bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial intelligence-based detection of paediatric appendicular skeletal fractures: performance and limitations for common fracture types and locations
Authors: Altmann-Schneider Irmhild , Kellenberger Christian J. , Pistorius Sarah-Maria , Saladin Camilla , Schäfer Debora , Arslan Nidanur , Fischer Hanna L. , Seiler Michelle 
Published: December 2023

Abstract
Background
Research into artificial intelligence (AI)-based fracture detection in children is scarce and has disregarded the detection of indirect fracture signs and dislocations.

Objective
To assess the diagnostic accuracy of an existing AI-tool for the detection of fractures, indirect fracture signs, and dislocations.

Materials and methods
An AI software, BoneView (Gleamer, Paris, France), was assessed for diagnostic accuracy of fracture detection using paediatric radiology consensus diagnoses as reference. Radiographs from a single emergency department were enrolled retrospectively going back from December 2021, limited to 1,000 radiographs per body part. Enrolment criteria were as follows: suspected fractures of the forearm, lower leg, or elbow; age 0–18 years; and radiographs in at least two projections.

Results
Lower leg radiographs showed 607 fractures. Sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were high (87.5%, 87.5%, 98.3%, 98.3%, respectively). Detection rate was low for toddler’s fractures, trampoline fractures, and proximal tibial Salter-Harris-II fractures. Forearm radiographs showed 1,137 fractures. Sensitivity, specificity, PPV, and NPV were high (92.9%, 98.1%, 98.4%, 91.7%, respectively). Radial and ulnar bowing fractures were not reliably detected (one out of 11 radial bowing fractures and zero out of seven ulnar bowing fractures were correctly detected). Detection rate was low for styloid process avulsions, proximal radial buckle, and complete olecranon fractures. Elbow radiographs showed 517 fractures. Sensitivity and NPV were moderate (80.5%, 84.7%, respectively). Specificity and PPV were high (94.9%, 93.3%, respectively). For joint effusion, sensitivity, specificity, PPV, and NPV were moderate (85.1%, 85.7%, 89.5%, 80%, respectively). For elbow dislocations, sensitivity and PPV were low (65.8%, 50%, respectively). Specificity and NPV were high (97.7%, 98.8%, respectively).

Conclusions
The diagnostic performance of BoneView is promising for forearm and lower leg fractures. However, improvement is mandatory before clinicians can rely solely on AI-based paediatric fracture detection using this software.

Graphical Abstract
Global stability and parameter analysis reinforce therapeutic targets of PD-L1-PD-1 and MDSCs for glioblastoma
Authors: Anderson Hannah G. , Takacs Gregory P. , Harris Duane C. , Kuang Yang , Harrison Jeffrey K. , Stepien Tracy L. 
Published: December 2023

AbstractGlioblastoma (GBM) is an aggressive primary brain cancer that currently has minimally effective treatments. Like other cancers, immunosuppression by the PD-L1-PD-1 immune checkpoint complex is a prominent axis by which glioma cells evade the immune system. Myeloid-derived suppressor cells (MDSCs), which are recruited to the glioma microenviroment, also contribute to the immunosuppressed GBM microenvironment by suppressing T cell functions. In this paper, we propose a GBM-specific tumor-immune ordinary differential equations model of glioma cells, T cells, and MDSCs to provide theoretical insights into the interactions between these cells. Equilibrium and stability analysis indicates that there are unique tumorous and tumor-free equilibria which are locally stable under certain conditions. Further, the tumor-free equilibrium is globally stable when T cell activation and the tumor kill rate by T cells overcome tumor growth, T cell inhibition by PD-L1-PD-1 and MDSCs, and the T cell death rate. Bifurcation analysis suggests that a treatment plan that includes surgical resection and therapeutics targeting immune suppression caused by the PD-L1-PD1 complex and MDSCs results in the system tending to the tumor-free equilibrium. Using a set of preclinical experimental data, we implement the approximate Bayesian computation (ABC) rejection method to construct probability density distributions that estimate model parameters. These distributions inform an appropriate search curve for global sensitivity analysis using the extended fourier amplitude sensitivity test. Sensitivity results combined with the ABC method suggest that parameter interaction is occurring between the drivers of tumor burden, which are the tumor growth rate and carrying capacity as well as the tumor kill rate by T cells, and the two modeled forms of immunosuppression, PD-L1-PD-1 immune checkpoint and MDSC suppression of T cells. Thus, treatment with an immune checkpoint inhibitor in combination with a therapeutic targeting the inhibitory mechanisms of MDSCs should be explored.
Triage Subsurface – Unter Tage Training für medizinische Ersthelfer in der virtuellen Welt
Authors: Harthum Birgit , Schrom-Feiertag Helmut , Wenighofer Robert 
Published: December 2023

ZusammenfassungGroßschadenslagen mit einem Massenanfall von Verletzten sind schon per se für Einsatzkräfte herausfordernd. Findet so eine Situation in einer Umgebung wie einem Tunnel statt, steigt die Komplexität dramatisch. Der Zugang ist schwieriger, die Bergung komplizierter, die Sicht oftmals beeinträchtigt und die Umgebung fordert die Gesundheit der Rettungskräfte zusätzlich durch Rauchentwicklung oder Kontamination durch chemische Stoffe. Koordination und (Zeit)-Management der Situation ist also unter Tage ein noch wichtigerer Faktor als sonst. Das Europäische Forschungsprojekt MED1stMR entwickelt eine Mixed Reality Trainingslösung, die medizinische Einsatzkräfte auf solche Situationen noch besser vorbereiten soll. Im Zuge des Projektes werden die Mixed Reality Trainingslösung und Realübungen verglichen, um Rückschlüsse auf die Gestaltung des Trainingssystems liefern zu können. Das Technologie-Forschungsprojekt besteht aus einem multidisziplinären Konsortium, geleitet vom AIT – Austrian Institute of Technology. Der Fokus ist, Technologie, Wissenschaft und End-Nutzer Rettungsorganisationen zusammenzubringen, um gemeinsam eine effiziente Trainingslösung entwickeln zu können. Zwei Jahre wurde gemeinsam an der Lösung gearbeitet, nun wird sie an sechs Standorten von Partner-Rettungsorganisationen getestet, um die Technologieakzeptanz zu erforschen und wissenschaftliche Daten der Einsatzkräfte zu erheben, die der Weiterentwicklung des Systems und diverser Algorithmen dienen. Auch zwei Realübungen sind Teil des Projektes. Eine der Realübungen fand an der Montanuniversität Leoben in den Tunnelanlagen des Zentrums am Berg statt und lieferten relevante Daten über die Gestaltung der virtuellen Szenarien sowie zu Stresssituation der Einsatzkräfte in realer Umgebung. Realübungen sind teuer und werden daher seltener durchgeführt – die Umsetzung der Übung in der Anlage des Zentrums am Berg erleichterte die Durchführung und tragen damit ihren Beitrag zur Sicherheit von Tunnelanlagen. Die Realübung lieferte einen wesentlichen Beitrag für die Übertragung und Gestaltung der virtuellen Trainingsszenarien unter Tage und ermöglicht es, ein immersives Trainingserlebnis zu schaffen, das die realen Bedingungen und Herausforderungen im Tunnel präzise wiedergibt und gleichzeitig die Trainingsziele effektiv erfüllt.
Existence and verification of Nash equilibria in non-cooperative contribution games with resource contention
Authors: Troquard Nicolas 
Published: December 2023

AbstractIn resource contribution games, a class of non-cooperative games, the players want to obtain a bundle of resources and are endowed with bags of bundles of resources that they can make available into a common for all to enjoy. Available resources can then be used towards their private goals. A player is potentially satisfied with a profile of contributed resources when his bundle could be extracted from the contributed resources. Resource contention occurs when the players who are potentially satisfied, cannot actually all obtain their bundle. The player’s preferences are always single-minded (they consider a profile good or they do not) and parsimonious (between two profiles that are equally good, they prefer the profile where they contribute less). What makes a profile of contributed resources good for a player depends on their attitude towards resource contention. We study the problem of deciding whether an outcome is a pure Nash equilibrium for three kinds of players’ attitudes towards resource contention: public contention-aversity, private contention-aversity, and contention-tolerance. In particular, we demonstrate that in the general case when the players are contention-averse, then the problem is harder than when they are contention-tolerant. We then identify a natural class of games where, in presence of contention-averse preferences, it becomes tractable, and where there is always a Nash equilibrium.
Why are Actions but not Emotions Done Intentionally, if both are Reason-Responsive Embodied Processes?
Authors: Nes Anders 
Published: December 2023

AbstractEmotions, like actions, this paper argues, are typically embodied processes that are responsive to reasons, where these reasons connect closely with the agent’s desires, intentions, or projects. If so, why are emotions, nevertheless, typically passive in a sense in which actions are not; specifically, why are emotions not cases of doing something intentionally? This paper seeks to prepare the ground for answering to this question by showing that it cannot be answered within a widely influential framework in the philosophy of action that has been dubbed the Standard Conception of Action, shared by such diverse theorists as G.E.M. Anscombe, Donald Davidson, Jennifer Hornsby, Michael Smith. and Michael Thompson. The Standard Conception approaches agency via the notion of someone’s doing something intentionally, and links the latter notion closely to that of doing something for a reason, so as to imply ‘Anscombe’s Thesis’ that, if someone is doing something for a reason, they are doing it intentionally. The paper shows how emotions, as reason-responsive embodied processes, counterexemplify this claim. Qua processes, they can aptly be described in the progressive, as cases of trembling with fear, exploding with anger, etc. They are a kind of ‘doing’ something, for a reason, yet not intentionally.
Inheritance of cellulose, hemicellulose and lignin content in relation to seed oil and protein content in oilseed rape
Authors: Yusuf Abdusaheed Olabisi , Möllers Christian 
Published: December 2023

AbstractOilseed rape is worldwide an important oil and protein crop. Its oil is valued because of its excellent quality. The oil extracted meal is marketed as a lower value by-product for feeding livestock. Recently, interest in vegetable proteins has increased to use the oilseed rape protein as an alternative vegetable source for human consumption. However, the use of the protein rich meal for food production is greatly limited by the presence of residual glucosinolate, phenolic acid esters and crude fibre contents which affect its techno-functional properties, taste and colour. Further reducing contents of glucosinolates, cellulose, hemicellulose and indigestible lignin, is expected to enhance protein content and quality. To this end, two half-sib DH populations were tested in replicated field experiments. Inheritance of individual seed fibre components in relation to each other and to oil, protein and glucosinolate content were investigated. The DH populations were genotyped with Brassica 15K SNP Illumina chip, QTL were mapped and candidate genes were identified using the high quality long read reference genome of Express 617. Novel QTL for fibre components were identified that co-located to each other, with QTL for oil, protein and glucosinolate content, and with opposite direction of additive effects. The parallel investigation of two half-sib DH populations gave insight into the direction of the additive effects which depended on the indvidual parents. The results provide additional understanding of genetic loci underlying the seed quality traits which may help achieving the breeding goals in oilseed rape.
Teachers’ Considerations for a Digitalised Learning Context of Preschool Science
Authors: Papantonis Stajcic Maria , Nilsson Pernilla 
Published: December 2023

AbstractThis study investigates preschool teachers’ considerations for including digital tools in science teaching to develop children’s learning of science content. Due to the ongoing digitalisation and demands in society, the utilisation of digital tools has increased significantly in educational settings. Recent research about digital tools in early childhood education focuses on various aspects of technology implementation. However, there is a research gap in which considerations underpin preschool teachers’ choices of what, why and how they integrate digital tools into science teaching. The data generation was conducted by different methods. The reflection tool Content Representations (CoRe) is used to make the preschool teachers’ considerations explicit when reflecting on planning science teaching regarding specific science content formulated as Big Ideas. Further, video stimulated recall interviews capture the preschool teachers’ considerations on their interactions with children in science activities and using digital tools. The Refined Consensus Model (RCM) of Pedagogical Content Knowledge (PCK) was employed as a theoretical framework for analysing and interpreting data around an entire teaching cycle. Some of the teachers’ considerations for including digital tools involve accessing children’s learning, making the abstract concrete and stimulating children’s engagement and learning. Further, the findings indicate that the considerations concerned knowledge about teachers’ personal PCK (pPCK) and enacted PCK (ePCK) aspects.
Perspectives of California Legislators on Institutional Barriers and Facilitators to Non-Partisan Research Evidence Use in State Health Policymaking
Authors: Ashtari Neda , Abbasi Justin , Barnert Elizabeth 
Published: December 2023

Abstract
Background
Bridging the translational gap between research evidence and health policy in state legislatures requires understanding the institutional barriers and facilitators to non-partisan research evidence use. Previous studies have identified individual-level barriers and facilitators to research evidence use, but limited perspectives exist on institutional factors within legislatures that influence non-partisan research evidence use in health policymaking.

Objective
We describe the perspectives of California state legislators and legislative staff on institutional barriers and facilitators of non-partisan research evidence use in health policymaking and explore potential solutions for enhancing use.

Design
Case study design involving qualitative interviews.

Participants
We interviewed 24 California state legislators, legislative office staff, and legislative research staff.

Approach
Semi-structured recorded interviews were conducted in person or by phone to identify opportunities for enhancing non-partisan research evidence use within state legislatures. We conducted thematic analyses of interview transcripts to identify (1) when research evidence is used during the policymaking process, (2) barriers and facilitators operating at the institutional level, and (3) potential solutions for enhancing evidence use.

Results
Institutional barriers to non-partisan research evidence use in health policymaking were grouped into three themes: institutional policies, practices, and priorities. Interviews also revealed institutional-level facilitators of research evidence use, including (1) access and capacity to engage with research evidence, and (2) perceived credibility of research evidence. The most widely supported institutional-level solution for enhancing evidence-based health policymaking in state legislatures involved establishing independent, impartial research entities to provide legislators with trusted evidence to inform decision-making.

Conclusions
Potential institutional-level changes within state legislatures may enhance evidence use in health policymaking, leading to improved health outcomes and lower healthcare costs for states.
Coastal Cliffs of the Rio Grande do Norte State: Geoheritage Characterization and Valorization in Northeast Brazil
Authors: dos Santos Costa Silas Samuel , Junior João Correia Saraiva , Lima Zuleide Maria Carvalho , do Nascimento Marcos Antonio Leite , da Silva Matheus Lisboa Nobre 
Published: December 2023

AbstractThe Rio Grande do Norte state, Northeast Brazil, has 410 km of coastal extension, being 107 km approximately composed of cliffs excavated in Cenozoic sedimentary rocks. In the last three decades, these cliffs are passing for transformations due the human occupation, tourism activity and climatic changes. To inventory the geoheritage present in these cliffs is essential to support management plans that include investment to conserving part of this Earth’s natural history. This paper aims to inventory, classify, assess and value the Potiguar cliffs as geological sites. From 46 outcrops that Saraiva Junior (2021) studied to describe the cliffs’ geomorphology, 20 geological sites were selected, these are used in national and international publications and divided in three frameworks: Miocene-Pliocene sedimentation, Pleistocene sedimentation and Cenozoic tectonics. These 20 cliffs were inventoried using the Brilha (2016) methodology in order to qualify and quantify the scientific value, potential educational and touristic uses and degradation risk. The Ponta do Mel, Barra de Tabatinga, Ponta dos Três Irmãos, Barreira do Inferno, Ponta do Reduto and Morro do Chapéu were defined as the most valuable cliffs on the scientific aspect. We proposed monitoring planning based on the cliff’s baselines, protection measures based on the specific threats and three geotouristic routes based on the geographic distribution of the sites and the frameworks.
Multiple attribute group decision making approach for selection of robot under induced bipolar neutrosophic aggregation operators
Authors: Jamil Muhammad , Afzal Farkhanda , Maqbool Ayesha , Abdullah Saleem , Akgül Ali , Bariq Abdul 
Published: December 2023

AbstractIn current piece of writing, we bring in the new notion of induced bipolar neutrosophic (BN) AOs by utilizing Einstein operations as the foundation for aggregation operators (AOs), as well as to endow having a real-world problem-related application. The neutrosophic set can rapidly and more efficiently bring out the partial, inconsistent, and ambiguous information. The fundamental definitions and procedures linked to the basic bipolar neutrosophic (BN) set as well as the neutrosophic set (NS), are presented first. Our primary concern is the induced Einstein AOs, like, induced bipolar neutrosophic Einstein weighted average (I-BNEWA), induced bipolar neutrosophic Einstein weighted geometric (I-BNEWG), as well as their different types and required properties. The main advantage of employing the offered methods is that they give decision-makers a more thorough analysis of the problem. These strategies whenever compare to on hand methods, present complete, progressively precise, and accurate result. Finally, utilizing a numerical representation of an example for selection of robot, for a problem involving multi-criteria community decision making, we propose a novel solution. The suitability ratings are then ranked to select the most suitable robot. This demonstrates the practicality as well as usefulness of these novel approaches.
Automatic algorithm design of distributed hybrid flowshop scheduling with consistent sublots
Authors: Zhang Biao , Lu Chao , Meng Lei-lei , Han Yu-yan , Hu Jiang , Jiang Xu-chu 
Published: December 2023

AbstractThe present-day globalized economy and diverse market demands have compelled an increasing number of manufacturing enterprises to move toward the distributed manufacturing pattern and the model of multi-variety and small-lot. Taking these two factors into account, this study investigates an extension of the distributed hybrid flowshop scheduling problem (DHFSP), called the distributed hybrid flowshop scheduling problem with consistent sublots (DHFSP_CS). To tackle this problem, a mixed integer linear programming (MILP) model is developed as a preliminary step. The NP-hard nature of the problem necessitates the use of the iterated F-Race (I/F-Race) as the automated algorithm design (AAD) to compose a metaheuristic that requires minimal user intervention. The I/F-Race enables identifying the ideal values of numerical and categorical parameters within a promising algorithm framework. An extension of the collaborative variable neighborhood descent algorithm (ECVND) is utilized as the algorithm framework, which is modified by intensifying efforts on the critical factories. In consideration of the problem-specific characteristics and the solution encoding, the configurable solution initializations, configurable solution decoding strategies, and configurable collaborative operators are designed. Additionally, several neighborhood structures are specially designed. Extensive computational results on simulation instances and a real-world instance demonstrate that the automated algorithm conceived by the AAD outperforms the CPLEX and other state-of-the-art metaheuristics in addressing the DHFSP_CS.
Adaptive learning point cloud and image diversity feature fusion network for 3D object detection
Authors: Yan Weiqing , Liu Shile , Liu Hao , Yue Guanghui , Wang Xuan , Song Yongchao , Xu Jindong 
Published: December 2023

Abstract3D object detection is a critical task in the fields of virtual reality and autonomous driving. Given that each sensor has its own strengths and limitations, multi-sensor-based 3D object detection has gained popularity. However, most existing methods extract high-level image semantic features and fuse them with point cloud features, focusing solely on consistent information from both sensors while ignoring their complementary information. In this paper, we present a novel two-stage multi-sensor deep neural network, called the adaptive learning point cloud and image diversity feature fusion network (APIDFF-Net), for 3D object detection. Our approach employs the fine-grained image information to complement the point cloud information by combining low-level image features with high-level point cloud features. Specifically, we design a shallow image feature extraction module to learn fine-grained information from images, instead of relying on deep layer features with coarse-grained information. Furthermore, we design a diversity feature fusion (DFF) module that transforms low-level image features into point-wise image features and explores their complementary features through an attention mechanism, ensuring an effective combination of fine-grained image features and point cloud features. Experiments on the KITTI benchmark show that the proposed method outperforms state-of-the-art methods.