bluuebunny/crossref_metadata_2025_split huggingface dataset:
Geometric Mean Type of Proportional Reduction in Variation Measure for Two-Way Contingency Tables
Authors: Urasaki Wataru , Wada Yuki , Nakagawa Tomoyuki , Tahata Kouji , Tomizawa Sadao 
Published: January 2024

AbstractTraditional analysis of two-way contingency tables with explanatory and response variables focuses on the independence of two variables. However, if the variables do not show independence or a clear relationship, the analysis shifts to the degree of association. Various measures have been proposed to calculate the degree of association. One is the proportional reduction in variation (PRV) measure. This measure describes the PRV from the marginal distribution to the conditional distribution of the response variable. Although conventional PRV measures can assess the association of the entire contingency table, they cannot accurately assess the association for each explanatory variable. In this paper, we propose a geometric mean type of PRV (geoPRV) measure, which aims to sensitively capture the association of each explanatory variable to the response variable. Our approach uses a geometric mean, and enabling analysis without underestimating the values when the cells in the contingency table are partially biased. The geoPRV measure can be constructed using any function that satisfies specific conditions. This approach has practical advantages, and in special cases, conventional PRV measures can be expressed as geometric mean types.
Fuzzy inference system using genetic algorithm and pattern search for predicting roof fall rate in underground coal mines
Authors: Sahu Ayush , Sinha Satish , Banka Haider 
Published: January 2024

AbstractOne of the most dangerous safety hazard in underground coal mines is roof falls during retreat mining. Roof falls may cause life-threatening and non-fatal injuries to miners and impede mining and transportation operations. As a result, a reliable roof fall prediction model is essential to tackle such challenges. Different parameters that substantially impact roof falls are ill-defined and intangible, making this an uncertain and challenging research issue. The National Institute for Occupational Safety and Health assembled a national database of roof performance from 37 coal mines to explore the factors contributing to roof falls. Data acquired for 37 mines is limited due to several restrictions, which increased the likelihood of incompleteness. Fuzzy logic is a technique for coping with ambiguity, incompleteness, and uncertainty. Therefore, In this paper, the fuzzy inference method is presented, which employs a genetic algorithm to create fuzzy rules based on 109 records of roof fall data and pattern search to refine the membership functions of parameters. The performance of the deployed model is evaluated using statistical measures such as the Root-Mean-Square Error , Mean-Absolute-Error, and coefficient of determination ($$R_2$$

R
2

). Based on these criteria, the suggested model outperforms the existing models to precisely predict roof fall rates using fewer fuzzy rules.
A novel evaluation method of natural gas hydrate saturation in reservoirs based on the equivalent medium theory
Authors: Zhang Qiangui , Li Quanshan , Fan Xiangyu , Chen Yufei , Wang Zhaoxiang , Yao Bowei , Wei Na , Zhao Jun 
Published: January 2024

AbstractNatural gas hydrate saturation (NGHS) in reservoirs is one of the critical parameters for evaluating natural gas hydrate resource reserves. Current widely-accepted evaluation methods developed for evaluating conventional natural gas saturation in reservoirs, to some extents, are not sufficient enough to obtain accurate predicted results. In light of the equivalent medium theory, the natural gas hydrate is regarded as the fluid (Mode A) when NGHS is relatively low, while it is regarded as the rock matrix (Mode B) when NGHS is high. Two mathematical model are then developed for evaluating NGHS at Mode A and B. Experimental verification shows that R2 of the predicted results based upon the proposed model is 0.968, and the average absolute relative error percentage is 8.90%. The error of the predicted results gradually decreases with increasing NGHS, whereas increases with increasing confining pressure. In addition, the proposed model has been applied to the 142.9–147.7 m well section of Well DK-1 in the permafrost region, Qilian Mountains. The results show that the error of the predicted results is less than 13.92%, with its average error being 10.51%. The predicted value gradually increases with its error decreasing as the depth continues to increase, which is consistent with the change behavior of measured data. NGHS evaluation method proposed in this paper fully considers the occurrence form of natural gas hydrate in reservoirs. The model parameters are easy to determine and the predicted results are reliable.
Post-earthquake fire risk and loss assessment in urban areas
Authors: Vitorino Hugo , Khiali Vahid , Rodrigues Hugo 
Published: January 2024

AbstractPost-earthquake fire (PEF) poses a significant threat to urban structures and may contribute to the collapse of seismically damaged buildings as well as result in catastrophic human casualties and loss of properties. There is some attention of the researchers to further study the impact of PEF risk in urban buildings and its socio-economical aspects on urban life. However, the nature of such phenomenon has not yet been fully understood in depth and many aspects are still unknown. In this paper, a review of the state-of-the-art of the previous PEF events, PEF risk estimation, fire ignition models and probabilistic loss assessment are presented. This work aims to present the main observations regarding the PEF events in history and give a review of the studies that were developed to better understand the PEF phenomenon. It is also presented some mitigation measures that could be helpful to reduce or prevent post-earthquake fires.
Exploring the link between basic ICT competence beliefs and technology use for student teachers and teachers: A use of bifactor exploratory structural equation models
Authors: Rubach Charlott , Quast Jennifer , Porsch Raphaela , Arndt Mona 
Published: January 2024

AbstractBasic ICT competence beliefs consist of a range of competence dimensions, including communication and collaboration and safety and security. Yet, unclear is if (student) teachers need to overall feel competence in using technology, or is feeling competent in specific competence dimensions sufficient for the use of technology. This study addressed this research lack and examined (a) basic ICT competence beliefs and technology use in different stages of teacher education and (b) the association between basic ICT competence beliefs and various hardware and software used. Data included 338 teachers and 343 student teachers from Germany and were analyzed using bifactor exploratory structural equation models. These models allow us to simultaneously account for general basic ICT competence beliefs (G-Factor) and specific basic ICT competence beliefs (S-Factors). The study found that teachers, on average, reported higher basic ICT competence beliefs than student teachers. For both student teachers and teachers, the use of wikis and forum was linked to higher general ICT competence beliefs. The use of LMS was linked to basic ICT competence beliefs in safety and security and problem solving. The study also identified unique links in both groups, such as wiki and weblog use being linked to higher general ICT competence beliefs for teachers. In various teacher education phases, we can emphasize the need to promote basic ICT competence beliefs to empower teachers in the digital transformation. By interpreting these results, however, it should be noted that our analysis only focused on utilizing particular hardware and software; replications of findings are also needed.
A Versatile Model for Clustered and Highly Correlated Multivariate Data
Authors: Zhang Yingjuan , Einbeck Jochen 
Published: January 2024

AbstractFor the analysis of multivariate data with an approximately one-dimensional latent structure, it is suggested to model this latent variable by a random effect, allowing for the use of mixed model methodology for dimension reduction purposes. We implement this idea through the mixture-based approach for the estimation of random effect models, hence conveniently enabling clustering of observations along the latent linear subspace, and derive the estimators required for the ensuing EM algorithm under several error variance parameterizations. A simulation study is conducted, and several important inferential problems, including clustering, projection, ranking, regression on covariates, and regression of an external response on the predicted latent variable, are considered and illustrated by real data examples.
Digital humanities in the era of digital reproducibility: towards a fairest and post-computational framework
Authors: Joyeux-Prunel Béatrice 
Published: January 2024

AbstractReproducibility has become a requirement in the hard sciences, and its adoption is gradually extending to the digital humanities. The FAIR criteria and the publication of data papers are both indicative of this trend. However, the question that arises is whether the strict prerequisites of digital reproducibility serve only to exclude digital humanities from broader humanities scholarship. Instead of adopting a binary approach, an alternative method acknowledges the unique features of the objects, inquiries, and techniques of the humanities, including digital humanities, as well as the social and historical contexts in which the concept of reproducibility has developed in the human sciences. In the first part of this paper, I propose to examine the historical and disciplinary context in which the concept of reproducibility has developed within the human sciences, and the disciplinary struggles involved in this process, especially for art history and literature studies. In the second part, I will explore the question of reproducibility through two art history research projects that utilize various computational methods. I argue that issues of corpus, method, and interpretation cannot be separated, rendering a procedural definition of reproducibility impractical. Consequently, I propose the adoption of ‘post-computational reproducibility’, which is based on FAIREST criteria as far as digital corpora are concerned (FAIR + Ethics and Expertise, Source mention + Time-Stamp), but extended to include further sources that confirm computational results with other non-computational methodologies.
Classification of Brain Tumors: A Comparative Approach of Shallow and Deep Neural Networks
Authors: Montoya Sebastián Felipe Álvarez , Rojas Alix E. , Vásquez Luis Fernando Niño 
Published: January 2024

AbstractBrain tumors can be generated anywhere in the brain, with an extensive size range and morphology that makes it challenging to identify and classify. Classifying brain tumors is essential for developing personalized treatment plans. Different types of brain tumors have different responses to treatment, and an accurate classification can help medical professionals develop treatment plans tailored to each patient’s needs. Therefore, this case study aimed to classify T1-weighted contrast-enhanced images of three types of tumors through various approaches, from shallow neural networks to fine-tuning deep neural networks trained. Comparing shallow and deep neural network approaches could help to understand the trade-offs between their performance, interoperability, interpretability, benefits, limitations, scopes, and overall, choosing the best method for a given problem.
Cubic elasticity of porous materials produced by additive manufacturing: experimental analyses, numerical and mean-field modelling
Authors: Kowalczyk-Gajewska Katarzyna , Maj Michał , Bieniek Kamil , Majewski Michał , Opiela Kamil C. , Zieliński Tomasz G. 
Published: January 2024

AbstractAlthough the elastic properties of porous materials depend mainly on the volume fraction of pores, the details of pore distribution within the material representative volume are also important and may be the subject of optimisation. To study their effect, experimental analyses were performed on samples made of a polymer material with a predefined distribution of spherical voids, but with various porosities due to different pore sizes. Three types of pore distribution with cubic symmetry were considered and the results of experimental analyses were confronted with mean-field estimates and numerical calculations. The mean-field ‘cluster’ model is used in which the mutual interactions between each of the two pores in the predefined volume are considered. As a result, the geometry of pore distribution is reflected in the anisotropic effective properties. The samples were produced using a 3D printing technique and tested in the regime of small strain to assess the elastic stiffness. The digital image correlation method was used to measure material response under compression. As a reference, the solid samples were also 3D printed and tested to evaluate the polymer matrix stiffness. The anisotropy of the elastic response of porous samples related to the arrangement of voids was assessed. Young’s moduli measured for the additively manufactured samples complied satisfactorily with modelling predictions for low and moderate pore sizes, while only qualitatively for larger porosities. Thus, the low-cost additive manufacturing techniques may be considered rather as preliminary tools to prototype porous materials and test mean-field approaches, while for the quantitative and detailed model validation, more accurate additive printing techniques should be considered. Research paves the way for using these computationally efficient models in optimising the microstructure of heterogeneous materials and composites.
To be forgotten or to be fair: unveiling fairness implications of machine unlearning methods
Authors: Zhang Dawen , Pan Shidong , Hoang Thong , Xing Zhenchang , Staples Mark , Xu Xiwei , Yao Lina , Lu Qinghua , Zhu Liming 
Published: January 2024

AbstractThe right to be forgotten (RTBF) allows individuals to request the removal of personal information from online platforms. Researchers have proposed machine unlearning algorithms as a solution for erasing specific data from trained models to support RTBF. However, these methods modify how data are fed into the model and how training is done, which may subsequently compromise AI ethics from the fairness perspective. To help AI practitioners make responsible decisions when adopting these unlearning methods, we present the first study on machine unlearning methods to reveal their fairness implications. We designed and conducted experiments on two typical machine unlearning methods (SISA and AmnesiacML) along with a retraining method (ORTR) as baseline using three fairness datasets under three different deletion strategies. Results show that non-uniform data deletion with the variant of SISA leads to better fairness compared to ORTR and AmnesiacML, while initial training and uniform data deletion do not necessarily affect the fairness of all three methods. This research can help practitioners make informed decisions when implementing RTBF solutions that consider potential trade-offs on fairness.
Answer Agnostic Question Generation in Bangla Language
Authors: Fahad Abdur Rahman , Al Nahian Nazme , Islam Md Ahanaf , Rahman Rashedur M. 
Published: January 2024

AbstractQuestion generation (QG) from a given context paragraph is a demanding task in natural language processing for its practical applications and prospects in various fields. Several studies have been conducted on QG in high-resource languages like English, however, very few have been done on resource-poor languages like Arabic and Bangla. In this work, we propose a finetuning method for QG that uses pre-trained transformer-based language models to generate questions from a given context paragraph in Bangla. Our approach is based on the idea that a transformer-based language model can be used to learn the relationships between words and phrases in a context paragraph which allows the models to generate questions that are both relevant and grammatically correct. We finetuned three different transformer models: (1) BanglaT5, (2) mT5-base, (3) BanglaGPT2, and demonstrated their capabilities using two different data formatting techniques: (1) AQL—All Question Per Line, (2) OQL—One Question Per Line, making it a total of six different variations of QG models. For each of these variants, six different decoding algorithms: (1) Greedy search, (2) Beam search, (3) Random Sampling, (4) Top K sampling, (5) Top- p Sampling, 6) a combination of Top K and Top-p Sampling were used to generate questions from the test dataset. For evaluation of the quality of questions generated using different models and decoding techniques, we also fine-tuned another transformer model BanglaBert on two custom datasets of our own and created two question classifier (QC) models that check the relevancy and Grammatical correctness of the questions generated by our QG models. The QC models showed test accuracy of 88.54% and 95.76% in the case of correctness and relevancy checks, respectively. Our results show that among all the variants of the QG, the mT5 OQL approach and beam decoding algorithm outperformed all the other ones in terms of relevancy (77%) and correctness (96%) with 36.60 Bleu_4, 48.98 METEOR, and 63.38 ROUGE-L scores.
The regulatory quality in the light of environmental, social and governance framework at world level
Authors: Costantiello Alberto , Leogrande Angelo 
Published: January 2024

AbstractThis article investigates the determinants of Regulatory Quality-RQ for 193 countries in the period 2011–2020.The database used is the World Bank's Environmental, Social, and Governance-ESG dataset. The analysis was conducted through usage of Ordinary Least Squares OLS, Panel Data with Fixed Effects and Panel Data with Random Effects. Results show that the variables that have the most positive impact on RQ, among others, are “GHG Net Emission”, “Mean Drought Index”, and “Heat Index” while the variables that have the most negative impact on RQ are among others “Renewable Energy Consumption”, “Voice and Accountability” and “Rule of Law”. Furthermore, the k-Means algorithm optimized with the Elbow Method has been applied and five clusters were found. In adjunct, eight machine learning algorithms have been confronted to predict the value of RQ. Results show that the best predictor is Polynomial Regression. The predictive level of RQ for the analysed countries is expected to diminish of − 1.29%. In the end, a alysis with the Euclidean distance is discussed.
The distributional impact of FEMA’s community rating system
Authors: Brent Daniel A. , Ren Yongwang , Wrenn Douglas H. 
Published: January 2024

AbstractCommunity Rating System (CRS) incentivizes investments in risk reduction above NFIP standards using discounts on insurance premiums. These discounts are cross-subsidized by increasing premiums in non-CRS communities. We examine the distribution of these subsidies and find that redistribution does occur, but the gains and losses are not economically large with 95% of households gaining or losing no more than 0.3% of household income. We also examine their relationship with other community characteristics and find that the strongest predictor of premium reductions is the underlying flood risk level within the community. Thus, CRS appears to reduce the cost of living in the riskier communities.
How cy pres promotes transdisciplinary convergence science: an academic health center for women’s cardiovascular and brain health
Authors: Villablanca Amparo , Dugger Brittany N. , Nuthikattu Saivageethi , Chauhan Joohi , Cheung Samson , Chuah Chen-Nee , Garrison Siedah L. , Milenkovic Dragan , Norman Jennifer E. , Oliveira Luca Cerny , Smith Bridgette P. , Brown Susan D. 
Published: January 2024

Abstract
Cardiovascular disease (CVD) is largely preventable, and the leading cause of death for men and women. Though women have increased life expectancy compared to men, there are marked sex disparities in prevalence and risk of CVD-associated mortality and dementia. Yet, the basis for these and female-male differences is not completely understood. It is increasingly recognized that heart and brain health represent a lifetime of exposures to shared risk factors (including obesity, hyperlipidemia, diabetes, and hypertension) that compromise cerebrovascular health. We describe the process and resources for establishing a new research Center for Women’s Cardiovascular and Brain Health at the University of California, Davis as a model for: (1) use of the cy pres principle for funding science to improve health; (2) transdisciplinary collaboration to leapfrog progress in a convergence science approach that acknowledges and addresses social determinants of health; and (3) training the next generation of diverse researchers. This may serve as a blueprint for future Centers in academic health institutions, as the cy pres mechanism for funding research is a unique mechanism to leverage residual legal settlement funds to catalyze the pace of scientific discovery, maximize innovation, and promote health equity in addressing society’s most vexing health problems.
Echericetus novellusn. gen. n. sp. (Cetacea, Mysticeti, Eomysticetidae), an Oligocene baleen whale from Baja California Sur, Mexico
Authors: Hernández-Cisneros Atzcalli Ehécatl , Schwennicke Tobias , Rochín-Bañaga Heriberto , Tsai Cheng-Hsiu 
Published: January 2024

AbstractAmong the several evolutionary lineages of the baleen whales (Mysticeti), the eomysticetids are an ancient successful family that retain possibly nonfunctional teeth and functional baleen, a transitional stage between toothed and baleen-assisted filter-feeding mysticetes. The patchy fossil record leaves gaps in eomysticetid paleobiology interpretations, but their diversity and widespread geographical distribution can be a relevant proxy to understanding the evolution of crown Mysticeti. Here we describe a new baleen whale,Echericetus novellusn. gen. n. sp., from the Oligocene of Mexico (slightly older than 27.95 million years ago). This new taxon has morphological features that show its affinity to Eomysticetidae, such as the intertemporal region longer than wide, the elongate and oval temporal fossa, and a well-developed and lobate coronoid process of the mandible. Similarly, our cladistic analyses confirm the inclusion ofEchericetusin the Eomysticetidae.Echericetusreinforces our notion of the eomysticetid diversity and disparity. Geographically, the existence ofEchericetusfrom Mexico also indicates that eomysticetid inhabited subtropical regions in the Northern Hemisphere. Last, our discovery of a new eomysticetid from the Oligocene of Mexico provides new insights into the distribution patterns and habitat use of Eomysticetidae, essential to further explain the demise of this transitional lineage between toothed and baleen-bearing whales.UUID:http://zoobank.org/00cfc802-e2da-465c-9bb6-59347be00164