs 4.0 system on the logistics economic park and improve the data prediction and data early warning sensitivity of the economic management information system in the logistics economic park, this study recombs the basic model of the logistics economic park and puts forward the development model of the inward logistics economic park. Based on this model, a logistics economic big data system is built, and a data early warning model based on machine learning is designed. In the simulation verification, it is found that the original data demand of machine learning data early warning mode is smaller, the data sensitivity early warning cycle is longer, and the data early warning sensitivity is higher under the same conditions. Finally, it is considered that the machine learning linear programming algorithm model has high applicability in the government economic management process of inward logistics economic park.
Recognition and Error Correction Techniques for Piano Playing Music Based on Convolutional Cyclic Hashing Method
Authors: Wang Dan
Published: April 2022

Music as a sound symbol can express what people think; music is both a form of social behavior and can promote people’s emotional communication; music is also a form of entertainment and can enrich people’s spiritual life. In this paper, we propose a new convolutional recurrent hashing method CRNNH, which uses multilayer RNN to learn to discriminate piano playing music using convolutional feature map sequences. Firstly, a convolutional feature map sequence learning preserving similarity hash function is designed consisting of multilayer convolutional feature maps extracted from multiple convolutional layers of a pretrained CNN; secondly, a new deep learning framework is proposed to generate hash codes using a multilayer RNN, which directly uses the convolutional feature maps as input to preserve the spatial structure of the feature maps; finally, a new loss function is proposed to preserve the semantic similarity and balance of the hash codes, while considering the quantization error generated when the hash layer outputs binary hash codes. The experimental results illustrate that the proposed CRNNH can obtain better performance compared to other hashing methods.
Hybrid Music Recommendation Algorithm Based on Music Gene and Improved Knowledge Graph
Authors: Zhang Tingting , Liu Shengnan
Published: April 2022

Combining music as a specific recommendation object, a hybrid recommendation algorithm based on music genes and improved knowledge graph is proposed for the traditional single recommendation algorithm that cannot effectively solve the accuracy problem in music recommendation. The algorithm first gives the recommendation pattern of music genes and gets the relevant recommendation results through the genetic preference analysis. After that, the algorithm in this paper utilizes item and user label information and knowledge graphs from two different domains to enrich and mine the potential information of users and items. In addition, deep learning method is applied to extract low-dimensional, abstract deep semantic features of users and items, based on which, score prediction is performed. The mixed-mode based recommendation addresses the drawbacks of these two recommendations and can adopt different weighting strategies in different situations. The advantages of music gene and knowledge graph-based recommendation algorithms are combined via this method. The experimental results indicate that the algorithm in this paper outperforms other existing recommendation algorithms.
Transcriptomics Coupled to Proteomics Reveals Novel Targets for the Protective Role of Spermine in Diabetic Cardiomyopathy
Authors: Wei Can , Song Tao , Yuan Hui , Li Xiaoxue , Zhang Xinying , Liang Xiao , Fan Ying
Published: April 2022

Background. Diabetic cardiomyopathy (DbCM) is the main complication and the cause of high mortality of diabetes. Exploring the transcriptomics and proteomics of DbCM is of great significance for understanding the biology of the disease and for guiding new therapeutic targets for the potential therapeutic effect of spermine (SPM). Methods and Results. By using a mouse DbCM model, we analyzed the overall transcriptome and proteome of the myocardium, before/after treatment with SPM. The general state and cardiac structure and function changes of each group were also compared. Diabetes induced an increased blood glucose and serum triglyceride content, a decreased body weight, serum insulin level, and cardiac function-related indexes, accompanied by disrupted myocardial tissue morphology and ultrastructure damage. Using RNA sequencing (RNA-seq), we identified thousands of differentially expressed genes (DEGs) in DbCM with or without SPM treatment. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis demonstrated that the DEGs were significantly enriched in lipid metabolism and amino acid metabolism pathways. Specifically, quantitative real-time PCR (qRT-PCR) confirmed that SPM protected DbCM by reversing the expressions of lipid metabolism and amino acid metabolism-related genes, including Alox15, Gm13033, pla2g12a, Ptges, Pnpla2, and Acot1. To further reveal the pathogenesis of DbCM, we used proteome-based data-independent acquisition (DIA) and identified 139 differentially expressed proteins (DEPs) with 67 being upregulated and 72 being downregulated in DbCM. Venn intersection analysis showed 37 coexpressed genes and proteins in DbCM, including 29 upregulation and 8 downregulation in DbCM. In the protein-protein interaction (PPI) network constructed by the STRING database, the metabolism-related coexpressed genes and proteins, such as Acot2, Ephx2, Cyp1a1, Comt, Acox1, Hadhb, Hmgcs2, Acot1, Inmt, and Cat, can interact with the identified DEGs and DEPs. Conclusion. The biomarkers and canonical pathways identified in this study may hold the key to understand the mechanisms of DbCM pathobiology and provide new targets for the therapeutic effect of SPM against DbCM by targeting lipid and amino acid metabolism pathways.
A Clinical Study on the Brain Protection Effect of Propofol Anesthesia on Patients Undergoing Acute Craniocerebral Trauma Surgery Based on Blockchain
Authors: Li Hu , Li Jinfeng , Su Peng , Zhang JianJun , Ma Ding
Published: April 2022

In order to explore the effects of propofol anesthesia system based on block chain on brain tissue and brain function protection, 88 patients undergoing craniocerebral trauma surgery are selected for prospective study. The anesthetic dose of propofol during operation in the control group is determined according to general information and personal situation. The team uses blockchain to process the patient’s clinical data and general information to calculate the optimal dose of propofol. The changes of hemodynamic indexes of patients in the two groups are compared, including dural incision (T1), 1 h after craniotomy (T2), end of surgery (T3), and 6 h after surgery (T4). The changes of neuron-specific enolase (NSE) and central neuron-specific protein (S100β) at T1, T2, T3, and T4 in 2 groups are observed and recorded. The changes of malondialdehyde (MDA) at T1, T2, T3, and T4 are compared between the two groups. Based on the analysis of clinical data and general information of patients on blockchain, the optimal intraoperative anesthetic dose of propofol for patients can effectively protect the brain function of patients, and the intraoperative anesthesia state is relatively stable, and the changes and improvements of intraoperative indicators are good.
NLRP3 Knockout Protects against Lung Injury Induced by Cerebral Ischemia–Reperfusion
Authors: Xu Qingxue , Ye Yingze , Wang Zhuo , Zhu Hua , Li Yina , Wang Jin , Gao Wenwei , Gu Lijuan
Published: April 2022

Background and Purpose. Stroke-associated pneumonia (SAP) is a common complication after stroke that increases the mortality of patients. Although there have been many studies suggesting that stroke can increase patient susceptibility to pneumonia, it is still unknown whether the treatment of stroke can also improve lung injury. We used NLRP3-knockout (NLRP3-KO) mice to verify that an improvement in brain injury would also be beneficial to lung injury and further confirm the relationship between stroke and pneumonia. Methods. C57/BL6 wild-type (WT) and NLRP3-KO mice were used to construct middle cerebral artery occlusion (MCAO) models. 2,3,5-Triphenyltetrazolium chloride (TTC) was used to evaluate brain damage, and neurological deficits were assessed. Then, lung tissue injury was examined in the different groups of mice by hematoxylin-eosin (HE) staining. Inflammation (macrophage and neutrophil infiltration, NLRP3-associated inflammatory molecules) and oxidative stress (reactive oxygen species, ROS) in the lungs were comprehensively examined by immunofluorescence staining and Western blotting. Results. First, our findings demonstrated that NLRP3 knockout had a protective effect against cerebral ischemia–reperfusion injury after MCAO. Second, by reducing brain damage after MCAO, lung inflammation was also alleviated. Immunofluorescence staining showed that NLRP3-KO-MCAO mice had reduced inflammatory effector molecule (caspase-1 and IL-1β) expression and macrophage and neutrophil infiltration in the lung, as well as remissive oxidative stress state in the lung, compared with WT-MCAO mice. We also observed a decrease in phosphorylated p65 (p-p65) (an NF-κB factor) in NLRP3-KO-MCAO mice, suggesting that the NF-κB pathway was involved in the protective effect of NLRP3 gene knockout on stroke-induced lung injury. Conclusions. NLRP3 inflammasome knockout not only is beneficial for cerebral ischemia–reperfusion injury but also reduces the severity of poststroke lung injury by reducing brain damage. It has been confirmed that there is a relationship between central insult and peripheral organ injury, and protecting the brain can prevent peripheral organ damage.
Implementation and Design of a Zero-Day Intrusion Detection and Response System for Responding to Network Security Blind Spots
Authors: Choi Won-Seok , Lee Si-Young , Choi Seong-Gon
Published: April 2022

We propose a zero-day intrusion detection and response system (ZDRS) for responding to network security blind spots. An existing detection and response system for the analysis of zero-day attacks uses a full-packet storage method; thus, the longer the time required to recognize a zero-day attack, the higher is the packet storage capacity and inspection cost. To solve the storage capacity and inspection cost problems, we design an architecture for ZDRS for a retroactive security check (RSC) using a first-N packet storage method. For fast verification of the RSC result, we propose a drill-down session metadata searching algorithm using session and flow metadata. The ZDRS comprises a network processing unit and a security processing unit. The ZDRS network processing unit generates metadata for the RSC verification and efficiently stores packets using the first-N packet storage method. The ZDRS security processing unit performs the RSC and RSC verification using the drill-down session metadata searching algorithm. For ZDRS performance analysis, we implemented ZDRS and analyzed the storage efficiency, detection efficiency, and detection speed of ZDRS at the campus level. As a performance analysis result of implementation, the amount of data storage decreased from 3.4 terabyte to 62 gigabyte compared to the full-packet storage method by 1.82%, and storage efficiency increased by 54.84 times. Furthermore, the detection rate of 99.55% based on the first 5-kilobyte size compared to the full-packet storage method was confirmed.
A Privacy-Preserving Exchange-Based Routing Protocol for Opportunistic Networks
Authors: Jiang Qingfeng
Published: April 2022

In opportunistic networks, nodes forward messages according to routing utility deduced from encounter information. When nodes simply communicate with real IDs, private information such as encounter time and location will be leaked. The private information can be preserved by anonymizing node real IDs, but ID anonymity will also prevent nodes from collecting real ID-based encounter information to compute routing utility. To address this problem, a privacy-preserving exchange-based routing protocol (PPERP) for opportunistic networks is proposed in this paper. In PPERP, nodes are classified into delivery nodes and nondelivery nodes. Two encountering nondelivery nodes exchange approximately equivalent anonymous encounter records. Then, when the exchanged encounter records are sent to a delivery node, it will compute the routing utilities to nondelivery nodes without knowing their real IDs. In PPERP, a node can directly compute its routing utility without relying on any trusted third party, so PPERP is suitable for opportunistic networks in which nodes usually are sparsely distributed and encounter opportunistically. Bilinear mapping technique is adopted to ensure the confidentiality, integrity, and nonrepudiation of encounter records. The security analysis and performance evaluation show that the proposed protocol is secure, efficient, and practical.
Impact of Economic Globalization on International Relations under the Background of Wireless Communication and Artificial Intelligence
Authors: Liu Lijia
Published: April 2022

Politics and economics are closely related, and it is not an exaggeration to say that it depends on the latter. Therefore, changes in international relations can be analyzed in the context of economic development. After entering the twenty-first century, the development of information technology and the rapid improvement of transportation capabilities have made the cost of human beings’ cross-regional trade lower and lower; the production and circulation of products all over the world have gradually formed an integration and have been integrated more and more closely. The circulation of resources and even financial capital is accelerating to be integrated, and the global economy is rapidly integrating. The shock of any major economy will almost cause instability in the global economy. Against this background, the political relations between countries are generally relatively harmonious. Although there are occasional regional conflicts, they are generally relatively peaceful. Especially in recent years, under the background that wireless communication and artificial intelligence decision-making have brought drastic changes to human production and life, the economic closeness of countries has reached an unprecedented level. At the same time, the complexity and volatility of international relations have also increased considerably. In order to better cope with this complex form, we are required to discuss the trends of international economic development in the context of today’s technology. The purpose of this research is to analyze the impact of economic globalization on international relations under the background of wireless communication and artificial intelligence, and to sort out the new features of economic globalization in this context, and its possible implications for international relations’ impact. Then, it further discusses how my country can better safeguard its own rights and interests through the adjustment of international policies under such a background.
Association of Single Nucleotide Polymorphism rs17580 with Smoking and Pulmonary Tuberculosis
Authors: Li Tao , Han Shunli
Published: April 2022

This study aimed to investigate the correlation between SERPINA1 single nucleotide polymorphism (SNP) rs17580, smoking, and pulmonary tuberculosis (TB). A total of 420 TB patients (observation group) and 640 patients without pulmonary disease (control group) were randomly included. The frequencies of different genotypes were counted in both groups, and the correlation between SNP genotypes and the occurrence of TB was analyzed. Statistical models were performed to analyze the correlation between rs17580 and TB and the correlation between rs17580. The frequencies of genotypes TT, TA, and AA at the rs17580 locus in patients with TB were not statistically different from those in the control group (

P
>
0.05

), and the distributions of the two groups were in accordance with the Hardy–Weinberg equilibrium law. rs17580 was analyzed in dominant, recessive, and codominant models, exhibiting no statistical difference (

P
>
0.05

). There was no significant difference among the three genotypes in the 1–5 typing (χ2 = 1.034,

P
=
0.998

), and the difference between T and C was not statistically significant (χ2 = 0.012,

P
=
0.999

). There was a significant difference between the three genotypes between the smoking group and the nonsmoking group in TB patients (

P
<
0.05

). There was no significant difference among three genotypes in the alcoholic group and the nonalcoholic group in TB patients (

P
>
0.05

). There was no statistical difference in the time to cure among the 3 genotypes in TB patients (

P
>
0.05

). A type mutation of rs17580 in the SERPINA1 gene was strongly associated with a higher risk of development of TB in smoking patients.
Reasoning Methods of Unmanned Underwater Vehicle Situation Awareness Based on Ontology and Bayesian Network
Authors: Yao Hongfei , Han Chunsong , Xu Fengxia
Published: April 2022

When unmanned underwater vehicles (UUVs) perform tasks, the marine environment situation information perceived by their sensors is insufficient and cannot be shared; moreover, the reasoning efficiency of the situation information is not high. To deal with these problems, this paper proposes an ontology‐based situation awareness information expression method, using the Bayesian network method to reason about situation information. First, the situation awareness information is determined in uncertain events when performing tasks in the marine environment. The core and application ontologies of UUV situation awareness are also established. Subsequently, semantic rules are constructed, and uncertain events are identified through the corresponding semantic rules. The Jess inference engine is used to update the ontology model. Because the ontology cannot reason about uncertainty, it is transformed into the Bayesian network to reason about the impacts of uncertain events on tasks. Simulation experiments verify the effectiveness and accuracy of the situation awareness reasoning method that combines the ontology and the Bayesian network.
GCP-Net: A Gating Context-Aware Pooling Network for Cervical Cell Nuclei Segmentation
Authors: Yang Guihua , Huang Jinjie , He Yongjun , Chen Yuanjian , Wang Tao , Jin Cong , Sengphachanh Phienphommalinh
Published: April 2022

Accurate segmentation of cervical nuclei is an essential step in the early diagnosis of cervical cancer. Still, there are few studies on the segmentation of clustered nuclei in clusters of cells. Because of the complexities of high cell overlap, blurred nuclei boundaries, and clustered cells, the accurate segmentation of clustered nuclei remains a pressing challenge. In this paper, we purposefully propose a GCP-Net deep learning network to handle the challenging cervical cluster cell images. The proposed U-Net-based GCP-Net consists of a pretrained ResNet-34 model as encoder, a Gating Context-aware Pooling (GCP) module, and a modified decoder. The GCP module is the primary building block of the network to improve the quality of feature learning. It allows the GCP-Net to refine details of feature maps leveraging multiscale context gating and Global Context Attention for the spatial and texture dependencies. The decoder block including Global Context Attention- (GCA-) Residual Block helps build long-range dependencies and global context interaction in the decoder to refine the predicted masks. We conducted extensive comparative experiments with seven existing models on our ClusteredCell dataset and three typical medical image datasets, respectively. The experimental results showed that the GCP-Net obtained promising results on three evaluation metrics AJI, Dice, and PQ, demonstrating the superiorities and generalizability of our GCP-Net for automatic medical image segmentation in comparison with some SOAT baselines.
Identification of Five N6-Methylandenosine-Related ncRNA Signatures to Predict the Overall Survival of Patients with Gastric Cancer
Authors: Yue Qingfang , Zhang Yuan , Bai Jun , Duan Xianglong , Wang Haipeng
Published: April 2022

Noncoding ribonucleic acids (ncRNAs) are involved in various functions in the formation and progression of different tumors. However, the association between N6-methyladenosine-related ncRNAs (m6A-related ncRNAs) and gastric cancer (GC) prognosis remains elusive. As such, this research was aimed at identifying m6A-related ncRNAs (lncRNAs and miRNAs) in GC and developing prognostic models of relevant m6A-related ncRNAs and identifying potential biomarkers regulated by m6A. In this study, the m6A2Target database, Starbase database, and The Cancer Genome Atlas (TCGA) were used to screen m6A-related ncRNAs. And then, we performed integrated bioinformatics analyses to determine prognosis-associated ncRNAs and to develop the m6A-related ncRNA prognostic signature (m6A-NPS) for GC patients. Finally, five m6A-related ncRNAs (including lnc-ARHGAP12, lnc-HYPM-1, lnc-WDR7-11, LINC02266, and lnc-PRIM2-7) were identified to establish m6A-NPS. The predictive power of m6A-NPS was better in the receiver operating characteristic (ROC) curve analysis of the training set (area under the curve (AUC), >0.6). The m6A-NPS could be utilized to classify patients into high- and low-risk cohorts, and the Kaplan-Meier analysis indicated that participants in the high-risk cohort had a poorer prognosis. The entire TCGA dataset substantiated the predictive value of m6A-NPS. Significant differences in TCGA molecular GC subtypes were observed between high- and low-risk cohorts. The ROC curve analysis indicated that m6A-NPS had better predictive power than other clinical characteristics of GC prognosis. Uni- and multivariate regression analyses indicated m6A-NPS as an independent prognostic factor. Furthermore, the m6A status between the low-risk cohort and high-risk cohort was significantly different. Differential genes between them were enriched in multiple tumor-associated signaling pathways. In summary, five m6A-related ncRNA signatures that could forecast the overall survival of patients with GC were identified.
Optimization of Data Distributed Network System under Uncertainty
Authors: Sahoo Laxminarayan , Sen Supriyan , Tiwary Kalishankar , Samanta Sovan , Senapati Tapan
Published: April 2022

The major network design or data distributed problems may be described as constrained optimization problems. Constrained optimization problems include restrictions imposed by the system designers. These limitations are basically due to the system design’s physical limitations or functional requirements of the network system. Constrained optimization is a computationally challenging job whenever the constraints/limitations are nonlinear and nonconvex. Furthermore, nonlinear programming methods can easily deal same optimization problem if somehow the constraints are nonlinear and convex. In this paper, we have addressed a distributed network design problem involving uncertainty that transmits data across a parallel router. This distributed network design problem is a Jackson open‐type network design problem that has been formulated based on the M/M/1 queueing system. Because our network design problem is a nonlinear, convex optimization problem, we have employed a well‐known Kuhn–Tucker (K‐T) optimality algorithm to solve the same. Here, we have used triangular fuzzy numbers to express uncertain traffic rates and data processing rates. Then, by applying α‐level interval of fuzzy numbers and their corresponding parametric representation of α‐level intervals, the associated network design problem has been transformed to its parametric form and later has been solved. To obtain the optimal data stream rate in terms of interval and to illustrate the applicability of the entire approach, a hypothetical numerical example has been exhibited. Finally, the most important results have been reported.