bluuebunny/crossref_metadata_2025_split huggingface dataset:
Efficient Machine Reading Comprehension for Health Care Applications: Algorithm Development and Validation of a Context Extraction Approach
Authors: Nguyen Duy-Anh , Li Minyi , Lambert Gavin , Kowalczyk Ryszard , McDonald Rachael , Vo Quoc Bao 
Published: January 2024

Background
Extractive methods for machine reading comprehension (MRC) tasks have achieved comparable or better accuracy than human performance on benchmark data sets. However, such models are not as successful when adapted to complex domains such as health care. One of the main reasons is that the context that the MRC model needs to process when operating in a complex domain can be much larger compared with an average open-domain context. This causes the MRC model to make less accurate and slower predictions. A potential solution to this problem is to reduce the input context of the MRC model by extracting only the necessary parts from the original context.

Objective
This study aims to develop a method for extracting useful contexts from long articles as an additional component to the question answering task, enabling the MRC model to work more efficiently and accurately.

Methods
Existing approaches to context extraction in MRC are based on sentence selection strategies, in which the models are trained to find the sentences containing the answer. We found that using only the sentences containing the answer was insufficient for the MRC model to predict correctly. We conducted a series of empirical studies and observed a strong relationship between the usefulness of the context and the confidence score output of the MRC model. Our investigation showed that a precise input context can boost the prediction correctness of the MRC and greatly reduce inference time. We proposed a method to estimate the utility of each sentence in a context in answering the question and then extract a new, shorter context according to these estimations. We generated a data set to train 2 models for estimating sentence utility, based on which we selected more precise contexts that improved the MRC model’s performance.

Results
We demonstrated our approach on the Question Answering Data Set for COVID-19 and Biomedical Semantic Indexing and Question Answering data sets and showed that the approach benefits the downstream MRC model. First, the method substantially reduced the inference time of the entire question answering system by 6 to 7 times. Second, our approach helped the MRC model predict the answer more correctly compared with using the original context (F1-score increased from 0.724 to 0.744 for the Question Answering Data Set for COVID-19 and from 0.651 to 0.704 for the Biomedical Semantic Indexing and Question Answering). We also found a potential problem where extractive transformer MRC models predict poorly despite being given a more precise context in some cases.

Conclusions
The proposed context extraction method allows the MRC model to achieve improved prediction correctness and a significantly reduced MRC inference time. This approach works technically with any MRC model and has potential in tasks involving processing long texts.
Path of integration of psychological education and ideological education in colleges and universities for the cultivation of college students’ emotional regulation ability in the background of the Internet
Authors: Lin Yingtao 
Published: January 2024

Abstract
In this paper, based on the machine learning algorithm of psychological education and civic education research, the decision tree algorithm and gradient enhancement tree are used, and then construct the crisis early warning model of the integration of civic education and psychological education teaching are constructed. According to the requirements of psychological education and ideological education in colleges and universities for the cultivation of students’ college students’ emotional regulation ability, the model can be divided into four layers for research. Combining the research objectives and hypotheses, the research on the path of integrating the teaching of college psychology and Civics with machine learning is designed. The questionnaire is designed and distributed to obtain research data, and statistical analysis is used to empirically analyze the integrated teaching of college psychology and Civic and Political education in the context of the Internet. The results show that in the model analysis, after suffering from positive stimuli at the moment of t=3, the intensity of positive emotions gradually increased, and the intensity of negative emotions gradually decreased, and the model accurately reflected the emotional changes of college students when suffering from stressful events. On the statistical analysis of the teaching path, the correlation coefficients were 0.948, 0.935, 0.942, 0.905, and 0.827, and the strongest correlation was found between the sense of acquisition of Civic and Political knowledge and the sense of acquisition of integrated teaching of Psychological Education and Civic and Political Education. This study gives full play to the maximum value of the infiltration of the contents of Civic and Political Education and Psychological Education, which is of great significance to the cultivation of college students’ emotional regulation ability.
Visualization and Analysis of Knowledge Graph for the Integration of Traditional Culture and Rural Tourism Industry
Authors: Ai Rong , Song Jianwei , Xie Xiaowei 
Published: January 2024

Abstract
In order to promote the integrated development of culture and the tourism industry, this paper explores the integration of traditional culture and the rural tourism industry. Firstly, a bottom-up knowledge graph construction method is designed based on the DGCN relational extraction model, Muhead-CU-FL-BE entity extraction model and Neo4j high-performance graph database. Then, based on the domain and characteristics of traditional culture and rural tourism industry integration, the knowledge graph of rural traditional culture and tourism industry integration is constructed from three aspects: line results, ontology model and description of this paper. Finally, the performance of the knowledge graph constructed in this paper is tested on the relevant dataset, and the visualization analysis of industrial integration is carried out through the constructed knowledge graph. The results show that the overall performance of the relationship extraction algorithm in this paper is around 0.7, the entity extraction algorithm has the best performance, and the overall performance is around 0.8, and the ratio of the public ancestor nodes of 5 times linking in the 5th and 6th layers is greater than 0.65. The centrality of culture and tourism industry, cultural and tourism fusion, high-quality development, culture and tourism, and industry fusion are 0.78, 0.60, 0.58, and 0.80. The centrality of the keywords is 0.78, 0.60, 0.58, and 0.80, respectively, 1.00, and the strength of the salient values of each keyword is concentrated around 1~3.
Prediction of Criminal Suspect Characteristics with Application of Wavelet Neural Networks
Authors: Lei Yang , Huang Bin 
Published: January 2024

Abstract
Crime incidents grow at a certain rate every year and become more complicated day by day, but at this stage, the development of the field of crime analysis and prediction is still lagging, so this paper applies a wavelet neural network to predict the characteristics of criminal suspects. Analyze the applicability of the WNN model, explore the principle of criminal suspect feature prediction, and study the basis of wavelet neural network analysis. Establish the face feature recognition framework, use the Morlet function and Mexican Hat function pair to optimize and improve the wavelet neural network, and construct the model evaluation index for evaluating the model prediction effect. Preprocessing the suspect feature point data by using the grayscale reflection in Haar features. Simulation experiments are used to analyze the performance of different algorithms and the prediction effect of criminal suspect features on wavelet neural networks. The proposed algorithm in this study is superior and has a lesser number of parameters than ResNet. From the loss curves of different algorithms, the algorithm proposed in this study has a faster-descending loss curve and a smaller loss rate in the epoch value of [100, 200] interval. The overall range of the evaluation indexes of the algorithms proposed in this study is above 88%, and the highest accuracy rate can reach 94.234%, which is a good performance of the algorithms and accurate prediction.
Machine learning-based design of a linear self-resistant attitude control system for UAV string level
Authors: Xiao Yingnan 
Published: January 2024

Abstract
This paper mainly investigates the attitude control method of the quadrotor against unknown external interference and improves the control accuracy for the subsequent design of the control algorithm by establishing a more accurate mathematical model of the quadrotor. The extended Kalman filtering algorithm is used to obtain the real-time attitude state of the vehicle for attitude solving. The inertial guidance fusion uses the Kalman filter algorithm with delay correction to estimate the vehicle’s position and velocity information. Finally, the attitude control method of serial linear self-immunity control is proposed, which estimates and compensates for the external perturbation and internal uncertainty in real-time by linear expansion observer, while the position controller is designed by using PIV control. The simulation study analyzes that this paper’s method reduces the UAV attitude angle maximum error magnitude between about 1.04° and 4.07°compared with the traditional ADRC and serial PID. The maximum tracking error of pitch angle under white noise interference is only 0.37°using the control method of this paper, and the fluctuation amplitude is reduced by 0.81 on average, which shows a stronger anti-interference ability.
Research on Artificial Intelligence Data Based on Image Texture Characterization and Aerobics Teaching Mode in Colleges and Universities
Authors: Song Xiaoling 
Published: January 2024

Abstract
In this paper, we first studied the action recognition method based on image texture features, established a human body model, and realized the extraction of skeletal data and joint calibration. Based on the skeleton information, the aerobic movements are recognized, the difference between the aerobics movement sequence and the standard movement sequence is calculated, and the movements are scored. Then, the database for aerobic movements was established, and an AI-assistant aerobics teaching model was developed. Finally, the effect of the teaching method was analyzed through indicators of recognition effect, skill impact, and students’ learning interests. The results show that the accuracy of the system’s action recognition reaches 95%, and the overall response time is between 5.2-6.4s, with high real-time performance. And it has a significant effect on students’ movement participation, active interest, and independent learning behavior, with p-values of 0.013, 0.041, and 0.036, respectively, p<0.05. This study promotes the development and innovation of aerobics, which can scientifically adjust training countermeasures and enhance the skill level of aerobics athletes.
Exploring the method of calculating the amount of overlapping enjoyment of enterprise income tax benefits
Authors: Zong Zhijuan , Guan Yu 
Published: January 2024

Abstract
The adjustment of enterprises’ stacked enjoyment of preferences in the revision of the 2021 corporate income tax return is a highlight. This paper takes the cost of capital use model as the basis to study the maximization of the net value of capital obtained by adopting the same optimal capital accumulation, and the utility mechanism of the reduced tax rate preferences and the halved levy preferences are studied, respectively. Finally, with high-tech enterprises as the main object of empirical research, we studied the effect mechanism of superimposed enjoyment of enterprise income tax concessions on R&D and analyzed the calculation process of the amount of superimposed enjoyment of income tax concessions. The results show that to tax-adjusted income of 60 million yuan to calculated through the original method of filing method to enjoy 1 million yuan of preferences, while the use of superimposed enjoyment of tax breaks and preferences amounted to 1,335,200 yuan. In this paper, the calculation of tax benefits enjoyed by using stacking is more accurate to reflect policy requirements and maintain the tax interests of high-tech and other enterprises.
Construction of college music information teaching mode under the background of Internet
Authors: Yan Li Hai 
Published: January 2024

Abstract
The traditional music teaching method in the informationization era has been difficult to adapt to the needs of modern teaching and must be reformed in the direction of informationization. In this paper, based on the closure, inflection point and outer enclosing box features of the stroke line element, the recognition of handwritten notes is carried out from the three categories of straight line segments, folded line segments and quadratic curves. Meanwhile, for the binarized music score image, the multi-directional LBP features for spectral line detection are improved, and the computation method of multi-scale spectral line detection LBP features is established. The Manhattan distance is used to evaluate and select the features, which are inputted into XGBoost for classification and recognition training based on the statistical distribution characteristics of the features. Note recognition and spectral line recognition are applied to college music teaching, and the effectiveness of teaching is explored. In the rhythm-recognition path, the recognition teaching based on multi-scale and multi-directional LBP features led to an increase in students’ mastery of the musical score by 2.8 and in the phrasing and segmentation path by 3.5. Informational teaching led to a deepening of students’ mastery of the notes and musical scores.
The Innovation of “Six Steps” Teaching Model of College English under Data Chain Mode
Authors: Li Mingxia , Zhao Fang , Feng Cheng 
Published: January 2024

Abstract
In this paper, under the data chain model, the form of envelope modulation is first utilized to identify the lexical feature quantity precision based on the horizontal energy clustering point of lexical distribution. Then, the reconstruction of the teaching model is accomplished by clustering assessment of modulation means and optimal experience through subtractive clustering. Finally, the multivariate array parameter distribution method is used to realize the node optimization deployment design of the data chain system, and at the same time, the teaching quality weight coefficients are obtained according to the overlap probability and the energy domain detection probability. After applying the six-step teaching model, the passing rate of the third level is about 40%, and the scoring rate of the experimental group is higher than that of the control group by 1.64%. When the vocabulary frequency is 200HZ, the P-value is 0.0039 less than 0.05 effect is significant, which can enhance the students’ subjective initiative, and the English teaching mode realizes a better innovative effect.
The Dilemma of Annual Reporting for Turkish Companies for 2022
Authors: Ilter Cenap , Canakcioglu Mustafa 
Published: January 2024

Abstract
Turkey has had another year of high inflation and high devaluation in its economic history. Inflation has been a continuing phenomenon in Turkey for many years. The paper analyzes the consequences of high inflation and devaluation rates on financial statements. Inflation and devaluation create difficulties both for local and foreign subsidiary companies` financial reporting. A model company`s transactions have been used to show the effects of inflation, devaluation on the financial statements. Application of different IAS based reporting methods will create discrepancies among the companies of foreign subsidiaries. Another discrepancy is due to which inflation rate to be applied for IAS 29 purposes. There are two inflation rates being discussed by the public. One is declared by the government statistical agency called “TurkStat” and the other one declared by the non-governmental academicians` organization called “ENAG”. ENAG`s inflation rate-being more than twice of the TurkStat`s makes the case even more interesting. Since government is not going to allow the inflation accounting rules for financial reporting for local/tax purposes for 2022 fiscal year, companies with revenues exceeding expenses and assets exceeding their liabilities will be declaring profits and paying taxes whilst in fact, high inflation and devaluation are sweeping away those profits.
Analysis of the Financial Performance of Energy Companies in the Current Context in Romania
Authors: Bircea Ioan , Sardi Tamara 
Published: January 2024

Abstract
After a period of pandemic (COVID-19), the liberalisation of electricity markets (on 1 January 2021), the start of the war in Ukraine, the imbalances between energy supply and demand at European level, energy policies at EU (towards green energy) and national level, prices are rising massively and consumption is falling for both individuals and companies, with a direct impact on the financial performance of companies in this sector. The assessment of the financial performance of companies in the energy sector was based on a set of indicators calculated from both financial statement and market data. The analysis was carried out during 2019-30 September 2023 and targeted two producers S.N.G.N. Romgaz S.A., S.N. Nuclearelectrica SA, a carrier C.N.T.E.E. Transelectica SA and a distributor Societatea Energetică Electrică S.A.
The Effects of Crises on the Living Standards in Romania and Hungary in the 21st Century – Similarities and Differences
Authors: Siposné Nándori Eszter , Zsidó Kinga Emese 
Published: January 2024

Abstract
Our research focuses on the analysis of the effects of the crises of the last 20 years on the development of the standard of living in Romania and Hungary. In the research, we focus primarily on the economic crisis of 2008 and the economic and social shock caused by the COVID-19 pandemic. The study includes an overview of the economic and social situation of Romania and Hungary, the presentation of the concept of crisis, and the indicators that describe the standard of living (GDP per capita, the Gini coefficient, indicators of poverty and social exclusion and indicators that describe the development of the labor market). In our analysis, we examine and compare the evolution of indicators measuring the standard of living in Romania and Hungary during the crises.
The Impact of Digitalization in Academic Marketing – A Comparative Study
Authors: Dobre Erika-Alexandra , Ciucan-Rusu Liviu 
Published: January 2024

Abstract
Using as a starting point the continuous transformation that the academic environment bears under the impact of the digitization phenomenon, our paper aims at a scientific investigation on the online presence of the educational offer of two universities. Current methods issued from theoretical and professional models have allowed us to highlight a series of criteria that reflect the quality and performance of online digital promotion of admission services in the case of two representative universities in Romania. Thus, we can say that, by building an operational dashboard, educational digital marketing can benefit from decision support tools, useful both in branding policy and in operationalizing online presence and communication.
Sex determination by reference to dental plaster casts of young adults
Authors: Wendei Evans R. , Faulkner Keith D.B. 
Published: January 2024

Abstract
Dental plaster casts of young Caucasians were examined by dentists with the objective of determining the sex of the subjects from whom they were taken. The first hypothesis tested was that dentists cannot distinguish the sexes, and second, that dentists cannot distinguish between the sexes from such an examination. The results of both tests were consistent with chance findings. However, half of the casts were perceived by 75 percent of dentists as being of a specific sex, which suggested that dentists shared some unfounded prejudices. It is concluded that dentists cannot differentiate sex by inspection alone of the general form and presentation of shape and size of dentate arches.
Morphometry of neural structures in the mouse periodontal ligament mesial to the mandibular first molar
Authors: Freezer Simon R. , Sims Milton R. 
Published: January 2024

Abstract
The periodontal ligament mesial to the mandibular first molars of three mice was analysed stereologically between the alveolar crest and the tooth apex. Ultrathin tissue sections were collected at statistically predetermined intervals, 50 and 200 microns apart, examined in the TEM and quantified using standard point counting procedures (Gundersen et al. 1988). Findings indude evidence that, in the mouse, some unmyelinated axons arise from myelinated axons and that anatomically discrete arrangements of unmyelinated axons occur in the apericytic wall sections of postcapillary-sized venules. Morphometric data indicate that unmyelinated axons constitute approximately 95 percent of all periodontal axons. The greatest relative proportion of myelinated axons is in the bone third of the ligament, at depths between 600 and 800 microns, where the ratio of unmyelinated to myelinated axons is 5:1.
Ultrasttucturally, the ligament contains a variety of anatomically discrete neural structures in juxtaposition to periodontal blood vessels. These structures indude nerve endings contiguous with K-cells, partially exposed terminal axons, preterminal and terminal axons protruding into the vessel lumina, and lamellated receptors. Mitochondria-rich terminals and fine nerve endings approximated pericytes in the walls of postcapillary-sized venules and arteriovenous anastomoses. Typically, these neural structures were characterised by the presence of an associated oxytalan fibre meshwork. This study also provides quantitative parameters for axon distribution within the ligament.
Comparative effect of probiotic and antibiotic on honey bees colony functional traits
Authors: Besharati Maghsoud , Bavand Reza , Paya Hamid , Lackner Maximilian 
Published: January 2024

Abstract
The purpose of this study was to evaluate the effect of different levels of probiotic on colony functional traits, honey quantitative and qualitative parameters and gut microbial flora in honey bees. A total of 56 hives were randomly allocated to 8 treatments with 7 replicates in the city of Firouzkoh (Iran) for about 60 days. Experimental treatments that were included: control, 2 g antibiotic, 1 g probiotic, 2 g probiotic, 3 g probiotic, 1 g antibiotic + 1 g probiotic, all per liter of syrup for each hive. The results showed that hives fed with treatments 2 g or 3 g probiotic per liter of syrup had higher hive population and honey production than the other treatments (p<0.05). Adding 2 g or 3 g probiotic per liter of syrup increased lactobacillus counts in bees’ intestines compared to the other treatments (p<0.05). Also, supplementation of 1 g or 2 g probiotic and 2 g antibiotic per liter of syrup for each hive decreased Escherichia coli counts in the intestine compared to the other treatments (p<0.05). The adding 1 g or 2 g probiotic per liter of syrup for each hive increased the levels of fructose and glucose in honey compared to the other treatments (p<0.05). The supplementation of 1 g or 2 g probiotic per liter of syrup decreased sucrose in honey compared to the other treatments (p<0.05). The results of the present study suggest that probiotic might be used as a feed additive for increased honey quality and gut microbial flora improvement in honey bees.