bluuebunny/crossref_metadata_2025_split huggingface dataset:
Experimental constraints on the nature of multiphase solid inclusions and their bearing on mantle wedge metasomatism, Bohemian Massif
Authors: Acosta-Vigil Antonio , Kotková Jana , Čopjaková Renata , Wirth Richard , Hermann Jörg 
Published: June 2024

AbstractThis study tests experimentally the hypothesis that calculated bulk compositions of multiphase solid inclusions present in minerals of ultrahigh pressure rocks, can be equated to the composition of the former trapped fluids. We investigated samples from the ultrahigh pressure garnet peridotites of the Bohemian Massif, spatially associated with ultrahigh pressure crustal rocks and representing a former subduction interface environment. Inclusions present in garnets, composed of amphibole + Ba-mica kinoshitalite + carbonates (dolomite + magnesite + norsethite), were taken to their entrapment conditions of c. 4.5 GPa and 1075 ºC. They (re)crystallized into a garnet fringe at the boundary between inclusion and host garnet, kinoshitalite ± olivine, carbonatite melt, and a hydrous fluid. Although the latter may have exsolved from the carbonatite melt upon quenching, microstructures suggest it was present at trapped conditions, and mass balance indicates that it corresponds to a Na-K-Cl-F-rich saline aqueous fluid (brine). Experiments demonstrate the stability of kinoshitalite at 4.5 GPa and 1075 ºC, and suggest that Ba-rich mica + carbonatite melt + brine coexisted at near-peak conditions. Barium is compatible in the carbonatite melt and mica with respect to the brine, with a partition coefficient between carbonatite melt and mica of ≈ 2.5–3. The garnet fringe formed from incongruent reaction of the former inclusion assemblage due to reversing the fluid(s)-host garnet reaction that occurred upon natural cooling/decompression. Loss of H2 or H2O from the inclusions due to volume diffusion through garnet and/or decrepitation, during geological timeframes upon decompression/cooling, may have prevented rehomogenization to a single homogeneous fluid. Our study shows that great care is needed in the interpretation of multiphase solid inclusions present in ultrahigh pressure rocks.
Sleep duration and quality in permanent night work: an observational field study
Authors: Nabe-Nielsen Kirsten , Larsen Ann Dyreborg , Arup Anne Emily Saunte Fiehn , Sallerup Mette , Schlünssen Vivi , Hansen Åse Marie , Garde Anne Helene 
Published: June 2024

Abstract
Purpose
Permanent night workers conceivably show better adaptation of circadian rhythms to night work than shift workers and therefore better possibilities of obtaining sufficient sleep of good quality after night shifts. We investigated the effect of night shifts including number of consecutive shifts on sleep among self-selected permanent night workers, and studied if the effect of night shifts differed between morning and evening types and compared with 3-shift workers.

Methods
The study population included 90 permanent night workers followed for 14 days (warehouse workers, 1228 observation days, 80% males). For comparison, we included 70 3-shift workers followed for 26 days (police officers, 1774 observation days, 100% men). Total sleep time (TST), primary sleep duration (PSD), and sleep efficiency were assessed by actigraphy. Measures of sleep quality and diurnal type were self-reported.

Results
Among permanent night workers, TST, PSD, difficulties falling asleep, disturbed sleep, and the number of awakenings decreased after night shifts compared with days without night work. Sleep efficiency, difficulties awakening, and non-refreshing sleep increased. More consecutive night shifts were associated with shorter TST and PSD. Sleep outcomes did not differ by diurnal type. Permanent night workers had fewer awakenings after night shifts than 3-shift workers, but no other differences were observed.

Conclusion
This study does not provide evidence that supports recommendation of permanent night work to reduce adverse effects of night shifts on sleep. A limited number of consecutive night shifts is recommended to reduce accumulation of sleep debt.
Computational Modeling of Tool-Rock Frictional Contact With Anisotropic Damage
Authors: Zhou Yaneng , Voyiadjis George Z. 
Published: June 2024

AbstractThe mechanism of frictional contact is investigated between a blunt tool and quasi-brittle rocks with anisotropic damage. A recently developed anisotropic elasto-plastic-damage model is further validated using experimental results under monotonic and cyclic loadings for rocks and concrete. A finite element model of tool-rock frictional contact is validated by analytical results in the two asymptotic regimes of an elastoplastic rock. The mesh sensitivity is reduced using a fracture energy-based method for anisotropic damage. The tool-rock frictional contact is predominantly controlled by three dimensionless parameters: $$\eta$$
η
, $$\xi$$
ξ
, and $$\zeta$$
ζ
, which characterize elastoplasticity, brittleness, and anisotropic damage, respectively. The newly introduced damage coefficient $$\zeta$$
ζ
controls the ratio of damage in different directions. As the elastoplastic parameter $$\eta$$
η
increases with more plastic deformation, the dimensionless average contact stress $${\widetilde{\Pi }}$$

Π
~

increases and then slightly varies before stabilizing. As the brittleness number $$\xi$$
ξ
increases in a more brittle mode, the contact stress $${\widetilde{\Pi }}$$

Π
~

generally decreases. When the damage coefficient $$\zeta$$
ζ
decreases from isotropic to anisotropic damage, the contact stress $${\widetilde{\Pi }}$$

Π
~

generally increases. The magnitudes of the average contact stress in numerical modeling are closer to experimental results when considering anisotropic damage.
Neurosurgery and the glymphatic system
Authors: Eide Per Kristian 
Published: June 2024

Abstract
The discovery of the glymphatic system has fundamentally altered our comprehension of cerebrospinal fluid transport and the removal of waste from brain metabolism. In the past decade, since its initial characterization, research on the glymphatic system has surged exponentially. Its potential implications for central nervous system disorders have sparked significant interest in the field of neurosurgery. Nonetheless, ongoing discussions and debates persist regarding the concept of the glymphatic system, and our current understanding largely relies on findings from experimental animal studies. This review aims to address several key inquiries: What methodologies exist for evaluating glymphatic function in humans today? What is the current evidence supporting the existence of a human glymphatic system? Can the glymphatic system be considered distinct from the meningeal-lymphatic system? What is the human evidence for glymphatic-meningeal lymphatic system failure in neurosurgical diseases? Existing literature indicates a paucity of techniques available for assessing glymphatic function in humans. Thus far, intrathecal contrast-enhanced magnetic resonance imaging (MRI) has shown the most promising results and have provided evidence for the presence of a glymphatic system in humans, albeit with limitations. It is, however, essential to recognize the interconnection between the glymphatic and meningeal lymphatic systems, as they operate in tandem. There are some human studies demonstrating deteriorations in glymphatic function associated with neurosurgical disorders, enriching our understanding of their pathophysiology. However, the translation of this knowledge into clinical practice is hindered by the constraints of current glymphatic imaging modalities.
MMG 3DOF model identification with uncertainty of observation and hydrodynamic maneuvering coefficients using MCMC method
Authors: Mitsuyuki Taiga , Kuribayashi Kouki , Fernandez Ricardo Francisco Suarez , Shimozawa Hyuga , Kakuta Ryo , Niki Ryosuke , Matsushita Rintaro 
Published: June 2024

AbstractThe trajectory prediction using ship maneuverability mathematical models is one of the essential technologies implemented in autonomous surface ship. Several ship maneuverability mathematical models and each one with a particular hydrodynamic coefficient approximation using towing tank tests are existed. However, it is presented difficult to directly inverse estimate the hydrodynamic maneuvering coefficients of a ship maneuverability mathematical model from operational data consisting of ship trajectory and maneuvering operation records. This paper proposed a method for estimating the hydrodynamic maneuvering coefficients of the MMG 3DOF model using three types of time-series ship motions (surge, sway, and yaw velocity) as observed data. In the assumption of this paper, there is uncertainty in observations and the hydrodynamic maneuvering coefficients of the MMG 3DOF model. The proposed method outputs samples of the simultaneous posterior probability distribution of the hydrodynamic maneuvering coefficients by the MCMC method using the observed data and stochastic model. A robust trajectory with a wide range can be presented by conducting ship maneuvering simulations using these samples. To verify the feasibility of the proposed method, this paper conducted observation system simulation experiments (OSSE) using the KVLCC2 L7 model and applied the proposed method to several free-running model ship tests. Results showed that on the assumption that MMG 3DOF model can explain the ship's state and trajectory in real world, the proposed method can estimate the ship hydrodynamic maneuvering coefficients of the MMG 3DOF model corresponding to the observed ship trajectory and control data including the error of observed data.
Evaluation of vibration behavior at different sensing positions on gearboxes
Authors: Knoll Erich , Rothemund Markus , Otto Michael , Rupprecht Bernhard , Ochs Matthias , Vogel-Heuser Birgit , Brederlow Ralf , Stahl Karsten 
Published: June 2024

AbstractMonitoring systems for rolling contacts in machinery are widespread. There are multiple solutions in various applications like Health Usage Monitoring Systems (HUMS) for Helicopters and smart bearings from different manufacturers. The number of solutions and possibilities is rising. This paper outlines how the vibrational behavior depends on the measuring position outside and inside the gearbox. The effect of the sensor location is evaluated by examining different locations close to the main excitation of gearboxes—the loaded tooth contact—as well as positions on the gearbox housing. Based on the results, recommendations for an optimal sensor position for gear vibration monitoring are given by considering the efforts of sensor integration. The results can be used to select sensors with the lowest cost to support the widespread gear vibration monitoring.Important locations for health monitoring are the gear meshes inside the gearbox. Due to the requirement of high power density, a compact design is mandatory. This does not allow for sensor application close to the gear meshes but only on the outside of the gearbox. An impediment of signal quality and less chance of early damage detection is the consequence. Hence, efforts have been made to directly integrate multiple sensors inside the gear body without negatively impacting the package.For the investigation of the vibration transmission behavior, an FZG gear test rig is selected. These standardized test rigs are approved and established all around the world for the creation and evaluation of various gear failure data. The presented test rig has been modified to be able to sense vibration in the gear wheel, at the gear wheel, and on the gearbox housing to evaluate different sensor positions and vibration sensors for their suitability on vibration measurement.
Evaluating learned feature aggregators for writer retrieval
Authors: Mattick Alexander , Mayr Martin , Seuret Mathias , Kordon Florian , Wu Fei , Christlein Vincent 
Published: June 2024

AbstractTransformers have emerged as the leading methods in natural language processing, computer vision, and multi-modal applications due to their ability to capture complex relationships and dependencies in data. In this study, we explore the potential of transformers as feature aggregators in the context of patch-based writer retrieval, with the objective of improving the quality of writer retrieval by effectively summarizing the relevant features from image patches. Our investigation underscores the complexity of leveraging transformers as feature aggregators in patch-based writer retrieval. While we have experimented with various model configurations, augmentations, and learning objectives, the performance of transformers in this task has room for improvement. This observation highlights the challenges in this domain and emphasizes the need for further research to enhance their effectiveness. By shedding light on the limitations of transformers in this context, our study contributes to the growing body of knowledge in the field of writer retrieval and provides valuable insights for future research and development in this area.
Perspective matters: a systematic review of immersive virtual reality to reduce racial prejudice
Authors: Higgins Sarah , Alcock Stephanie , Aveiro Bianca De , Daniels William , Farmer Harry , Besharati Sahba 
Published: June 2024

AbstractIn the wake of the COVID-19 pandemic and the rise of social justice movements, increased attention has been directed to levels of intergroup tension worldwide. Racial prejudice is one such tension that permeates societies and creates distinct inequalities at all levels of our social ecosystem. Whether these prejudices are present explicitly (directly or consciously) or implicitly (unconsciously or automatically), manipulating body ownership by embodying an avatar of another race using immersive virtual reality (IVR) presents a promising approach to reducing racial bias. Nevertheless, research findings are contradictory, which is possibly attributed to variances in methodological factors across studies. This systematic review, therefore, aimed to identify variables and methodological variations that may underlie the observed discrepancies in study outcomes. Adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this systematic review encompassed 12 studies that employed IVR and embodiment techniques to investigate racial attitudes. Subsequently, two mini meta-analyses were performed on four and five of these studies, respectively — both of which utilised the Implicit Association Test (IAT) as a metric to gauge these biases. This review demonstrated that IVR allows not only the manipulation of a sense of body ownership but also the investigation of wider social identities. Despite the novelty of IVR as a tool to help understand and possibly reduce racial bias, our review has identified key limitations in the existing literature. Specifically, we found inconsistencies in the measures and IVR equipment and software employed, as well as diversity limitations in demographic characteristics within both the sampled population and the embodiment of avatars. Future studies are needed to address these critical shortcomings. Specific recommendations are suggested, these include: (1) enhancing participant diversity in terms of the sample representation and by integrating ethnically diverse avatars; (2) employing multi-modal methods in assessing embodiment; (3) increasing consistency in the use and administration of implicit and explicit measures of racial prejudice; and (4) implementing consistent approaches in using IVR hardware and software to enhance the realism of the IVR experience.
Quantitative MRI methods for the assessment of structure, composition, and function of musculoskeletal tissues in basic research and preclinical applications
Authors: Casula Victor , Kajabi Abdul Wahed 
Published: June 2024

AbstractOsteoarthritis (OA) is a disabling chronic disease involving the gradual degradation of joint structures causing pain and dysfunction. Magnetic resonance imaging (MRI) has been widely used as a non-invasive tool for assessing OA-related changes. While anatomical MRI is limited to the morphological assessment of the joint structures, quantitative MRI (qMRI) allows for the measurement of biophysical properties of the tissues at the molecular level. Quantitative MRI techniques have been employed to characterize tissues’ structural integrity, biochemical content, and mechanical properties. Their applications extend to studying degenerative alterations, early OA detection, and evaluating therapeutic intervention. This article is a review of qMRI techniques for musculoskeletal tissue evaluation, with a particular emphasis on articular cartilage. The goal is to describe the underlying mechanism and primary limitations of the qMRI parameters, their association with the tissue physiological properties and their potential in detecting tissue degeneration leading to the development of OA with a primary focus on basic and preclinical research studies. Additionally, the review highlights some clinical applications of qMRI, discussing the role of texture-based radiomics and machine learning in advancing OA research.
A multifaceted survey on privacy preservation of federated learning: progress, challenges, and opportunities
Authors: Saha Sanchita , Hota Ashlesha , Chattopadhyay Arup Kumar , Nag Amitava , Nandi Sukumar 
Published: June 2024

AbstractFederated learning (FL) refers to a system of training and stabilizing local machine learning models at the global level by aggregating the learning gradients of the models. It reduces the concern of sharing the private data of participating entities for statistical analysis to be carried out at the server. It allows participating entities called clients or users to infer useful information from their raw data. As a consequence, the need to share their confidential information with any other entity or the central entity called server is eliminated. FL can be clearly interpreted as a privacy-preserving version of traditional machine learning and deep learning algorithms. However, despite this being an efficient distributed training scheme, the client’s sensitive information can still be exposed to various security threats from the shared parameters. Since data has always been a major priority for any user or organization, this article is primarily concerned with discussing the significant problems and issues relevant to the preservation of data privacy and the viability and feasibility of several proposed solutions in the FL context. In this work, we conduct a detailed study on FL, the categorization of FL, the challenges of FL, and various attacks that can be executed to disclose the users’ sensitive data used during learning. In this survey, we review and compare different privacy solutions for FL to prevent data leakage and discuss secret sharing (SS)-based security solutions for FL proposed by various researchers in concise form. We also briefly discuss quantum federated learning (QFL) and privacy-preservation techniques in QFL. In addition to these, a comparison and contrast of several survey works on FL is included in this work. We highlight the major applications based on FL. We discuss certain future directions pertaining to the open issues in the field of FL and finally conclude our work.
Software advancements in automatic epilepsy diagnosis and seizure detection: 10-year review
Authors: Handa Palak , Lavanya  , Goel Nidhi , Garg Neeta 
Published: June 2024

AbstractEpilepsy is a chronic neurological disorder that may be diagnosed and monitored using routine diagnostic tests like Electroencephalography (EEG). However, manual introspection and analysis of EEG signals is presently difficult and repetitive task even for experienced neuro-technologists with high false-positive rates and inter- and intra-rater reliability. Software advancements using Artificial Intelligence (AI) algorithms have the potential to early detect and predict abnormal patterns observed in EEG signals. The present review focuses on systematically reporting software advancements and their implementation using hardware systems in automatic epilepsy diagnosis and seizure detection for the past 10 years. Traditional, hybrid, and end-to-end AI-based pipelines and associated EEG datasets have been discussed. The review summarizes and compares reported articles, datasets, and patents through various subjective and objective parameters in this field. Latest advancements demonstrate that AI-based pipelines can reduce the introspection time by at least 50% without compromising the diagnostic accuracy or abnormal event detection. A significant rise in hardware implementation of software-based pipelines, end-to-end deep learning architectures for real-time analysis, and granted patents has been noticed since 2011. More than twenty-eight datasets have been developed to automatically diagnose epileptic EEG signals from 2001 to 2023. Extensive analysis using explainability tools, cross-dataset generalizations, reproducibility analysis, and ablation experiments can further improve the existing AI-based pipelines in this field. There is a need for the development of standardized protocols for data collection and its AI pipeline for a robust, inter- and intra-rater reliability-free, and real-time automatic epilepsy diagnosis.
Trustworthy cyber-physical power systems using AI: dueling algorithms for PMU anomaly detection and cybersecurity
Authors: Cali Umit , Catak Ferhat Ozgur , Halden Ugur 
Published: June 2024

AbstractEnergy systems require radical changes due to the conflicting needs of combating climate change and meeting rising energy demands. These revolutionary decentralization, decarbonization, and digitalization techniques have ushered in a new global energy paradigm. Waves of disruption have been felt across the electricity industry as the digitalization journey in this sector has converged with advances in artificial intelligence (AI). However, there are risks involved. As AI becomes more established, new security threats have emerged. Among the most important is the cyber-physical protection of critical infrastructure, such as the power grid. This article focuses on dueling AI algorithms designed to investigate the trustworthiness of power systems’ cyber-physical security under various scenarios using the phasor measurement units (PMU) use case. Particularly in PMU operations, the focus is on areas that manage sensitive data vital to power system operators’ activities. The initial stage deals with anomaly detection applied to energy systems and PMUs, while the subsequent stage examines adversarial attacks targeting AI models. At this stage, evaluations of the Madry attack, basic iterative method (BIM), momentum iterative method (MIM), and projected gradient descend (PGD) are carried out, which are all powerful adversarial techniques that may compromise anomaly detection methods. The final stage addresses mitigation methods for AI-based cyberattacks. All these three stages represent various uses of AI and constitute the dueling AI algorithm convention that is conceptualised and demonstrated in this work. According to the findings of this study, it is essential to investigate the trade-off between the accuracy of AI-based anomaly detection models and their digital immutability against potential cyberphysical attacks in terms of trustworthiness for the critical infrastructure under consideration.