bluuebunny/crossref_metadata_2025_split huggingface dataset:
Machine learning classification of permeable conducting spheres in air and seawater using electromagnetic pulses
Authors: Thomas Ryan , Salmon Brian , Holloway Damien , Olivier Jan 
Published: July 2024

Abstract
This paper presents machine learning classification on simulated data of permeable conducting spheres in air and seawater irradiated by low frequency electromagnetic pulses. Classification accuracy greater than 90% was achieved. The simulated data were generated using an analytical model of a magnetic dipole in air and seawater placed 1.5–3.5 m above the center of the sphere in 50 cm increments. The spheres had radii of 40 cm and 50 cm and were of permeable materials, such as steel, and non-permeable materials, such as aluminum. A series RL circuit was analytically modeled as the transmitter coil, and an RLC circuit as the receiver coil. Additive white Gaussian noise was added to the simulated data to test the robustness of the machine learning algorithms to noise. Multiple machine learning algorithms were used for classification including a perceptron and multiclass logistic regression, which are linear models, and a neural network, 1D convolutional neural network (CNN), and 2D CNN, which are nonlinear models. Feature maps are plotted for the CNNs and provide explainability of the salient parts of the time signature and spectrogram data used for classification. The pulses investigated, which expand the literature, include a two-sided decaying exponential, Heaviside step-off, triangular, Gaussian, rectangular, modulated Gaussian, raised cosine, and rectangular down-chirp. Propagation effects, including dispersion and frequency dependent attenuation, are encapsulated by the analytical model, which was verified using finite element modeling. The results in this paper show that machine learning methods are a viable alternative to inversion of electromagnetic induction (EMI) data for metallic sphere classification, with the advantage of real-time classification without the use of a physics-based model. The nonlinear machine learning algorithms used in this work were able to accurately classify metallic spheres in seawater even with significant pulse distortion caused by dispersion and frequency dependent attenuation. This paper presents the first effort towards the use of machine learning to classify metallic objects in seawater based on EMI sensing.
Phonon transport in vacancy induced defective stanene/hBN van der Waals heterostructure
Authors: Hassan Mehady , Das Priom , Paul Plabon , Morshed AKM Monjur , Paul Titan C 
Published: July 2024

Abstract
In this study, Non-Equilibrium Molecular Dynamics (NEMD) simulation is employed to investigate the phonon thermal conductivity (PTC) of Sn/hBN van der Waals heterostructures with different vacancy-induced defects. We deliberately introduce three types of vacancies in Sn/hBN bilayer point vacancies, bivacancies, and edge vacancies at various concentrations ranging from 0.25% to 2%, to examine their effects on PTC across temperatures from 100 K to 600 K. The key findings of our work are (i) PTC declines monotonically with increasing vacancy concentration for all types of vacancies, with a maximum reduction of ∼62% observed at room temperature compared to its pristine form. (ii) The position of defects has an impact on PTC, with a larger decrease observed when defects are present in the hBN layer and a smaller decrease when defects are in the Sn layer. (iii) The type of vacancy also influences PTC, with point vacancies causing the most substantial reduction, followed by bivacancies, and edge vacancies having the least effect. A 2% defect concentration results in a ∼62% decrease in PTC for point vacancies, ∼51% for bivacancies, and ∼32% for edge vacancies. (iv) Finally, our results indicate that for a given defect concentration, PTC decreases as temperature increases. The impact of temperature on thermal conductivity is less pronounced compared to the effect of vacancies for the defective Sn/hBN bilayer. The presence of vacancies and elevated temperatures enhance phonon-defect and phonon–phonon scattering, leading to changes in the phonon density of states (PDOS) profile and the distribution of phonons across different frequencies of Sn/hBN bilayer, thus affecting its thermal conductivity. This work offers new insights into the thermal behavior of vacancy-filled Sn/hBN heterostructures, suggesting potential pathways for modulating thermal conductivity in bilayer van der Waals heterostructures for applications in thermoelectric, optoelectronics, and nanoelectronics in future.
Effective estimates of ergodic quantities illustrated on the Bolyai-Rényi map
Authors: Pollicott Mark , Slipantschuk Julia 
Published: July 2024

Abstract
We present a practical and effective method for rigorously estimating quantities associated to top eigenvalues of transfer operators to very high precision. The method combines explicit error bounds of the Lagrange-Chebyshev approximation with an established min-max method. We illustrate its applicability by significantly improving rigorous estimates on various ergodic quantities associated to the Bolyai–Rényi map.
Fast Monte Carlo dose calculation in proton therapy
Authors: Holmes Jason , Feng Hongying , Zhang Lian , Fix Michael K , Jiang Steve B , Liu Wei 
Published: July 2024

Abstract
This article examines the critical role of fast Monte Carlo (MC) dose calculations in advancing proton therapy techniques, particularly in the context of increasing treatment customization and precision. As adaptive radiotherapy and other patient-specific approaches evolve, the need for accurate and precise dose calculations, essential for techniques like proton-based stereotactic radiosurgery, becomes more prominent. These calculations, however, are time-intensive, with the treatment planning/optimization process constrained by the achievable speed of dose computations. Thus, enhancing the speed of MC methods is vital, as it not only facilitates the implementation of novel treatment modalities but also leads to more optimal treatment plans. Today, the state-of-the-art in MC dose calculation speeds is 106–107 protons per second. This review highlights the latest advancements in fast MC dose calculations that have led to such speeds, including emerging artificial intelligence-based techniques, and discusses their application in both current and emerging proton therapy strategies.
Generation of high-density plasma via transparent electrode in capacitively coupled plasma
Authors: Moon Ho-Jun , Jung Jiwon , Park Junyoung , Chung Chin-Wook 
Published: July 2024

Abstract
The effect of transparent capacitively coupled plasmas (TCCP) is investigated by using a transparent ITO electrode instead of the metal electrode of capacitively coupled plasmas (CCP). To compare the CCP and the TCCP, the CCP is made by coating the powered electrode with metal and the TCCP is made by coating the powered electrode with Indium Tin Oxide (ITO) on the quartz window. To compare the properties of electrodes based on ITO electrodes and metal electrodes in the same size reactor, electrode voltage, electron temperature and electron density were measured at various applied RF powers and pressures at 13.56 MHz. The electron temperature decreases overall with pressure, regardless of the electrode type. We observed that the total voltage between the powered electrode and ground is about two times higher for the TCCP than for the CCP. In addition, the electron density of the TCCP increases significantly by about two times that of the CCP. In this TCCP, the voltage applied to the sheath is calculated based on the fact that the increase in electron density is related to the voltage applied to the sheath. The calculated voltages applied to each sheath of the CCP and the TCCP agree well with our experimental results. In addition, From the calculated total power absorption per unit area, it is found that the total power absorption per unit area is significantly higher in the TCCP than in the CCP.
Development of finger movement assistive gloves with pneumatic fabric actuators
Authors: Chung Jiwon , Yoon Jeong Eun , Kim Minseo , Shin Dongjun , Koo Sumin 
Published: July 2024

Abstract
We developed flexible, lightweight, and washable gloves with actuators to assist finger movements and improve ease of wearing. Performance and wearability were measured using standardized tests, triangulation of bending angles, electromyography (EMG), and grip strength. User satisfaction was measured using a survey. EMG sensors were attached to the flexor digitorum superficialis and extensor digitorum communis to capture movement data for grasping and releasing, lifting and putting down, and opening and closing an object with (a) gloves and an actuator, (b) gloves and no actuator, and (c) no gloves. The actuator-equipped glove weighed 31.4 g—lighter than in any earlier studies. In situation (a), the average EMG values for the four participants decreased, ranging from −2.06% to −44.1%, confirming the superior performance of the gloves. Survey results revealed high levels of user satisfaction. Our study offers insights into the development of rehabilitation robotic gloves that assist muscle movements and are easy to wear.
A Diamond Ring in X-Ray: Investigating the Diffuse Emission of Supernova Remnant G28.8+1.5
Authors: Zhang Kaiyue 
Published: July 2024

Abstract
Supernova remnants are products of supernova explosions, which are usually observed in the X-ray band. However, the diffuse emission from the galactic supernova remnant G28.8+1.5 has been overlooked following initial searches for pulsars and pulsar wind nebulae. This paper presents background-subtracted imaging and spatial-resolved spectral analyses using archival XMM-Newton data to reveal unprecedented details of the diffuse emission, providing updated estimates of distance and age. For the first time, an iron overabundance is revealed and the progenitor is suggested to be 9–15 M
⊙. These findings refine our understanding of G28.8+1.5 and contribute to a broader comprehension of the Galactic supernova remnant family.
Emergence of multivariate climate change signals
Authors: King Andrew D , Harrington Luke J , Hawkins Ed , Paik Seungmok , Lieber Ruby , Min Seung-Ki , Borowiak Alexander R 
Published: July 2024

Abstract
The emergence of a climate change signal relative to background variability is a useful metric for understanding local changes and their consequences. Studies have identified emergent signals of climate change, particularly in temperature-based indices with weaker signals found for precipitation metrics. In this study, we adapt climate analogue methods to examine multivariate climate change emergence over the historical period. We use seasonal temperature and precipitation observations and apply a sigma dissimilarity method to demonstrate that large local climate changes may already be identified, particularly in low-latitude regions. The multivariate methodology brings forward the time of emergence by several decades in many areas relative to analysing temperature in isolation. We observed particularly large departures from an early-20th century climate in years when the global warming signal is compounded by an El Niño-influence. The latitudinal dependence in the emergent climate change signal means that lower-income nations have experienced earlier and stronger emergent climate change signals than the wealthiest regions. Analysis based on temperature and precipitation extreme indices finds weaker signals and less evidence of emergence but is hampered by lack of long-running observations in equatorial areas. The framework developed here may be extended to attribution and projections analyses.
Mega project impact on urban growth and ecosystem service
Authors: Zuhra Fatema Tuz , Upaul Sharfan 
Published: July 2024

Abstract
In an effort to forecast and estimate the value of ecosystem services (ESVs) and land use and land cover changes (LULC), recent global events and research have centered on the evaluation of LULC. This study evaluates the effectiveness of an integrated approach using multi-layer perceptron-Markov chain analysis (MLP-MCA) and remote sensing data for monitoring land use and land cover (LULC) changes. The study area comprises the three Bangladeshi upazilas that the Padma Bridge connects: Shariatpur, Louhajang, and Janjira. The Ecosystem Services Valuation Database (ESVD) was used to evaluate the impacts of changes in land use and land cover (LULC) on Ecosystem Service Values (ESVs). The LULC of the study area was estimated for the years 2000, 2010, and 2020, and the forecast period for urban development was 2030 and 2040. Additionally, the monetary value of ecosystem services was evaluated across the time. As per the LULC change analysis, there is a projected 4.35% growth in the built-up area from 2000 to 2020. After the bridge is constructed, the built-up area is projected to increase by 7% in 2030 and 14% in 2040, according to the forecast. The exponential expansion of developed land has a paradoxical effect on the value of ecosystem services. From 2020 to 2040, it is expected that the ESVs for provision, regulation, culture, and support will experience a reduction of approximately 213.24 million USD/ha. Even if the establishment of a bridge results in economic stability and investment attraction for the nation, unforeseen ecosystem service losses cannot contribute to long-term, sustainable development. This study, by establishing a correlation between urban area expansion and the deterioration of ecosystem services, can serve as a standard and basis for guiding management decisions.
Polarization dynamics: a study of individuals shifting between political communities on social media
Authors: Albanese Federico , Feuerstein Esteban , Balenzuela Pablo 
Published: July 2024

Abstract
Individuals engaging on social media often tend to establish online communities where interactions predominantly occur among like-minded peers. While considerable efforts have been devoted to studying and delineating these communities, there has been limited attention directed towards individuals who diverge from these patterns. In this study, we examine the community structure of re-post networks within the context of a polarized political environment at two different times. We specifically identify individuals who consistently switch between opposing communities and analyze the key features that distinguish them. Our investigation focuses on two crucial aspects of these users: the topological properties of their interactions and the political bias in the content of their posts. Our analysis is based on a dataset comprising 2 million tweets related to US President Donald Trump, coupled with data from over 100 000 individual user accounts spanning the 2020 US presidential election year. Our findings indicate that individuals who switch communities exhibit disparities compared to those who remain within the same communities, both in terms of the topological aspects of their interaction patterns (pagerank, degree, betweenness centrality.) and in the sentiment bias of their content towards Donald Trump.
Exploring the space of graphs with fixed discrete curvatures
Authors: Roost Michelle , Devriendt Karel , Zucal Giulio , Jost Jürgen 
Published: July 2024

Abstract
Discrete curvatures are quantities associated to the nodes and edges of a graph that reflect the local geometry around them. These curvatures have a rich mathematical theory and they have recently found success as a tool to analyze networks across a wide range of domains. In this work, we consider the problem of constructing graphs with a prescribed set of discrete edge curvatures, and explore the space of such graphs. We address this problem in two ways: first, we develop an evolutionary algorithm to sample graphs with discrete curvatures close to a given set. We use this algorithm to explore how other network statistics vary when constrained by the discrete curvatures in the network. Second, we solve the exact reconstruction problem for the specific case of Forman–Ricci curvature. By leveraging the theory of Markov bases, we obtain a finite set of rewiring moves that connects the space of all graphs with a fixed discrete curvature.
Benchmarking machine learning interatomic potentials via phonon anharmonicity
Authors: Bandi Sasaank , Jiang Chao , Marianetti Chris A 
Published: July 2024

Abstract
Machine learning approaches have recently emerged as powerful tools to probe structure-property relationships in crystals and molecules. Specifically, machine learning interatomic potentials (MLIPs) can accurately reproduce first-principles data at a cost similar to that of conventional interatomic potential approaches. While MLIPs have been extensively tested across various classes of materials and molecules, a clear characterization of the anharmonic terms encoded in the MLIPs is lacking. Here, we benchmark popular MLIPs using the anharmonic vibrational Hamiltonian of ThO2 in the fluorite crystal structure, which was constructed from density functional theory (DFT) using our highly accurate and efficient irreducible derivative methods. The anharmonic Hamiltonian was used to generate molecular dynamics (MD) trajectories, which were used to train three classes of MLIPs: Gaussian approximation potentials, artificial neural networks (ANN), and graph neural networks (GNN). The results were assessed by directly comparing phonons and their interactions, as well as phonon linewidths, phonon lineshifts, and thermal conductivity. The models were also trained on a DFT MD dataset, demonstrating good agreement up to fifth-order for the ANN and GNN. Our analysis demonstrates that MLIPs have great potential for accurately characterizing anharmonicity in materials systems at a fraction of the cost of conventional first principles-based approaches.
Application of deep learning-based fuzzy systems to analyze the overall risk of mortality in glioblastoma multiforme
Authors: Yang Cheng-Hong , Cheung Tin-Ho , Chuang Li-Yeh 
Published: July 2024

Abstract
Glioblastoma multiforme (GBM) is the most aggressive brain cancer in adults, with 3.2–3.4 cases per 100 thousand. In the US, brain cancer does not rank in the top 10 causes of death, but it remains in the top 15. Therefore, this research proposes a fuzzy-based GRUCoxPH model to identify missense variants associated with a high risk of all-cause mortality in GBM. The study combines various models, including fuzzy logic, gated recurrent units (GRUs), and Cox proportional hazards regression (CoxPh), to identify potential risk factors. The dataset is derived from TCGA-GBM clinicopathological information and mutations to create four risk score models: GRU, CoxPH, GRUCoxPHAddition, and GRUCoxPHMultiplication, analyzing nine risk factors of the dataset. The Fuzzy-based GRUCoxPH model achieves an average accuracy of 87.64%, outperforming other models. This model demonstrates its ability to classify and identify missense variants associated with mortality in GBM, potentially advancing cancer research.
Usefulness of point of care ultrasound in older adults: a multicentre study across different geriatric care settings in Spain and the United Kingdom
Authors: Reinoso-Párraga Paola Pastora , González-Montalvo Juan Ignacio , Menéndez-Colino Rocío , Perkisas Stany , Rivera-Deras Ivón , Garmendia-Prieto Blanca , Arain Salman Javed , Tung-Chen Yale , Vilches-Moraga Arturo 
Published: July 2024

Abstract

Background
Point of care ultrasound (POCUS) is an imaging technique performed bedside. To date, few published studies have reported the usefulness of multiorgan POCUS in Geriatrics. The objective of this study was to describe the utility of multiorgan POCUS in the care of older adults admitted to geriatric care settings.

Methodology
Observational retrospective study of patients admitted to geriatric settings in Spain and UK. Multiorgan POCUS was performed when there was a specific clinical suspicion or unexplained torpid clinical course despite physical examination and complementary tests. A geriatrician with a certificate degree in comprehensive ultrasound and long-standing experience in POCUS carried out POCUS. All patients underwent multiorgan POCUS in a cephalo-caudal manner.

Results
Out of 368 patients admitted to geriatric units, 29% met the inclusion criteria. Average age was 85.9 years (SD ± 6.1). POCUS identified 235 clinically significant findings (2.2 per patient). Findings were classified as 37.9% confirmed diagnosis, 16.6% ruled out diagnosis, 14.9% unsuspected relevant diagnoses and 30.6% clinical follow-ups. POCUS findings led to changes in pharmacological and non-pharmacological treatment in 66.3 and 69.2% respectively, resulted in completion or avoidance of invasive procedures in 17.8 and 15.9%, respectively, facilitating early referrals to other specialities in 14.9% and avoiding transfers in 25.2% of patients.

Conclusion
Multiorgan POCUS is a tool that aids in the assessment and treatment of patients receiving care in geriatrics units. These results show the usefulness of POCUS in the management of older adults and suggest its inclusion in any curriculum of Geriatric Medicine speciality training.
HbA2 levels in children with β-thalassemia trait associated with iron deficiency: A perspective for pediatricians
Authors: Değermenci Şeyda , Aslan Deniz 
Published: July 2024

Abstract

Objectives
A critical factor in β-thalassemia trait screening is a hemoglobin A2 (HbA2) level of 3.5% or higher. In children with iron deficiency, HbA2 levels decrease, and diagnosis may be missed. Studies with adult carriers have yielded conflicting results on this issue. The effectiveness of HbA2–based thalassemia screening in carrier children with iron deficiency has not been studied before.

Methods
In this study, among 213 children with β-thalassemia trait, those with iron deficiency were determined based on ferritin value (&lt;15 ng/mL), and their HbA2 levels were examined. We compared HbA2 levels of iron-deficient and iron-sufficient carriers and examined the correlation between low HbA2 levels and ferritin level. Because ferritin is an acute-phase reactant, similar evaluations were made by using transferrin saturation as the criterion for iron deficiency.

Results
The median HbA2 value of iron-deficient carrier children was 4.1% and within the diagnostic range (≥3.5%) in the majority of children. Median HbA2 levels in iron-deficient carriers differed from levels in iron-sufficient carriers (4.1% vs 4.9%, P &lt; .007). No correlation was present between low HbA2 levels and ferritin levels (0.226). Furthermore, among children without iron deficiency, there were individuals with low HbA2 levels (26.9%). Similar results were obtained when transferrin saturation was used.

Conclusions
Hemoglobin A2 can be used as a screening test in children with β-thalassemia trait, despite accompanying iron deficiency. Low HbA2 levels in these children may be the result of underlying thalassemia mutation, not the result of accompanying iron deficiency. Therefore, in suspected cases of β-thalassemia trait, evaluation should continue, regardless of iron status or treatment.