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Peer-to-peer lending, a novel element of Internet finance that links lenders and borrowers via online platforms, has generated large profits for investors. However, borrowers’ missed payments have negatively impacted the industry’s sustainable growth. It is imperative to create a system that can correctly predict loan defaults to lessen the damage brought on by defaulters. The goal of this study is to fill the gap in the literature by exploring the feasibility of developing prediction models for P2P loan defaults without relying heavily on personal data while also focusing on identifying key variables influencing borrowers’ repayment capacity through systematic feature selection and exploratory data analysis. Given this, this study aims to create a computational model that aids lenders in determining the approval or rejection of a loan application, relying on the financial data provided by applicants. The selected dataset, sourced from an open database, contains 8578 transaction records and includes 14 attributes related to financial information, with no personal data included. A loan dataset is first subjected to an in-depth exploratory data analysis to find behaviors connected to loan defaults. Subsequently, diverse and noteworthy machine learning classification algorithms, including Random Forest, Support Vector Machine, Decision Tree, Logistic Regression, Naïve Bayes, and XGBoost, were employed to build models capable of discerning borrowers who repay their loans from those who do not. Our findings indicate that borrowers who fail to comply with their lenders’ credit policies, pay elevated interest rates, and possess low FICO ratings are at a higher likelihood of defaulting. Furthermore, elevated risk is observed among clients who obtain loans for small businesses. All classification models, including XGBoost and Random Forest, successfully developed and performed satisfactorily and achieved an accuracy of over 80%. When the decision threshold is set to 0.4, the

Keyword extraction from Knowledge Bases underpins the definition of relevancy in Digital Library search systems. However, it is the pertinent task of Joint Relation Extraction, which populates the Knowledge Bases from which results are retrieved. Recent work focuses on fine-tuned, Pre-trained Transformers. Yet, F1 scores for scientific literature achieve just 53.2, versus 69 in the general domain. The research demonstrates the failure of existing work to evidence the rationale for optimisations to finetuned classifiers. In contrast, emerging research subjectively adopts the common belief that Natural Language Processing techniques fail to derive context and shared knowledge. In fact, global context and shared knowledge account for just 10.4% and 11.2% of total relation misclassifications, respectively. In this work, the novel employment of semantic text analysis presents objective challenges for the Transformer-based classification of Joint Relation Extraction. This is the first known work to quantify that pipelined error propagation accounts for 45.3% of total relation misclassifications, the most poignant challenge in this domain. More specifically, Part-of-Speech tagging highlights the misclassification of complex noun phrases, accounting for 25.47% of relation misclassifications. Furthermore, this study identifies two limitations in the purported bidirectionality of the Bidirectional Encoder Representations from Transformers (BERT) Pre-trained Language Model. Firstly, there is a notable imbalance in the misclassification of right-to-left relations, which occurs at a rate double that of left-to-right relations. Additionally, a failure to recognise local context through determiners and prepositions contributes to 16.04% of misclassifications. Furthermore, it is highlighted that the annotation scheme of the singular dataset utilised in existing research, Scientific Entities, Relations and Coreferences (SciERC), is marred by ambiguity. Notably, two asymmetric relati

Introduction The purpose of this study was to explore the students' perceptions and performance in prosthodontics theory exam. Methods A cross-sectional descriptive study was conducted on 560 (80.82%) students of different levels (third, fourth and fifth years) to explore their opinions and performance with regard to a number of issues on a prosthodontics theory exam (exam evaluation, exam preparation, exam material, exam timing). Demographic data were also collected. Descriptive statistics were generated and Chi-square test, independent sample t-test, ANOVA test and Pearson's correlation coefficient were used to examine the associations between different variables. The significance level was set at p < .05. Results Students' responses regarding exam evaluation was influenced by their gender, study level, high-school Grade Point Average (GPA) and undergraduate cumulative GPA. Perceived exam difficulty was significantly affected by gender (p = .03) and study level (p < .001), and negatively correlated to both high-school GPA (p < .001) and university GPA (p = .03). The vast majority (88.2%) depended on lecture hand-outs and lecture notes for study. Exam material and preparation were not significantly affected by any of the demographic variables with most respondents (76.8%) thinking that the lectures blended with prosthodontics laboratories/clinics would improve their understanding of the exam material. The suggested best time to conduct the exam was early afternoon (31.6%). Student performance was significantly affected by the study level (p < .001) and cumulative GPA (p < .001) with significant positive correlation between the high-school GPA and the mark in the exam (r = .29, p < .001) and by the amount of time students spent for exam preparation (p < .001). Those students who reported using textbooks to prepare for the exam got significantly higher marks (66.1 ± 8.7) compared to the students who did not (62.8 ± 9.7) (p = .03). Conclusions Course level, GPA and ge

Brazing of Mo to W and to graphite is achieved using BNi2 paste (containing Ni, Cr, Si, and B). For the Mo/W or Mo/graphite joint, the joining area consists of a diffusion area and a brazing area. The diffusion area is composed of MoNi and Mo, which is formed by diffusion of the Mo substrate into the braze during brazing. The brazing area of the Mo/W joint contains Ni(ss) (solid solution), Cr(ss), Ni3B, CrB, and Ni4W, while the brazing area of the Mo/graphite joint mainly comprises Ni(ss), MoNi, Ni3B, and CrB. A continuous chromium carbide layer is formed at the brazing area/graphite interface in the Mo/graphite joint due to the reaction of Cr in the BNi2 braze with the graphite. Nanoindentation measurements of the joints show that the diffusion area exhibits the highest hardness and elastic modulus in the joints. The shear strengths of the Mo/W and Mo/graphite joints are 58.1 ± 16.0 and 13.0 ± 4.0 MPa, respectively. The Mo/W and Mo/graphite joints fracture after the shear tests in the W and graphite sides, respectively, near the joining area, indicating that both fractures are caused by the stress concentration in the corresponding areas.

BACKGROUND: For placement of intraradicular posts the intracanal filling material has to be removed. If drills are employed for this purpose, extra widening of the canal, incomplete cracks or root perforation are probable when inappropriate size of drill is used. OBJECTIVE: This in vitro study assessed the efficacy of radiographs taken after completion of root canal therapy in selecting the appropriate-sized Peeso reamer for post space preparation. METHODS: Canals of 53 extracted maxillary and mandibular teeth of different types were cleaned and shaped. Then with acrylic resin 3-dimensional model of the intracanal space of each tooth was fabricated. Next, all canals were filled with gutta-percha and teeth were radiographed buccolingually. Based on these radiographs two observers selected a Peeso reamer that best matched each canal’s diameter. The diameter of the selected Peeso reamer was compared to the diameter of the corresponding resin model of each canal by two independent observers and the difference was measured. The data were analyzed by paired sample t-test using SPSS version 22. RESULTS: The diameter of the selected Peeso reamers ranged from 0.21 mm smaller to 0.12 mm larger than the diameter of intracanal spaces. The difference between reamer and resin model was less than 0.1 mm in 75% of the cases. CONCLUSION: The result of this study suggests that post-operation endodontic radiographs are reliable means for selecting a size of Peeso reamer that does not encroach on dentinal wall during removal of intracanal filling material and post space preparation.

Three-dimensional (3D) printing is becoming more prevalent in the dental sector due to its potential to save time for dental practitioners, streamline fabrication processes, enhance precision and consistency in fabricating prosthetic models, and offer cost-effective solutions. However, the effect of aging in artificial saliva of this type of material has not been explored. To assess the physical and mechanical properties of the two types of 3D-printed materials before and after being subjected to artificial saliva, a total of 219 acrylic resin specimens were produced. These specimens were made with two types of 3D-printed materials, namely, NextDent (ND) and Formlabs (FLs), and a Schottlander heat-cured (HC) resin material that was used as a control. Water sorption and solubility specimens (n = 5) were tested after three months of storage in artificial saliva. Moreover, the Vickers hardness, Martens hardness, flexural strength/modulus, and impact strength were evaluated both under dry conditions and after three months of storage in artificial saliva. The degree of conversion (DC), elemental analysis, and filler content were also investigated. The ANOVA showed that 3D-printed resins had significantly greater sorption than the control group (p < 0.05). However, the flexural strength values of the 3D-printed materials were significantly greater (p < 0.05) than those of the heat-cured material. The DC of the 3D-printed resins was lower than that of the control group, but the difference was not significant (p > 0.05). The 3D-printed materials contained significantly more filler than the control (p < 0.05). Moreover, the artificial saliva had a significant effect on the Vickers hardness for all tested groups and on the Martens hardness for the control group only (p < 0.05). Compared with conventional heat-cured materials, 3D-printed denture base materials demonstrated relatively poorer performance in terms of sorption, solubility, and DC but exhibited either comparable or

The spread of misinformation in football transfer news has become a growing concern. To address this challenge, this study introduces a novel approach by employing ensemble learning techniques to identify key factors for predicting such misinformation. The performance of three ensemble learning models, namely Random Forest, AdaBoost, and XGBoost, was analyzed on a dataset of transfer rumours. Natural language processing (NLP) techniques were employed to extract structured data from the text, and the veracity of each rumor was verified using factual transfer data. The study also investigated the relationships between specific features and rumor veracity. Key predictive features such as a player’s market value, age, and timing of the transfer window were identified. The Random Forest model outperformed the other two models, achieving a cross-validated accuracy of 95.54%. The top features identified by the model were a player’s market value, time to the start/end of the transfer window, and age. The study revealed weak negative relationships between a player’s age, time to the start/end of the transfer window, and rumor veracity, suggesting that for older players and times further from the transfer window, rumors are slightly less likely to be true. In contrast, a player’s market value did not have a statistically significant relationship with rumor veracity. This study contributes to the existing knowledge of misinformation detection and ensemble learning techniques. Despite some limitations, this study has significant implications for media agencies, football clubs, and fans. By discerning the credibility of transfer news, stakeholders can make informed decisions, reduce the spread of misinformation, and foster a more transparent transfer market.