bluuebunny/crossref_metadata_2025 huggingface dataset:
Analysis of Adrenocorticotropic Hormone and Cortisol Levels in Acute Respiratory Distress Syndrome COVID-19 Patients
Authors: Marpaung Ferdy Royland , Mayasari Victoria , Sidjabat Hanna , Soehita Sidarti , Semedi Bambang Pujo , Parwati Ida , Wihastuti Titin Andri , Iskandar Agustin , Aryati
Published: September 2022

Objective. SARS-CoV-2 infection may cause multiple organ failure. However, scarce information can be found on the impact on the endocrine system. This study was conducted to determine plasma Adrenocorticotropic hormone (ACTH) and plasma cortisol levels in a cohort of COVID-19 patients with Acute Respiratory Distress Syndrome (ARDS). Methods. A prospective cohort study was conducted on COVID-19 patients who manifested ARDS and were admitted to the ICU of Dr. Soetomo Tertiary Hospital, Surabaya, Indonesia. Morning plasma ACTH and plasma total cortisol were measured on 45 recruited patients. The outcome of the patient was justified based on the survivance on days 7th and 30th during the follow-up with groupings of surviving for survived patients and nonsurvive for deceased patients. Results. The ACTH and cortisol median were 1.06 (0.5–64.57) pg/mL and 17.61 (0.78–75) μg/dL, respectively. Both parameters were assembled to allow the allocation of the 45 subjects into the survive and nonsurvive groups. There was a moderate correlation between ACTH and cortisol levels in all groups (

r
=
0.46

,

p
<
0.002

) and particularly ACTH and cortisol levels in COVID-19 patients who survived on the 7th-day and 30th-day follow-up (

r
=
0.518

and

r
=
0.568

, respectively, with

p
<
0.05

). It is important to note that there was no correlation for an individual parameter, either ACTH only or cortisol only, compared to the outcome among patients with various comorbid. Conclusion. ACTH or cortisol alone has no correlation to the outcome of these patients. Therefore, further study of the potential use of corticosteroid treatments guided by ACTH and cortisol levels in reducing the risk of ARDS warrants further investigation.
Application of Visual Recognition Based on BP Neural Network in Architectural Design Optimization
Authors: Liang Rui , Wang Po-Hsun , Hu Linhui
Published: September 2022

In order to establish the mapping relationship between architectural design parameters and building performance and optimize architectural design parameters, an architectural design optimization method based on BP neural network is proposed. The selected main design parameters of building ventilation include spacing coefficient, air outlet area, and height from the bottom of the window sill to the ground. Take the comprehensive performance of building ventilation design as the main optimization objective to optimize the building design. First, nine groups of samples of building optimization design are obtained through uniform experimental design. Then, based on the architectural design sample data obtained by BP neural network training, the mapping relationship between architectural design parameters and building performance is established, and based on this mapping, the optimal design parameters of the building are calculated. The research results have a certain reference value for architectural design optimization.
Evaluation Method of Vocal Music Teaching Quality for Music Majors Based on the Theory of Multiple Intelligences
Authors: Li Dongxia
Published: September 2022

The quality evaluation of vocal music teaching for music majors is of great significance to music education. Based on the theory of multiple intelligences, this paper constructs a model for evaluating the quality of vocal music teaching for music majors and introduces the theory of multiple intelligences into the operation form, design requirements, and recommended lesson examples of music teaching unit design. The experimental data and questionnaire data collected by the model verify that this operation is beneficial to the improvement of students’ music listening scores, vocal music comprehension scores, and total scores and solves the quantitative problem of vocal music teaching quality evaluation. In the simulation process, the engineering testing and analysis method uses the correlation rate and correlation strength as the analysis indicators, comparing the platforms for the quality evaluation of vocal music teaching in music majors and the corresponding quality evaluation data preprocessing process. The experimental results show that the algorithm performance evaluation is carried out based on the three aspects of quality evaluation association rules, algorithm running time, and algorithm memory consumption. The multiple intelligence algorithm is applied to vocal music teaching analysis of association rules for quality evaluation: when 4000 tasks call for 20–40 virtual resources, the total time spent is reduced by 61.7%, which has a significant positive effect on the knowledge expansion and ability improvement of music majors.
Effect of Forest Management on Carbon Stock of Tropical Moist Afromontane Forest
Authors: Daba Damena Edae , Dullo Bikila Workineh , Soromessa Teshome
Published: September 2022

Moist tropical forests have a significant role in provisioning and regulating ecosystem services. However, these forests are under threat of deforestation and forest degradation. In Ethiopia, the moist evergreen Afromontane forests have the potential for carbon storage and support a high diversity of plant species. However, it is under severe threat of deforestation and degradation.This investigation was conducted to obtain adequate information on the carbon stock potential of the moist Afromontane forest of southwestern Ethiopia. A comparison of carbon stock was conducted between disturbed and undisturbed forests. A systematic sampling design was applied for recording woody species and soil data. A total of 100 main plots of 400 m2 were laid to record trees and shrubs with a diameter at breast height (DBH) ≥ 5 cm. The soil data were collected from 1 m2 subplots established at the four corners and the center of each main plot. The DBH and height were measured to calculate the aboveground carbon of trees and shrubs with DBH ≥ 5 cm. A total of 68 tree and shrub species belonging to 59 genera and 33 families were recorded. The mean carbon stock density was 203.80 ± 12.38 t·ha–1 (aboveground carbon stock) and 40.76 ± 2.47 t·ha–1 (belowground carbon stock). The highest proportion of aboveground carbon (t·ha–1) (42.34%) was contributed by a few tree individuals with DBH > 70 cm. The soil organic carbon stock (SOCS) (t·ha–1) for the depth of 0–30 cm is ranging from 58.97 to 198.33 across plots; the mean is 117.16 ± 3.15. The carbon stored in the moist Afromontane forest indicates its huge potential for climate change mitigation. Therefore, for the enhancement of forest biodiversity and carbon sequestration effective conservation measure and sound management approach is essential.
Damage Evolution of Rocks Containing a Set of Cross-Joints Based on Acoustic Emission Characteristics
Authors: Li Xiao , Wang Xinquan , Wang Kangyu , Diao Hongguo , Zhang He
Published: September 2022

The problem of damage rupture of jointed rocks is a hot topic of research in underground tunnel engineering. In this paper, the damage evolution characteristics of rocks containing a set of cross-joints are investigated based on particle flow code (PFC). Firstly, the jointed rock models, such as rock models with different joint cross-angles and with different joint lengths, are established based on PFC. Then, the uniaxial compression mechanical properties and acoustic emission (AE) evolution characteristics of different jointed rocks are analyzed. Finally, the damage evolution characteristics of jointed rocks are discussed based on the damage variable defined by AE counts. The research results show that the uniaxial compressive strength (UCS) and elastic modulus (E) of the jointed rock are highest when the joint cross-angle β is 60°. The UCS and E of jointed rocks show a trend of decrease with the increase of joint length. The overall trend of the AE evolution of jointed rocks does not change with the change of joint cross-angle and joint length. However, the time step of the occurrence of acoustic emission (crack initiation time) has changed. The damage evolution of jointed rocks shows three stages with the increase of stress (time): damage-free stage, slow damage increase stage, and sharp damage increase stage. The fluctuation characteristics of the damage evolution of jointed rocks are different with the change of β and LB. The damage rupture of jointed rocks begins to sprout from and expand along the tip of the joints.
Visual Information Computing and Processing Model Based on Artificial Neural Network
Authors: Wang Junling , Liu Shuhan
Published: September 2022

This paper analyzes the parallel and serial information processing structure of visual system and proposes a visual information processing model with three layers: visual receptor layer, visual information conduction and relay layer, and information processing layer of visual information computing and processing area. Based on the analysis, abstraction, and simplification of the biological prototype of each layer in the visual system, a framework model of an artificial neural system corresponding to the visual system is proposed. An artificial neural network model is proposed to simulate the mechanism of visual attention. A network model is formed by introducing the saliency mask map as additional information on the benchmark network, and the selective enhancement operation is performed on the extracted features in different regions according to the mask map. The experimental results show that the visual computing processing network model can effectively improve the classification performance of the network when the appropriate saliency mask is used. The visual information computing and processing model network can work effectively for different data sets and different structures of the benchmark network, which is a universal network model. The complexity of visual information computing and processing model network is very small, and the improvement of network performance is not at the cost of increasing model complexity, but in the way of improving network efficiency. The performance of artificial neural network visual information computation and processing model is directly related to the performance of saliency map used as mask map.
Application of Big Data Technology to Promote Agricultural Structure Adjustment and High-Quality Development of Modern Agriculture
Authors: Ju Xiaoxian
Published: September 2022

The implementation of the strategy of rural revitalization is a major ministerial work made by the Nineteenth National Congress of the Communist Party of China and is the general grasp of contemporary agriculture, peasants, and rural work. In recent years, with the rapid development of remote sensing technology and deep learning technology, the demand for the technology for the classification of crops on satellite remote sensing images based on deep learning technology has increased in agricultural insurance and land survey. Therefore, this paper trains one, which is 85.9%–92.8%, the accuracy of corn classification is 77%–93%, and the accuracy of forest classification is 77%–87.6%. Subsequently, the overall accuracy of classifying all directories through the multi-temporal validation data set between May 2017 and October 2017 reached 92.6%. Such a multi-time combination method can be used for monthly, timely, and efficient iteration of agricultural insurance and crop yield estimation, which will be more accurate each time. These methods can also be further applied to the growth and change monitoring of large agricultural planting areas, adding bricks and tiles to China’s agricultural remote sensing. If the countryside is to be revitalized, agriculture must develop rapidly at the same time, industries must flourish, ecology must be livable, rural customs must be civilized, and life must be prosperous. Modern agriculture is a comprehensive circulation system with high yield, high quality, low consumption, ecology, environmental protection, and high efficiency. The development of modern agriculture is inseparable from the industrialization of agriculture, the globalization of agriculture, the digitization of agriculture, the integration of agriculture, the adjustment of agricultural structure, and agricultural innovation. Only the continuous development of modern agriculture can make rural revitalization enter a new journey.
Mathematical Model Design of the Traditional Dress Recognition Algorithm Based on Digital Watermarking Technology
Authors: Wang Yue , Li Wei , Zhang Yaojun
Published: September 2022

According to the characteristics of traditional clothing, clothing identification is studied, and clothing identification and clothing culture learning are effectively combined to find a new method for the inheritance of national culture and strive to make contributions to the inheritance of national culture. According to the requirement of the traditional garment identification watermark monitoring system, a self-synchronous digital watermarking algorithm is designed and implemented. Watermark is embedded in the time domain, and feature information is extracted from traditional clothing by means of mean filtering and replaced by watermark to achieve the purpose of embedding information. Blind detection is realized without the participation of the original image. The difference between the traditional costume embedded with watermark and the original traditional costume is almost imperceptible. It can effectively resist synchronous attacks including clipping and time shifting, showing good robustness. Imperceptibility and robustness can be adjusted freely by embedding strength. The HOG + SVM algorithm is applied to minority clothing classification and recognition. By comparing different classifiers, it is concluded that the classifier trained by the support vector machine algorithm has the best classification effect on ethnic clothing. In order to improve the classification effect, the classical algorithm of color, texture, and shape feature extraction was combined with SVM to conduct experiments on the clothing database collected and sorted out in Yunnan ethnic minority communities, and finally, we verified that the HOG feature combined with the SVM classification algorithm achieved good results in the classification of ethnic clothing.
The Influence of Data Length on the Performance of Artificial Intelligence Models in Predicting Air Pollution
Authors: AlOmar Mohamed Khalid , Khaleel Faidhalrahman , AlSaadi Abdulwahab Abdulrazaaq , Hameed Mohammed Majeed , AlSaadi Mohammed Abdulhakim , Al-Ansari Nadhir
Published: September 2022

Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues.
Solar Power Generation in Smart Cities Using an Integrated Machine Learning and Statistical Analysis Methods
Authors: Almadhor Ahmad , Mallikarjuna K. , Rahul R. , Chandra Shekara G. , Bhatia Rishu , Shishah Wesam , Mohanavel V. , Suresh Kumar S. , Thimothy Sojan Palukaran
Published: September 2022

Presently, photovoltaic systems are an essential part of the development of renewable energy. Due to the inherent dependence of solar energy production on climate variations, forecasting power production using weather data has a number of financial advantages, including dependable proactive power trading and operation planning. Megacity electricity generation is regarded as a current research problem in the modern features of urban administration, particularly in developing nations such as Iran. Machine learning could be used to identify renewable resources like transformational participation (TP) and photovoltaic (PV) technology; based on resident motivational strategies, the smart city concept offers a revolutionary suggestion for supplying power in a metropolitan region. The sustainable development agenda is introduced at the same time as this approach. Therefore, the article’s goals are to estimate Mashhad, Iran’s electrical power needs using machine learning technologies and to make innovative suggestions for motivating people to generate renewable energy based on the expertise of experts. The potential of solar power over the course of a year is then assessed in our research study in Mashhad, Iran, using the solar photovoltaic modelling tool. The present idea in this research uses linear regression techniques to forecast utilising artificial neural networks (ANN). The most important factor in sizing the installation of solar power producing units is the daily mean sun irradiation. The amount of power that will be produced by solar panels can be estimated using the mean sun irradiance at a particular spot. A precise prediction can also be used to determine the complexity of the system, return on investment (ROI), and system load metrics. Several regression techniques and solar irradiance-related metrics have been combined to forecast the mean sun irradiation in terms of kilowatt hours per square metre. Azimuth and zenith factors considerably enhance the performance of the model, as demonstrated by the proposed method. The results of this study demonstrate 99.9% reliability rate for ANN model prediction of the electrical power usage during the summer and winter seasons. Thus, the maximum of power requirement during the hottest and coolest periods can be managed by using the photovoltaic system’s renewable power projections.
Effect of Mirena Intrauterine Device on Endometrial Thickness, Quality of Life Score, and Curative Effect in Patients with Perimenopausal Abnormal Uterine Bleeding
Authors: Yu Ying , Zhou Zhi , Wang Liping , Liu Jie
Published: September 2022

Objective. To study the effect of Mirena intrauterine device (IUD) on endometrial thickness, life quality score, and curative effect in patients with perimenopausal abnormal uterine bleeding. Methods. Eighty patients with perimenopausal abnormal uterine bleeding cured from January 2020 to December 2021 were enrolled as the object of study. According to random number table, the patients were classified into the study (

n
=
40

) and control (

n
=
40

) groups. The control cases were cured with medroxyprogesterone. The study cases were cured with Mirena IUD. The effective rate of clinical therapies was evaluated after 3 months of treatment. The endometrial thickness, menstrual volume score, and life quality score (WHOQOL-BREF) was measured after 1 month, 2 months, and 3 months of treatment. Results. The effective rate of patients with Mirena IUD for 3 months was higher compared to the control group (

P
<
0.05

). The endometrial thickness and menstrual volume scores of study cohort after 1 month, 2 months, and 3 months following treatment were remarkably lower than those before treatment (

P
<
0.05

) and were considerably lower than those of control cohort (

P
<
0.05

). The hemoglobin level of the studied cases after 1 month, 2 months, and 3 months after therapy was remarkably upregulated (

P
<
0.05

) and was greatly higher compared to the controlled cases (

P
<
0.05

). After 3-month treatment, the WHOQOL-BREF score of the study group was higher compared to the control group (

P
<
0.05

). Conclusion. The Mirena IUD is far more effective in the treatment of perimenopausal abnormal uterine bleeding and is helpful in reducing the thickness of the endometrium. Patients’ menstrual flow can be controlled, and anemia can be corrected; thus, patients improve their quality of life and health status and can be considered for further promotion.
Vibrational Characteristics of Au‐Doped Si Nanowires: A Molecular Dynamics Study with a Modified Embedded Atom Method Potential Developed for Si‐H‐Au Systems
Authors: Liu Bin , Zhang Yun’an , Wang Xinfeng , Li Yanjiao , Yue Yingjuan
Published: September 2022

Silicon nanowires (SiNWs) are promising structures as resonators for applications in ultrasensitive force and mass transducers. In the present work, large‐scale molecular dynamics simulations were conducted to explore the effect of gold (Au), a commonly used catalyst, on the vibrational properties of [1 0 0] and [1 1 0] SiNWs. The force field was established using the modified embedded atom method (MEAM) formalism. The parameterization focused on the properties of the hydrogen‐covered SiNWs doped with Au, involving the formation energy as well as the structural and vibrational characteristics. The vibrational characteristics of a clamped‐clamped SiNW were examined through excitation using a sinusoidal velocity field after energy minimization and thermal equilibration. Different doping concentrations and distributions can significantly change the frequency response, quality factor Q, and beat phenomenon. The natural frequency f0 decreased with increasing Au concentration, whereas the effect of the impurity distribution on f0 was negligible. Furthermore, Q was sensitive to the Au concentration and distribution, and the increased concentration led to an overall increment in Q, accompanied by considerably increased scattering. Besides, the beat period of [1 1 0] SiNWs showed a strong positive correlation with the concentration. The vibration along the elementary axes cannot produce the beat phenomenon. Deflection of the elementary axes in a very small range would lead to a higher energy dissipation rate. In addition, owing to the considerable difference between the atomic masses of Au and Si, randomly distributed Au atoms significantly disturb the symmetry of the SiNWs. However, the elementary axes of the [1 1 0] SiNWs remained stable, even in the models with extremely radial or longitudinal impurity segregation. In contrast, we failed to capture the distinguishable elementary axes for most of the doped [1 0 0] samples. The features described above indicate that the impurity Au can effectively tune the resonant frequency of the [1 1 0] SiNWs, meanwhile, causes no large frequency shift that could be potentially caused by the deflection of the elementary coordinate system.
A Novel Concept of Fuzzy Subsemigroup (Ideal)
Authors: Che Yu-Hong , Liu Qi
Published: September 2022

This paper focuses on a generalized definition of fuzzy subsemigroup (ideal) on semigroup. Let L be a completely distributive lattice; we introduce the definition of L‐fuzzy ideal and also the novel concept of subsemigroup (ideal) on semigroup. Then, we discuss the necessary and sufficient conditions of L‐fuzzy subsemigroup (ideal) measure using the four level cuts of an L‐fuzzy set. Moreover, we study the properties of L‐fuzzy subsemigroup (ideal) measure. As an application of L‐fuzzy subsemigroup (ideal) measure, we obtain the L‐fuzzy convexities on a semigroup and bijective semigroup homomorphic mapping is an L‐fuzzy isomorphism.
Generalization of (Q,L)-Fuzzy Soft Subhemirings of a Hemiring
Authors: Geetha K. , Anitha N. , Noeiaghdam S. , Fernandez-Gamiz U. , Santra S.S. , Khedher K.M.
Published: September 2022

This paper investigates the properties and results of (Q,L)-fuzzy soft subhemirings ((Q,L)-FSSHR) of a hemiring R. The motivation behind this study is to utilize the concept of L-fuzzy soft set of a hemiring and to derive a few specific outcomes on (Q, L)-FSSHR. The concepts of strongest Q-fuzzy soft set relation, Q-isomorphism, pseudo-Q-fuzzy soft coset, and some of their related properties are implemented while analyzing the results. Finally, the properties are verified with a numerical example from the 2000 AMS subject classification: 05C38, 05A15, and 15A18.
Bioinformatic Analysis Identifies of Potential miRNA-mRNA Regulatory Networks Involved in the Pathogenesis of Lung Cancer
Authors: Hao Dexun , Li Yanshuang , Shi Jiang , Jiang Junguang
Published: September 2022

Objective. The purpose of the present study was to explore the biomarkers related to lung cancer based on the bioinformatics method, which might be new targets for lung cancer treatment. Methods. GSE17681 and GSE18842 were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed miRNAs (DEMs) and genes (DEGs) in lung cancer samples were screened via the GEO2R online tool. DEMs were submitted to the mirDIP website to predict target genes. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted via uploading DEGs to the DAVID database. The protein-protein interaction network (PPI) of the DEGs was analyzed by STRING’s online tool. Then, the PPI network was visualized using Cytoscape 3.8.0. Results. 46 DEMs were identified in GSE17681, and the website predicted that there were 873 target genes of these DEMs. 1029 DEGs were identified in the GSE18842 chip. GO analysis suggested that the co-DEGs participated in the canonical Wnt signaling pathway, regulation of the Wnt signaling pathway, a serine/threonine kinase signaling pathway, the Wnt signaling pathway, and cell-cell signaling by Wnt. KEGG analysis results showed the co-DEGs of GSE17681 and GSE18842 were related to the Hippo signaling pathway and adhesion molecules. In addition, six hub genes that were related to lung cancer were identified as hub genes, including mTOR, NF1, CHD7, ETS1, IL-6, and COL1A1. Conclusions. The present study identified six hub genes that were related to lung cancer, including mTOR, NF1, CHD7, ETS1, IL-6, and COL1A1, which might be a potential target for lung cancer.
A Novel Method Based on ERP and Brain Graph for the Simultaneous Assessment of Various Types of Attention
Authors: Jami Ali Esmaili , Khalilzadeh Mohammad Ali , Ghoshuni Majid , Khalilzadeh Mohammad Mahdi
Published: September 2022

Assessment of attention is of great importance as one of human cognitive abilities. Although neuropsychological tests have been developed and used to evaluate the ability to pay attention, their validity and reliability have been reduced due to some limitations such as the presence of intervention factors, including human factors, limited range of languages, and cultural influences. Therefore, direct outputs of the brain system, represented by event-related potentials (ERPs), and the analysis of its function in cognitive activities have become very important as a complementary tool to assess various types of attention. This research tries to assess 4 types of attention including sustained, alternative, selective, and divided, using an integrated visual-auditory test and brain signals simultaneously. Thus, the electroencephalogram (EEG) data were recorded using 19 channels, and the integrated visual and auditory (IVA-AE) test was simultaneously performed on twenty-eight healthy volunteers including 22 male and 6 female subjects with the average age of 27 ± 5.3 years. Then ERPs related to auditory and visual stimuli with synchronous averaging technique were extracted. A topographic brain mapping (topo-map) was plotted for each frame of stimulation. Next, an optical flow method was implemented on different topo-maps to obtain motion vectors from one map to another. After obtaining the overall brain graph of an individual, some features were extracted and used as measures of local and global connectivity. The extracted features were consequently evaluated along with the parameters of the IVA test by support vector machine regression (SVM-R). The volume of attention was then quantified by combining the IVA parameters. Ultimately, estimation accuracy of each type of attention including focused attention (86.1%), sustained attention (83.4%), selective attention (80.9%), and divided attention (79.9%) was obtained. According to the present study, there is a significant relationship between response control and attention indicators of the IVA test as well as ERP brain signals.
Research on Enterprise Data Management Strategy Analysis System Based on Knowledge Mining Model
Authors: Li Yongkang , Duan Boyan
Published: September 2022

The aim of this study is to solve the problem of low decision-making efficiency, this paper discusses the method of decision analysis based on business application data and it puts forward the enterprise data management system based on knowledge mining. The system uses the improved decision tree algorithm for data management, which optimizes the selection of the best threshold segmentation point in the process of discretizing the continuous value feature attributes. An improved decision tree algorithm is proposed and applied to enterprise data management. A method to optimize threshold segmentation points is proposed, which reduces the partition of threshold points and reduces the time complexity of the algorithm. The experimental results show that the operation efficiency and classification accuracy of the model on the abalone dataset are due to the traditional C4.5 algorithm, which can effectively deal with the analysis and management knowledge mining of large-scale enterprise operation data.
Effect of Vacuum Sealing Drainage on Soft Tissue Injury of Traumatic Fracture and Its Effect on Wound Recovery
Authors: Nie Pengfei , Zhang Canhong
Published: September 2022

Purpose. The current work is mainly to explore the effect of vacuum sealing drainage (VSD) on soft tissue injury (STI) caused by traumatic fractures (TFs) and its effect on wound recovery. Methods. We first selected 90 patients with TF STI from May 2019 to May 2021, of which 40 patients (control group) received routine treatment, and the other 50 patients (observation group) were treated with VSD. The curative effect, rehabilitation (changing dressing frequency, healing time, and hospitalization time), pain severity, patient comfort, and complications were evaluated and compared. Results. The observation group exhibited a higher total effective rate, lower dressing change frequency, complication rate, and shorter healing time and hospital stay than the control group, which are statistically significant. Statistically milder pain sensation and better patient comfort were also determined in the observation group. Conclusions. VSD is effective and safe in the treatment of TF-induced sexually transmitted infections, which can effectively accelerate wound recovery while reducing pain sensation and improving patient comfort, with clinical promotion value.
Analysis of Placement Priorities and Nursing Countermeasures of Transnasally Inserted Intestinal Obstruction Catheters in Patients with Acute Small Bowel Obstruction
Authors: Qian Xiaoli , Yan Wei
Published: September 2022

Objective. To explore the placement priorities and analysis of nursing countermeasures of transnasally inserted intestinal obstruction catheters in patients with acute small bowel obstruction (ASBO). Methods. One hundred and three patients with ASBO treated in our hospital from May 2016 to February 2022 were enrolled to this study. Patients who received individual nursing for transnasally inserted intestinal obstruction catheters were considered as the observation group (n = 59) and those who received traditional nursing were regarded as the control group (n = 44). The symptom relief time, daily gastrointestinal decompression, bowel sound recovery time, exhaust, defecation recovery time, and gas-liquid plane disappearance time were compared between both groups. The abdominal pain was evaluated by visual analogue scale (VAS), and the psychology of patients was evaluated by self-rating anxiety scale (SAS) and self-rating depression scale (SDS). Patients’ clinical efficacy and incidence of adverse effects were counted, and quality of life was assessed using the short form 36 (SF-36) health survey questionnaire. Results. In the observation group, the improvement time of clinical symptoms and VAS, SAS, and SDS scores after intubation were lower than those of the control group, while the total clinical treatment efficiency was higher (

P
<
0.05

). As to the adverse effects, the observation group was lower than the control group (

P
<
0.05

). Also, the SF-36 scores were higher than those of the control group in all dimensions (

P
<
0.05

). Conclusion. The individual nursing strategy implemented for the transnasally inserted intestinal obstruction catheter can effectively shorten the recovery of ASBO patients and improve their treatment outcome and prognosis quality of life.
Cross-Plane DDoS Attack Defense Architecture Based on Flow Table Features in SDN
Authors: Yue Meng , Yan Qingxin , Zheng Han , Wu Zhijun
Published: September 2022

Software-Defined Networking (SDN) actualizes the separation of control and forwarding and innovates network functionalities with a logically centralized controller. Contemporary SDN infrastructure exposes the potential bottlenecks which are prone to engage in distributed denial of service attack (DDoS) thus posing an ever-increasing threat. This paper adopts the idea of “cross-plane collaboration” accomplishing DDoS attack defense and incorporates a two-phase project deploying the lightweight detection mechanism in data layer and the fine-grained filtering model in control layer. The coadjutant detection mechanism introduces a novel three-dimensional entropy consisting of five flow table features performing real-time feature detection; the defense strategy schedules an attack classification algorithm based on neural network by means of extracting four flow rule features designed to locate compromised interfaces occupied by malicious traffic. Extensive experiments are implemented to demonstrate the method we proposed brings excellent superiority. The detection rate of the classification filtering model is 99.4%, and it is real-time, with a detection time of 0.51s. In addition, the method of cross-layer defense reduces the CPU utilization of the controller.
Mitigating Risks in the Disaster Management Cycle
Authors: Tay Huay Ling , Banomyong Ruth , Varadejsatitwong Paitoon , Julagasigorn Puthipong
Published: September 2022

Purpose. The disaster management cycle (DMC) is often considered part of the essential efforts to handle disaster risk and consists of four key phases, namely preparedness, response, recovery, and mitigation. The purpose of this conceptual article is two‐fold. The first is to identify dominant risks, and the second is to propose risk mitigation strategies for these four phases in the DMC. Design/Methodology/Approach. The study uses primary and secondary data to identify the dominant risks in each DMC phase. The primary data sources include responses from an online questionnaire and transcripts from three semi‐structured interviews with stakeholders in the humanitarian supply chain. The secondary data sources include practitioner reports and archival data triangulation. Findings. The findings reveal five dominant risk factors in the DMC and classify them within the DMC phases, which are (1) demand risk, (2) supply risk, (3) operational risk, (4) infrastructure risk, and (5) disruption risk. The severity and frequency of each risk vary in each DMC phase. We found that several supply chain strategies (SCSs), such as raising risk awareness and horizontal and vertical collaboration and coordination among the key stakeholders in the DMC, can be essential risk mitigation strategies that apply across the four DMC phases. Research Limitations/Implications. The study highlighted dominant risks and the appropriate SCSs for mitigating the risk factors within each DMC phase. These findings are encapsulated in a conceptual framework for guiding risk prioritisation, decision‐making, and policy‐making. Our study has several limitations. First, although we followed a systematic process in computing the risk scores based on the likelihood of occurrences and impacts, the scores are nonetheless considered subjective perceptions of the respondents. Second, the number of respondents was limited. Broader coverage of respondents across geographical regions will provide further insights into the perspectives on the relevant risk factors in the DMC phases. This leaves possibilities for future research, comparison with other risk computation methods, and evaluation. Originality/Value. This study is one of the few that collected multiple data to extend the knowledge of risk identification and mitigation within the DMC.
Analysis Methods for the Planning and Dissemination Mode of Radio and Television Assisted by Artificial Intelligence Technology
Authors: Jia Zhihua
Published: September 2022

In the context of the development of artificial intelligence (AI) technology, the planning and dissemination of radio and television must not only learn to quickly and deeply process a large amount of content and extract structured information such as classification, themes, tags, and styles from information sources, but also learn to use audience behaviors to explore and discover potential content, jump out of the editor’s subjective vision, and realize personalized content presentation. On the basis of summarizing and analyzing previous research results, this study expounded the research status and significance of the planning and dissemination mode of radio and television, elaborated on the development background, current status, and future challenges of AI technology, introduced the methods and principles of computer vision, natural language processing, semantic analysis, and video generation, discussed the content production, interview, and editing of radio and television, explored the review, archiving, and system solutions of radio and television, proposed the planning strategies for radio and television assisted by AI technology, analyzed the smart distribution, reception, and feedback of radio and television dissemination, discussed the interactive application and virtual reality of radio and television dissemination, analyzed the dissemination mode of radio and television assisted by AI technology, and finally carried out a case application and its analysis. The study results show that AI technology can be used for smart lyrics, smart writing, smart broadcasting, smart translation, smart recommendation, smart stripping, and smart retrieval, and so on. The AI-assisted radio and television planning gives audiences the right to choose news, provides them with more targeted news content by means of data capture and resource integration, and caters to audiences’ all-round and in-depth information needs through a question-and-answer method, innovating dissemination logic and expression approaches, and enhances the audience’s sense of experience and leadership in obtaining information. The AI-assisted radio and television dissemination mode has the advantages of improving the effectiveness of information communication, promoting the optimization and integration of news resources, and enhancing audience stickiness.
Estimation of Groundwater Quality in Arid Region or Semiarid Region by Using Statistical Methods and Geographical Information System Technique
Authors: Alothman Ahmed A. S. , Ahmed Mohamed Saad , Radjarejesri S. , Ramkumar G. , Prasad Ram , Lakshmi Panuganti , Sillanpaa Mika , Thanappan Subash
Published: September 2022

This study takes place in year of 2021 during the premonsoon and postmonsoon seasons and twenty water samples were collected. Chemical factors like chloride, fluoride, sulphate, nitrate, and phosphate were measured in water samples. There is a significant difference in anion dominance between pre- and postmonsoonal (PRM and POM) water samples. The following anions are

HC

O

3

>
TDS
>
C

l

−

>
S

O

4

2
−

>
TH
>
N

O

3

>

F

−

>
P

O

4

and

TDS
>
HC

O

3

−

>
S

O

4

2
−

>
C

l

−

>
TH
>
N

O

3

−

>

F

−

>
P

O

4

−

. In both seasons, particular way of cations is

Na

+

>

Ca

2
+

>

Mg

2
+

>

K

+

. SAR, Kelly’s ratio, residual sodium carbonate, potential salinity, permeability index, and sodium percentage were used to evaluate the irrigation water quality. To investigate the geochemical regulation and hydrogeochemistry of groundwater, two approaches were used: according to the findings, groundwater in various parts of the investigation was determined to be unfit for human consumption.
Anomaly Detection in QAR Data Using VAE-LSTM with Multihead Self-Attention Mechanism
Authors: Rong Chuitian , OuYang Shuxin , Sun Huabo
Published: September 2022

With the rapid development of the aviation industry, it is particularly important to ensure the safe flight of aircraft. How to find potential hazards in the process of aircraft flight has always been one of the important topics of civil aviation research. At present, the Quick Access Recorder (QAR) is the most widely used equipment to store the data recorded on aircraft. QAR data contain a lot of valuable and unexplored information, which records the true status of the aircraft in detail. Therefore, finding abnormal data from QAR data lays an important foundation for obtaining the cause of abnormality and providing a guarantee for flight. In this paper, in order to discover the abnormal information in the QAR data, we applied a VAE-LSTM model with a multihead self-attention mechanism. Compared to the VAE and LSTM models alone, our model performs much better in anomaly detection and prediction, detecting all types of anomalies. We conducted extensive experiments on real-world QAR data sets to prove the efficiency and accuracy of our proposed neural network model. The experimental results proved that our proposed model can outperform state-of-the-art models under different experimental settings.
Deep Learning of Subject Context in Ideological and Political Class Based on Recursive Neural Network
Authors: Jiang Tingting , Gao Xiang
Published: September 2022

Ideological and political education is the most important way to cultivate students’ humanistic qualities, which can directly determine the development of other qualities. However, at present, the direction of ideological and political innovation in higher vocational colleges is vague. In response to this problem, this study proposes a model based on HS-EEMD-RNN. First, the ensemble empirical mode decomposition (EEMD) method is used to decompose the measured values, and then the recurrent neural network (RNN) is used to train each component and the remaining items. Finally, through the mapping relationship obtained by the model, the response prediction value of each component and the remaining items can be obtained. In the RNN training process, the harmony search (HS) algorithm is introduced to optimize it, and the noise is systematically denoised. Perturbation is used to obtain the optimal solution, thereby optimizing the weight and threshold of the RNN and improving the robustness of the model. The study found that, compared with EEMD-RNN, HS-EEMD-RNN has a better effect, because HS can effectively improve the training and fitting accuracy. The fitting accuracy of the HS-EEMD-RNN model after HS optimization is 0.9918. From this conclusion, the fitting accuracy of the HS-EEMD-RNN model is significantly higher than that of the EEMD-RNN model. In addition, four factors, career development, curriculum construction, community activities, and government support, have obvious influences on ideological and political classrooms in technical colleges. The use of recurrent neural networks in the research direction of deep and innovative research on the subject context of ideological and political classrooms can significantly improve the prediction accuracy of its development direction.
Determination of Scopolamine Distribution in Plasma and Brain by LC-MS/MS in Rats
Authors: Chen Juan , Lu Anjing , Tan Daopeng , Zhang Qianru , Lu Yanliu , Qin Lin , He Yuqi
Published: September 2022

Scopolamine, as a tropane alkaloid found in plants such as belladonna and datura, is used clinically as a transdermal patch and is highly neurotoxic. This study aimed to develop a simple, sensitive, and selective LC-MS/MS method for the determination of the content and distribution of scopolamine in rat plasma and brain after drug administration. In our study, sample pretreatment consisted of protein precipitation with acetonitrile followed by nitrogen blow concentration. Gradient elution of scopolamine and internal standard was performed on a ZORBAX Eclipse Plus C18 (2.1

∗

100 mm, 3.5 μm) column with water containing 0.1% formic acid (v/v) and acetonitrile as a mobile phase. Those samples were quantified in ESI positive ion mode using an API 4000 triple quadrupole mass spectrometer. The results showed that scopolamine was linear in the calibration range of 2–2500 ng/mL, and the selectivity, accuracy, precision, matrix effect, stability, and recovery of the method were within acceptable limits. The method has been validated and has been successfully used for toxicokinetic studies of scopolamine. After intraperitoneal injection, the time to peak toxic concentrations of scopolamine in rats was 0.5 h. The concentrations of scopolamine in the hippocampus and cortex were much higher than those in the striatum, indicating that the likely targets of its neurotoxic damage were the hippocampus and cortex. Overall, this study provides the basis for the neurotoxicity of scopolamine and provides a reference for its toxicokinetic studies.
Study of the Reform of College Mathematics Blended Teaching Supported by Intelligent Technology
Authors: Han Xiaofeng
Published: September 2022

The rapid development of artificial intelligence, “Internet +,” big data, 5G, and other technologies in the twenty-first century has not only brought great changes to the field of education but also brought unprecedented opportunities and challenges to the reform and innovation of mathematics teaching. The use of intelligent technology to carry out the reform of college mathematics teaching in depth and effectively improve the quality of teaching has become a hot topic discussed by the majority of mathematics teachers. This paper carefully analyzes and sorts out the problems existing in the current intelligent college mathematics teaching and systematically studies the related theories and design principles of the blended teaching mode. The ideas and approaches of the reform of college mathematics blended teaching supported by intelligent technology are deeply discussed from the aspects of improving the level of teachers, setting up a blended teaching team supported by intelligent technology, optimizing the informatization construction of teaching environment and establishing rich teaching resources, building an online and offline classroom teaching system, personalized learning under the background of microclass and cloud class, cross-university blended learning in the network environment, and mathematics precision teaching evaluation and optimization using big data.