bluuebunny/crossref_metadata_2025_split huggingface dataset:
Convolutional neural network for UAV image processing and navigation in tree plantations based on deep learning
Authors: Xiao Shuiqing 
Published: July 2023

Abstract
In this study, we show a new way for a small unmanned aerial vehicle (UAV) to move around on its own in the plantations of the tree using a single camera only. To avoid running into trees, a control plan was put into place. The detection model looks at the image heights of the trees it finds to figure out how far away they are from the UAV. It then looks at the widths of the image between the trees without any obstacles to finding the largest space. The purpose of this research is to investigate how virtual reality (VR) may improve student engagement and outcomes in the classroom. The emotional consequences of virtual reality on learning, such as motivation and enjoyment, are also explored, making this fascinating research. To investigate virtual reality’s potential as a creative and immersive tool for boosting educational experiences, the study adopts a controlled experimental method. This study’s most significant contributions are the empirical evidence it provides for the efficacy of virtual reality in education, the illumination of the impact VR has on various aspects of learning, and the recommendations it offers to educators on how to make the most of VR in the classroom.
La religiosidad en Gouverneurs de la rosée de Jacques Roumain: entre la inmovilidad y la redención
Authors: Hurtado Ruiz Pablo Mauricio 
Published: July 2023

El presente trabajo analiza la religiosidad en la novela Gouverneurs de la rosée del escritor haitiano Jacques Roumain. Para ello, se establece que la religión que profesan los personajes corresponde a un sincretismo entre el catolicismo y el vuduísmo, en el que se manifiesta el conflicto entre la divinidad impuesta por el esclavismo y las divinidades heredadas de África. Se analiza la postura inmovilista que esta religiosidad significa para los habitantes de Fonds-Rouge, oponiéndose a la visión marxista del trabajo que presenta Manuel, el protagonista de la novela. Finalmente, pese a las críticas a la religión, el destino impuesto por las deidades del vudú, se cumple inexorablemente en contraposición al catolicismo que se cuestiona por ser una religión de blancos.
Nacionalidades y etnicidades “otras” en el Gran Caribe
Authors: Caballero De la Hoz Amilkar 
Published: July 2023

El número 30 de la Revista Cuadernos de Literatura del Caribe e Hispanoamérica pretende revisar narrativas y problemáticas que han dado forma a los pueblos del Gran Caribe y a formas de nacionalidades y etnicidades “otras”. Se trata de construcciones y nociones que se renuevan constantemente y que generan reformulaciones de esas nacionalidades. Los artículos que se presentan a continuación abordan estas temáticas desde ópticas y metodologías diversas.
Analysis of asymmetric multipathing with a generalization of the double-beam method
Authors: Krüger F. , Scherbaum F. , Weber M. , Schlittenhardt J. 
Published: July 2023

AbstractAn array method integrating source and receiver array beamforming is presented. The method is a generalization of previous work (Krüger et al., 1993) to independent variation of slowness and azimuth in both the source and the receiver array. Compared to the analysis capabilities of a single array, the resolution is enhanced, and asymmetric scattering configurations and multipathing can be studied. The generalized double-beam method (DBM) is applied to a data set consisting of nuclear explosions detonated in Eastern Kazakhstan and recorded at the Yellowknife array in Northern Canada. The multipathing found for PcP and PdP is interpreted in terms of lateral inhomogeneity in the lowermost mantle below Severnaya Zemlya. Indications for Rayleigh-to-P-wave scattering observed for the Shagan River and Degelen subsites of the former main test site of the USSR in Eastern Kazakhstan can be related to the structure in the vicinity of the two subsites.
Value of dynamic enhanced magnetic resonance image-based model in predicting low expression of HER-2 in breast cancer
Authors: Zheng Lu , Sun Chenyu , Meng Muzi , Chen Eric , Tang Tong , Chen Xiao , Wang Zhitao , Zhao Ru 
Published: July 2023

Abstract

Objective
This study aimed to evaluate the feasibility of evaluating early low expression of HER-2 in patients with breast cancer by applying Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) based imaging features, which could potentially optimize treatment for patients.
Method
Clinical and pathology data of 294 female patients with invasive ductal carcinoma confirmed by puncture or surgical pathology were collected. Regions of interest (ROI) were mapped. Features were then extracted from the original Magnetic Resonance Imaging (MRI) image data. Relevant features were screened out by Mann-Whitney U test. Cross-validated LASSO regression was used for feature selection. Inner and outer 10-fold cross-validation (CV) models were used. The inner 10-fold CV was used to select the best model during the Linear SVC modeling in training set, and an outer 10-fold CV was used to validate the efficiency in validation set. Model performance was evaluated by using receiver operator curve (ROC) analysis. The average accuracy, sensitivity, and specificity were calculated.
Results
After model selection using the inner 10-fold CV in Linear SVC modeling and validation using the outer CV, the average accuracy, sensitivity, and specificity of the validation set were 79.6%, 73.7%, and 85.6%, respectively. The average area under curve (AUC) of ROC analysis was 0.87. The diagnostic efficiency of the replacement dataset after 1000 permutation tests was compared with the original dataset, and the average accuracy, sensitivity, and specificity were all less than 0.05. The differences were all statistically significant. The model established after cross-validation could classify patients as HER2 low expression or HER2 positive. The classification efficiency of the model was higher than the chance level.
Conclusion
DCE-MRI imaging model can help predict the low expression of HER2 receptor in breast cancer with a high predictive efficiency, which can provide a new method for clinical diagnosis of non-invasive HER2 status.
Evaluation of fluid-fluid and rock-fluid interfacial interactions using silica nanofluids and crude oil for a deepwater carbonate Pre-salt field
Authors: Dias Nathália Pereira , Ferraz Helen Conceição , Nicolini João Victor , Couto Paulo , Drexler Santiago , Balbino Tiago Albertini 
Published: July 2023

Abstract

The biggest challenges for E&P activities are the high viscosity of the oil, the geology of the formation, the high interfacial tensions (IFT) between fluids and the reservoir wetting conditions. Enhanced Oil Recovery (EOR) methods are applied to modify fluid-fluid and fluid-rock interactions in the reservoir, facilitating the oil displacement and, consequently, increasing the recovery factor. In this work, the use of silica nanofluids, with and without amphoteric surfactant, as EOR method to reduce the IFT and to change the wettability conditions of reservoir rock were evaluated. For experimental tests, crude oil from a reservoir in a Brazilian Pre-salt field was used as oleic phase. After data treatment, silica nanofluids (0.02 wt%) with surfactant (0.03 wt%) proved to be more effective in reducing the IFT of the system. However, the increase of silica nanoparticles (SiNPs) concentration promoted an increase in the IFT of the system, indicating a mechanical barrier effect. For nanofluids without surfactant, no significant change in IFT was observed with an increase in the concentration of SiNPs for both distilled water and brine (1,000 ppm) dispersant fluids. The significant reduction of the angle is observed for nanofluids with 0.02 wt% SiNP. Finally, the Amott test was performed in a carbonate rock sample to reaffirm the action of these chemicals in oil recovery, corroborating the potential of nanofluids to EOR applications. Thus, this work might contribute to a more rational design of nanoEOR strategies and technological innovations in carbonate reservoirs, especially those addressed to the South American Deepwater sector.
In search of new stratification strategies: Tissue proteomic profiling of papillary thyroid microcarcinoma in patients with localized disease and lateral neck metastases
Authors: Matovinovic Filip , Novak Rudjer , Hrkac Stela , Salai Grgur , Mocibob Marko , Pranjic Marija , Košec Andro , Bedekovic Vladimir , Grgurevic Lovorka 
Published: July 2023

Abstract

Introduction: Papillary thyroid carcinomas (PTC) are the most common thyroid malignancies that are often diagnosed as microcarcinomas when the tumour is less than one centimetre in diameter. Currently, there are no valid stratification strategies that would reliably assess the risk of lateral neck metastases and optimize surgical treatment. Materials and methods: Aiming to find potential tissue biomarkers of metastatic potential, we conducted a cross-sectional proteomic study on formalin-fixed paraffin-embedded tissues of metastatic (N = 10) and non-metastatic (N = 10) papillary thyroid microcarcinoma patients. Samples were analysed individually using liquid-chromatography / mass spectrometry, and the differentially expressed proteins (DEP) were functionally annotated. Results: We identified five overexpressed DEPs in the metastatic group (EPB41L2, CSE1L, GLIPR2, FGA and FGG) with a known association to tumour biology. Using bioinformatics tools, we found markedly different profiles of significantly enriched biological processes between the two groups. Conclusion: The identified DEPs might have a role as potential tissue biomarkers for PTC metastases. However, further prospective research is needed in order to confirm our findings.
Carbon emission reduction potential of renewable energy, remittance, and technological innovation: empirical evidence from China
Authors: voumik liton chandra 
Published: July 2023

Abstract

Although the effects on environmental quality have not yet been extensively studied, some studies demonstrate that renewable energy, remittances, and technical innovation contribute to a country's socioeconomic development. In light of this, the current study utilizes annual data from 1990 to 2020 to evaluate the impact of renewable energy, remittances, and technical innovation on carbon dioxide (CO2) emissions in China. By using the autoregressive distributed lag (ARDL) bounds testing, the research was able to reveal persistent associations between CO2 and the regressors. Long- and short-run ARDL results also demonstrated that CO2 emissions rise alongside economic development, but the using renewable energy, remittances, and technical advancement help to lower emissions in China. The reliability of these results was determined using fully modified ordinary least squares (FMOLS), dynamic ordinary least square (DOLS), and canonical cointegrating regression (CCR). In light of these significant determining, several policy suggestions are proposed.
Konkurencyjność kosztowa polskich gospodarstw mlecznych na arenie europejskiej w perspektywie zmian polityki rolnej UE po 2014 roku
Authors: Parzonko Andrzej 
Published: July 2023

Celem artykułu było przedstawienie zagadnienia konkurencyjności kosztowej produkcji mleka w gospodarstwach z Polski i wybranych krajach europejskich. Do porównań wykorzystano badania prowadzone w ramach IFCN oraz z Powszechnego Spisu Rolnego 2010. Jak wynika z przeprowadzonych analiz, większość gospodarstw w Polsce jest niekonkurencyjna pod względem kosztów produkcji mleka w stosunku do typowych gospodarstw z Niemiec, Francji, Danii i Holandii. Sytuacja wygląda nieco lepiej w przypadku gospodarstw dysponujących większa skalą produkcji (utrzymujących powyżej 65 krów). Słabością polskich gospodarstw jest niska efektywność techniczna. Głównie niska wydajność pracy i produkcyjność ziemi.
FPGA-based Intelligent Logistics Management for Universities
Authors: Zhao Zhao 
Published: July 2023

Abstract
With the strong drive for economic and cultural development, China’s higher education has flourished and formed a higher education system and scale with typical Chinese characteristics, and the healthy development of higher education cannot be separated from the strong support and guarantee of university logistics. Based on FPGA technology, this paper explains in detail the design method and computing steps of FPGA technology to build a university intelligent logistics management data analysis system. The performance evaluation is carried out for the system designed in this paper, and the data analysis of the indicator examples is carried out by using five examples of logistics management service indicators of the University of W, namely campus environment, property service, commercial service, residence management service, and utility maintenance. From the performance evaluation data of the FPGA university intelligent logistics management system, the average accuracy, precision, recall, and AUC values of the FPGA-based university logistics management system are 91.90%, 95.78%, 97.28%, and 97.41%, respectively. From the index instance analysis, the average evaluation values of the five services of very satisfied, satisfied, generally satisfied, and dissatisfied are 24.89%, 25.16%, 40.97%, and 8.98%, compared with the previous average values of very satisfied and satisfied evaluations increased by 18.36% and 4.61%, respectively, and the average values of generally satisfied and unsatisfied evaluations decreased by 7.65% and 15.31%, respectively. The FPGA-based intelligent management system of colleges and universities has higher stability and stronger data analysis ability, which can more effectively propose corresponding reform directions for the problems and provide technical support to promote more intelligent logistics management of colleges and universities. At the same time, it enables university teachers and students to carry out relevant academic research and study life without any worries, contributes technical power to promote the overall healthy development of universities, and provides new research directions to broaden the development field of FPGA technology.
Research on the innovation path of teaching interior design majors in universities based on big data analysis
Authors: Hou Weiwei 
Published: July 2023

Abstract
Presently, the teaching of interior design in most higher education institutions still uses more traditional and conservative teaching methods, which to some extent, hinders the reform of interior design teaching in the era of big data. Interior design originally required students to grasp the operation fully and use CAD, 3D, and other design software, which makes the operation of complex commands, and model construction diversity, difficult for students to learn. This situation still uses the traditional lecture-based indoctrination teaching can play a very small role. This paper first focuses on the field of interior design education, based on the background of the times, provides a deep overview of the current situation of the relevant research involved in interior design, and explains the purpose, ideas, content, and methods of this paper. Secondly, we use data mining technology to build a smart classroom to visually present interior design. Finally, a comparison experiment between the traditional teaching mode of interior design and the smart teaching mode of big data is carried out, and three groups of experimental subjects are investigated after nearly one month of teaching experience. The results of this study show that the big data wisdom teaching model proposed in this paper compared with the traditional teaching model, through the one-month teaching experience post-test results, the average increase of 7 points in school A, 11 points in school K, and 14 points in school Z. The wisdom teaching system based on big data built in this paper enhanced the relevance and initiative of interior design students’ learning, improved students’ interest in learning and classroom efficiency and promoted students’ comprehensive ability while playing a positive and active role.
The Evolution of Marxist Theory of Financial Capital in the ACC Payment Model
Authors: Tang Jingyun 
Published: July 2023

Abstract
Since the late 1970s, a series of profound changes have occurred in developed capitalist countries. The most prominent is the increasing virtualization and financialization of the capitalist economy. Financial leverage has been increasing under the ACC payment model in the financial supply chain. In order to study the evolution of Marxist financial capital theory under the ACC payment model, this paper mathematically models supply chain finance under the ACC payment model by analyzing early payment, immediate payment, and credit payment in the process of ordering, receiving, and selling in the supply chain, followed by the monopoly position of financial capital in the U.S. economy and U.S. national income under economic virtualization. From 1995 to 2016, total U.S. financial assets as a percentage of GDP grew from 113.6% to 425.5%, a growth rate of 274.56%. The average percentage of U.S. government bonds to GDP was 39.16%. U.S. non-financial business as a percentage of GDP has grown at an average annual rate of only 1.62% over the last 20 years. Under economic virtualization, the median annual U.S. household income has largely trended downward since 2002, and in 2016 the median annual U.S. household income fell to $49,100, essentially the same as in 1995. Annual per capita income is also declining in the U.S.; in 2016 was already nearly $2,200 less than in 1995. The ACC payment model allows financial capital to add value by detaching itself from the production process, and the faster it adds value, the more it stimulates credit expansion. From a Marxist perspective, the bourgeoisie’s use of virtual financial capital for the continuous plundering of global capital has led to unbalanced global economic development and a growing gap between the rich and poor of people, thus intensifying the basic contradictions of capitalism, which is a warning for the development of the global economy nowadays.
Biosafety management strategy based on multiple linear regression model
Authors: Zhao Chao , Xiao Zhanpeng , Zhang Te , Xiao Na 
Published: July 2023

Abstract
With the rise of artificial intelligence technology in the context of digital development of big data and information, the biosafety management method under the traditional model can hardly fit the development requirements of the times, and the biosafety management under the traditional model has various problems such as slow manual efficiency, tedious management work, and humanization of work. This paper focuses on studying a biosafety management strategy based on a multiple regression model, then analyzes and compares the two methods of the one-dimensional linear regression model and the multiple linear regression model. The study is conducted under the three indicators of biosafety management weight score. Arithmetic means to score and significance of 100 enterprises (schools, pharmaceutical companies, hospitals, and other types of these four major types) under the multiple linear regression model. The results show schools topped the list with a biosafety management weight score of 0.793. The arithmetic mean scores of the four categories of enterprises were ranked in descending order: schools, hospitals, pharmaceutical plants, and other categories. The size of the enterprise has a significant and positive effect on biosafety management, which means that the larger the size of the enterprise, the more resources the enterprise has under its control, and then the implementation of biosafety management and the improvement of its performance will be guaranteed. Analyzing the efficiency of biosafety management in hospitals based on three different methods over the past 20 years, it was found that the efficiency of biosafety management based on multiple linear regression models fluctuated from 60% to 95% compared to the other two methods. This study promotes university laboratories as important and safe bases for society to cultivate highly qualified and skilled personnel in line with the new era of production, management, and service lines and to contribute to the country’s experimental research work. This study has substantial reference value for biosafety management and promotes bioscience development in China.
Innovative Strategies for Cultivating Journalism and Communication Talents in the All-Media Era Based on the Light GBM Model
Authors: Pan Yan 
Published: July 2023

Abstract
Under the development background of digitalization of big data and information, artificial intelligence and the Internet of Things are rising like mushrooms. It is difficult for the traditional journalism communication teaching mode in colleges and universities to keep pace with the development of the times, and there are several problems in the traditional teaching methods, such as teaching being too rigid, lack of personalized teaching, and teaching means being too backward. This paper focuses on the innovative strategy of journalism communication talents training in the era of full media based on Light GBM model. Firstly, we understand the principle and construction idea of Light GBM model by viewing the information, comparing the difference between XG Boost algorithm and Light GBM two algorithms, analyzing the advantages and limitations of Light GBM, optimizing the algorithm by establishing the hyperparameters of Light GBM model under grid search algorithm, understand the teaching status of college journalism broadcasting majors through survey and analysis, based on Light model under the analysis of feedback to propose targeted innovation strategies, 30 times ten-fold hierarchical cross-validation study of the three algorithms. The mean and standard deviation results of 11 dichotomous classification models accuracy and F1 values under the three algorithms were compared. The results show that in 11 sets of dichotomous data sets, the optimized Light GBM algorithm has 9 sets of experimental accuracy wins and 7 sets of experimental F1 values wins, with 81.8% accuracy and 63.6% of F1 values wins. The optimized Light GBM algorithm had seven experiments that beat the other two groups of algorithms in comparing the accuracy and F1 value. This study can accurately predict students’ learning behaviors to improve the professional performance of journalism and communication students in a targeted manner and has a reference value for the innovation of journalism and communication teaching mode in colleges and universities in the context of big data and all-media era, and this study has great historical significance for the development of Chinese journalism industry.