bluuebunny/crossref_metadata_2025_split huggingface dataset:
Flowering and fruiting characteristics of Kokum [Garcinia indica (Thouars) Choisy] germplasm collected for Industrial Use from the Konkan Region of India
Authors: Latha M. , Suma A. , Venkatesan K. , Pradheep K. , Thirumalaisamy P. P. , Vikram H. C. 
Published: October 2024

Abstract
Kokum [Garcinia indica (Thouars) Choisy] is a multi-purpose tree with culinary, cosmetics and pharmaceutical uses found popular in Konkan region of India. ICAR-NBPGR Regional Station, Thrissur made a systematic exploration and collection missions on kokum over a period from 1987–2004 and collected 31 accessions from various parts of Konkan region to characterize them using morphological characters and also to investigate their flowering and fruiting behaviour. Totally, 85 trees planted in Field Gene Bank in which only 67 trees (23 accessions) survived during field establishment. Among them, 32 (47.8%) were male and remaining 25 identified as female (37.3%) and 10 as co-sexual (14.9%). Fruit bearing ability noticed only in both female and co-sexual trees with huge variation in fruits. Morphological characterization of 33 trees with fruiting (23 accessions) for 35 morphological traits revealed presence of considerable amount of variation among them on basis of CV%. Highest positive correlation observed between fresh seed weight and fresh kernel weight (0.92). Cluster analysis formed four major clusters with Cluster I and II comprising male/bisexual genotypes and Cluster III and IV with female genotypes and it clearly distinguished male/bisexual genotypes from female ones. PCA analysis accounted 65.6% of genetic variation present among accessions by first three most informative PCs. Superior accessions identified for important traits will be more useful in industrial aspects of preparing kokum butter and juice. Further, seasonal difference identified in fruiting and maturity of fruits well-before onset of monsoon may be exploited by kokum industries for drying and processing of fruits.
How Does the Tibetan Plateau Land Thermal Initial Condition Influence the Subseasonal Prediction of 2020 Record‐Breaking Mei‐Yu Rainfall
Authors: Fan Yalan , Yang Jing , Bao Qing , Ma Tingting , Wu Guoxiong , Xue Yongkang , Shi Chunxiang , Liu Yimin , Qi Xin 
Published: October 2024

AbstractAccurate subseasonal prediction of heavy rainfall is helpful for disaster mitigation but challenging. The land thermal condition of Tibetan Plateau (TP), usually with climate memory ranging from weeks to seasons, has been seen as a potential predictability source for subseasonal prediction. Aiming at 2020 record‐breaking Mei‐yu rainfall, this study attempts to investigate whether and how the influence of initial TP surface thermal condition near late June influences the July rainfall prediction over the Middle and Lower Yangtze River Region (MLYR), based on two contrasting prediction experiments using a global climate ensemble prediction system. The results show that the most distinguishable change in the downstream prediction in July is the anomalous low‐tropospheric cyclone and the associated increased rainfall over MLYR corresponding to the warmer initial condition of surface TP. Influenced by the invasion of the positive potential vorticity (PV) center that generated over TP and propagated eastward, this low‐level cyclone anomaly over MLYR is formed within the first week of prediction, and persists for the next 3 weeks maintained by the positive feedback between the low‐level cyclone and middle‐tropospheric latent heating over MLYR in the prediction. This study confirmed the significant effect of TP initial thermal condition on downstream prediction ahead of 3 weeks during the Mei‐yu season (peak summer) with strong land–atmosphere coupling over TP.
Light Scattering From High‐Porosity 3D Simulants of the Lunar Regolith at Small Phase Angles
Authors: Lee Mingyeong , Jeong Minsup , Choi Young‐Jun 
Published: October 2024

AbstractLunar regolith consists of unconsolidated grains with high porosity, called the fairy castle structure. It is closely linked to the lunar opposition effect, which is the effect where brightness sharply increases as the phase angle approaches 0. However, owing to the Earth's gravity, it is difficult to reproduce the structure to study the physical characteristics of the lunar fairy castle structure in the laboratory. We designed a lunar fairy castle structure model for 3D printing. These models had high porosity and were simplified to tree‐like shapes. Various porous conditions of the surface were considered, represented by the number of trees, maximum trunk length, and maximum branch angle. In this study, a laboratory experiment was conducted to measure the reflectance of simulants with a fairy castle structure within a small phase angle range from 1.4 to 5.0. The result is analyzed for the sample porosity with the tangential slope of the reflectance S, which denotes the strength of the opposition effect. In addition, the results of this study were compared with lunar observation data. The porous samples exhibited a relatively large S value. The influence of branch length and attachment angle was very weak in this study. Samples with a porosity between 0.78 and 0.82 represent the similar S values to the lunar observation data, a mean porosity of lunar regolith. In conclusion, our findings suggest a potential correlation between porosity and the opposition effect in printed samples, proposing a new research approach for understanding the lunar opposition effect.
RocMLMs: Predicting Rock Properties Through Machine Learning Models
Authors: Kerswell Buchanan , Cerpa Nestor G. , Tommasi Andréa , Godard Marguerite , Padrón‐Navarta José Alberto 
Published: October 2024

AbstractMineral phase transformations significantly alter the bulk density and elastic properties of mantle rocks and consequently have profound effects on mantle dynamics and seismic wave propagation. These changes in the physical properties of mantle rocks result from evolution in the equilibrium mineralogical composition, which can be predicted by the minimization of the Gibbs Free Energy with respect to pressure (P), temperature (T), and chemical composition (X). Thus, numerical models that simulate mantle convection and/or probe the elastic structure of the Earth's mantle must account for varying mineralogical compositions to be self‐consistent. Yet coupling Gibbs Free Energy minimization (GFEM) approaches with numerical geodynamic models is currently intractable for high‐resolution simulations because prediction speeds of widely‐used GFEM programs (100–102 ms) are impractical in many cases. As an alternative, this study introduces machine learning models (RocMLMs) that have been trained to predict thermodynamically self‐consistent rock properties at arbitrary PTX conditions between 1–28 GPa and 773–2,273 K, and dry mantle compositions ranging from fertile (lherzolitic) to refractory (harzburgitic) end‐members define9d with a large data set of published mantle compositions. RocMLMs are 101–103 times faster than GFEM calculations or GFEM‐based look‐up table approaches with equivalent accuracy. Depth profiles of RocMLMs predictions are nearly indistinguishable from reference models PREM and STW105, demonstrating good agreement between thermodynamic‐based predictions of density, Vp, and Vs and geophysical observations. RocMLMs are therefore capable, for the first time, of emulating dynamic evolution of density, Vp, and Vs due to partial melting and refertilization of dry mantle rocks in high‐resolution numerical geodynamic models.
CRBN modulates synuclein fibrillation via degradation of DNAJB1 in mouse model of Parkinson disease
Authors: Akber Uroos , Jung Jun-Hyung , Yoon Heewoong , Seo Jiwon , Park Chul-Seung 
Published: October 2024

AbstractCereblon (CRBN) is a substrate recruiter for CRL4CRBN E3 ubiquitin ligase system playing a plethora of pivotal roles for biological systems. Here, we identified DNAJB1 (DJ1) as endogenous substrate of CRBN and report how CRBN influences the aggregation and toxicity of alpha-synuclein (α-SYN) via modulation of DJ1. CRBN interferes with molecular activities of DJ1 in vitro, in cells, and in vivo resulting in a reduced disaggregation of α-SYN fibrils, increased formation of preformed fibrils (PFFs) of α-SYN, and high susceptibility of mice to MPTP and PFF-induced neurotoxicity. Depletion of Crbn improves the behavioral and biochemical responses of mice towards neurotoxic insult. Finally, we designed a peptide inhibitor to inhibit the recruitment of DJ1 to CRBN for ubiquitination, resulting in an enhanced supply of DJ1 to counteract the toxicity of aggregated α-SYN. Our data has important implications for development of CRBN-targeting therapies that could prevent or delay progression of neurodegenerative synucleinopathy.
Integrated non-reciprocal magneto-optics with ultra-high endurance for photonic in-memory computing
Authors: Pintus Paolo , Dumont Mario , Shah Vivswan , Murai Toshiya , Shoji Yuya , Huang Duanni , Moody Galan , Bowers John E. , Youngblood Nathan 
Published: October 2024

AbstractProcessing information in the optical domain promises advantages in both speed and energy efficiency over existing digital hardware for a variety of emerging applications in artificial intelligence and machine learning. A typical approach to photonic processing is to multiply a rapidly changing optical input vector with a matrix of fixed optical weights. However, encoding these weights on-chip using an array of photonic memory cells is currently limited by a wide range of material- and device-level issues, such as the programming speed, extinction ratio and endurance, among others. Here we propose a new approach to encoding optical weights for in-memory photonic computing using magneto-optic memory cells comprising heterogeneously integrated cerium-substituted yttrium iron garnet (Ce:YIG) on silicon micro-ring resonators. We show that leveraging the non-reciprocal phase shift in such magneto-optic materials offers several key advantages over existing architectures, providing a fast (1 ns), efficient (143 fJ per bit) and robust (2.4 billion programming cycles) platform for on-chip optical processing.
AI-Generated Annotations Dataset for Diverse Cancer Radiology Collections in NCI Image Data Commons
Authors: Murugesan Gowtham Krishnan , McCrumb Diana , Aboian Mariam , Verma Tej , Soni Rahul , Memon Fatima , Farahani Keyvan , Pei Linmin , Wagner Ulrike , Fedorov Andrey Y. , Clunie David , Moore Stephen , Van Oss Jeff 
Published: October 2024

AbstractThe National Cancer Institute (NCI) Image Data Commons (IDC) offers publicly available cancer radiology collections for cloud computing, crucial for developing advanced imaging tools and algorithms. Despite their potential, these collections are minimally annotated; only 4% of DICOM studies in collections considered in the project had existing segmentation annotations. This project increases the quantity of segmentations in various IDC collections. We produced high-quality, AI-generated imaging annotations dataset of tissues, organs, and/or cancers for 11 distinct IDC image collections. These collections contain images from a variety of modalities, including computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). The collections cover various body parts, such as the chest, breast, kidneys, prostate, and liver. A portion of the AI annotations were reviewed and corrected by a radiologist to assess the performance of the AI models. Both the AI’s and the radiologist’s annotations were encoded in conformance to the Digital Imaging and Communications in Medicine (DICOM) standard, allowing for seamless integration into the IDC collections as third-party analysis collections. All the models, images and annotations are publicly accessible.
Synthesis, structure characterization, and corrosion properties of duplex electroless Ni-P/Ni-B and Ni-P/Ni-B-W coatings on mild steel
Authors: Ahmed S. Y. , Mahmoud S. B. , Shoeib M. A. 
Published: October 2024

AbstractThis study investigates the formation of duplex electroless Ni-P/Ni-B and Ni-P/Ni-B-W alloys through electroless plating process coatings on mild steel using hypophosphite and sodium borohydride as a reducing agent, employing heat-treated. Electroless plating is affordable and suitable for coating convoluted structures. Duplex electroless on mild steel was characterized by X-ray diffraction (XRD), Energy-dispersive X-ray spectrometry (EDS), scanning electron microscopy (SEM) were used to examine the surface and cross-sectional morphologies of the duplex coating, and finally study electrochemical corrosion properties. The analysis reveals that duplex coating yields a thicker, more homogeneous coating with a characteristic cauliflower morphology and spherical nodular structures. The coating was initially amorphous, but finally crystallized when heated to 400 °C. More corrosion resistance was found in the Ni-P/Ni-B and Ni-P/Ni-B-W layers when Ni-B served as the outer covering. This study focuses on the important effects of varying tungsten concentrations and heat treatment on the corrosion resistance, surface quality, and microstructural characteristics of duplex coatings. Showed improved corrosion resistance when exposed to 0.5 g/L of Na2WO4.
Application of quality indicators and critical lessons learned assessment as a research approach for the evaluation of rescue missions during terrorist attacks
Authors: Wurmb Thomas , Kurz Sebastian , Schwarzmann Gerhard , Trautner Herbert , Kinstle Uwe , Wagenhäuser Ulrich , Koch Florian , Münch Markus , Meybohm Patrick , Kippnich Maximilian 
Published: October 2024

AbstractIn Wuerzburg, Germany, a terrorist attack and a killing rampage occurred five years apart (2016 and 2021). Following a structured evaluation of the rescue mission in 2016, a bundle of quality indicators and ten “lessons learned” were defined. Aim of the presented study was to compare the two rescue missions and to critically review the lessons learned from 2016 for their implementation and feasibility. An interdisciplinary and inter-professional group of experts analyzed the data using predefined quality indicators. All lessons defined in 2016 were critically reviewed and qualified as either lessons learned or lessons identified. While seven out of ten lessons were successfully implemented after 2016 (lessons learned), three lessons didn´t work and were recategorized as lessons identified (communication, zoning and the mutual exchange of different tactical approaches). Our results demonstrate that the conclusions drawn in 2016 have helped to improve the performance of the rescue forces in 2021. In addition, the identified lessons are now the basis for further improving emergency and disaster preparedness. It is important to understand, that the process of preparedness improvement is not completed with the definition of lessons identified. These must first be integrated into response plans and then trained intensively. A lesson identified only becomes a lesson learned once it has been successfully applied.
Development and assessment of a machine learning tool for predicting emergency admission in Scotland
Authors: Liley James , Bohner Gergo , Emerson Samuel R. , Mateen Bilal A. , Borland Katie , Carr David , Heald Scott , Oduro Samuel D. , Ireland Jill , Moffat Keith , Porteous Rachel , Riddell Stephen , Rogers Simon , Thoma Ioanna , Cunningham Nathan , Holmes Chris , Payne Katrina , Vollmer Sebastian J. , Vallejos Catalina A. , Aslett Louis J. M. 
Published: October 2024

AbstractEmergency admissions (EA), where a patient requires urgent in-hospital care, are a major challenge for healthcare systems. The development of risk prediction models can partly alleviate this problem by supporting primary care interventions and public health planning. Here, we introduce SPARRAv4, a predictive score for EA risk that will be deployed nationwide in Scotland. SPARRAv4 was derived using supervised and unsupervised machine-learning methods applied to routinely collected electronic health records from approximately 4.8M Scottish residents (2013-18). We demonstrate improvements in discrimination and calibration with respect to previous scores deployed in Scotland, as well as stability over a 3-year timeframe. Our analysis also provides insights about the epidemiology of EA risk in Scotland, by studying predictive performance across different population sub-groups and reasons for admission, as well as by quantifying the effect of individual input features. Finally, we discuss broader challenges including reproducibility and how to safely update risk prediction models that are already deployed at population level.
Rapid modulation in music supports attention in listeners with attentional difficulties
Authors: Woods Kevin J. P. , Sampaio Gonçalo , James Tedra , Przysinda Emily , Hewett Adam , Spencer Andrea E. , Morillon Benjamin , Loui Psyche 
Published: October 2024

AbstractBackground music is widely used to sustain attention, but little is known about what musical properties aid attention. This may be due to inter-individual variability in neural responses to music. Here we find that music with amplitude modulations added at specific rates can sustain attention differentially for those with varying levels of attentional difficulty. We first tested the hypothesis that music with strong amplitude modulation would improve sustained attention, and found it did so when it occurred early in the experiment. Rapid modulations in music elicited greater activity in attentional networks in fMRI, as well as greater stimulus-brain coupling in EEG. Finally, to test the idea that specific modulation properties would differentially affect listeners based on their level of attentional difficulty, we parametrically manipulated the depth and rate of amplitude modulations inserted in otherwise-identical music, and found that beta-range modulations helped more than other modulation ranges for participants with more ADHD symptoms. Results suggest the possibility of an oscillation-based neural mechanism for targeted music to support improved cognitive performance.
Enhanced composite controller for PV/PMSG/PEMFC and BESS‐based DC microgrids voltage regulation: Integrating integral terminal sliding mode controller and recursive backstepping controller
Authors: Islam Md. Saiful , Bushra Israt Jahan , Roy Tushar Kanti , Ghosh Subarto Kumar , Oo Amanullah Maung Than 
Published: October 2024

AbstractThe variability of renewable energy sources due to weather patterns often leads to a mismatch between power generation and consumption within microgrids (MGs). This challenge is exacerbated when integrating bio‐renewable units, complicating stability maintenance in MGs. This research work introduces a novel solution to address this issue: a composite controller merging an integral terminal sliding mode controller with a recursive backstepping controller for direct current MGs (DCMGs). The proposed DCMG incorporates solar photovoltaic units, wind farms based on permanent magnet synchronous generators, proton exchange membrane fuel cells fuelled by hydrogen gas, an electrolyser, battery energy storage systems, and DC loads. First, a comprehensive mathematical model for the components within the DCMG is developed to design the proposed composite controller. This controller not only overcomes the inherent limitations and convergence issues of conventional SMCs but also ensures stable DC‐bus voltage and maintains power balance across various operational conditions. Moreover, a fuzzy logic‐based energy management system is introduced to regulate power flow, considering factors like battery state of charge and renewable energy sources' total power output. The control Lyapunov function confirms the DCMG system's asymptotic stability. Finally, the proposed controller's effectiveness is validated through simulations on both MATLAB/Simulink and Arduino Mega 2650 processor‐in‐the‐loop platforms under various operational conditions. In both platforms, the proposed controller surpasses an existing controller in terms of settling time, overshoot, and tracking error of the DC‐bus voltage.
Hybrid switched‐capacitor‐based boost DC–DC converter with reduced voltage stress
Authors: Van Tan Luong , Nguyen Thanh Hai 
Published: October 2024

AbstractThis article presents the new hybrid switched‐capacitor (SC)‐based transformerless DC–DC converter with a high step‐up capability and common ground features. The proposed hybrid SC‐based converter provides low voltage stress across semiconductor devices to utilize MOSFETs with lower Rdson and low voltage ratings. In addition, the diodes in SC cells with low voltage stress can reduce the reverse recovery power loss of the converter. Thus, the overall efficiency of the proposed hybrid SC‐based converter can be increased. The converter circuit, operating principle, and design guidelines of the proposed hybrid SC‐based converter are discussed. To confirm the performance of the proposed hybrid SC‐based converter, a detailed comparison study with the conventional hybrid SC converter is also presented. Finally, a laboratory prototype with 200 W and 25 to 200 V is set up to verify the effectiveness of the proposed hybrid SC‐based converter.
High order recovery of geometric interfaces from cell-average data
Authors: Cohen Albert , Somacal Agustin , Mula Olga 
Published: October 2024

We consider the problem of recovering multivariate characteristic functions u := χΩ from cell-average data on a coarse grid, motivated in particular by the accurate treatment of interfaces in finite volume schemes. While linear recovery methods are known to perform poorly, nonlinear strategies based on local reconstructions of the jump interface Γ := ∂Ω by geometrically simpler interfaces may offer significant improvements. We study two main families of local reconstruction schemes the first one based on nonlinear least-squares fitting, the second one based on the explicit computation of a polynomial-shaped curve fitting the data, which yields simpler numerical computations and high order geometric fitting. For each of them, we derive a general theoretical framework which allows us to control the recovery error by the error of best approximation up to a fixed multiplicative constant. Numerical tests in 2d illustrate the expected approximation order of these strategies. Several extensions are discussed, in particular the treatment of piecewise smooth interfaces with corners.
Le numérique en ETP : une révolution en marche
Authors: Cohen Jean-David 
Published: October 2024

Le numérique en ETP revêt différentes formes comme l’autonomie par la technologie du patient diabétique et va au-delà de la réalisation par les équipes de programmes en distanciel. La téléETP qui monopolise pourtant quelque peu les débats se matérialise d’ailleurs par l’existence de parcours d’ETP entièrement numériques proposés par des entreprises. Cette offre se décline aussi avec la mise en contact de patients experts au service de pairs. À un autre pôle, c’est la parole du patient qui est au centre du numérique et se trouve écoutée, précieusement analysée. Aussi, l’étendue de la e-ETP nécessite d’être explorée et, afin d’y voir plus clair et de façon dynamique quant à la finalité des outils numériques, une cartographie pourrait être la solution. L’e-ETP est en effet un tournant à appréhender dans la relation de soins. Dans ce contexte, il est également légitime de se poser la question de la place du soignant et d’un lien possiblement fragilisé, l’irruption de l’IA ne faisant qu’amplifier ces interrogations. La technologie doit cependant être maîtrisée et non nous diriger et, à cet instant, l’idée d’une autonomie du patient potentiellement renforcée, voire « débridée » doit nous satisfaire.