bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Point-Cluster-Partition Architecture for Weighted Clustering Ensemble
Authors: Li Na , Xu Sen , Xu Heyang , Xu Xiufang , Guo Naixuan , Cai Na 
Published: May 2024

AbstractClustering ensembles can obtain more superior final results by combining multiple different clustering results. The qualities of the points, clusters, and partitions play crucial roles in the consistency of the clustering process. However, existing methods mostly focus on one or two aspects of them, without a comprehensive consideration of the three aspects. This paper proposes a three-level weighted clustering ensemble algorithm namely unified point-cluser-partition algorithm (PCPA). The first step of the PCPA is to generate the adjacency matrix by base clusterings. Then, the central step is to obtain the weighted adjacency matrix by successively weighting three layers, i.e., points, clusters, and partitions. Finally, the consensus clustering is obtained by the average link method. Three performance indexes, namely F, NMI, and ARI, are used to evaluate the accuracy of the proposed method. The experimental results show that: Firstly, as expected, the proposed three-layer weighted clustering ensemble can improve the accuracy of each evaluation index by an average value of 22.07% compared with the direct clustering ensemble without weighting; Secondly, compared with seven other methods, PCPA can achieve better clustering results and the proportion that PCPA ranks first is 28/33.
Hyping the Hypothetical: Talk and Temporality in US Supreme Court Oral Arguments
Authors: Gibson David R. 
Published: May 2024

AbstractThe US Supreme Court conducts much of its business through talk, including during oral arguments, where a central activity is the consideration of hypotheticals posed by justices. Using conversation analysis, I examine a key segment of the oral arguments for Citizens United v. FEC, one that arguably changed the course of campaign finance history. I identify the conversational devices employed to advance and contest one particular hypothetical, involving an imagined ban on books, subject to a speech-exchange system that differentially empowers justices to dictate both the terms of the discussion and the time afforded the advocate to respond to any particular question. The article offers the first disciplined qualitative analysis of interaction during oral arguments, illustrates the place of temporality in legal reasoning and argumentation, and makes several contributions to conversation analysis: it advances the study of institutional talk to a new legal setting, identifies some ways in which the machinery of talk can be harnessed for rhetorical effect, and demonstrates the analytical utility of prior knowledge of what a participant arrives to an encounter equipped to say.
A novel hybrid deep learning model for early stage diabetes risk prediction
Authors: Bülbül Mehmet Akif 
Published: May 2024

AbstractDiabetes is a prevalent global disease that significantly diminishes the quality of life and can even lead to fatalities due to its complications. Early detection and treatment of diabetes are crucial for mitigating and averting associated risks. This study aims to facilitate the prompt and straightforward diagnosis of individuals at risk of diabetes. To achieve this objective, a dataset for early stage diabetes risk prediction from the University of California Irvine (UCI) database, widely utilized in the literature, was employed. A hybrid deep learning model comprising genetic algorithm, stacked autoencoder, and Softmax classifier was developed for classification on this dataset. The performance of this model, wherein both the model architecture and all hyperparameters were specifically optimized for the given problem, was compared with commonly used methods in the literature. These methods include K-nearest neighbor, decision tree, support vector machine, and convolutional neural network, utilizing tenfold cross-validation. The results obtained with the proposed method surpassed those obtained with other methods, with higher accuracy rates than previous studies utilizing the same dataset. Furthermore, based on the study’s findings, a web-based application was developed for early diabetes diagnosis.
Out of the fog: fog computing-enabled AI to support smart marketing management
Authors: Hornik Jacob , Ofir Chezy , Rachamim Matti 
Published: May 2024

AbstractMarketing and consumer research use a variety of data and electronic measurement devices for research, theory-building, and applied decision-making. Managing data deluge produced by ‘smart devices’ and internet of things (IoT) actuators and sensors is one of the challenges faced by managers when using IoT systems. With the advent of the cloud-based IoT and artificial intelligence, which are advancing a ‘smart world’ and introducing automation in many application areas, such as ‘smart marketing,’ a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent language models like, ChatGPT and Bart, and technologies like nanotechnology. The article introduces the marketing community to a recent computing development: IoT-driven fog computing (FC)—an emerging concept that decentralizes operations, management, and data into the network utilizing a distributed and federated computing paradigm. Although numerous research studies have been published on ‘smart’ applications, none hitherto have been conducted on fog-based smart marketing. FC is considered a novel computational system, which can mitigate latency and improve bandwidth utilization for autonomous marketing applications requiring real-time processing of ‘big data’ typical of smart marketing ecosystems.
Enhancing Geosıte Accessibility Assessment: A New Mcdm Model
Authors: Bilgiç Sevgi , Karadeniz Enes , Selman Er  
Published: May 2024

AbstractGeosites are significant geological heritage areas that serve as field laboratories for researchers. They offer insights into geological processes that have occurred from the past to the present and hold high potential for geotourism. To preserve and make geosites sustainable, their accessibility is a prerequisite. For this purpose, a model consisting of various parameters was developed in accordance with the Multi-Criteria Decision Making (MCDM) method, identifying 183 geosite areas that have been determined by experts and are subjects of literature. As a result, four evaluation criteria were established, including accessibility, access to service/attractiveness areas, scientific importance, and geomorphological features. Taking these parameters into account, a collective evaluation map was obtained using GIS-based AHP, drawing upon literature and expert opinions. The study concluded that the most critical parameter for accessibility to geosites is accessibility itself, accounting for 50% of the importance. Other parameters have influenced the accessibility of geosites in the following order of importance: 25% land, 15% scientific importance, and 10% service/attractiveness. Consequently, the areas where accessibility to geosites in Türkiye is most convenient are those with the highest road density. Road density, in turn, exhibits a pattern based on the characteristics of topography, population, and trade factors. Additionally, a parallel outcome emerged between areas that are more suitable in terms of these factors and the density of geosites. As a validation of the newly created model, the accessibility of Türkiye's areas recognized by UNESCO was found to be very high, indicating that the model is a suitable and convenient method that can be applied to different areas.
Monitoring changes in hydric properties of treated stone material with conservation products by time-sequential IR thermography
Authors: Perez-Ema Natalia , Alvarez de Buergo Monica , Gomez-Heras Miguel 
Published: May 2024

AbstractA methodological approach for a semi-quantitative non-destructive testing (NDT) of the effects on hydric properties after the application of different conservation treatments (commercial ethylsilicate and siloxane compounds with consolidant, water repellent or consolidant + water repellent properties) is presented in this study. The NDT used for this purpose is a simplified method of time-sequential infrared thermography (IRT) on stone (granite and marble, treated and untreated) from the Roman theatre of Merida (Spain), which was listed by UNESCO as World Heritage Site in 1993.As water evaporation modifies temperature intensely, IRT enables monitoring the response of the stone samples during water absorption-evaporation processes. The comparative analysis between treated and non-treated specimens was performed by calculating the rate of surface apparent temperature change (ΔoC/h) value. The capillary rising test, monitored by IRT, clearly showed a different response of the samples treated with water-repellent treatments, proving their efficiency. Samples treated with organosilicic compounds showed a large and rapid drop in apparent surface temperature, indicating that water accesses rapidly.Results from the evaporation test also show this difference. The surface apparent temperature increased immediately in samples with water-repellent treatments, in which water does not enter inside stone, but remains on the surface. Samples with ethyl silicate, again, show a greater difference with respect to untreated samples, taking longer to recover surface apparent temperature, because the water is retained inside the stone for longer.This study proved simplified time-sequential IRT as a reliable technique for assessing the efficacy of conservation treatments applied to stone materials in laboratory.
Algorithmic Decision-Making, Agency Costs, and Institution-Based Trust
Authors: Dowding Keith , Taylor Brad R. 
Published: May 2024

AbstractAlgorithm Decision Making (ADM) systems designed to augment or automate human decision-making have the potential to produce better decisions while also freeing up human time and attention for other pursuits. For this potential to be realised, however, algorithmic decisions must be sufficiently aligned with human goals and interests. We take a Principal-Agent (P-A) approach to the questions of ADM alignment and trust. In a broad sense, ADM is beneficial if and only if human principals can trust algorithmic agents to act faithfully on their behalf. This mirrors the challenge of facilitating P-A relationships among humans, but the peculiar nature of human-machine interaction also raises unique issues. The problem of asymmetric information is omnipresent but takes a different form in the context of ADM. Although the decision-making machinery of an algorithmic agent can in principle be laid bare for all to see, the sheer complexity of ADM systems based on deep learning models prevents straightforward monitoring. We draw on literature from economics and political science to argue that the problem of trust in ADM systems should be addressed at the level of institutions. Although the dyadic relationship between human principals and algorithmic agents is our ultimate concern, cooperation at this level must rest against an institutional environment which allows humans to effectively evaluate and choose among algorithmic alternatives.
On duration effects in non-life insurance pricing
Authors: Lindholm Mathias , Nazar Taariq 
Published: May 2024

AbstractThe paper discusses duration effects on the consistency of mean parameter and dispersion parameter estimators in exponential dispersion families (EDFs) that are the standard models used for non-life insurance pricing. Focus is on the standard generalised linear model assumptions where both the mean and variance, conditional on duration, are linear functions in terms of duration. We derive simple convergence results that highlight consequences when the linear conditional moment assumptions are not satisfied. These results illustrate that: (i) the resulting mean estimators always have a relevant asymptotic interpretation in terms of the duration adjusted actuarially fair premium—a premium that only agrees with the standard actuarial premium using a duration equal to one, given that the expected value is linear in the duration; (ii) deviance based estimators of the dispersion parameter in an EDF should be avoided in favour of Pearson estimators; (iii) unless the linear moment assumptions are satisfied, consistency of dispersion and plug-in variance estimators can not be guaranteed and may result in spurious over-dispersion. The results provide explicit conditions on the underlying data generating process that will lead to spurious over-dispersion that can be used for model checking. This is illustrated based on real insurance data, where it is concluded that the linear moment assumptions are violated, which results in non-negligible spurious over-dispersion.
Copyright and Open Access to Scientific Publishing
Authors: Esteve Asunción 
Published: May 2024

AbstractThe internet has broadened the communication of digitized journals and books among scholars and the perception that academic commercial publishers use copyright law to restrict the free circulation of scientific knowledge. Open access is changing the business model of academic publishing to the extent that copyright law is increasingly being viewed as needing to be balanced against the right to benefit from science. Some have called for copyright law to be revised to promote open access to academic publishing. The question of just how copyright law should be revised to achieve this is today more topical than ever. However, there is a need to clarify and question the role that copyright law should play and there is much to be gained from consideration of the role that competition law can play. Additionally, initiatives to implement open access have been taken by stakeholders (academic authors, publishers, universities, libraries, and research funding agencies) such as open access policies and the new “read and publish” agreements between publishers and universities’ libraries. But the transition towards sustainable universal open access will be a long, complex process since the interaction between these stakeholders can lead to conflicts of interest. This article also evaluates these initiatives and suggests the best approach.
A strategic framework for optimal decisions in football 1-vs-1 shot-taking situations: an integrated approach of machine learning, theory-based modeling, and game theory
Authors: Yeung Calvin , Fujii Keisuke 
Published: May 2024

AbstractComplex interactions between two opposing agents frequently occur in domains of machine learning, game theory, and other application domains. Quantitatively analyzing the strategies involved can provide an objective basis for decision-making. One such critical scenario is shot-taking in football, where decisions, such as whether the attacker should shoot or pass the ball and whether the defender should attempt to block the shot, play a crucial role in the outcome of the game. However, there are currently no effective data-driven and/or theory-based approaches to analyzing such situations. To address this issue, we proposed a novel Shooting Payoff Computation (SPC) framework to analyze such scenarios based on game theory, where we estimate the expected payoff with machine learning (ML) models, and additional features for ML models were extracted with a theory-based shot block model. Conventionally, successes or failures (1 or 0) are used as payoffs, while a success shot (goal) is extremely rare in football. Therefore, we proposed the Expected Probability of Shot On Target (xSOT) metric to evaluate players’ actions even if the shot results in no goal; this allows for effective differentiation and comparison between different shots and even enables counterfactual shot situation analysis. In our experiments, we have validated the SPC framework by comparing it with baseline and ablated models. Furthermore, we have observed a high correlation between the xSOT and existing metrics. This alignment of information suggests that xSOT provides valuable insights. Lastly, as an illustration, we studied optimal strategies in the World Cup 2022 and analyzed a shot situation in EURO 2020.
In vitro and in silico screening and identification of potential bioactive anthraquinones of Morinda lucida benth against pathogenic bacterial target proteins
Authors: Adekunle Oluwatobi David , Adeleke Oluwafeyikemi Ajoke , Odugbemi Adeshina Isaiah , Faboro Esther Oremeyi , Lajide Labunmi 
Published: May 2024

Abstract
Background
The use of plant extracts as antibiotics is gaining more attention because bacteria are one of the biggest threats to global health and the resistance of bacteria to antibiotics in humans and animals is increasing. Antibacterial susceptibility is used to determine which specific antibiotics a particular bacterium is sensitive to. This research is focused on the phytochemical, in vitro antibacterial susceptibility, and in silico analysis of Morinda lucida root extracts against gram-negative Pseudomonas aeruginosa and gram-positive Staphylococcus aureus. The root of M. lucida was extracted separately with ethanol, chloroform, and ethyl acetate.

Results
Crude extracts of M. lucida had active antibacterial activity and were a promising natural antibiotic when compared to gentamicin. The in vitro results showed that the extracts of M. lucida had good susceptibility properties against the two drug-resistant bacteria while the in silico showed that 2-hydroxy-1-methoxy anthraquinone is predicted to have a better susceptibility with S. aureus while alizarin has better susceptibility properties against P. aeruginosa. Finally, the MD simulation studies of Alizarin and 9,10-Anthracenedione 2-hydroxy-1- methoxy- define the stability of protein–ligand complexes within a 50 ns time scale.
Unveiling the patterns: exploring social and clinical characteristics of frequent mental health visits to the emergency department—a comprehensive systematic review
Authors: Zhang Zhonghao , Das Soumitra 
Published: May 2024

Abstract
Background
Frequent presenters (FPs) are a group of individuals who visit the hospital emergency department (ED) frequently for urgent care. Many among the group present with the main diagnosis of mental health conditions. This group of individual tend to use ED resources disproportionally and significantly affects overall healthcare outcomes. No previous reviews have examined the profiles of FPs with mental health conditions.

Aims
This study aims to identify the key socio-demographic and clinical characteristics of patients who frequently present to ED with a mental health primary diagnosis by performing a comprehensive systematic review of the existing literature.

Method
PRISMA guideline was used. PubMed, PsycINFO, Scopus and Web of Science (WOS) were searched in May 2023. A manual search on the reference list of included articles was conducted at the same time. Covidence was used to perform extraction and screening, which were completed independently by two authors. Inclusion and exclusion criteria were defined.

Results
The abstracts of 3341 non-duplicate articles were screened, with 40 full texts assessed for eligibility. 20 studies were included from 2004 to 2022 conducted in 6 countries with a total patient number of 25,688 (52% male, 48% female, mean age 40.7 years old). 27% were unemployed, 20% married, 41% homeless, and 17% had tertiary or above education. 44% had a history of substance abuse or alcohol dependence. The top 3 diagnoses are found to be anxiety disorders (44%), depressive disorders (39%) schizophrenia spectrum and other psychotic disorders (33%).

Conclusion
On average, FPs are middle-aged and equally prevalent in both genders. Current data lacks representation for gender-diverse groups. They are significantly associated with high rates of unemployment, homelessness, lower than average education level, and being single. Anxiety disorder, depressive disorder, and schizophrenia spectrum disorders are the most common clinical diagnoses associated with the group.
Praxis, Persistence, and Public Archaeology: Disrupting the Mission Myth at La Purísima Concepción
Authors: Brown Kaitlin M. 
Published: May 2024

AbstractThis article introduces a model that harnesses praxis as a powerful tool for critique, knowledge, and action within the realm of public archaeology. The adopted framework focuses on persistence as a middle-range methodology that bridges the material past to activist and collaborative-based projects. Recent research at Mission La Purísima Concepción in Lompoc, California, shows the effectiveness of this model and its real-world application. Visitors to California missions encounter the pervasive “Mission Myth”—a narrative that systematically overlooks and marginalizes Indigenous presence while perpetuating ideas of White hegemony and Eurocentrism. Archaeological excavations in the Native rancheria and collaboration with members of the Chumash community help resist notions of Indigenous erasure. By activating notions of persistence through public archaeology, this study contributes to dismantling entrenched terminal narratives, paving the way for a more accurate representation of the past and fostering a more inclusive archaeological practice.
Social Welfare Functions and Health Policy: A New Approach
Authors: Adler Matthew D. 
Published: May 2024

Abstract
The social welfare function (SWF) framework converts the possible outcomes of governmental policy choice into vectors (lists) of interpersonally comparable well-being numbers, measuring the lifetime well-being of each individual in the population of interest. The SWF proper is a rule for ranking these vectors. The utilitarian SWF adds up well-being numbers. A prioritarian SWF adds up well-being numbers plugged into a strictly increasing and strictly concave transformation function. Governmental policies are conceptualized as probability distributions over well-being vectors. A recent literature applies the SWF framework to health policy. This article first provides a brief overview of the SWF framework and then reviews some of the key concepts and findings that have emerged from this literature. One such concept is the “social value of risk reduction” (SVRR): the marginal social value (as calculated by the SWF) per unit of reduction in fatality risk for a given individual. The SVRR is the analogue, within the SWF framework, to the value-of-statistical-life (VSL) concept within benefit–cost analysis. This article explicates the SVRR concept and reports on recent theoretical findings and simulations that illustrate the properties of utilitarian and prioritarian SVRRs and their differences from VSL.
“We are 9 degrees and sunny”: the use of personal pronouns with weather predicates
Authors: Ghomeshi Jila , Duncan Mercedes 
Published: May 2024

AbstractThis squib discusses a construction heard during weather reports on CBC Radio One in Manitoba whereby personal pronouns appear as the subjects of weather predicates. We show that the use of we/you in statements like we are minus 15 degrees and Brandon, you are sunny is unlike other non-prototypical uses for personal pronouns that have been noted in the literature and argue they index place rather than person. We note that the deictic coordinates of the utterance that are spelled out by these pronouns are necessary for a felicitous interpretation of weather statements, even though they are typically implicit. This implicit deixis, in turn, sheds light on a long-standing claim that weather-it, in contrast to a true expletive, is ‘quasi-argumental’ (Chomsky 1981). That is, we suggest that the deictic coordinates of an utterance are ‘quasi-arguments’.