bluuebunny/crossref_metadata_2025_split huggingface dataset:
Analysis of preschool children’s outdoor play behaviours
Authors: Cakan Ayse , Acer Dilek 
Published: July 2024

AbstractThis research aimed to observe preschoolers' outdoor play behaviours, gather their opinions about the outdoors and examine their drawings related to outdoor activities. Methodology: Using a mixed methods approach, children aged 60 to 69 months were studied over a 4-week period in a preschool setting. Observation, interviews, and drawing tasks were used as data sources, ensuring triangulation. The children's outdoor play behaviours were video-recorded, and their opinions were taken through one-on-one interviews. The children were asked to draw pictures of the materials they wanted to see in the playground. This study explored how the outdoor environment impacts children's play behaviour. Research Findings: Using content analysis and frequency distribution, we found that the most frequently played in the schoolyard was functional play, and they most often played alone and solitarily. Physical conditions, such as the schoolyard grounds, material diversity, and material types within the play environment, affected children's outdoor play behaviours. The schoolyard setting led children to consider alternative play types and toys. The children stated that they mostly preferred to play in the park area and playhouse within the schoolyard. The children also stated that they wanted to see fixed park components, sports, dramatic play, arts, entertainment, and natural areas. In addition, the observed play behaviours and children's views regarding the schoolyard supported one another. Practice and Policy: These results support the theory that the environment impacts behaviour and show that outdoor spaces tailored to children's wishes and needs are an effective way to encourage quality play behaviours.
How AI Works: Reconfiguring Lifelong Learning
Authors: Palenski Ted , Hills Libby , Unnikrishnan Sagarika , Eynon Rebecca 
Published: July 2024

AbstractLifelong learning is a current policy focus in many countries, with AI technologies promoted as both the motivation for the need for lifelong learning (due to its assumed role in social change) and as an important way to ‘deliver’ learning across the life course. Such policies tend to be instrumental and technologically deterministic, and there is a need to properly theorize the relationships between AI and lifelong learning to better inform policy and practice. In this paper, we examine the ways that academic communities conceptualize AI and lifelong learning, based on a thematic analysis of existing academic literature in contexts beyond formal education. We identify three groups of research, which vary according to their engagement with theories of learning and AI technology and how AI ‘works’. In group 1 (working AI), AI is assumed to contribute to increased efficiency of humans and learning; in group 2 (working with AI), AI is implemented and conceptualized as a peer or colleague; and in group 3 (reconfiguring AI), AI is viewed as part of a wider reconfiguration of humans and their contexts. This latter group, though least well represented in the literature, holds promise in advancing a postdigital research agenda that focuses not solely on how AI works to increase efficiency, but how people are increasingly working, learning, and living with AI, thus moving beyond exclusively instrumental, economic, and technologically deterministic concerns.
A hyperparameter study for quantum kernel methods
Authors: Egginger Sebastian , Sakhnenko Alona , Lorenz Jeanette Miriam 
Published: July 2024

AbstractQuantum kernel methods are a promising method in quantum machine learning thanks to the guarantees connected to them. Their accessibility for analytic considerations also opens up the possibility of prescreening datasets based on their potential for a quantum advantage. To do so, earlier works developed the geometric difference, which can be understood as a closeness measure between two kernel-based machine learning approaches, most importantly between a quantum kernel and a classical kernel. This metric links the quantum and classical model complexities, and it was developed to bound generalization error. Therefore, it raises the question of how this metric behaves in an empirical setting. In this work, we investigate the effects of hyperparameter choice on the model performance and the generalization gap between classical and quantum kernels. The importance of hyperparameters is well known also for classical machine learning. Of special interest are hyperparameters associated with the quantum Hamiltonian evolution feature map, as well as the number of qubits to trace out before computing a projected quantum kernel. We conduct a thorough investigation of the hyperparameters across 11 datasets, and we identify certain aspects that can be exploited. Analyzing the effects of certain hyperparameter settings on the empirical performance, as measured by cross validation accuracy, and generalization ability, as measured by geometric difference described above, brings us one step closer to understanding the potential of quantum kernel methods on classical datasets.
Hybrid genetic optimization for quantum feature map design
Authors: Pellow-Jarman Rowan , Pillay Anban , Sinayskiy Ilya , Petruccione Francesco 
Published: July 2024

AbstractKernel methods are an import class of techniques in machine learning. To be effective, good feature maps are crucial for mapping non-linearly separable input data into a higher dimensional (feature) space, thus allowing the data to be linearly separable in feature space. Previous work has shown that quantum feature map design can be automated for a given dataset using NSGA-II, a genetic algorithm, while both minimizing circuit size and maximizing classification accuracy. However, the evaluation of the accuracy achieved by a candidate feature map is costly. In this work, we demonstrate the suitability of kernel-target alignment as a substitute for accuracy in genetic algorithm-based quantum feature map design. Kernel-target alignment is faster to evaluate than accuracy and does not require some data points to be reserved for its evaluation. To further accelerate the evaluation of genetic fitness, we provide a method to approximate kernel-target alignment. To improve kernel-target alignment and root mean squared error, the final trainable parameters of the generated circuits are further trained using COBYLA to determine whether a hybrid approach applying conventional circuit parameter training can easily complement the genetic structure optimization approach. A total of eight new approaches are compared to the original across nine varied binary classification problems from the UCI machine learning repository, showing that kernel-target alignment and its approximation produce feature map circuits enabling comparable accuracy to the previous work but with larger margins on training data (in excess of 20% larger) that improve further with circuit parameter training.
Durability and Functionality of Conventional Polymeric Packaging Materials in Reusable Packaging Systems
Authors: Tenhunen-Lunkka Anna , Parikka Risto , Korostavyi Artur , Palola Sarianna , Ronkainen Helena 
Published: July 2024

AbstractReusable packaging is increasingly recognised as a sustainable solution to address issues related to increasing packaging waste generation and harmful environmental impacts. The packaging sector consumes a significant portion of Europe’s plastic production, which is mostly single-use packaging. There is a sense of urgency to develop sustainable reusable alternatives. Unlike single-use packaging, the longevity of reusable plastic packaging is paramount, necessitating material durability and functionality. This study examines the durability of conventional polymeric packaging materials (polyethylene terephthalate (PET), polypropylene, high-density polyethylene) and investigates the effectiveness of scratch resistance additives. A specialised test set-up was created to evaluate the performance of various polymers in reusable takeaway food packaging scenarios. The research included scratch testing, wear analysis and wash cycles to assess the reuse potential of these materials. Qualitative measures were employed to compare material characteristics. Results indicated that polymer type significantly affects hardness and scratch performance, while additives had minimal impact on hardness and modulus. Although current scratch resistance additives do not effectively mitigate wear in reusable packaging in the experimental scenarios, tailored additives could present potential in increasing the durability and longevity of reusable packaging. PET demonstrated notable recovery post-washing, suggesting that washing conditions could influence material resilience. This study emphasises the need for further research to optimise scratch resistance additives and understand the impact of washing conditions on different polymers, aiming to enhance the design and longevity of reusable packaging systems.
Epithelial to Mesenchymal Transition in Neuroblastoma: Mechanisms and Therapeutic Considerations
Authors: Jahangiri Leila 
Published: July 2024

Abstract
Purpose of Review
Neuroblastoma (NB) is a solid tumour of the extracranial region in children, and it originates from the developing sympathetic nervous system. Metastasis is present in circa 70% of NB cases at the time of diagnosis and may be accompanied by poor prognosis and resistance to treatment. As such, metastasis is an important step in NB progression and is the main cause of fatality in this cancer. Furthermore, metastasis is linked to epithelial to mesenchymal transition (EMT).

Recent Findings
EMT is characterised by the loss of the stationary compact epithelial cell arrangement and increased motility and invasion of these cells and may also be associated with patient outcomes, including treatment resistance, recurrence, and poor survival.

Summary
This study aims to gain mechanistic insight into EMT and review signalling pathways, proteins, non-coding RNA, and microenvironmental factors that may contribute to this process in NB. Further, drug compounds that affect EMT in NB, and proteins and non-coding RNA that impact EMT and treatment resistance will be reviewed. A better comprehension of EMT in NB will deepen our understanding of this cancer and provide new avenues for clinical interventions and therapeutic gain.
Evaluation of weather generator tools to estimate climate conditions in different agro ecological zones of North Shewa, Ethiopia
Authors: Ayele Biruk Getaneh , Mengistu Tsegaye Getachew , Woldemariam Ayele Desalegn 
Published: July 2024

AbstractThe information on climatic condition is difficult to obtain, expensive, and time-consuming so as to make timely decision on agricultural activities. As a scientific effort, this study was conducted to assess the temporal changes and trends of rainfall and temperature, to know the performance of weather generator (WG) tools in capturing the temporal and spatial distribution of rainfall, maximum temperature (Tmax) and minimum temperature (Tmin) and to evaluate the performance of WG in simulating the observed rainfall, Tmax and Tmin by using statistical methods. Mann–Kendall's trend analysis revealed that rainfall had non-significant (P < 0.05) decreasing trends, while Tmax and Tmin had an increasing significant (P < 0.05) trends at all stations. NASA POWER data followed by NewlocClim exactly capture the temporal changes of rainfall, Tmax, and Tmin in all stations except Debre Birhan and Mehal Meda. NewlocClim well captures rainfall at Alem ketema, while NASA well simulates rainfall at Debre Birhan and Majete stations. However Had-GEM2-ES, MRI-CGCM3, and CSIRO-Mk3.6.0 were not handling the spatial variability of observed rainfall at all stations. Similarly, some WGs showed moderate to good performance in capturing the spatial distributions of Tmax and Tmin. The smallest RMSE and CV, the highest R and d values were observed in NASA POWER and NewlocClim for rainfall, Tmax and Tmin. Therefore, NASA and NewlocClim are more accurate with goodness of fit to estimate rainfall, Tmax and Tmin in most of the stations to access weather data for ungagged stations for timely and reasonable decision-making on agriculture.
Ethics and the use of generative AI in professional editing
Authors: Otmar Renée , Michael Rose , Mullins Sharon , Day Katherine 
Published: July 2024

AbstractGenerative artificial intelligence (GnAI) has garnered significant attention worldwide across diverse industries, including in book publishing. To date, more attention has been paid to its potential in creative collaboration and less to the editorial possibilities of its application. Interest has accelerated since the breakthrough of a new Large Language Model in late 2022. This paper engages with the ethical and industrial implications of using GnAI in a creative context, namely literary publishing. It raises crucial questions about intellectual property, trust, the author–editor relationship and publishing professionals’ evolving roles in shaping quality literature. Using a published story as a test case, we compare edits using GnAI with those by professional editors over multiple drafts and at different stages of editorial development. We consider the potential ethical implications of the use of GnAI in literary fiction editing, highlighting the principles and practices that underpin professional editing to consider how these may or may not translate in the use of GnAI. This is followed by a discussion of the risks and opportunities in using GnAI in editing literary texts in the trade publishing context.
Understanding the explosion risk presented by ammonium nitrate and aluminium home-made explosives detonated as surface charges in hexahedral main charge containers
Authors: Collett Gareth , Akhavan Jacqueline , Critchley Richard 
Published: July 2024

AbstractAmmonium nitrate and aluminium (AN–Al) has been used as a typical homemade explosive (HME) by non-state actors since the turn of the century. Despite the regulation applied to ammonium nitrate above 16% nitrogen content and an aluminium particle size below 200 µm, their use has been widespread in Afghanistan, Columbia, Iraq, Syria and Yemen. Containers used to utilise AN–Al as a man-portable improvised explosive device (IED) are typically hexahedral in shape, not the spherical or hemispherical geometries used to theorise risk mitigation. This is particularly important in post-blast investigation where explosives of a non-ideal nature are often used in non-spherical containers.Given the breadth of HME available to criminals, the explosion performance of forty hexahedral containers filled with AN–Al of unknown manufacture is examined. Performance of the AN–Al is determined through the surface detonation of these containers on alluvial soil, with apparent crater volume compared to theoretical calculations for spherical charges of TNT detonated in that same medium. A conversion factor for hexahedral main charges to spherical charges is then established to achieve more accurate predictions of the explosion risk using Kingery-Bulmash and Bowen curves. The paper provides worked examples for practical application and a methodology by which predictions of charge mass in other mediums such as asphalt can be determined.
A Class of Polynomial Recurrences Resulting in $$({\text {n}}/{\text {log}}\,\,{\text {n}}, {\text {n}}/{\text {log}}^2{\text {n}})$$–Asymptotic Normality
Authors: Hitczenko Paweł 
Published: July 2024

AbstractWe consider sequences of polynomials that satisfy differential–difference recurrences. Polynomials satisfying such recurrences frequently appear as generating polynomials of integer valued random variables that are of interest in discrete mathematics. It is, therefore, of interest to understand the properties of such polynomials and their probabilistic consequences. We identify a class of polynomial recurrences that lead to a normal law with the expected value and the variance proportional to $$n/\log ~n$$

n
/
log

n

and $$n/\log ^2n$$

n
/

log
2

n

, respectively. Examples include Stirling numbers of the second kind and other polynomials concerning set partitions as well as polynomials related to Whitney numbers of Dowling lattices.
Differences in the clinical management of shoulder pain by physician specialty: data from the 2007–2019 National Ambulatory Medical Care Survey
Authors: Liu Yujian , Lu Jian , Liu Guangwu 
Published: July 2024

Abstract
Objectives
The aim of this study was to identify the differences in the clinical management of shoulder pain by primary care physicians (PCPs) and non-primary care physicians (non-PCPs) from the National Ambulatory Medical Care Survey (NAMCS) dataset.

Methods
This cross-sectional study included ambulatory care visits for shoulder pain by using NAMCS data from 2007 to 2019. Descriptive statistics were presented to assess patient-level and visit-level characteristics of the sampled visits. By controlling for patient-level and visit-level covariates, we conducted a multivariable logistic regression to evaluate the influence of primary care physician status on the utilization of health services (pain medications, PT referral, health education/counseling, and diagnostic imaging) for shoulder pain.

Results
There were 74.43 million ambulatory care visits by adults with shoulder pain during the study period, and nearly one-third of these shoulder visits were made to PCPs. As compared with non-PCPs, PCPs had higher adjusted odds of prescribing narcotic analgesics (adjusted odds ratio [OR] = 1.62, 95% confidence interval [CI]: 1.04–2.51), skeletal muscle relaxants (adjusted OR = 2.71, 95% CI: 1.65–4.45), other pain medications (adjusted OR = 1.87, 95% CI: 1.13–3.07), and lower odds of prescribing PT (adjusted OR = 0.34, 95% CI: 0.21–0.55) and MRI (adjusted OR = 0.46, 95% CI: 0.25–0.84).

Conclusion
We observed significant differences in the services ordered or provided by PCPs versus non-PCPs for shoulder pain in ambulatory care settings. These results may reveal the higher reliance of pharmacological approaches, coupled with the potential under-utilization of PT during the ambulatory shoulder care provided by PCPs compared to non-PCPs in the United States.
Using economic analysis to inform health resource allocation: lessons from Malawi
Authors: Rao Megha , Nkhoma Dominic , Mohan Sakshi , Twea Pakwanja , Chilima Benson , Mfutso-Bengo Joseph , Ochalek Jessica , Hallett Timothy B. , Phillips Andrew N. , McGuire Finn , Woods Beth , Walker Simon , Sculpher Mark , Revill Paul 
Published: July 2024

AbstractDespite making remarkable strides in improving health outcomes, Malawi faces concerns about sustaining the progress achieved due to limited fiscal space and donor dependency. The imperative for efficient health spending becomes evident, necessitating strategic allocation of resources to areas with the greatest impact on mortality and morbidity. Health benefits packages hold promise in supporting efficient resource allocation. However, despite defining these packages over the last two decades, their development and implementation have posed significant challenges for Malawi. In response, the Malawian government, in collaboration with the Thanzi la Onse Programme, has developed a set of tools and frameworks, primarily based on cost-effectiveness analysis, to guide the design of health benefits packages likely to achieve national health objectives. This review provides an overview of these tools and frameworks, accompanied by other related analyses, aiming to better align health financing with health benefits package prioritization. The paper is organized around five key policy questions facing decision-makers: (i) What interventions should the health system deliver? (ii) How should resources be allocated geographically? (iii) How should investments in health system inputs be prioritized? (iv) How should equity considerations be incorporated into resource allocation decisions? and (v) How should evidence generation be prioritized to support resource allocation decisions (guiding research)? The tools and frameworks presented here are intended to be compatible for use in diverse and often complex healthcare systems across Africa, supporting the health resource allocation process as countries pursue Universal Health Coverage.
Investigation of lawsuit process duration using machine learning and process mining
Authors: Vercosa Luiz , Silva Vinicius , Cruz Jaqueline , Bastos-Filho Carmelo , Bezerra Byron L. D. 
Published: July 2024

AbstractDelays in legal proceedings significantly impact both corporate finances and individual livelihoods. Traditional methods for managing these delays typically rely on subjective assessments of what constitutes a reasonable process duration. This study explores a more precise approach by integrating machine learning and process mining techniques to enhance prediction of legal proceedings’ overall time. Diverging from previous works that either utilized machine learning analysis or process mining in isolation, this research synergizes these approaches. We applied process mining clustering techniques to over 60,000 cases from Brazilian labor courts to segment cases based on their procedural movements, creating clusters. These clusters, along with other procedural characteristics, such as case subject, class, and digital status, were then incorporated into a feature set for regression modelling. We employed linear regression, support vector regressor, and gradient boosting based methods to develop models that predicted case duration. The gradient boosting model demonstrated the best performance with an $$R^2$$

R
2

-score of 0.87. Furthermore, our analysis identifies time bands where the model performs better and employs explainable AI techniques to elucidate key features influencing case durations. The clustering features emerged among the most significant for the task. The proposed combined approach offers a comprehensive method for analyzing and forecasting legal case timelines and also shows the potential of process mining clustering techniques to improve the analysis.
Use of multivariate techniques to regionalize rainfall patterns in semiarid Botswana
Authors: Nkoni Godiraone , Mphale Kgakgamatso , Mbangiwa Nicholas , Samuel Sydney , Molosiwa Rejoice 
Published: July 2024

AbstractMonthly precipitation data from 58 synoptic stations throughout Botswana, spanning 1981–2016, were used in this study. The data were examined using multivariate analysis to determine regions exhibiting distinct precipitation variability patterns and regimes. To accomplish this, the T-mode of principal component analysis was applied to the correlation matrix of the data. Based on the maximum loading values of the rotational principal component scores, the T-mode indicated three separate subregions with varying precipitation patterns over time. Four clusters with distinct rainfall patterns were identified when cluster analysis was performed on the principal component scores. An assessment of the homogeneity of the clusters was performed using L-moment’s heterogeneity measure (H). Statistical analysis was employed to model annual rainfall data using five commonly used rainfall analysis probability distribution functions: normal, lognormal, gamma, Weibull, and Gumbel. The probability distributions with the greatest fit were determined based on the maximum overall score, which was calculated by adding the individual point scores of three chosen goodness-of-fit tests. Each cluster exhibited distinct probability distribution functions, with the gamma, Gumbel, lognormal, and Weibull distributions providing the most accurate descriptions.