bluuebunny/crossref_metadata_2025 huggingface dataset:
Self-Driving Tour Travel Route Planning Model Construction Using Recurrent Neural Network
Authors: Wang Nan
Published: April 2022

With the continuing increase of people’s living standards, tourism has now become the primary mode of holiday and leisure activity. Scientific and reasonable route planning can best meet people’s essential needs, provide detailed planning in terms of travel time and cost, resolve the impact of various external factors, and realize the complementary benefits of scenic tourism while ensuring that each factor configuration is optimized. External factors such as trip time, cost, travel date, and transport mode all have an impact on route planning. In order to develop a mathematical model and gather other information, it is frequently essential to determine two or more of these influencing elements. The ideal plan for the influencing elements is to first develop an optimal self-driving trip route that meets all of the requirements. This strategy is becoming increasingly popular among the general public, and it focuses on how to spend the least amount of money in order to visit more tourist attractions, construct the most reasonable routes, and provide circumstances for tourists to save time and money. The purpose of this study is to examine the topic of self-driving travel route planning and to suggest the creation of a route planning model. In this paper, a self-driving journey route planning model is built using a recurrent neural network model. The driving route is designed with a high-speed priority strategy, and the travel route selection problem takes into account the consumption condition as well as the quantity of scenic sites. The developed model is used to optimize self-driving trip routes, and the results are promising.
Clinical Efficacy of Acupuncture for the Treatment of Rheumatoid Arthritis: Meta-Analysis of Randomized Clinical Trials
Authors: Li Hongchao , Man Siliang , Zhang Liang , Hu Lidong , Song Hui
Published: April 2022

Objective. Acupuncture has been used by rehabilitation specialists as an adjunctive treatment for the symptomatic treatment of rheumatoid arthritis (RA). This meta-analysis aims to evaluate the efficacy of acupuncture in treating patients with RA. Methods. A comprehensive search was conducted in CBM, CNKI, PubMed, CENTRAL, Web of Science, and Embase from their inception up to March 2022. All randomized controlled trials (RCTs) without the language restriction, concerning the patients with RA treated with acupuncture, were included. Two reviewers independently assessed the risk of bias with the Cochrane Risk of Bias Assessment tool. Weight mean difference (MD) and 95% CI were calculated, and data were pooled with random effects model. Results. A total of eleven RCTs involving 796 patients with RA met the established inclusion criteria. This systematic review indicated the efficacy of acupuncture as an adjunctive treatment for patients with RA. Invasive acupuncture could reduce significantly in pain (MD = -1.00, 95% CI: −1.96 to −0.05,

P
=
0.040

), health assessment questionnaire (HAQ, MD = −0.20, 95% CI: −0.30 to −0.11,

P
<
0.001

), physician global assessment (PhGA, MD = −0.98, 95% CI: −1.23 to −0.72,

P
<
0.001

), tender joint count (TJC, MD = −1.24, 95% CI: −2.11 to −0.37,

P
=
0.005

), C-reactive protein (CRP, MD = −1.81, 95% CI: −3.32 to −0.29,

P
=
0.019

), and erythrocyte sedimentation rate (ESR, MD = −3.03, 95% CI: −5.80 to −0.26,

P
=
0.032

), while compared to control group. Laser acupuncture could reduce HAQ (MD = −0.15, 95% CI: −0.28 to −0.01,

P
=
0.034

), the RA quality of life questionnaire (RAQoL, MD = −2.32, 95% CI: −4.40 to −0.25,

P
=
0.028

), CRP (MD = −35.24, 95% CI: −36.49 to −33.99,

P
<
0.001

), and interleukin-6 (IL-6, MD = −29.63, 95% CI: −49.34 to −9.92,

P
=
0.003

), while compared to control group. No adverse events associated with acupuncture were reported. Conclusion. Available evidence suggests that acupuncture is beneficial for relieving pain and ameliorating quality of life and health index in patients with RA; thereby, it should be available as an adjunctive nonpharmacological treatment in rehabilitation programmes.
Qibin Tongbian Decoction in the Treatment of Diabetic Constipation and Its Influence on the Intestinal Environment and the Incidence of Adverse Reactions: A Randomized Trial
Authors: Tang Cunxiang , Pu Wenyuan , Shan Zhaowei
Published: April 2022

Objective. This study aimed to study the clinical efficacy of Qibin Tongbian decoction in the treatment of diabetic constipation and its influence on the intestinal environment and the incidence of adverse reactions. Methods. In this prospective study, a total of 62 patients with diabetic constipation admitted to our hospital from June 2020 to March 2021 were recruited and assigned at a ratio of 1 : 1 to receive either traditional treatment (control group) or Qibin Tongbian decoction (experimental group). Outcome measures included clinical efficacy, intestinal environment, and incidence of adverse reactions. Results. Qibin Tongbian decoction was associated with significantly higher efficacy and a lower TCM symptom score versus conventional treatment (

P
<
0.05

). Qibin Tongbian decoction resulted in significantly lower fasting plasma glucose (FPG) levels and 2-hour postprandial plasma glucose (2hPG) levels versus conventional treatment (

P
<
0.05

). Patients receiving Qibin Tongbian decoction showed significantly lower constipation symptom scores and PAC-QOL scores versus conventional treatment (

P
<
0.05

). Qibin Tongbian decoction resulted in a significantly larger number of bifidobacteria and enterococci and a smaller number of yeasts versus conventional treatment (

P
<
0.05

). Qibin Tongbian decoction was associated with a significantly lower incidence of adverse reactions and recurrence rate versus conventional treatment (

P
<
0.05

). Conclusion. Qibin Tongbian decoction combined with conventional TCM therapy yields a promising effect in the treatment of diabetic constipation. It can improve the intestinal environment of patients, mitigate the symptoms of constipation, enhance the quality of life of patients, and reduce the incidence of adverse reactions, so it is worthy of promotion and application.
Differential Human Learning Optimization Algorithm
Authors: Zhang Pinggai , Wang Ling , Du Jiaojie , Fei Zixiang , Ye Song , Fei Minrui , Pardalos Panos M.
Published: April 2022

Human Learning Optimization (HLO) is an efficient metaheuristic algorithm in which three learning operators, i.e., the random learning operator, the individual learning operator, and the social learning operator, are developed to search for optima by mimicking the learning behaviors of humans. In fact, people not only learn from global optimization but also learn from the best solution of other individuals in the real life, and the operators of Differential Evolution are updated based on the optima of other individuals. Inspired by these facts, this paper proposes two novel differential human learning optimization algorithms (DEHLOs), into which the Differential Evolution strategy is introduced to enhance the optimization ability of the algorithm. And the two optimization algorithms, based on improving the HLO from individual and population, are named DEHLO1 and DEHLO2, respectively. The multidimensional knapsack problems are adopted as benchmark problems to validate the performance of DEHLOs, and the results are compared with the standard HLO and Modified Binary Differential Evolution (MBDE) as well as other state-of-the-art metaheuristics. The experimental results demonstrate that the developed DEHLOs significantly outperform other algorithms and the DEHLO2 achieves the best overall performance on various problems.
Investigation on the Relationship between Sleep Quality and Depression and Anxiety in Hospitalized Patients with Different Levels of AECOPD
Authors: Liu Dingfang , Zou Ying , Wang Yumei
Published: April 2022

Purpose. To explore the correlations between sleep quality and depression and anxiety in hospitalized patients with acute exacerbation of chronic obstructive pulmonary disease (AECOPD). Methods. A total of 131 AECOPD patients treated at No.6th People’s Hospital affiliated to Shanghai Jiao Tong University School from January 2018 to December 2020 were selected and divided into the classes I (

n
=
30

), II (

n
=
23

), III (

n
=
37

), and IV (

n
=
41

) based on the severity of AECOPD. The Pittsburgh sleep quality index (PSQI) and the hospital anxiety and depression scale (HADS) were employed to assess the quality of sleep and anxiety/depression in AECOPD patients. The Pearson correlation analysis explored the associations between PSQI score and HADS anxiety/depression score. Binary logistic regression was utilized to determine risk factors for anxiety/depression in AECOPD patients. Results. The PSQI score, HADS anxiety score, and HADS depression score in patients with AECOPD class III and class IV were significantly higher than those of classes I and II. The severe anxiety and depression ratio were significantly different among patients with classes I–IV AECOPD. The severity of AECOPD was significantly correlated with the PSQI score (

r
=
0.51

,

P
<
0.001

), HADS anxiety score (

r
=
0.66

,

P
<
0.001

), and HADS anxiety score (

r
=
0.65

,

P
<
0.001

). Binary logistic regression analysis showed that the COPD duration, the severity of AECOPD, and PSQI score were the risk factors for anxiety and depression in patients with AECOPD. Conclusions. Poor sleep quality, anxiety, and depression are common in patients with AECOPD. Improvement of sleep quality may help to alleviate anxiety and depression in AECOPD patients.
Quality Assessment Method of Information Model Reform of Higher Mathematics Education Based on Big Data
Authors: Li Jiao , Xue Jiao , Fu Hongjuan
Published: April 2022

The quality of higher mathematics teaching in colleges and universities is related to a variety of influencing factors, and the changing laws are very complex, which makes the current model unable to accurately evaluate the quality of higher mathematics teaching in colleges and universities. In order to solve the deficiencies in the current teaching quality evaluation process in colleges and universities and improve the correct rate of college teaching quality evaluation, a quality evaluation method based on big data is proposed, which reforms the information model of college teaching quality evaluation. The literature related to mathematics teaching quality evaluation is researched and analyzed, and the influencing factors of higher mathematics teaching quality evaluation in colleges and universities are established. Then, collect the data of the influencing factors of higher mathematics teaching quality in colleges and universities, and establish a learning sample of college teaching quality evaluation through the star rating of the teaching quality of colleges and universities by experts. Finally, the neural network and decision tree of big data technology are introduced to train the learning samples to form an evaluation model of higher mathematics teaching quality in colleges and universities. The results show that our method can achieve high accuracy.
Computer Vision-Based Medical Cloud Data System for Back Muscle Image Detection
Authors: Qi Xuanye
Published: April 2022

The fast development of image recognition and information technology has influenced people’s life and industry management mode not only in some common fields such as information management, but also has very much improved the working efficiency of various industries. In the healthcare field, the current highly disparate doctor–patient ratio leads to more and more doctors needing to undertake more and more patient treatment tasks. Back muscle image detection can also be considered a task in medical image processing. Similar to medical image processing, back muscle detection requires first processing the back image and extracting semantic features by convolutional neural networks, and then training classifiers to identify specific disease symptoms. To alleviate the workload of doctors in recognizing CT slices and ultrasound detection images and to improve the efficiency of remote communication and interaction between doctors and patients, this paper designs and implements a medical image recognition cloud system based on semantic segmentation of CT images and ultrasound recognition images. Accurate detection of back muscles was achieved using the cloud platform and convolutional neural network algorithm. Upon final testing, the algorithm of this system partially meets the accuracy requirements proposed by the requirements. The medical image recognition system established based on this semantic segmentation algorithm is able to handle all aspects of medical workers and patients in general in a stable manner and can perform image segmentation processing quickly within the required range. Then, this paper explores the effect of muscle activity on the lumbar region based on this system.
Transient Pressure Behavior of a Horizontal Well in a Naturally Fractured Gas Reservoir with Dual-Permeability Flow and Stress Sensitivity Effect
Authors: Wang Guiqin , Zhang Rui , Cui Liangliang
Published: April 2022

Dual-permeability flow and stress sensitivity effect are two fundamental issues that have been widely investigated in transient pressure analysis for horizontal wells. However, few attempts have been made to simulate the combined effects of dual-permeability flow and stress-dependent fracture permeability on the pressure transient dynamics of a horizontal well in a naturally fractured gas reservoir. In this approach, an analytical model is proposed to integrate the complexities of pressure-dependent PVT properties, dual-permeability flow behavior, and stress-dependent fracture permeability characteristics. The nonlinearity of the mathematical model is weakened by using Pedrosa’s transform formulation. Then, the Laplace integral transformation and separation of variables are applied to solve the model. Based on the solution of the mathematical model, a series of new-type curves are drawn to make a precise observation of different flow regimes. The main differences between the proposed model and the traditional models are discussed, and the effects of the permeability modulus of fractures, storability ratio, interporosity flow factor, and skin factor on transient pressure response are also examined. The results show that there are obvious differences in transient pressure dynamic curves between the proposed model and traditional models. The stress sensitivity effect plays a significant role in the intermediate flow period and the late-time pseudoradial flow period. The dual-permeability flow behavior mainly affects the early transient and interporosity flow stages. The proposed model can accurately simulate the transient pressure behaviors of a horizontal well in a naturally fractured gas reservoir with a dual-permeability flow and stress sensitivity effect. The novel model can be used to interpret pressure signals with accurate matching results and more reasonable interpreted parameters.
Dietary Pattern or Weight Loss: Which One Is More Important to Reduce Disease Activity Score in Patients with Rheumatoid Arthritis? A Randomized Feeding Trial
Authors: Sadeghi Alireza , Tabatabaiee Mojtaba , Mousavi Mir Ali , Mousavi Seyedeh Neda , Abdollahi Sabet Somayae , Jalili Nooshin
Published: April 2022

Objectives. Herein, disease activity score 28 (DAS 28) was compared between patients with rheumatoid arthritis (RA) receiving the Mediterranean dietary pattern (MD) and low-fat diet. Subjects/Methods. Overweight and obese RA patients aged 15–75 y participated in this randomized feeding trial. Participants were randomized to MD (n = 51) and low-fat high-carbohydrate diet (n = 53) for 12 weeks. The control group followed their regular diet (n = 50). Participants completed the form of tender and swollen joint counts before the study enrollment and after 12 weeks to compute DAS 28. Results. Weight loss was not statistically significant between the MD and LF-HC groups. DAS 28 significantly decreased in MD compared to the LF-HC group (

p
=
0.02

) and controls (

p
=
0.001

). Adjusting for the baseline variables, MD reduced DAS 28 by 76% (95% CI = −0.45, −0.2;

p
=
0.03

) after 12 weeks of intervention. The baseline serum ESR level showed 99.8% effect on DAS 28 score at the end (95% CI = 0.014, 0.035;

p
<
0.001

). Conclusions. The MD showed beneficial effects on DAS 28 compared to the LF-HC diet in patients with RA, regardless of weight loss. It is a better dietary choice for pain reduction in patients with RA. The trial is registered at Iranian Registry of Clinical Trials (IRCT20200929048876N2).
Line Loss Prediction of Distribution Network Based on BP Neural Network
Authors: Huang Haoran
Published: April 2022

Distribution network line loss calculation refers to the calculation of the electrical energy loss, which is generated by all components in a distribution network system in a given period of time. Distribution network, as the end of the power network, is directly connected with users; there are many equipment on the network; the system has impedance, electric energy in the process of conversion, transmission and distribution with a lot of loss, so the calculation of distribution network line loss has very important economic significance. In addition, with the development of power network construction, the rationality of distribution network topology design and the comparison of practical effects of various measures need the guidance of line loss calculation. Line loss and line loss rate is one of the main criteria to reflect the operation of distribution system. Reducing line loss of distribution system is very important for the effective use of power and economic operation of distribution system. In order to better find the effective reduction method and lay the foundation for the scientific establishment of linear contraction targets. This study uses a line loss calculation method based on BP neural network. The method uses the super matching property of the network to map the complex nonlinear relationship between the line loss and the characteristic variables and stores the evolution of the line loss changes with the changes of the structural and operational variables of the distribution line. On the basis of analyzing the theoretical methods, management methods, and various loss reduction measures of power loss calculation, this study also analyzes the status quo and existing problems of line loss analysis and calculation and collects data from line loss calculation and theoretical line loss calculation methods. This study discusses in detail the method and idea of the application of the improved neural network in the estimation of distribution network line loss, and it is used to predict the line loss in a certain area, and the prediction result is good.
Magnetosomes: A Tool for Targeted Drug Delivery in the Management of Cancer
Authors: Puri Ritika , Arora Vimal , Kabra Atul , Dureja Harish , Hamaal Shailendra
Published: April 2022

The last two decades of developments in drug formulations and novel drug delivery systems have been seen as the beginning of a new era leading to increased patient adherence and pharmacological response to the therapeutic regimen. One of the most difficult tasks is efficiency and target‐specific drug delivery or the extent of delivery at any given site of interest. Many currently designed drug delivery systems are precisely tailored to maximize the delivery of a particular form of drug by reducing the degradation or loss of the drug. In case of cancer treatment, the targeted drug delivery is of utmost importance as the anticancer agents are not having the ability to differentiate between healthy and tumor cells resulting in adverse effects and/or systemic toxicity. The targeted drug delivery is thus designed to focus on preventing side effects and encouraging the accumulation of the drug at the targeted site; one such promising drug delivery system is magnetosome drug delivery, i.e., drug delivery using magnetosomes (biological magnetic nanoparticles). In this article, we have summarized the system for design, development, and mode of drug delivery using magnetosomes along with the recent developments made in this field to facilitate the diagnosis and treatment of cancer.
The Multiple Pharmacologic Functions and Mechanisms of Action of Guizhi Fuling Formulation
Authors: Gao Jie , Yang Jianmei , Lu Zhiyuan , Dong Xianwen , Xu Ying
Published: April 2022

Objectives. Guizhi Fuling Formulation (GZFL), a traditional Chinese medical formulation, consists of Cinnamomi Ramulus, Paeoniae Radix Alba (or Paeoniae Radix Rubra), Moutan Cortex, Persicae Semen, and Poria, with multiple therapeutic functions such as sedation, antitumor activity, anti-inflammation, and neuroprotection. However, its clinical applications remain relatively fragmented, and the underlying mechanisms of GZFL in different diseases are still not very certain. Further research and summary in both application and mechanisms remain to be needed for human health and the best use of GZFL. Therefore, we summarized the multiple pharmacologic effects and possible mechanisms of action of GZFL according to recent 17 years of research. Methods. We retrieved four English and two Chinese databases using these keywords (the formulation name or its synonyms) and searched articles written in English from January 2006 up to February 2022. Key Findings. GZFL exhibits multiple pharmacologic advantages in gynecologic diseases and other expanding diseases such as cancer, blood, and vascular disease, renal failure, inflammation, and brain injury. Possibly due to its diverse bioactive components and pharmacologic activities, GZFL could target the multiple signaling pathways involved in regulating blood circulation, inflammatory and immune factors, proliferation, apoptosis, and so on. Conclusion. This review suggests that GZFL displays promising therapeutic effects for many kinds of diseases, which have been beyond the scope of the original prescription for gynecologic diseases. In this way, we wish to provide a reference and recommendation for further preclinic and clinic studies.
Short-Term Demand Forecasting Method in Power Markets Based on the KSVM–TCN–GBRT
Authors: Yang Guang , Du Songhuai , Duan Qingling , Su Juan
Published: April 2022

With the consumption of new energy and the variability of user activity, accurate and fast demand forecasting plays a crucial role in modern power markets. This paper considers the correlation between temperature, wind speed, and real-time electricity demand and proposes a novel method for forecasting short-term demand in the power market. Kernel Support Vector Machine is first used to classify real-time demand in combination with temperature and wind speed, and then the temporal convolutional network (TCN) is used to extract the temporal relationships and implied information of day-ahead demand. Finally, the Gradient Boosting Regression Tree is used to forecast daily and weekly real-time demand based on electrical, meteorological, and data characteristics. The validity of the method was verified using a dataset from the ISO-NE (New England Electricity Market). Comparative experiments with existing methods showed that the method could provide more accurate demand forecasting results.
Methotrexate-Associated Lymphoproliferative Disorder in a Patient with Psoriasis: A Case Report and Review of the Literature
Authors: Afonso Carolina , Roque Adriana , Almeida Cátia , Pimentão Maria Beatriz , Julião Maria José , Silva Rodolfo , Geraldes Catarina , Gomes Marília
Published: April 2022

Iatrogenic immunodeficiency-associated lymphoproliferative disorders (LPDs) are heterogeneous clinicopathological entities developing in patients receiving immunosuppression. Outside the posttransplant setting, methotrexate (MTX), a drug commonly used for the treatment of autoimmune diseases, is an immunosuppressive agent frequently reported to be associated with LPD. MTX-associated LPD (MTX-LPD) includes a spectrum of lymphocytic proliferations, ranging from polyclonal hyperplasia to malignant lymphoma. MTX-LPD diagnosis can be challenging, as signs and symptoms are often nonspecific and may overlap with those of several other conditions, including exacerbation of the underlying autoimmune disease. Spontaneous regression of LPD after MTX discontinuation is characteristic of MTX-LPD, therefore avoiding chemotherapeutic intervention in a significant proportion of patients. Other cases, however, should receive chemotherapy.
Inhibition of the Vancomycin Resistance in Staphylococcus aureus in Egypt Using Silver Nanoparticles
Authors: Salah Nouran M. , Saafan Amal E. , Salem Eman H. , El Rabey Haddad A. , Alsieni Mohammed A. , Alatawi Fuad A. , Alalawy Adel I. , Abeer Mohammed A. B.
Published: April 2022

Staphylococcus aureus is a major human pathogen that is sometimes resistant to vancomycin. In this study, the prevalence of vancomycin‐resistant Staphylococcus aureus (VRSA) was studied. 100 isolates of S. aureus were identified based on biochemical and molecular evidence. The antibiotic susceptibility of the studied isolates was tested against 13 antibiotics by the disc diffusion method that showed 24 vancomycin‐resistant isolates. The minimum inhibitory concentrations (MICs) were estimated by the agar dilution method to determine vancomycin intermediate‐resistant S. aureus (VISA) and VRSA. The resistance gene cluster (vanA, vanR, vanH, and vanY) was amplified by PCR and then sequenced. Amplification of vanA and vanR genes showed that they are present in 21.4% and 14.3% of VRSA isolates, respectively, whereas none of the studied genes has been detected in VISA strains. A significant antimicrobial effect toward VRSA isolates using silver nanoparticles (AgNPs) synthesized from S. aureus and rosemary leaves was recorded. This study confirmed the existence of VRSA strains in Egypt. Furthermore, the use of silver nanoparticles inhibits these vancomycin‐resistant S. aureus strains in vitro.
Adoption of Two-Dimensional Ultrasound Gastrointestinal Filling Contrast on Artificial Intelligence Algorithm in Clinical Diagnosis of Gastric Cancer
Authors: Xu Da , Liu Rong , Xu Huiping , Zhang Zhijian , Li Wei , Zhang Yi , Zhang Wenjun
Published: April 2022

This research was aimed to explore the value of gastrointestinal filling contrast-enhanced ultrasound (CEUS) and computed tomography (CT-)-enhanced scanning based on artificial intelligence (AI) algorithm in the evaluation of gastric cancer staging. 102 patients with gastric cancer were selected as the research objects. All of them underwent CEUS of gastrointestinal filling and 64-slice spiral CT before surgery. In addition, an improved mean shift algorithm was proposed based on differential optical flow and deep convolutional neural network (D-CNN), which was applied in image processing. The predicted positive rate (PPR), sensitivity, specificity, and accuracy of gastric cancer in different stages by CEUS and CT were calculated using pathological diagnosis results as the gold standard. 17 patients with T1 stage, 41 patients with T2-T3 stage, and 35 patients with T4 stage were detected by CEUS. 13 patients with T1 stage, 34 patients with T2-T3 stage, and 30 patients with T4 stage were detected by CT enhanced examination. The PPRs of CEUS for T1, T2-T3, and T4 stages of gastric cancer were higher than those of CT enhanced (

P
<
0.05

). The PPR of CEUS for N0 staging of gastric cancer was higher than that of CT enhanced (

P
<
0.05

), and it for N3 staging of gastric cancer was lower than that of CT enhanced (

P
<
0.05

). From the analysis of M staging of gastric cancer, the PPRs of CEUS for M0 and M1 staging of gastric cancer were not statistically different from the PPRs of CT enhanced (

P
>
0.05

). The sensitivity (95.6%), specificity (81.82%), and accuracy (94.12%) of CEUS in assessing resectability were significantly higher than those of CT enhancement (89.01%, 63.67%, and 86.27%, respectively), and the differences were statistically significant (

P
<
0.05

). In summary, CEUS gastrointestinal filling based on the D-CNN algorithm could better improve the display rate of the tissue lesions around the stomach. It also helped to judge the lesion progress, the depth of infiltration, and lymph node metastasis of the lesion. In addition, it had excellent performance in evaluating the resectability of gastric cancer before surgery and had clinical promotion value.
Model Optimization of Agricultural Machinery Information Control System Based on Artificial Intelligence
Authors: Wang Chunhang , An Zhe , Jairueng Supalux , Ruekkasaem Lakkana , Jayasudha M. , Singh Bhupesh Kumar
Published: April 2022

Agricultural mechanization information in our country has the main problems existing in the management and utilization. The analysis of China’s agricultural mechanization management model and related software is presented based on combining modern science and technology as well as the development of agricultural mechanization management information system based on network software to standardize the management information collection, processing, storage and transmission, agricultural mechanization management information science, standardization, automation, etc. According to the analysis, the output target speed after fusion is more stable, and the stability is increased by 59.59% compared with the single-point GNSS velocity measurement data, and by 18.32% compared with the data measured by the binocular vision velocity measurement system. It has realized the goal of accurate speed measurement from low speed to high speed. In particular, it has solved the problems such as vehicles unable to complete positioning and vehicle skidding caused by trees blocking GNSS satellite signals during field operations.
Model Predictive Control-Based Collision Avoidance for Autonomous Surface Vehicles in Congested Inland Waters
Authors: Yuan Wei , Gao Pengcheng
Published: April 2022

Compared with open waters, congested inland waters have narrow waterways, many river-crossing bridges, a high density of navigation, and high current velocity in some sections. In this study, an improved collision avoidance algorithm based on model predictive control (MPC) is proposed to solve the problem of collision avoidance for autonomous surface vehicles (ASVs) in congested inland waters. First, considering the influence of current, the collision avoidance problem of ASVs is transformed into a nonlinear programming problem, and the kinematics of ASVs and the boundary of the channel are regarded as its inequality constraints. Next, since ASVs cannot perform large-scale collision avoidance in congested inland waters, the strategy of reducing the speed and slightly changing the yaw angle is adopted to realize collision avoidance. Then, an improved dynamic bumper model is used to model the safe zone of ASVs and dynamic obstacles, which improves the efficiency of the algorithm and the safety of ASVs. Finally, the collision avoidance rules and the evaluation function of the collision avoidance maneuver are constructed in the cost function of the algorithm. The simulation experiments in different encounter scenarios show that the proposed algorithm significantly improves the rationality and compliance of ASVs’ autonomous collision avoidance in congested inland waters.
On Stability for State Constrained Hybrid Systems via Barrier Lyapunov Functions
Authors: Li Liangliang , Jiang Wenlin
Published: April 2022

In this paper, the stability of familiar state constrained hybrid systems is considered. In the first, we prove the invariant set stability for the state constrained impulsive hybrid systems. Specifically, a robust control feedback method is applied for state constrained uncertain impulsive hybrid systems. With the auxiliary matrix assistance, some convergence criteria are derived to guarantee robust stability for state constrained uncertain hybrid systems with output disturbance by constructing the symmetric and asymmetric barrier Lyapunov functions (BLF), respectively. Finally, two comparative examples with simulations show that the proposed results are effective and superior.
[Retracted] Developing a New Interval Type‐2 Hesitant Fuzzy TOPSIS‐Based Fuzzy Best‐Worst Multicriteria Decision‐Making Method for Competitive Pricing in Supply Chain
Authors: Salimian Farhad , Damiri Maryam , Ramezankhani Mahyar , Fariman Saeed Khakshouri
Published: April 2022

Corporate social responsibility based on competitive supply chain pricing has been considered the most efficient strategy of a business globally. In an MCDM challenge, a competent supply chain pricing considerations solutions supplier considers multiple possibly competing for qualitative and quantitative criteria. This study presented a new extended decision‐making and FBWM method based on type‐2 fuzzy sets TOPSIS for ranking the corporate social responsibility criteria based on competitive supply chain pricing factors. The aim of the ranking is to learn how a company and its supply chain may accomplish joint‐optimization of economic, environmental, and social performance by transparently and strategically integrating sustainability into business operations. To verify the efficacy of the suggested strategy, it is compared to a benchmarking model. IT2F VIKOR is the subject of this benchmarking model. The significance weights of the competitive supply chain pricing selection criterion are determined from BWM for both techniques. The results show that the most probable dimension of social responsibility in knowledge‐based businesses in the oil sector, according to the research, is the environmental dimension in social responsibility, which may produce competitive supply chain pricing mechanisms based on profitability. Other findings revealed that the elements of competitive supply chain pricing factors in oil companies regarding social responsibility are ranked as follows: customer, product, and competitive supply chain pricing procedures. As a result, maintaining customer values is a major price element in establishing contacts, based on the company’s strategic connection with the economy and regional and worldwide partners.
Therapeutic Effect of Nanotitanium Oxide Combined with Exercise Rehabilitation Training on Wrist Joint Injury of Boxers
Authors: Li Xiaohua , Li Jianbo , Zhang Meifang
Published: April 2022

The rapid development of society promotes the rapid improvement of science and technology. On this basis, the development of nanotechnology has also made great achievements and has been applied to all walks of life. Among them, nanotitanium oxide provides favorable conditions for the medical treatment of various joint injuries. And sports rehabilitation training also plays an irreplaceable role in the treatment of joint injury, promoting the rapid recovery of joint injury. Boxing is a sport in which boxing gloves are worn for fighting. It has both amateur and professional competitions. The purpose of this paper is to study the therapeutic effect of nanotitanium oxide combined with sports rehabilitation training on the wrist joint injury of boxers. According to the therapeutic effect of the research, it can provide useful treatment basis for clinical treatment and help boxers recover wrist joint injury, so as to carry out boxing training later. The method adopted in this paper is as follows: first, the subjects were randomly divided into nanotitanium oxide combined with sports rehabilitation training group, nanotitanium oxide treatment group, sports rehabilitation training treatment group, and control group. Then, the subjects were tested with the score table, and the score data were recorded. Finally, chi‐square test was used to test the experimental results. In conclusion, through the research and comparison, it is found that nanotitanium oxide combined with sports rehabilitation training can reduce the pain of boxers’ wrist joint injury; after the treatment of wrist joint injury of boxers by nanotitanium oxide combined with sports rehabilitation training, the pain score decreased, and the daily function was improved; the PT score before the experiment was 15%, and the peak torque score after the experiment was 26%. The Pt/BW score was 22% before the experiment and 37% after the experiment. This reflects the strength of wrist muscle contraction, judging that the stability of wrist joint is enhanced after the experiment; nanotitanium oxide combined with exercise rehabilitation training can increase the sensitivity of wrist joint after the treatment of boxer wrist joint injury.
Multimedia Urban Road Path Optimization Based on Genetic Algorithm
Authors: Ma Fangfang
Published: April 2022

In order to study multimedia urban road path optimization based on genetic algorithm, a dynamic path optimization based on genetic algorithm is proposed. Firstly, for the current situation of traffic congestion, time constraints are strictly considered based on the traditional hard time window logistics distribution vehicle scheduling problem model. Then, the mathematical model is established, and the optimal solution is solved by the combination of decomposition coordination algorithm and genetic algorithm. We divide multiple customers into different customer groups and determine the service object order of each express car in each customer group, so as to obtain the most valuable scheduling scheme. Finally, in the process of solving the model, the relevant and reliable distribution basis for enterprise distribution is collected, including customer geographical coordinates, demand, delivery time window, unit cost required for loading and unloading, loading and unloading time, and penalty cost to be borne by distribution enterprises after early arrival and late arrival. Using the improved genetic algorithm, the optimal solution of each objective function is actually obtained in about 140 generations, which is faster than that before the improvement. Using the genetic algorithm based on sequence coding, a hybrid genetic algorithm is constructed to solve the model problem. Through the comparative analysis of experimental data, it is known that the algorithm has good performance, is a feasible algorithm to solve the VSP problem with time window, and can quickly obtain the vehicle routing scheduling scheme with reference value.
A Comparative Study of Endometriosis and Normal Endometrium Based on Ultrasound Observation
Authors: Jiao Lin , Wang Jue , Zhu Lingling
Published: April 2022

In order to compare the microscopic ultrastructure of eutopic endometrium and normal endometrium in patients with endometriosis, to study the specific pathogenesis of endometriosis. In this paper, on the basis of using B-ultrasound technology, several patients with endometriosis were subjected to B-ultrasound to observe the ultrastructure of the eutopic uterine endometrium and compared with the pictures of normal endometrium to carry out the specific analysis between the two ultrastructural comparisons. This study is based on the analysis of B-ultrasound images of patients with endometriosis, compares the difference between their ultrastructure and normal human body, and conducts specific pathological diagnosis and analysis to find out the impact of the endometrium in place. The specific factors of the occurrence of lesions and the corresponding treatment methods are proposed. The experimental results show that the ultrastructure of endometriosis eutopic endometrium is different from that of normal endometrium. The microvilli of secretory cells and the cilia of ciliated cells of the former are abnormally increased and lengthened, and they are superior to B-ultrasound technology. The success rate of the examination is 93.75%, which can play an important role in the specific examination process of patients with endometriosis, as one of the actual indicators of detection. Under the electron microscope, microvilli are tiny finger-like protrusions extending from the cell membrane and the cytoplasm on the free surface of the cell, surrounded by the cell membrane and perpendicular to the cell membrane surface.
Pedometric Mapping of Soil Classes: A Case Study of San Mateo de Otao, Peru
Authors: Mestanza Carlos J. , Nazario Julio C.
Published: April 2022

Conventional soil maps are designed based on expert criteria, a characteristic that reduces their reproducibility and generates subjective uncertainty. Pedometric mapping uses mathematical and statistical principles, which makes it the opposite of conventional mapping. It was proposed to apply the pedometric mapping in San Mateo de Otao and find out its characteristics against the conventional one. Satellite and field data were used to extract covariables (soil-forming factors) and soil classes. The data were modeled with Naïve Bayes, global uncertainty was calculated by resubstitution, cross-validation and retention, and local uncertainty with the confusion and Shannon indices. A low uncertainty map was obtained with six identified soil classes, relief, and parent material having the most important covariates. We conclude that pedometric mapping has considerable advantages over conventional mapping and its application is possible under the context of soil survey in Peru.
Design of English Teaching Corpus Resource-Sharing System Based on XML Technology
Authors: Bing Li
Published: April 2022

It is well known that vocabulary is the cornerstone of language learning. Therefore, vocabulary teaching is very important in English language teaching. However, time-consuming and inefficient vocabulary education has always been a long-standing issue. Teachers utilize traditional teaching methods, which are monotonous, so students lose interest and desire in studying, resulting in a drop in academic performance and even resistance to English courses, creating a vicious circle. Therefore, improving the efficiency of vocabulary teaching has become a hot topic in the field of English education today. Corpus technology has begun to infiltrate the realm of English education as the times and technology have progressed. Because of its vast capacity and real corpus, the corpus is gradually being used in English classes. Therefore, this research will explore the application of corpus-based XML technology data-driven teaching in junior high school English vocabulary teaching. Data-driven vocabulary teaching based on corpus resources has been shown to considerably improve students’ vocabulary learning effects, but its promotion requires the assistance of education departments as well as the development of instructors’ professional theoretical understanding. In order to solve the problem of data processing and data sharing of heterogeneous data in the process of digital campus construction, this study aims at the campus network application system, using data exchange technology and XML’s good extensibility, self-description, separation of form and content, etc. It realizes data sharing and sharing in heterogeneous databases.
Application of GPS and Accelerometers in Predicting Physical Activity Patterns
Authors: Mo Mingzhu
Published: April 2022

To reduce the workload, to predict the physical activity mode with fewer variables, and to construct a path to predict PAM based on temporal and spatial data generated by physical activity and the amount of activity, this paper mainly uses the literature, logical analysis, and inductive method to sort out and summarize the basic methods and models in predicting physical activity mode using GPS and accelerometer at home and abroad and to construct a path from equipment. The process involves selecting and determining the predictors, collecting data, and using supervised learning algorithms and unsupervised learning algorithms. The joint use of GPS and accelerometers is fully capable of predicting physical activity patterns and can realize the method of predicting physical activity patterns based on the spatiotemporal data and the amount of activity generated by physical activity, although GPS and accelerometers have shortcomings in predicting PAM in terms of positioning error, missing data, and wearing position and mode.
Propofol Prevents the Growth, Migration, Invasion, and Glycolysis of Colorectal Cancer Cells by Downregulating Lactate Dehydrogenase Both In Vitro and In Vivo
Authors: Wu Zhichao , Wang Han , Shi Zu-An , Li Yalan
Published: April 2022

Colorectal cancer (CRC) is one of the most frequently diagnosed gastrointestinal malignancies worldwide and has high rates of morbidity and mortality. Propofol has been reported to have certain anticancer properties. However, the role and mechanism of propofol in CRC are not entirely clear. CRC cells were treated with propofol and/or LDH-overexpression plasmids, and a mouse xenograft model of CRC was also established and treated with propofol. Cell viability, migration, and invasion were evaluated by CCK-8, wound healing, and transwell assays; the expression of related proteins was confirmed by western blotting; indexes of the glycolytic pathway were analyzed using specialized kits; tumor growth in mice was measured; pathological tissue structure was assessed by H&E staining; and 8-OHDG expression was determined by an immunochemistry assay. Our results verified that propofol could effectively prevent the malignant behaviors of CRC cells by suppressing cell viability, migration, and invasion and accelerating apoptosis. We also discovered that propofol could attenuate the glycolytic pathway in CRC cells. Moreover, we proved that lactate dehydrogenase (LDH) was required for the inhibitory effects of propofol on the growth of CRC cells, including glycolysis in CRC cells. Furthermore, our results showed that propofol could not only significantly inhibit tumor growth and glycolysis, but also ameliorate the pathological structure of CRC tumors. The current results proved that propofol could attenuate the malignant progression of CRC by preventing LDH activity, suggesting that propofol might be an effective therapeutic agent for CRC.
A New Optimization Approach Based on Bipolar Type-2 Fuzzy Soft Sets
Authors: Alsager Kholood M. , Alfahhad Sumaiah A.
Published: April 2022

The main objective of this research study is to amplify the schematic representation of human reasoning by launching the most generalized fantastic theories of bipolar type-2 fuzzy set (BT2FS) and bipolar type-2 fuzzy soft set (BT2FS

 

f

S). These incredible models are exclusively developed for the simultaneous capturing of both polarity and abstruseness inherent in equivocal interpretations. The proposed BT2FS

 

f

S theory renders an outstanding parameterized framework that skilfully wipes out the high-order uncertainty of imprecise knowledge-based systems. First, we keenly provide the formal structure of both proposed models along with the deep exploration of their elementary properties. Moreover, we explore rudimentary set-theoretical operations of developed frameworks inclusive of equality, subset, complement, union, and intersection with their noticeable results. We brilliantly formulate a highly proficient algorithm using proposed theory to disentangle the real-world multiattribute decision-making conundrums with two-sided ambiguous information. Finally, we holistically scrutinize an empirical analysis for the selection best way to cultivate the dessert city to demonstrate the remarkable accountability of the proposed methodology.