bluuebunny/crossref_metadata_2025 huggingface dataset:
Discussion on Health Service System of Mobile Medical Institutions Based on Internet of Things and Cloud Computing
Authors: Tang Jinzhou
Published: January 2022

Because modern human beings pay more and more attention to physical health, and there are many problems in the traditional medical service system, human beings have a higher and higher voice for the new medical model. At present, there are many researches on the application of modern science and technology to put forward solutions to medical development, but they generally pay attention to some details and ignore the construction of the whole medical service system. In order to solve the problems of low efficiency of the traditional medical model, difficult communication between doctors and patients, unreasonable allocation of medical resources, and so on, this article proposes establishing a perfect medical and health service system. First, the correlation functions are used, such as cosine correlation, to calculate the correlation of various medical products, and then the correlation measurement methods of cloud computing and the Internet of Things are used to realize the network connection of smart medical equipment, efficiently store, calculate and analyze health data, and realize online outpatient services, health file management, data analysis, and other functions. Then, the energy consumption formula of the wireless transceiver was used to reduce the resource loss in the operation of the system. Then, we use the questionnaire to understand the current situation of mobile medical and put forward improvement suggestions. This article also scores the performance of the system. The experimental results show that the performance rating of traditional medical institutions is B, while the model rating of mobile medical institutions is a, and the efficiency is optimized by 4.42%.
Group Mobility Model for Complex Multimission Cooperation of UAV Swarm
Authors: Gu Xiaoyan , He Feng , Wang Rongwei , Chen Liang
Published: January 2022

In the unmanned aerial vehicle (UAV) swarm combat system, multiple UAVs’ collaborative operations can solve the bottleneck of the limited capability of a single UAV when they carry out complicated missions in complex combat scenarios. As one of the critical technologies of UAV collaborative operation, the mobility model is the basic infrastructure that plays an important role for UAV networking, routing, and task scheduling, especially in high dynamic and real-time scenarios. Focused on real-time guarantee and complex mission cooperative execution, a multilevel reference node mobility model based on the reference node strategy, namely, the ML-RNGM model, is proposed. In this model, the task decomposition and task correlation of UAV cluster execution are realized by using the multilayer task scheduling model. Based on the gravity model of spatial interaction and the correlation between tasks, the reference node selection algorithm is proposed to select the appropriate reference node in the process of node movement. This model can improve the real-time performance of individual tasks and the overall mission group carried out by UAVs. Meanwhile, this model can enhance the connectivity between UAVs when they are performing the same mission group. Finally, OMNeT++ is used to simulate the ML-RNGM model with three experiments, including the different number of nodes and clusters. Within the three experiments, the ML-RNGM model is compared with the random class mobility model, the reference class mobility model, and the associated class mobility model for the network connectivity rate, the average end-to-end delay, and the overhead caused by algorithms. The experimental results show that the ML-RNGM model achieves an obvious improvement in network connectivity and real-time performance for missions and tasks.
A Matter of Time: Delayed Presentation and Rapid Progression from Gonadotropin-Independent to Gonadotropin-Dependent Precocious Puberty following Successful Treatment for a Leydig Cell Tumor
Authors: Naotunna C. R. , Siriwardana D. N. , Lakmini B. C. , Samarasinghe M. , Atapattu N.
Published: January 2022

Leydig cell tumors, most often benign, are a rare cause of isosexual gonadotropin-independent precocious puberty in boys due to secretion of testosterone. Very rarely do these tumors produce estrogen, causing gynecomastia. Testicular sparing surgery is the mainstay of treatment currently although radical orchidectomy was the choice in the past. Following surgery, clinical signs improve along with a revision of biochemical changes. Occasionally, it has been reported few children are progressed to gonadotropin-dependent precocious puberty (GDPP) after initial clinical and biochemical recovery. Gonadotropin receptor analogs have been successful on them to halt the progression of puberty, and growth hormone administration has been used to optimize the adult height. Here, we report a case of a 10-year-old boy who presented very late due to failure in recognition of features of puberty due to a Leydig cell tumor. Even though he underwent successful radical orchidectomy, just within 1 month following surgery, he entered GDPP in contrast to the published cases where it was earliest detected at 3 months.
Artificial Intelligence Education System Based on Differential Evolution Algorithm to Optimize SVM
Authors: Long Weilin , Gao Yi
Published: January 2022

The artificial intelligence education system promotes the rooting of artificial intelligence in the education field and accelerates its entry into the era of intelligent education. This article focuses on the development of the artificial intelligence education system and proposes an artificial intelligence education system based on differential evolution algorithm optimization support vector machine. First, the processing of educational demand information data is automated, then a differential evolution algorithm is built to optimize the support vector machine model, and the model is used to implement various educational tasks to achieve automated education. The test results show that the model classification accuracy, classification recall rate, classification accuracy rate, and F1-score value are 4 items. Performances have been improved to improve the efficiency of education work and provide a reference for exploring the application and practice of artificial intelligence in education.
hMSCs Migrate under the Chemotaxis of CXCL-13 and Enhance Islet B Cell Activity through p-AKT Signaling Pathway in High-Glucose Environment
Authors: Li Yongtao , Bian Wenshan , Jiang Yang , Liu Danyang , Shen Lei
Published: January 2022

As a common clinical chronic disease, the incidence of diabetes is increasing year by year. According to the latest statistics from the International Diabetes Federation, as of 2019, the global prevalence of diabetes has reached 8.3%. This study aims to investigate the effect of CXCL-13 on the migration ability of human mesenchymal stem cells (hMSCs) and to clarify the specific molecular mechanism of the protective effect of hMSCs on islet B cells. The hMSCs were cultured in high-glucose environment, and the effect of CXCL-13 on the migration ability of hMSCs was determined by Transwell experiment. After coculture of hMSCs and islet B cells, the activity of cells was detected by CCK8 assay, the expression of Ki-67 in cells was detected by RT-PCR, and the expression of P53 was detected by Western blot to investigate the effect of hMSCs on the proliferation and apoptosis of islet B cells. The effect of hMSCs on the function of islet B cells was determined by glucose stimulated insulin secretion experiment. Transwell experiment results showed that CXCL-13 could promote the migration of hMSCs to islet B cells in high-glucose environment. The results of CCK-8 showed that the cell activity in the coculture group was significantly higher than that of the other groups, and RT-PCR showed that the expression of Ki-67 was significantly increased in the coculture group of hMSCs and islet B cells. The results of Western blot showed that the expression of P53 was significantly decreased in the coculture group, and the glucose stimulated insulin secretion test showed that insulin secretion was significantly increased. It was found that after the inhibition of ATK, cell activity was significantly reduced, and apoptosis was significantly increased. Meanwhile, the expression of Ki-67 was inhibited, the expression of P-53 was significantly increased, and insulin secretion was significantly reduced. To sum up, in a high-glucose environment, CXCL-13 effectively promoted the migration of hMSCs, and hMSCs protected the activity and function of islet B cells through Akt signaling pathway.
Investigating the Effect of Probiotics as Natural Preservatives on the Microbial and Physicochemical Properties of Yogurt-Marinated Chicken Fillets
Authors: Masoumi Behzad , Abbasi Azam , Mazloomi Seyed Mohammad , Shaghaghian Samaneh
Published: January 2022

Microbial flora of probiotic yogurt can have nutraceutical properties and improve beneficial health effects of food products. In this study, microbial and physicochemical properties of raw chicken fillets dipped in regular yogurt and probiotic yogurt containing Lactobacillus casei, preserved at 4°C for 9 days, were studied. In this regard, microbial analysis (Staphylococcus aureus, fecal coliform, yeasts, and molds) and physicochemical analysis (pH, water holding capacity, cooking loss percentage, Thiobarbituric acid reactive substances, and texture profile analysis) were performed during the storage time. In comparison with control samples, the amount of Staphylococcus aureus, fecal coliforms, yeast and mold counts, pH index, malondialdehyde value, and cooking loss percentage reduced significantly (

P
<
0.05

) in the chicken fillets treated with regular and probiotic yogurt, while the water holding capacity increased in six days. It was concluded that probiotic and regular yogurts reduced microbial growth and improved the physicochemical quality of chicken fillets during storage and cooking time.
Destruction Feature Extraction of Prefabricated Residential Building Components Based on BIM
Authors: Zhao Peili , Liu Xiaohong , Liang Zhisheng
Published: January 2022

In order to improve the damage feature extraction effect of prefabricated residential building components and improve the structural stability of prefabricated residential components, this paper applies BIM technology to the structural feature analysis of prefabricated residential components. Moreover, this paper adopts the simple superposition method and combines the first strength theory of material mechanics to derive the formula for calculating the cracking torque of prefabricated residential building components under compound torsion. In addition, based on the variable-angle space truss model, this paper uses a simple superposition method to derive the calculation formula for the ultimate torque of composite torsion of fabricated residential building components and applies it to the BIM fabricated residential model. Finally, this paper constructs an intelligent BIM prefabricated residential building construction damage characteristic monitoring system. Through experimental research, it can be seen that the intelligent BIM prefabricated residential building construction damage feature monitoring system proposed in this paper can monitor the damage characteristics of prefabricated residential building construction and can predict the evolution of subsequent building features.
An Approach of Integral Equations in Complex-Valued
b
-Metric Space Using Commuting Self-Maps
Authors: Bantan Rashad A. R. , Rehman Saif Ur , Mehmood Shahid , Almutiry Waleed , Alahmadi Amani Abdullah , Elgarhy Mohammed
Published: January 2022

This paper is aimed at establishing some unique common fixed point theorems in complex-valued

b

-metric space under the rational type contraction conditions for three self-mappings in which the one self-map is continuous. A continuous self-map is commutable with the other two self-mappings. Our results are verified by some suitable examples. Ultimately, our results have been utilized to prove the existing solution to the two Urysohn integral type equations. This application illustrates how complex-valued

b

-metric space can be used in other types of integral operators.
Passenger Flow Prediction Using Smart Card Data from Connected Bus System Based on Interpretable XGBoost
Authors: Zou Liang , Shu Sisi , Lin Xiang , Lin Kaisheng , Zhu Jiasong , Li Linchao
Published: January 2022

Bus passenger flow prediction is a critical component of advanced transportation information system for public traffic management, control, and dispatch. With the development of artificial intelligence, many previous studies attempted to apply machine learning models to extract comprehensive correlations from transit networks to improve passenger flow prediction accuracy, given that the variety and volume of traffic data have been easily obtained. The passenger flow on a station is highly affected by various factors such as the previous time step, peak hours or nonpeak hours, and extracting the key features from the data is essential for a passenger flow prediction model. Although the neural networks,

k

-nearest neighbor, and some deep learning models have been adopted to mine the temporal correlations of the passenger flow data, the lack of interpretability of the influenced variables is still a big problem. Classical tree-based models can mine the correlations between variables and rank the importance of each variable. In this study, we presented a method to extract passenger flow of different routes on the station and implemented a XGBoost model to find the contributions of variables to the prediction of passenger flow. Comparing to benchmark models, the proposed model can reach state-of-the-art prediction accuracy and computational efficiency on the real-world dataset. Moreover, the XGBoost model can interpret the predicted results. It can be seen that period is the most important variable for the passenger flow prediction, and so the management of buses during peak hours should be improved.
Natural Language Processing with Improved Deep Learning Neural Networks
Authors: Zhou YiTao
Published: January 2022

As one of the core tasks in the field of natural language processing, syntactic analysis has always been a hot topic for researchers, including tasks such as Questions and Answer (Q&A), Search String Comprehension, Semantic Analysis, and Knowledge Base Construction. This paper aims to study the application of deep learning and neural network in natural language syntax analysis, which has significant research and application value. This paper first studies a transfer-based dependent syntax analyzer using a feed-forward neural network as a classifier. By analyzing the model, we have made meticulous parameters of the model to improve its performance. This paper proposes a dependent syntactic analysis model based on a long-term memory neural network. This model is based on the feed-forward neural network model described above and will be used as a feature extractor. After the feature extractor is pretrained, we use a long short-term memory neural network as a classifier of the transfer action, and the characteristics extracted by the syntactic analyzer as its input to train a recursive neural network classifier optimized by sentences. The classifier can not only classify the current pattern feature but also multirich information such as analysis of state history. Therefore, the model is modeled in the analysis process of the entire sentence in syntactic analysis, replacing the method of modeling independent analysis. The experimental results show that the model has achieved greater performance improvement than baseline methods.
Three-Dimensional Reconstruction of Two-Dimensional Cardiovascular Angiography Image Sequences by Local Threshold Segmentation Algorithm
Authors: Yu Shenming
Published: January 2022

The study focused on the extraction of cardiovascular two-dimensional angiography sequences and the three-dimensional reconstruction based on the local threshold segmentation algorithm. Specifically, the two-dimensional cardiovascular angiography sequence was extracted first, and Gaussian smoothing was adopted for image preprocessing. Then, optimize maximum between-class variance (OSTU) was compared with the traditional two-dimensional OSTU and fast two-dimensional OSTU and applied in the segmentation of cardiovascular angiography images. It was found that the cardiovascular structure itself was continuous, the contrast agent diffused relatively evenly in the blood vessel, and the gray level of the blood vessel was also continuous. The degree of smoothness was consistent in all directions by Gaussian smoothing, avoiding the direction deviation of the smoothened image. The operation time (0.59 s) of the optimize OSTU was significantly shorter than that of traditional OSTU (35.68 s) and fast two-dimensional OSTU (6.34 s) (

P
<
0.05

). The local threshold segmentation algorithm can realize the continuous edge extraction of blood vessels and accurately reflect the stenosis of blood vessels. The results of blood vessel diameter measurement showed that the diameter from the end of blood vessel to the intersection varied linearly from 5.5 mm to 9.0 mm. In short, the optimize OSTU demonstrated good segmentation effects and fast calculation time; it successfully extracted continuous two-dimensional cardiovascular angiography images and can be used in three-dimensional reconstruction of cardiovascular images.
Virtual Adversarial Training-Based Semisupervised Specific Emitter Identification
Authors: Xie CunXiang , Zhang LiMin , Zhong ZhaoGen
Published: January 2022

Deep learning is a new direction of research for specific emitter identification (SEI). Radio frequency (RF) fingerprints of the emitter signal are small and sensitive to noise. It is difficult to assign labels containing category information in noncooperative communication scenarios. This makes network models obtained by conventional supervised learning methods perform unsatisfactorily, leading to poor identification performance. To address this limitation, this paper proposes a semisupervised SEI algorithm based on bispectrum analysis and virtual adversarial training (VAT). Bispectrum analysis is performed on RF signals to enhance individual discriminability. A convolutional neural network (CNN) is used for RF fingerprint extraction. We used a small amount of labelled data to train the CNN in an adversarial manner to improve the antinoise performance of the network in a supervised model. Virtual adversarial samples were calculated for VAT, which made full use of labelled and large unlabelled training data to further improve the generalization capability of the network. Results of numerical experiments on a set of six universal software radio peripheral (USRP; model B210) devices demonstrated the stable and fast convergence performance of the proposed method, which exhibited approximately 90% classification accuracy at 10 dB. Finally, the classification performance of our method was verified using other evaluation metrics including receiver operating characteristic and precision-recall.
Validation of a Saliva-Based Test for the Molecular Diagnosis of SARS-CoV-2 Infection
Authors: Bulfoni Michela , Sozio Emanuela , Marcon Barbara , De Martino Maria , Cesselli Daniela , De Carlo Chiara , Martinella Romina , Migotti Angelica , Vania Eleonora , Zanus-Fortes Agnese , De Piero Jessica , Nencioni Emanuele , Tascini Carlo , Isola Miriam , Curcio Francesco
Published: January 2022

Background. Since the beginning of the pandemic, clinicians and researchers have been searching for alternative tests to improve the screening and diagnosis of the SARS-CoV-2 infection. Currently, the gold standard for virus identification is the nasopharyngeal (NP) swab. Saliva samples, however, offer clear, practical, and logistical advantages but due to a lack of collection, transport, and storage solutions, high-throughput saliva-based laboratory tests are difficult to scale up as a screening or diagnostic tool. With this study, we aimed to validate an intralaboratory molecular detection method for SARS-CoV-2 on saliva samples collected in a new storage saline solution, comparing the results to NP swabs to determine the difference in sensitivity between the two tests. Methods. In this study, 156 patients (cases) and 1005 asymptomatic subjects (controls) were enrolled and tested simultaneously for the detection of the SARS-CoV-2 viral genome by RT-PCR on both NP swab and saliva samples. Saliva samples were collected in a preservative and inhibiting saline solution (Biofarma Srl). Internal method validation was performed to standardize the entire workflow for saliva samples. Results. The identification of SARS-CoV-2 conducted on saliva samples showed a clinical sensitivity of 95.1% and specificity of 97.8% compared to NP swabs. The positive predictive value (PPV) was 81% while the negative predictive value (NPV) was 99.5%. Test concordance was 97.6% (Cohen’s

Kappa
=
0.86

; 95% CI 0.81-0.91). The LoD of the test was 5 viral copies for both samples. Conclusions. RT-PCR assays conducted on a stored saliva sample achieved similar performance to those on NP swabs, and this may provide a very effective tool for population screening and diagnosis. Collection of saliva in a stabilizing solution makes the test more convenient and widely available; furthermore, the denaturing properties of the solution reduce the infective risks belonging to sample manipulation.
Semantic Graph Neural Network: A Conversion from Spam Email Classification to Graph Classification
Authors: Pan Weisen , Li Jian , Gao Lisa , Yue Liexiang , Yang Yan , Deng Lingli , Deng Chao
Published: January 2022

In this study, we propose a method named Semantic Graph Neural Network (SGNN) to address the challenging task of email classification. This method converts the email classification problem into a graph classification problem by projecting email into a graph and applying the SGNN model for classification. The email features are generated from the semantic graph; hence, there is no need of embedding the words into a numerical vector representation. The method performance is tested on the different public datasets. Experiments in the public dataset show that the presented method achieves high accuracy in the email classification test against a few public datasets. The performance is better than the state-of-the-art deep learning-based method in terms of spam classification.
Research on Chest Disease Recognition Based on Deep Hierarchical Learning Algorithm
Authors: Li Lingling , Long Yangyang , Huang Bangtong , Chen Zihong , Liu Zheng , Yang Zekun
Published: January 2022

Chest X-ray has become one of the most common ways in diagnostic radiology exams, and this technology assists expert radiologists with finding the patients at potential risk of cardiopathy and lung diseases. However, it is still a challenge for expert radiologists to assess thousands of cases in a short period so that deep learning methods are introduced to tackle this problem. Since the diseases have correlations with each other and have hierarchical features, the traditional classification scheme could not achieve a good performance. In order to extract the correlation features among the diseases, some GCN-based models are introduced to combine the features extracted from the images to make prediction. This scheme can work well with the high quality of image features, so backbone with high computation cost plays a vital role in this scheme. However, a fast prediction in diagnostic radiology is also needed especially in case of emergency or region with low computation facilities, so we proposed an efficient convolutional neural network with GCN, which is named SGGCN, to meet the need of efficient computation and considerable accuracy. SGGCN used SGNet-101 as backbone, which is built by ShuffleGhost Block (Huang et al., 2021) to extract features with a low computation cost. In order to make sufficient usage of the information in GCN, a new GCN architecture is designed to combine information from different layers together in GCNM module so that we can utilize various hierarchical features and meanwhile make the GCN scheme faster. The experiment on CheXPert datasets illustrated that SGGCN achieves a considerable performance. Compared with GCN and ResNet-101 (He et al., 2015) backbone (test AUC 0.8080, parameters 4.7M and FLOPs 16.0B), the SGGCN achieves 0.7831 (−3.08%) test AUC with parameters 1.2M (−73.73%) and FLOPs 3.1B (−80.82%), where GCN with MobileNet (Sandler and Howard, 2018) backbone achieves 0.7531 (−6.79%) test AUC with parameters 0.5M (−88.46%) and FLOPs 0.66B (−95.88%).
A Large-Scale
k
-Nearest Neighbor Classification Algorithm Based on Neighbor Relationship Preservation
Authors: Song Yunsheng , Kong Xiaohan , Zhang Chao
Published: January 2022

Owing to the absence of hypotheses of the underlying distributions of the data and the strong generation ability, the

k

-nearest neighbor (kNN) classification algorithm is widely used to face recognition, text classification, emotional analysis, and other fields. However, kNN needs to compute the similarity between the unlabeled instance and all the training instances during the prediction process; it is difficult to deal with large-scale data. To overcome this difficulty, an increasing number of acceleration algorithms based on data partition are proposed. However, they lack theoretical analysis about the effect of data partition on classification performance. This paper has made a theoretical analysis of the effect using empirical risk minimization and proposed a large-scale

k

-nearest neighbor classification algorithm based on neighbor relationship preservation. The process of searching the nearest neighbors is converted to a constrained optimization problem. Then, it gives the estimation of the difference on the objective function value under the optimal solution with data partition and without data partition. According to the obtained estimation, minimizing the similarity of the instances in the different divided subsets can largely reduce the effect of data partition. The minibatch

k

-means clustering algorithm is chosen to perform data partition for its effectiveness and efficiency. Finally, the nearest neighbors of the test instance are continuously searched from the set generated by successively merging the candidate subsets until they do not change anymore, where the candidate subsets are selected based on the similarity between the test instance and cluster centers. Experiment results on public datasets show that the proposed algorithm can largely keep the same nearest neighbors and no significant difference in classification accuracy as the original kNN classification algorithm and better results than two state-of-the-art algorithms.
Dynamic Response of Bridge‐Landslide Parallel System under Earthquake
Authors: Liang Yu , Wu Honggang , Lai Tianwen , Lei Hao , Zhu Mingzhe , Ma Kun , Ji Zhiyang , Ren Jiankai
Published: January 2022

In order to further understand the instability mechanism and geohazard causation when the main sliding path of the slope body is parallel to the path of the bridge, the corresponding bridge‐landslide parallel system is constructed for shaking table tests. This paper summarizes the combination forms of bridge‐landslide model under different position and focused on the slope body located above the bridge deck. Firstly, based on the shaking table test results of El Centro (1940), the failure behavior of bridge‐landslide parallel system was evaluated, and the changes of acceleration and deformation of bridge pile were subsequently analyzed. Then, the interaction bridge structure and sliding body were explained by the spectral features. The main conclusions are as follows. First, in the model test, the landslide belongs to the thrust‐type landslide. Due to the barrier function of the bridge, the main failure site of landslide occurs in the middle and trailing edge of slope body. At the same time, the acceleration value of earthquake waves is 0.3 g, which is the key to this variation. Second, the acceleration response of the measuring points on the bridge pile and landslide increases with the increase of ground elevation. If the slope structure is damaged severely, the deformation response of weak interlayer is inconsistent with the surrounding soil structure. Third, with the increase of excitation power, the dominant frequency of bridge‐landslide parallel system gradually transitions from low to high frequency rate, and the interaction of the parallel system weakens the influence of river direction on frequency. Finally, under the same working condition, the dynamic response of the measuring points has obvious regularity with the change of situation. But the response of the same points is not regular due to the different earthquake excitation intensity.
Long Noncoding RNA ZFAS1 Protects HK-2 Cells against Sepsis‐Induced Injury through Targeting the miR3723p/PPARα Axis
Authors: Hei Bingchang , Yue Caifang , Sun Yao
Published: January 2022

In septic acute kidney injury, one of the main purposes of long noncoding RNA (lncRNA) ZFAS1 is still unclear. This study is intended to analyze the effects of lncRNA ZFAS1 on the septic AKI in the HK-2 cell line. Materials and Methods. In order to construct an in vitro model of septic AKI, HK-2 cells have been treated with lipopolysaccharides. CCK-8 assay has been utilized to check the viability of HK-2 cells. The contents of inflammatory cytokines (that includes IL-1β, TNF-α, and IL-6) have been marked with enzyme-linked immune sorbent assay (ELISA). Cell apoptosis was assessed by TUNEL staining. To detect the expression of lncRNA ZFAS1 and microRNA-372-3p, quantitative reverse-transcription PCR has been used. And to confirm the connection among genes, luciferase reporter assay has been applied. Results. Overexpression of ZFAS1 alleviated LPS‐induced HK-2 cell injury. ZFAS1 positively regulated expression of α receptor activated by peroxisome proliferation (PPARα) through competitive linkage with miR-372-3p. In addition, over expression of miR-372-3p counteracted the protective effect of upward regulation of ZFAS1 on LPS-induced HK-2 cell damage, which could be reversed by over expression of PPARα. Conclusion. It is concluded that, in LPS-induced HK-2 cell injury, ZFAS1 has a protective role via modulating the miR-372-3p/PPARα axis, suggesting the potential of ZFAS1 as a protective target for septic AKI.
New-Onset Ulcerative Colitis in a Young Caucasian Woman with Unclassified Arteritis
Authors: Roberts Bryan
Published: January 2022

Takayasu arteritis is a rare disease mostly found in Asian populations. Cases have been reported in patients with inflammatory bowel disease, suggesting possible genetic linkage. The objective of this clinical case report is to highlight a rare finding of arteritis signs and symptoms in a 32-year-old Caucasian woman (likely early that it did not yet meet classification for official diagnosis as Takayasu arteritis) who subsequently was diagnosed with ulcerative colitis a few months later. The patient presented to the hospital with throbbing neck pain and tenderness around the area of her right carotid artery distribution, nonspecific visual changes, and bilateral upper extremity paresthesia, with significant findings of 50-69% right carotid artery stenosis on a recent outpatient carotid Doppler ultrasound. Based on additional laboratory, clinical, and advanced imaging findings at the hospital, a diagnosis of arteritis not yet classifiable as Takayasu arteritis was made, and the patient was treated with corticosteroids. Unfortunately, she developed bradycardia that was later attributed to the corticosteroid regimen and the medication was discontinued. By follow-up in the clinic, the patient’s carotidynia improved, but now, she reported a three-month history of bloody stools. Colonoscopy and pathology findings were consistent with ulcerative colitis, and the patient was started on mesalamine. The association of inflammatory bowel disease and Takayasu arteritis should not be overlooked, as future treatment methods and early, continuous surveillance may be critical in improving quality of life and avoiding serious complications.
Nursing Value Analysis and Risk Assessment of Acute Gastrointestinal Bleeding Using Multiagent Reinforcement Learning Algorithm
Authors: Liu Fang , Liu Xiaoli , Yin Changyou , Wang Hongrong
Published: January 2022

Gastrointestinal bleeding (GIB) indicates an issue in the digestive system. Blood can be found in feces or vomiting; however, it is not always visible, even if it makes the stool appear darkish or muddy. The bleeding can range in harshness from light to severe and can be dangerous. It is advised that nursing value analysis and risk assessment of patients with GIB is essential, but existing risk assessment techniques function inconsistently. Machine learning (ML) has the potential to increase risk evaluation. For evaluating risk in patients with GIB, scoring techniques are ineffective; a machine learning method would help. As a result, we present а unique machine learning-based nursing value analysis and risk assessment framework in this research to construct a model to evaluate the risk of hospital-based interventions or mortality in individuals with GIB and make a comparison to that of other rating systems. Initially, the dataset is collected, and preprocessing is done. Feature extraction is done using local binary patterns (LBP). Classification is performed using a fuzzy support vector machine (FSVM) classifier. For risk assessment and nursing value analysis, machine learning-based prediction using a multiagent reinforcement algorithm is employed. For improving the performance of the proposed system, we use spider monkey optimization (SMO) algorithm. The performance metrics like classification accuracy, area under the receiver-operating characteristic curve (AUROC), area under the curve (AUC), sensitivity, specificity, and precision are analyzed and compared with the traditional approaches. In individuals with GIB, the suggested technique had a good–excellent prognostic efficacy, and it outperformed other traditional models.
Research on Personalized Minority Tourist Route Recommendation Algorithm Based on Deep Learning
Authors: Liu Guanglu
Published: January 2022

With the improvement of living standards, more and more people are pursuing personalized routes. This paper uses personalized mining of interest points of ethnic minority tourism demand groups, extracts customer data features in social networks, and constructs data features of interesting topic factors, geographic location factors, and user access frequency factors, using LDA topic models and matrix decomposition models to perform feature vectorization processing on user sign-in records and build deep learning recommendation model (DLM). Using this model to compare with the traditional recommendation model and the recommendation model of a single data feature module, the experimental results show the following: (1) The fitting error of DLM recommendation results is significantly reduced, and its recommendation accuracy rate is 50% higher than that of traditional recommendation algorithms. The experimental results show that the DLM constructed in this paper has good learning and training performance, and the recommendation effect is good. (2) In this method, the performance of the DLM is significantly higher than other POI recommendation methods in terms of the accuracy or recall rate of the recommendation algorithm. Among them, the accuracy rates of the top five, top ten, and top twenty recommended POIs are increased by 9.9%, 7.4%, and 7%, respectively, and the recall rate is increased by 4.2%, 7.5%, and 14.4%, respectively.
Rehabilitation of Sepsis Patients with Acute Kidney Injury Based on Intelligent Medical Big Data
Authors: Xia Yanmei , Wang Xiuzhe , Wu Weidong , Shi Haipeng
Published: January 2022

The objective of this study was to explore rehabilitation of patients with acute kidney injury (AKI) treated with Xuebijing injection by using intelligent medical big data analysis system. Based on Hadoop distributed processing technology, this study designed a medical big data analysis system and tested its performance. Then, this analysis system was used to systematically analyze rehabilitation of sepsis patients with AKI treated with Xuebijing injection. It is found that the computing time of this system does not increase obviously with the increase of cases. The results of systematic analysis showed that the glomerular filtration rate (59.31 ± 3.87% vs 44.53 ± 3.53%) in the experimental group was obviously superior than that in the controls after one week of treatment. The levels of urea nitrogen (9.32 ± 2.21 mmol/L vs. 14.32 ± 0.98 mmol/L), cystatin C (1.65 ± 0.22 mg/L vs. 2.02 ± 0.13 mg/L), renal function recovery time (6.12 ± 1.66 days vs. 8.66 ± 1.17 days), acute physiology and chronic health evaluation system score (8.98 ± 2.12 points vs. 12.45 ± 2.56 points), sequential organ failure score (7.22 ± 0.86 points vs. 8.61 ± 0.97 points), traditional Chinese medicine (TCM) syndrome score (6.89 ± 1.11 points vs. 11.33 ± 1.23 points), and ICU time (16.43 ± 2.37 days vs. 12.15 ± 2.56 days) in the experimental group were obviously lower than those in the controls, and the distinctions had statistical significance (

P
<
0.05

). The significant efficiency (37.19% vs. 25.31%) and total effective rate (89.06% vs. 79.06%) in the experimental group were obviously superior than those in the controls, and distinction had statistical significance (

P
<
0.05

). In summary, the medical big data analysis system constructed in this study has high efficiency. Xuebijing injection can improve the renal function of sepsis patients with kidney injury, and its therapeutic effect is obviously better than that of Western medicine, and it has clinical application and promotion value.
Inflammatory Cytokines, Adipocytokines, and Th17/Treg Balance in Patients with Nonalcoholic Fatty Liver Disease following Administration of Dahuang Zhechong Pills
Authors: Duan Xiaohua , Lv Jianlin , Jiang Hebei , Zheng Kefei , Chen Yulin
Published: January 2022

Objectives. The occurrence and development of nonalcoholic fatty liver disease (NAFLD) is related to lipid peroxidation, imbalance of inflammatory response factors, and immune function disorder. This study was conducted with the purpose of investigating the expression levels of inflammatory cytokines and adipocytokines and Th17/Treg balance in NAFLD patients treated with Dahuang Zhechong pills (DHZCPs). Methods. The study recruited 100 NAFLD patients who were then arranged into the test group and control group. Patients in the test group were treated with DHZCPs, while patients in the control group were untreated. Peripheral TH17 and Treg cells were detected by flow cytometry, and peripheral IL-17, IL-10, hs-CRP, and TNF-α expression levels were determined by enzyme-linked immunosorbent assay (ELISA) methods. The concentrations of ghrelin, leptin, and adiponectin were quantitatively examined. Results. The levels of TC, TG, ALT, and AST were declined but the level of HDL-C was increased in NAFLD patients treated with DHZCPs compared with untreated patients (

P
<
0.05

). The ratio of Th17/Treg in NAFLD patients treated with DHZCPs was (1.52 ± 0.21), which was significantly lower than (2.39 ± 0.45) of untreated patients (

P
<
0.05

). The levels of IL-17, hs-CRP, and TNF-α were lower, but the level of IL-10 was higher in NAFLD patients treated with DHZCPs than that in untreated patients (

P
<
0.05

). The expression levels of ghrelin and adiponectin in NAFLD patients treated with DHZCPs were evidently higher than those in untreated patients (

P
<
0.01

), and the expression level of leptin in NAFLD patients treated with DHZCPs was evidently lower than that in untreated patients (

P
<
0.01

). Conclusions. Administration of DHZCPs regulates the immune function of NAFLD patients by keeping Th17/Treg balance and affecting the levels of inflammatory cytokines and adipocytokines.
Construction of Internet Business Innovation Network Model and Data Image Research under the Background of Game Theory
Authors: Bai Haiying
Published: January 2022

Facing the fierce market competition, enterprises not only need a deeper understanding and concept renewal in marketing theory but also need a set of standardized, practical, and efficient technical means and methods. Therefore, it is urgent to develop advanced marketing analysis tools and marketing decision-making methods. Based on game theory and neural network model, this study simplifies the existing research methods. By introducing different models, an image analysis and decision-making model based on game theory and neural network is constructed. It mainly aims at enterprise decision-making. The method used is to simulate various decision-making processes of enterprises by establishing neural network model and game model. At the same time, the image simulation of the model is carried out. The results show that the highest market share of the selected products is 36.1%, and the highest brand awareness is 9 points. The product with the second market share has better quality, 8 brand awareness points, and the highest dealer fee (2.3 yuan). Market share is less affected by product price and dealer expenses. The accuracy of the designed market share neural network model is 93%. This shows that the increase of market share is not realized simply by reducing the price but by increasing the profits of distributors and improving the brand image. Market leaders have the greatest revenue and profits. There is a positive correlation between the efforts of managers and the results achieved. Internet employees’ work effort is positively correlated with their basic salary. Different decisions have different effects on business models. The research of this paper provides a new idea for the innovation and development of Internet enterprise business model.
Multispectral Image under Tissue Classification Algorithm in Screening of Cervical Cancer
Authors: Wang Pei , Wang Shuwei , Zhang Yuan , Duan Xiaoyan
Published: January 2022

The objectives of this study were to improve the efficiency and accuracy of early clinical diagnosis of cervical cancer and to explore the application of tissue classification algorithm combined with multispectral imaging in screening of cervical cancer. 50 patients with suspected cervical cancer were selected. Firstly, the multispectral imaging technology was used to collect the multispectral images of the cervical tissues of 50 patients under the conventional white light waveband, the narrowband green light waveband, and the narrowband blue light waveband. Secondly, the collected multispectral images were fused, and then the tissue classification algorithm was used to segment the diseased area according to the difference between the cervical tissues without lesions and the cervical tissues with lesions. The difference in the contrast and other characteristics of the multiband spectrum fusion image would segment the diseased area, which was compared with the results of the disease examination. The average gradient, standard deviation (SD), and image entropy were adopted to evaluate the image quality, and the sensitivity and specificity were selected to evaluate the clinical application value of discussed method. The fused spectral image was compared with the image without lesions, it was found that there was a clear difference, and the fused multispectral image showed a contrast of 0.7549, which was also higher than that before fusion (0.4716), showing statistical difference (

P
<
0.05

). The average gradient, SD, and image entropy of the multispectral image assisted by the tissue classification algorithm were 2.0765, 65.2579, and 4.974, respectively, showing statistical difference (

P
<
0.05

). Compared with the three reported indicators, the values of the algorithm in this study were higher. The sensitivity and specificity of the multispectral image with the tissue classification algorithm were 85.3% and 70.8%, respectively, which were both greater than those of the image without the algorithm. It showed that the multispectral image assisted by tissue classification algorithm can effectively screen the cervical cancer and can quickly, efficiently, and safely segment the cervical tissue from the lesion area and the nonlesion area. The segmentation result was the same as that of the doctor's disease examination, indicating that it showed high clinical application value. This provided an effective reference for the clinical application of multispectral imaging technology assisted by tissue classification algorithm in the early screening and diagnosis of cervical cancer.
Artificial Intelligence Segmentation Model-Based Computed Tomography Angiography Image in the Diagnosis of Congenital Aortic Constriction
Authors: Zheng Tao , Shao Guofeng , Zhou Qingyun , Wang Qinning , Ye Mengmeng
Published: January 2022

This study was to analyze the impacts of the image segmentation model and computed tomography angiography (CTA) on the clinical diagnosis of aortic constriction under the background of artificial intelligence. In this study, 126 patients with congenital aortic constriction (CAC) diagnosed by surgery were selected as the research objects and routine digital subtraction angiography (DSA) and CTA were performed. Then, the traditional active contour model (AC model) was optimized based on the local area information to construct a new image segmentation model for intelligent segmentation and reconstruction of the CTA images of patients. The results revealed that compared with the AC model and the image segmentation model based on region growth (RG model) obtained from angiography segmentation, the algorithm constructed in this study showed a smaller segmentation range for angiography images and more accurate segmentation results. The quantitative data results suggested that the evolution times and running time of the constructed model were less than those of the AC and RG models

P
<
0.05

. Based on the gold standard of DSA examination results, there were 122 correctly diagnosed cases, 3 missed diagnosed cases, and 1 misdiagnosed by CTA, so the diagnosis coincidence rate was 96.83%. Compared with DSA, the average inner diameter and average pressure difference of patients with precatheter, paracatheter, and postcatheter type were not greatly different in CTA

P
>
0.05

. The CTA examination suggested there were 154 cases with intracardiac structural abnormalities, with a detection rate of 86.52%; there were 32 cases of cardiac-vascular connection abnormalities, with a detection rate of 100%; and there were 79 extracardiac vascular abnormalities, with the detection rate of 95.18%. It indicated that the optimized image segmentation model based on local area information proposed in this paper has excellent segmentation performance for CT angiography images and has good segmentation effect and efficiency. The CTA based on the artificial intelligence image segmentation model showed a better diagnostic effect on abnormal heart-vascular connection and abnormal extracardiac blood vessels and can be used as an effective examination method for clinical diagnosis of CAC.