bluuebunny/crossref_metadata_2025 huggingface dataset:
On Radical of Intuitionistic Fuzzy Primary Submodule
Authors: Taherpour Abbas , Ghalandarzadeh Shaban , Rad Parastoo Malakooti , Safari Parvin
Published: August 2022

In this paper, we further study the theory of Intuitionistic fuzzy submodules and we will define intuitionistic fuzzy primary submodule with the help of the definition of a radical submodule, and we also study the properties of these submodules. Furthermore, homomorphic image and preimage of intuitionistic fuzzy primary submodule are investigated. This manuscript is online on arXiv by the link https://arxiv.org/abs/2207.09772.
Design Innovation of Ethnic Cultural Wisdom Tourism Products Based on Digital Information Technology
Authors: Zhou Yi , Chen Shaohan
Published: August 2022

With the improvement of people’s living standards, ethnic tourism has begun to develop in an all-round way, and correspondingly, ethnic cultural products have become more and more dazzling. However, the current tourists’ satisfaction with ethnic cultural tourism products is generally low, so the innovative design of tourism products is an important means to improve the satisfaction of tourism products. The purpose of this paper was to use digital information technology to study the innovation of national cultural wisdom tourism product design and to optimize tourism products. This paper first expounded the experiential design of ethnic cultural tourism products which applies the TEAR theory, and the ASEB grid analysis introduced the four factors of theme, environment, activity, and reaction, analyzed the experience of tourism from the perspective of tourists, then introduced the extraction and development process of digital information technology, and then built a three-dimensional digital model of ethnic cultural tourism product design to lay a solid foundation for product design. In addition, this paper took the development of ethnic cultural tourism products in Yunnan ethnic villages as a case to investigate and analyze the tourist market and tourist needs and finally put forward some product design ideas and suggestions. Through the research and analysis of tourists, it is found that there are more young and middle-aged tourists in Yunnan ethnic villages, and most of them travel with their families. According to the survey of tourists’ preferences, more than 80% of tourists like high-quality and low-cost tourism products, and nearly 50% of tourists want to gain a thinking experience in tourist attractions. The most popular forms of tourists were ethnic art and traditional culture. Therefore, designers can design tourism products according to the preferences of tourists and improve the satisfaction of tourism products.
Application of Carbon Fiber Cement-Based Composites in Improving Construction Durability
Authors: Xu Xiaoming
Published: August 2022

In order to improve the durability of building construction, this study proposes a construction method based on carbon fiber and cement-based composites. Analyze the mechanical properties of carbon fiber cement-based composites, combined with experiments to further analyze its advantages in building construction, to achieve the purpose of building durability construction. The experimental results show that the elastic modulus of CFRC samples with a 0.6% carbon fiber volume percentage is the largest. When the content of carbon fiber increases within a certain range, the elastic modulus of CFRC samples will also increase. Conclusion. The application of carbon fiber cement-based composites in building construction can effectively improve the durability of buildings and further improve the quality of buildings.
Optimal Decisions in the Closed‐Loop Supply Chain considering Capacity Constraints and Stochastic Demand under the Cap‐and‐Trade Regulation
Authors: Wang Jian , Shao Wenxuan
Published: August 2022

In the current context of resource scarcity and increased pollution, enterprises need to better allocate capacity and achieve carbon emission reduction. We examine the optimal decision problem for a closed‐loop supply chain (CLSC) considering capacity constraints and stochastic demand under a cap‐and‐trade system. A two‐level CLSC comprising a supplier and a manufacturer is established. To make the model more realistic, we treat demand as a random variable, consider the manufacturer’s production capacity within a certain upper limit, and allow the manufacturer to make investments to expand capacity to complete production demand. Based on this CLSC, the collection activities of the supplier and manufacturer are considered. Meanwhile, government carbon limits and carbon trading controls on the manufacturer are considered based on carbon reduction by enterprises. Through modeling and analysis, optimal decisions are derived for the amount of capacity investment, level of carbon emission reduction, and collection price variables. The results indicate that supplier collection has a positive effect on the sustainability of the CLSC. Moreover, the government’s carbon quota and carbon trading control of manufacturers can effectively reduce the carbon emissions of the CLSC, encourage enterprises to reduce carbon emissions, and improve the sustainability of the CLSC.
Impact of Silicon Carbide Particles Weight Percentage on the Microstructure, Mechanical Behaviour, and Fractography of Al2014 Alloy Composites
Authors: Vasanth Kumar H. S. , Revanna K. , Kumar Nithin , Sathyanarayana N. , Madeva N. , Manjunath G. A. , Adisu H.
Published: August 2022

The microstructure, mechanical characteristics, and tensile fractography of Al2014 alloy reinforced composites with 5 and 10% wt. % of 150 to 160 micron-sized SiC particles were investigated in this study. The melt stir process was used to make composites comprising 5 and 10% SiC particles in the Al2014 alloy. To increase the wettability between Al matrix SiC particles, SiC particles were warmed to 300 degrees Celsius before being disseminated into the Al2014 alloy matrix in two phases. SEM, EDS, and XRD were used to examine the specimens micro-structurally. ASTM standards were used to evaluate the mechanical characteristics of micro SiC particles reinforced Al alloy composites with a content of 5 to 10%. SEM and EDS microstructural tests revealed the existence and dissemination of micro SiC particles in the Al2014 matrix. The addition of 5 and 10% weight % of micro SiC particles to Al2014 alloy improved its hardness, ultimate, yield, and compression strength. With the addition of 10 wt. % of SiC, the hardness, ultimate strength, yield strength, and compression strength of Al2014 alloy improved to 15.96%, 35.2%, 43.8%, and 38.7%, respectively. Furthermore, the presence of SiC particles reduced the ductility of Al2014 alloy. SEM microphotographs were used to examine the manufactured Al2014 with SiC composites for tensile fracture analysis, revealing the various mechanisms intricate in tensile fracture.
Characterization of Natural Precious Opal Using Modern Spectroscopic Techniques in Ethiopia: The Case from Delanta, South Wollo
Authors: Ejigu Assamen Ayalew , Ketemu Desalegn Gezahegn , Endalew Sisay Awoke , Assen Wudu Yimer
Published: August 2022

The purpose of this study was to conduct characterization of the Delanta natural opals obtained from different mining sites. Characteristics of opals mined from different geological locality were examined. Six opal samples were collected from the main mining sites found in different localities from Delanta district and characterized using Fourier infrared spectroscopy, scanning electron microscopy, X-ray diffraction, and inductively coupled plasma optical emission spectroscopy characterization techniques. The scanning electron microscope micrographs showed that all the samples have relatively semi-ordered (semi-crystalline) structural surface morphologies composed of tiny spherical particles. The performed X-ray diffraction analysis showed that the samples have main peaks at 20.0°, 21.72°, 36.9°, 44.0°, and 56.85° with 2

θ

values between 10° and 60°. The five diffraction peaks are characteristic peak positions of opal-CT. The Fourier infrared spectroscopy data indicated that the prominent absorption bands for cristobalite at 620 and 520 cm−1 were absent from the spectrum. Instead, three bands in the low frequency region centered at 470, 791, and 1100 cm−1 were observed because of the absorptions of the silicate framework (Si-O) vibrations. red−1−1 Moreover,spectra feature of molecular water and silanol (Si-OH) groups with broadabsorption band centered around 3447 cm-1 andthe O-H stretching vibration of watermolecules and water bending vibration withan absorption band centered around 1634 cm-1were observed.. These values were consistently comparable with literature data. The inductively coupled plasma optical emission spectroscopy analysis showed that four main impurities (>500 parts per million) were Ca (∼1,750–4,730 parts per million), Al (∼1,990–4,319  parts per million, K (∼1,670–3,895 parts per million), and Na (∼595–3,723 parts per million). In general, this study revealed that all opal samples taken from six mining sites in Delanta district were found to be opal-CT as per the results of X-ray diffraction and Fourier infrared spectroscopy investigations.
Synchronous Behavior of Coupled Oscillatory Network with Different Interconnection Patterns
Authors: Gao Zhongshe , Liang Xuefeng , Yang Lixin
Published: August 2022

This paper concerns with the synchronous behavior of coupled oscillatory networks with positive and negative interconnections. Furthermore, the coupling strength is related to the frequencies of oscillators, and we study the dynamical behavior of power networks with weight coefficient between oscillators. Most importantly, time delay is inevitable in the propagation power through the transmission line. Thus, the impact of time delay on synchronous state of oscillatory network is discussed. Simulation results show that the impact of positive interconnection is not obvious on the synchronous state of power network. Nevertheless, for negative coupling, large coupling strength between oscillators decreases the synchronous performance. In addition, we find that when the weight coefficient is even, the synchronous ability of network increases with the increase in weight coefficient whatever the coupling strategy and topological structure be. However, when weight parameter is odd, the entire network with negative coupling cannot achieve synchronization. Finally, our results show that time delay has a critical value τ = 1, and the evolutions of order parameters are different under different values of time delay.
Prevalence and Management Recommendations for Disease-Related Malnutrition in Chronic Kidney Disease Patients with and without Diabetes
Authors: Dai Li-Li , Li Wei-Li , Zheng Ding-Feng , Wang Wei-Hong , Xie Hao-Fen , Ma Jian-Wei
Published: August 2022

Objective. To investigate the nutritional risk, malnutrition, severe malnutrition, and malnutrition prevalence of different stages in chronic kidney disease (CKD) patients with and without diabetes mellitus using the Global Leadership Initiative on Malnutrition (GLIM), and to analyze the causes of malnutrition and to improve the clinical outcomes of patients for early intervention. Methods. A total of 683 patients with CKD who were hospitalized in our hospital from January 2020 to January 2021 were enrolled and divided into subgroups 1 to 5 according to whether they were complicated with diabetes and glomerular filtration rate. Using the second step of the malnutrition (GLIM) diagnostic tool and 2 previously commonly used malnutrition assessment methods (body mass index <18.5 kg/m2 with poor general condition, 3 points for nutritional deficiency in nutritional risk screening), combined with clinical research on the main causes of malnutrition, the intervention measures were discussed. Results. The prevalence of malnutrition was 16.7% (114/683) in the patients included in the survey using the diagnostic criteria of malnutrition (GLIM) (excluding whole body muscle mass index). The prevalence of malnutrition in CKD patients with and without diabetes was 23.7% and 12.6%, respectively. The overall prevalence rate of severe malnutrition was 14.2%, and the prevalence rates of those with and without diabetes were 19.0% and 11.4%, respectively; the results of the two methods of malnutrition assessment showed that the prevalence of malnutrition in CKD patients with diabetes was higher than that in the uncombined group. There was no severe malnutrition in patients with CKD stages 1 and 2. From CKD stage 3 onwards, the severe malnutrition in the diabetic group was significantly higher than that in the uncombined group. Conclusion. With the progression of CKD, the incidence of malnutrition also gradually increased, indicating that malnutrition is related to primary diseases and concomitant diseases. Attention should be paid to the malnutrition of CKD patients with diabetes, and clinical medical staff need to pay early attention to various diseases that lead to the progression of CKD, such as diabetes, primary nephropathy, and other factors, to prevent complications and delay the progression of CKD.
Correlation Study between Levels of Gastrin, Serum IGF-1, and GHBP and Growth and Development in Children with Short Stature Based on Big Data Analysis
Authors: Hua Chen , Yu Dan
Published: August 2022

Objective. To analyze the correlation between the levels of gastrin, serum IGF-1, and GHBP and growth and development in children with short stature (SS) using the big data. Methods. By means of retrospective analysis, the clinical data of 42 children with SS admitted to our hospital from October 2020 to October 2021 were selected as the study group, while 30 children with the healthy physical examination results in the corresponding period were selected as the control group to measure the growth and development indices and the levels of gastrin, serum IGF-1, and GHBP. The Pearson correlation analysis was used for the relationship between the levels of gastrin, serum IGF-1, and GHBP and growth and development indices in children with SS, and the targeted intervention measures were formulated by the analysis of experimental data. Results. Compared with the study group, the height, weight, and bone mineral density (BMD)

Z

-scores of children in the control group were obviously higher (

P
<
0.001

). The levels of gastrin, serum IGF-1, and GHBP in the study group were markedly lower than those in the control group (

P
<
0.05

). The Pearson correlation analysis showed that the gastrin, serum IGF-1, and GHBP of children were positively correlated with growth and development indices (

P
<
0.001

). The levels of gastrin, serum IGF-1, and GHBP in children were distinctly improved after treatment (

P
<
0.05

). Conclusion. The gastrin, serum IGF-1, and GHBP are closely related to the SS, and the effective clinical intervention can better improve the above indicators of children to promote their growth and development.
Comprehensive Analysis of N6-Methyladenosine RNA Methylation Regulators in the Diagnosis and Subtype Classification of Acute Myocardial Infarction
Authors: Wang Xianpei , Wu Ying , Guo Ruoyao , Zhao Linwei , Yan Juanjuan , Gao Chuanyu
Published: August 2022

Acute myocardial infarction (AMI) is still a huge danger to human health. Sensitive markers are necessary for the prediction of the risk of AMI and would be beneficial for managing the incidence rate. N6-methyladenosine (m6A) RNA methylation regulators have been confirmed to be involved in the development of various diseases. However, their function in AMI has not been fully elucidated. The purpose of this study was to determine the expression of m6A RNA methylation regulators in AMI as well as their possible functions and prognostic values. The GEO database was used to get the gene expression profiles of patients with and without AMI, and bioinformatics assays of genes with differently expressed expression were performed. We establish two separate m6A subtypes, and relationships between subtypes and immunity were studied. In this study, we identified IGF2BP1, FTO, RBM15, METTL3, YTHDC2, FMR1, and HNRNPA2B1 as the seven major m6A regulators. A nomogram model was developed and confirmed. The consensus clustering algorithm was conducted to categorize AMI patients into two m6A subtypes from the identified m6A regulators. Patients who have activated T-cell activities were found to be in clusterA; they may have a better prognosis as a result. Importantly, we found that patients with high METTL3 expressions had an increased level of Activated.CD4.T.cell and Type.2.T.helper.cell, while having a decreased level of CD56bright.natural.killer.cell, Macrophage, Monocyte, Natural.killer.cell, and Type.17.T.helper.cell. Overall, a diagnostic model of AMI was established based on the genes of IGF2BP1, FTO, RBM15, METTL3, YTHDC2, FMR1, and HNRNPA2B1. Our investigation of m6A subtypes may prove useful in the developments of therapy approaches for AMI.
Automated Detection and Characterization of Colon Cancer with Deep Convolutional Neural Networks
Authors: Hasan Md Imran , Ali Md Shahin , Rahman Md Habibur , Islam Md Khairul
Published: August 2022

Colon cancer is a momentous reason for illness and death in people. The conclusive diagnosis of colon cancer is made through histological examination. Convolutional neural networks are being used to analyze colon cancer via digital image processing with the introduction of whole-slide imaging. Accurate categorization of colon cancers is necessary for capable analysis. Our objective is to promote a system for detecting and classifying colon adenocarcinomas by applying a deep convolutional neural network (DCNN) model with some preprocessing techniques on digital histopathology images. It is a leading cause of cancer-related death, despite the fact that both traditional and modern methods are capable of comparing images that may encompass cancer regions of various sorts after looking at a significant number of colon cancer images. The fundamental problem for colon histopathologists is differentiating benign from malignant illnesses to having some complicated factors. A cancer diagnosis can be automated through artificial intelligence (AI), enabling us to appraise more patients in less time and at a decreased cost. Modern deep learning (MDL) and digital image processing (DIP) approaches are used to accomplish this. The results indicate that the proposed structure can accurately analyze cancer tissues to a maximum of 99.80%. By implementing this approach, medical practitioners will establish an automated and reliable system for detecting various forms of colon cancer. Moreover, CAD systems will be built in the near future to extract numerous aspects from colonoscopic images for use as a preprocessing module for colon cancer diagnosis.
Bioactivity of Exosomes Derived from Trained Natural Killer Cells versus Non‐Trained One: More Functional and Antitumor Activity
Authors: Mohammadi Fateme , Hashemi Zahra Sadat , Forooshani Ramin Sarrami , Alizadeh Shaban
Published: August 2022

Background. Natural killer (NK) cells are cytotoxic lymphocytes of the innate immune system, capable of killing viral‐infected and cancerous cells. NK cell‐mediated immunotherapy has remarkably changed the current paradigm of cancer treatment in recent years. It emerged as a safe and effective therapeutic approach for patients with advanced‐stage leukemia. Several immune‐escape mechanisms can be enacted by cancer cells to avoid NK‐mediated killing. Exosomes released by NK cells that carry proteins and miRNAs can exert an antitumor effect. In the present study, we hypothesized that maybe exosomes derived from trained natural killer cells show more antitumor effect in comparison to non‐trained one. Methods. PBMC was separated by the Ficoll method and cultured with IL‐2 for 21 days to expand NK cells. The NK cells were co‐cultured with K562 for 72 hours and exosome‐derived co‐cultured (as trained) and natural killer cell‐derived exosomes (as non‐trained) were extracted by Exo kit. The exosomes were confirmed by dynamic light scattering (DLS), transmission electron microscopy (TEM), flow cytometry, and western blotting. The K562 cells were separately treated by trained and non‐trained exosomes and MTT assay, apoptosis, and real‐time PCR were performed. Results. Based on flow cytometry, CD56 marker was 89.7% and 40.1% for NK cells and NK‐derived exosomes, respectively. CD63 and CD9 were positive for exosomes by western blotting. The morphology of exosome was confirmed by TEM. Treated K562 cells by trained exosomes indicated the diminished cell viability and higher apoptosis. Furthermore, the trained exosomes showed up‐regulation in both P53 and caspase3 genes as compared with non‐trained sample. Discussion. Trained Exos showed a potent inhibitory effect on proliferation and induced apoptosis on K562 cell lines compared to the same dose of non‐trained Exos. According to the results of qRT‐PCR, trained Exos exerted an antitumor activity through up‐regulation of caspase 3 and P53 in the apoptotic signaling pathway in tumor cells. Our findings indicate an effective action of trained Exos against cancer cells.
Grouping-Based Reliable Privacy Preservation for Blockchain-Assisted Data Aggregation in Mobile Crowdsensing
Authors: Li Yajie , Wang Guanghui , Yang Haochen , Zuo Fang , Yu Junyang , He Xin
Published: August 2022

Privacy-preserving data aggregation is an important technology for mobile crowdsensing. Blockchain-assisted data aggregation enables the traceability of sensing data to improve the trustworthiness of data aggregation results. However, directly using blockchains for data aggregation may introduce the risk of privacy leakage because all nodes, including malicious nodes, can access the data on blockchains. In this paper, we propose a grouping-based reliable privacy-preserving data aggregation (RPPDA) method using private blockchains for mobile crowdsensing. First, the sensing nodes are divided into multiple groups, and each group maintains a private blockchain to store the data aggregation records, which avoids the leakage of the aggregated results and ensures the traceability of the sensory data. Then, a zero-sum noise-adding mechanism is utilized to not only preserve the private information during aggregation and ensure the correctness of the aggregated results but also improve the efficiency of privacy preservation. Furthermore, we theoretically prove the correctness, privacy, efficiency, and reliability of the proposed RPPDA algorithm. Real-world and simulated experiments demonstrate the effectiveness and advantages of the proposed RPPDA algorithm in terms of correctness, efficiency, and privacy.
Enhancement of Local Crowd Location and Count: Multiscale Counting Guided by Head RGB-Mask
Authors: Ren Guoyin , Lu Xiaoqi , Wang Jingyu , Li Yuhao
Published: August 2022

Background. In crowded crowd images, traditional detection models often have the problems of inaccurate multiscale target count and low recall rate. Methods. In order to solve the above two problems, this paper proposes an MLP-CNN model, which combined with FPN feature pyramid can fuse the feature map of low-resolution and high-resolution semantic information with less computation and can effectively solve the problem of inaccurate head count of multiscale people. MLP-CNN “mid-term” fusion model can effectively fuse the features of RGB head image and RGB-Mask image. With the help of head RGB-Mask annotation and adaptive Gaussian kernel regression, the enhanced density map can be generated, which can effectively solve the problem of low recall of head detection. Results. MLP-CNN model was applied in ShanghaiTech and UCF_ CC_ 50 and UCF-QNRF. The test results show that the error of the method proposed in this paper has been significantly improved, and the recall rate can reach 79.91%. Conclusion. MLP-CNN model not only improves the accuracy of population counting in density map regression, but also improves the detection rate of multiscale population head targets.
Early Warning and Management Method of Abnormal Performance of Tourist Scenic Spots Assisted by Image Recognition Technology
Authors: Song Zhaozhen , Lu Jing
Published: August 2022

Scenic area is a product of the improvement of living standards and the improvement of economic level. Many types of scenic spots have been developed in most areas. The development of scenic spots will affect the economic development level of a region, and it will also affect the living standards of local residents because the construction of scenic spots will consume a lot of financial and human resources. If the scenic area can be managed well, it will bring greater economic benefits to the local area. However, if the scenic area fails to operate, it can affect the local finances. This requires local managers to be able to grasp the development of the scenic area, which will avoid abnormal performance. However, the performance management of scenic spots is more difficult for local managers, and more cumbersome data will be involved here. This study uses the convolutional neural network (CNN) method to realize the image recognition technology of the characteristics of the scenic area’s flow of people and tourists’ preferences, and these characteristics will be displayed to the managers in the form of images. In this study, the collaborative filtering algorithm can be used to complete the active recommendation of abnormal performance of tourist scenic spots. This also enables CNN to achieve collaborative monitoring. The research results show that image recognition technology can better assist managers to manage the abnormal performance of scenic spots. CNN also has good accuracy in predicting related features such as the flow of people in scenic spots. The similarity index of the three features exceeds 0.9. This has achieved a high accuracy for anomaly detection of tourist attractions. The largest similarity index has reached 0.963.
A Proposal to Employ Artificial Intelligence Applications in Developing Prince Sattam Bin Abdulaziz University Students’ Future Skills
Authors: Faraj Alyaa Omar Kamel
Published: August 2022

This study aimed to identify the requirements for employing artificial intelligence applications, determine the future skills to be acquired by Prince Sattam Bin Abdulaziz University (PSAU) students, and develop a proposal for the requirements of employing artificial intelligence applications in the development of students’ future skills. To achieve this aim, the author used the descriptive method where an author-developed 50-item questionnaire was administered to 150 faculty members. The participating faculty members rated the extent to which the requirements were present and the level of students’ mastery of future skills. The results revealed the significance of integrating artificial intelligence in university education to accelerate progression towards the achievement of the fourth aim of sustainable development and to activate the substantial role of universities in developing students’ future skills. The study identified the requirements for employing artificial intelligence applications in developing students’ future skills. The identified requirements fell into five areas: the learning environment, faculty members, courses, students, and graduates. The future skills that were identified included soft skills, lifelong learning skills, and digital skills. The study ended with a proposal to employ artificial intelligence applications in developing the future skills of PSAU students.
Emergency Presentations of Meckel’s Diverticulum in Adults
Authors: Mohammed Ayad Ahmad , Rasheed Mohammed Mohammed
Published: August 2022

Introduction. Meckel’s diverticulum is the commonest congenital anomaly of the gastrointestinal tract in humans that is commonly encountered during surgical practice as the cause of the patient’s presentation or as an incidental finding during other unrelated procedures. Most clinical symptoms are caused due to its complications. Results. The mean age of the involved patients was 24.79 years with slight male predominance, 62.9% males compared to 37.1% females. The mean length of the diverticulum was 55.21 cm. The most common emergency presentation was right lower quadrant abdominal pain in 31% of the patients, intestinal obstruction in 28.6%, acute lower abdominal pain and guarding and acute abdomen in 18.6% and 15.7% of patients, respectively, bleeding per rectum in 2.9%, acute right upper quadrant abdominal pain in 1.4%, and obstructed paraumbilical hernia containing the diverticulum in one patient. Perforation of the Meckel’s diverticulum was reported in 18.6%. Histopathological examination showed acute inflammation in the wall of the diverticulum in 37.1%, lymphoid hyperplasia in 24.3%, hemorrhagic necrosis in 22.9%, and chronic inflammation in 8.6%. Ectopic mucosa was detected in 50% of the cases, gastric mucosa was detected in 42.86%, ectopic pancreatic mucosa was detected in 5.71%, and both gastric and pancreatic types in 1.43%. Conclusion. Long diverticula are more liable to develop complications. At surgery, inspection and palpation of the wall of the diverticulum must be done for any evidence of inflammation, necrosis, perforation, or abnormal thickening of the walls of the diverticulum. Resection of the segment of the bowel that contains the diverticulum with primary anastomosis is preferable to other procedures due to the risk of leaving behind an abnormal heterotopic mucosa.
Evaluation of Uterotonic Activity, Acute Oral Toxicity, and Phytochemical Composition of Uvariodendron anisatum Verdc. Root Extracts
Authors: Benson Muthee Kanji , Maitho Timothy E. , Kanja Laetitia Wakonyu , Onyancha Jared Misonge
Published: August 2022

Over 80% of cultural societies in low-income countries use plant preparations in traditional medicine with unknown potency and safety profiles. Uvariodendron anisatum root extracts are used by some Kenyan herbalists. However, the claims of the plant to remove retained placenta during birth have remained uninvestigated. Therefore, the current study evaluated its uterotonic activities. Acute toxicity in Wistar rats and the phytochemical composition of the plant were also studied. The plant was collected from Embu County in Kenya. The water and ethanol extracts were prepared by maceration. Uterine strips were isolated from primed mature female Wistar rats and used to study the uterotonic activities of the extracts. De Jalon’s solution and oxytocin were used as negative and positive controls, respectively. Acute oral toxicity studies were done following the OECD 423 guideline and phytochemical screening were based on standard phytochemical procedures. The study met all the approval requirements before commencement. Data obtained from the uterotonic activity were analysed by using GraphPad Prism Version 8.0.1 software and expressed as a percentage increase or decrease of mean as mean ± SEM relative to the controls. The findings of acute oral toxicity were expressed using LD50. Additionally, the phytochemical components of the U. anisatum were tabulated. The uterotonic effect of Uvariodendron anisatum root water extract was higher than that of ethanol extract. A single dose of the Uvariodendron anisatum root water extract at 2000 mg/kg did not cause mortality in the tested Wistar rats. Besides, there were no changes in hematological and biochemical parameters. The extracts did not reveal changes in the gross morphology of the liver, kidney, heart, and lung of the tested Wistar rats. However, the histopathological studies of Uvariodendron anisatum root water extracts exhibited toxicity in the liver, kidney, and lung tissues of Wistar rats at a concentration of 2000 mg/kg. Alkaloids, glycosides, saponins, phytosterols, terpenes, proteins, phenols, and oils were recorded in Uvariodendron anisatum. The findings from this study provided scientific evidence which is useful in validating the use of Uvariodendron anisatum extracts in the stimulation of the uterus during birth.
A Systematic Study on the Application of Information Technology in Teaching Japanese Translation Courses
Authors: Xin Ying
Published: August 2022

Technological for the purpose of interlingual communication has gone through tremendous changes because of mechanical progressions. The significance of two important technological advancements in current translation is as follows: PC helped translation apparatuses and machine translation, which is talked about in this article. These innovations have further developed translation efficiency and quality, supported overall communication, and underlined the developing requirement for state-of-the-art specialized answers for the deep rooted challenge of language boundaries. These instruments, then again, give extensive obstacles and vulnerability to the deciphering calling and industry. People have proposed new standards for several disciplines and their own lifestyles based on computer technology. Because of its significant data advantage, network information technology plays a key role in traditional language translation. Thus, it is basic to comprehend how to more readily involve network data assets in the Japanese translation process, as well as to investigate changes in network data assets and merge the commonsense headway of the Japanese translation process, in light of the significant issue of Japanese learning and exploration.
The Role of Jinhuang Powder to Prevent Adverse Effects of Subcutaneous Injection of Enoxaparin Sodium
Authors: Zhang Meng , Zhang Xiang , Wang Chunlan , Shen Yangfang , Fu Jianan
Published: August 2022

Objective. To determine the role of Jinhuang Powder to prevent adverse effects of subcutaneous injection of enoxaparin sodium. Methods. The clinical data of 97 patients with cervical cancer who were treated with subcutaneous injection of enoxaparin through the lower margin of the deltoid muscle of the upper arm in Zhejiang Tumor Hospital from August 2020 to August 2021 were retrospectively analyzed. All patients were divided into the control group (n = 39) and the research group (n = 58) according to the different use time periods of Jinhuang Powder. The research group was treated with Jinhuang Powder and enoxaparin sodium at the same time. The control group started to use Jinhuang Powder after the adverse reactions occurred. The induration, subcutaneous bleeding events, and pain were statistically analyzed. Results. The incidence of induration (3.4% vs 15.4%,

P
=
0.036

) and subcutaneous hemorrhage (37.9% vs 76.9%,

P
=
0.003

) in the research group was significantly lower than that in the control group. The pain in the research group was lighter than that in the control group (grade 0–4 pain: 70% vs 28.2%, 19% vs 30.8%, 8.6% vs 23.1%, 1.7% vs 12.8%, 1.7% vs 5.1%,

P
=
0.001

). Conclusion. Preventive use of Jinhuang Powder can significantly reduce the incidence of subcutaneous induration and subcutaneous bleeding and can effectively alleviate the local pain of injection. It is worthy of further study to clarify its role and mechanism.
A Framework for Interactive Medical Image Segmentation Using Optimized Swarm Intelligence with Convolutional Neural Networks
Authors: Kaushal Chetna , Islam Md Khairul , Althubiti Sara A. , Alenezi Fayadh , Mansour Romany F.
Published: August 2022

Recent improvements in current technology have had a significant impact on a wide range of image processing applications, including medical imaging. Classification, detection, and segmentation are all important aspects of medical imaging technology. An enormous need exists for the segmentation of diagnostic images, which can be applied to a wide variety of medical research applications. It is important to develop an effective segmentation technique based on deep learning algorithms for optimal identification of regions of interest and rapid segmentation. To cover this gap, a pipeline for image segmentation using traditional Convolutional Neural Network (CNN) as well as introduced Swarm Intelligence (SI) for optimal identification of the desired area has been proposed. Fuzzy C-means (FCM), K-means, and improvisation of FCM with Particle Swarm Optimization (PSO), improvisation of K-means with PSO, improvisation of FCM with CNN, and improvisation of K-means with CNN are the six modules examined and evaluated. Experiments are carried out on various types of images such as Magnetic Resonance Imaging (MRI) for brain data analysis, dermoscopic for skin, microscopic for blood leukemia, and computed tomography (CT) scan images for lungs. After combining all of the datasets, we have constructed five subsets of data, each of which had a different number of images: 50, 100, 500, 1000, and 2000. Each of the models was executed and trained on the selected subset of the datasets. From the experimental analysis, it is observed that the performance of K-means with CNN is better than others and achieved 96.45% segmentation accuracy with an average time of 9.09 seconds.
Analysis on Influencing Factors of Consumer Trust in E-Commerce Marketing of Green Agricultural Products Based on Big Data Analysis
Authors: Dong Luyan
Published: August 2022

In order to avoid consumers’ irrational purchase behavior due to e-commerce marketing and affect the normal operation of e-commerce market, this paper analyzes the influencing factors of consumer trust in e-commerce marketing of green agricultural products based on big data analysis. We construct the influencing factor model of consumer trust and analyze the types of influencing factors of consumer trust. The K-means clustering method is used to collect the relevant data of green agricultural products’ e-commerce marketing and consumers’ irrational purchase behavior in the big data environment. The influencing factors of consumers’ trust in green agricultural products’ e-commerce marketing are analyzed with the professional degree, credibility, and attraction of e-commerce marketing as independent variables; consumers’ purchase behavior as dependent variables; and consumers’ purchase emotion as intermediary variables. The experimental results show that the designed method has certain reliability and good effectiveness, and plays a positive role in irrational purchase behavior.
Influence of Management Education on Enterprise Scientific and Technological Innovation Based on K-Means Clustering Algorithm
Authors: Ma Jun , Li Zhigang , Liu Zichan , Zhang Shan , Lai Wenling
Published: August 2022

Scientific and technological innovation is the source of the survival and development of enterprises and the key to the realization of the goal of prosperity. In recent years, more and more companies have begun to focus on technological innovation, but the results are not significant, so companies have begun to explore the factors that affect their technological innovation. The management is the helm of the development of the enterprise, the main body of the company’s actual production activities, and the direct person in charge of the company’s management. Its influence on the innovation of the enterprise is self-evident, and the education level of the management directly determines the manager’s ability and vision. However, the current research on management mainly focuses on the position change of management and the rights of management and does not involve the level of education of management. Based on this, this article started from the management education and subdivided it with the K-means clustering algorithm, so as to explore the impact of management education on the technological innovation of enterprises. The experiment showed that there was a significant positive correlation between the educational level of management and the technological innovation ability of enterprises, and the correlation coefficient was 1.521. It fully shows that the management with a higher education background will promote the enterprise to carry out scientific and technological innovation practice and continuously improve the enterprise’s innovation ability.
Performance Analysis of Marine Ecological Environment Governance Based on DPSIR and Entropy Weight TOPSIS Model
Authors: Shengde Hu , Zhonghua Li
Published: August 2022

In order to solve the global marine ecological environment problems and ensure their own marine strategic interests, we should participate in the global marine ecological environment governance in an all-round way. There are still some problems in the evaluation system, supervision mechanism, and performance evaluation of the marine ecological environment. Considering the impact and analysis on the ecological environment in the effect of marine governance, this paper puts forward the performance analysis of marine ecological environment governance based on DPSIR (Drive Pressure State Impact Response) and entropy weight TOPSIS model and constructs the performance index evaluation system of marine ecological environment governance combined with the current situation of marine ecological environment governance. Using entropy TOPSIS (technique for order performance by similarity to ideal solution) model and geographic detector, this paper explores the influencing factors of governance performance. From the perspective of marine ecological environment governance effect, the governance effect is good. The influence of pressure factors such as ecological environment, population, and economy is generally large. The main causes of marine environmental pressure are the massive amount of industrial wastewater, the utilization of water resources in coastal areas, and the employees involved in the sea. Through the factor detection method of geographic detector, the strength calculation of the three criteria of pressure-state-response which affect the performance level of marine environmental governance is carried out, and the pressure layer index, state layer index, and response layer index have good response. In marine science and technology innovation ability, government investment in marine construction has a significant impact on environmental governance performance.
Association of Choline Intake with Blood Pressure and Effects of Its Microbiota-Dependent Metabolite Trimethylamine-N-Oxide on Hypertension
Authors: He Guo-dong , Liu Xiao-cong , Lu An-shang , Feng Ying-qing
Published: August 2022

Background. The association of total choline (TC) intake and its metabolite trimethylamine-N-oxide (TMAO) with hypertension and blood pressure (BP) has not been elucidated. Methods. For the population study, the association of TC intake with hypertension, as well as blood pressure, was determined through logistic along with multiple linear regression analysis from the National Health and Nutrition Examination Survey 2007 to 2018, respectively. For the animal experimental study, spontaneously hypertensive rats (SHRs) were assigned to the water group or water containing 333 mg/L or 1 g/L TMAO group. After 22 weeks treatment of TMAO, blood pressure measurement, echocardiography, and histopathology of the heart and arteries were evaluated. Results. No significant association of TC with hypertension was observed but the trend for ORs of hypertension was decreased with the increased level of TC. Negative association between TC and BP was significant in quintile 4 and quintile 5 range of TC, and the negative trend was significant. The SHR-TMAO groups showed significant higher urine output levels in contrast with the SHR-water group. No difference of diastolic BP was observed, but there was a trend towards lower systolic BP with the increase doses of TMAO in the SHR group. The SHR 1 g/L TMAO rats had a remarkably lower systolic blood pressure than the SHR-water group. Echocardiography showed a diastolic dysfunction alleviating effect in the 1 g/L TMAO group. Conclusion. High TC intake was not linked to elevated risk of hypertension. An inverse relationship of choline intake with systolic BP was observed. The mechanism for the beneficial effect of TC might be associated with the diuretic effect of its metabolite TMAO.
Piano Teaching Improvement Based on Machine Learning and Artificial Intelligence
Authors: Zhang Nan
Published: August 2022

With the development of modern piano and the increasing demand of people for music hobbies, the traditional piano teaching mode had exposed more and more problems. Artificial intelligence accelerated the development of music education to a certain extent and effectively improved the teaching effect of teachers to piano lovers. If artificial intelligence and machine learning were organically integrated, the application of intelligent piano can be more wide. Therefore, this paper proposed a piano teaching model based on machine learning and artificial intelligence, which aimed to provide support for improving the quality of piano teaching. Firstly, based on the analysis of machine learning-related theories, this paper expounded the characteristics of neural network in data processing and gave the process of the integration of machine learning and artificial intelligence and its application advantages in music teaching. Secondly, according to the interactive needs between intelligent piano and learners, this paper put forward intelligent piano teaching assistance methods and constructed an intelligent piano teaching service management system by improving the teaching information resource base. Finally, it analyzed the influence of artificial intelligence on piano teaching from different angles. In order to test the effectiveness of the piano teaching mode proposed in this paper, the piano learners under different teaching modes were investigated and counted. From the piano teaching effect and the evaluation results of the piano practitioners on the teaching mode, it was known that compared with the traditional piano teaching mode, the piano teaching mode proposed in this paper had achieved remarkable results in both theoretical teaching and practical operation. It can significantly improve the quality of piano teaching and promote the enthusiasm of learners.
Risk Assessment of Debris Flow in Huyugou River Basin Based on Machine Learning and Mass Flow
Authors: Li Jiazheng , Lv Yiqing
Published: August 2022

The Huyugou river basin is a typical debris flow river basin in the Shanxi Province, which has great harm after the outbreak and seriously affects the safety of people’s lives and property. Therefore, it is urgent to carry out debris flow risk assessment. In this paper, a machine learning algorithm is implemented to assess the disaster susceptibility of each branch gully in a river basin of the Huyugou. Furthermore, its high-susceptibility branch gully and main gully were selected as the starting points of debris flow simulation for numerical simulation. The machine learning algorithm is implemented in a cloud-edge platform to minimize the model training and prediction times. Under the simulated rainfall conditions of major debris flow disasters, e.g., the one that occurred in 1996, the accuracy rate reached 84%. The results show that the debris flow susceptibility of each branch gully in the study area is mainly affected by the peak flow rate of the river basin, the length of the main gully, and the relative height difference of the river basin. The total risk area of debris flow is 1.91 × 105 m2, and the high-risk area accounts for 52.18% of the total area. It is mainly located in the upper part of the main gully accumulation area and the confluence of each channel and the main gully. The middle-risk area accounts for 36.14% of the total area, and the low-risk area accounts for less. We also observed significant reduction, from 34.68% to 36.98%, in the training and prediction times of the machine learning models when implemented over the proposed edge-cloud framework. The reappearance of debris flow in the study area is relatively accurate, which provides a certain scientific basis for the risk assessment of debris flow in the future.