syedzayyan/paper_mill_benchmark huggingface dataset:
Design and Application of Land Resource Management System Based on Internet of Things

In order to further solve the problems existing in the process of land resource management of relevant departments of land and resource management, realize the information management of land resources, and promote the efficient utilization of land resources and scientific information management, this paper proposes a land resource management system based on internet of things technology.With the help of resource allocation and management algorithm based on the needs of internet of things users and land resource management requirements, this paper constructs a land resource management system based on web and mobile network platform.The system development takes java as the background programming language, constructs the database based on oracle 9i, and uses gis/gps and other electronic map functions to realize the accurate display and resource sharing of land resource information.In the system test, 60, 120, 240, and 360 concurrent users are simulated to access the system at the same time.The connection success rates of the corresponding system are 97.2%, 97.5%, 98.3%, and 96.6%, respectively, which fully verifies the good performance of the system; it can be widely used in land resource information management.

Journal: Wireless Communications and Mobile Computing (2022)
Publication types: Research Article;
Acupuncture for Knee Osteoarthritis: A Systematic Review of Randomized Clinical Trials with Meta-Analyses and Trial Sequential Analyses

Knee osteoarthritis (koa) can cause chronic pain and seriously affect the quality of patient lives.The continued emergence of high‐quality rcts requires us to update the quality of evidence.This study aims to evaluate the efficacy of acupuncture for koa patients and calculate the required information size (ris) to determine whether further clinical studies are required.Methods .We searched pub med, embase, wos, cbm, cnki, vip, who ictrp, chi ctr, and grey literature to collect randomized controlled trials (rcts) of acupuncture for koa from inception to december 2021.A meta‐analysis was performed according to the cochrane systematic review method by using review manager 5.4 and tsa 0.9.5.10 beta, and grade was used to evaluate the quality of the evidence.Trial sequential analysis was used to control random errors and calculate the required information size.Results .Eleven rcts with 2484 patients were included in our meta‐analysis, meeting the inclusion criteria for the meta‐analysis.The meta‐analysis indicated that acupuncture had beneficial effect on knee osteoarthritis in reducing pain [ n = 2387; smd = −0.12, 95% ci (‐0.20, ‐0.04); i 2 = 0 % ] and improved patients function activities [ n = 2408; md = −1.25, 95% ci (‐1.97, ‐0.53); i 2 = 0 % ], but true acupuncture showed no significant effect in relieve patient’s stiffness [ n = 1337; md = −0.07, 95% ci (‐0.30, 0.15); i 2 = 0 % ].We pooled the studies which found no significant difference in improving the quality life of mental [ n = 1462; smd = 0.02, 95% ci (‐0.23, 0.27); i 2 = 78 % ] and patients physical health (sf‐36 or sf‐12) [ n = 1745; smd = 1.01, 95% ci (‐0.08, 2.11); i 2 = 0 % ] compared with sham acupuncture.The pain and function tsa graphs indicated that cumulative z‐curves intersected with the traditional level of statistical favoring acupuncture, and more rcts will required in the future studies.Conclusion .Acupuncture has beneficial effect on pain relief and improves function activities, and this treatment can be recommended as a beneficial alternative therapy in patients with koa, particularly for chronic patients and those currently undergoing long‐term pain and help them increasing quality of life.But it should be further verified through more rcts in function.Available studies suggested that acupuncture was superior to sham acupuncture in reduce pain and function as verified by tsa.

Journal: BioMed Research International (2022)
Publication types: Meta-Analysis;Review Article;
A2/B1 Promotes NRF2 mRNA Stability and Inhibits Ferroptosis and Cell Proliferation in Breast Cancer Cells

Breast cancer is a highly harmful malignant tumor, which poses a great threat to women’s body and mind, and the mortality rate ranks second among all women’s diseases.The incidence rate accounts for 7‐10% of various malignant tumors in the whole body, second only to uterine cancer in women, and has become the main cause of threatening women’s health.Advanced breast cancer is often considered an incurable disease.The family of heterogeneous nuclear ribonucleoprotein complexes is composed of about 20 hn rnp proteins with molecular weights ranging from 32 to 120 k da, and they are named according to their molecular weights.Among them, hn rnpa2 and hn rnpb1 are the two most important members of the hn rnp family, both derived from the same gene on chromosome 7p15.Therefore, research to understand the molecular mechanism and process of breast cancer progression has an important role in promoting the current medical research on breast cancer treatment methods.Therefore, studying the mechanism of tumorigenesis is the key to tumor prevention and treatment.Therefore, this paper proposes that a2/b1 promotes the stability of nrf2 m rna and inhibits ferroptosis and cell proliferation in breast cancer cells.The article mainly introduces the disease diagnosis method based on artificial neural network and its neural network algorithm.In the experimental part, the activity of hn rnp a2/b1 on cancer cells is deeply studied.The results show that the absorbance of the mtt method increases continuously with the extension of the culture time, and the maximum reaches 1.2.This fully shows that its absorption capacity is very strong, especially after 24 hours, the absorption rate rises from 0.6 to 0.9, which shows that 24 hours is the best absorption time.And it can also be found that hn rnpa2/b1 has a significant inhibitory effect on breast cancer cells; it can reduce the effect on breast cancer cell cycle and apoptosis.

Journal: BioMed Research International (2023)
Publication types: Research Article;
Application of Rotationally Symmetrical Triangulation Stereo Vision Sensor in National Dance Movement Detection and Recognition

Information acquisition is an important branch of information science.It is the product of the development and cross-integration of traditional sensing technology and other multidisciplines.It is characterized by the high precision, high speed, integration, and intelligence of information acquisition.An important part of information acquisition, it studies how to acquire the geometric structure and scale information of objects in three-dimensional space.With the continuous development of the level, the acquisition of 3d information has become more and more important in scientific research and industrial production.This paper takes 3d information acquisition technology as the main line.From the perspectives of fusion, effectiveness, physical limit, and measurement efficiency, high-resolution 3d information acquisition techniques are studied.In this paper, the upper and lower limb movements of each behavioral segment are segmented according to the difference.An omnidirectional stereo vision sensor composed of a single camera and two secondary conical mirrors is designed, which solves the problems of large size, narrow field of view, and asynchronous image acquisition of traditional binocular vision sensors, effectively ensuring image perspective.The invariance of projection avoids image distortion caused by curved mirror imaging and reduces the difficulty of subsequent work.The three-dimensional delaunay triangulation algorithm is used to replace the traditional poisson reconstruction algorithm to generate the triangular mesh model.The triangular mesh model obtained by the multiview stereo reconstruction algorithm is relatively rough.This article uses zbrush’s zremesher and geometric-divide functions to smooth and simplify the model.The results show that the accuracy of the algorithm proposed in this paper is as high as 89.25% in the motion recognition of body elements.The dynamic mapping method is used to map the texture of the triangular mesh model, so that the realism of the model reaches 91.23%.

Journal: Wireless Communications and Mobile Computing (2022)
Publication types: Research Article;
A Study on the Relationship between Mental Resilience, Work-Family Conflict, and Anxiety of Nurses in Shandong, China

Based on freud’s personality theory, 839 nurses from 5 public hospitals in shandong province were selected by the convenience sampling method.A cross‐sectional survey was conducted to investigate the correlation among resilience, work‐family conflict (wfc), and anxiety (sas).The purpose of this study was to explore the relationship among resilience, wfc, and sas of these nurses and to provide evidence with reducing wfc and sas for the nurses.The results showed that the total score of resilience, wfc, and sas was 58.00 ± 18.27, 53.46 ± 13.29, and 49.98 ± 14.73, respectively.There was 47.68% of the nurses that had anxiety, and 10.97% of the nurses had severe anxiety.There were significant differences in the score of sas among the length of service, self‐perceived health status, confidence in nursing professional development, wfc, and resilience ( p &lt; 0.05).This study draws the following conclusions: the proportion of anxiety is high, and the level of resilience is lower than the domestic norm.Length of service, self‐perceived health status, confidence in nursing professional development, wfc, and resilience were the important influencing factors of anxiety.It is suggested that hospital managers should pay attention to the mental health of nurses, take active intervention measures according to the influencing factors of sas, improve nurses’ psychological resilience, reduce wfc and anxiety, improve nurses’ mental health and well‐being, and ensure nursing safety.

Journal: BioMed Research International (2022)
Publication types: Research Article;
A Study on the Evaluation of Polyenoic Vegetable Oils and Their Female Health Benefits Based on Time Series Analysis Model: The Case of Peony Seed Oil

Polyenoic vegetable oils mainly contain polyenoic acids such as linoleic acid and linolenic acid, as well as active ingredients such as ve, phytosterols, mineral elements, and squalene.Among them, schisandra oil, kiwi seed oil, grape seed oil, maitake fruit oil, and evening primrose seed oil all contain up to 80% or more polyenoic acids.Studies have shown that polygenic vegetable oils have the effects of assisting in lowering blood lipids, antioxidation, delaying ageing, anti-inflammation, sun protection, moisturizing, slimming and weight loss, etc.They can be widely used in nutritional and healthy edible oils, health food, skin care, and cosmetic products and have great prospects for development and utilization.This paper explores the application of artificial neural networks in the analysis of data.A nonlinear time series prediction method based on the bp algorithm is proposed.The prediction accuracy is much higher than that of the traditional method.

Journal: Journal of Healthcare Engineering (2022)
Publication types: Research Article;
A Robust and Novel Approach for Brain Tumor Classification Using Convolutional Neural Network

Brain tumors are the most common and aggressive illness, with a relatively short life expectancy in their most severe form.Thus, treatment planning is an important step in improving patients’ quality of life.In general, image methods such as computed tomography (ct), magnetic resonance imaging (mri), and ultrasound images are used to assess tumors in the brain, lung, liver, breast, prostate, and so on.X‐ray images, in particular, are utilized in this study to diagnose brain tumors.This paper describes the investigation of the convolutional neural network (cnn) to identify brain tumors from x‐ray images.It expedites and increases the reliability of the treatment.Because there has been a significant amount of study in this field, the presented model focuses on boosting accuracy while using a transfer learning strategy.Python and google colab were utilized to perform this investigation.Deep feature extraction was accomplished with the help of pretrained deep cnn models, vgg19, inception v3, and mobile net v2.The classification accuracy is used to assess the performance of this paper.Mobile net v2 had the accuracy of 92%, inception v3 had the accuracy of 91%, and vgg19 had the accuracy of 88%.Mobile net v2 has offered the highest level of accuracy among these networks.These precisions aid in the early identification of tumors before they produce physical adverse effects such as paralysis and other impairments.

Journal: Computational Intelligence and Neuroscience (2021)
Publication types: Research Article;
A Retrospective Analysis of 1,595 Cases: Comparing the Characteristics of Total Knee Arthroplasty between Tibetans Living in the Plateau and Han of the Sichuan Basin

Total knee arthroplasty is an effective treatment for end‐stage knee arthritis.Studies’ date have shown that the demand for knee replacements continues to increase worldwide.Although racial disparities have been previously reported in the utilization of total knee arthroplasty in western countries, however, there are few similar studies in china.Objectives .Retrospective analysis of medical records identified the characteristics of tibetan patients who had undergone total knee arthroplasty living in plateau and their differences with hans living in sichuan basin.Methods .The patients with unilateral primary total knee arthroplasty in sichuan orthopedic hospital from 2015 to 2020 were enrolled.Analyze and compare the demographic characteristics (age, body mass index, and occupation) and pathogenesis characteristics (disease duration, exacerbation period, treatment methods, etc.) of the plateau tibetans and the hans of the sichuan basin.Results .1595 eligible patients were reviewed, including 541 tibetan patients and 1054 han patients, the average age of tibetan patients was lower than that of han patients ( p &lt; 0.001); the average bmi index of tibetan patients was higher than that of han patients ( p &lt; 0.001).And obese people account for more; the occupational distribution of tibetan patients is more concentrated, mainly farmers.Tibetan patients have a longer course of disease than han patients ( p &lt; 0.001).Conclusions .The social background and geographical environment of the tibetan population are different from those of the han.Tibetans living on plateaus have an earlier illness, a longer course of illness, a more obvious trend of younger, age and fewer opportunities for regular treatment during the illness.

Journal: BioMed Research International (2022)
Publication types: Research Article;
A Precise Medical Imaging Approach for Brain MRI Image Classification

Magnetic resonance imaging (mri) is an accurate and noninvasive method employed for the diagnosis of various kinds of diseases in medical imaging.Most of the existing systems showed significant performances on small mri datasets, while their performances decrease against large mri datasets.Hence, the goal was to design an efficient and robust classification system that sustains a high recognition rate against large mri dataset.Accordingly, in this study, we have proposed the usage of a novel feature extraction technique that has the ability to extract and select the prominent feature from mri image.The proposed algorithm selects the best features from the mri images of various diseases.Further, this approach discriminates various classes based on recursive values such as partial z-value.The proposed approach only extracts a minor feature set through, respectively, forward and backward recursion models.The most interrelated features are nominated in the forward regression model that depends on the values of partial z-test, while the minimum interrelated features are diminished from the corresponding feature space under the presence of the backward model.In both cases, the values of z-test are estimated through the defined labels of the diseases.The proposed model is efficiently looking the localized features, which is one of the benefits of this method.After extracting and selecting the best features, the model is trained by utilizing support vector machine (svm) to provide the predicted labels to the corresponding mri images.To show the significance of the proposed model, we utilized a publicly available standard dataset such as harvard medical school and open access series of imaging studies (oasis), which contains 24 various brain diseases including normal.The proposed approach achieved the best classification accuracy against existing state-of-the-art systems.

Journal: Computational Intelligence and Neuroscience (2022)
Publication types: Research Article;
A Novel Necroptosis-Related lncRNA Signature for Osteosarcoma

S.Osteosarcoma (os) is easy to metastasis.Necroptosis-related long noncoding rna (lnc rna) (nrlnc rna) plays a vital role in the tumorigenesis of many malignant tumors.Nonetheless, there have been few studies investigating the relations between nrlnc rna and os.During the investigation, nrlnc rnas in os were confirmed and characterized and their relationships with prognoses were investigated.Methods.Nrlnc rnas were downloaded from the cancer genome atlas (tcga) os expression data and clinical-pathological information.First, univariate cox regression and lasso regression analyses were used to screen for prognostic-related nrlnc rnas.Second, multivariate regression analyses were used to establish a prognostic nomogram for predicting individual survival probability.Survival analyses demonstrated that high-risk patients (hrps) had a poor prognosis.In addition, gene set enrichment analyses (gsea) were used to identify gene function in high- and low-risk groups based on the survival mode.Results.The 7 nrlnc rnas (ac004812.2, ac022915.1, ac073073.2, ac090559.1, al512330.1, ddn-as1, and sencr) were shown to have a distinct difference and were used to construct an nrlnc rna signature.Using the signature as a risk score was an independent factor for os patients.The signature divided os patients into the high- and low-risk groups.Furthermore, the seven lnc rnas were significantly enriched in cell migration and metabolism.Conclusions.The 7 nrlnc rna survival models have the potential to serve as therapeutic targets and molecular biomarkers for patients with os, as well as to precisely predict os prognoses.

Journal: Computational and Mathematical Methods in Medicine (2022)
Publication types: Research Article;
A Novel Missense Variant in the Gene PPP2R5D Causes a Rare Neurodevelopmental Disorder with Increased Phenotype

Ppp2r5d‐related neurodevelopmental disorder, which is mainly caused by de novo missense variants in the ppp2r5d gene, is a rare autosomal dominant genetic disorder with about 100 patients and a total of thirteen pathogenic variants known to exist globally so far.Here, we present a 24‐month‐old chinese boy with developmental delay and other common clinical characteristics of ppp2r5d‐related neurodevelopmental disorder including hypotonia, macrocephaly, intellectual disability, speech impairment, and behavioral abnormality.Trio‐whole exome sequencing (wes) and sanger sequencing were performed to identify the causal gene variant.The pathogenicity of the variant was evaluated using bioinformatics tools.We identified a novel pathogenic variant in the ppp2r5d gene (c.620g&gt;t, p.trp207leu).The variant is located in the variant hotspot region of this gene and is predicted to cause ppp2r5d protein dysfunction due to an increase in local hydrophobicity and unstable three‐dimensional structure.We report a novel pathogenic variant of ppp2r5d associated with ppp2r5d‐related neurodevelopmental disorder from a chinese family.Our findings expanded the phenotypic and mutational spectrum of ppp2r5d‐related neurodevelopmental disorder.

Journal: BioMed Research International (2021)
Publication types: Case Report;
Relationship between Business Administration Ability and Innovation Ability Formation of University Students Based on Data Mining and Empirical Research

With the continuous development of science and technology, the scale of education is slowly expanding, but at the same time, what is more important is the quality of higher education.This issue has also become a hot topic of concern for the society nowadays.Higher education, which is at the top of the education chain, has the important task of training talents for society.The cultivation of talents includes not only the ability to innovate but also the ability to manage business.The purpose of this paper is to study and empirically analyze the relationship between business administration competency and innovation competency formation among university students based on data mining (dm).This paper mainly uses the k-means algorithm in dm to extract relevant data and then conducts a questionnaire survey to analyze the innovation ability and business administration ability of university students; the results showed that 24 respondents considered their innovation ability is relatively poor, accounting for 20.98%; 137 respondents considered their innovation ability is average, accounting for 47.9% of the total, nearly half; and 65 respondents thought that their innovation ability is relatively strong.In the end, 60 respondents thought that their innovation ability is very strong and half of the respondents thought that their innovation ability is generally poor.Generally speaking, the tests have a strong ability to innovate.The evaluation is generally average.

Journal: Mobile Information Systems (2021)
Publication types: Research Article;
Prediction of the Forwarding Volume of Campus Microblog Public Opinion Emergencies Using Neural Network

With the continuous expansion of colleges and universities, the proportion of college students among netizens has increased rapidly.They have become an important component of chinese netizens and the main audience of weibo.It is easier for online public opinion to form in some social media platforms than in others.We explain places for political conversation in chinese cyberspace in terms of interaction, which results in various types of political discussion, based on research on authoritarian deliberation.The crisis of college network public opinion caused by the improper use of weibo by college students occurs frequently.College network public opinion has become an important factor affecting the development and stability of colleges and universities.Social network rumor forwarding behavior refers to whether users forward specific rumors.Also, there is no surety about the truthfulness of the rumor.Taking weibo as an example, this paper proposes a neural network-based model for predicting the forwarding volume of public opinion emergencies on campus weibo.It solves the problem of low prediction accuracy of traditional svm and other models.The data-driven experimental findings suggest that the technique described in this study can increase the accuracy of predicting forwarding volume in unexpected campus public opinion events.

Journal: Mobile Information Systems (2022)
Publication types: Research Article;