bluuebunny/crossref_metadata_2025 huggingface dataset:
Focusing of an Atomic Beam for the Efficient Loading of an Atom Chip
Authors: Afanasiev A. E. , Bykova D. V. , Skakunenko P. I. , Balykin V. I.
Published: July 2022

A method has been proposed to increase the rate of loading of atoms in a U-magneto-optical trap near an atom chip. The method is based on the focusing of a slow atomic beam into the localization region of the atom chip. The overdamped focusing regime has been considered. In this case, the focal length is independent of the initial transverse velocity of atoms. It has been shown that the focusing of the atomic beam makes it possible to increase the loading rate in the localization region 250 μm in diameter by a factor of 160.
Spectral Maximum in the Terahertz Photoconductance of a Quantum Point Contact
Authors: Kazantsev D. M. , Alperovich V. L. , Tkachenko V. A. , Kvon Z. D.
Published: July 2022

The disappearance of the giant terahertz photoconductance of a quantum point contact under the increase in the photon energy, which was discovered experimentally (Otteneder et al., Phys. Rev. Appl. 10, 014015 (2018)) and studied by the numerical calculations of the photon-stimulated transport (O. A. Tkachenko et al., JETP Lett. 108, 396 (2018)), is explained by the momentum conservation upon absorption of photons by tunneling electrons and on the base of perturbation theory calculations.
Abundant solutions for the Lakshmanan–Porsezian–Daniel equation in an optical fiber through Riemann–Hilbert approach
Authors: Guo Han-Dong , Xia Tie-Cheng , Tong Li-Ning
Published: July 2022

The integrable Lakshmanan–Porsezian–Daniel (LPD) equation originating in nonlinear fiber is studied in this work via the Riemann–Hilbert (RH) approach. First, we give the spectral analysis of the Lax pair, from which an RH problem is formulated. Afterwards, by solving the special RH problem with reflectionless under the conditions of irregularity, the formula of general [Formula: see text]-soliton solutions can be obtained. In addition, the localized structures and dynamic behaviors of the breathers and solitons corresponding to the real part, imaginary part and modulus of the resulting solution [Formula: see text] are shown graphically and discussed in detail. Unlike 1- or 2-order breathers and solitons, 3-order breathers and soliton solutions rapidly collapse when they interact with each other. This phenomenon results in unbounded amplitudes which imply that higher-order solitons are not a simple nonlinear superposition of basic soliton solutions.
Improved Maximum Angle Estimate for Longest-Edge Bisection
Authors: Korotov Sergey , Fredrik Lund Lars , Eivind Vatne Jon
Published: July 2022

In this note we construct an upper estimate on the maximum angles of triangles generated by the longest-edge bisection algorithms applied to a triangle. This upper bound considerably improves upon one inherited from the well-known minimum angle estimation, thus providing tighter two-sided estimation of the angles generated by bisections. This estimate also guarantees the validity of the maximum angle condition, widely used in finite element analysis and computer graphics.
On the possible spacetime structures of rotating loop quantum black holes
Authors: Chen Che-Yu
Published: July 2022

To date, a mathematically consistent construction of effective rotating black hole models in the context of Loop Quantum Gravity (LQG) is still lacking. In this work, we start with the assumption that rotating LQG black hole metrics can be effectively obtained using Newman–Janis Algorithm. Then, based on a few extra fair assumptions on the seed metric functions, we make a conjecture on what a rotating LQG black hole would generically look like. Our general arguments and conclusions can be supported by some known specific examples in the literature.
Geodesic structure of the quantum-corrected Schwarzschild black hole surrounded by quintessence
Authors: Nozari Kourosh , Hajebrahimi Milad
Published: July 2022

By considering the back-reaction of the spacetime through the spherically symmetric quantum fluctuations of the background metric, Kazakov and Solodukhin removed the singularity of the Schwarzschild black hole. This regular Schwarzschild black hole has a spherical central region with a radius of the order of the Planck length. On the other hand, due to the positively accelerating expansion of the Universe, it seems that there exists a universal repulsive force known as dark energy. In the framework of quantum field theories, the quintessence field is a candidate model for investigating and modeling dark energy. Accordingly, by taking into account the quintessential matter field in the background of the Schwarzschild black hole, Kiselev gained the metric of this black hole surrounded by quintessence. By combining these two above ideas, in this study, we consider the quantum-corrected Schwarzschild black hole surrounded by quintessence to investigate null and time-like geodesics structure. Generally, this study points out that black holes are quantum-gravitational objects. We will show that the accelerated expansion of the Universe, instead of dark energy, happens because of the presence of quantum effects in this setup. Also, due to the presence of the central Planck-size sphere, the regular black hole has been possessed a shifting over radial coordinate in its inner structure.
OPPORTUNITY ALERTNESS, RISK-TAKING AND DIVERSIFICATION BY SMALL- AND MEDIUM-SIZED FARMERS
Authors: CALLE MARCELO , WATSON ADAM , LAI JOHN , PORTER WENDELL A.
Published: July 2022

In many developed economies, the struggle to survive finds many small farms disappearing. Diversification is recognized as an important strategy for sustaining farms of this scale, addressing food security issues and creating a more resilient food system. This study aims to analyze farmers’ intentions to diversify into new business opportunities and how opportunity alertness and risk-taking propensity affect their intentions. These relationships are examined using data collected from 166 small and medium-sized farmers in five regions within Florida. The results indicate that for small and medium-sized farmers, opportunity alertness and risk-taking propensity have a positive effect on diversification intentions across seven different types of activities. Implications are drawn for theory and practice.
Review—New Approaches for the Detection of Lithium Carbonate: The First Line Treatment for Bipolar Disorder
Authors: Mouawad Lynn
Published: July 2022

Bipolar disorder is known to be a group of affective disorders depicted by depressive manic or hypomanic disorders. Indeed, considered as an oldie as its pharmaceutical usage started in the 19th -century lithium is also a goodie used as the first-line treatment for bipolar disorder, also it is considered to be the only treatment with anti-suicidal effects. During treatment, lithium levels should be monitored as its therapeutic levels (0.5–0.8 mM) in the blood are close to intoxication levels (>1.5 mM) and to severe intoxication levels >2.5 mM that might induce coma. That is why it is necessary for patients diagnosed with bipolar disorder to monitor lithium levels frequently. In fact, there are many traditional analytical techniques for lithium detection. Still, these are associated with limitations as they are very expensive, time-consuming, and not found in all laboratories. So, the need to develop a cost-effective, sensitive, and easy-to-handle devices has grown. Affinity sensors constituted a promising potential for the sensitive detection of lithium. This review, to my knowledge, is the first review highlighting the different types of biosensors developed for lithium-ion detection while explaining the mode of action of each biosensor, and talking about all possible biological fluids that can be used for the detection of this drug.
Highway Traffic Flow Prediction Model Construction Based on the Gray Theory and BP Neural Network
Authors: Jiang Zaiyang
Published: July 2022

The short-term traffic flow prediction and modeling of highways are the core content and important foundation of highway management decision-making support systems. It is of great significance to improving the level of highway management. Based on the macrodynamic traffic flow model, this article establishes a method for establishing a highway traffic flow prediction model based on the BP neural network theory and gray theory. We collected and carry out modeling and prediction of highway traffic flow data near a certain station. It is learned from the prediction results that the traffic flow prediction model based on the BP neural network and gray theory has a high degree of reliability.
Integrative Analysis and Experimental Validation Indicated That SNHG17 Is a Prognostic Marker in Prostate Cancer and a Modulator of the Tumor Microenvironment via a Competitive Endogenous RNA Regulatory Network
Authors: Li Jinling , Yu Hongyuan , Yao Jinlong , Jiang Zhenming , Li Zhenhua , Cui Xiaolu
Published: July 2022

The incidence of prostate cancer (PC) is growing rapidly worldwide, and studies uncovering the molecular mechanisms driving the progression and modulating the immune infiltration and antitumor immunity of PC are urgently needed. The long noncoding RNA SNHG family has been recognized as a prognostic marker in cancers and contributes to the progression of multiple cancers, including PC. In this study, we aimed to clarify the prognostic values and underlying mechanisms of SNHGs in promoting the progression and modulating the tumor microenvironment of PC through data mining based on The Cancer Genome Atlas (TCGA) database. We identified that within the SNHG family, SNHG17 was most correlated with the overall survival of PC patients and could act as an independent predictor. Moreover, we constructed a competitive endogenous RNA (ceRNA) network by which SNHG17 promotes progression and potentially inhibits the immune infiltration and immune response of prostate cancer. By interacting with miR-23a-3p/23b-3p/23c, SNHG17 upregulates the expression of UBE2M and OTUB1, which have been demonstrated to play critical roles in the tumorigenesis of human cancers, more importantly promoting cancer cell immunosuppression and resistance to cytotoxic stimulation. Finally, we examined the correlation between SNHG17 expression and the clinical progression of PC patients based on our cohort of 52 PC patients. We also verified the SNHG17/miR-23a/OTUB1 axis in RV-1 and PC-3 cells by dual luciferase and RIP assays, and we further identified that SNHG17 promoted cellular invasive capacity by modulating OTUB1. In summary, the current study conducted a ceRNA-based SNHG17-UBE2M/OTUB1 axis and indicated that SNHG17 might be a novel prognostic factor associated with the progression, immunosuppression, and cytotoxic resistance of PC.
Visual Object Tracking Based on Deep Neural Network
Authors: Diao Zhifeng , Sun Fanglei
Published: July 2022

Computer vision systems cannot function without visual target tracking. Intelligent video monitoring, medical treatment, human-computer interaction, and traffic management all stand to benefit greatly from this technology. Although many new algorithms and methods emerge every year, the reality is complex. Targets are often disturbed by factors such as occlusion, illumination changes, deformation, and rapid motion. Solving these problems has also become the main task of visual target tracking researchers. As with the development for deep neural networks and attention mechanisms, object-tracking methods with deep learning show great research potential. This paper analyzes the abovementioned difficult factors, uses the tracking framework based on deep learning, and combines the attention mechanism model to accurately model the target, aiming to improve tracking algorithm. In this work, twin network tracking strategy with dual self-attention is designed. A dual self-attention mechanism is used to enhance feature representation of the target from the standpoint of space and channel, with the goal of addressing target deformation and other problems. In addition, adaptive weights and residual connections are used to enable adaptive attention feature selection. A Siamese tracking network is used in conjunction with the proposed dual self-attention technique. Massive experimental results show our proposed method improves tracking performance, and tracking strategy achieves an excellent tracking effect.
Face Mask-Wearing Detection Model Based on Loss Function and Attention Mechanism
Authors: Wang Zhong , Sun Wu , Zhu Qiang , Shi Peibei
Published: July 2022

Face mask-wearing detection is of great significance for safety protection during the epidemic. Aiming at the problem of low detection accuracy due to the problems of occlusion, complex illumination, and density in mask-wearing detection, this paper proposes a neural network model based on the loss function and attention mechanism for mask-wearing detection in complex environments. Based on YOLOv5s, we first introduce an attention mechanism in the feature fusion process to improve feature utilization, study the effect of different attention mechanisms (CBAM, SE, and CA) on improving deep network models, and then explore the influence of different bounding box loss functions (GIoU, CIoU, and DIoU) on mask-wearing recognition. CIoU is used as the frame regression loss function to improve the positioning accuracy. By collecting 7,958 mask-wearing images and a large number of images of people without masks as a dataset and using YOLOv5s as the benchmark model, the mAP of the model proposed in the paper reached 90.96% on the validation set, which is significantly better than the traditional deep learning method. Mask-wearing detection is carried out in a real environment, and the experimental results of the proposed method can meet the daily detection requirements.
The Relationship between VEGFC Gene Polymorphisms and Autoimmune Thyroiditis
Authors: Gao Chaoqun , Zhu Jie , Qin Qiu , Yang Xiaorong , Jiang Yanfei , Zhang Jinan
Published: July 2022

Background. Autoimmune thyroid diseases (AITDs), representative autoimmune diseases, mainly consist of Graves’ disease (GD) and Hashimoto’s thyroiditis (HT). In this passage, we investigated the association between vascular endothelial growth factor C (VEGFC) gene polymorphisms and AITDs. Methods. A total of 1084 patients with AITDs and 794 healthy controls were tested for VEGFC gene genotypes in four single nucleotide polymorphisms (SNPs) by high‐throughput sequencing, and the correlation between VEGFC gene polymorphisms and AITDs was statistically analyzed. Results. The genotype distribution of rs3775194 was statistically associated with AITDs compared with the control group. Rs3775194 was associated with AITDs under the overdominant model, both before and after adjusting for confounding factors, while the other three SNPs were not associated with GD and HT. There was a prominent discrepancy between male healthy controls and male AITD patients under overdominant model in rs3775194 and the recessive model in rs11947611. The genotype distribution of rs3775194 was statistically related to male HT. Conclusion. These results reveal the correlation between VEGFC mutation and AITD susceptibility.
Adrenal Mass in a 70-Year-Old Woman
Authors: Karimi Kiana , Nikzad Mohsen , Kulivand Sohrab , Borzouei Shiva
Published: July 2022

Introduction. Adrenocortical carcinoma is a rare endocrine malignancy with a bimodal age distribution pattern that affects women more than men. More than half of the patients present with hormone excess manifestations such as Cushing’s syndrome and virilization. Non-functional tumors usually are diagnosed incidentally following imaging studies due to a mass effect or metastatic disease. Surgical resection is considered the best curative treatment for these tumors. Case Presentation. A 70-year-old woman presented with a 3-month history of diffuse intermittent abdominal discomfort, weight loss, and additional hair growth. Imaging investigations revealed a large 187 × 85 × 140 mm mass between the liver and upper pole of the right kidney which has displaced the adjacent structures. Hormonal evaluations detected high levels of cortisol and adrenal androgens. She underwent open adrenalectomy and right nephrectomy due to severe adhesion of the mass. Histopathological evaluations revealed adrenocortical carcinoma and the patient received adjuvant radiotherapy. Conclusion. Precise physical examination, hormonal evaluation, and imaging studies play a key role in differentiating malignant adrenal masses in all patients, especially in those with vague symptoms. Radical excision of the mass and appropriate adjuvant chemotherapy or radiotherapy improve the outcome for patients.
Optimization of UAV Airfoil Based on Improved Particle Swarm Optimization Algorithm
Authors: Jiang Tieying , Jiang Liang
Published: July 2022

Airfoil optimization is an essential task in the aerodynamic layout design of the unmanned aerial vehicle (UAV). An objective optimization function was constructed based on the airfoil power factor and handling stability at various attack angles. The parametric mathematical model of the airfoil and aerodynamic parameter proxy model of airfoil were constructed using the Hicks-Henne improved function and CFD solution sample, focusing on the issues with particle swarm optimization algorithms such as slow convergence, a tendency to fall into local optimal solutions, and oscillation at a late stage; an optimization method for the low-speed airfoil of a small UAV based on improved particle swarm optimization was developed. When compared to standard particle swarm optimization, selective regenerative particle swarm optimization, and improved particle swarm optimization, the results indicate that the maximum thickness of the optimized rear airfoil decreases from 19.77% to 18.76%, the number of iterations decreases from 112 to 31, and the search speed of the improved particle swarm optimization significantly improves; the CFD results indicate that the optimized rear airfoil exhibits superior aerodynamic performance. On average, the airfoil’s maximum lift-to-drag ratio is increased by 11.9%, its maximum power factor is increased by 12.5%, and its pitching moment is reduced by 8.4%. Within the UAV’s speed range, the aerodynamic performance is stable.
Analysis of Computer Network Technology on New Media Problem-Based Learning (PBL) Teaching Method
Authors: Liu Mingzhu , Mu Xian
Published: July 2022

Today’s skill development can support modern technologies to understand in a simple manner. The teaching methods with problem-based learning (PBL) have improved exploration of collaborative inquiry. The deep understanding of concepts is possible with computer network technology through critical analysis. The reasoning, decision, and defending concepts are solving unfamiliar directions of teaching methods. The computer network technologies are making PBL efficient with teaching method. The 3 factors like learning goals, projecting elements, and teaching practices are maintained by computer network technology. This study solely uses students from one major as the experimental subjects, ignoring the fact that students from other majors have diverse logical thinking styles. As a result, the author will raise the sample size and kind of study items in the next research project. Structuralism is a scientifically educational paradigm that, with its particular vision of information, learning, and students, has become the theoretical foundation of teaching and has offered theoretical direction for many instructors’ instruction. MBPBL learning paradigm outperforms standard PBL and its N-Gain score is obtained as N-

Gain
>
70

,

30
≤
g
≤
70

, and N-

Gain
<
30

. Training and teaching methods must also be updated on a regular basis. As an emerging technology in the realm of education, virtual reality (VR) technology may assist instructors in implementing immersion instruction. The virtual environment provided by virtual reality technology transcends time and place, allowing students to master new skills in a new “real” setting.
Design and Implementation of Smart Hydroponics Farming Using IoT‐Based AI Controller with Mobile Application System
Authors: Ramakrishnam Raju S. V. S. , Dappuri Bhasker , Ravi Kiran Varma P. , Yachamaneni Murali , Verghese D. Marlene Grace , Mishra Manoj Kumar
Published: July 2022

Hydroponics is the soil less agriculture farming, which consumes less water and other resources as compared to the traditional soil‐based agriculture systems. However, monitoring of hydroponics farming is a challenging task due to the simultaneous supervising of numerous parameters, nutrition suggestion, and plant diagnosis system. But the recent technological developments are quite useful to solve these problems by adopting the artificial intelligence‐based controlling algorithms in agriculture sector. Therefore, this article focuses on implementation of mobile application integrated artificial intelligence based smart hydroponics expert system, hereafter referred as AI‐SHES with Internet of Things (IoT) environment. The proposed AI‐SHES with IoT consists of three phases, where the first phase implements hardware environment equipped with real‐time sensors such as NPK soil, sunlight, turbidity, pH, temperature, water level, and camera module which are controlled by Raspberry Pi processor. The second phase implements deep learning convolutional neural network (DLCNN) model for best nutrient level prediction and plant disease detection and classification. In third phase, farmers can monitor the sensor data and plant leaf disease status using an Android‐based mobile application, which is connected over IoT environment. In this manner, the farmer can continuously track the status of his field using the mobile app. In addition, the proposed AI‐SHES also develops the automated mode, which makes the complete environment in automatic control manner and takes the necessary actions in hydroponics field to increase the productivity. The obtained simulation results on disease detection and classification using proposed AI‐SHES with IoT disclose superior performance in terms of accuracy, F‐measure with 99.29%, and 99.23%, respectively.
Discussion on the Construction of Enterprise Internal Audit Informatization
Authors: Li Shenglu
Published: July 2022

With the popularization and application of enterprise, internal audit has gradually emerged. Therefore, enterprises need to strengthen information technology in enterprise audit, the development of information technology in information construction, and the integration of audit information, so as to accelerate the growth of enterprises. This article mainly uses the internal audit information of insurance company A as an example to analyze the existing problems in the current audit information structure and propose relevant solutions.
Isolation and Characterization of Natural Cellulose from Oxytenanthera abyssinica (Lowland Ethiopian Bamboo) Using Alkali Peroxide Bleaching Stages Followed by Aqueous Chlorite in Buffer Solution
Authors: Worku Limenew Abate , Bachheti Rakesh Kumar , Tadesse Mesfin Getachew
Published: July 2022

Plants are the primary sources of cellulose. This paper is aimed at isolating cellulose from Oxytenanthera abyssinica via chemical treatments. The thermal behavior, functional group, chemical composition, crystallinity, and morphology of raw (ROA), dewaxed (DOA), alkali-treated (AOA), and bleached (BOA) fibers were examined. TGA, FTIR, DSC, DTA, XRD, and SEM were used for characterization techniques. The effects of chemical treatments were examined by determining the content of cellulose, hemicellulose, lignin, and ash. The cellulose content in the ROA improved from49.26±0.13 wt% to86.01±0.02 wt% due to the removal of noncellulose components using waxing, alkali treatment, and bleaching with alkali peroxide bleaching stages followed by aqueous chlorite in buffer solution. The highest content of cellulose and holocellulose was exhibited in the BOA samples with a yield of86.01±0.02 wt% and97.61±0.17 wt%, respectively. ROA had greater hemicellulose (21.31±0.15 wt%), lignin (20.63±0.12 wt%), and ash content (3.30±0.11 wt%) in comparison to AOA and BOA. The XRD data showed a change in crystallinity after each treatment. Because of the high amount of crystalline cellulose, the XRD results revealed that BOA has a higher crystallinity index (CrI) (59.89%) and peak intensity than AOA, DOA, and ROA. The strength of the FTIR peaks increased in the order of ROA, DOA, AOA, and BOA, indicating that pretreatment causes hemicellulose and lignin to be gradually removed from the Oxytenanthera abyssinica fiber. The TGA, DTG, DTA, and DSC data also confirmed that BOA has the highest thermal stability due to the high content of cellulose. The SEM analysis showed a morphological change in the surface due to chemical treatment. These results confirmed that through chemical pretreatment, a high amount of cellulose was produced from Oxytenanthera abyssinica. Even though Oxytenanthera abyssinica is commonly grown in Ethiopia, few studies have been done on it, and no works have been carried out to isolate and characterize cellulose from the plant. Thus, the findings in this work will encourage researchers to use Oxytenanthera abyssinica as a source of cellulose for various applications, including the manufacture of cellulose nanocrystals, polymer matrix biofilters, green biocomposite reinforcing agents, and hydrogel synthesis.
Incentive Nursing can Effectively Improve the ESCA Level of Patients with Endometrial Cancer after Laparoscopic Hysterectomy
Authors: Zong Ju , Chen Liping , Chen Jie
Published: July 2022

Objective. To investigate the effect of incentive nursing on the rehabilitation of patients with endometrial cancer after laparoscopic hysterectomy. Methods. A total of 110 patients with endometrial cancer receiving laparoscopic hysterectomy in our hospital from October 2017 to July 2020 were recruited and assigned at a ratio of 1 : 1 to receive either routine nursing (control group) or incentive nursing plus routine care (study group). Outcome measures included the self-rating anxiety scale (SAS) score, Hamilton depression scale (HAMD) score, the exercise of self-care agency (ESCA) score, postoperative rehabilitation outcomes, the incidence of nursing complications, and nursing satisfaction. Results. Patients receiving incentive nursing showed significantly lower SAS scores and HAMD scores, and a higher ESCA score versus patients given routine care alone (

P
<
0.05

). Incentive care resulted in a shorter length of hospital stay and postoperative time of out-of-bed activities and exercises versus routine care (

P
<
0.05

). Incentive care was associated with a significantly lower incidence of nursing complications and higher nursing satisfaction versus routine nursing (

P
<
0.05

).Conclusion. Incentive nursing can effectively improve the ESCA level of patients, promote postoperative recovery, and reduce the incidence of complications, so it is worthy of clinical promotion.
English Teaching Quality Evaluation Based on Analytic Hierarchy Process and Fuzzy Decision Tree Algorithm
Authors: Zhu Huiyang
Published: July 2022

China’s economy has become more globalized in the twenty-first century. English has become the primary means of communication and trade between people and other countries as the economy has rapidly developed. As a result, colleges and universities have increased the English proficiency and teaching quality criteria for college students. The success of college instructors’ English instruction is judged using teaching quality as a primary metric. The procedure of assessing the quality of English instruction is complicated. Using appropriate assessment indexes and procedures, a scientific and sensible evaluation system for English education should be developed. This study uses the AHP and fuzzy decision tree algorithms to investigate the quality of teaching (preferably English) and gives teaching evaluation data to university administrators, which is useful to enhance the quality of teaching (preferably English) in colleges. First, a hierarchical analysis-based evaluation index system (EIS) for English teaching is established, followed by a detailed description of the decision tree algorithm’s calculation process and the application of the fuzzy decision tree algorithm to the evaluation of teaching (preferably English) quality. Finally, the practical application effect of the algorithm is tested. The results show that the accuracy of this algorithm is higher than other algorithms, and it is helpful to improve the efficiency of evaluating English teaching quality.
Quantitative Evaluation of Chronic Obstructive Pulmonary Disease and Risk Prediction of Acute Exacerbation by High-Resolution Computed Tomography
Authors: Hua Qifeng , Chen Guoping , Yang Yin , Leng Shaoyi , Zhao Zhenzhen , Bai Feng , Hu Xiaowei , Qiu Liyan , Yu Zhe , Zhang Hongbin , Shi Jiapei , Dai Qi
Published: July 2022

Objective. It is imperative to popularize the tertiary prevention of chronic obstructive pulmonary disease (COPD) and to improve the diagnosis and treatment. Methods. COPD patients were divided into mild (n = 18), moderate (n = 20), severe (n = 24), and extremely severe (n = 22) groups for performing high-resolution computed tomography (HRCT) and pulmonary function test. Serum procalcitonin (PCT) and high-sensitivity C-reactive protein (hs-CRP) were detected, and the occurrence rate of acute exacerbation COPD (AECOPD) was recorded during a 12-months follow-up period. Results. With an increase in the severity grade, the HRCT indexes, including emphysema index (EI), 1st and 15th percentile of inspiratory attenuation distribution (Perc1 and Perc15), ratio of expiratory/inspiratory mean lung density (MLDex/in) and lung volume (LVex/in), and ratio of the wall thickness to the outer diameter of the lumen (TDR), as well as percentage of the wall area to the total cross-sectional area (WA%) were increased with a decreased change in relative lung volume with attenuation values between −860 and −950 HU (RVC−860to −950) and lumen area (Ai). These were correlated with the ratio of forced expiratory volume in 1 sec (FEV1) over forced vital capacity (FVC) (FEV1/FVC), the percentage of FEV1 the predicted value (FEV1%), and ratio of residual volume to total lung volume (RV/TLC). Body mass index, MLDex/in, FEV1%, FEV1/FVC, and PCT had a predictive value to AECOPD, with the combined AUC of 0.812. Conclusions. HRCT imaging effectively classifies the severity of COPD, which combined with BMI, PFT, and serum PCT can predict the risk of AECOPD.
Recommendation of Online Business English Learning Resources Integrating Attention Mechanism and Collaborative Filtering Model
Authors: Yang Zhemei , Lv Dongbin
Published: July 2022

Faced with massive resources, many learners find it difficult to quickly screen out useful content for themselves. In order to help learners acquire the required network resources quickly and accurately, the birth of a personalized recommendation system solves this problem perfectly. A collaborative filtering algorithm has been widely used in the field of personalized recommendation. However, due to the limitation of the model, the recommendation effect has not been further improved. The single weakness of a collaborative filtering algorithm to recommend learning resources is difficult to meet the needs of learners to acquire personalized resources. This paper proposes a recommendation algorithm for business English online learning resources based on an attention mechanism and collaborative filtering model. The learner vector and learning resource vector are mapped to multispace, and the learner-learning resource interaction is done from multiple angles. The final learner representation vector and learning resource representation vector are aggregated by a two-level attention mechanism to predict scores. Through teaching practice in student associations, it is found that students from different backgrounds have different preferences for business English online learning resources. This method has a positive impact on online learning. This study aims to provide some references for English education resource recommendations. The results at Precision@K and Recall@K prove that the proposed model has better recommendation ability.
Experimental and Theoretical Study of Internal Pressure Loads on Boundary Walls under Gusty Wind Conditions
Authors: Chevula Sreenadh , Maddula Satya Prasad
Published: July 2022

This work contains an acceptable solution to the most practical interest to reduce the internal pressure load variations acting on closed envelops (i.e., inner building walls, fairing of rocket, and missile payloads) under gusty wind conditions. To simulate the gusty wind conditions (i.e., tangential unsteady flow condition), an experimental setup of a wind tunnel with a sinusoidal gust generating mechanism has been established in IDR of the Universidad Politécnica de Madrid (IDR/UPM), Spain. The wind tunnel has been equipped with an air reservoir with vent holes. The pressure jumps shown across the vent holes are studied as a function of sinusoidal gust frequency, volume of the air reservoir, and vent hole size. A theoretical model (based on the mass conservation equation and polytropic law gas evolution) has been proposed to predict the pressure jumps and variations under gusty wind conditions. At the end of the work, theoretical and experimental results have been studied and compared. The relationship between pressure loss coefficient

ξ

and flow coefficient

α

under unsteady flow conditions has been evaluated.
Exploratory Research on the Practice of College English Classroom Teaching Based on Internet and Artificial Intelligence
Authors: Jia Yunjie
Published: July 2022

With the rapid development of cloud computing and mobile Internet technology, the information of optional services in the network is exploding and the problem of information overload is becoming more and more serious. The recommendation system can recommend suitable English online classes for students according to their interests and needs, effectively reduce the data load, help students extract effective information from the mass of information, and make accurate recommendations. Aiming at the problems of data sparsity and cold start in the recommendation system, this paper proposes a recommendation method to improve the collaborative recommendation algorithm in college English online classroom teaching practice. Based on the extracted student tag feature information, this method uses spectral clustering algorithm to cluster similar students and transforms the original high-dimension scoring matrix into several lower-dimension subscoring matrices. Then, the implicit meaning model is used to locally predict the missing score in the subscore matrix. Finally, after obtaining the missing score, the improved neighborhood-based collaborative recommendation algorithm is used to predict the global score of the target student. Experiments are performed on commonly used public data sets. Compared with the traditional recommendation algorithm, the proposed algorithm has higher recommendation accuracy and better RMSE performance.
Analyzing Potato Market Participation, Market Surplus, and Market Outlet Choice on Small Farm Household Level in Lemo District, Southern Ethiopia
Authors: Demisse Chaimiso , Melese Mesfin , Alemu Mebratu , Berhanu Afework , Sinore Tamrat
Published: July 2022

This study was aimed to analyze potato market participation, market surplus, and market outlet choice in Lemo District of southern Ethiopia. To achieve the objective, primary and secondary data sources were employed and 202 potato producers were selected following simple random sampling techniques. The collected data were analyzed by using Heckman second-stage and multivariate probit models to identify factors that determine potato market participation decision and potato farmer market outlet choice, respectively. The Heckman’s first-stage (probit) result indicates that education level, sex, membership in the cooperative, distance to the nearest market, remittent from family members, own transportation facilities, seed types, frequency of extension contact, lagged market price, and amount of credit that farm household used for potato production significantly (

P
<

) determined producers’ participation in potato market. Heckman second-stage (OLS) result showed that education level, cooperative membership, family size, total cropland size, oxen owned, types of seed, and lagged market price were significantly (

P
<

) determined potato marketed surplus. Multivariate probit results showed the probability to choose wholesalers (

P
<

), retailers (

P
<

), collectors (

P
<

), and consumers (

P
<

) market outlets who were significantly affected by age, educational levels, sex, farm experiences, distance to near market, family size, remittance, land size, product supplied to the market, own transport facility, and extension service. Therefore, we recommend that the government and concerned stakeholders should focus on promoting improved potato seed, encouraging education, promoting farmers’ cooperatives, empowering females, strengthening rural-urban infrastructure, and protecting informal labor trading.
Cultural and Creative Product Design and Image Recognition Based on Deep Learning
Authors: Li Ren , Wang Chunbin
Published: July 2022

In today’s technological world, advanced intelligence technologies such as deep learning (DL) techniques are widely applied in various fields. In this study, people are going to research cultural and creative product design and image recognition based on deep learning. Cultural creative products are referred to as products that are designed by taking inspiration from the cultural aspects. The use of cultural and creative products has increased among the people, thus creating a fair market. Artificial intelligence deep learning (DL) is employed for the design of culturally creative objects. Deep learning is referred to as a machine learning technique that is used to teach machines to imitate human behaviour so that computers can learn from examples. The proposed system utilizes image recognition technique which is referred as the ability of computer systems to identify objects from an image. The image recognition technique integrates machine vision technology, which uses cameras and artificial intelligent software for recognising images. This technology is widely used for various functions, such as self-driven cars, image content searches, and machine vision robots. In our proposed system, image recognition based on deep learning is used in the design of cultural and creative products through the utilisation of randomized algorithms. The system is found to deliver more accurate solutions when compared with the existing LDA, HMM, and optimization algorithms.
Roll of Newtonian and Non-Newtonian Motion in Analysis of Two-Phase Hepatic Blood Flow in Artery during Jaundice
Authors: Singh Abha , Khan Rizwan Ahmad , Kushwaha Sumit , Alshenqeeti Tahani
Published: July 2022

Biomathematics is an interdisciplinary subject consisting of mathematics and biology, which is widely applicable for the analysis of biological problems. In this paper, we provide a mathematical model of two-phase hepatic blood flow in a jaundice patient’s artery. The blood flow is thought to be a two-phased process. The clinical data of a jaundice patient (blood pressure and hemoglobin) is gathered. To begin, hemoglobin is transformed into hematocrit, and blood pressure is turned to a decline in blood pressure. For the examination of hepatic arteries in Newtonian and non-Newtonian movements, a mathematical model is constructed. The relationship between two-phase blood flow flux and blood pressure reduction in the hepatic artery is established. For various hematocrit levels, the blood pressure decrease is determined. The patient’s states are defined by the slope of the linear relationship between computed blood pressure decrease and hematocrit.
Mechanism of Action of Yin Nourishing and Heat Clearing Prescription in Treating Cough Variant Asthma Based on Network Pharmacology and Molecular Docking Verification
Authors: Zhang Yin , Cui Yixin , Chen Qi , Li Fagen , Li Shaodan
Published: July 2022

Objective. To explore the mechanism of action of the yin nourishing and heat clearing prescription in treating cough variant asthma (CVA) based on network pharmacology (NP). Methods. The active ingredients and targets of the yin nourishing and heat clearing prescription were screened using the Traditional Chinese Medicine System Pharmacology Analysis Platform (TCMSP); CVA targets were screened by the GeneCards, NCBI gene, and OMIM databases to construct the component-target network and the protein-protein interaction (PPI) network. GO functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the target genes were performed to construct the component-disease-pathway-target biological network. Moreover, CVA-related core target structures with high values were subjected to molecular docking (MD) with the active components. Results. We found 265 eligible targets in the prescription and 1115 CVA-related genes. The medicine targets were intersected with disease targets, which yielded 148 common targets. After topology analysis, 66 key targets were screened. Upon GO functional annotation, 2408 biological processes, 153 molecular functions, and 162 KEGG pathways were enriched. Molecular docking results suggested that the major active ingredients of the prescription showed high affinity to the key targets, among which AKT1 might be the most important target. Conclusions. Active ingredients might act on AKT1, IL-6, VEGFA, IL-1B, and JUN to suppress eosinophil accumulation, decrease histamine release, suppress airway inflammation, regulate the airway immune microenvironment, increase autophagy in lung tissue, inhibit mucus production, and reduce airway resistance and hyperresponsiveness, thus treating CVA. Our findings provide a reference for further research and clinical applications of the prescription.
An Improved Music Composing Technique Based on Neural Network Model
Authors: Liang Mingheng
Published: July 2022

Traditionally, music was considered an analog signal that had to be made by hand. In recent decades, music has been highlighted by technology that can autonomously compose a suite of music without any human interaction. To achieve this goal, this article suggests an autonomous music composition technique based on long short-term memory recurrent neural networks. Firstly, the music collection is split into music sequences based on unit time, and the Meier cepstrum coefficients of music audio are retrieved as features during music preprocessing. Secondly, the training samples composed of feature vectors processed by data were trained and predicted by short- and long-term memory models. Finally, the generated music sequence is spliced and fused to get new music. This article designs and performs experiments to demonstrate that our results are promising. From experimental results, this work gained that our model has the maximum accuracy of 99% and the lowest loss rate of 0.03.
The Effect of Individual Nursing on Anxiety and Depression in Patients with Temporomandibular Disorders
Authors: Ke XiaoQiu , Shao Xia , Wang Jie , Zhuo JinFeng
Published: July 2022

Objective. This study aimed to explore the effects of individualized nursing in patients with temporomandibular disorders (TMD). Methods. From June 2019 to April 2021, 80 patients with TMD were admitted to the First Affiliated Hospital of Wenzhou Medical University. Among them, 40 patients (control group, CG) received routine nursing and 40 patients (experimental group, EG) received individualized nursing. Functional exercise compliance, pain score, maximum mouth opening, nursing satisfaction questionnaire, self‐rating anxiety scale, and self‐rating depression scale were investigated. Results. From June 2019 to April 2021, 81 patients with TMD were admitted to the First Affiliated Hospital of Wenzhou Medical University. Among them, 40 patients (control group) received routine care and 41 patients (experimental group) received individual care. There were no significant differences in mouth opening and pain score between the two groups before surgery (P > 0.05), but there were significant differences in mouth opening and pain score between the two groups 3 weeks after surgery. Patients’ anxiety and depression were assessed by the SAS and SDS scores. Before nursing, the control group and experimental group (P < 0.05) had no significant difference. After nursing, the score of both groups decreased (P < 0.05). However, the score was lower in the experimental group, compared to the control group (P < 0.01). Conclusion. In summary, individualized nursing can improve patients’ physical condition and reduce negative emotions and complications. In light of this, the study needs further verification by a large sample randomized controlled trial.
SLC6A14 Depletion Contributes to Amino Acid Starvation to Suppress EMT‐Induced Metastasis in Gastric Cancer by Perturbing the PI3K/AKT/mTORC1 Pathway
Authors: Guo Qie , Xu Wen , Li Xiao , Sun Jia-Lin , Gu Xiao-Ce , Jing Fan-Bo
Published: July 2022

Metastasis is the main obstacle for the treatment of gastric cancer (GC), leading to low survival rate and adverse outcomes in CG patients. SLC6A14, a general amino acid transporter, can import all the essential amino acids in a manner dependent on the NaCl‐generated osmotic gradients. Herein, we constructed GC cell sublines with high (SGC7901‐M and MKN28‐M) and low (MKN28‐NM and SGC7901‐NM) metastatic ability. Putative functional genes advancing GC metastasis were identified using mRNA microarray analysis and High‐Content Screening. In particular, most significant change with a dampening trend in the migration potentiality of GC cells emerged after SLC6A14 gene was silenced. SLC6A14 expression was positively correlated with the migrated capability of different GC cell lines, and SLC6A14 was also constitutively expressed in GC patients with venous or lymphatic invasion, lymph node, or distant metastasis and poor prognosis, thus prompting SLC6A14 as a nonnegligible presence in supporting GC migration and invasion. Consistently, SLC6A14 depletion drastically depressed GC metastasis in vitro and in vivo. Most importantly, pharmacological blockade and gene silence of SLC6A14 both restricted epithelial‐mesenchymal transition‐ (EMT‐) driven GC metastasis, in which attenuated activation of the PI3K/AKT/mTORC1 pathway caused by amino acid starvation was involved. In summary, it is conceivable that targeting SLC6A14 has a tremendous promising for the treatment of metastatic GC.