Problems In Field Research Articles

Application of Mendelian randomized research method in oncology research: bibliometric analysis

BackgroundCancer has always been a difficult problem in the medical field, and with the gradual deepening of Genome-wide association studies (GWAS), Mendelian randomization methods have been increasingly used to study cancer pathogenesis. In this study, we examine the literature on Mendelian cancer, summarize the status of the research, and analyze the development trends in the field.MethodsPublications on “Mendelian Randomization - Cancer” were retrieved and downloaded from the Web of Science Core Collection database. CiteSpace 6.2.R4, VOSviewer 1.6.19, Scimago Graphica 1.0.38, Bibliometrix R-package, and a bibliometric online analysis platform were used for data analysis and visualization. An in-depth analysis of country or region, authors, journals, keywords, and references was performed to provide insights into the content related to the field.ResultsA total of 836 articles were included in the analysis; 643 authors from 72 countries had published articles related to the field. China and Harvard University (among countries and institutions, respectively) had the highest number of articles. Martin, Richard M and Smith, George Davey were the largest contributors. A total of 27 cancers have been studied, with breast, colorectal, and liver cancers being the most studied.ConclusionThis study is the first to use bibliometric methods to visualize the application of Mendelian randomization analysis in the field of cancer, revealing research trends and research frontiers in the field. This information will provide a strong reference for cancer researchers and epidemiologic researchers.

Using the Rstudio Environment in Researching Problems in Mathematics, Physics and Economics

The purpose of the article is to study the possibilities of using the RStudio environment to solve problems in mathematics, physics and economics. In particular, the emphasis is on the integration of statistical analysis tools, the use of a set of mathematical functions and operations that ensure efficiency and accuracy in calculations. Research methods. An analysis of scientific papers, methodological recommendations and examples of RStudio application in various fields of knowledge related to mathematics, physics and economics was conducted, the functionality of the RStudio environment was tested on practical tasks, and the results obtained using RStudio were compared with the results obtained using other methods to assess the effectiveness of the environment. The scientific novelty lies in the use of R functionality, which allows developing and implementing new methods and approaches to data analysis and solving scientific problems. Conclusions. The RStudio environment demonstrates its effectiveness for solving problems in mathematics, physics and economics due to a wide range of functions for numerical modeling, statistical analysis and data visualization. Using RStudio allows you to significantly simplify the process of solving complex mathematical problems, in particular, it contributes to the integration of mathematical methods in the study of physical phenomena and economic processes, which allows you to form new approaches to data analysis and modeling. RStudio can be used as an effective tool in education, contributing to the formation of analytical thinking and programming skills in students engaged in mathematical, physical and economic problems. In particular, the use of the Tukey criterion and the functions of the RStudio environment for comparing data in large arrays, such as the results of experiments or measurements. The results of the study confirm the feasibility of implementing RStudio in the practice of teaching and scientific research, and also indicate the need for further research into the capabilities of the environment in solving problems in scientific fields.

Synthesis of Boundary Conditions in Polygonal Magnetic Domains Using Deep Neural Networks

In this paper, the authors approach the problem of boundary condition synthesis (also defined as field continuation) in a doubly connected domain by the use of a Neural Network-based approach. In this innovative method, given a field problem (magnetostatic, in the test case shown here), a set of Finite Element Method simulations is performed in order to define the training set (in terms of the potential over a domain) by solving the direct problem; subsequently, the Neural Network is trained to perform the boundary condition synthesis. The performances of different Neural Networks are compared, showing the accuracy and computational efficiency of the method. Moreover, domains externally bounded by two different kinds of polygonal contours (L-shaped and three-segments, respectively) are considered. As for the latter, the effect of the concavity/convexity of the boundary is deeply investigated. To sum up, a classical field continuation problem turns out to be revisited and solved with an innovative approach, based on deep learning.

An Enhanced and Highly Efficient Semi‐Implicit Combined Lagrange Multiplier Approach Preserving Original Energy Law for Dissipative Systems

ABSTRACTRecently, a new Lagrange multiplier approach was introduced by Cheng, Liu, and Shen, which has been broadly used to solve various challenging phase field problems. To design original energy‐stable schemes, they have to solve a nonlinear algebraic equation to determine the introduced Lagrange multiplier, which can be computationally expensive, especially for large‐scale and long‐time simulations involving complex nonlinear terms. In this article, we propose an essential improved technique to modify this issue, which can be seen as a semi‐implicit combined Lagrange multiplier approach. In general, the newly constructed schemes keep all the advantages of the Lagrange multiplier method and significantly reduce the computation costs. Besides, the new proposed second‐order backward difference formula (BDF2) scheme dissipates the original energy, as opposed to a modified energy for the classic Lagrange multiplier approach. In addition, we establish a general framework for extending our constructed method to dissipative systems. Finally, several examples have been presented to demonstrate the effectiveness of the proposed approach.

Economic Impact Study of the New Crown Epidemic

The outbreak's impact on the world as a whole, particularly the world economy, has been enormous. Although there have been studies on the impact of the epidemic on certain areas and sectors, there is a lack of comparative summaries and a lack of a more comprehensive exploration of the results of the epidemic. This paper aims to synthesise a number of papers to summarise and analyse the impact of the outbreak on the economy in China, such as a decrease in consumption and the number of products produced. In the future, people should identify the existing problems in various fields, solve the problems by combining the actual situation of the current era, and the state should summarise the relevant laws and introduce relevant policies to accelerate the economic recovery speed, help the economic development, and build the country into a rich, intense, democratic, civilised, harmonious and modern socialist power as soon as possible.

Research on a Panoramic Image Stitching Method for Images of Corn Ears, Based on Video Streaming

Background: Corn is the main grain crop grown in China, and the ear shape index of corn is an important parameter for breeding new varieties, including ear length, diameter, row number of ears, row number of grains per ear, and so on. Objective: In order to solve the problem of limited field of view associated with computer detection of the corn ear shape index against a complex background, this paper proposes a panoramic splicing method for corn ears against a complex background, which can splice 10 corn ear panoramic images at the same time, to improve information collection efficiency, display comprehensive information, and support data analysis, so as to realize automatic corn seed examination. Methods: A summary of corn ear panoramic stitching methods under complex backgrounds is presented as follows: 1. a perceptual hash algorithm and histogram equalization were used to extract video frames; 2. the U-Net image segmentation model based on transfer learning was used to predict corn labels; 3. a mask preprocessing algorithm was designed; 4. a corn ear splicing positioning algorithm was designed; 5. an algorithm for irregular surface expansion was designed; 6. an image stitching method based on template matching was adopted to assemble the video frames. Results: The experimental results showed that the proposed corn ear panoramic stitching method could effectively solve the problems of virtual stitching, obvious stitching seams, and too-high similarity between multiple images. The success rate of stitching was as high as 100%, and the speed of single-corn-ear panoramic stitching was about 9.4 s, indicating important reference value for corn breeding and disease and insect detection. Discussions: Although the experimental results demonstrated the significant advantages of the panoramic splicing method for corn ear images proposed in this paper in terms of improving information collection efficiency and automating corn assessment, the method still faces certain challenges. Future research will focus on the following points: 1. addressing the issue of environmental interference caused by diseases, pests, and plant nutritional status on the measurement of corn ear parameters in order to enhance the stability and accuracy of the algorithm; 2. expanding the dataset for the U-Net model to include a wider range of corn ears with complex backgrounds, different growth stages, and various environmental conditions to improve the model’s segmentation recognition rate and precision. Recently, our panoramic splicing algorithm has been deployed in practical applications with satisfactory results. We plan to continue optimizing the algorithm and more broadly promote its use in fields such as corn breeding and pest and disease detection in an effort to advance the development of agricultural automation technology.

A novel smart window based on co-crosslinked hydrogel with temperature self-adaptability and anti-freezing functions for building energy saving

Thermochromic smart windows based on hydrogels with adaptive regulation of solar radiation have attracted an increasing attention due to their potential in temperature management. However, hydrogels with extremely high water content may freeze, leading to the risk of transparency reduction and even window breakage in cold environment. In this study, co-crosslinked P(NIPAM-co-AM)@GLY@PVA hydrogel with excellent solar radiation regulation and anti-freezing performance was prepared by introducing antifreeze glycerol (GLY) and polymer polyvinyl alcohol (PVA). By adjusting the amount of GLY, PVA and hydrophilic AM, P(NIPAM-co-AM)@GLY@PVA hydrogel showed a satisfactory thermochromic performance, with a luminous transmittance (Tlum) of 68.05 %, a solar modulation ability (ΔTsol) of 62.11 %, a lower critical solution temperature (LCST) of ∼24 °C and an antifreeze temperature of −15 °C. The thermal management experiments proved that the thermochromic@anti-freezing (TCA) smart window assembled based on P(NIPAM-co-AM)@GLY@PVA hydrogel had an excellent performance in managing indoor temperature and cyclic stability. This work may open a new avenue to solve problems in the application field of hydrogel-based smart windows

Efficient Integration of CNN and LSTM for Data Mining Observation Algorithms: A Systematic Review

An extensive analysis of the combination of Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNN) is presented in this research. within data mining applications. CNNs, optimized for spatial data processing, and LSTMs, made to manage data in a sequential fashion, are combined to leverage their respective strengths in feature extraction and temporal pattern recognition. By analyzing over 60 published studies, we evaluate the effectiveness of CNN-LSTM models across diverse domains such as medical image analysis, financial time series forecasting, stock price prediction, and sentiment analysis. The review reveals that CNN-LSTM hybrids significantly outperform traditional methods in activities requiring both temporally and spatially understanding. However, challenges such as model interpretability, hyperparameter optimization, and the need for diverse datasets remain. Future research should focus on optimizing these models for specific applications, improving scalability, and addressing limitations like data scarcity. This review underscores the significant potential of CNNLSTM integration in advancing data mining techniques and solving complex problems in various fields. We present the related work of some of the most important publication in the field and classify over 60 published papers from different perspective. This review intends to classify the major techniques, methods, algorithms in the domain of medical images, financial time series analysis, stock price and social media.

Dynamic description of soliton solutions of nonlinear schrödinger equation with higher order dispersion and nonlinear terms

Abstract The article presents an analytical solution for the higher-order nonlinear Schrödinger equation (NLSE), which describes the propagation of short light pulses in monomode optical fibers. Various traveling wave solutions are obtained using the generalized exponential rational function method, a technique with substantial applications in physics and mathematics. Additionally, the parameters leading to the occurrence of optical bright and multipeak solitons in this medium are provided along with their formation conditions. The derived solutions are graphically displayed to enhance the understanding of the model’s physical phenomena. This approach is credible, potent, and successful in solving a wide variety of different models of this kind that arise in the applied sciences. Its robustness, strength, and efficiency make it suitable for addressing various higher-order nonlinear problems in current research fields, extending beyond the models encountered in the applied sciences.

An seamless stitching method for large field equivalent center projection image based on rotating camera

Digital cameras are limited by a narrow field of view and a large photosensitive unit, resulting in images with a small frame size and low resolution. This reduces the acquisition range and measurement accuracy of stereo vision in close-range photogrammetry, making it difficult to meet the requirements for precise close-range photogrammetry in high-precision industrial engineering fields, and limiting the significant development of digital close-range photogrammetry. For this reason, based on the characteristics of ground close-range photogrammetry, this paper proposes a large-format image acquisition method for rotating cameras. By designing a simple and structurally relaxed rotating camera, a rigorous seamless stitching model for large-format images is constructed, forming a large-format equivalent central projection image acquisition mechanism that meets the requirements of precise close-range photogrammetry. Finally, the effectiveness of the proposed method is verified through experiments. The results show that the proposed method effectively increases the coverage of a single camera station. The large-format image obtained through three degrees of rotation increases the image size from 916 × 687 pixels in a single image to 4977 × 671 pixels in a large-format image. This method solves the problem of the small view field of digital cameras, complementing the theory of precision close-range photogrammetry and providing necessary theoretical support for technological development in the field of precision industrial engineering.

Spherical basis functions in Hardy spaces with localization constraints

Subspaces obtained by the orthogonal projection of locally supported square-integrable vector fields onto the Hardy spaces H+(S) and H−(S), respectively, play a role in various inverse potential field problems since they characterize the uniquely recoverable components of the underlying sources. Here, we consider approximation in these subspaces by a particular set of spherical basis functions. Error bounds are provided along with further considerations on norm-minimizing vector fields that satisfy the underlying localization constraint. The new aspect here is that the used spherical basis functions are themselves members of the subspaces under consideration.

An advanced nanoparticle reinforced carbon fiber laminates for low frequency sound insulation

Low frequency noise is a common engineering problem in the industrial field. This study utilizes graphite particles to enhance the low frequency sound insulation of carbon fiber laminates significantly. The effects of grasphite particles with different mass and mesh on sound insulation of laminates are investigated by theory and experiment. The analytical model is proposed based on the Galerkin method for the laminates’ motion equations. The effective elastic properties of the samples are calculated using the Halpin-Tasi model and the rule of mixture. Two groups of samples are prepared for sound insulation measurement. The comparison between the theoretical and experimental results shows that the theory can well predict the sound insulation of the nanoparticle reinforced laminates. A significant improvement in low-frequency sound transmission loss of 3–8 dB is achieved without altering the laminates' thickness. However, the mesh variation of the graphite particles has little effect on the sound insulation of laminates.

Research of the application of medical bionic robot

Abstract. Medical bionic robotics is one of the research hotspots in today's medical field, which is widely used in minimally invasive surgery, soft bionic robots, endoscopic instruments, and so on. Through reading and organizing related literature, this paper will describe the research status of medical bionic robot application in the form of a review, analyze the existing research results, find the research gaps, explore the optimization direction, and try to put forward the corresponding solutions. Medical bionic robot application technology can still be improved in many aspects to realize more comprehensive functions. Current research work not only explores knowledge or problems in specific fields, but also lays a foundation for future research and provides valuable reference information. This reference may include aspects such as methodology, data analysis techniques, experimental design ideas, theoretical frameworks, etc., which can help other researchers to conduct deeper or broader exploration in this field.

Using Deep Learning, Optuna, and Digital Images to Identify Necrotizing Fasciitis

Necrotizing fasciitis, which is categorized as a medical and surgical emergency, is a life-threatening soft tissue infection. Necrotizing fasciitis diagnosis primarily relies on computed tomography (CT), magnetic resonance imaging (MRI), ultrasound scans, surgical biopsy, blood tests, and expert knowledge from doctors or nurses. Necrotizing fasciitis develops rapidly, making early diagnosis crucial. With the rapid progress of information technology and systems, in terms of both hardware and software, deep learning techniques have been employed to address problems in various fields. This study develops an information system using convolutional neural networks (CNNs), Optuna, and digital images (CNNOPTDI) to detect necrotizing fasciitis. The determination of the hyperparameters in convolutional neural networks plays a critical role in influencing classification performance. Therefore, Optuna, an optimization framework for hyperparameter selection, is utilized to optimize the hyperparameters of the CNN models. We collect the images for this study from open data sources such as Open-i and Wikipedia. The numerical results reveal that the developed CNNOPTDI system is feasible and effective in identifying necrotizing fasciitis with very satisfactory classification accuracy. Therefore, a potential future application of the CNNOPTDI system could be in remote medical stations or telemedicine settings to assist with the early detection of necrotizing fasciitis.

A Kernel approach for extending nonparametric multivariate analysis of variance in high-dimensional settings

The nonparametric multivariate analysis of variance (NPMANOVA) testing procedure has been proven to be a valuable tool for comparing groups. In the present paper, we propose a kernel extension of this technique in order to effectively confront high-dimensionality, a recurrent problem in many fields of science. The new method is called kernel multivariate analysis of variance (KMANOVA). The basic idea is to take advantage of the kernel framework: we propose to project the data from the original data space to a Hilbert space generated by a given kernel function and then perform the NPMANOVA method in the reproducing kernel Hilbert space (RKHS). Dispersion of the embedded points can be measured by the distance induced by the inner product in the RKHS but also by many other distances best suited in high-dimensional settings. For this purpose, we study two promising distances: a Manhattan-type distance and a distance based on an orthogonal projection of the embedded points in the direction of the group centroids. We show that the NPMANOVA method and the KMANOVA method with the induced distance are essentially equivalent. We also show that the KMANOVA method with the other two distances performs considerably better than the NPMANOVA method. We illustrate the advantages of our approach in the context of genetic association studies and demonstrate its usefulness on Alzheimer’s disease data. We also provide a software implementation of the method that is available on GitHub https://github.com/8699vicente/Kmanova.

A deep learning framework for solving the prediction and reconstruction problem of Bingham fluid flow field

As a typical non-Newtonian fluid, Bingham fluid is employed in a multitude of fields, including petroleum, construction, and the chemical industry. However, due to the intricate intrinsic properties of Bingham fluids and the necessity for precision and efficacy in specific engineering applications, the rapid and precise prediction and reconstruction of its flow field information has become a challenge and a focal point of contemporary research. In this paper, we introduce a novel deep-learning approach to address the two-dimensional laminar motion of Bingham fluids. The proposed Papanastasiou Regularization Physics-Informed Neural Network (PR-PINN) framework effectively predicts and reconstructs the flow field of Bingham fluids. Initially, the framework applies Papanastasiou regularization to the governing equations of Bingham fluids, enhancing the network's adaptability to solving the flow field problem by incorporating boundary conditions and an adaptive weight assignment strategy. We consider two scenarios: equal-diameter circular pipe flow and conical pipe flow. The PR-PINN network is utilized for flow field prediction and reconstruction. Our results show that PR-PINN achieves high accuracy in flow field prediction and can reconstruct velocity and pressure fields using limited measurement data. Based on these findings, we explore the impact of boundary constraints, the effect of large intrinsic parameters on prediction accuracy, and the influence of measurement points and boundary constraints on flow field reconstruction. In summary, the PR-PINN network exhibits satisfactory performance and significant potential for predicting and reconstructing Bingham fluid flow fields.

Formation of cultural identity of students as a subject of pedagogical discussion

Introduction. Geopolitical challenges of our time encourage teachers to search for optimal means to counteract the deformation of the ideological positions of students, the mixing of cultures, and the devaluation of traditional cultural and historical values. In this context, the formation of cultural identity among students and schoolchildren, which unites all-Russian and regional cultural meanings at the content level, acquires great theoretical and practical significance. As an example, the Novgorod cultural identity is given as an integrative symbol of the cultural and historical heritage of Russia and the Novgorod region. The purpose of the study is the scientific substantiation of the concepts of “cultural code” and “cultural identity” as pedagogical phenomena containing national and regional components. Materials and methods. Using theoretical methods (interdisciplinary analysis, synthesis, generalization), a study of scientific sources was carried out, which made it possible to identify the relationship between the concepts of “cultural code” and “cultural identity”. In order to determine the components and levels of formation of cultural identity among students, a survey was conducted of 327 students and schoolchildren of the Novgorod region aged 14 to 21 years. Statistical data processing was carried out using the Google Forms service. Results. Based on a theoretical analysis of scientific sources, it was established that cultural identity as a conscious position of young people can be purposefully formed by pedagogical means, and the cultural code can be generated and transmitted by various institutions of socialization. The survey data recorded a fairly high level of development of the cognitive component of the cultural identity of students and an average level of development of the affective and behavioral components. Based on the results obtained, the problematic field of cultural categories was translated into the plane of pedagogical discussion and their characteristics as social and pedagogical phenomena were given. Conclusion. The conducted research made it possible to identify the relationship between the concepts of “cultural code” and “cultural identity”, to determine the general civil and regional aspects of the cultural identity of students. The comprehensive educational program for training future teachers developed by the authors, which includes the history of education and enlightenment of the Novgorod region, the theoretical foundations of educational tourism, museum and excursion pedagogy, will be of practical interest for university teachers and school teachers who carry out educational work with students.

Construction of electric dyadic Green’s functions in free space based on elliptic cylindrical coordinate system

Abstract A set of vector wave functions is defined in the elliptic cylindrical coordinate system. These vector wave functions can not only describe the propagation characteristics of electromagnetic waves in the elliptic cylindrical coordinate system but also meet the requirements of the electromagnetic field equation. By studying the orthogonality of these vector wave functions, the free space magnetic dyadic Green function is derived by the Ohm-Rayleigh method. The free space electric dyadic Green function is derived from the magnetic dyadic Green function. The special electromagnetic field boundary value problem is transformed into a Green function problem by the electric dyadic Green function. The distribution of the electromagnetic field on the boundary can be obtained by solving the Green function, and then the characteristics of the electromagnetic field can be analyzed. The core of this method is to transform the electromagnetic field problem into a Green function problem, thus simplifying the solving process. This method enriches the electromagnetic field theory and provides a new way to solve the practical electromagnetic field problem.

A Transferable Meta-Learning Phase Prediction Model for High-Entropy Alloys Based on Adaptive Migration Walrus Optimizer

The phases of high-entropy alloys (HEAs) are crucial to their material properties. Although meta-learning can recommend a desirable algorithm for materials designers, it does not utilize the optimal solution information of similar historical problems in the HEA field. To address this issue, a transferable meta-learning model (MTL-AMWO) based on an adaptive migration walrus optimizer is proposed to predict the phases of HEAs. Firstly, a transferable meta-learning algorithm frame is proposed, which consists of meta-learning based on adaptive migration walrus optimizer, balanced-relative density peaks clustering, and transfer strategy. Secondly, an adaptive migration walrus optimizer model is proposed, which adaptively migrates walruses according to the changes in the average fitness value of the population over multiple iterations. Thirdly, balanced-relative density peaks clustering is proposed to cluster the samples in the source and target domains into several clusters with similar distributions, respectively. Finally, the transfer strategy adopts the maximum mean discrepancy to find the most matching historical problem and transfer its optimal solution information to the target domain. The effectiveness of MTL-AMWO is validated on 986 samples from six datasets, including 323 quinary HEAs, 366 senary HEAs, and 297 septenary HEAs. The experimental results show that the MTL-AMWO achieves better performance than other algorithms.

LOGISTICS AND EFFICIENCY OF HEAVY TRAFFIC

Background. The article is devoted to the study of the organization of the heavyweight movement. The relevance of the topic is related to the steady increase in the average weight of freight trains operating on heavily loaded routes of Russian railways and the corresponding need for a comprehensive assessment of the potential of heavy traffic. Purpose: to characterize the problematic issues of logistics of heavy traffic in the conditions of the Russian Federation, caused by mixed traffic, different landscape and climate of our country, as well as the reorientation of cargo flows to the east, are considered. Methods. Methods of comparative analysis, logistics, norming of operational work of railroads, visualization were used. Results. An analytical review of the problematic field of the organization of heavy traffic from the standpoint of logistics has been performed. The peculiarities of logistics are revealed, relevant development tracks and challenges arising in the organization of heavy traffic are formulated. An integral indicator is proposed – the Index of the development potential of heavy traffic for a comprehensive assessment of the effectiveness of organizing both a separate route and related multiplicative effects. Conclusion. It is shown that the use of the indicator will provide a scientifically sound solution to the problem of increasing the capacity of railways in answering the problematic issues of heavy traffic identified in the study.