bluuebunny/crossref_metadata_2025_split huggingface dataset:
Knowledge transfer in lifelong machine learning: a systematic literature review
Authors: Khodaee Pouya , Viktor Herna L. , Michalowski Wojtek 
Published: July 2024

AbstractLifelong Machine Learning (LML) denotes a scenario involving multiple sequential tasks, each accompanied by its respective dataset, in order to solve specific learning problems. In this context, the focus of LML techniques is on utilizing already acquired knowledge to adapt to new tasks efficiently. Essentially, LML concerns about facing new tasks while exploiting the knowledge previously gathered from earlier tasks not only to help in adapting to new tasks but also to enrich the understanding of past ones. By understanding this concept, one can better grasp one of the major obstacles in LML, known as Knowledge Transfer (KT). This systematic literature review aims to explore state-of-the-art KT techniques within LML and assess the evaluation metrics and commonly utilized datasets in this field, thereby keeping the LML research community updated with the latest developments. From an initial pool of 417 articles from four distinguished databases, 30 were deemed highly pertinent for the information extraction phase. The analysis recognizes four primary KT techniques: Replay, Regularization, Parameter Isolation, and Hybrid. This study delves into the characteristics of these techniques across both neural network (NN) and non-neural network (non-NN) frameworks, highlighting their distinct advantages that have captured researchers’ interest. It was found that the majority of the studies focused on supervised learning within an NN modelling framework, particularly employing Parameter Isolation and Hybrid for KT. The paper concludes by pinpointing research opportunities, including investigating non-NN models for Replay and exploring applications outside of computer vision (CV).
Political Organisational Silence and the Ethics of Care: EU Migrant Restaurant Workers in Brexit Britain
Authors: Reeves Laura J. , Bristow Alexandra 
Published: July 2024

AbstractIn this paper, we explore the experiences of EU migrants working in UK restaurants in the aftermath of the Brexit vote. We do so through a care ethics lens, which we bring together with the integrative approach to organisational silence to consider the ethical consequences of the organisational policies of political silence adopted by the restaurant chains in our qualitative empirical study. We develop the concept of political organisational silence and probe its ethical dimensions, showing how at the organisational level it falls short of constituting a practice of caring for migrant workers in politically divisive and hostile times. We argue that organisational policies of political silence emphasise the exploitative nature of the business of (im)migration, which prioritises concern for profits over care for the needs of others. Organisations refuse caring responsibility for migrant workers, leaving care to the migrants themselves and their co-workers and managers. Whilst peer-care practices partially fill this politically silent care-vacuum, this leaves individuals to negotiate difficult tensions without institutional support at a time of increased uncertainty, complexity, hostility, violence, and vulnerability. Drawing lessons from our study and its aftermath, we call for a care manifesto to inform the business of (im)migration, which would need to include caring political responsibility towards migrant workers exercised through caring political organisational voice as well as silence.
ADAR1 prevents ZBP1-dependent PANoptosis via A-to-I RNA editing in developmental sevoflurane neurotoxicity
Authors: Yang Huiling , Xu Sen , Hong Xinya , Liu Yusi , Qian Shaojie , Lou Yifei , Wang Wenyuan 
Published: July 2024

AbstractIt is well established that sevoflurane exposure leads to widespread neuronal cell death in the developing brain. Adenosine deaminase acting on RNA-1 (ADAR1) dependent adenosine-to-inosine (A-to-I) RNA editing is dynamically regulated throughout brain development. The current investigation is designed to interrogate the contributed role of ADAR1 in developmental sevoflurane neurotoxicity. Herein, we provide evidence to show that developmental sevoflurane priming triggers neuronal pyroptosis, apoptosis and necroptosis (PANoptosis), and elicits the release of inflammatory factors including IL-1β, IL-18, TNF-α and IFN-γ. Additionally, ADAR1-P150, but not ADAR1-P110, depresses cellular PANoptosis and inflammatory response by competing with Z-DNA/RNA binding protein 1 (ZBP1) for binding to Z-RNA in the presence of sevoflurane. Further investigation demonstrates that ADAR1-dependent A-to-I RNA editing mitigates developmental sevoflurane-induced neuronal PANoptosis. To restore RNA editing, we utilize adeno-associated virus (AAV) to deliver engineered circular ADAR-recruiting guide RNAs (cadRNAs) into cells, which is capable of recruiting endogenous adenosine deaminases to promote cellular A-to-I RNA editing. As anticipated, AAV-cadRNAs diminishes sevoflurane-induced cellular Z-RNA production and PANoptosis, which could be abolished by ADAR1-P150 shRNA transfection. Moreover, AAV-cadRNAs delivery ameliorates developmental sevoflurane-induced spatial and emotional cognitive deficits without influence on locomotor activity. Taken together, these results illustrate that ADAR1-P150 exhibits a prominent role in preventing ZBP1-dependent PANoptosis through A-to-I RNA editing in developmental sevoflurane neurotoxicity. Application of engineered cadRNAs to rectify the compromised ADAR1-dependent A-to-I RNA editing provides an inspiring direction for possible clinical preventions and therapeutics.
Extragradient method with feasible inexact projection to variational inequality problem
Authors: Millán R. Díaz , Ferreira O. P. , Ugon J. 
Published: July 2024

AbstractThe variational inequality problem in finite-dimensional Euclidean space is addressed in this paper, and two inexact variants of the extragradient method are proposed to solve it. Instead of computing exact projections on the constraint set, as in previous versions extragradient method, the proposed methods compute feasible inexact projections on the constraint set using a relative error criterion. The first version of the proposed method provided is a counterpart to the classic form of the extragradient method with constant steps. In order to establish its convergence we need to assume that the operator is pseudo-monotone and Lipschitz continuous, as in the standard approach. For the second version, instead of a fixed step size, the method presented finds a suitable step size in each iteration by performing a line search. Like the classical extragradient method, the proposed method does just two projections into the feasible set in each iteration. A full convergence analysis is provided, with no Lipschitz continuity assumption of the operator defining the variational inequality problem.
Is artificial intelligence use related to self-control, self-esteem and self-efficacy among university students?
Authors: Rodríguez-Ruiz Joaquín , Marín-López Inmaculada , Espejo-Siles Raquel 
Published: July 2024

Abstract
The present study aimed to analyse if self-control, self-esteem and self-efficacy are related to the use of artificial intelligence tools. These tools are being incorporated to educational practices, but there is a lack of empirical evidence about the relation between artificial intelligence use by students and their personal and psychological characteristics. Drawing a profile of students concerning their use of artificial intelligence is imperative in order to design effective learning strategies. This was a cross-sectional study including 1 761 undergraduate students enrolled in different degrees related to education and psychology. Data collection was conducted using validated self-reports that showed appropriate psychometric properties. According to linear regression analyses, low levels of self-control were related to a higher frequency of artificial intelligence use. Logistic regression analyses showed that self-control and self-efficacy were associated with using artificial intelligence to solve daily doubts, due to the need of interacting with someone and to do academic tasks instead of the student. Moreover, higher scores in self-esteem decreased the odds of using artificial intelligence due to the need of interacting with someone. Educators should take into account these findings when implementing the use of artificial intelligence in their educational strategies with university students.
Preschool Children’s Agency in Play-activities with Science Content
Authors: Henriksson Anna , Fridberg Marie , Leden Lotta 
Published: July 2024

AbstractThe Swedish preschool educational tradition is based on a holistic view where care, play, learning, and teaching are intertwined. Previous research argues for the need to develop teaching approaches that simultaneously direct attention towards content and children’s perspectives. The aim of this study is to contribute with knowledge about how preschool children’s agency is supported in activities that integrate play and science, with a special focus on the use of representations. The empirical data consists of video-observations of teachers’ and children’s (2-4-years) participation in activities that integrate play and science. In the analysis, the theoretical framework of Play-Responsive Early Childhood Education and Care (PRECEC) is used. Here, teaching and play are understood as a mutual activity. In these mutual activities, teachers and children are constantly shifting back and forth betweenas if(fantasy) andas is(reality) (Pramling et al., 2019). Three narratives have been constructed that exemplify how children’s agency is supported in activities that integrate play and science. These narratives highlight how children’s initiatives are given space to evolve and enrich both play and science; how children’s initiatives become valuable for teachers’ opportunities totriggerand further explore science content and; how teacher’s constantly must respond to children’s different initiatives and balance between them. Implications for preschool teachers and preschool practice are discussed in relation to children’s agency in science teaching in preschool.
Assessing the quality of conceptual knowledge through dynamic constructions
Authors: Vízek Lukáš , Samková Libuše , Star Jon R. 
Published: July 2024

AbstractIn this contribution, we address the gap that has appeared in mathematics education research and practice with the emergence of dynamic geometry environments and build on the opportunities these environments offer to school geometry. In our qualitative empirical study, we investigate how to elaborate on the general model of conceptual knowledge to make it applicable to dynamic geometry tasks, specifically to tasks including dynamic geometric constructions. We present a design of dynamic constructions of quadrilaterals that comply with Euclidean constructions, derive an assessment instrument based on them, and study what information the instrument can provide about the quality of students’ conceptual knowledge. We present the results in the form of an assessment framework consisting of an example of the assessment instrument and an ordered system of qualitative categories serving as an assessment codebook for interpreting students’ responses in terms of the quality of conceptual knowledge. To clarify the relations between the assessment framework and the general model of conceptual knowledge, we establish a system of subdimensions of conceptual knowledge that indicates how conceptual knowledge can be understood in the context of dynamic geometric constructions and identifies the conceptual knowledge needed to achieve individual categories of the assessment framework.
Multivariate Bayesian models with flexible shared interactions for analyzing spatio-temporal patterns of rare cancers
Authors: Retegui Garazi , Etxeberria Jaione , Ugarte María Dolores 
Published: July 2024

AbstractRare cancers affect millions of people worldwide each year. However, estimating incidence or mortality rates associated with rare cancers presents important difficulties and poses new statistical methodological challenges. In this paper, we expand the collection of multivariate spatio-temporal models by introducing adaptable shared spatio-temporal components to enable a comprehensive analysis of both incidence and cancer mortality in rare cancer cases. These models allow the modulation of spatio-temporal effects between incidence and mortality, allowing for changes in their relationship over time. The new models have been implemented in INLA using r-generic constructions. We conduct a simulation study to evaluate the performance of the new spatio-temporal models. Our results show that multivariate spatio-temporal models incorporating a flexible shared spatio-temporal term outperform conventional multivariate spatio-temporal models that include specific spatio-temporal effects for each health outcome. We use these models to analyze incidence and mortality data for pancreatic cancer and leukaemia among males across 142 administrative health care districts of Great Britain over a span of nine biennial periods (2002–2019).
Narrative Ability in Autism and First-Degree Relatives
Authors: Nayar Kritika , Landau Emily , Martin Gary E. , Stevens Cassandra J. , Xing Jiayin , Sophia Pirog , Guilfoyle Janna , Gordon Peter C. , Losh Molly 
Published: July 2024

AbstractNarrative is an important communication skill for sharing personal experiences and connecting with others. Narrative skills are often impacted in autism spectrum disorder (ASD) and have important consequences for social interactions and relationships. Subtle differences in narrative have also been reported among first-degree relatives of autistic individuals, suggesting that narrative may also be an etiologically important language-related skill that is influenced by genes associated with ASD. This study examined narrative ability and related visual attention during narration in ASD and first-degree relatives of individuals with ASD (siblings and parents) to understand how narrative and related attentional styles may be variably impacted across the spectrum of ASD genetic influence. Participants included 56 autistic individuals, 42 siblings of autistic individuals, 49 controls, 161 parents of autistic individuals, and 61 parent controls. Narratives were elicited using a wordless picture book presented on an eye tracker to record concurrent gaze. Findings revealed parallel patterns of narrative differences among ASD and sibling groups in the use of causal language to connect story elements and the use of cognitive and affective language. More subtle differences within the domain of causal language were evident in ASD parents. Parallel patterns in the ASD and sibling groups were also found for gaze during narration. Findings implicate causal language as a critical narrative skill that is impacted in ASD and may be reflective of ASD genetic influence in relatives. Gaze patterns during narration suggest similar attentional mechanisms associated with narrative among ASD families.
Predictors of Change in Wellbeing and Mental Health of Parents of Autistic Pre-Schoolers
Authors: Green Cherie C. , Smith Jodie , Bent Catherine A. , Chetcuti Lacey , Uljarević Mirko , Benson Paul R. , Hudry Kristelle 
Published: July 2024

AbstractParenting is a rewarding experience but is not without its challenges. Parents of Autistic children face additional challenges, and as a result can experience lower levels of wellbeing and more mental health problems (i.e., depression, anxiety, stress). Previous studies have identified concurrent correlates of wellbeing and mental health. However, few have investigated predictors of subsequent wellbeing and mental health, or of change over time, among parents of pre-school aged autistic children. We examined child-, parent-, and family/sociodemographic factors associated with change in parents’ mental health and wellbeing across three timepoints (spanning approximately one year) among 53 parents of Autistic pre-schoolers (M = 35.48, SD = 6.36 months. At each timepoint, parents reported lower wellbeing and greater mental health difficulties compared to normative data. There was no significant group-level change over time in parent outcomes. However, individual variability in short-term (~ 5 months) wellbeing and mental health change was predicted by a combination of child- and parent-related factors, while variability in medium-term (~ 10 months) change was predicted by parent factors alone. Parents’ description of their child and their relationship predicted change in both wellbeing and mental health. Furthermore, participating in a parent-mediated intervention (available to a subgroup) was a significant predictor of change in wellbeing. Our findings highlight potentially modifiable factors (e.g., learning healthier coping strategies) that may positively impact both short- and medium-term change in parental outcomes.
Memory Efficient Deep Learning-Based Grasping Point Detection of Nontrivial Objects for Robotic Bin Picking
Authors: Dolezel Petr , Stursa Dominik , Kopecky Dusan 
Published: July 2024

AbstractPicking up non-trivial objects from a bin with a robotic arm is a common task of modern industrial processes. Here, an efficient data-driven method of grasping point detection, based on an attention squeeze parallel U-shaped neural network (ASP U-Net) for the bin picking task, is proposed. The method directly provides all necessary information about the feasible grasping points of objects, which are randomly or regularly arranged in a bin with side walls. Moreover, the method is able to evaluate and select the optimal grasping point among the feasible ones for two types of end effectors, i.e., a vacuum cup and a parallel gripper. The key element of the utilized ASP U-Net neural network is the transformation of a single RGB-Depth image of the bin containing nontrivial objects into a schematic grey-scale frame, where the positions and poses of the grasping points are coded into gradient geometric shapes. The experiments carried out in this study include a comprehensive set of scenes with randomly scattered, ordered, and semi-ordered objects arranged in impeccable or deformed bins. The results indicate outstanding accuracy with more than acceptable computational requirements. Additionally, the scaling possibilities of the method can offer extremely lightweight implementations, applicable, for example, to battery-powered edge-computing devices with low RAM capacity.
Upwind Summation-by-parts Finite Differences: error Estimates and WENO methodology
Authors: Jiang Yan , Wang Siyang 
Published: July 2024

AbstractHigh order upwind summation-by-parts finite difference operators have recently been developed. When combined with the simultaneous approximation term method to impose boundary conditions, the method converges faster than using traditional summation-by-parts operators. We prove the convergence rate by the normal mode analysis for such methods for a class of hyperbolic partial differential equations. Our analysis shows that the penalty parameter for imposing boundary conditions affects the convergence rate for stable methods. In addition, to solve problems with discontinuous data, we extend the method to also have the weighted essentially nonoscillatory property. The overall method is stable, achieves high order accuracy for smooth problems, and is capable of solving problems with discontinuities.
Risk assessment of street trees failure by non-experts: a case study from the North of Israel
Authors: Zur Shalhevet Leibovitz , Bahat Shay , Amara Ronza , Farber Yair 
Published: July 2024

Abstract
Street trees are valuable to urban environments, providing environmental, aesthetic, and social benefits. However, local authorities and municipalities are under pressure to cut down street trees because of concerns about fallen trees and broken branches. The problem is that this risk assessment can be done only by agronomists (experts) in some places, which is limiting. The idea is to train the local municipality’s gardeners (non-experts) to conduct risk assessments for street trees using a criteria-defined protocol developed by the Israeli Ministry of Agriculture. In our case study, we compare a risk assessment on street trees done by gardeners to a risk assessment done by an agronomist. The findings showed that most non-experts agreed with every criterion in the protocol. The strength of the relationship between the expert score and the non-experts’ average score was high and significant. Most criteria had a high or complete agreement between the gardeners and the agronomist. The visible risks criterion has a medium agreement, while the height–diameter ratio criterion has the lowest agreement between the gardeners and the agronomist. Local authorities and municipalities have room to rely on their gardeners (after a short training) to assess the risks of street trees falling when a criterion-defining assessment protocol is used.
On imperfective suffixes in Russian
Authors: Biskup Petr 
Published: July 2024

AbstractThis article is concerned with imperfective suffixes in Russian. It argues that there are at least three imperfective suffixes, the progressive morpheme, the iterative marker and the habitual suffix. It is shown that they differ in morphosyntactic and semantic properties. As to their structural properties, the progressive suffix is the lowest marker; the iterative morpheme attaches in a higher position; and the habitual morpheme is structurally the highest element. Thus, the article argues against a unitary approach to the iterative suffix and the habitual morpheme. Semantically, the iterative suffix has a pluractional meaning, whereas the habitual suffix is a vague generic quantifier. As to their phonological properties, the three markers are homophonous; they are spelled out as an -yva- allomorph. In this respect, the suffixes differ from the morphological aspect operator encoding (im)perfectivity in the aspectual head, which is phonologically empty. Further, it is shown that there are certain prefixes in the verbal morphosyntactic structure that are placed between the progressive projection and the iterative projection.
Augmented reality for endoscopic transsphenoidal surgery: evaluating design factors with neurosurgeons
Authors: Higa Jennifer , Nkatha Sonia , Ramirez Herrera Roxana , Marcus Hani , Yoo Soojeong , Blandford Ann , Opie Jeremy 
Published: July 2024

Abstract

Purpose
This study investigates the potential utility of augmented reality (AR) in the endoscopic transsphenoidal approach (TSA). While previous research has addressed technical challenges in AR for TSA, this paper explores how design factors can improve AR for neurosurgeons from a human-centred design perspective.

Methods
Preliminary qualitative research involved observations of TSA procedures (

$$n=2$$

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=
2

) and semi-structured interviews with neurosurgeons (

$$n=4$$

n
=
4

). These informed the design of an AR mockup, which was evaluated with neurosurgeons (

$$n=3$$

n
=
3

). An interactive low-fidelity prototype—the “AR-assisted Navigation for the TransSphenoidal Approach (ANTSA)”—was then developed in Unity 3D. A user study (

$$n=4$$

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=
4

) evaluated the low-fidelity prototype of ANTSA through contextual interviews, providing feedback on design factors.

Results
AR visualisations may be beneficial in streamlining the sellar phase and reducing intraoperative errors such as excessive or inadequate exposure. Key design recommendations include a lean mesh rendering, an intuitive colour palette, and optional structure highlighting.

Conclusion
This research presents user-centred design guidelines to improve sensemaking and surgical workflow in the sellar phase of TSA, with the goal of improving clinical outcomes. The specific improvements that AR could bring to the workflow are discussed along with surgeons’ reservations and its possible application towards training less experienced physicians.