bluuebunny/crossref_metadata_2025_split huggingface dataset:
The effect of the viscoelastic support and GRPL-reinforced foam material on the thermomechanical vibration response of piezomagnetic sandwich nanosensor plates
Authors: Eroğlu Mustafa , Esen İsmail , Koç Mehmet Akif 
Published: January 2024

AbstractThis paper investigates the vibration characteristics of a sandwich nanosensor plate composed of piezoelectric materials, specifically barium and cobalt, in the upper and lower layers, and a core material consisting of either ceramic (silicon nitride) or metal (stainless steel) foams reinforced with graphene (GPRL). The study utilized the novel sinosoidal higher-order deformation theory and nonlocal strain gradient elasticity theory. The equations of motion for nanosensor sandwich graphene were derived using Hamilton's principle, considering the thermal, electroelastic, and magnetostrictive characteristics of the piezomagnetic surface plates. These equations were then solved using the Navier method. The core element of the sandwich nanosensor plate can be represented using three distinct foam variants: a uniform foam model, as well as two symmetric foam models. The investigation focused on analyzing the dimensionless fundamental natural frequencies of the sandwich nanosensor plate. This analysis considered the influence of three distinct foam types, the volumetric graphene ratio, temperature variation, nonlocal parameters, porosity ratio, electric and magnetic potential, as well as spring and shear viscoelastic support. Furthermore, an analysis was conducted on the impact of the metal and ceramic composition of the central section of the sandwich nanosensor plate on its dimensionless fundamental natural frequencies. In this context, the use of ceramic as the central material results in a mean enhancement of 33% in the fundamental natural frequencies. In contrast, the incorporation of graphene into the core material results in an average enhancement of 27%. The thermomechanical vibration behavior of the nanosensor plate reveals that the presence of graphene-supported foam and a viscoelastic support structure in the core layer leads to an increase in thermal resistance. This increase is dependent on factors such as the ratio of graphene, porosity ratio of the foam, and parameters of the viscoelastic support. Metal foam or ceramic foam has been found to enhance thermal resistance when compared to solid metal or ceramic core materials. The analysis results showed that it is important to take into account the temperature-dependent thermal properties of barium and cobalt, which are piezo-electromagnetic materials, and the core layer materials ceramics and metal, as well as the graphene used to strengthen the core. The research is anticipated to generate valuable findings regarding the advancement and utilization of nanosensors, transducers, and nano-electromechanical systems engineered for operation in high-temperature environments.
Effect of optical flow and user VR familiarity on curvature gain thresholds for redirected walking
Authors: Lee Jieun , Hwang Seokhyun , Ataya Aya , Kim SeungJun 
Published: January 2024

AbstractVirtual reality (VR) locomotion should allow users to move freely in the virtual space while staying within the tracking area in the real space. The redirected walking (RDW) technique enables users to walk naturally in an unlimited virtual space within a limited tracking area by rotating the virtual scene view. However, conflicting visual and vestibular signals during RDW can lead to user discomfort and decreased immersion. To avoid user discomfort, an RDW gain should be within the detection threshold (DT) range. However, a large angle of walking redirection is required when physically avoiding obstacles or escaping from a narrow space, so DT expansion is necessary. In this study, to change the curvature DT range and enhance RDW performance, we proposed an optical flow (OF)-generating vection in a virtual environment. Further, we investigate methods to reduce user discomfort and increase RDW efficiency considering familiar and unfamiliar VR users. The findings showed that the introduction of OF led to a reduction in the DT range for all users, irrespective of the OF’s direction. However, conditions with OF resulted in an extended DT range for users familiar with VR while concurrently diminishing the DT range for those who were VR unfamiliar. To delve further, our analysis indicated that when both the OF and redirecting directions were identical, the RDW performance was robust to VR familiarity, whereas in opposing directions, the DT range increased for VR-familiar users. Our study findings suggested using OF for the RDW technique and extending its applicability in virtual environments.
Review of the Free Research Software for Computer-Assisted Interventions
Authors: Amla Zaiba , Khehra Parminder Singh , Mathialagan Ashley , Lugez Elodie 
Published: January 2024

AbstractResearch software is continuously developed to facilitate progress and innovation in the medical field. Over time, numerous research software programs have been created, making it challenging to keep abreast of what is available. This work aims to evaluate the most frequently utilized software by the computer-assisted intervention (CAI) research community. The software assessments encompass a range of criteria, including load time, stress load, multi-tasking, extensibility and range of functionalities, user-friendliness, documentation, and technical support. A total of eight software programs were selected: 3D Slicer, Elastix, ITK-SNAP, MedInria, MeVisLab, MIPAV, and Seg3D. While none of the software was found to be perfect on all evaluation criteria, 3D Slicer and ITK-SNAP emerged with the highest rankings overall. These two software programs could frequently complement each other, as 3D Slicer has a broad and customizable range of features, while ITK-SNAP excels at performing fundamental tasks in an efficient manner. Nonetheless, each software had distinctive features that may better fit the requirements of certain research projects. This review provides valuable information to CAI researchers seeking the best-suited software to support their projects. The evaluation also offers insights for the software development teams, as it highlights areas where the software can be improved.
Exploring the use of metacognitive monitoring cues following a diagram completion intervention
Authors: Noushad Babu , Van Gerven Pascal W. M. , de Bruin Anique B. H. 
Published: January 2024

AbstractStudying texts constitutes a significant part of student learning in health professions education. Key to learning from text is the ability to effectively monitor one’s own cognitive performance and take appropriate regulatory steps for improvement. Inferential cues generated during a learning experience typically guide this monitoring process. It has been shown that interventions to assist learners in using comprehension cues improve their monitoring accuracy. One such intervention is having learners to complete a diagram. Little is known, however, about how learners use cues to shape their monitoring judgments. In addition, previous research has not examined the difference in cue use between categories of learners, such as good and poor monitors. This study explored the types and patterns of cues used by participants after being subjected to a diagram completion task prior to their prediction of performance (PoP). Participants’ thought processes were studied by means of a think-aloud method during diagram completion and the subsequent PoP. Results suggest that relying on comprehension-specific cues may lead to a better PoP. Poor monitors relied on multiple cue types and failed to use available cues appropriately. They gave more incorrect responses and made commission errors in the diagram, which likely led to their overconfidence. Good monitors, on the other hand, utilized cues that are predictive of learning from the diagram completion task and seemed to have relied on comprehension cues for their PoP. However, they tended to be cautious in their judgement, which probably made them underestimate themselves. These observations contribute to the current understanding of the use and effectiveness of diagram completion as a cue-prompt intervention and provide direction for future research in enhancing monitoring accuracy.
Exploring data mining and machine learning in gynecologic oncology
Authors: Idlahcen Ferdaous , Idri Ali , Goceri Evgin 
Published: January 2024

AbstractGynecologic (GYN) malignancies are gaining new and much-needed attention, perpetually fueling literature. Intra-/inter-tumor heterogeneity and “frightened” global distribution by race, ethnicity, and human development index, are pivotal clues to such ubiquitous interest. To advance “precision medicine” and downplay the heavy burden, data mining (DM) is timely in clinical GYN oncology. No consolidated work has been conducted to examine the depth and breadth of DM applicability as an adjunct to GYN oncology, emphasizing machine learning (ML)-based schemes. This systematic literature review (SLR) synthesizes evidence to fill knowledge gaps, flaws, and limitations. We report this SLR in compliance with Kitchenham and Charters’ guidelines. Defined research questions and PICO crafted a search string across five libraries: PubMed, IEEE Xplore, ScienceDirect, SpringerLink, and Google Scholar—over the past decade. Of the 3499 potential records, 181 primary studies were eligible for in-depth analysis. A spike (60.53%) corollary to cervical neoplasms is denoted onward 2019, predominantly featuring empirical solution proposals drawn from cohorts. Medical records led (23.77%, 53 art.). DM-ML in use is primarily built on neural networks (127 art.), appoint classification (73.19%, 172 art.) and diagnoses (42%, 111 art.), all devoted to assessment. Summarized evidence is sufficient to guide and support the clinical utility of DM schemes in GYN oncology. Gaps persist, inculpating the interoperability of single-institute scrutiny. Cross-cohort generalizability is needed to establish evidence while avoiding outcome reporting bias to locally, site-specific trained models. This SLR is exempt from ethics approval as it entails published articles.
A hybrid method to solve reliability-cost-oriented bi-objective machine configuration problem for a flow shop system
Authors: Yeh Cheng-Ta , Yeng Louis Cheng-Lu , Lin Yi-Kuei , Chao Yu-Lun 
Published: January 2024

AbstractMachine configuration is a crucial strategic decision in designing a flow shop system (FSS) and directly affects its performance. This involves selecting device suppliers and determining the number of machines to be configured. This study addresses a bi-objective optimization problem for an FSS that considers repair actions and aims to determine the most suitable machine configuration that balances the production reliability and purchase cost. A nondominated sorting genetic algorithm II (NSGA-II) is used to determine all the Pareto solutions. The technique for order preference by similarity to an ideal solution is then used to identify a compromise alternative. It is necessary to assess the production reliability of any machine configuration identified by the NSGA-II. The FSS under the machine configuration is modeled as a multistate flow shop network, and Absorbing Markov Chain and Recursive Sum of Disjoint Products are integrated into the NSGA-II for reliability evaluation. The experimental results of solar cell manufacturing demonstrate the applicability of the proposed hybrid method and validate the efficiency of the NSGA-II compared with an improved strength Pareto evolutionary algorithm.
Exploring the Experiences of Compassion Fatigue Amongst Peer Support Workers in Homelessness Services
Authors: Steenekamp Bronwyn Leigh , Barker Stephanie L. 
Published: January 2024

AbstractPeer support workers have lived experiences of the challenges their clients face. While research has shown peer work can benefit recovery, the negative consequences have not been clearly addressed. This study aimed to explore the experiences of compassion fatigue amongst peer support workers in homelessness services, and the coping strategies used. An explorative qualitative design was adopted. Six peer support workers, in homelessness services, were recruited via snowball sampling. Data was collected using semi-structured interviews and analysed using thematic analysis. Five themes were identified: relentless nature of working in homelessness services, change, making meaning of past experiences, organisational support, and personal coping strategies. There were two novel findings: (1) multiple clients recounting traumatic experiences, and (2) being unfairly blamed for lack of progress, exacerbating compassion fatigue. The findings of this study furthers limited research on compassion fatigue and can be used to develop protocols and practices for organisations that utilise peer support.
A Mechanical Theory of Growth
Authors: Chen Yi-chao 
Published: January 2024

AbstractA theory of growth is developed, utilizing the notion of a directional density function that captures the number and distribution of the material particles and their changes in time. A spatial (or Eulerian) description of kinematics is adopted, and the constitutive theory for a growing body is developed that relates the stress to the directional density function. The equation that governs the evolution of the directional density function is derived. An example of internal surface growth is presented.
Synbit: synthesizing bidirectional programs using unidirectional sketches
Authors: Yamaguchi Masaomi , Matsuda Kazutaka , David Cristina , Wang Meng 
Published: January 2024

AbstractWe propose a technique for synthesizing bidirectional programs from the corresponding unidirectional code plus input/output examples. The core ideas are: (1) constructing a sketch using the given unidirectional program as a specification, and (2) filling the sketch in a modular fashion by exploiting the properties of bidirectional programs. These ideas are enabled by our choice of programming language, HOBiT, which is specifically designed to maintain the unidirectional program structure in bidirectional programming, and keep the parts that control bidirectional behavior modular. To evaluate our approach, we implemented it in a tool called Synbit and used it to generate bidirectional programs for intricate microbenchmarks, as well as for a few larger, more realistic problems. We also compared Synbit to a state-of-the-art unidirectional synthesis tool on the task of synthesizing backward computations. This is an extended version of the paper “Synbit: Synthesizing Bidirectional Programs using Unidirectional Sketches”, published at OOPSLA 2021. In addition to the OOPSLA’21 paper, this journal will contain additional formalization and detailed examples.
Mass spectrometry developments of 232Th and 238U radiopurity measurements for LEGEND
Authors: Kaizer Jakub , Bujdoš Marek , Buompane Raffaele , Dulanská Silvia , Ješkovský Miroslav , Kontuľ Ivan , Nisi Stefano , Povinec Pavel P. , Sirignano Carmina , Terrasi Filippo , Zeman Jakub 
Published: January 2024

AbstractThe LEGEND collaboration has been developing a 76Ge-based double-beta decay experimental program where precise radiopurity measurements of ultraclean materials are crucial. Ultralow concentrations of thorium and uranium, the main contributors to the detector background via their decay products, can be determined by inductively coupled plasma mass spectrometry (ICPMS) and accelerator mass spectrometry (AMS). Here we shall present recent developments in thorium and uranium mass spectrometry methods, together with basics of separation chemistry applied to process different samples. The new possibilities to measure 232Th and 238U by ICPMS and AMS at the Comenius University in Bratislava are discussed as well.
An architectural framework of elderly healthcare monitoring and tracking through wearable sensor technologies
Authors: Alsadoon Abeer , Al-Naymat Ghazi , Jerew Oday D. 
Published: January 2024

AbstractThe growing elderly population in smart home environments necessitates increased remote medical support and frequent doctor visits. To address this need, wearable sensor technology plays a crucial role in designing effective healthcare systems for the elderly, facilitating human–machine interaction. However, wearable technology has not been implemented accurately in monitoring various vital healthcare parameters of elders because of inaccurate monitoring. In addition, healthcare providers encounter issues regarding the acceptability of healthcare parameter monitoring and secure data communication within the context of elderly care in smart home environments. Therefore, this research is dedicated to investigating the accuracy of wearable sensors in monitoring healthcare parameters and ensuring secure data transmission. An architectural framework is introduced, outlining the critical components of a comprehensive system, including Sensing, Data storage, and Data communication (SDD) for the monitoring process. These vital components highlight the system's functionality and introduce elements for monitoring and tracking various healthcare parameters through wearable sensors. The collected data is subsequently communicated to healthcare providers to enhance the well-being of elderly individuals. The SDD taxonomy guides the implementation of wearable sensor technology through environmental and body sensors. The proposed system demonstrates the accuracy enhancement of healthcare parameter monitoring and tracking through smart sensors. This study evaluates state-of-the-art articles on monitoring and tracking healthcare parameters through wearable sensors. In conclusion, this study underscores the importance of delineating the SSD taxonomy by classifying the system's major components, contributing to the analysis and resolution of existing challenges. It emphasizes the efficiency of remote monitoring techniques in enhancing healthcare services for the elderly in smart home environments.
Crop index insurance as a tool for climate resilience: lessons from smallholder farmers in Nigeria
Authors: Aina Ifedotun , Ayinde Opeyemi , Thiam Djiby , Miranda Mario 
Published: January 2024

AbstractCrop farmers in arid and semi-arid regions face a covariate risk that is of paramount importance, namely, the threat of catastrophic crop loss triggered by natural disasters and climate hazards. Such an occurrence not only jeopardizes the livelihoods of these farmers but may also lead to persistent poverty. Promoting sustainable development requires control over climate-related shocks, which negatively affect the most vulnerable population in the developing world. This study investigates the option of choosing crop area-yield index insurance to mitigate the adverse effects associated with climate stress. In addition, we assessed the amount farmers are willing to pay for an insurance product that insures yields below 70% of the local average in the study area. Our results show that access to extension services and economic association membership offers farmers social capital and encourages their decision to purchase index insurance. We also find that the long-run welfare impact of index insurance policy on vulnerable households could be significant as an alternative insurance mechanism to traditional insurance. This study contributes to filling the gaps on the uptake of index insurance and provide guidance to policymakers in their approach to mitigating the effects of climate change on crop production in Nigeria.
Drivers of sustainable tourism in Europe: how to design efficient business strategies
Authors: Bassi Francesca , Martín Juan Antonio Marmolejo 
Published: January 2024

AbstractThis article studies the willingness of the citizens of the 27 EU countries to change their travel and tourism habits to assume a more sustainable behavior. The study wants to contribute to the recent literature on the topic of interconnections between tourism and sustainability. The data comes from the Flash Eurobarometer survey 499, involving more than 25,000 European citizens. The survey took place in October 2021 and wanted to analyze travel behavior and the impact of the Covid-19 pandemic on it, booking channels and information sources for travel preparation, reasons for selecting destinations, options and information on sustainable tourism. The hierarchical structure of the data—citizens within countries—is assumed applying a multilevel approach of analysis that considers heterogeneity between and within countries. The estimation of the multilevel latent class model allowed to identify seven groups of European citizens similar by their willingness to adopt tourism-related sustainability practices, and the association of these latent groups with the 27 European countries. Using sociodemographic variables, it was also possible to profile these groups as well as to describe the typical citizen belonging to each cluster. Moreover, drivers of sustainable tourism are identified, both at country and citizen level. The results of the analyses give many useful information for strategic management in the tourism sector.
Predictive tissue markers in testicular germ cell tumors: Immunohistochemical expression of MLH1 and REV-7 proteins
Authors: Spinos Theodoros , Zografos Eleni , Koutsoukos Konstantinos , Zagouri Flora , Kosmas Christos , Driva Tatiana S. , Goutas Dimitrios , Gakiopoulou Charikleia , Agrogiannis George , Theochari Eirini , Tzavara Chara , Lazaris Andreas C. 
Published: January 2024

Abstract
Purpose
Testicular Germ Cell Tumors (TGCTs) are the most frequent solid malignancies in young adult men. Regardless of differences in their cell of origin, all TGCTs are considered highly curable malignancies. However, approximately 3–5% of all TGCTs do not respond to platinum-based chemotherapies. The purpose of our paper is to investigate whether immunohistochemical expression of MLH1 and REV-7 can be used as predictive tissue markers for TGCTs.

Material and Methods
The main demographic and clinicopathological characteristics of 64 male patients with TGCTs who underwent orchiectomy from 2007 to 2022 were retrospectively obtained from two large Oncology Clinics in Greece. Both patients with chemosensitive and chemoresistant disease were included. Immunohistochemical staining for MLH1 and REV-7 proteins was applied in specimens of these patients.

Results
31 seminomas and 33 non-seminomas were included. 48 patients had chemosensitive disease, while 16 had chemoresistant disease. 53 specimens showed preserved MLH1 expression, while 11 specimens had lost MLH1 expression. Expression of MLH1 was only significantly associated with patients’ age. 16 specimens showed positive REV-7 expression, while 48 specimens were REV-7 negative. Interestingly, 50% of patients with chemoresistant disease and 16,7% of patients with chemosensitive disease were REV-7 positive. This difference was statistically significant. Moreover, REV-7 positivity was significantly associated with chemoresistance, various clinicopathological parameters and patients’ prognosis and survival.

Conclusion
Loss of MLH1 expression was only found to be significantly associated with lower patients’ age. Positive immunohistochemical REV-7 expression was significantly associated with various clinicopathological parameters, while it was also associated with significantly lower survival and greater hazard. REV-7 positive percentages were significantly higher in patients with chemoresistant disease. Our findings imply that immunohistochemical staining for REV-7 could potentially be used as a predictive tissue marker for TGCT tumors. Moreover, targeting of REV-7 protein, could represent a potential therapeutic strategy for chemoresistant TGCT cases. The implementation of well-designed studies on a larger scale is of utmost importance, in order to draw safer conclusions. Additional studies are needed so as to draw safer conclusions.