bluuebunny/crossref_metadata_2025_split huggingface dataset:
Implementing a Photodocumentation Program
Authors: Lai Eric K. , Slavik Evan , Ganim Bessie , Perry Laurie A. , Treuting Caitlin , Dee Troy , Osborne Melissa , Presley Cieara , Towbin Alexander J. 
Published: August 2024

AbstractThe widespread availability of smart devices has facilitated the use of medical photography, yet photodocumentation workflows are seldom implemented in healthcare organizations due to integration challenges with electronic health records (EHR) and standard clinical workflows. This manuscript details the implementation of a comprehensive photodocumentation workflow across all phases of care at a large healthcare organization, emphasizing efficiency and patient safety. From November 2018 to December 2023, healthcare workers at our institution uploaded nearly 32,000 photodocuments spanning 54 medical specialties. The photodocumentation process requires as few as 11 mouse clicks and keystrokes within the EHR and on smart devices. Automation played a crucial role in driving workflow efficiency and patient safety. For example, body part rules were used to automate the application of a sensitive label to photos of the face, chest, external genitalia, and buttocks. This automation was successful, with over 50% of the uploaded photodocuments being labeled as sensitive. Our implementation highlights the potential for standardizing photodocumentation workflows, thereby enhancing clinical documentation, improving patient care, and ensuring the secure handling of sensitive images.
Strategies for ammonia recovery from wastewater: a review
Authors: Farghali Mohamed , Chen Zhonghao , Osman Ahmed I. , Ali Israa M. , Hassan Dalia , Ihara Ikko , Rooney David W. , Yap Pow-Seng 
Published: August 2024

AbstractThe circular economy requires advanced methods to recycle waste matter such as ammonia, which can be further used as a fuel and a precursor of numerous value-added chemicals. Here, we review methods for the recovery of ammonia from wastewater with emphasis on biological and physicochemical techniques, and their applications. Biological techniques involve nitrification, denitrification, and anammox processes and the use of membrane bioreactors. Physicochemical techniques comprise adsorption, membrane filtration, ion exchange, chemical precipitation, ammonia stripping, electrochemical oxidation, photocatalytic oxidation, bioelectrochemical systems, and membrane hybrid systems. We found that nitrification and anammox processes in membrane bioreactors stand out for their cost-effectiveness, reduced sludge production, and energy efficiency. The use of struvite precipitation is an efficient, environmentally friendly, and recyclable method for ammonia removal. Membrane hybrid systems are promising for ammonia recovery, nutrient concentration, and wastewater treatment, with applications in fertilizer production and water purification. Overall, nitrogen removal ranges from 28 to 100%, and nitrogen recovery ranges from 9 to 100%.
Contrasting Linguistic Patterns in Human and LLM-Generated News Text
Authors: Muñoz-Ortiz Alberto , Gómez-Rodríguez Carlos , Vilares David 
Published: August 2024

AbstractWe conduct a quantitative analysis contrasting human-written English news text with comparable large language model (LLM) output from six different LLMs that cover three different families and four sizes in total. Our analysis spans several measurable linguistic dimensions, including morphological, syntactic, psychometric, and sociolinguistic aspects. The results reveal various measurable differences between human and AI-generated texts. Human texts exhibit more scattered sentence length distributions, more variety of vocabulary, a distinct use of dependency and constituent types, shorter constituents, and more optimized dependency distances. Humans tend to exhibit stronger negative emotions (such as fear and disgust) and less joy compared to text generated by LLMs, with the toxicity of these models increasing as their size grows. LLM outputs use more numbers, symbols and auxiliaries (suggesting objective language) than human texts, as well as more pronouns. The sexist bias prevalent in human text is also expressed by LLMs, and even magnified in all of them but one. Differences between LLMs and humans are larger than between LLMs.
Machine learning-based hyperspectral wavelength selection and classification of spider mite-infested cucumber leaves
Authors: Mandrapa Boris , Spohrer Klaus , Wuttke Dominik , Ruttensperger Ute , Dieckhoff Christine , Müller Joachim 
Published: August 2024

AbstractTwo-spotted spider mite (Tetranychus urticae) is an important greenhouse pest. In cucumbers, heavy infestations lead to the complete loss of leaf assimilation surface, resulting in plant death. Symptoms caused by spider mite feeding alter the light reflection of leaves and could therefore be optically detected. Machine learning methods have already been employed to analyze spectral information in order to differentiate between healthy and spider mite-infested leaves of crops such as tomatoes or cotton. In this study, machine learning methods were applied to cucumbers. Hyperspectral data of leaves were recorded under controlled conditions. Effective wavelengths were identified using three feature selection methods. Subsequently, three supervised machine learning algorithms were used to classify healthy and spider mite-infested leaves. All combinations of feature selection and classification methods yielded accuracy of over 80%, even when using ten or five wavelengths. These results suggest that machine learning methods are a powerful tool for image-based detection of spider mites in cucumbers. In addition, due to the limited number of wavelengths, there is also substantial potential for practical application.
Functional attributes of conifers expanding into temperate semi-arid grasslands modulate carbon and nitrogen fluxes in response to prescribed fire
Authors: Gay Justin D. , Currey Bryce , Davis Kimberley T. , Brookshire E. N. Jack 
Published: August 2024

AbstractFire exclusion is a key factor driving conifer expansion into temperate semi-arid grasslands. However, it remains unclear how reintroducing fire affects the aboveground storage of carbon (C) and nitrogen (N) in the expanding tree species and belowground in soils. To assess the impacts of fire reintroduction C and N pools and fluxes in areas of conifer expansion we targeted a region of the Northern Great Plains that has experienced extensive woody plant expansion (WPE) of two species: ponderosa pine (Pinus ponderosa) and juniper (Juniperus spp). We quantified tree mortality of both species to estimate the amount of dead biomass C and N produced by a recent prescribed fire, in addition to changes in soil C, pyrogenic C (PyC), and N concentrations across a woody-cover gradient using a before/after/control experimental design. Post-fire soil chemical analysis revealed a 2 year increase in mineral soil C, PyC and N, suggesting the return of fire led to the transfer of partially combusted plant organic matter back to the soil. Further, we found that functional trait differences between the two species influenced the distribution of living conifer biomass-N prior to fire. Despite junipers having 41% less total aboveground biomass than ponderosa, they contained two times more aboveground N. Prescribed fire resulted in 88% mortality of all mature juniper stems and increased fire severity correlated with greater pre-fire juniper cover. Ponderosa mortality varied by size class, with > 40 cm stem diameter class having only 28% mortality. High mortality and greater aboveground N storage in juniper biomass, compared to ponderosa, led to 77% of the total conifer biomass N lost. Consequently, the functional attributes of expanding trees differentially contribute to fluxes of C and N after the return of fire, with junipers acting as conduits for N movement due to their relatively higher N content in less fire-resistant tissues and ponderosa serving as important and more stable storage pools for C. Together, these findings highlight the importance of considering species-specific traits when planning WPE management strrategies at landscape-scales, particularly when goals include C storage or soil nutrient status.
Graphical abstract
Meta-analysis of Interventions for Monitoring Accuracy in Problem Solving
Authors: Janssen Noortje , Lazonder Ard W. 
Published: August 2024

AbstractAccurate monitoring of performance in problem-solving tasks is an important prerequisite for students’ future academic success. A wide variety of interventions aiming to enhance students’ monitoring accuracy have been developed, but their effectiveness is not apparent from the individual studies in which they have been examined. This meta-analysis classified these interventions in terms of how they target students’ monitoring and investigated their relative effectiveness to improve monitoring accuracy in problem-solving tasks. Findings across the 35 included studies indicated that all interventions combined have a small positive effect on students’ monitoring accuracy (g = 0.25). Intervention type moderated the findings. Interventions on the whole task, metacognitive knowledge, and external standards improved monitoring accuracy. On the other hand, interventions targeting the timing of metacognitive judgment negatively impacted monitoring accuracy and significantly differed from all other interventions. Exploratory moderator analyses of study features indicated that secondary school students benefited least from the interventions compared to primary school students and adults, laboratory studies showed larger effects than classroom studies, and interventions were more effective for retrospective confidence judgments than for judgments of learning. For educational practice, interventions targeting the whole task, metacognitive knowledge, and external standards are recommended, while reconsideration and possibly discontinuation of timing interventions is needed.
Challenges in Promoting Self-Regulated Learning in Technology Supported Learning Environments: An Umbrella Review of Systematic Reviews and Meta-Analyses
Authors: Prasse Doreen , Webb Mary , Deschênes Michelle , Parent Séverine , Aeschlimann Franziska , Goda Yoshiko , Yamada Masanori , Raynault Audrey 
Published: August 2024

AbstractSupporting learners’ self-regulated learning (SRL) processes and skills is crucial for effective learning, especially in online learning environments. In recent years, research on SRL and how it can be supported by technology has proliferated, resulting in many systematic reviews. The aims of this umbrella review are to provide orientation in a growing field, to identify challenges in the design of computer-assisted SRL (CA-SRL) supports and to derive future research needs. We identified and analysed 31 systematic reviews and meta-analyses that investigated SRL supports in computer-based, online and blended learning environments. The synthesis of the reviews highlights the critical importance of adopting comprehensive approaches in designing and implementing CA-SRL supports which integrate a variety of direct and indirect CA-SRL supports across the entire SRL cycle. The findings also call for greater precision in defining and categorising CA-SRL supports and their theoretical foundations to enhance comparability of research in this area. Finally, we conclude by providing recommendations for future research and development to effectively promote SRL for learners.
Neutron Activation Analysis Reveals Jamaican Origin of Afro-Caribbean Ware Excavated from the Cayman Islands
Authors: Petras Elysia M. , MacDonald Brandi L. 
Published: August 2024

AbstractAfro-Caribbean ware is Caribbean-made pottery manufactured both at the craft and industrial scale by enslaved and free potters of African descent. Previous sourcing studies have shown historic variation in centers of production and market distribution of these wares across the Caribbean. We used Neutron Activation Analysis (NAA) on a clay sample and 13 low-fired coarse earthenware sherds excavated from the historic-period Jackson Wall Manor site on Grand Cayman. We found that the Grand Cayman clay was compositionally inconsistent with any previously analysed ceramic sherds in the University of Missouri Research Reactor (MURR) NAA Caribbean database, and that the ceramic samples excavated on Grand Cayman showed high statistical correlation with Jamaican ceramic groups. These findings suggest that coarse earthenware on Grand Cayman was transported from Jamaica, a nearby colony with established potteries and markets, rather than being produced locally. Jamaican yabbas, which combined African and European manufacturing technique and forms, were imported into Grand Cayman to fit local needs.
Understanding Artificial Intelligence Diffusion through an AI Capability Maturity Model
Authors: Hansen Hans Fredrik , Lillesund Elise , Mikalef Patrick , Altwaijry Νajwa 
Published: August 2024

Abstract
The recent advancements in the field of Artificial Intelligence (AI) have sparked a renewed interest in how organizations can potentially leverage and gain value from these technologies. Despite the considerable hype around AI, recent reports indicate that a very small number of organizations have managed to successfully implement these technologies in their operations. While many early studies and consultancy-based reports point to factors that enable adoption, there is a growing understanding that adoption of AI is rather more of a process of maturity. Building on this more nuanced approach of adoption, this study focuses on the diffusion of AI through a maturity lens. To explore this process, we conducted a two-phased qualitative case study to explore how organizations diffuse AI in their operations. During the first phase, we conducted interviews with AI experts to gain insight into the process of diffusion as well as some of the key challenges faced by organizations. During the second phase, we collected data from three organizations that were at different stages of AI diffusion. Based on the synthesis of the results and a cross-case analysis, we developed a capability maturity model for AI diffusion (AICMM), which was then validated and tested. The results highlight that AI diffusion introduces some common challenges along the path of diffusion as well as some ways to mitigate them. From a research perspective, our results show that there are some core tasks associated with early AI diffusion that gradually evolve as the maturity of projects grows. For professionals, we present tools for identifying the current state of maturity and providing some practical guidelines on how to further implement AI technologies in their operations to generate business value.
Determinants of artificial intelligence adoption: research themes and future directions
Authors: Khanfar Ahmad A. , Kiani Mavi Reza , Iranmanesh Mohammad , Gengatharen Denise 
Published: August 2024

AbstractThe adoption of artificial intelligence (AI) systems is on the rise owing to their many benefits. This study conducted a bibliometric analysis to identify (1) how the literature on AI adoption has evolved over the past few years, (2) key themes associated with AI adoption in the literature, and (3) the gaps in the literature. To achieve these objectives, we utilised the Biblioshiny of R-package bibliometric analysis tool to analyse the AI adoption literature. A total of 91 articles were reviewed and analysed in this study. Four major themes were identified: AI, machine learning, the unified theory of acceptance and use of technology (UTAUT) model and the technology acceptance model (TAM). Using a content analysis of the identified themes, the study gained additional insight into the studies on AI adoption. Previous studies have been limited to specific industries and systems, and adoption theories like the UTAUT and TAM have also been utilised to a limited extent. Directions for future studies were provided.
A second homotopy group for digital images
Authors: Lupton Gregory , Musin Oleg , Scoville Nicholas A. , Staecker P. Christopher , Treviño-Marroquín Jonathan 
Published: August 2024

AbstractWe define a second (higher) homotopy group for digital images. Namely, we construct a functor from digital images to abelian groups, which closely resembles the ordinary second homotopy group from algebraic topology. We illustrate that our approach can be effective by computing this (digital) second homotopy group for a digital 2-sphere.
Accuracy of Mothers’ Perception of Birth Size to Predict Birth Weight Data in Bangladesh
Authors: Khan Jahidur Rahman , Bakar K. Shuvo , Awan Nabil , Muurlink Olav , Homaira Nusrat 
Published: August 2024

Abstract
Objectives
The prevalence of low birth weight (LBW) is an important indicator of child health and wellbeing. However, in many countries, decisions regarding care and treatment are often based on mothers’ perceptions of their children’s birth size due to a lack of objective birth weight data. Additionally, birth weight data that is self-reported or recorded often encounters the issue of heaping. This study assesses the concordance between the perceived birth size and the reported or recorded birth weight. We also investigate how the presence of heaped birth weight data affects this concordance, as well as the relationship between concordance and various sociodemographic factors.

Methods
We examined 4,641 birth records reported in the 2019 Bangladesh Multiple Indicator Cluster Survey. The sensitivity-specificity analysis was performed to assess perceived birth size’s ability to predict LBW, while Cohen’s Kappa statistic assessed reliability. We used the kernel smoothing technique to correct heaping of birth weight data, as well as a multivariable multinomial logistic model to assess factors associated with concordance.

Results
Maternally-perceived birth size exhibited a low sensitivity (63.5%) and positive predictive value (52.6%) for predicting LBW, but a high specificity (90.1%) and negative predictive value (93.4%). There was 86.1% agreement between birth size and birth weight-based classifications (Kappa = 0.49, indicating moderate agreement). Smoothed birth weight data did not improve agreement (83.4%, Kappa = 0.45). Of the sociodemographic factors, early marriage was positively associated with discordance (i.e., overestimation).

Conclusions
An important consideration when calculating the LBW prevalence is that maternally perceived birth size is not an optimal proxy for birth weight. Focus should be placed on encouraging institutional births and educating community health workers and young mothers about the significance of measuring and recording birth weight.
Robust Optimization Using the Mean Model with Bias Correction
Authors: Oliver Dean S. 
Published: August 2024

Abstract
Optimization of the expected outcome for subsurface reservoir management when the properties of the subsurface model are uncertain can be costly, especially when the outcomes are predicted using a numerical reservoir flow simulator. The high cost is a consequence of the approximation of the expected outcome by the average of the outcomes from an ensemble of reservoir models, each of which may need to be numerically simulated. Instead of computing the sample average approximation of the objective function, some practitioners have computed the objective function evaluated on the “mean model,” that is, the model whose properties are the means of properties of an ensemble of model realizations. Straightforward use of the mean model without correction for bias is completely justified only when the objective function is a linear function of the uncertain properties. In this paper, we show that by choosing an appropriate transformation of the variables before computing the mean, the mean model can sometimes be used for optimization without bias correction. However, because choosing the appropriate transformation may be difficult, we develop a hierarchical bias correction method that is highly efficient for robust optimization. The bias correction method is coupled with an efficient derivative-free optimization algorithm to reduce the number of function evaluations required for optimization. The new approach is demonstrated on two numerical porous flow optimization problems. In the two-dimensional well location problem with 100 ensemble members, a good approximation of the optimal location is obtained in 10 function evaluations, and a slightly better (nearly optimal) solution using bias correction is obtained using 216 function evaluations.
Motivating and demotivating teaching styles: A comparison among planned, adopted, and perceived
Authors: Wang Jiawei , Meng Weiqing , Xing Qiang , Moè Angelica 
Published: August 2024

AbstractGrounded in Self-Determination Theory (SDT), this study aimed at comparing autonomy-supportive (e.g., the teacher allows students to choose a project they are interested in from several options), structuring (e.g., the teacher breaks down problem-solving steps into manageable parts to help students understand), controlling (e.g., the teacher strictly enforces classroom rules and punishes non-compliance), and chaotic (e.g., despite some students being disruptive in class, the teacher does not intervene) planned, adopted, and student-perceived (de)motivating teaching styles. A mixed method was adopted by involving 20 secondary school teachers and their 38 students in focus group interviews and in filling in a measure of (de)motivating teaching styles. While the questionnaire data did not reveal significant differences, the focus group interviews uncovered noteworthy distinctions. The data from focus group interviews with teachers and students revealed that the proportion of teachers adopting a controlling teaching style is higher than those who had initially planned to use this style. Simultaneously, the proportion of teachers adopting a controlling style is lower than the proportion of students perceiving this style. Thematic analysis of data gathered from focus group interviews with teachers identified five main themes influencing teachers’ adoption of teaching styles: “teacher factors”, “school factors”, “student factors”, “management and relationships”, and “teaching methods and design”. Overall, this study provides a more comprehensive understanding of (de)motivating teaching styles in China. Educational implications are discussed.
Steepest Geometric Descent for Regularized Quasiconvex Functions
Authors: Daniilidis Aris , Salas David 
Published: August 2024

AbstractWe establish existence of steepest descent curves emanating from almost every point of a regular locally Lipschitz quasiconvex functions, where regularity means that the sweeping process flow induced by the sublevel sets is reversible. We then use max-convolution to regularize general quasiconvex functions and obtain a result of the same nature in a more general setting.