bluuebunny/crossref_metadata_2025_split huggingface dataset:
Time course of EEG power during creative problem‐solving with insight or remote thinking
Authors: Bieth Théophile , Ovando‐Tellez Marcela , Lopez‐Persem Alizée , Garcin Béatrice , Hugueville Laurent , Lehongre Katia , Levy Richard , George Nathalie , Volle Emmanuelle 
Published: December 2023

AbstractProblem‐solving often requires creativity and is critical in everyday life. However, the neurocognitive mechanisms underlying creative problem‐solving remain poorly understood. Two mechanisms have been highlighted: the formation of new connections among problem elements and insight solving, characterized by sudden realization of a solution. In this study, we investigated EEG activity during a modified version of the remote associates test, a classical insight problem task that requires finding a word connecting three unrelated words. This allowed us to explore the brain correlates associated with the semantic remoteness of connections (by varying the remoteness of the solution word across trials) and with insight solving (identified as a Eurêka moment reported by the participants). Semantic remoteness was associated with power increase in the alpha band (8–12 Hz) in a left parieto‐temporal cluster, the beta band (13–30 Hz) in a right fronto‐temporal cluster in the early phase of the task, and the theta band (3–7 Hz) in a bilateral frontal cluster just prior to participants' responses. Insight solving was associated with power increase preceding participants' responses in the alpha and gamma (31–60 Hz) bands in a left temporal cluster and the theta band in a frontal cluster. Source reconstructions revealed the brain regions associated with these clusters. Overall, our findings shed new light on some of the mechanisms involved in creative problem‐solving.
How do intangible assets and financial constraints affect stock returns in Vietnam before and during the COVID‐19 pandemic?
Authors: Duong Khoa Dang , Huynh Tran Ngoc , Truong Linh Thi Diem 
Published: December 2023

AbstractWe are the first to determine the effect of intangible intensity (INTANG) on cross‐sectional stock returns after controlling financial constraints in the Vietnam stock market. Our sample includes 37,938 firm‐month observations from 488 non‐financial firms from October 2008 to February 2021. We employ Fama and MacBeth regressions and portfolio analysis methodologies to estimate the impact of intangible assets and financial constraints on stock returns. Our findings show that a percentage increase in INTANG empowers stock returns by 0.922%. Meanwhile, the cross‐sectional stock returns decrease by 0.506% when the financial constraints index increases by a percentage point. Moreover, the results suggest that intangible assets in the entire sample and before COVID‐19 empower the stock return cross‐sectionally. Our findings are robust after employing alternative INTANG proxies. Our findings support the risk‐based explanation, the pecking order theory, and prior literature. Our findings suggest governments should promote intellectual property and copyright regulations to encourage Small and Medium Enterprises (SMEs) to expand intangible assets. Furthermore, investors can utilize our suggested models to construct their portfolios efficiently.
Does crop insurance influence crop yield impacts of warming temperatures? A farm‐level analysis
Authors: Regmi Madhav , Tack Jesse , Featherstone Allen M. 
Published: December 2023

AbstractWe examine the effects of the federal crop insurance program on crop production under extreme heat using farm‐level data from Kansas. We find that insured dryland corn is 38% more sensitive to extreme heat than uninsured dryland corn. Results also suggest that a uniform C warming scenario leads to yield reductions of 16.7% (11.8%) for insured (uninsured) corn production; and 6.0% (4.4%) for insured (uninsured) wheat production. Warming had a stronger impact with reduced rainfall, especially on insured farms compared with uninsured farms, while altered growing seasons showed potential benefits for wheat but limited improvements for corn.
Development of nanoparticle‐based drug delivery system for inflammation treatment and diagnosis
Authors: Qin Qin , Wang Mao , Zou Yu , Yang Dujiang , Deng Yuchuan , Lin Shuyu , Song Yiran , Li Rui , Zheng Yu 
Published: December 2023

AbstractInflammation is a prevalent pathological process that accompanies the onset and progression of numerous acute and chronic diseases including rheumatoid arthritis, sepsis, pancreatitis, atherosclerosis, ischaemic brain/heart disease and so forth. However, conventional anti‐inflammatory drugs have certain disadvantages such as nonspecific tissue distribution, low bioavailability, and a short half‐life, resulting in off‐target side effects and limited efficacy in disease control. To address these issues, nanoparticles have emerged as a novel therapeutic paradigm in this field to attain inflammation targeting and improve drug pharmacokinetic properties via the well‐recognized enhanced permeability and retention (EPR) effect at the inflammatory site. Existing reviews are predominantly centered on inflammatory pathology introduction and vector design. As a necessary complement, this review mainly elaborates on the introduction of inflammation core events, the history of the drug delivery system for anti‐inflammatory drugs, the action mechanism of inflammation targeting vectors, nanoparticle classification based on targeting moiety, methods to combine targeting moiety with core nanoparticles, techniques to assess targetability in vitro and in vivo and finally the challenges and prospects in this field. The information provided herein offers practical guidance to researchers seeking to develop and evaluate inflammation targeting vectors rationally.
Chalcones As Broad‐Spectrum Antimicrobial Agents: A Comprehensive Review And Analysis Of Their Antimicrobial Activities
Authors: Nawaz Tariq , Tajammal Affifa , Qurashi Aisha Waheed 
Published: December 2023

AbstractThe rise of drug‐resistant microbes necessitates the development of new antimicrobial agents. Increasing resistance among multidrug‐resistant microbes has spurred research efforts to overcome drug resistance through novel scaffolds and strategies. Chalcones, distinguished by their chemical structure of 1,3‐diphenylprop‐2‐en‐1‐one, have exhibited a multifaceted array of biological activities, prominently including antimicrobial and antiviral properties. Modifying the structure by adding substituent groups to the aromatic ring enhances potency, reduces toxicity, and expands pharmacological effects. This review highlights the potential of chalcones and analogues in preventing diseases affected by diverse antibiotic resistance genes, including viruses, bacteria, fungal spores, and Plasmodiidae. Our analysis underscores the fact that a multitude of chalcone compounds effectively obstruct various molecular targets pivotal in the development of antibiotic resistance, thus rendering bacteria vulnerable to conventional antibacterial agents and potentially obliterating resistance mechanisms. Certain chalcone compounds exhibit higher activity levels compared to traditional antibiotics like vancomycin and tetracycline. Additionally, Docking and SAR studies of chalcones summarize their role in developing novel drugs. Chalcones and their derivatives demonstrate promising broad‐spectrum antimicrobial activities. Investigating structure‐activity relationships (SAR) enhances our understanding of chalcone‐based drug design, enabling the development of more effective therapeutic interventions against the pressing global health challenge of antimicrobial resistance.
Phosphine‐Catalyzed C‐Methoxylation of para‐Quinone Methides with Methanol and Deuterated Methanol as C1 Sources
Authors: Zhong Yuan , Zhang Dan , Li Guichen , Jia Zhilong , Hao Long , Wang Xin 
Published: December 2023

AbstractIn this work, we developed a phosphine‐catalyzed C‐methoxylation process of para‐quinone methides (p‐QMs). By employing a metal‐free phosphine catalyst [P(4‐MeC6H4)3] together with methanol and deuterated methanol (d4, d1, and d3) as C1 sources, a series of methoxylated diarylmethanes and the corresponding deuterated products were obtained without additional solvent under mild reaction conditions.
Evaluating ChatGPT text mining of clinical records for companion animal obesity monitoring
Authors: Fins Ivo S. , Davies Heather , Farrell Sean , Torres Jose R. , Pinchbeck Gina , Radford Alan D. , Noble Peter‐John 
Published: December 2023

AbstractBackgroundVeterinary clinical narratives remain a largely untapped resource for addressing complex diseases. Here we compare the ability of a large language model (ChatGPT) and a previously developed regular expression (RegexT) to identify overweight body condition scores (BCS) in veterinary narratives pertaining to companion animals.MethodsBCS values were extracted from 4415 anonymised clinical narratives using either RegexT or by appending the narrative to a prompt sent to ChatGPT, prompting the model to return the BCS information. Data were manually reviewed for comparison.ResultsThe precision of RegexT was higher (100%, 95% confidence interval [CI] 94.81%–100%) than that of ChatGPT (89.3%, 95% CI 82.75%–93.64%). However, the recall of ChatGPT (100%, 95% CI 96.18%–100%) was considerably higher than that of RegexT (72.6%, 95% CI 63.92%–79.94%).LimitationsPrior anonymisation and subtle prompt engineering are needed to improve ChatGPT output.ConclusionsLarge language models create diverse opportunities and, while complex, present an intuitive interface to information. However, they require careful implementation to avoid unpredictable errors.
Integrated EPANET‐MSX process models of chlorine and its by‐products in drinking water distribution systems
Authors: Fisher Ian H. 
Published: December 2023

AbstractEPANET and its commercial derivatives are the most widely‐used software packages for modeling free chlorine and its by‐products in drinking water distribution systems. Yet, they are not sufficiently accurate, general, or efficient for deriving optimal chlorine dosing strategies at different seasonal temperatures. To overcome EPANET's limitations, an integrated set of rigorously validated multispecies process models are proposed for application within the EPANET‐MSX environment. An executable (command‐line) version of these MSX models is supplied for use either within the MSX environment or embedded in commercial versions of MSX. A new general method was devised to obtain output of any intermediate coefficient or variable involved in the simulation. This overcomes MSX's limited output options. When the debugged models were applied to a real distribution system, the optimal chlorine dose for summer required almost double the chlorine dose needed in winter. A lower initial dose combined with a downstream booster dose required less chlorine in total. Formal optimization techniques are needed to efficiently obtain similar strategies in more complex systems.Practitioner Points
EPANET water quality models are not accurate or general enough for deriving optimal chlorine dosing strategies in distribution systems.
Integrated EPNET‐MSX models of chlorine reactions in bulk water and at pipe walls, and associated by‐product formation, overcome EPANET’s limitations.
To verify model authenticity, a general technique was developed to obtain values of coefficients and variables within an EPANET‐MSX simulation.
EPANET‐MSX command lines implementing these integrated EPANET‐MSX models are presented with verified results for optimal initial and booster dosing strategies.
Optimal summer dosing in a real system of rough pipes was almost double that required in winter.
Numerical solution for cross nanofluid flow in porous medium over stretching cylinder under MHD and radiation effects
Authors: Munazza F. , Farooq M. Asif 
Published: December 2023

AbstractThe aim of the present study is to analyze heat source in unsteady MHD nanofluid flow subjected to convective boundary conditions past a stretching cylinder incorporated with variable thermal properties. This study is significant in industries involving chemical processing, oil and gas, and manufacturing. The problem possesses a nonsimilar solution when similarity variables are utilized in transforming governing partial differential equations into ordinary differential equations. The numerical solution is found using MATLAB packagebvp4c. The velocity, temperature, and concentration curves for various parameters are plotted graphically. The values for the coefficient of skin friction, heat transfer, and mass transfer rate are tabulated for the unsteadiness parameter, Weissenberg number, Prandtl number, Schmidt number, heat source/sink, thermal conductivity parameter, thermal and solutal Biot number, thermophoresis and Brownian parameter, radiation parameter, porosity parameter, and magnetic parameter. It is noted that, rise in porosity (–) and magnetic parameter (–) contributes to the decrease in velocity profile. It is found that, radiation effects help in incrementing rate of heat and mass transfer into fluid when radiation parameter ranges from to . Moreover, present results are in good agreement with the literature published in a limiting context.
A Topological Clustering of Individuals
Authors: Abdesselam Rafik 
Published: December 2023

AbstractThe clustering of objects-individuals is one of the most widely used approaches to exploring multidimensional data. The two common unsupervised clustering strategies are Hierarchical Ascending Clustering (HAC) and k-means partitioning used to identify groups of similar objects in a dataset to divide it into homogeneous groups. The proposed Topological Clustering of Individuals, or TCI, studies a homogeneous set of individual rows of a data table, based on the notion of neighborhood graphs; the columns-variables are more-or-less correlated or linked according to whether the variable is of a quantitative or qualitative type. It enables topological analysis of the clustering of individual variables which can be quantitative, qualitative or a mixture of the two. It first analyzes the correlations or associations observed between the variables in a topological context of principal component analysis (PCA) or multiple correspondence analysis (MCA), depending on the type of variable, then classifies individuals into homogeneous group, relative to the structure of the variables considered. The proposed TCI method is presented and illustrated here using a real dataset with quantitative variables, but it can also be applied with qualitative or mixed variables.
COVID-19 Pandemic: A Methodological Model for the Analysis of Government’s Preventing Measures and Health Data Records
Authors: Chadjipadelis Theodore , Magopoulou Sofia 
Published: December 2023

AbstractThe study aims to investigate the associations between the government’s response measures during the COVID-19 pandemic and weekly incidence data (positivity rate, mortality rate and testing rate) in Greece. The study focuses on the period from the detection of the first case in the country (26th February 2020) to the first week of 2022 (08th January 2022). Data analysis was based on Correspondence Analysis on a fuzzy-coded contingency table, followed by Hierarchical Cluster Analysis (HCA) on the factor scores. Results revealed distinct time periods during which interesting interactions took place between control measures and incidence data.
Trends in Data Stream Mining
Authors: Gama João 
Published: December 2023

AbstractLearning from data streams is a hot topic in machine learning and data mining. This article presents our recent work on the topic of learning from data streams. We focus on emerging topics, including fraud detection and hyper-parameter tuning for streaming data. The first study is a case study on interconnected by-pass fraud. This is a real-world problem from high-speed telecommunications data that clearly illustrates the need for online data stream processing. In the second study, we present an optimization algorithm for online hyper-parameter tuning from nonstationary data streams.
Model Based Clustering of Functional Data with Mild Outliers
Authors: Anton Cristina , Smith Iain 
Published: December 2023

AbstractWe propose a procedure, called CFunHDDC, for clustering functional data with mild outliers which combines two existing clustering methods: the functional high dimensional data clustering (FunHDDC) [1] and the contaminated normal mixture (CNmixt) [3] method for multivariate data. We adapt the FunHDDC approach to data with mild outliers by considering a mixture of multivariate contaminated normal distributions. To fit the functional data in group-specific functional subspaces we extend the parsimonious models considered in FunHDDC, and we estimate the model parameters using an expectation-conditional maximization algorithm (ECM). The performance of the proposed method is illustrated for simulated and real-world functional data, and CFunHDDC outperforms FunHDDC when applied to functional data with outliers.
Some Issues in Robust Clustering
Authors: Hennig Christian 
Published: December 2023

AbstractSome key issues in robust clustering are discussed with focus on the Gaussian mixture model based clustering, namely the formal definition of outliers, ambiguity between groups of outliers and clusters, the interaction between robust clustering and the estimation of the number of clusters, the essential dependence of (not only) robust clustering on tuning decisions, and shortcomings of existing measurements of cluster stability when it comes to outliers.
Detecting Differences in Italian Regional Health Services During Two Covid-19 Waves
Authors: Palazzo Lucio , Ievoli Riccardo 
Published: December 2023

AbstractDuring the first twowaves of Covid-19 pandemic, territorial healthcare systems have been severely stressed in many countries. The availability (and complexity) of data requires proper comparisons for understanding differences in performance of health services. We apply a three-steps approach to compare the performance of Italian healthcare system at territorial level (NUTS 2 regions), considering daily time series regarding both intensive care units and ordinary hospitalizations of Covid-19 patients. Changes between the two waves at a regional level emerge from the main results, allowing to map the pressure on territorial health services.
Political and Religion Attitudes in Greece: Behavioral Discourses
Authors: Panagiotidou Georgia , Chadjipadelis Theodore 
Published: December 2023

AbstractThe research presented in this paper attempts to explore the relationship between religious and political attitudes. More specifically we investigate how religious behavior, in terms of belief intensity and practice frequency, is related to specific patterns of political behavior such as ideology, understanding democracy and his set of moral values. The analysis is based on the use of multivariable methods and more specifically Hierarchical Cluster Analysis and Multiple Correspondence Analysis in two steps. The findings are based on a survey implemented in 2019 on a sample of 506 respondents in the wider area of Thessaloniki, Greece. The aim of the research is to highlight the role of people’s religious practice intensity in shaping their political views by displaying the profiles resulting from the analysis and linking individual religious and political characteristics as measured with various variables. The final output of the analysis is a map where all variable categories are visualized, bringing forward models of political behavior as associated together with other factors such as religion, moral values and democratic attitudes.
The Death Process in Italy Before and During the Covid-19 Pandemic: A Functional Compositional Approach
Authors: Scimone Riccardo , Menafoglio Alessandra , Sangalli Laura M. , Secchi Piercesare 
Published: December 2023

AbstractIn this talk, based on [1], we propose a spatio-temporal analysis of daily death counts in Italy, collected by ISTAT (Italian Statistical Institute), in Italian provinces and municipalities. While in [1] the focus was on the elderly class (70+ years old), we here focus on the middle class (50–69 years old), carrying out analogous analyses and comparative observations. We analyse historical provincial data starting from 2011 up to 2020, year in which the impacts of the Covid-19 pandemic on the overall death process are assessed and analysed. The cornerstone of our analysis pipeline is a novel functional compositional representation for the death counts during each calendar year: specifically, we work with mortality densities over the calendar year, embedding them in the Bayes space B2 of probability density functions. This Hilbert space embedding allows for the formulation of functional linear models, which are used to split each yearly realization of the mortality density process in a predictable and an unpredictable component, based on the mortality in previous years. The unpredictable components of the mortality density are then spatially analysed in the framework of Object Oriented Spatial Statistics. Via spatial downscaling of the results obtained at the provincial level, we obtain smooth predictions at the fine scale of Italian municipalities; this also enable us to perform anomaly detection, identifying municipalities which behave unusually with respect to the surroundings.
Typology of Motivation Factors for Employees in the Banking Sector: An Empirical Study Using Multivariate Data Analysis Methods
Authors: Sousa Áurea , Silva Osvaldo , Batista M. Graça , Cabral Sara , Bacelar-Nicolau Helena 
Published: December 2023

AbstractLeadership has been considerate as a competitive advantage for organizations, contributing to their success and effective and efficient performance. Motivation, on the other hand, is assumed as a basic competence of leadership. Therefore, the main purpose of this paper is to know the perceptions of bank employees on the main motivational factors in the organizational context. Data analysis was performed based on several statistical methods, among which the Categorical Principal Component Analysis (CatPCA) and some agglomerative hierarchical clustering algorithms from VL (V for Validity, L for Linkage) parametrical family, applied to the items that aim to assess the aspects most valued by bankers in the work context. The CatPCA allowed to extract four principal components which explain almost 70% of the total data variance. The dendrograms provided by the hierarchical clustering algorithms over the same data, exhibit four main branches, which are associated with different main motivational factors. Moreover, CatPCA and clustering results show an important correspondence concerning the main motivations in this sector.
Generalized Spatio-Temporal Regression with PDE Penalization
Authors: Arnone Eleonora , Cunial Elia , Sangalli Laura M. 
Published: December 2023

AbstractWe develop a novel generalised linear model for the analysis of data distributed over space and time. The model involves a nonparametric term 5, a smooth function over space and time. The estimation is carried out by the minimization of an appropriate penalized negative log-likelihood functional, with a roughness penalty on 5 that involves space and time differential operators, in a separable fashion, or an evolution partial differential equation. The model can include covariate information in a semi-parametric setting. The functional is discretized by means of finite elements in space, and B-splines or finite differences in time. Thanks to the use of finite elements, the proposed method is able to efficiently model data sampled over irregularly shaped spatial domains, with complicated boundaries. To illustrate the proposed model we present an application to study the criminality in the city of Portland, from 2015 to 2020.