syedzayyan/paper_mill_benchmark huggingface dataset:
miR-365 secreted from M2 Macrophage-derived extracellular vesicles promotes pancreatic ductal adenocarcinoma progression through the BTG2/FAK/AKT axis

Clinical and experimental evidence indicates that tumour‐associated macrophages support cancer progression.Moreover, macrophage‐derived extracellular vesicles (evs) are involved in pathogenesis of multiple cancers, yet the functions of molecular determinants in which have not been fully understood.Herein, we aim to understand whether macrophage modulates pancreatic ductal adenocarcinoma (pdac) progression in an ev‐dependent manner and the underlying mechanisms.Micro rna (mi r)‐365 was experimentally determined to be enriched in the evs from m2 macrophages (m2‐evs), which could be transferred into pdac cells.Using a co‐culture system, m2‐evs could enhance the proliferating, migrating and invading potentials of pdac cells, while inhibition of mi r‐365 in m2‐evs could repress these malignant functions.B‐cell translocation gene 2 (btg2) was identified to be a direct target of mi r‐365, while the focal adhesion kinase (f/atp)‐dependent tyrosine kinase (akt) pathway was activated by mi r‐365.We further demonstrated that overexpression of btg2 could delay the progression of pdac in vitro, whereas by impairing btg2‐mediated anti‐tumour effect, m2‐ev‐mi r‐365 promoted pdac progression.For validation, a nude mouse model of tumorigenesis was established, in which we found that targeting m2‐ev‐mi r‐365 contributed to suppression of tumour growth.Collectively, m2‐evs carry mi r‐365 to suppress btg2 expression, which activated fak/akt pathway, thus promoting pdac development.

Journal: Journal of Cellular and Molecular Medicine (JCMM) (2021)
Publication types: Research Article;
Improved Newton Iterative Algorithm for Fractal Art Graphic Design

Fractal art graphics are the product of the fusion of mathematics and art, relying on the computing power of a computer to iteratively calculate mathematical formulas and present the results in a graphical rendering.The selection of the initial value of the first iteration has a greater impact on the final calculation result.If the initial value of the iteration is not selected properly, the iteration will not converge or will converge to the wrong result, which will affect the accuracy of the fractal art graphic design.Aiming at this problem, this paper proposes an improved optimization method for selecting the initial value of the gauss-newton iteration method.Through the area division method of the system composed of the sensor array, the effective initial value of iterative calculation is selected in the corresponding area for subsequent iterative calculation.Using the special skeleton structure of newton’s iterative graphics, such as infinitely finely inlaid chain-like, scattered-point-like composition, combined with the use of graphic secondary design methods, we conduct fractal art graphics design research with special texture effects.On this basis, the newton iterative graphics are processed by dithering and matlab-based mathematical morphology to obtain graphics and then processed with the help of weaving cad to directly form fractal art graphics with special texture effects.Design experiments with the help of electronic jacquard machines proved that it is feasible to transform special texture effects based on newton's iterative graphic design into jacquard fractal art graphics.

Journal: Complexity (2020)
Publication types: Research Article;
Research on Load Balancing and Caching Strategy for Central Network

The central network architecture takes the information content as the processing object and can better meet the user access needs.However, the traditional network node load balancing and caching strategy has been difficult to adapt to the development of the central network architecture.Therefore, this article proposed a load balancing and cache management strategy based on the central network in order to provide a reference for the architecture and application of the central network.Based on the analysis of the existing network architecture, this article expounded the networking form and characteristics of the central network system.By analyzing the load scheduling requirements of network nodes, combined with the transmission characteristics of resource requests in the central network, a cache node load balancing method suitable for the requirements of the central network was proposed based on the existing load balancing algorithms.In addition, by analyzing the cache management strategy and its impact on the performance of the network system, a cache node information forwarding model with resource requests as the object in the central network environment was given.Finally, through experimental test and comparative analysis, the results showed that the method proposed in this article had certain feasibility and effectiveness.Compared with other typical strategies, the load balancing and caching strategy proposed in this article can better meet the user needs of the central network.

Journal: Journal of Electrical and Computer Engineering (2022)
Publication types: Research Article;
Identifying Smart Strategies for Effective Agriculture Solution Using Data Mining Techniques

Agricultural producers and enterprises face a dizzying array of decisions every day, and the many factors that influence them are incredibly complex.Agricultural planning relies heavily on accurately calculating the yields of the various crops that will be used.If you want realistic and successful solutions, data mining is an essential component.Researchers in this study are looking for ways to evaluate agricultural data and extract valuable information from the results in order to increase agricultural output.Use of the cart and random forest algorithms is a data mining technique that may be used to various datasets.It is possible to recognise the effects of various climatic and other factors on agricultural output using the matlab software and data mining methods, and a potential strategy is highlighted.

Journal: Journal of Food Quality (2022)
Publication types: Research Article;
Clinical Study on Prevention of Irinotecan-Induced Delayed-Onset Diarrhea by Hot Ironing with Moxa Salt Packet on Tianshu and Shangjuxu

To study the clinical efficacy of hot ironing of the tianshu and shangjuxu with moxa salt packet to prevent irinotecan (cpt-11)-induced delayed-onset diarrhea (iidd).Methods.A randomized controlled study was conducted on a sample of 120 patients with advanced colorectal cancer who were hospitalized in our oncology department and treated with folfiri chemotherapy regimen from february 2018 to july 2021.They were equally divided into study group (n = 60) and control group (n = 60) according to whether they were treated with hot ironing with moxa salt packs or not.The general conditions, occurrence of iidd, occurrence of delayed chemotherapy due to iidd, time of occurrence and duration of iidd, karnofsky performance score (kps) score, occurrence of leukopenia, and myelosuppression were compared between the two groups.Result.The incidence of grade 1, 2, 3, and 4 diarrhea in the study group was 11.67% (7/60), 5.00% (3/60), 3.33% (2/60), and 0.00% (0/60), respectively, while the incidence of grade 1, 2, 3, and 4 diarrhea in the control group was 21.67% (13/60), 8.33% (5/60), 10.00% (6/60), and 3.33% (2/60).The incidence of severe diarrhea and total diarrhea in the study group was (3.33% and 20.00%) lower than that in the control group (13.33% and 43.33%) ( p &lt; 0.05 ).The incidence of delayed chemotherapy was lower in the study group (8.33%) (1/12) than in the control group (23.08%) (6/26) but the difference between the groups was not statistically significant ( p &gt; 0.05 ).The time to onset of iidd in the study group (6.45 ± 1.53) days was comparable to that in the control group (6.40 ± 1.77 days) ( p &gt; 0.05 ), but the duration of iidd in the study group (3.25 ± 1.05 days) was shorter than that in the control group (5.70 ± 1.72 days) ( p &lt; 0.05 ).After treatment, the incidence of kps improvement, stabilization, and reduction in the study group was 38.33% (23/60), 51.67% (31/60), and 10.00% (6/60), respectively, the incidence of kps improvement, stabilization, and reduction in the control group was 23.33% (14/60), 50.00% (30/60), and 26.67% (16/60), respectively, and the percentage of kps reduction in the study group was less than that in the control group ( p &lt; 0.05 ).During the observation period after treatment, the total incidence of leucopenia in the study group was 11.67% (7/60) which is lower than 31.67% (19/60) in the control group ( p &lt; 0.05 ).During the observation period after treatment, the incidence of iii°+°iv myelosuppression in the study group was 5.00% (3/60) which is lower than 25.00% (15/60) in the control group ( p &lt; 0.05 ).Conclusion.The hot ironing with moxa salt packet on tianshu and shangjuxu was more effective in preventing iidd, which could reduce the incidence and severity of iidd, shorten the duration of diarrhea and significantly increase the quality of life of patients with no significant adverse effects.

Journal: Emergency Medicine International (2022)
Publication types: Clinical Study;Research Article;
Innovation and Research of Digital Artwork Design Based on Big Data and Integrated Media

We are in the era of big data, and art, which has developed along with human history, has been given new vehicles and forms of creation with the use of integrated media technology.This study first introduces artwork and its definition and classification, then briefly describes the definition and development of digital art, and focuses on the development, contribution, and drawbacks of digital artwork nft (nonfunctional token).This study divided the design model into four layers based on the neural network calculation of factorization machine to build, and the data of each layer was analyzed and processed to build the fnn algorithm model.It was found that the largest proportion of nft artworks circulating in the trading market is in paintings, at 55%.The hsv color space was selected as the color perception model, and then the picture pixel positions were determined, and redundant pixels were eliminated.To avoid data overflow, the spatial memory occupied by the pixels of the painting was calculated.The results showed that the growth of pixel space complexity was smooth and converged to a straight line, indicating that the original pixel data extracted in this experiment was stable.The circle was selected as the basic guideline for the creation of four digital art paintings with an overall uniform but innovative style.

Journal: Journal of Function Spaces (2022)
Publication types: Research Article;
Application of GA in Furniture Modeling Style Design

With the rapid development of the national economy, the construction industry is unprecedentedly active and has made great achievements.However, most traditional structural design is based on the personal experience of designers.In order to improve the production efficiency of furniture, it is necessary to optimize it.Since the evaluation criteria mainly depend on people’s subjective thoughts, it is difficult to describe the individual’s adaptive function.In this paper, an interactive innovation evolution system based on evolutionary algorithm is proposed.Taking full advantage of the evolution function of genetic algorithm and the modeling advantages of acis platform, a prototype innovation evolution system is developed based on acis/hoops platform with the genetic algorithm based on tree structure as the innovation support, which can help designers complete the innovation modeling design.The results show that the total processing time of the sorting scheme obtained by this algorithm is reduced by 2475.2 s, the production efficiency is increased by 20.6%, and the job waiting time is reduced by 15079.2 s.The results show that the algorithm is feasible and effective in solving the furniture production scheduling problem.The furniture production scheduling scheme solved by genetic algorithm can provide certain reference value for production personnel to formulate production scheduling scheme and improve production efficiency.

Journal: Journal of Function Spaces (2022)
Publication types: Research Article;
Optimization of the Intelligent Sports Health Management System Based on Big Data Technology and Internet of Things

People often suffer from unpredictable injuries during physical exercise.One of the important reasons is the absence of a scientific sports health management system.Therefore, the construction of such a scientific and effective system has gradually attracted the attention of scholars, which is of great significance to realizing people’s scientific and personalized physical fitness.An intelligent sports health management system based on big data analysis and the internet of things (io t) is constructed to solve this problem.The system consists of the user, io t, cloud, system analysis, evaluation, and data layers.Firstly, a new multilabel feature selection algorithm is proposed in the system analysis layer.The suggested multilabel feature selection algorithm maps the sample space to the label space through the l 21 norm.Then, the consistency of various topologies is guaranteed by combining with feature popularity so that the factors affecting user health can be better selected.Secondly, the experiment is compared with scls, ssfs, and six other multilabel feature selection algorithms in 6 classic medical multilabel datasets.Experimental results under five indexes show the effectiveness and superiority of the proposed feature selection algorithm.Finally, the feasibility of the proposed intelligent sports management system is analyzed.

Journal: Journal of Function Spaces (2022)
Publication types: Research Article;
Analysis of Consumer Behavior Data Based on Deep Neural Network Model

This paper divides the research modes of consumer purchase behavior characteristics into three categories: experience-driven mode, theory-driven mode, and data-driven mode.An analysis algorithm based on customer consumption behavior is proposed, and the idea of combining customer consumption behavior factors such as satisfaction and loyalty is proposed.Through comparison, it is pointed out that the data-driven model is most suitable for analyzing the characteristics of online consumers’ purchasing behavior.Using the decision support of knowledge base, different service schemes for customers with different evaluation degrees are realized.In order to improve the accuracy of sample classification and maximize the output function, genetic algorithm is used to optimize the samples.A deep neural network structure algorithm is proposed to classify customer transaction data samples.In this algorithm, the sheep nodes are not fixed, but the number of hidden layers and unit nodes of the neural network are dynamically determined according to the sample training.The research excavates various kinds of valuable information such as consumer preferences and consumption structure from the huge consumption data of consumers.It is not only helpful for enterprises to analyze consumers’ consumption behavior and organize production but also helpful for enterprises to realize the concept of personalization.

Journal: Journal of Function Spaces (2022)
Publication types: Research Article;
Modeling and Simulation of Real Estate Valuation Based on Genetic Neural Network Model

China’s real estate market is developing rapidly, but the house price is abnormal.The nonlinear relationship between housing characteristics and real estate value is difficult to calculate, resulting in the difficulty of house price prediction.Based on this, the relationship model between characteristic variables and house prices is constructed by using the machine learning method.At the same time, genetic algorithm is used to screen the specific values.The experimental results show that the optimized model converges in 56 iterations; in the application test, the research model found that in 90% of the test samples, the error between the predicted value and the actual measured value shall not exceed 10%.Experiments show that the genetic algorithm is effective in optimizing the bp house price valuation model and improves the calculation efficiency and valuation accuracy of the valuation model.

Journal: Journal of Function Spaces (2022)
Publication types: Research Article;
Optimization of Three-Dimensional Model of Landscape Space Based on Big Data Analysis

Based on virtual reality technology, landscape 3d modeling provides users with the possibility to construct a simulated garden landscape environment design effect online, so it has high requirements for accuracy.With the continuous improvement of precision requirements, the number of people involved in the construction of 3d models is also increasing, which puts forward higher requirements for modeling.Based on this, this paper studies the optimization strategy of landscape space 3d model based on big data analysis.Based on the analysis of the establishment of the 3d model and the related algorithm research, this paper analyzes the optimal design of the 3d model under the background of big data.In the 3d modeling of the edge folded area, it is based on the traditional quadratic error measurement grid simplification algorithm, combined with the vertex error matrix to simplify, so as to shorten the modeling time.Based on an efficient search algorithm, an adaptive nonsearch fractal image compression and decoding method is proposed in the image compression and decoding stage of 3d modeling.The search is performed by specifying the defined area block.Finally, an experiment is designed to analyze the performance of the optimization algorithm.The results show that the improved edge folding region algorithm can reduce errors on the basis of ensuring image quality, and the adaptive search algorithm can shorten the search time and improve the compression rate.This method provides a technical reference for the visualization experience and simulation system of garden landscape design and improves the presentation quality of virtual garden landscape design scenes.

Journal: Journal of Function Spaces (2022)
Publication types: Research Article;
Risk Prediction Algorithm of Social Security Fund Operation Based on RBF Neural Network

In order to ensure the benign operation of the social security fund system, it is necessary to understand the social security fund facing all aspects of the risk, more importantly to know the relationship between different risks.Based on rbf, the interpretative structure model is applied to draw the risk correlation hierarchy diagram, which provides a scientific risk management method for the social security fund.Rbf neural network is used to build the risk warning model of social security fund operation.Then, put forward the corresponding risk treatment scheme to the warning signal.Finally, the rbf neural network is used for comprehensive risk warning.In this paper, the risk warning of social security fund operation is the research object, and the corresponding risk treatment scheme is put forward for the warning signal.This paper uses an improved ant colony algorithm to optimize the parameters of the rbf neural network, which overcomes the shortcomings of the traditional rbf neural network such as slow convergence, ease of falling into local extremes, and low accuracy, and improves the generalization ability of the rbf neural network.It has the characteristics of good output stability and fast convergence speed.On this basis, the prediction model based on the improved ant colony-rbf neural network is established, and the matlab software calculation tool is used for accurate calculation, which makes the prediction results of coal mine safety risk more accurate and provides more reliable decision basis for decision makers.The results show that the network has small calculation error, fast convergence, and good generalization ability.

Journal: International Journal of Antennas and Propagation (2021)
Publication types: Research Article;
Digital Technology Empowers Grain Supply Chain Optimization Simulation

The issue of the balance of food supply and demand has always been the main issue of national and even world food security.There are many factors that affect food supply and demand, and the factors are interrelated.Therefore, it is necessary to study this complex issue in a systematic way in order to provide a reliable theoretical basis for the country to formulate effective policy measures.Based on the analysis of the current situation of grain supply and demand, this paper uses system dynamics (sd) to carry out digital elevation model (dem) and latitude correction for land surface temperature (lst).The lst, combined with the normalized difference vegetation index (ndvi), has initially constructed a temperature vegetation dryness index (tvdi) model; it has constructed five subsystems including arable land, production capacity, import, population, and consumption.This paper proposes a food supply chain network construction model from the dimensions of the food supply chain network’s information flow, logistics, and business flow.Through detailed empirical analysis of each subsystem, we judge the development trend of the total grain system, perform operational tests and historical tests on the simulation results of the model to judge the rationality of the model system structure and simulation prediction, and give the simulation results.Finally, based on the forecast results, targeted countermeasures and suggestions are proposed.

Journal: Complexity (2021)
Publication types: Research Article;
Painting Color Editing System Based on Virtual Reality Sensor Technology

In order to solve the problem of large color matching error in color matching system, this paper proposes a painting color editing system based on virtual reality sensing technology.Apply virtual reality technology (technology) to the proposed system design, and design the system framework based on the three‐tier architecture, which includes data access layer, business logic layer, and presentation layer.The hardware includes spectrophotometer, central processing unit, storage device, and virtual reality somatosensory interaction device.The software part takes bp neural network algorithm as the core, trains and processes the three color values, constructs the automatic matching model of painting colors according to the training results, and realizes the logical operation and analysis of the system software.The experimental results show that although there is still a certain error between the painting color matching output by the system and the actual color matching of the test samples, the maximum absolute error of the matching is relatively small, which is less than 0.05.The error between 6 groups of actual matching samples and predicted matching samples is &lt;1.5.Although the designed system has some errors with the expected color matching, the error is small, so the color difference does not affect the visual presentation effect.

Journal: Journal of Sensors (2023)
Publication types: Research Article;
Preparation of PCL Electrospun Fibers Loaded with Cisplatin and Their Potential Application for the Treatment of Prostate Cancer

Prostate cancer is a global fatal type of cancer.It is a type of cancer that affect men.Signs and symptoms of the disease include blood in the urine, pain when one micturates, and difficulties in penis erection.Cisplatin chemotherapy is a principal treatment normally given to the prostate cancer patients.Nonetheless, on its own, cisplatin loses efficacy once administered due to liver pass effects and other biochemical attacks.In this paper, we looked at preparation of pcl nanoparticles loaded with cisplatin and their potential for the treatment of prostate cancer.Pcl nanoparticles protect cisplatin from biochemical attack, thus increasing drug efficacy.Incorporation of p-glycoprotein inhibitors in pcl nanoparticles (nps) loaded with cisplatin could improve prostate cancer treatment even more.

Journal: Emergency Medicine International (2022)
Publication types: Research Article;