bluuebunny/crossref_metadata_2025 huggingface dataset:
EiMOL: A Secure Medical Image Encryption Algorithm based on Optimization and the Lorenz System
Authors: Singh KN , Singh OP , Singh Amit Kumar , Agrawal Amrit Kumar
Published: September 2022

Nowadays, the demand for digital images from different intelligent devices and sensors has dramatically increased in smart healthcare. Due to advanced low-cost and easily available tools and software, manipulation of these images is an easy task. Thus, the security of digital images is a serious challenge for the content owners, healthcare communities, and researchers against illegal access and fraudulent usage. In this article, a secure medical image encryption algorithm,
EiMOL
, based on optimization and the Lorenz system, is proposed for smart healthcare applications. In the first stage, an optimized random sequence (ORS) is generated through directed weighted complex network particle swarm optimization using the genetic algorithm (GDWCN-PSO). This random number matrix and the Lorenz system are adopted to encrypt plain medical images, obtaining the cipher messages with a relationship to the plain images. According to our obtained results, the proposed
EiMOL
encryption algorithm is effective and resistant to the many attacks on benchmark Kaggle and Open-i datasets. Further, extensive experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art approaches.
Epidemiology and Determinants of Vitamin D Deficiency in Eastern Nepal: A Community-Based, Cross-Sectional Study
Authors: Sherchand O. , Baranwal J. K. , Gelal B.
Published: September 2022

Objective. To estimate the prevalence of vitamin D deficiency in the eastern part of Nepal and identify the sociodemographic factors associated with it. Methods. A descriptive cross-sectional study was carried out among 324 participants between the ages 18 and 65 years from the Sunsari and Morang districts of Nepal. A semi-structured questionnaire helped obtain sociodemographic data followed by anthropometric measurements and blood sampling. 25(OH)D level was measured by chemiluminescence immunoassay (CLIA) via a fully automated Maglumi 1000 analyzer (SNIBE Co., Ltd., China). Serum 25(OH)D was classified as deficient, insufficient, and sufficient (<20 ng/ml, 20–29 ng/ml, and 30–100 ng/ml, respectively). The chi-square test was used to analyze the sociodemographic variables followed by a post hoc analysis. Significant variables were subjected to multivariate logistic regression. Result. 181(55.9%) of the study population had vitamin D deficiency. There was significant association between vitamin D status and time of maximum sun exposure (chi square test = 11.1,

p
=
0.02

), duration of sun exposure (chi-square test = 15.1,

p
=
0.004

), type of meat intake (Fisher’s exact test is 16.4,

p
=
0.01

), frequency of fish intake (Fisher’s exact test is 19.3,

p
=
0.001

), and frequency of dairy intake (chi-square test=11.2,

p
=
0.02

). In multivariate regression, consumption of dairy products ≥3/week had lower OR (95% CI) (0.3(0.1–0.8)

p
:
0.02

) and weekly fish consumption had lower OR (95% CI) (0.06(0.008–0.6)

p
:
0.01

) for vitamin D deficiency. Conclusion. The prevalence of vitamin D deficiency was relatively high in eastern Nepal. This highlights the need to create public awareness regarding the importance of bare skin sun exposure, nutritional sources of vitamin D, and the need to implement food fortification policies by the government.
Professional Talent Training System for Landscape Engineering Based on K-Means Algorithm
Authors: Zhou Shifeng
Published: September 2022

As the economy and society are developing and changing continuously, the garden industry is also developing and changing. The landscape industry has become a major focus of research on how to bring the training methods of the landscape technology profession more in line with the changing times, and how to make the training methods more in line with the needs of the industry by using modern personnel training methods. And it is important to find a good k-means algorithm to match the development of landscape engineering professionals. In this experiment, a combination of telephone interviews and questionnaires was used to ask questions about the landscape engineering profession. The respondents were the principals of the enterprise or relevant technical personnel. They learned what abilities the landscape engineering professionals needed by the society should have, and then the components were appropriate. The teaching system is used to conduct experimental teaching for landscape engineering majors, and there is also a landscape engineering professional control group for comparative analysis. The experimental results show that 27.45% of the students in the experimental group have 60–70 credits, while only 10.34% of the students in the control group have credits in this interval. The gap between the students in the two classes is very large, mainly because the experimental group pays attention to the combination of practice; practice and theory can better promote students’ mastery and application of professional knowledge. Moreover, 66.67% of the students in this experimental group found jobs in their majors. It can be seen from this that this system of cultivating talents for landscape engineering is very useful.
An Improved Inverse Kinematics Solution for a Robot Arm Trajectory Using Multiple Adaptive Neuro-Fuzzy Inference Systems
Authors: Refaai Mohamad Reda A.
Published: September 2022

Inverse kinematics of robots is a critical topic in the robotics field. Although there are conventional ways of solving inverse kinematics, soft computing is an important technology that has lately gained prominence due to its ability to reduce the complexity of the inverse kinematics problem. This paper presents an inverse kinematics solution using multiple adaptive neuro-fuzzy inference systems (MANFIS). Different models were established by employing various methods of identification. Subtractive Clustering (SCM), Fuzzy C-Means Clustering (FCM), and Grid Partitioning (GP) are the three methods used in this study. This work is being carried out on a 5-DOF articulated robot arm, which is commonly used in industry. A mathematical model is built based on the Denavit-Hartenberg (DH) approach. Following confirmation that the kinematic findings of the mathematical model match the actual observed values of the robot arm, two types of data sets are generated: a random data set and a systematic data set based on a trajectory. The data sets are then utilized to train and evaluate ANFIS models and choose the optimal models to develop MANFIS model. Thus, the prediction and experimental data are compared to assess the performance of the MANFIS model.
Assessment of Mineralogical and Geochemical Composition of Oligocene/Eocene Black Shale Deposits in Beni Suef Area, Egypt
Authors: Soliman Samar R. , Salama Yasser F. , El-Sayed Mohamed I. , Abdel-Fattah Mohamed I. , Abd-Allah Zakaria M.
Published: September 2022

Black shale deposits are widespread in the Paleogene successions in the Beni Suef area, Egypt. In this work, the mineralogical composition, total organic carbon (TOC), and total sulfur (TS) contents of black shales were determined. The organic matter (OM), thermal maturation, and kerogen type can be determined using Rock-Eval pyrolysis. The results show that smectite and kaolinite are the dominant clay minerals in the analyzed samples. Al2O3 and SiO2 as major elements and Zr as a trace element are dominant constituents of terrigenous origin of the Dabaa and El Fashn formations. The total organic carbon content reaches 2.27 wt% at the El Fashn Formation in the Ghaida Al-Sharqia quarry but 0.36 wt% in the Oligocene Dabaa Formation in the Maydoum quarry. However, the total sulfur content reaches 0.58 wt% in the Dabaa Formation but 1.52 wt% in the El Fashn Formation. These results confirm that (1) the Dabaa Formation was formed in oxidizing environments and oxidized the high volume of OM and (2) the El Fashn Formation accumulated in reducing environments of mostly suboxic conditions. The Rock-Eval pyrolysis results of the black shale deposits reflected a kerogen type III of terrestrial origin of the El Fashn Formation due to the high OM content preservation in suboxic shallow marine environments. The Tmax and production index parameters of Rock-Eval pyrolysis reﬂected that the black shale deposits in the El Fashn Formation are immature, but with surface retorting, it can generate natural gas.
CX3CL1 Derived from Bone Marrow Mesenchymal Stem Cells Inhibits Aβ1-42-Induced SH-SY5Y Cell Pathological Damage through TXNIP/NLRP3 Signaling Pathway
Authors: Guo Chuan , Li Qinxuan , Xiao Jiujia , Zhou Xuhui , Tian Meichen , Xie Lei , Xia Xun
Published: September 2022

Alzheimer’s disease (AD) is the most commonly seen neurodegenerative brain disorder. The paracrine effects of mesenchymal stem cells (MSCs) signify to trigger immunomodulation and neural regeneration. However, the role and mechanism of bone marrow MSC- (BMSC-) derived CX3CL1 in AD remains elusive. In this study, Aβ1-42-intervened SH-SY5Y cells were used for AD cell model construction. pcDNA-ligated CX3CL1 overexpression plasmids were transfected into BMSCs. The levels of soluble and membrane-bound CX3CL1 were detected by ELISA and Western blotting (WB), respectively. The growth, apoptosis, and pathology of AD model cells were evaluated by CCK-8, flow cytometry, immunofluorescence, morphology observation, biochemical examination, and WB. It was found that Aβ1-42 significantly reduced CX3CL1 expression either in soluble or membrane-bound form, cell viability, relative protein expression of synaptic markers, SOD, CAT, and GSH-Px contents, as well as Trx protein expression; in addition, it enhanced the apoptosis rate, the relative expression of cleaved caspase-3, Aβ, tau, p-Tau, Iba1, MDA, TXNIP, and NLRP3 in SH-SY5Y cells; however, the above effects were prominently reversed by the coculture of BMSCs. Moreover, overexpression of CX3CL1 in BMSCs observably strengthened the corresponding tendency caused by BMSCs. In conclusion, through the TXNIP/NLRP3 pathway, CX3CL1 derived from BMSCs inhibited pathological damage in Aβ1-42-induced SH-SY5Y.
The Role of Tetra Hybrid Reinforcements on the Behavior of Aluminum Metal Matrix Composites
Authors: Ashebir Demeke Abay , Mengesha Getinet Asrat , Sinha Devendra Kumar
Published: September 2022

Hybrid aluminum matrix composites (HAMCs) are a new class of advanced materials that can be customized and engineered to achieve specific properties for specific applications in specific environments. HAMCs find a wide range of popularity in the transportation sector because of lower noise and lower fuel consumption over other materials. This research aims to synthesize, characterize, and test the physicomechanical characteristics of tetra hybrid (SiC, Al2O3, Gr, and sugarcane bagasse ash (SCBA)) reinforced HAMCs via powder metallurgy (PM) processing. Tetra hybrid reinforced HAMCs were synthesized using a pure Al matrix with fixed wt% of primary reinforcements (5 wt% SiC and 5 wt% Al2O3) and varying wt% of secondary reinforcements such as 0.5, 2.5, 4.5, and 6.5 wt% Gr and 0.5, 2.5, 4.5, and 6.5 wt% SCBA. It mainly focused on phase purity investigation using XRD, thermal analysis using TGA‐DTA, and surface area and micropore size analysis using BET and physicomechanical tests to explore the materials’ behavior of the newly synthesized HAMCs. The increase in wt% of secondary reinforcements decreases both the density and porosity while increasing the hardness and compressive strength up to a certain level above which it begins to reverse because of the increase in wt% of hard particles of SiC, Al2O3, and SCBA. The Vickers hardness and compressive strength of the AS4 HAMC with 10 wt% (SiC+Al2O3) and 9 wt% (Gr+SCBA) were improved by 446.40% and 209.75%, respectively. The newly synthesized tetra hybrid reinforced HAMCs showed superior physicomechanical properties compared to pure Al and single and double reinforced HAMCs. As a result, the new tetra hybrid reinforced HAMC material is predicted to have potential applications in automotive, aerospace, defense, and various other structural applications.
SchizoGoogLeNet: The GoogLeNet-Based Deep Feature Extraction Design for Automatic Detection of Schizophrenia
Authors: Siuly Siuly , Li Yan , Wen Peng , Alcin Omer Faruk
Published: September 2022

Schizophrenia (SZ) is a severe and prolonged disorder of the human brain where people interpret reality in an abnormal way. Traditional methods of SZ detection are based on handcrafted feature extraction methods (manual process), which are tedious and unsophisticated, and also limited in their ability to balance efficiency and accuracy. To solve this issue, this study designed a deep learning-based feature extraction scheme involving the GoogLeNet model called “SchizoGoogLeNet” that can efficiently and automatically distinguish schizophrenic patients from healthy control (HC) subjects using electroencephalogram (EEG) signals with improved performance. The proposed framework involves multiple stages of EEG data processing. First, this study employs the average filtering method to remove noise and artifacts from the raw EEG signals to improve the signal-to-noise ratio. After that, a GoogLeNet model is designed to discover significant hidden features from denoised signals to identify schizophrenic patients from HC subjects. Finally, the obtained deep feature set is evaluated by the GoogleNet classifier and also some renowned machine learning classifiers to find a sustainable classification method for the obtained deep feature set. Experimental results show that the proposed deep feature extraction model with a support vector machine performs the best, producing a 99.02% correct classification rate for SZ, with an overall accuracy of 98.84%. Furthermore, our proposed model outperforms other existing methods. The proposed design is able to accurately discriminate SZ from HC, and it will be useful for developing a diagnostic tool for SZ detection.
Overexpressed Thrombospondin 2 Induced Osteogenic Differentiation of Valve Interstitial Cells via Inhibition of Akt/NF-κB Signaling Pathway to Promote Calcific Aortic Valve Disease Development
Authors: Yu Cheng , Wu Danna , Zhao Chong , Wu Chaoguang
Published: September 2022

Thrombospondin 2 (THBS2) is reported to participate in the development of calcific aortic valve disease (CAVD), while the effects are not elucidated completely. The study aimed to explore the role and mechanism of THBS2 in CAVD. Differentially expressed genes related to stenosis and sclerosis were screened through Limma package based on data from Gene Expression Omnibus (GEO), and the functional enrichment analysis was performed by the Database for Annotation, Visualization and Integrated Discovery (DAVID) database. The immunoreactivity of THBS2 in CAVD and normal samples was detected through immunohistochemistry. Valve interstitial cells (VICs) were transfected with short hairpin RNA against THBS2 (shTHBS2) and THBS2 overexpression plasmid and treated with LY294002 (Akt inhibitor) and induced osteogenic differentiation. The expression of THBS2 in CAVD and normal samples and the levels of THBS2, osteocalcin, Runx2, SPARC, COL1A2, COL1A1, SPP1, CTGF, MMP-2, MMP-13, Akt, p-Akt, p65, p-p65, and nuclear p65 in VICs were tested by qRT-PCR and Western blot. ALP activity was assessed using colorimetry. Calcic nodule formation was measured by Alizarin Red staining. THBS2 and PI3K-Akt pathway were differentially enriched in stenosis samples when compared with those in sclerosis samples. THBS2 expression was upregulated in CAVD and positively correlated with ALP activity, calcic nodule formation, osteogenic differentiation-related (osteocalcin, Runx2, SPARC, COL1A2, COL1A1, SPP1, and CTGF) and extracellular matrix– (ECM–) related (MMP-2 and MMP-13) factors in the process of osteogenic differentiation. ShTHBS2 suppressed ALP activity, calcic nodule formation, and osteogenic differentiation/ECM-related molecules while upregulating p-Akt/Akt, p-p65/p65, and nuclear p65 expressions in VICs during osteogenic differentiation. However, THBS2 overexpression had the opposite effect to shTHBS2, and LY294002 reversed the effect of shTHBS2. Collectively, overexpressed THBS2 induces the osteogenic differentiation of VICs via inhibiting Akt/NF-κB pathway to promote the development of CAVD.
Coefficient Estimates for Some Classes of Biunivalent Function Associated with Jackson
q
-Difference Operator
Authors: Ali Ekram E. , Lashin Abdel Moneim , Albalahi Abeer M.
Published: September 2022

Using the Jackson

q

-difference operator, we present two new subclasses of biunivalent functions. Furthermore, we estimate the initial Taylor-Maclaurin coefficients

a

2

and

a

3

of functions belonging to these new subclasses. Our results generalize some of the previously related works of several authors.
Meeting the Unmet Need in the Management of MDR Gram-Positive Infections with Oral Bactericidal Agent Levonadifloxacin
Authors: Mehta Yatin , Mishra K. C. , Paliwal Yashesh , Rangappa Pradeep , Sinha Sharmili , Bhapkar Sandeep
Published: September 2022

Levonadifloxacin (intravenous) and its oral prodrug alalevonadifloxacin are broad-spectrum antibacterial agents developed for the treatment of difficult-to-treat infections caused by multidrug-resistant Gram-positive bacteria, especially methicillin-resistant Staphylococcus aureus, atypical bacteria, anaerobic bacteria, and biodefence pathogens as well as Gram-negative bacteria. Levonadifloxacin has a well-defined mechanism of action involving a strong affinity for DNA gyrase as well as topoisomerase IV. Alalevonadifloxacin with widely differing solubility and oral bioavailability has pharmacokinetic profile identical to levonadifloxacin. Unlike existing MRSA drugs such as vancomycin and linezolid, which cause unfavorable side effects like nephrotoxicity, bone-marrow toxicity, and muscle toxicity, levonadifloxacin/alalevonadifloxacin has demonstrated superior safety and tolerability features with no serious adverse events. Levonadifloxacin/alalevonadifloxacin could be a useful weapon in the battle against infections caused by resistant microorganisms and could be a preferred antibiotic of choice for empirical therapy in the future.
Prevalence of Emergence Delirium and Associated Factors among Older Patients Who Underwent Elective Surgery: A Multicenter Observational Study
Authors: Tesfaye Mekonin Gezahegn , Kelbesa Olika Megersa , Birhanu Wedajo Mitiku , Tolasa Badada Ashanafi , Dukessa Dubiwak Abebe , Tageza Ilala Tajera , Gebre Mamo Nigatu
Published: September 2022

Background. Emergence delirium is a common and serious postoperative complication in older surgical patients. It occurs at any time in the perioperative period, during or immediately following emergence from general anesthesia. Unfortunately, it is highly associated with postoperative complications such as a decrease in functional capacity, prolonged hospital stay, an increase in health care costs, and morbidity and mortality. The goal of this study was to determine the prevalence of emergence delirium and associated factors among older patients who underwent elective surgery in the teaching hospitals of Ethiopia at the postanesthesia care unit in 2021. Methods. A multicenter prospective observational study was conducted at the postanesthetic care unit in the four teaching hospitals of Ethiopia. Older surgical patients admitted to the postanesthesia care unit who underwent elective surgery in the four teaching hospitals of Ethiopia were selected by using simple random sampling. Pretested structured questionnaire was used to collect data. Data were entered into EpiData (version 4.6) and exported to the SPSS (version 25.0). Binary logistic regression was used to identify factors independently associated with the emergence delirium. Results. Out of 384 older patients included in the study, the prevalence of emergence delirium was 27.6%. Preoperative low hemoglobin levels (AOR: 2.0, 95% CI; 1.77–3.46), opioid (AOR: 8.0, 95% CI; 3.22–27.8), anticholinergic premedications (AOR: 8.5, 95% CI; 6.85–17.35), and postoperative pain (AOR: 3.10, 95 CI; 2.07–9.84) at PACU were independently associated with emergence delirium. Conclusion. The prevalence of emergence delirium was high among older patients who underwent elective surgery. Opioid and anticholinergic premedication, low preoperative hemoglobin, and the presence of postoperative pain were independently associated with the emergence delirium. Adequate preoperative optimization and postoperative analgesia may reduce the prevalence of emergence delirium.
Efficient Hybrid Blind Watermarking in DWT‐DCT‐SVD with Dual Biometric Features for Images
Authors: Mokashi Bhargavi , Bhat Vandana S. , Pujari Jagadeesh D. , Roopashree S. , Mahesh T. R. , Alex D. Stalin
Published: September 2022

In the modern era of virtual computers over the notional environment of computer networks, the protection of influential documents is a major concern. To bring out this motto, digital watermarking with biometric features plays a crucial part. It utilizes advanced technology of cuffing data into digital media, i.e., text, image, video, or audio files. The strategy of cuffing an image inside another image by applying biometric features namely signature and fingerprint using watermarking techniques is the key purpose of this study. To accomplish this, a combined watermarking strategy consisting of Discrete Wavelet Transform, Discrete Cosine Transform, and Singular Value Decomposition (DWT‐DCT‐SVD) is projected for authentication of image that is foolproof against attacks. Here, singular values of watermark1 (fingerprint) and watermark2 (signature) are obtained by applying DWT‐DCT‐SVD. Affixing both the singular values of watermarks, we acquire the transformed watermark. Later, the same is applied to cover image to extract the singular values. Then we add these values to the cover image and transformed watermark to obtain a final watermarked image containing both signature and fingerprint. To upgrade the reliability, sturdiness, and originality of the image, a fusion of watermarking techniques along with dual biometric features is exhibited. The experimental results conveyed that the proposed scheme achieved an average PSNR value of about 40 dB, an average SSIM value of 0.99, and an embedded watermark resilient to various attacks in the watermarked image.
Optimization of Cold Chain Distribution Route with Mixed Time Window considering Customer Priority
Authors: Liu Shouchen , Zhang Cheng
Published: September 2022

In order to study the mixed time window vehicle routing optimization problem based on customer priority, a customer differentiation management strategy based on customer priority is proposed. Combined with the main factors affecting customer priority evaluation and the characteristics of vehicle routing problem with mixed time windows, a comprehensive evaluation index affecting customer priority was first established and DBSCAN clustering algorithm was used for clustering analysis of customer priority to solve the optimization problem of cold chain distribution route considering customer priority. Fuzzy time window of refrigerated vehicles was then constructed with trapezoidal fuzzy number, and a mathematical programming model was built with an objective function for minimizing the sum of fixed, green, penalty, refrigeration, and cargo damage costs. Two scenarios of out-of-stock and not-out-of-stock were designed. Finally, an improved genetic algorithm was used to solve the model, and the rationality of the model was verified through a case of imported fruit distribution in Xiamen City. Results showed that the proposed method can effectively solve the routing problem of refrigerated trucks considering customer priority. Moreover, the findings of this study can provide a new approach for solving the routing optimization problem of refrigerated trucks considering customer priority.
The Public Sentiment Analysis of Double Reduction Policy on Weibo Platform
Authors: Jia Weichen , Peng Jun
Published: September 2022

Weibo platform is an indispensable transmission channel in education policy release and dissemination. The events and sentiments contained in education policies microblogs include the public sentiment and support the general management and guidance scientifically and efficiently. This study constructs a dataset based on the “Double Reduction Policy” relevant microblogs and comments. The policy events are extracted by Latent Dirichlet Allocation (LDA) model and Language Technology Platform (LTP). Based on the emotion dictionary, an attention-based BiLSTM model is constructed to classify the public sentiments. The experimental results reveal four themes: “industry impact,” “institutional supervision,” “public feedback,” and “policy implementation.” The distribution conforms to the development trend of online public sentiments.
Research on Online Education Resources Recommendation Based on Deep Learning
Authors: Wang Xu
Published: September 2022

For the problem of knowledge overload in the process of online learning and the traditional algorithm’s poor recommendation accuracy and real-time performance in the massive educational resources, a deep learning-based recommendation model for online educational resources is proposed. First, attribute features of learners and learning resources are extracted, and then text features of learning resources are extracted, and attention fusion of features at multiple different scales is performed using a multiscale fusion strategy. Finally, the fused features are used as input to the multilayer perceptron to train the classification model. Through testing a variety of educational resources, it is verified that the model in this paper has better real-time performance while maintaining high detection accuracy and outperforms the mainstream comparison model in several indexes, which have a certain application value. It provides a new way of thinking for educational platforms to build real-time educational resource recommendations.
Bernardi Integral Operator and Its Application to the Fourth Hankel Determinant
Authors: Khan Abid , Haq Mirajul , Cotîrlă Luminiţa-Ioana , Oros Georgia Irina
Published: September 2022

In recent years, the theory of operators got the attention of many authors due to its applications in different fields of sciences and engineering. In this paper, making use of the Bernardi integral operator, we define a new class of starlike functions associated with the sine functions. For our new function class, extended Bernardi’s theorem is studied, and the upper bounds for the fourth Hankel determinant are determined.
Application of Collaborative Optimization in Urban Fresh Product Logistics Inventory and Distribution System
Authors: Li Xiaoli
Published: September 2022

People’s requirements for material needs and living standards are gradually increasing, and consumers’ demand for fresh, fruit, and vegetable cold chain foods is also increasing. This paper takes urban fresh agricultural products cold chain logistics as the research object, establishes a collaborative optimization model of urban fresh agricultural products cold chain logistics inventory and distribution based on distribution centers, proposes a partitioning solution strategy for the multidistribution center problem, and proposes a collaborative optimization in urban fresh agricultural products logistics inventory and distribution system. The application of collaborative optimization in urban fresh product logistics inventory and distribution system is proposed.
Prediction of Functional Genes in Primary Varicose Great Saphenous Veins Using the lncRNA-miRNA-mRNA Network
Authors: Wei Jiabo , Zhu Haihong , Zhang Qijun , Zhang Qin
Published: September 2022

Background. Long noncoding RNAs (lncRNAs) have been widely suggested to bind with the microRNA (miRNA) sites and play roles of competing endogenous RNAs (ceRNAs), which can thus affect and regulate target gene and mRNA expression. Such lncRNA-related ceRNAs are identified to exert vital parts in vascular disease. Nonetheless, it remains unknown about how the lncRNA-miRNA-mRNA network functions in the varicose great saphenous veins. Methods. This study acquired the lncRNA and mRNA expression patterns from the GEO database and identifies the differentially expressed mRNAs and lncRNAs by adopting the R software “limma” package. Then, miRcode, miRDB, miRTarbase, and TargetScan were used to establish the miRNA-mRNA pairs and lncRNA-miRNA pairs. In addition, the lncRNA-miRNA-mRNA ceRNA network was constructed by using Cytoscape. Protein-protein interaction, Gene Ontology functional annotations, and Kyoto Encyclopedia of Genes and Genomes enrichment were carried out to examine the candidate hub genes, the functions of genes, and the corresponding pathways. Results. In line with the preset theory, we constructed ceRNA network comprising 12 lncRNAs, 38 miRNAs, and 149 mRNAs. Kyoto Encyclopedia of Genes and Genomes analysis indicated that the PI3K/Akt signaling pathway played a vital part in the development of varicose great saphenous veins. AC114730, AC002127, and AC073342 were significant biomarkers. At the same time, we predicted the potential miRNA, which may exert a significant influence on the varicose great saphenous veins, namely, miR-17-5p, miR-129-5p, miR-1297, miR-20b-5p, and miR-33a-3p. Conclusion. By performing ceRNA network analysis, our study detects new lncRNAs, miRNAs, and mRNAs, which can be applied as underlying biomarkers of varicose great saphenous veins and as therapeutic targets for the treatment of varicose great saphenous veins.
Cloud Data Integrity Verification Algorithm Based on Data Mining and Accounting Informatization
Authors: Wang Junli , Yang Xiqian , Li Zhi
Published: September 2022

Data integrity verification means that the data in the cloud are uploaded by the user. In addition to the user’s own update of the data, any external factors including the cloud service provider’s data are destroyed, tampered with, and lost, and the data are not updated in a timely manner. Any inconsistencies in the actual data required can be detected by the user. This article aims to study cloud data integrity verification algorithms based on data mining and accounting informatization. This article proposes data mining technology, accounting information to help business managers assist management work. The article uses Company H as an example to illustrate the accounting information system and proposes a new information management strategy based on it. The CBF algorithm and data integrity verification algorithm are used to study the cloud storage data integrity verification protocol, the cloud data integrity verification model is constructed, the data program flow design is analyzed, and the time-consuming operation of file data insertion is analyzed. The experiment in this paper uses 16 standard mathematical calculation formulas to strengthen the analysis. The results show that the study of cloud data integrity verification algorithms based on data mining and accounting information is beneficial to the integrity and protection of data. When the number of documents added increases from 0 to 400, the document agreement shows an upward trend, and the agreement in this paper basically fluctuates between 10 and 80.
Lite-3DCNN Combined with Attention Mechanism for Complex Human Movement Recognition
Authors: Zhu Maochang , Bin Sheng , Sun Gengxin
Published: September 2022

Three-dimensional convolutional network (3DCNN) is an essential field of motion recognition research. The research work of this paper optimizes the traditional three-dimensional convolution network, introduces the self-attention mechanism, and proposes a new network model to analyze and process complex human motion videos. In this study, the average frame skipping sampling and scaling and the one-hot encoding are used for data pre-processing to retain more features in the limited data. The experimental results show that this paper innovatively designs a lightweight three-dimensional convolutional network combined with an attention mechanism framework, and the number of parameters of the model is reduced by more than 90% to only about 1.7 million. This study compared the performance of different models in different classifications and found that the model proposed in this study performed well in complex human motion video classification. Its recognition rate increased by 1%–8% compared with the C3D model.
Taiwan Passengers’ Willingness to Pay for Air Sleeper Seats
Authors: Kuo Chung-Wei
Published: September 2022

To provide passengers with a better flight experience and make their journey more comfortable, airlines are diversifying their services to attract more customers. One of these services includes the development of a sleeper seat. A sleeper seat, also known as family couch in Taiwan, mainly provides a row of 3–4 economy class seats that allow passengers to lie down. Although Taiwan’s China Airlines originally offered this service, it stopped due to poor sales; however, other international airlines continue to implement similar services. Regarding previous research exploring air service issues, almost no research has focused on sleeper seats. Accordingly, this article focuses on exploring Taiwanese passengers’ willingness to pay (WTP) for the sleeper seat service and the influencing factors. The contingent valuation method (CVM) was used to construct the price scenario of passengers’ WTP. To avoid estimation bias and thus provide more reliable results, the spike model was used to estimate passengers’ WTP for the sleeper seat service and the influencing factors. According to the results, when considering multiple variables, people were willing to increase the price paid to use parent-child cabins by NTD 11,194, which was about 75% of the original price but lower than the preferential price offered by the airlines, indicating a gap between the amount people were willing to pay and the airlines’ pricing. In addition, if passengers were traveling with children or if the passengers had higher flight frequencies and personal incomes, they were willing to purchase the sleeper seat service. However, people who had not experienced the sleeper seat service were unwilling to purchase it. This paper is practical to reflect current aviation industry marketing, and the research results contribute to the existing literature and provide a reference for Taiwan’s civil aviation industry when relaunching the sleeper seat service in the future.
Prediction of Thermal Conductivity of a Rock Wool Board by Computer X-Ray Tomography Technique Scanning and Random Generation-Growth Model
Authors: Li Xiaoguang , Zhang Rui , Zhang Diya , Fan Xin , Guo Meiqi , Qin Jinyi
Published: September 2022

Thermal conductivity of rock wool boards was investigated in this study. Although distribution of fibers in a realistic rock wool board is unclear, it can be simulated by computer X-ray tomography technique (CT) followed by rearrangement through the random generation-growth (RGG) model. An ideal CT-RGG structure model of rock wool boards (CT-random generation-growth model) was established by simplifying material properties based on the mesostructure parameters of the RGG model. Thermal conductivity of rock wool boards with different apparent densities and fiber diameters was studied, and the CT-RGG model was analyzed by explicit jump (EJ) diffusion equations solved by the fast Fourier transform method. We found that thermal conductivity of a single rock wool fiber can be successfully determined. Simulation and measurement results show that thermal conductivity increases consistently with the increase of apparent density and fiber diameter, particularly when the apparent density of rock wool board is greater than 140 kg m−3. Compared with the existing theoretical models, the proposed method does not depend on the empirical parameters; therefore, it is useful in designing and optimizing the thermal conductivity of rock wool boards.
Evaluation of Efficacy of Adjuvant Radiotherapy in Well-Differentiated Liposarcoma Patients with Positive Surgical Margins: A Population-based Study
Authors: Wang Haoran , Chen Boran , Cen Ying , Chen Junjie
Published: September 2022

Background. The use of adjuvant radiotherapy (RT) for well-differentiated liposarcoma (WD-LPS) patients with positive surgical margins is unclear. We aim to compare the overall survival (OS) and cancer-specific survival (CSS) of well-differentiated liposarcoma patients with positive surgical margins in an RT group and non-RT group. Methods. WD-LPS patients with positive margins from 2000 to 2018 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database and divided into two groups: RT group and non-RT group. Kaplan–Meier survival analysis with the log-rank test was performed to evaluate the difference of overall survival (OS) and cancer-specific survival (CSS) between groups. Univariate and multivariate Cox proportional hazard analyses were performed to identify important prognostic factors of OS and CSS. Analyses were adjusted using propensity-score matching. Results. We identified 2549 patients: 377 (14.79%) with RT and 2172 (85.21%) without RT. The median age was 61 years, and the median follow-up time was 68 months. The log-rank test revealed that there was no significant difference of CSS between RT and non-RT groups (

P
=
0.81

). The 5-year and 10-year CSS were comparable (

P
=
0.418

and

P
=
0.987

). Additionally, the use of RT was neither an independent prognostic factor for OS nor CSS. Age, sex, tumor site, the use of chemotherapy were independent prognostic factors for OS after propensity score matching, while race and the tumor site were independent prognostic factors for CSS. Conclusion. Adjuvant RT had no significant improvement on OS and CSS of WD-LPS patients with positive surgical margins.
Prospective Study on Professional Competence Development Planning of Higher Education Teachers in the Context of Big Data
Authors: Ji Xiaochuan
Published: September 2022

Teachers play a key role in education, and education plays a key role in national development. The combination of these two key points fully confirms the key role of teachers in national development and social prosperity. Strengthening the teaching force is the most important basic work for educational development. At present, improving teacher training, professional quality, and teaching ability has become an important measure to improve the quality of personnel training in vocational training institutions. Only through in-depth analysis of training problems and targeted reforms can we improve the quality of training and make teacher training truly serve vocational training. Therefore, this study explores the problems and causes and proposes countermeasures by conducting a prospective study on vocational competence planning of higher vocational teachers under big data, based on a full analysis of Chinese and foreign literature.
Plant Growth Promoting and Abiotic Stress Tolerant Chickpea (Cicer arietinum L.) Rhizobial Isolates from Some Areas of South Wollo Zone, Ethiopia
Authors: Menelih Asmamaw , Hailu Faris , Adal Mussa
Published: September 2022

Chickpea (Cicer arietinum L) is an important pulse crop prized for its high protein content and is grown as a food source worldwide, including Ethiopia. However, the yield of chickpea is low due to low soil fertility and the ever-changing abiotic stresses. Therefore, this study aimed at isolation, characterization, and selection of chickpea rhizobia effective in their nitrogen fixation and abiotic stress tolerance potential. As a result, 150 nodule samples were collected from three districts of chickpea-producing areas in South Wollo. The nodules were crushed, and the rhizobia were isolated and characterized by using standard microbiological procedures. Based on the presumptive tests conducted, 103 (68.7%) of the rhizobial isolates were tentatively categorized as Rhizobium species. Regarding phosphate solubilization, only 48 (46.7%) solubilized phosphate with a solubilization index ranging from 2.1 to 2.7 mm. Twenty-four (50%) of the isolates were found to be hydrogen cyanide producers. Among the rhizobial isolates tested under greenhouse conditions, 37 (77.1%) of them induced nodulation on their host plant (chickpea). Their symbiotic effectiveness evaluation test confirmed that 16(47.1%), 6(17.6%), 26.47%, and 3(8.8%) were highly effective, effective, low effective, and ineffective, respectively. Of the authenticated rhizobial isolates, 12 (35.5%) of them, including WuCR-15, 16, 17, 18, 19, 20, 23, 30, 31, 32, 36, 38, and 48, accumulated higher shoot dry matter than the positive control. Isolates WuCR- 11, 17, and 36 showed resistance to low and high extreme abiotic stresses of pH, temperature, and salt. Consequently, rhizobial isolates, WuCR- 11, 17, and 36, which were effective and competent in all the tested parameters, were recommended as good rhizobial candidates for applications under greenhouse and field conditions.
Lightweight Authenticated Privacy-Preserving Secure Framework for the Internet of Vehicles
Authors: Jamjoom Mona , Abulkasim Hussein , Abbas Safia
Published: September 2022

The integration of the Internet of things and dynamic vehicles has become unavoidable in advanced transportation systems. The vehicles communicate wirelessly with other vehicles, cloud servers, and roadside units via open communication channels. The openness of the above-mentioned entities and their communication tools introduces new difficulties, such as data privacy, vulnerabilities, data integrity, and scalability. To enhance data privacy and ensure security, a lightweight authenticated privacy-preserving secure framework is presented in the Internet of vehicles deployment. The proposed scheme can achieve confidentiality, privacy, authenticity, integrity, anonymity, and perfect forward secrecy based on blockchain technology, elliptic curve cryptography, and a one-way hash function. The proposed framework is secure against well-known cyberattacks, including replay attacks, man-in-the-middle attacks, traffic analysis attacks, and DDoS attacks. The security analysis shows the robustness of the proposed scheme. Finally, the results demonstrate that the proposed framework is efficient and feasible in practice.
Optimized Design for Reliability of Pointer Irrigation Machine Components for Intelligent Computing
Authors: Liu Chengwen , Liu Guoliang , Zhang Haitao
Published: September 2022

The application of intelligent technology has realized the transformation of people’s production and lifestyle, and also promoted the development and transformation of the agricultural field. At present, the application of agricultural intelligence is getting stronger and stronger; using its intelligent advanced methods and technologies, this paper aimed to achieve the optimization of sprinkler irrigation machine parts in the intelligent network environment to promote the rapid development of agriculture, and proposed the use of the NSGA-II algorithm in intelligent computing to guide the integration of artificial intelligence and pointer sprinkler parts, which helps to analyze and solve the objective problem of machine failure and parts damage in agriculture. In the study of the sprinkler gear system, from the perspective of gear efficiency, since it is optimized according to the minimum efficiency point of the fourth gear of the gear reducer, compared with the gear efficiency of 49.05% before this point, the efficiency of this point after optimization is 59.45%, and the minimum efficiency point will be increased by 21.2%. And because the energy loss unrelated to the power loss load will be greatly reduced, these energy losses have a greater relationship with the structure of the gearbox. In terms of each gear, compared with the previous period, the efficiency of the first gear was increased by 8.5% to 15.9%; the efficiency of the second gear increased by 8.7% to 17.4%; the efficiency of the third gear increased by 9.4% to 18.7%; and the efficiency of the fourth gear increased by 10.1% to 21.2%. Therefore, it is currently necessary to optimize the components of the sprinkler irrigation machine.
Research on Dual-Dimensional Entity Association-Based Question and Answering Technology for Smart Medicine
Authors: Zhai Pengjun , Fang Yu , Cui Xue
Published: September 2022

With the development of the Internet of Things, intelligent medical devices and intelligent consultation platforms have been rapidly popularized, providing great convenience for medical treatment to patients and consultation to doctors. In the face of large-scale medical electronic information data, how to automatically and accurately learn professional knowledge and realize application is very important. The existing intelligent medical question answering models typically use query expansion to improve the accuracy of model matching answers but ignore the corresponding entity association between questions and answers, and the method of randomly generating negative samples cannot train the model to capture more semantic information. To solve these problems, a question answering method based on dual-dimensional entity association for intelligent medicine is proposed. This method learns semantics from the dual-dimension of question and answer respectively. In the question dimension, query extension words with strong relevance to query intention are obtained through entity association in the medical knowledge graph. In the answer dimension, answer sentences are segmented and sampled by employing a variety of similarity distances to generate negative samples in different ranges, provide different levels of correlation information between entities for model training, and then integrate the trained model to improve the accuracy and robustness of the question answering model. The experimental results show that the question answering model proposed in this paper has a good improvement in accuracy.
Effect of Multidisciplinary Team Continuous Nursing on Glucose and Lipid Metabolism, Pregnancy Outcome, and Neonatal Immune Function in Gestational Diabetes Mellitus
Authors: Qi Shuping , Dong Yanmei
Published: September 2022

Objective. To investigate the effect of multidisciplinary team (MDT) continuous nursing on glucose and lipid metabolism, pregnancy outcome, and neonatal immune function in gestational diabetes mellitus (GDM). Methods. A total of 90 patients with gestational diabetes mellitus (GDM) from January 2018 to December 2019 were recruited and assigned to receive routine care (routine group) or MDT continuous care (study group) according to different nursing methods. Outcome measures included glucose and lipid metabolism, pregnancy outcomes, and neonatal immune function. Results. There were no significant differences in glucose and lipid metabolism indices and self-rating anxiety scale (SAS) scores, before nursing. After nursing, MDT continuous care resulted in significantly lower levels of fasting blood glucose (FBG), 2 h postprandial blood glucose (2hPBG), glycosylated hemoglobin (HbAlc), triglyceride (TG), and homeostasis model insulin resistance index (HOMA-IR) versus routine care. After nursing, the SAS scores in the two groups were significantly decreased, with lower results in the study group. Patients in the study group showed better compliance than those in the routine group. MDT continuous care was associated with a significantly lower incidence of premature rupture of fetal membranes, cesarean section, premature delivery, macrosomia, and hypoglycemia versus routine nursing. There were no significant differences in immunoglobulin (Ig) A and IgM levels. Patients in the study group showed a higher IgG level and lower CD3, CD4, CD8, and CD4/CD8 levels than those in the routine group. Conclusion. MDT continuous nursing could effectively regulate glucose and lipid metabolism and improve pregnancy outcomes and neonatal immune function in patients with GDM.