bluuebunny/crossref_metadata_2025 huggingface dataset:
Investigation of E-Commerce Security and Data Platform Based on the Era of Big Data of the Internet of Things
Authors: Dai Zhiqiang , Guo Xin
Published: August 2022

The Internet of Things (IoT) can combine a wide range of information space with physical space and use related technologies to provide effective information interaction between objects. Nowadays, a large amount of Internet data transmitted by E-commerce communication data is vulnerable to interference, intrusion, and other attacks, posing a major threat and challenge to data security. Based on the IoT data analysis platform Hadoop, starting from the data, the problems faced by the E-commerce information security are analyzed and the E-commerce security is improved. Through the comparison of Delphi method, fuzzy comprehensive evaluation method, weighted linear evaluation method, grey cluster analysis method, analytic hierarchy process, and using the complex fuzzy evaluation method of the vague set theory, the security weights of the first and second levels are calculated according to the vague entropy value of each index attribute. It provides a useful reference for E-commerce informatization security evaluation methods. The first-level and second-level comprehensive evaluations of the vague set are used to detect and analyze E-commerce network security, transaction security, data security, and physical security. The weight of each indicator occupies a total of 100% and the final risk status is close to 0. Therefore, it can be concluded that the risk status of the E-commerce information security is “safe”.
Prediction of Telecommunication Network Fraud Crime Based on Regression-LSTM Model
Authors: Gao Youyang , Yin Dechun , Zhao Xiaoliang , Wang Yu , Huang Yan
Published: August 2022

Telecommunication network fraud crimes frequently occur in China. Predicting the number and trend of telecommunication network fraud will be of great significance to combating crimes and protecting the legal property of citizens. This paper proposes a combined model of predicting telecommunication network fraud crimes based on the Regression-LSTM model. First, we find that there is a strong correlation between privacy data illegally sold on the dark web and telecommunication network fraud data. Hence, this paper constructs a Linear Regression model using the privacy data illegally sold on the dark web to predict the number of telecommunication network fraud crimes. Second, an LSTM prediction model is constructed using the data of telecommunication network fraud cases on China Judgments Online based on the time-series feature of telecommunication network fraud crimes. Third, this paper uses the error reciprocal method to combine the two models for prediction. In addition, this paper selects the monthly data set of telecommunication network fraud occurring in 2021 for experimental evaluation. The experimental results show that the accuracy of the Regression-LSTM model constructed in this paper is 86.80%, and the RMSE is 0.149. Compared with the ARIMA, Linear Regression, LSTM, Additive-ARIMA-LSTM, and Multiplicative-ARIMA-LSTM models, the Regression-LSTM model proposed has the highest prediction accuracy.
On the Performance Supremum of CFO Based Physical Layer Identification
Authors: Zeng Shuiguang , Chen Yin , Li Xufei , Shen Yulong , Zhao Dongmei , Zhu Jinxiao , Shiratori Norio
Published: August 2022

Physical layer identification is an emerging technique that exploits physical layer features to identify wireless devices. The identification accuracy and the device quantity that can be identified at most are significant for the identification scheme. Existing works primarily focus on the feature correlation analysis for multifeature selection without investigating the least upper bound (supremum) of the performance of a single feature. The supremum indicates the limit of the performance, which is another sight for evaluating the quality of features and improving the performance of the identification scheme. Therefore, this paper first investigates the supremum of the performance of the most commonly used physical layer feature, i.e., carrier frequency offset (CFO). Specifically, we offer a rigorous mathematical analysis and derive the closed-form expression of the supremum of identification accuracy based on the max-min distance analysis (MMDA) criterion. And then, the supremum of the number of distinguishable devices is also analyzed. Finally, we conducted a simulation study to verify the theoretical analysis result.
Biodistribution of a Mitochondrial Metabolic Tracer, [18F]F-AraG, in Healthy Volunteers
Authors: Levi Jelena , Duan Heying , Yaghoubi Shahriar , Packiasamy Juliet , Huynh Lyna , Lam Tina , Shaikh Faiq , Behera Deepak , Song Hong , Blecha Joseph , Jivan Salma , Seo Youngho , VanBrocklin Henry F.
Published: August 2022

Purpose. [18F]F-AraG is a radiolabeled nucleoside analog that shows relative specificity for activated T cells. The aim of this study was to investigate the biodistribution of [18F]F-AraG in healthy volunteers and assess the preliminary safety and radiation dosimetry. Methods. Six healthy subjects (three female and three male) between the ages of 24 and 60 participated in the study. Each subject received a bolus venous injection of [18F]F-AraG (dose range: 244.2–329.3 MBq) prior to four consecutive PET/MR whole-body scans. Blood samples were collected at regular intervals and vital signs monitored before and after tracer administration. Regions of interest were delineated for multiple organs, and the area under the time-activity curves was calculated for each organ and used to derive time-integrated activity coefficient (TIAC). TIACs were input for absorbed dose and effective dose calculations using OLINDA. Results. PET/MR examination was well tolerated, and no adverse effects to the administration of [18F]F-AraG were noted by the study participants. The biodistribution was generally reflective of the expression and activity profiles of the enzymes involved in [18F]F-AraG’s cellular accumulation, mitochondrial kinase dGK, and SAMHD1. The highest uptake was observed in the kidneys and liver, while the brain, lung, bone marrow, and muscle showed low tracer uptake. The estimated effective dose for [18F]F-AraG was 0.0162 mSv/MBq (0.0167 mSv/MBq for females and 0.0157 mSv/MBq for males). Conclusion. Biodistribution of [18F]F-AraG in healthy volunteers was consistent with its association with mitochondrial metabolism. PET/MR [18F]F-AraG imaging was well tolerated, with a radiation dosimetry profile similar to other commonly used [18F]-labeled tracers. [18F]F-AraG’s connection with mitochondrial biogenesis and favorable biodistribution characteristics make it an attractive tracer with a variety of potential applications.
AOMC: An Adaptive Point Cloud Clustering Approach for Feature Extraction
Authors: Zhang Zhengyu , Jin Mengdi
Published: August 2022

Point cloud local feature extraction places an important part of point cloud deep learning neural networks. Accurate extraction of point cloud features is still a challenge for deep learning networks. Oversampling and feature loss of point cloud model are important problems in the accuracy of image point cloud deep learning network. In this paper, we propose an adaptive clustering method for point cloud feature extraction—adaptive optimal means clustering (AOMC)—and apply it to point cloud deep learning network tasks. This method solves the problem of determining the number of clustering centers in the process of point cloud feature extraction so that the feature points contain the whole point cloud model and avoid the problem of losing detail features. Specifically, according to the loss characteristics of point cloud clustering, AOMC selects a different number of clustering centers for various models. Moreover, in light of the density distribution of the point cloud, the radius of the clustering subset is determined. This method effectively improves the accuracy of the point cloud deep learning network on object classification and parts segmentation. Our method reaches demand on Modelnet10 and shapenetcore_partanno_segmentation_benchmark datasets. In terms of deep learning network optimization, it has good performance. Additionally, our method has high accuracy and low algorithm complexity.
Green Nano‐Biotechnology: A New Sustainable Paradigm to Control Dengue Infection
Authors: Zohra Tanzeel , Khalil Ali Talha , Saeed Faryal , Latif Bushra , Salman Muhammad , Ikram Aamer , Ayaz Muhammad , Murthy H. C. Ananda
Published: August 2022

Dengue is a growing mosquito‐borne viral disease prevalent in 128 countries, while 3.9 billion people are at high risk of acquiring the infection. With no specific treatment available, the only way to mitigate the risk of dengue infection is through controlling of vector, i.e., Aedes aegypti. Nanotechnology‐based prevention strategies like biopesticides with nanoformulation are now getting popular for preventing dengue fever. Metal nanoparticles (NPs) synthesized by an eco‐friendly process, through extracts of medicinal plants have indicated potential anti‐dengue applications. Green synthesis of metal NPs is simple, cost‐effective, and devoid of hazardous wastes. The recent progress in the phyto‐synthesized multifunctional metal NPs for anti‐dengue applications has encouraged us to review the available literature and mechanistic aspects of the dengue control using green‐synthesized NPs. Furthermore, the molecular bases of the viral inhibition through NPs and the nontarget impacts or hazards with reference to the environmental integrity are discussed in depth. Till date, major focus has been on green synthesis of silver and gold NPs, which need further extension to other innovative composite nanomaterials. Further detailed mechanistic studies are required to critically evaluate the mechanistic insights during the synthesis of the biogenic NPs. Likewise, detailed analysis of the toxicological aspects of NPs and their long‐term impact in the environment should be critically assessed.
Online Loan Default Prediction Model Based on Deep Learning Neural Network
Authors: Li Baodong
Published: August 2022

With the rapid development of Internet loans and the demand for Internet loans, Internet-based loan default prediction is particularly important. P2P online lending is based on Internet technology. With the popularization of personal PCs and mobile terminals, the borrower’s financing cost has been reduced to a large extent, and the efficiency of the borrower’s capital utilization has also been improved to a considerable level. Making full use of the existing data of the online lending platform, integrating third-party data, and predicting the default behavior of users are the major directions of future development. This paper mainly studies the network loan default prediction model based on DPNN. This paper first analyzes the problems and risks of the P2P online lending platform, then introduces the principle and characteristics of BPNN in detail, and determines the credit risk rating process for online lending based on BPNN. With the help of data analysis and processing software, after cleaning and variable selection of credit customer data provided by lending clubs, a set of corresponding online lending default risk assessment models are established through BPNN. This paper simulates the network loan default assessment model of the BPNN model and compares it with the support vector machine and regression model. The experimental results show that the highest accuracy rate of the BPNN model is 98.01% and the highest recall rate is 99.82%, which is better than the other two models; the AUC value of BPNN is 0.79, which is significantly higher than that of support vector machine and regression model. The above results show that the online loan default prediction model based on DPNN has high application value in practice. Predicting the probability of customer default risk in advance will help reduce the risk of P2P companies and lenders, improve the competitiveness of P2P lending institutions, and promote the development of domestic P2P platforms to be more stable.
The Application of Cloud Computing Service and Voicematic Identification Intelligence in the Supply Chain during the COVID-19 Pandemic
Authors: Li Jian , Song Rui , Lai Bin
Published: August 2022

After the outbreak of the COVID-19 pandemic, cloud computing and voice recognition services have provided a more critical role in the enterprise supply chain management process. Speech emotion recognition technology can identify the content and emotion of the speaker according to the content and tone of speech. Analyzing the attitude and tone behind a statement through language and high-level information acquisition is very difficult. Cloud computing technology is becoming more widely used each day and is the cornerstone of enterprise information development. The anti-globalization caused by COVID-19 has made enterprises pay increasing attention to supply chain management. Determining how to optimize and integrate the supply chain has become an urgent problem for enterprises. Based on the work and research completed in this field, this paper first analyzes the algorithm and model used in speech recognition and then tests the system. To improve the utilization rate of enterprise resources and enterprise industrial benefits, this paper puts forward the optimization scheme of the enterprise supply chain from the perspective infrastructure construction to product development to backstage management and procurement.
Real‐Time Explainable Multiclass Object Detection for Quality Assessment in 2‐Dimensional Radiography Images
Authors: Naddaf-Sh Sadra , Naddaf-Sh M-Mahdi , Zargarzadeh Hassan , Dalton Maxim , Ramezani Soodabeh , Elpers Gabriel , Baburao Vinay S. , Kashani Amir R.
Published: August 2022

Quality inspection and defect detection play a critical role in infrastructure safety and integrity specially when it comes to aging infrastructure mostly owned by governments around the world. One of the prevalent inspections performed in the industry is nondestructive testing (NDT) using radiography imaging. Growing demand, shortage of experts, diversity of required skills, and specific regional standards with a time‐limited requirement of inspection results make automated inspection an urgent need. Therefore, utilizing artificial intelligence‐ (AI‐) based tools as an assistive technology has become a trend for industrial applications, which automates repeated tasks and provides increased confidence before and during the inspection operation. Most of the works in quality assessment are focused on the classification of few categories of defects and mostly performed on public or noncomprehensive research datasets. In this work, a scalable, efficient, and real‐time deep learning family of models for detection and classification of 10 various categories of weld characteristics on a real‐world industrial dataset is presented. The models are evaluated and compared against each other, various critical hyperparameters and components are optimized, and local explainability of models is discussed. Additionally, AutoAugment for object detection and various techniques are utilized and investigated. The best performance for object detection and classification for 10 class models is reached by mean average precision of 72.4% and top‐1 accuracy of 90.2%, respectively. Also, the fastest object detection model is able to evaluate a full 15360 × 1024 pixels weld image in 0.39 seconds. Finally, the proposed models are deployable on edge‐devices to perform as assistant to NDT experts or auditing professionals.
Confidence Neutrosophic Number Linear Programming Methods Based on Probability Distributions and Their Applications in Production Planning Problems
Authors: Lv Gangding , Du Shigui , Ye Jun
Published: August 2022

A neutrosophic number linear programming (NN-LP) method reveals a useful tool for solving optimal production planning problems in an indeterminate environment. The optimal feasible solutions of the decision variables and the objective function in the NN-LP method are obtained only depending on the subjectively specified uncertain range of neutrosophic numbers without considering some distribution and confidence level of product sample data. Due to the lack of some probability distribution and confidence level/interval in indeterminate situations, existing indeterminate optimization methods are difficult to guarantee the reliability and credibility of the optimal interval feasible solutions. To solve these problems, this study proposes confidence neutrosophic number linear programming methods based on some probability distributions to objectively determine the confidence level/interval of a sample dataset/multivalued set in the NN-LP problems and strengthen the reliability and credibility of the optimal interval feasible solutions. Therefore, this article first introduces a transformation method from product sample datasets (multivalued sets) with normal and log-normal distributions to confidence neutrosophic numbers (CNNs) (confidence intervals) from a probabilistic point of view. Then, CNN linear programming (CNN-LP) models and solution methods are proposed according to the normal and log-normal distributions and confidence levels of product sample datasets to obtain the optimal interval feasible solutions. Furthermore, the proposed CNN-LP methods are applied to two real cases in a manufacturing company of Shaoxing City in China to perform the production planning problems under normal and log-normal distributions and 95% CNN/confidence interval of the product sample datasets. Compared with existing related linear programming methods, the proposed CNN-LP methods can make the optimal interval feasible solutions more reasonable and credible/reliable than the existing linear programming methods and overcome the defects of the existing linear programming methods in indeterminate situations.
Profiling of Plant Derived Natural Constituents by Using Magnetic Resonance Techniques
Authors: Anand Anupama , Sharma Anshu , Kaur Saini Harpreet , Sharma Somesh , Sharma Ruchi , Thakur Chahat , Priyanka  , Atanassova Maria , Caruso Gianluca , Pasdaran Ardalan
Published: August 2022

Plants are reservoirs of naturally occurring chemical constituents with a wide range of structural diversity. These biological compounds can be derived from different parts of plants such as leaves, barks, seeds, seed coats, flowers, and roots. A broad array of secondary metabolic compounds is present in the plants such as antibiotics, alkaloids, antimicrobials, food-grade pigments, and phenolics which have been reported to possess numerous health-related benefits, including antioxidant, anti-inflammatory, anticancer, and antiobesity activities. Therefore, the identification and detection of these compounds are of utmost importance in order to utilise their benefits into various fields. Wherein, magnetic resonance techniques, such as NMR (nuclear magnetic resonance), MRI (magnetic resonance imaging), and EPR (electron paramagnetic resonance), being far more reproducible, nondestructive, than other analytical techniques such as liquid chromatography, mass spectroscopy, and high-performance liquid chromatography cover a much wider dynamic range of metabolites with easy sample preparation techniques with high speed and fidelity. Hence, these magnetic resonance techniques have been proven to be extremely useful in plant metabolite profiling and disease metabolomics, along with structural elucidation of bioactive compounds from plant sources. Therefore, the present review focuses on the effectiveness of magnetic resonance for the detection of plant-derived metabolites that may lead to new areas of research in various fields such as drug discovery and development, metabolomics, combinatorial chemistry, and assessing overall food safety and quality.
“Bacterial Consortium”: A Potential Evolution of Fecal Microbiota Transplantation for the Treatment of Clostridioides difficile Infection
Authors: Quaranta Gianluca , Ianiro Gianluca , De Maio Flavio , Guarnaccia Alessandra , Fancello Giovanni , Agrillo Chiara , Iannarelli Federica , Bibbo Stefano , Amedei Amedeo , Sanguinetti Maurizio , Cammarota Giovanni , Masucci Luca
Published: August 2022

Fecal microbiota transplantation (FMT) consists of infusion of feces from a donor to a recipient patient in order to restore the resident microbial population. FMT has shown to be a valid clinical option for Clostridioides difficile infections (CDI). However, this approach shows several criticalities, such as the recruiting and screening of voluntary donors. Our aim was to evaluate the therapeutic efficacy of a synthetic bacterial suspension defined “Bacterial Consortium” (BC) infused in the colon of CDI patients. The suspension was composed by 13 microbial species isolated by culturomics protocols from healthy donors’ feces. The efficacy of the treatment was assessed both clinically and by metagenomics typing. Fecal samples of the recipient patients were collected before and after infusion. DNA samples obtained from feces at different time points (preinfusion, 7, 15, 30, and 90 days after infusion) were analyzed by next‐generation sequencing. Before infusion, patient 1 showed an intestinal microbiota dominated by the phylum Bacteroidetes. Seven days after the infusion, Bacteroidetes decreased, followed by an implementation of Firmicutes and Verrucomicrobia. Patient 2, before infusion, showed a strong abundance of Proteobacteria and a significant deficiency of Bacteroidetes and Verrucomicrobia. Seven days after infusion, Proteobacteria strongly decreased, while Bacteroidetes and Verrucomicrobia increased. Metagenomics data revealed an “awakening” by microbial species absent or low concentrated at time T0 and present after the infusion. In conclusion, the infusion of selected bacteria would act as a trigger factor for “bacterial repopulation” representing an innovative treatment in patients with Clostridioides difficile infections.
Neutrophil to Lymphocyte Ratio and Platelet to Lymphocyte Ratio in Poststroke Depression: A Systematic Review and Meta-Analysis
Authors: Sarejloo Shirin , Abadifard Erfan , Othman Zhian Jamal , Zafarani Fatemeh , Khanzadeh Monireh , Sadigh-Eteghad Saeed , Farajdokht Fereshteh , Mohammadpoorasl Asghar , Khanzadeh Shokoufeh
Published: August 2022

Objectives. Evidence shows that stroke-induced inflammatory responses play an essential role in the development of poststroke depression (PSD). The goal of this systematic review and meta-analysis was to critically evaluate the literature regarding the use of the neutrophil to lymphocyte ratio (NLR) as a reliable means to detect early PSD development, to help clinicians institute early interventions and improve outcomes. Methods. Electronic databases, including Web of Science, PubMed, Google Scholar, and Scopus, were searched, and eight studies were included. We assessed the certainty of the associations with GRADE methods. Results. We found that patients with PSD had higher NLR than the stroke patients with no depression (

SMD
=
0.51

;

CI
 
95
%
=
0.29

-0.73,

p
<
0.001

). Also, we found a significantly higher PLR in the patients with PSD when compared to the stroke patients with no depression (

SMD
=
0.66

;

CI
 
95
%
=
0.19

-1.13,

p
<
0.001

). Conclusion. These findings indicated that NLR and PLR could be considered inexpensive biomarkers for the prediction of PSD.
Rapid Text Retrieval and Analysis Supporting Latent Dirichlet Allocation Based on Probabilistic Models
Authors: Gnanavel S. , Mani Vinodhini , Sreekrishna M. , Amshavalli R. S. , Reta Gashu Yomiyu , Duraimurugan N. , Srinivasa Rao Namburi
Published: August 2022

Text mining, also known as text analysis, is the process of converting unstructured text data into meaningful and functional information. Text mining uses different AI technologies to automate data and generate valuable insights, allowing enterprises to make data-based decisions. Text mining enables the user to extract important content from text data sets. Text analysis encourages machine learning ability for research areas such as medical and pharmaceutical innovation fields. Apart from this, text analysis converts inaccessible data into a structured format, which can be used for further analysis. Text analysis emphasizes facts and relationships from large data sets. This information is extracted and converted into structured data for visualization, analysis, and integration as structured data and refines the information using machine-learning methods. Like most things related to Natural Language Processing, text mining can seem like a difficult concept to understand. But the fact is, it does not have to be. This research article will go through the basics of text mining, clarify its different methods and techniques, and make it easier to understand how it works. We implemented Latent Dirichlet Allocation techniques for mining the data from the data set; it works properly and will be in future development data mining techniques.
A Comprehensive Nursing Model Combined with High-Quality Nursing Intervention for Antiviral Therapy in Patients with Chronic Hepatitis B
Authors: Yang Wei , Tian Xiaofeng , Liang Jing
Published: August 2022

Objective. To investigate the effect of comprehensive nursing combined with high-quality nursing intervention on the compliance, anxiety, and mental state of patients with chronic hepatitis B undergoing antiviral therapy. Methods. A total of 100 patients with chronic hepatitis B admitted to China-Japan Union Hospital from December 2017 to August 2020 were recruited and assigned to receive either routine nursing (control group, n = 50) or comprehensive nursing plus high-quality nursing (observation group, n = 50) via the random number table method. The outcome measures included compliance, psychological state, and treatment effects. Results. Before the nursing, there was no significant difference in the compliance scores between the two groups of patients

P
>
0.05

. After the nursing, the observation group had higher compliance scores than the control group

P
<
0.001

. Before the intervention, the two groups had similar Self-Rating Anxiety Scale (SAS) scores

P
>
0.05

. After the intervention, patients in the observation group showed lower SAS scores and a lower incidence of negative emotions as compared to the control group

P

<

0.001

. The observation group showed a better outcome in terms of quality of life, Medical Coping Modes Questionnaire (MCMQ) scores, and the General Self-Efficacy Scale (GSES) scores when compared to those of the control group

P
<
0.001

. A higher treatment effective rate was witnessed in the observation group in contrast to the control group

P
<
0.001

. Conclusion. The comprehensive nursing model combined with high-quality nursing intervention improves the psychological state and compliance of patients with chronic hepatitis B, with favorable treatment efficiency, which shows good potential for clinical promotion.
Research on Classroom Emotion Recognition Algorithm Based on Visual Emotion Classification
Authors: Yuan Qinying
Published: August 2022

In this paper, we construct a classroom emotion recognition algorithm by classifying visual emotions for improving the quality of classroom teaching. We assign weights to the training images through an attention mechanism network and then add a designed loss function so that it can focus on the feature parts of face images that are not obscured and can characterize the target emotion, thus improving the accuracy of facial emotion recognition under obscuration. Analyze the salient expression features of classroom students and establish a classification criteria and criteria library. The videos of classroom students’ facial expressions are collected, a multi-task convolutional neural network (MTCNN) is used for face detection and image segmentation, and the ones with better feature morphology are selected to build a standard database. A visual motion analysis method with the fusion of overall and local features of the image is proposed. To validate the effectiveness of the designed MTCNN model, two mainstream classification networks, VGG16 and ResNet18, were tested and compared with MTCNN by training on RAF-DB, masked dataset, and the classroom dataset constructed in this paper, and the final accuracy after training was 78.26% and 75.03% for ResNet18 and VGG16, respectively. The results show that the MTCNN proposed in this paper has a better recognition effect. The test results of the loss function also show that it can effectively improve the recognition accuracy, and the MTCNN model has an accuracy of 93.53% for recognizing students’ facial emotions. Finally, the dataset is extended with the training method of expression features, and the experimental study shows that the method performs well and can carry out recognition effectively.
Linc00312 Single Nucleotide Polymorphism as Biomarker for Chemoradiotherapy Induced Hematotoxicity in Nasopharyngeal Carcinoma Patients
Authors: Guo Zhen , Wang Ya-Jing , He Bin-Sheng , Zhou Ji
Published: August 2022

Background. Linc00312 is downregulated in nasopharyngeal carcinoma (NPC) and associates with poor treatment efficacy. Genetic variations are the main cause of individual differences in treatment response. The objective of this study is to explore the relationship between genetic variations of linc00312 and the risk of chemoradiotherapy induced toxic reactions in NPC patients. Methods. We used a bioinformatics approach to select 3 single nucleotide polymorphisms (SNPs) with regulatory feature in linc00312 (rs12497104, rs15734, and rs164966). 505 NPC patients receiving chemoradiotherapy with complete follow-up data were recruited. Genotyping was carried out by MassARRAY iPLEX platform. Univariate logistic and multivariate logistic regression were used to analyze the risk factors responsible for toxic reactions of NPC patients. Results. Our result demonstrated that linc00312 rs15734 (

G
>
A

) was significantly associated with severe leukopenia in NPC patients underwent chemoradiotherapy (AA vs. GG,

OR
=
3.145

,

P
=
0.029

). In addition, the risk of severe leukopenia was remarkably increased to 5.635 times (

P
=
0.034

) in female with rs15734 AA genotype compared to male with rs15734 GG genotype. Moreover, patients with rs12497104 (

G
>
A

) AA genotype showed a 67.5% lower risk of thrombocytopenia than those with GG genotype (

P
=
0.030

). Remarkably, the younger patients (

age
<
47

) with rs12497104 AA genotype displayed a 90% decreased risk of thrombocytopenia compared with older patients (

age
≥
47

) carrying rs12497104 GG genotype (

P
=
0.030

). Conclusions. Genetic variations of linc00312 affect the risk of chemoradiotherapy induced hematotoxicity in nasopharyngeal carcinoma patients and may serve as biomarkers for personalized medicine.
Effect of Nursing Model Based on Rosenthal Effect on Self-Efficacy and Cognition of Life Meaning in Patients with Non-Small-Cell Lung Cancer
Authors: Mao Linghua , Lu Huaqin , Lu Yangyang
Published: August 2022

Objective. To study the influence of nursing model based on Rosenthal effect on self-efficacy and cognition of life meaning in patients with non-small-cell lung cancer (NSCLC). Methods. 120 patients with NSCLC treated in the hospital were selected from November 2020 to November 2021 and were randomly divided into the nursing group and the Rosenthal group, with 60 cases in each group. The nursing group received routine nursing intervention, while the Rosenthal group was intervened by nursing model based on the Rosenthal effect, and both groups were intervened for 1 month. The self-efficacy (General Self-Efficacy Scale (GSES)), negative emotions (Hospital Anxiety and Depression Scale (HADS)), self-burden (Self-Perceived Burden Scale for Cancer Patients (SPBS-CP)), meaning of life (Meaning of Life Scale for Advanced Cancer Patients (MiLS)), and quality of life (Functional Assessment of Cancer Therapy-Lung (FACT-L)) were compared between the two groups before and after intervention. Results. After 1 month of intervention, the scores of GSES and MiLS of patients in the two groups were significantly higher than those before intervention, and the scores in the Rosenthal group were significantly higher than those in the nursing group (

P
<
0.05

). The scores of HADS, SPBS-CP, and FACT-L in the two groups were significantly lower than those before intervention, and the scores were significantly lower in the Rosenthal group than those in the nursing group (

P
<
0.05

). Conclusion. Nursing model intervention based on Rosenthal effect enhances the self-efficacy and meaning of life and reduces the negative emotions and self-burden in patients with NSCLC.
Participation in Targeted Poverty Alleviation and Enterprise Innovation Investment: Analysis of the Mediating Effect Test Model Based on Financing Constraints
Authors: Gao Ting , Wang Huaiming
Published: August 2022

In China, enterprises participating in targeted poverty alleviation can be provided with a strategic resource to enhance their ultimate value creation ability and directly win reputation and obtain sustainable competitive advantages, thus enhancing their innovation ability. In this study, the influence and mechanism of the participation in targeted poverty alleviation on enterprise innovation investment are empirically examined based on financing constraints with the data of nonfinancial listed companies in Shanghai and Shenzhen from 2012 to 2019 as the object. The research suggests that enterprises taking the initiative to participate in targeted poverty alleviation can first have increased innovation investment and especially enhanced exploratory innovation ability. Second, enterprises’ participation in targeted poverty alleviation can reduce financing constraints of enterprises’ innovation, improve their resource allocation efficiency, and stimulate their innovation vitality primarily through the influence of resource, reputation, and information. This study enriches the theoretical research on the economic consequences of targeted poverty alleviation and provides a reference for guiding enterprises in practice to fulfill social responsibilities to promote enterprises’ innovation vigor and boost their high-quality development.
Information Disclosure of Network Platform and Corporate Social Responsibility Based on Cloud Computing
Authors: Shengyong Yang
Published: August 2022

With the development of information and cloud computing technology, the global data volume shows an explosive growth trend. Information has become the “circulating currency” supporting the data operation analysis of various industries, and it is the key for the website platform to obtain the core competitiveness. However, with the expansion of network transaction scale, information disclosure problems between various network platforms begin to appear. Users will continue to receive various marketing emails, SMS, and even harassing calls, which has brought great problems to users’ life and led to users’ refusal to disclose personal information. The network platform cannot obtain accurate information from users, so it cannot analyze and predict the bias of user behavior to provide personalized services, which undoubtedly hinders the development of the network platform. Therefore, how to promote users’ information disclosure has become an urgent problem for network platform managers. Based on the multidimensional development theory, combined with the factors at the website and user level, this paper discusses its influencing factors and mechanism and develops the relevant research on privacy disclosure in a more in-depth and comprehensive way. Then, this paper studies corporate social responsibility. Stakeholders such as the government, the media, consumers, and the public all hope that enterprises will bear more social responsibility while obtaining economic benefits. Based on the analysis of relevant technologies of cloud computing network platform, this paper studies the information disclosure and corporate social responsibility of network platform so as to solve the existing problems.
A Study on the Application of Extended Care Based on the Biopsychosocial Medicine Model in People with Abnormal Tumor Markers on Physical Examination
Authors: Wang Wenting , Lin Chunyu , Yu Hong , Zhou Shuai
Published: August 2022

The purpose of this study was to explore the value of extended care based on a biopsychosocial medicine model in patients with abnormal tumor markers on physical examination. One hundred and fifty‐two cases with abnormal primary screening tumor markers who were examined in our medical examination center between January 2020 and January 2022 were selected as the subjects of this study and divided into intervention and control groups according to the random number table method, with 76 cases in each group. The control group was given the usual extended care intervention and the intervention group was given the extended care intervention based on the biopsychosocial medicine model. The compliance rates of regular follow‐up, reasonable diet, appropriate exercise, and regular rest were compared between the two groups. After the intervention, the disease‐related knowledge score in the intervention group was higher than that in the control group (P < 0.05). The compliance rates of regular return visits, reasonable diet, appropriate exercise, and regular routines in the intervention group were higher than those in the control group (P < 0.05). After the intervention, the scores of psychological states such as anxiety, depression, and post‐traumatic growth in the intervention group were better than those in the control group (P < 0.05). After the intervention, the total scores of objective support, subjective support, support utilization, and social support in the intervention group were higher than those in the control group (P < 0.05). After the intervention, the intervention group had higher positive coping scores and lower negative coping scores than the control group (P < 0.05). Continuing care based on the biopsychosocial model of medicine is effective in people with abnormal tumor markers on medical screening. It can improve the knowledge about the disease, increase the compliance rate, improve negative emotions, psychological status, and social support, and promote a more positive way of responding.
A Support Vector Machine Based Prediction on Sensitivity to Coal Ash Blast for Different Degrees of Deterioration
Authors: Zhang Jing , Wang Qingxia , Guo Wannian , Li Longlong
Published: August 2022

Coal ash blast is a potential hazard that causes serious disasters in coal mines. In explosion control, research work on coal ash sensitivity prediction is of practical importance to improve accuracy, reduce blindness of explosion protection measures, and strengthen targets. The potential and destructive characteristics of coal ash blast vary greatly from coal to coal, especially in coal mines with complex and changing environments, where the characteristics of coal ash blast show great variability under the influence of various factors. In addition, due to the lack of systematic and comprehensive understanding of the occurrence mechanism of coal ash blast, it is necessary to conduct systematic research on the occurrence mechanism of coal ash blast. Current coal ash blast sensitivity summarizes and concludes prediction methods to create reliable predictions for coal ash blast. A new general learning method, support vector machine (SVM), has been developed, which provides a unified framework for solving limited sample training problems and can better solve small sample training problems. With the purpose of determining the coal mine problem and coal ash sensitivity prediction sensitivity indicators and thresholds, the SVM method is used to set the sensitivity function of each prediction indicator, and the sensitivity of each prediction indicator for the proposed study mine is expressed quantitatively. The experimental results show that the prediction accuracy of SVM for positive and negative categories is 15.6% higher than that of BP neural network and 35.1% higher than that of Apriori algorithm. Therefore, the prediction effectiveness of the SVM algorithm is proved. Therefore, it is practical to adopt SVM method for prediction on sensitivity to coal ash blast and apply the latest statistical learning theory SVM to predict the risk of coal ash.
Susceptibility Evaluation of Fall Armyworm (Spodoptera frugiperda) Infesting Maize in Kenya against a Range of Insecticides
Authors: Gichere Savinda Njeri , Khakame Kakai Shem , Patrick Okoth
Published: August 2022

The fall armyworm, Spodoptera frugiperda (J. E. Smith), is a worldwide pest of gramineous crops and a major pest of corn. Kenya has, in the recent years, reported massive outbreaks of this pest causing huge economic losses in maize fields. The indiscriminate use of insecticides has led to the evolution of insecticide resistance. This presents serious challenges to the control of pests including fall armyworm. The fall armyworm infestation has greatly threatened food security in Kenya. Consequently, this has heightened the need to evaluate the susceptibility of the fall armyworm to commonly used insecticides in Kenya. In this study, thirteen populations of the fall armyworm were sampled from thirteen counties of Kenya and determined its susceptibility to a range of insecticides using leaf-dip bioassay method. The current study illustrated the high toxicity of spinetoram, spinosad, lufenuron, and pyridaben to fall armyworm while indoxacarb, deltamethrin, lambda-cyhalothrin, imidacloprid, and abamectin exhibited relatively low toxicity to fall armyworm. Possible cross-resistance between abamectin, imidacloprid, deltamethrin, indoxacarb, spinosad, spinetoram, and lufenuron was determined through pair-wise correlational analyses. Results of this study revealed no cross-resistance between lambda-cyhalothrin with all other insecticides tested. Susceptibility monitoring of the fall armyworm can be a valuable strategy in the control of fall armyworm in the field populations. This can help inform the policy to design management strategies that promote the judicious use of these chemicals and prolong their efficacy in the management of the fall armyworm in Kenya.
Model of Urban Marketing Strategy Based on Ecological Environment Quality
Authors: Sun Feihan , Ye Chongliang , Zheng Weirong , Miao Xumei
Published: August 2022

Ecological environment quality is increasingly becoming a critical priority in measuring the sustainable development of modern cities. Reflecting on the reality of contemporary urban development, we create a structural equation model with city image and ecological quality as the antecedents of sustainable urban development, reflect on and expand the existing models of sustainable urban development, and further explore the mechanisms of various urban marketing factors on the shaping of city image. The study found that the continuous improvement of city marketing assets contributes to the process of shaping sustainable city branding. City image has a direct and significant positive impact on city recognition, city reputation, and perceived quality. Ecological environment quality also has a direct effect on city recognition and no direct effect on perceived quality or city reputation. However, ecological environment quality can affect city’s reputation by acting on perceived quality through city recognition and then enhance the competitiveness of city sustainable development. The case practice of shaping a city’s image by ecological environment quality illustrates the pragmatic return of humanism in contemporary urban development. While the halo effect of city image still exists, a good city reputation becomes another important influence on perceived quality and sustainability. This study facilitates the development of urban sustainable development theory and the extension of urban branding innovation theory, which will promote international dialogue in the field of urban eco‐environment in China.
Conservation and Development of Residential-Type Historical and Cultural Blocks in Guangzhou under Subjective Evaluation
Authors: Ma Yue , Wang Zhongwei , Li Nan , Chen Jue
Published: August 2022

Historical and cultural blocks not only carry numerous cultural heritages but always undertake urban functions. As a major driver of urban context construction, historical and cultural blocks have never been abandoned by the times. Regardless of the purposes for which they were built in the first place, all historical and cultural blocks should be extended functionally to rejuvenate them in current and future developments. In particular, in single-functional residential-type historical and cultural blocks, the large numbers of traditional architectures left vacant by out-migrating residents can be redeveloped for other functions. The effectiveness of block conservation and development affects the cultural construction of a city. More importantly, it is closely related to all kinds of users in blocks, as users engage in in-depth interactions with environments and are best able to discover the merits of block conservation and development. Moreover, the conservation and optimal design of historical and cultural blocks must, ultimately, be based on the users’ needs. By collecting users’ subjective evaluation opinions about two typical residential-type historical and cultural blocks based on the existing evaluation index and putting them through quantitative analysis, this paper analyzes block status in many aspects. It identifies focuses on optimization, offering references and suggestions for future development.
A Hybrid Framework for Real-Time Dispatching of Airline Unit Load Devices under Demand Variations
Authors: Tang Ching-Hui , Hsieh Ping-Hsuan
Published: August 2022

This study is devoted to a new research topic in real-time airline operations, the redispatching of unit load devices (ULDs) under demand variations. We develop a new hybrid framework to solve the problem of ULD redispatch following the time-sequence decision-making required by airlines. The hybrid framework is developed by integrating techniques including the probability distribution technique to simulate different types of operational demand, the adjustable number of stages which is needed to meet the requirements of a decision-making process following a time sequence and the time pressure characteristic of real operations, and the scenario tree and probability rule approaches which are aimed and representing all possible demand scenarios for a stage, while the network flow technique is applied to represent the movement and location of ULDs at each airport over time and is used for the development of the associated mathematical model and the simulation. We performed a simulation of 2,000 cases based on different operational days and types of operational demand. The results show that this hybrid framework is able to achieve stability and also a small variability of both ULD operating costs and solution times, which could allow the airline to save on ULD operating costs, under demand variations in real-time operations.