bluuebunny/crossref_metadata_2025_split huggingface dataset:
PENGARUH MODEL PEMBELAJARAN MAKE A MATCH TERHADAP HASIL BELAJAR SISWA PADA MATA PELAJARAN BAHASA INGGRIS DI KELAS IV SD NEGERI 066050 MEDAN
Authors: Situmorang Anita S , Gaol Rumiris Lumban , Juliana Juliana , Sinaga Reflina , Simarmata Ester Julianda 
Published: December 2023

This study seeks to determine the impact of the make a match learning model on student learning outcomes in English class IV SD Negeri 066050 Medan Learning Year 2022/2023. The research method used is experimental quantitative research methodology. To obtain information in the form of data, a test instrument of 30 questions is needed. The number of research samples was 30 students based on purposive sample technique. To see the initial ability of students, the study conducted a Pre Test with the results representing a set of scores of 59.87 which fell into the category of less. The results of the Post Test are better than the results of the Pre Test given previously with the average value of the students reaching 80.17 in the excellent category. The results of the calculation of the correlation coefficient show an increase in the level of student learning success where 14rhitung"> ≥ 14 rtable"> l with the results of 0.770 ≥ 0.361. Furthermore, hypothesis testing is to compare the value of 14thitung "> ≥ 14ttabel"> . Obtained the value of 14thitung"> = 6.387 while 14ttable"> = 1.697. Because 14tcount"> ≥ 14ttable"> (6.387 ≥ 1.697) then H_a is accepted and H_o is rejected. Through the t-test, it can be concluded that the make a match learning model has a positive impact on student learning outcomes.
EFEKTIVITAS PENGGUNAAN MEDIA KARTU HILANG (KAHI) TERHADAP HASIL BELAJAR MATEMATIKA SISWA KELAS VI SD
Authors: Kirana Rara Netra Ihya , Sumaji Sumaji , Ermawati Diana 
Published: December 2023

The low mathematics learning outcomes of class VI students at SD 3 Purwosari is due to the fact that learning is still teacher-centered and has not utilized learning media so that effective and appropriate solutions and innovations are needed so that mathematics learning achieves the expected learning objectives. One of the innovations that can be applied to learning mathematics is the use of KAHI media. The aims of this study were 1) to examine the differences before and after applying KAHI media to students' mathematics learning outcomes, 2) to find out the increase in students' mathematics learning outcomes before and after applying KAHI Media. This research is a quantitative research type of pre-experimental design using one group pretest-posttest. The hypothesis test used is the Paired Sample t Test and the N-Gain test. The results of the Paired Sample t Test obtained a significance value of 0.000<0.05 indicating that there was a significant difference between learning before and after applying the KAHI media. The results of the N-Gain test obtained 45% results in the medium category, so it can be concluded that the use of KAHI media is effective for the mathematics learning outcomes of grade VI elementary school students.
Electrical Power Generator Faults Analysis Using Fault Tree and Bayesian Network
Authors: Touil Toufik , Lakehal Abdelaziz 
Published: December 2023

Abstract
This paper presents a model to predict Electrical Power Generator (EPG) faults. The fault tree (FT) model is developed and used to help maintenance engineers in fault analysis procedure of this rotating machine. By identifying the main, intermediate and basic events it’s possible to construct the FT with logical reasoning. The top dreaded event is defined. By using a Bayesian network (BN) as a complementary tool, fault prediction of the EPG becomes possible and easy. By using the developed BN, the probability of occurrence of the top event (EPG failure) is calculated. Also, by this approach, we can process complex information that causes system faults in an easy and simple way. The essential elements to do this analysis are the reliable and good exploitation of the information previously stored in the system. The use of the BN in combination with the FT gives the possibility of qualitative and quantitative analysis, diagnosis, and prediction of faults from the same Bayesian model. The flexibility of the proposed BN model in this paper allows better and precise decision making. Also, priorities regarding maintenance job are defined and resources are a priori prepared.
Bending Path Understanding Based on Angle Projections in Field Environments
Authors: Wang Luping , Wei Hui 
Published: December 2023

AbstractScene understanding is a core problem for field robots. However, many unsolved problems, like understanding bending paths, severely hinder the implementation due to varying illumination, irregular features and unstructured boundaries in field environments. Traditional three-dimensional(3D) environmental perception from 3D point clouds or fused sensors are costly and account poorly for field unstructured semantic information. In this paper, we propose a new methodology to understand field bending paths and build their 3D reconstruction from a monocular camera without prior training. Bending angle projections are assigned to clusters. Through compositions of their sub-clusters, bending surfaces are estimated by geometric inferences. Bending path scenes are approximated bending structures in the 3D reconstruction. Understanding sloping gradient is helpful for a navigating mobile robot to automatically adjust their speed. Based on geometric constraints from a monocular camera, the approach requires no prior training, and is robust to varying color and illumination. The percentage of incorrectly classified pixels were compared to the ground truth. Experimental results demonstrated that the method can successfully understand bending path scenes, meeting the requirements of robot navigation in an unstructured environment.
Self-Organized Operational Neural Networks for The Detection of Atrial Fibrillation
Authors: Zhang Junming , Dong Hao , Gao Jinfeng , Yao Ruxian , Li Gangqiang , Wu Haitao 
Published: December 2023

Abstract
Atrial fibrillation is a common cardiac arrhythmia, and its incidence increases with age. Currently, numerous deep learning methods have been proposed for AF detection. However, these methods either have complex structures or poor robustness. Given the evidence from recent studies, it is not surprising to observe the limitations in the learning performance of these approaches. This can be attributed to their strictly homogenous conguration, which solely relies on the linear neuron model. The limitations mentioned above have been addressed by operational neural networks (ONNs). These networks employ a heterogeneous network configuration, incorporating neurons equipped with diverse nonlinear operators. Therefore, in this study, to enhance the detection performance while maintaining computational efficiency, a novel model named multi-scale Self-ONNs (MSSelf-ONNs) was proposed to identify AF. The proposed model possesses a significant advantage and superiority over conventional ONNs due to their self-organization capability. Unlike conventional ONNs, MSSelf -ONNs eliminate the need for prior operator search within the operator set library to find the optimal set of operators. This unique characteristic sets MSSelf -ONNs apart and enhances their overall performance. To validate and evaluate the system, we have implemented the experiments on the well-known MIT-BIH atrial fibrillation database. The proposed model yields total accuracies and kappa coefficients of 98% and 0.95, respectively. The experiment results demonstrate that the proposed model outperform the state-of-the-art deep CNN in terms of both performance and computational complexity.
Lexical Diversity and Language Impairment
Authors: Zozuk Natalia Časnochová 
Published: December 2023

Abstract
The development of artificial intelligence tools has seen an enormous growth recently. Linguistic artificial intelligence tools are being successfully applied in the field of speech analysis and discourse. In our study, we used automatic NLP tools to detect differences in picture description in the discourse of people diagnosed with Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI) and healthy people. A measure of lexical diversity was used to compare discourse complexity. Transcripts of recordings of the probands within the EWA project were used in the study. From the multiple comparisons, we found that there is a statistically significant difference between healthy people and people suffering from MCI and AD. Our results indicate that healthy people have more lexical diversity than people suffering from MCI and AD – a more diverse vocabulary in spontaneous speech, in our case, when describing a picture.
Slovak Language Models for Basic Preprocessing Tasks in Python
Authors: Hládek Daniel , Harahus Maroš , Staš Ján , Pleva Matúš 
Published: December 2023

Abstract
We propose a Slovak language model for the spaCy library in Python. These models are easy-to-use for basic natural language processing tasks in a single package. The package contains several components for basic preprocessing tasks, such as tokenization, sentence boundary detection, syntactic parsing, lemmatization, named entity recognition, morphology analysis, and word vectors. It is based on the state-of-the-art monolingual SlovakBERT model. Named entity recognition is trained on a separate, publicly available WikiAnn database. The other statistical classifiers use a Slovak Dependency Treebank corpus. Morphological tags are compatible with the conventions of the Slovak National Corpus. The part of speech tags use conventions of the Universal Dependencies framework. We trained a separate word vector model on a web-based corpus. The training uses fastText with Floret modification. We present a series of experiments that confirm that the model performs similarly to other languages for all tasks. Training scripts and data are publicly available.
Distractor Generation for Lexical Questions Using Learner Corpus Data
Authors: Login Nikita 
Published: December 2023

Abstract
Learner corpora with error annotation can serve as a source of data for automated question generation (QG) for language testing. In case of multiple choice gapfill lexical questions, this process involves two steps. The first step is to extract sentences with lexical corrections from the learner corpus. The second step, which is the focus of this paper, is to generate distractors for the retrieved questions. The presented approach (called DisSelector) is based on supervised learning on specially annotated learner corpus data. For each sentence a list of distractor candidates was retrieved. Then, each candidate was manually labelled as a plausible or implausible distractor. The derived set of examples was additionally filtered by a set of lexical and grammatical rules and then split into training and testing subsets in 4:1 ratio. Several classification models, including classical machine learning algorithms and gradient boosting implementations, were trained on the data. Word and sentence vectors from language models together with corpus word frequencies were used as input features for the classifiers. The highest F1-score (0.72) was attained by a XGBoost model. Various configurations of DisSelector showed improvements over the unsupervised baseline in both automatic and expert evaluation. DisSelector was integrated into an opensource language testing platform LangExBank as a microservice with a REST API.
Validating a Scale for Innovation in Sustainable Water Management in the Manufacturing Sector: A Slovenian Study
Authors: Moretti Melita 
Published: December 2023

Abstract
Based on the results of our research, we have developed a reliable and valid scale for assessing the innovation success of sustainable development practices, with a specific focus on reducing drinking water consumption in the production sector, where so-called industrial water is used (for production, cooling, or both). We found that the assessment of innovation success in sustainable development management practices, specifically aimed at reducing drinking water consumption in the production sector, comprises two dimensions: measuring success in water use innovations and innovative success in water treatment and conservation. The study also found that the assessment of innovation success in sustainable development management practices, particularly focused on reducing drinking water consumption in the production sector, is influenced by reporting on sustainable water management, innovations in organizational systems, innovations in materials and processes for sustainable water use, and analysis with goal setting in water use innovations. This comprehensive analysis provides organizations without established metrics with a tool to identify potential areas for improvement, thereby enhancing ecological methods and increasing production efficiency.
Aspects of creating a corporate question-and-answer system using generative pre-trained language models
Authors: Golikov Aleksei , Akimov Dmitrii , Romanovskii Maksim , Trashchenkov Sergei 
Published: December 2023

The article describes various ways to use generative pre-trained language models to build a corporate question-and-answer system. A significant limitation of the current generative pre-trained language models is the limit on the number of input tokens, which does not allow them to work "out of the box" with a large number of documents or with a large document. To overcome this limitation, the paper considers the indexing of documents with subsequent search query and response generation based on two of the most popular open source solutions at the moment – the Haystack and LlamaIndex frameworks. It has been shown that using the open source Haystack framework with the best settings allows you to get more accurate answers when building a corporate question-and-answer system compared to the open source LlamaIndex framework, however, requires the use of an average of several more tokens. The article used a comparative analysis to evaluate the effectiveness of using generative pre-trained language models in corporate question-and-answer systems using the Haystack and Llamaindex frameworks. The evaluation of the obtained results was carried out using the EM (exact match) metric. The main conclusions of the conducted research on the creation of question-answer systems using generative pre-trained language models are: 1. Using hierarchical indexing is currently extremely expensive in terms of the number of tokens used (about 160,000 tokens for hierarchical indexing versus 30,000 tokens on average for sequential indexing), since the response is generated by sequentially processing parent and child nodes. 2. Processing information using the Haystack framework with the best settings allows you to get somewhat more accurate answers than using the LlamaIndex framework (0.7 vs. 0.67 with the best settings). 3. Using the Haystack framework is more invariant with respect to the accuracy of responses in terms of the number of tokens in the chunk. 4. On average, using the Haystack framework is more expensive in terms of the number of tokens (about 4 times) than the LlamaIndex framework. 5. The "create and refine" and "tree summarize" response generation modes for the LlamaIndex framework are approximately the same in terms of the accuracy of the responses received, however, more tokens are required for the "tree summarize" mode.
Efficacy of comprehensive dental treatment for lichenoid reactions of the mucoparodontal complex: A single-center pilot cohort study
Authors: Gileva O. S. , Libik T. V. , Rogozhnikov G. I. , Gibadullina N. V. , Rogozhnikov A. G. , Gavrilenko M. S. 
Published: December 2023

Background. Lichenoid reactions of the oral mucosa are currently recognized as a serious issue for dentists of various specialties, since their knowledge and competence in the diagnosis and pathogenetic treatment of lichenoid lesions of the oral cavity are yet to be improved. Following the data on high oncogenic potential in some forms of lichenoid reactions of oral cavity mucous membranes, the issue should be given serious considerations, and dentists should keep oncology-related suspicion when receiving these patients.Objective. To evaluate the efficacy of comprehensive dental treatment for lichenoid reactions of the mucoparodontal complex.Methods. A single-center pilot cohort study was conducted to evaluate the efficacy of the comprehensive treatment (combination of pharmacotherapy and physiotherapy) of patients with lichenoid reactions of the oral mucosa. The observation group consisted of 49 patients (15 males, 34 females) aged 58.92 ± 0.82 years with verified diagnoses of oral mucosa lichenoid reactions. The suggested treatment included pharmacotherapy (highly-potent topical steroids, calcineurin inhibitors) and physiotherapy in the form of low level laser therapy (635 nm, 10 W) provided via Matrix laser (Matrix, Russia). Treatment efficacy was assessed by the total scale of treatment efficacy, registering indicators of the area of focal lesions and the severity of pain syndrome, as well as by changes in dental quality of life indicators according to OHIP-49-RU (Oral Health Impact Profile) in the Russian version. Statistical processing of the data involved Student’s t-test and Wilcoxon t-test. Differences were considered significant at p ≤ 0.05. Data statistical analysis was performed using SPSS 13.0 (SPSS Inc., USA).Results. During the follow-up period, the indicators of the total scale of treatment efficacy decreased significantly (p < 0.001) in 100% of patients and amounted (as medians and quartiles): 6.0 (4.0–7.0) points before treatment and 1.0 (1.0–2.0) points after treatment to be objectified by a significant reduction in the area of focal lesions (by 63.2%, p < 0.001) and in pain syndrome (spontaneous pain from 2.0 (1.5–3.0) points before treatment to 0.0 (0.0–1.0) points after treatment; induced pain from 3.0 (2.0–3.0) points to 1.0 (0.0–1.0) points). Along with the clinical improvement, the scores in dental quality of life improved from 83.11 ± 2.58 to 39.94 ± 0.86 points, according to OHIP-49 RU (Oral Health Impact Profile).Conclusion. Early detection and differentiation of lichenoid reactions in oral mucous membranes ensure timely treatment using the developed protocol of comprehensive treatment (combination of pharmacotherapy and physiotherapy). The efficacy of the protocol was confirmed clinically and justified in relation to quality of life.
Comparative evaluation of efficiency of burn wound healing with derma-based hydrogel: a preclinical experimental study
Authors: Melkonyan K. I. , Alekseenko S. N. , Bykov I. M. 
Published: December 2023

Background. Burn wound healing is recognized as a complex process involving synergetic interactions between different cells, cytokines and growth factors. The adverse interactions can underlie chronicization of the process. Accordingly, the paper presents a relevant study into mechanisms of natural wound dressings, capable of influencing the processes of inflammation, angiogenesis, and skin resurfacing.Objective. To carry out a comparative evaluation of efficiency of burn wound healing with derma-based hydrogel according to the dynamics of proand anti-inflammatory factors.Methods. Development of a hydrogel material involved dermis samples of Landrace breed of pig, subjected to partial alkaline hydrolysis. In order to carry out a comparative evaluation of burn wound healing efficiency, the authors simulated direct thermal injury in three groups of sphinx (hairless) rats: group 1 (control group) — rats without treatment (n = 20), group 2 (comparison group) — rats treated with Levomekol ointment (n = 20), and group 3 (experimental group) — rats treated with hydrogel material (n = 20). Before and after injuring on days 1, 3, 7, 14, the content of cytokines interleukin-1β, interleukin-4, interleukin-6, interleukin-8, interleukin-10, tumor necrosis factor-α by enzyme immunoassay. The wound samples were explanted for histological examination on days 3, 7 and 14 after the beginning of the experiment. Statistical processing of the obtained results on DNA content in hydrogel, cytokine content in serum and morphometric data was performed using GraphPadPrism 6.04, Microsoft Excel 2016 (Microsoft, USA).Results. When determining the content dynamics of nonspecific markers of inflammation, an increase in the concentrations of interleukin-1β and tumor necrosis factor-α on day 1 after the hydrogel application was recorded, as well as an increase in interleukin-6 on days 3 and 7, while the concentrations of interleukin-8 did not change significantly throughout the experiment. Thus, dermal components are indicated to participate in the inhibition of acute-phase immune reactions. With regard to anti-inflammatory factors, the study revealed a decrease in the concentration of interleukin-10 on days 1 and 7, an increase in interleukin-4 on day 3 as compared to the control group, thereby indicating a pronounced anti-inflammatory effect and prolonged action of the hydrogel.Conclusion. Comparative analysis of the pro-inflammatory cytokines levels (interleukin-1β, interleukin-8) showed pronounced anti-inflammatory effects of the derma-based hydrogel material. Introduction of exogenous biological components of the extracellular matrix (collagen and its hydrolysates) had a significant influence on the regulation of anti-inflammatory cytokines synthesis, presumably contributing to faster successful epithelization and wound healing.
Prostate cancer morbidity in the Mari El Republic: A retrospective observational study
Authors: Pasynkov D. V. , Fatykhov R. I. 
Published: December 2023

Background. Prostate cancer maintains a relatively high standardized uptake value and share of patients followed up for 5 or more years. Accordingly, distant outcomes in these patients appear to be influenced by factors other than the underlying disease.Objective. To analyze the morbidity in prostate cancer patients with additional malignancies potentially linked with the decrease in the survival rate in the Mari El Republic.Methods. The present study involved 1434 prostate cancer patients firstly enrolled in the period from 2012 to 2021. A group of patients in this sample was identified with additional malignancies (other than prostate cancer) diagnosed within the period from 6 months prior to prostate cancer diagnosis to the end of 2021. Comparison of the incidence of malignancies among prostate cancer patients and the general population was performed via a 2 × 2 crosstab analysis by calculating the relative risk and its 95% confidence interval. The difference was considered significant when 95% confidence interval did not include 1. In addition, chi-square values and corresponding p-values were calculated. Statistical analyses were performed using SPSS 13.0 (SPSS Inc., USA) and Microsoft Excel 2007 (Microsoft Corporation, USA).Results. 31 (32.29%) additional malignancies were identified (prostate cancer was diagnosed within 6 months before prostate cancer diagnosis and up to 6 months thereafter), 7 additional malignancies (7.29%) were registered 6 months to 1 year after prostate cancer diagnosis, and 61 additional malignancies (63.54%) during the later period. The most common primary malignancies among all patients included: bladder cancer (relative risk = 15.23 [95% confidence interval: 10.42–22.26]), nonmelanoma skin cancer (relative risk = 3.77 [2.34–6.07]), colorectal cancer (relative risk = 2.10 [1.24–3.54]), gastric cancer (relative risk = 2.01 [1.08–3.73]), and kidney cancer (relative risk = 4.69 [2.51–8.75]).Conclusion. Within 7.1 years (median) of follow-up, additional malignancies develop in 6.70% of prostate cancer patients. These patients reveal the higher risk than the population average value, thereby constituting a risk group.