bluuebunny/crossref_metadata_2025_split huggingface dataset:
Detection resolving power of SiC Timepix3 detector to electrons, neutrons, ions and protons
Authors: Granja Carlos , Barber Corinne , Barna Sandra , Chancellor Jeffery , Chvatil David , Grevillot Loic , Inzalaco David , Jakubek Jan , Kohout Zdenek , Magrin Giulio , Marek Lukas , Mihai Radu , Oancea Cristina , Olsansky Vaclav , Olsen Trevor , Poklop Dusan , Pospisil Stanislav , Resch Andreas , Sagatova Andrea , Sivertz Michael , Stursa Jan , Sykora Rudolf , Wilhelm Ivan , Zach Vaclav , Zatko Bohumir 
Published: November 2024

Abstract
Silicon Carbide is a suitable semiconductor sensor for radiation measurement and nuclear
applications. It has been recently implemented as a position-sensitive and radiation imaging
device coupled to the Timepix3 ASIC chip in the form of a miniaturized radiation camera
MiniPIX-Timepix3 SiC. In this work we systematically evaluate the detection resolving power to
different radiation species: electrons, fast neutrons, ions and protons. Experimental calibrations
were made at well-defined reference fields in terms of particle type, energy and direction. The
spectral-sensitive tracking response and characteristic morphology of the particle tracks are
analyzed by pattern recognition algorithms. The low thickness of the radiation sensitive volume
(65 μm) of the SiC sensor limits the directional tracking response and angular
resolution. Three broad classes of particle-type events are resolved. The detector together with
suitable data processing can be used for radiation dosimetry and particle tracking tasks in space,
particle therapy, nuclear physics and nuclear reactors and particle accelerator environments.
Computational model for fractal dimension of anisotropic gear surfaces based on improved structure function method
Authors: Zhu Guodong , Huang Kang , Xiong Yangshou , Ding Wenhao , Peng Jiyou , Li Anqi 
Published: November 2024

Abstract
This paper analyzes the computational errors inherent in the structure function method (SFM) and proposes an improved method considering the effect of sampling length. The results reveal that smaller fractal dimensions correlate with larger calculation errors. While increasing the sampling length reduces errors, it is not the optimal solution. Compared with the traditional method, the improved structure function method (ISFM) markedly reduces computational errors across varying sampling lengths. Specifically, the computational maximum mistake diminishes from 16.55% to 0.46%. Then, an experimental method is introduced to approximate the fractal dimension of actual tooth profiles by extending the sampling length, which validates the accuracy of ISFM. Based on the ISFM, extensive experimental testing of tooth surface profile data establishes an interval model between the fractal dimension and surface roughness. This revised model corrects the inherent errors of traditional models and is more consistent with the actual rough surfaces. The interval values of fractal dimensions outputted are more consistent with the observed phenomenon, where the same roughness value can correspond to multiple contour curves exhibiting different fractal dimensions.
Enhancing the performance of hybrid bio-composites reinforced with natural fibers by using coupling agents
Authors: Alamry Ali , Islam Md Saiful , Hamdan Sinin anwar mohamad , Abdullah Masuk 
Published: November 2024

Abstract
Natural fiber-reinforced hybrid bio-composites are emerging as sustainable alternatives to traditional composites due to their environmental benefits and desirable properties. However, the interfacial interaction between natural fiber and polymer matrix is very weak. Thus, this study looks into the effect of maleic anhydride (MAH) coupling agents on the performance of natural fiber-reinforced hybrid biocomposites. The bio-composites were prepared using jute fiber, kenaf fiber, and polylactic acid (PLA) through the hot compression method. We treated both natural fibers with MAH coupling agents before using them in the production of biocomposites. Comprehensive characterization techniques, including tensile strength, modulus, and impact strength, were employed to evaluate the mechanical properties of the composites. The mechanical results indicated a significant improvement in mechanical properties for the bio-composites treated with coupling agents. The tensile strength of bio-composites increased by 35%, tensile modulus by 15%, and impact strength by 20% after modification with MAH coupling agents. The surface morphology and chemical interactions between the fiber and polymer matrix were investigated using SEM and FTIR studies. The FTIR result reveled that the intensity of C=O peaks enhanced after MAH treatment. Moreover, SEM images exposed better fiber dispersion and adhesion, corroborated by FTIR spectra showing enhanced chemical bonding where MAH reacted with the cellulose backbone of the fibers and formed fiber cellulose ester. Furthermore, TGA results revealed that adding MAH coupling agent to the fiber increased the thermal stability of biocomposites.
Dose compensation for decreased biological effective dose due to intrafractional interruption during radiotherapy: integration with a commercial treatment planning system
Authors: Yamaguchi Hikaru , Kawahara Daisuke , Koganezawa Akito S , Imano Nobuki , Murakami Yuji , Nishibuchi Ikuno , Shiba Eiji , Nagata Yasushi 
Published: November 2024

Abstract

Objective. While the biological effective dose (BED) has been used to estimate the damage to tumor cells in radiotherapy, BED does not consider intrafractional interruption (IFI) occurring during irradiation. We aim to develop a framework to evaluate the decrease in BED [

Δ
BED

] and to create a plan compensating for the decrease by IFI. Approach.

Δ
BED

was calculated using a model based on the microdosimetric kinetic model (MKM) for four brain tumor cases treated using a volumetric-modulated arc therapy. Four biologically compensated plans (BCPs) were created in the treatment planning system by a single-time optimization using a base plan considering

Δ
BED

created in in-house software and optimization objectives for the original clinically delivered plan to achieve a homogeneous BED distribution within the planning target volume (PTV). The BED-volume histogram was evaluated for non-compensated plan and BCP with different timepoint of interruption, a percentage of gantry rotation angle (GRA) before interruption in planned GRA,

η

and duration of interruption

τ

. Characteristics of the dose accumulation were analyzed for different collimator angle sets, Plan A (10°, 85°) and Plan B (45° and 315°), for the first case. Main Results. Hot spots in the

Δ
BED

distribution for

η

= 25%, 50%, and 75% were observed at superior-and-interior ends, central region, and peripheral region in PTV, respectively. These behaviors could be understood by characteristics of the MKM-based model producing maximum

Δ
BED

at 50% of the dose accumulation.

Δ
BED

50
%

ranged 4.5%–6.6%, 5.0%–7.3%, and 5.3%–7.7% for

τ

= 60, 90, and 120 min, respectively. Plan A showed fast dose accumulation at superior and inferior edges while slow on peripheries in the lateral dose profile. Plan B showed more homogeneous PD distributions than Plan A during irradiation. Significance. The developed framework successfully evaluated and compensated for the decreased BED distribution.
Liquid–film triboelectric nanogenerator with the volume effect for low-frequency blue energy harvesting and shock detection
Authors: Wang Weiyuan , Wang Hao , Wang Ping , Li Qiuhong , Zhao Ruoxi , Jin Yang 
Published: November 2024

Abstract
Blue energy is vital for sustainable development, providing a viable alternative to fossil fuels and contributing to climate change mitigation and environmental protection. Triboelectric nanogenerators (TENGs), a novel energy-harvesting technology, have garnered significant attention, particularly for their potential in wave energy harvesting. However, enhancing the performance of solid–liquid TENGs under low-frequency excitation remains challenging. This study addresses this challenge by leveraging the volume effect to substantially enhance the effective output power of liquid–film triboelectric nanogenerators (LF-TENGs). A 5.22-fold increase in output power was achieved, reaching 95.01 μW, compared to LF-TENGs without the volume effect. When integrated into a cathodic protection system, the LF-TENG reduced the open-circuit potential of aluminium by approximately 320 mV in a 3.5% NaCl solution. The electric energy generated from the triboelectric effect of the LF-TENG can enhance the corrosion protection of metal substrates in marine engineering through cathodic protection. Additionally, a ship shock protection sensor based on LF-TENG further validates its potential in sensing applications. These findings underscore the capability of LF-TENGs to advance marine energy harvesting and corrosion protection technologies, offering a promising solution for sustainable energy generation and infrastructure protection.
Statistical design of experiments for efficient performance characterization of protonic-ceramic electrolysis cells
Authors: Rand Peter W , Huang Jake D , Kim You-Dong , Meisel Charlie , Herradón Hernandez Carolina , Chmura Chris , O’Hayre Ryan P , Sullivan Neal P 
Published: November 2024

Abstract
Obtaining a cohesive understanding of performance in protonic-ceramic electrolysis cells is difficult due to the wide operating space coupled with low-throughput diagnostic techniques, sluggish system dynamics, and cell degradation. In this work, design of experiments (DOEs) methods are implemented to provide an efficient framework for understanding the phenomena that most strongly dictate cell performance. In addition to a more robust description of cell-level phenomena, mathematical equations are generated that accurately describe the complex relationship between the cell operating variables and cell performance metrics such as faradaic efficiency, cell potential, resistances, and energy conversion efficiency. Here, DOE is realized without the need to pre-select the most important operating variables based on a priori rationalizations. This is particularly valuable for system-level and technoeconomic analyses where the accurate prediction of cell/stack response over many operating conditions is required. The demonstrated experimental framework consists of a screening design and subsequent optimization design. The Plackett–Burman factor-screening design identifies temperature, current density, and steam content as having the largest impacts on cell performance, particularly faradaic efficiency. Increasing the electrolytic current density from 0.2 to 0.5 A cm−2 decreases polarization resistances by 74% due in large part to a negative-capacitance element that dominates at low frequency and high electrolysis bias. Impedance data highlights the connection of this negative feature to electronic leakage through the electrolyte and gas diffusion limitations. Additionally, increasing cell temperature from 500 to 600 ∘C is shown to decrease faradaic efficiency by 9% due to electrolyte dehydration and oxygen incorporation at high temperatures. The Box–Behnken optimization design then enables generation of regression equations to be used in response surfaces for data visualization and cohesive, multivariate analysis of cell operation.
Omnidirectional reflector based on Ta2O5 cylinder-filled IZO mesh structure for flexible and efficient blue TADF top-emission OLEDs
Authors: Ren Wanqi , Raju Tejas Dhanalaxmi , Yao Chenjie , Hong Seok Hee , Kim Tae Geun 
Published: November 2024

Abstract
Flexible top-emission organic light-emitting diodes (f-TEOLEDs) with a high aperture ratio can be used in next-generation wearable electronic applications. However, the advancement of f-TEOLEDs is being hindered by their low light extraction and poor mechanical stability. In this study, we introduce an omnidirectional reflector (ODR) consisting of an Ag/SiO2/Ta2O5 cylinder-embedded indium zinc oxide (IZO) mesh (c-mesh) structure that improves both the light extraction and mechanical flexibility of TEOLEDs using blue thermally activated delayed fluorescence emitters. The proposed ODR achieved a remarkable reflectance of over 96%, particularly in the transverse-electric mode. Furthermore, the Ta2O5 cylinders effectively compensated for the diverse void-induced depths in the IZO mesh, significantly reducing the leakage current between the electrode and the organic layers. In addition, the ODR electrodes exhibited outstanding mechanical stability. Moreover, even after being subjected to 2000 bending cycles over a 5 mm radius, the device luminance changed by less than 20%. Notably, the proposed f-TEOLEDs with Ag/SiO2/c-mesh electrodes demonstrated superior performance, achieving a low turn-on voltage (2.6 V), high current efficiency (33 cd·A−1), and power efficiency of 29.6 lm·W−1. Finally, the devices featured a narrow full width at half maximum of 27 nm under first-order microcavity effects.
Luminescent probes/conjugates derived from quantum dots, nanoparticles, and nanoclusters for cyanide (CN−) detection
Authors: Shellaiah Muthaiah , Manikandan E , Sun Kien Wen , Venkatachalam Vijayaraj 
Published: November 2024

Abstract
Advancing the development of the luminescent nanoprobes and conjugates toward the toxic analyte quantification plays a crucial role in environmental remedy. Cyanide ions (CN−) play vital role in many industries, such as electroplating, mining, textiles, metallurgy, etc. The highly toxic CN− may be discharged into the environment. The presence of CN− in aquatic environment at micromolar level could lead to fatal issue in humans and living beings, which requires scrutiny to maintain the ecosystem. Thus, diverse probes, such as organic fluorophores, noble metal nanoparticles/clusters (NPs/NCs), carbon/graphene quantum dots (CQDs/GQDs), cadmium telluride/selenide quantum dots (CdTe QDs/CdSe QDs), cadmium/zinc/silver sulfide quantum dots (CdS QDs/ZnS QDs/ Ag2S QDs), etc, were proposed toward the quantification of CN− ions with real-time applications. Among them, the luminescent probes/conjugates derived from QDs, NPs, and NCs are unique and display astonishing selectivity and sensitivity to CN−. The observed luminescent responses can be attributed to agglomeration, inner filter effect (IFE), fluorescence resonance energy transfer (FRET), chemiluminescence resonance energy transfer (CRET), etc Till now there is no review article reporting the applications of the luminescent probes/conjugates derived from QDs, NPs, and NCs for CN− quantification. In this review, the employment of these probes/conjugates for detecting CN− is illustrated with detailed underlying mechanisms and real-time applications. Furthermore, the design requirements, advantages, limitations, and directions of the luminescent probes/conjugates are delivered for future studies.
Enhancing water permeability in thin-film nanocomposite membranes utilizing electrospun recycled PET and graphene oxide
Authors: Zamisa Mantsopa Koena , Ray Suprakas Sinha , Madirisha Makungu Marco , Ojijo Vincent , Seadira Tumelo , Sadiku Rotimi Emmanuel , Kumar Neeraj , Orasugh Jonathan 
Published: November 2024

Abstract
Addressing the challenge of low permeability in Thin-Film Nanocomposite (TFNC) membranes is crucial for improving water filtration efficiency. Despite advancements in membrane technology, the interface between the substrate and active layer remains a critical research gap affecting overall permeability. This study aims to fill this gap using electrospun recycled polyethylene terephthalate (rPET) substrates combined with graphene oxide (GO). A vacuum-assisted self-assembly method was employed to coat microporous rPET substrates with GO. Extensive characterization techniques, including Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), x-ray diffraction (XRD), Fourier Transform Infrared Spectroscopy (FTIR), and Brunauer–Emmett–Teller (BET) analyses, demonstrated the uniform GO layer formation on rPET substrates, indicating enhanced structural and operational efficiency. The integration of GO resulted in a crystalline structure modification, improved surface morphology, and increased water permeability. The optimized rPET-GO membranes showcased a significant decrease in water contact angle to approximately 93 degrees, denoting enhanced hydrophilicity and, consequently, better permeability compared to uncoated rPET membranes. Despite increased hydrophilicity, the membranes exhibited reduced but stable permeability rates, highlighting the effectiveness of the GO and rPET blend in advancing membrane functionality. These findings mark a significant advancement in membrane technology, offering enhanced water permeability efficiency and paving the way for a substantial impact on sustainable water management. Additionally, this study underscores the importance of recycling in developing advanced materials for environmental applications.
Identifying cell types by lasso-constraint regularized Gaussian graphical model based on weighted distance penalty
Authors: Zhang Wei , Xu Yaxin , Zheng Xiaoying , Shen Juan , Li Yuanyuan 
Published: November 2024

Abstract
Single-cell RNA sequencing (scRNA-seq) technology is one of the most cost-effective and efficacious methods for revealing cellular heterogeneity and diversity. Precise identification of cell types is essential for establishing a robust foundation for downstream analyses and is a prerequisite for understanding heterogeneous mechanisms. However, the accuracy of existing methods warrants improvement, and highly accurate methods often impose stringent equipment requirements. Moreover, most unsupervised learning-based approaches are constrained by the need to input the number of cell types a prior, which limits their widespread application. In this paper, we propose a novel algorithm framework named WLGG. Initially, to capture the underlying nonlinear information, we introduce a weighted distance penalty term utilizing the Gaussian kernel function, which maps data from a low-dimensional nonlinear space to a high-dimensional linear space. We subsequently impose a Lasso constraint on the regularized Gaussian graphical model to enhance its ability to capture linear data characteristics. Additionally, we utilize the Eigengap strategy to predict the number of cell types and obtain predicted labels via spectral clustering. The experimental results on 14 test datasets demonstrate the superior clustering accuracy of the WLGG algorithm over 16 alternative methods. Furthermore, downstream analysis, including marker gene identification, pseudotime inference, and functional enrichment analysis based on the similarity matrix and predicted labels from the WLGG algorithm, substantiates the reliability of WLGG and offers valuable insights into biological dynamic biological processes and regulatory mechanisms.
Molecular group and correlation guided structural learning for multi-phenotype prediction
Authors: Zhou Xueping , Cai Manqi , Yue Molin , Celedón Juan C , Wang Jiebiao , Ding Ying , Chen Wei , Li Yanming 
Published: November 2024

Abstract
We propose a supervised learning bioinformatics tool, Biological gRoup guIded muLtivariate muLtiple lIneAr regression with peNalizaTion (Brilliant), designed for feature selection and outcome prediction in genomic data with multi-phenotypic responses. Brilliant specifically incorporates genome and/or phenotype grouping structures, as well as phenotype correlation structures, in feature selection, effect estimation, and outcome prediction under a penalized multi-response linear regression model. Extensive simulations demonstrate its superior performance compared to competing methods. We applied Brilliant to two omics studies. In the first study, we identified novel association signals between multivariate gene expressions and high-dimensional DNA methylation profiles, providing biological insights for the baseline CpG-to-gene regulation patterns in a Puerto Rican children asthma cohort. The second study focused on cell-type deconvolution prediction using high-dimensional gene expression profiles. Using Brilliant, we improved the accuracy for cell-type fraction prediction and identified novel cell-type signature genes.
Diffusion model assisted designing self-assembling collagen mimetic peptides as biocompatible materials
Authors: Wang Xinglong , Xu Kangjie , Ma Lingling , Sun Ruoxi , Wang Kun , Wang Ruiyan , Zhang Junli , Tao Wenwen , Linghu Kai , Yu Shuyao , Zhou Jingwen 
Published: November 2024

Abstract
Collagen self-assembly supports its mechanical function, but controlling collagen mimetic peptides (CMPs) to self-assemble into higher-order oligomers with numerous functions remains challenging due to the vast potential amino acid sequence space. Herein, we developed a diffusion model to learn features from different types of human collagens and generate CMPs; obtaining 66% of synthetic CMPs could self-assemble into triple helices. Triple-helical and untwisting states were probed by melting temperature (Tm); hence, we developed a model to predict collagen Tm, achieving a state-of-art Pearson’s correlation (PC) of 0.95 by cross-validation and a PC of 0.8 for predicting Tm values of synthetic CMPs. Our chemically synthesized short CMPs and recombinantly expressed long CMPs could self-assemble, with the lowest requirement for hydrogel formation at a concentration of 0.08% (w/v). Five CMPs could promote osteoblast differentiation. Our results demonstrated the potential for using computer-aided methods to design functional self-assembling CMPs.
A05 - HPB Interrogating the spatial biology of pancreatic cancer-associated fibroblasts
Authors: Bryce Adam , Dreyer Stephan , Froeling Fieke , Martinelli Silvia , Pennie Rachel , Officer-Jones Leah , Le Quesne John , Chang David 
Published: November 2024

Abstract

Background
Cancer-associated fibroblasts (CAFs) are key players in the fibrotic tumour microenvironment (TME) of pancreatic cancer. Recent evidence has demonstrated the existence of multiple CAF subtypes, however there is relatively little evidence validating and corroborating these CAF subtypes in resected human pancreatic cancer. Furthermore, the spatial interactions between CAF subtypes and the epithelial and immune compartments of the pancreatic cancer TME is not well understood.

Method
We designed and optimised a bespoke 8-plex immunofluorescence panel for the purposes of CAF subtype profiling and elucidation of CAF spatial biology: panCK, FAP, SMA, CTGF, S100A4, IL6, LIF, PDGFR. This was applied to a cohort of n=215 resected pancreatic cancer, well characterised with highly granular clinico-pathological and transcriptomic data through the Australian Pancreatic Cancer Genome Initiative. Spatial analysis was performed with novel computational biology techniques.

Results
We demonstrate seven CAF subtypes which cluster to form distinct, highly prognostic “enrichment profiles” (EPs). The cold EP represents quiescent CAFs in close proximity to immune cells (multivariate HR 0.45, 95% CI 0.30-0.68, p&lt;0.001), and the fibrotic EP represents activated CAFs in close proximity to epithelial cells (multivariate HR 2.22, 95% CI 1.48-3.35, p&lt;0.001). We also demonstrate the prognostic power of high spatial entropy (loss of tissue architecture, correlating with the fibrotic EP) vs low spatial entropy (preservation of tissue architecture, correlating with the cold EP) (HR 2.22, 95% CI 1.47-3.35, p&lt;0.001).

Conclusion
We begin to unravel the elusive spatial biology of pancreatic CAFs in a large resected human cohort. We see changes in immune cell infiltrate, modulation of cellular relationships, and greater tissue disorganisation as a result of CAF activation. These features are highly prognostic and we hypothesise they are driven by changes in crosstalk between TME compartments. Current work focuses on application of these techniques to a large resected neoadjuvantly-treated cohort, allowing further interrogation of the notorious fibrotic post-neoadjuvant pancreatic cancer TME. If accepted for presentation we look forward to presenting both cohorts simultaneously.